Repository: GeoscienceAustralia/PyRate Branch: master Commit: 3579612cbfec Files: 856 Total size: 5.7 MB Directory structure: gitextract_ml4uzp09/ ├── .coveragerc ├── .github/ │ ├── ISSUE_TEMPLATE/ │ │ ├── bug_report.md │ │ └── feature_request.md │ └── workflows/ │ └── build.yml ├── .gitignore ├── .pylintrc ├── Dockerfile ├── LICENSE ├── README.rst ├── __init__.py ├── docs/ │ ├── Makefile │ ├── algorithm.rst │ ├── aps.rst │ ├── authors.rst │ ├── conf.py │ ├── configuration.rst │ ├── contributing.rst │ ├── covariance.rst │ ├── demerror.rst │ ├── dependencies.rst │ ├── docker.rst │ ├── gamma.rst │ ├── gdal_python.rst │ ├── geometry.rst │ ├── history.rst │ ├── hpc.rst │ ├── ifgconstants.rst │ ├── index.rst │ ├── installation.rst │ ├── logger.rst │ ├── make.bat │ ├── modules.rst │ ├── mpiops.rst │ ├── mst.rst │ ├── orbital.rst │ ├── phase_closure.rst │ ├── prepifg_helper.rst │ ├── pyrate.conv2tif.rst │ ├── pyrate.correct.rst │ ├── pyrate.main.rst │ ├── pyrate.merge.rst │ ├── pyrate.prepifg.rst │ ├── ref_phs_est.rst │ ├── refpixel.rst │ ├── roipac.rst │ ├── scripts.rst │ ├── shared.rst │ ├── stack.rst │ ├── timeseries.rst │ ├── troubleshooting.rst │ ├── ubuntu.rst │ └── usage.rst ├── input_parameters.conf ├── pyrate/ │ ├── __init__.py │ ├── configuration.py │ ├── constants.py │ ├── conv2tif.py │ ├── core/ │ │ ├── .coveragerc │ │ ├── __init__.py │ │ ├── algorithm.py │ │ ├── aps.py │ │ ├── covariance.py │ │ ├── dem_error.py │ │ ├── gamma.py │ │ ├── gdal_python.py │ │ ├── geometry.py │ │ ├── ifgconstants.py │ │ ├── logger.py │ │ ├── mpiops.py │ │ ├── mst.py │ │ ├── orbital.py │ │ ├── phase_closure/ │ │ │ ├── __init__.py │ │ │ ├── closure_check.py │ │ │ ├── collect_loops.py │ │ │ ├── correct_phase.py │ │ │ ├── mst_closure.py │ │ │ ├── plot_closure.py │ │ │ └── sum_closure.py │ │ ├── prepifg_helper.py │ │ ├── ref_phs_est.py │ │ ├── refpixel.py │ │ ├── roipac.py │ │ ├── shared.py │ │ ├── stack.py │ │ └── timeseries.py │ ├── correct.py │ ├── default_parameters.py │ ├── main.py │ ├── merge.py │ └── prepifg.py ├── pytest.ini ├── requirements-dev.txt ├── requirements-plot.txt ├── requirements-test.txt ├── requirements.txt ├── scripts/ │ ├── apt_install.sh │ ├── ci_gdal_install.sh │ ├── ci_proj_install.sh │ ├── create_html_documentation.sh │ ├── gdal_calc_local.py │ ├── nci_install.sh │ ├── nci_load_modules.sh │ ├── nci_run_tests.sh │ ├── plot_ifgs.py │ └── prepifg.sh ├── setup.cfg ├── setup.py ├── tests/ │ ├── __init__.py │ ├── common.py │ ├── conftest.py │ ├── phase_closure/ │ │ ├── __init__.py │ │ ├── common.py │ │ ├── conftest.py │ │ ├── test_closure_check.py │ │ ├── test_collect_loops.py │ │ ├── test_mst_closure.py │ │ ├── test_plot_closure.py │ │ └── test_sum_closure.py │ ├── test_algorithm.py │ ├── test_aps.py │ ├── test_constants.py │ ├── test_conv2tif.py │ ├── test_correct.py │ ├── test_covariance.py │ ├── test_data/ │ │ ├── cropA/ │ │ │ ├── baseline_30 │ │ │ ├── coherence_30 │ │ │ ├── coherence_stats/ │ │ │ │ ├── coh_mean.tif │ │ │ │ ├── coh_median.tif │ │ │ │ └── coh_std.tif │ │ │ ├── dem_error_result/ │ │ │ │ ├── 20180106-20180319_ifg_20_dem_error.npy │ │ │ │ ├── 20180130-20180412_ifg_20_dem_error.npy │ │ │ │ ├── 20180412-20180518_ifg_20_dem_error.npy │ │ │ │ └── dem_error.tif │ │ │ ├── geometry/ │ │ │ │ ├── 20180106-20180130_VV_8rlks_base.par │ │ │ │ ├── 20180106-20180130_VV_8rlks_bperp.par │ │ │ │ ├── 20180106-20180319_VV_8rlks_base.par │ │ │ │ ├── 20180106-20180319_VV_8rlks_bperp.par │ │ │ │ ├── 20180106-20180412_VV_8rlks_base.par │ │ │ │ ├── 20180106-20180412_VV_8rlks_bperp.par │ │ │ │ ├── 20180106-20180518_VV_8rlks_base.par │ │ │ │ ├── 20180106-20180518_VV_8rlks_bperp.par │ │ │ │ ├── 20180106_VV_8rlks_eqa_to_rdc.lt │ │ │ │ ├── 20180130-20180307_VV_8rlks_base.par │ │ │ │ ├── 20180130-20180307_VV_8rlks_bperp.par │ │ │ │ ├── 20180130-20180412_VV_8rlks_base.par │ │ │ │ ├── 20180130-20180412_VV_8rlks_bperp.par │ │ │ │ ├── 20180307-20180319_VV_8rlks_base.par │ │ │ │ ├── 20180307-20180319_VV_8rlks_bperp.par │ │ │ │ ├── 20180307-20180331_VV_8rlks_base.par │ │ │ │ ├── 20180307-20180331_VV_8rlks_bperp.par │ │ │ │ ├── 20180307-20180506_VV_8rlks_base.par │ │ │ │ ├── 20180307-20180506_VV_8rlks_bperp.par │ │ │ │ ├── 20180307-20180530_VV_8rlks_base.par │ │ │ │ ├── 20180307-20180530_VV_8rlks_bperp.par │ │ │ │ ├── 20180307-20180611_VV_8rlks_base.par │ │ │ │ ├── 20180307-20180611_VV_8rlks_bperp.par │ │ │ │ ├── 20180319-20180331_VV_8rlks_base.par │ │ │ │ ├── 20180319-20180331_VV_8rlks_bperp.par │ │ │ │ ├── 20180319-20180506_VV_8rlks_base.par │ │ │ │ ├── 20180319-20180506_VV_8rlks_bperp.par │ │ │ │ ├── 20180319-20180518_VV_8rlks_base.par │ │ │ │ ├── 20180319-20180518_VV_8rlks_bperp.par │ │ │ │ ├── 20180319-20180530_VV_8rlks_base.par │ │ │ │ ├── 20180319-20180530_VV_8rlks_bperp.par │ │ │ │ ├── 20180319-20180623_VV_8rlks_base.par │ │ │ │ ├── 20180319-20180623_VV_8rlks_bperp.par │ │ │ │ ├── 20180331-20180412_VV_8rlks_base.par │ │ │ │ ├── 20180331-20180412_VV_8rlks_bperp.par │ │ │ │ ├── 20180331-20180506_VV_8rlks_base.par │ │ │ │ ├── 20180331-20180506_VV_8rlks_bperp.par │ │ │ │ ├── 20180331-20180518_VV_8rlks_base.par │ │ │ │ ├── 20180331-20180518_VV_8rlks_bperp.par │ │ │ │ ├── 20180331-20180530_VV_8rlks_base.par │ │ │ │ ├── 20180331-20180530_VV_8rlks_bperp.par │ │ │ │ ├── 20180331-20180623_VV_8rlks_base.par │ │ │ │ ├── 20180331-20180623_VV_8rlks_bperp.par │ │ │ │ ├── 20180331-20180717_VV_8rlks_base.par │ │ │ │ ├── 20180331-20180717_VV_8rlks_bperp.par │ │ │ │ ├── 20180412-20180506_VV_8rlks_base.par │ │ │ │ ├── 20180412-20180506_VV_8rlks_bperp.par │ │ │ │ ├── 20180412-20180518_VV_8rlks_base.par │ │ │ │ ├── 20180412-20180518_VV_8rlks_bperp.par │ │ │ │ ├── 20180506-20180518_VV_8rlks_base.par │ │ │ │ ├── 20180506-20180518_VV_8rlks_bperp.par │ │ │ │ ├── 20180506-20180530_VV_8rlks_base.par │ │ │ │ ├── 20180506-20180530_VV_8rlks_bperp.par │ │ │ │ ├── 20180506-20180611_VV_8rlks_base.par │ │ │ │ ├── 20180506-20180611_VV_8rlks_bperp.par │ │ │ │ ├── 20180506-20180623_VV_8rlks_base.par │ │ │ │ ├── 20180506-20180623_VV_8rlks_bperp.par │ │ │ │ ├── 20180506-20180705_VV_8rlks_base.par │ │ │ │ ├── 20180506-20180705_VV_8rlks_bperp.par │ │ │ │ ├── 20180506-20180717_VV_8rlks_base.par │ │ │ │ └── 20180506-20180717_VV_8rlks_bperp.par │ │ │ ├── geotiffs/ │ │ │ │ ├── cropA_20180106-20180130_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180106-20180130_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180106-20180319_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180106-20180319_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180106-20180412_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180106-20180412_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180106-20180518_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180106-20180518_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180130-20180307_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180130-20180307_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180130-20180412_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180130-20180412_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180307-20180319_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180307-20180319_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180307-20180331_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180307-20180331_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180307-20180506_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180307-20180506_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180307-20180530_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180307-20180530_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180307-20180611_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180307-20180611_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180319-20180331_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180319-20180331_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180319-20180506_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180319-20180506_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180319-20180518_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180319-20180518_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180319-20180530_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180319-20180530_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180319-20180623_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180319-20180623_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180331-20180412_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180331-20180412_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180331-20180506_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180331-20180506_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180331-20180518_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180331-20180518_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180331-20180530_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180331-20180530_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180331-20180623_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180331-20180623_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180331-20180717_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180331-20180717_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180412-20180506_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180412-20180506_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180412-20180518_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180412-20180518_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180506-20180518_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180506-20180518_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180506-20180530_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180506-20180530_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180506-20180611_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180506-20180611_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180506-20180623_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180506-20180623_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180506-20180705_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180506-20180705_VV_8rlks_flat_eqa_cc.tif │ │ │ │ ├── cropA_20180506-20180717_VV_8rlks_eqa_unw.tif │ │ │ │ ├── cropA_20180506-20180717_VV_8rlks_flat_eqa_cc.tif │ │ │ │ └── cropA_T005A_dem.tif │ │ │ ├── headers/ │ │ │ │ ├── cropA_20180106_VV_8rlks_eqa_dem.par │ │ │ │ ├── r20180106_VV_8rlks_mli.par │ │ │ │ ├── r20180106_VV_slc.par │ │ │ │ ├── r20180130_VV_8rlks_mli.par │ │ │ │ ├── r20180130_VV_slc.par │ │ │ │ ├── r20180307_VV_8rlks_mli.par │ │ │ │ ├── r20180307_VV_slc.par │ │ │ │ ├── r20180319_VV_8rlks_mli.par │ │ │ │ ├── r20180319_VV_slc.par │ │ │ │ ├── r20180331_VV_8rlks_mli.par │ │ │ │ ├── r20180331_VV_slc.par │ │ │ │ ├── r20180412_VV_8rlks_mli.par │ │ │ │ ├── r20180412_VV_slc.par │ │ │ │ ├── r20180506_VV_8rlks_mli.par │ │ │ │ ├── r20180506_VV_slc.par │ │ │ │ ├── r20180518_VV_8rlks_mli.par │ │ │ │ ├── r20180518_VV_slc.par │ │ │ │ ├── r20180530_VV_8rlks_mli.par │ │ │ │ ├── r20180530_VV_slc.par │ │ │ │ ├── r20180611_VV_8rlks_mli.par │ │ │ │ ├── r20180611_VV_slc.par │ │ │ │ ├── r20180623_VV_8rlks_mli.par │ │ │ │ ├── r20180623_VV_slc.par │ │ │ │ ├── r20180705_VV_8rlks_mli.par │ │ │ │ ├── r20180705_VV_slc.par │ │ │ │ ├── r20180717_VV_8rlks_mli.par │ │ │ │ └── r20180717_VV_slc.par │ │ │ ├── headers_13 │ │ │ ├── ifg_30 │ │ │ └── pyrate_mexico_cropa.conf │ │ ├── cropB/ │ │ │ └── 20180106-20180130_ifg.tif │ │ ├── gamma/ │ │ │ ├── 16x20_20090713-20090817_VV_4rlks_utm.tif │ │ │ ├── 16x20_20090713-20090817_VV_4rlks_utm.unw │ │ │ ├── 20160114-20160126_base.par │ │ │ ├── 20160114-20160126_base_init.par │ │ │ ├── cropped_lookup_table.lt │ │ │ ├── dem16x20_subset_from_gamma.tif │ │ │ ├── dem16x20raw.dem │ │ │ ├── dem16x20raw.dem.par │ │ │ ├── r20090713_VV.slc.par │ │ │ └── r20090817_VV.slc.par │ │ ├── geotiffs/ │ │ │ ├── cropA_20180106-20180130_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180106-20180130_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180106-20180319_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180106-20180319_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180106-20180412_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180106-20180412_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180106-20180518_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180106-20180518_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180106_VV_8rlks_eqa_dem.par │ │ │ ├── cropA_20180130-20180307_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180130-20180307_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180130-20180412_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180130-20180412_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180307-20180319_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180307-20180319_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180307-20180331_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180307-20180331_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180307-20180506_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180307-20180506_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180307-20180530_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180307-20180530_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180307-20180611_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180307-20180611_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180319-20180331_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180319-20180331_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180319-20180506_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180319-20180506_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180319-20180518_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180319-20180518_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180319-20180530_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180319-20180530_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180319-20180623_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180319-20180623_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180331-20180412_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180331-20180412_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180331-20180506_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180331-20180506_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180331-20180518_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180331-20180518_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180331-20180530_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180331-20180530_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180331-20180623_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180331-20180623_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180331-20180717_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180331-20180717_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180412-20180506_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180412-20180506_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180412-20180518_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180412-20180518_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180506-20180518_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180506-20180518_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180506-20180530_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180506-20180530_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180506-20180611_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180506-20180611_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180506-20180623_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180506-20180623_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180506-20180705_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180506-20180705_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_20180506-20180717_VV_8rlks_eqa_unw.tif │ │ │ ├── cropA_20180506-20180717_VV_8rlks_flat_eqa_cc.tif │ │ │ ├── cropA_T005A_dem.tif │ │ │ ├── r20180106_VV_slc.par │ │ │ ├── r20180130_VV_slc.par │ │ │ ├── r20180307_VV_slc.par │ │ │ ├── r20180319_VV_slc.par │ │ │ ├── r20180331_VV_slc.par │ │ │ ├── r20180412_VV_slc.par │ │ │ ├── r20180506_VV_slc.par │ │ │ ├── r20180518_VV_slc.par │ │ │ ├── r20180530_VV_slc.par │ │ │ ├── r20180611_VV_slc.par │ │ │ ├── r20180623_VV_slc.par │ │ │ ├── r20180705_VV_slc.par │ │ │ └── r20180717_VV_slc.par │ │ ├── headers/ │ │ │ └── geo_060619-060828.unw.rsc │ │ ├── incidence/ │ │ │ └── 128x2.tif │ │ ├── prepifg/ │ │ │ ├── obs/ │ │ │ │ ├── geo_060619-061002.unw │ │ │ │ ├── geo_060619-061002.unw.rsc │ │ │ │ ├── geo_070326-070917.unw │ │ │ │ └── geo_070326-070917.unw.rsc │ │ │ └── tif/ │ │ │ ├── 0_1lksx_1lksy_4cr.tif │ │ │ ├── 1_1lksx_1lksy_4cr.tif │ │ │ ├── geo_060619-061002.tif │ │ │ ├── geo_060619-061002_unw.tif │ │ │ ├── geo_070326-070917.tif │ │ │ └── geo_070326-070917_unw.tif │ │ ├── small_test/ │ │ │ ├── coherence/ │ │ │ │ ├── 20060619-20061002_utm.unw.cc │ │ │ │ ├── 20060828-20061211_utm.unw.cc │ │ │ │ ├── 20061002-20070219_utm.unw.cc │ │ │ │ ├── 20061002-20070430_utm.unw.cc │ │ │ │ ├── 20061106-20061211_utm.unw.cc │ │ │ │ ├── 20061106-20070115_utm.unw.cc │ │ │ │ ├── 20061106-20070326_utm.unw.cc │ │ │ │ ├── 20061211-20070709_utm.unw.cc │ │ │ │ ├── 20061211-20070813_utm.unw.cc │ │ │ │ ├── 20070115-20070326_utm.unw.cc │ │ │ │ ├── 20070115-20070917_utm.unw.cc │ │ │ │ ├── 20070219-20070430_utm.unw.cc │ │ │ │ ├── 20070219-20070604_utm.unw.cc │ │ │ │ ├── 20070326-20070917_utm.unw.cc │ │ │ │ ├── 20070430-20070604_utm.unw.cc │ │ │ │ ├── 20070604-20070709_utm.unw.cc │ │ │ │ ├── 20070709-20070813_utm.unw.cc │ │ │ │ └── coherence_17 │ │ │ ├── conf/ │ │ │ │ ├── pyrate1.conf │ │ │ │ ├── pyrate2.conf │ │ │ │ ├── pyrate_gamma_test.conf │ │ │ │ └── pyrate_roipac_test.conf │ │ │ ├── dem/ │ │ │ │ └── roipac_test_trimmed.tif │ │ │ ├── gamma_obs/ │ │ │ │ ├── 20060619-20061002_base.par │ │ │ │ ├── 20060619-20061002_utm.unw │ │ │ │ ├── 20060619-20061002_utm_unw.tif.aux.xml │ │ │ │ ├── 20060619_slc.par │ │ │ │ ├── 20060619_utm.dem │ │ │ │ ├── 20060619_utm.inc │ │ │ │ ├── 20060619_utm.lv_theta │ │ │ │ ├── 20060619_utm_dem.par │ │ │ │ ├── 20060828-20061211_base.par │ │ │ │ ├── 20060828-20061211_utm.unw │ │ │ │ ├── 20060828_slc.par │ │ │ │ ├── 20061002-20070219_base.par │ │ │ │ ├── 20061002-20070219_utm.unw │ │ │ │ ├── 20061002-20070430_base.par │ │ │ │ ├── 20061002-20070430_utm.unw │ │ │ │ ├── 20061002_slc.par │ │ │ │ ├── 20061106-20061211_base.par │ │ │ │ ├── 20061106-20061211_utm.unw │ │ │ │ ├── 20061106-20070115_base.par │ │ │ │ ├── 20061106-20070115_utm.unw │ │ │ │ ├── 20061106-20070326_base.par │ │ │ │ ├── 20061106-20070326_utm.unw │ │ │ │ ├── 20061106_slc.par │ │ │ │ ├── 20061211-20070709_base.par │ │ │ │ ├── 20061211-20070709_utm.unw │ │ │ │ ├── 20061211-20070813_base.par │ │ │ │ ├── 20061211-20070813_utm.unw │ │ │ │ ├── 20061211_slc.par │ │ │ │ ├── 20070115-20070326_base.par │ │ │ │ ├── 20070115-20070326_utm.unw │ │ │ │ ├── 20070115-20070917_base.par │ │ │ │ ├── 20070115-20070917_utm.unw │ │ │ │ ├── 20070115_slc.par │ │ │ │ ├── 20070219-20070430_base.par │ │ │ │ ├── 20070219-20070430_utm.unw │ │ │ │ ├── 20070219-20070604_base.par │ │ │ │ ├── 20070219-20070604_utm.unw │ │ │ │ ├── 20070219_slc.par │ │ │ │ ├── 20070326-20070917_base.par │ │ │ │ ├── 20070326-20070917_utm.unw │ │ │ │ ├── 20070326_slc.par │ │ │ │ ├── 20070430-20070604_base.par │ │ │ │ ├── 20070430-20070604_utm.unw │ │ │ │ ├── 20070430_slc.par │ │ │ │ ├── 20070604-20070709_base.par │ │ │ │ ├── 20070604-20070709_utm.unw │ │ │ │ ├── 20070604_slc.par │ │ │ │ ├── 20070709-20070813_base.par │ │ │ │ ├── 20070709-20070813_utm.unw │ │ │ │ ├── 20070709_slc.par │ │ │ │ ├── 20070813_slc.par │ │ │ │ ├── 20070917_slc.par │ │ │ │ ├── bad_epochs_headers │ │ │ │ ├── bad_epochs_ifms_17 │ │ │ │ ├── baseline_17 │ │ │ │ ├── cropped_lookup_table.lt │ │ │ │ ├── headers │ │ │ │ ├── ifms_17 │ │ │ │ └── tif_17 │ │ │ ├── linrate/ │ │ │ │ ├── linear_error.npy │ │ │ │ ├── linear_intercept.npy │ │ │ │ ├── linear_rate.npy │ │ │ │ ├── linear_rsquared.npy │ │ │ │ ├── linear_samples.npy │ │ │ │ └── tscuml_0.npy │ │ │ ├── mst/ │ │ │ │ ├── mst_geo_060619-061002.csv │ │ │ │ ├── mst_geo_060828-061211.csv │ │ │ │ ├── mst_geo_061002-070219.csv │ │ │ │ ├── mst_geo_061002-070430.csv │ │ │ │ ├── mst_geo_061106-061211.csv │ │ │ │ ├── mst_geo_061106-070115.csv │ │ │ │ ├── mst_geo_061106-070326.csv │ │ │ │ ├── mst_geo_061211-070709.csv │ │ │ │ ├── mst_geo_061211-070813.csv │ │ │ │ ├── mst_geo_070115-070326.csv │ │ │ │ ├── mst_geo_070115-070917.csv │ │ │ │ ├── mst_geo_070219-070430.csv │ │ │ │ ├── mst_geo_070219-070604.csv │ │ │ │ ├── mst_geo_070326-070917.csv │ │ │ │ ├── mst_geo_070430-070604.csv │ │ │ │ ├── mst_geo_070604-070709.csv │ │ │ │ └── mst_geo_070709-070813.csv │ │ │ ├── orbital_error_correction/ │ │ │ │ ├── ifg_orb_planar_1lks_method1_geo_060619-061002.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method1_geo_060828-061211.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method1_geo_061002-070219.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method1_geo_061002-070430.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method1_geo_061106-061211.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method1_geo_061106-070115.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method1_geo_061106-070326.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method1_geo_061211-070709.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method1_geo_061211-070813.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method1_geo_070115-070326.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method1_geo_070115-070917.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method1_geo_070219-070430.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method1_geo_070219-070604.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method1_geo_070326-070917.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method1_geo_070430-070604.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method1_geo_070604-070709.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method1_geo_070709-070813.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method2_geo_060619-061002.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method2_geo_060828-061211.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method2_geo_061002-070219.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method2_geo_061002-070430.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method2_geo_061106-061211.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method2_geo_061106-070115.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method2_geo_061106-070326.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method2_geo_061211-070709.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method2_geo_061211-070813.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method2_geo_070115-070326.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method2_geo_070115-070917.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method2_geo_070219-070430.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method2_geo_070219-070604.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method2_geo_070326-070917.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method2_geo_070430-070604.unw.csv │ │ │ │ ├── ifg_orb_planar_1lks_method2_geo_070604-070709.unw.csv │ │ │ │ └── ifg_orb_planar_1lks_method2_geo_070709-070813.unw.csv │ │ │ ├── ref_phase_est/ │ │ │ │ ├── ifg_orb_and_ref_phase_corrected_method2geo_060619-061002.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_corrected_method2geo_060828-061211.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_corrected_method2geo_061002-070219.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_corrected_method2geo_061002-070430.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_corrected_method2geo_061106-061211.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_corrected_method2geo_061106-070115.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_corrected_method2geo_061106-070326.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_corrected_method2geo_061211-070709.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_corrected_method2geo_061211-070813.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_corrected_method2geo_070115-070326.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_corrected_method2geo_070115-070917.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_corrected_method2geo_070219-070430.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_corrected_method2geo_070219-070604.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_corrected_method2geo_070326-070917.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_corrected_method2geo_070430-070604.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_corrected_method2geo_070604-070709.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_corrected_method2geo_070709-070813.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_correctedgeo_060619-061002.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_correctedgeo_060828-061211.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_correctedgeo_061002-070219.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_correctedgeo_061002-070430.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_correctedgeo_061106-061211.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_correctedgeo_061106-070115.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_correctedgeo_061106-070326.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_correctedgeo_061211-070709.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_correctedgeo_061211-070813.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_correctedgeo_070115-070326.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_correctedgeo_070115-070917.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_correctedgeo_070219-070430.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_correctedgeo_070219-070604.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_correctedgeo_070326-070917.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_correctedgeo_070430-070604.unw.csv │ │ │ │ ├── ifg_orb_and_ref_phase_correctedgeo_070604-070709.unw.csv │ │ │ │ └── ifg_orb_and_ref_phase_correctedgeo_070709-070813.unw.csv │ │ │ ├── roipac_obs/ │ │ │ │ ├── bad_epochs_ifms_17 │ │ │ │ ├── geo_060619-061002.unw │ │ │ │ ├── geo_060619-061002.unw.rsc │ │ │ │ ├── geo_060828-061211.unw │ │ │ │ ├── geo_060828-061211.unw.rsc │ │ │ │ ├── geo_061002-070219.unw │ │ │ │ ├── geo_061002-070219.unw.rsc │ │ │ │ ├── geo_061002-070430.unw │ │ │ │ ├── geo_061002-070430.unw.rsc │ │ │ │ ├── geo_061106-061211.unw │ │ │ │ ├── geo_061106-061211.unw.rsc │ │ │ │ ├── geo_061106-070115.unw │ │ │ │ ├── geo_061106-070115.unw.rsc │ │ │ │ ├── geo_061106-070326.unw │ │ │ │ ├── geo_061106-070326.unw.rsc │ │ │ │ ├── geo_061211-070709.unw │ │ │ │ ├── geo_061211-070709.unw.rsc │ │ │ │ ├── geo_061211-070813.unw │ │ │ │ ├── geo_061211-070813.unw.rsc │ │ │ │ ├── geo_070115-070326.unw │ │ │ │ ├── geo_070115-070326.unw.rsc │ │ │ │ ├── geo_070115-070917.unw │ │ │ │ ├── geo_070115-070917.unw.rsc │ │ │ │ ├── geo_070219-070430.unw │ │ │ │ ├── geo_070219-070430.unw.rsc │ │ │ │ ├── geo_070219-070604.unw │ │ │ │ ├── geo_070219-070604.unw.rsc │ │ │ │ ├── geo_070326-070917.unw │ │ │ │ ├── geo_070326-070917.unw.rsc │ │ │ │ ├── geo_070430-070604.unw │ │ │ │ ├── geo_070430-070604.unw.rsc │ │ │ │ ├── geo_070604-070709.unw │ │ │ │ ├── geo_070604-070709.unw.rsc │ │ │ │ ├── geo_070709-070813.unw │ │ │ │ ├── geo_070709-070813.unw.rsc │ │ │ │ ├── headers │ │ │ │ ├── ifms_17 │ │ │ │ ├── roipac_test_trimmed.dem │ │ │ │ └── roipac_test_trimmed.dem.rsc │ │ │ ├── stackrate/ │ │ │ │ ├── coh_sta.csv │ │ │ │ ├── errormap.csv │ │ │ │ └── stackmap.csv │ │ │ ├── tif/ │ │ │ │ ├── geo_060619-061002_unw.tif │ │ │ │ ├── geo_060828-061211_unw.tif │ │ │ │ ├── geo_061002-070219_unw.tif │ │ │ │ ├── geo_061002-070430_unw.tif │ │ │ │ ├── geo_061106-061211_unw.tif │ │ │ │ ├── geo_061106-070115_unw.tif │ │ │ │ ├── geo_061106-070326_unw.tif │ │ │ │ ├── geo_061211-070709_unw.tif │ │ │ │ ├── geo_061211-070813_unw.tif │ │ │ │ ├── geo_070115-070326_unw.tif │ │ │ │ ├── geo_070115-070917_unw.tif │ │ │ │ ├── geo_070219-070430_unw.tif │ │ │ │ ├── geo_070219-070604_unw.tif │ │ │ │ ├── geo_070326-070917_unw.tif │ │ │ │ ├── geo_070430-070604_unw.tif │ │ │ │ ├── geo_070604-070709_unw.tif │ │ │ │ ├── geo_070709-070813_unw.tif │ │ │ │ └── ifms_17 │ │ │ ├── time_series/ │ │ │ │ ├── ts_cum_interp0_method1.csv │ │ │ │ ├── ts_cum_interp0_method2.csv │ │ │ │ ├── ts_error_interp0_method1.csv │ │ │ │ ├── ts_incr_interp0_method1.csv │ │ │ │ └── ts_incr_interp0_method2.csv │ │ │ └── vcm/ │ │ │ ├── alpha.csv │ │ │ └── vcmt.csv │ │ └── system/ │ │ ├── gamma/ │ │ │ ├── 20060619-20061002_base.par │ │ │ ├── 20060619-20061002_utm.unw │ │ │ ├── 20060619-20061002_utm_unw.tif │ │ │ ├── 20060619_slc.par │ │ │ ├── 20060619_utm.dem │ │ │ ├── 20060619_utm_dem.par │ │ │ ├── 20060619_utm_dem.tif │ │ │ ├── 20060828-20061211_base.par │ │ │ ├── 20060828-20061211_utm.unw │ │ │ ├── 20060828-20061211_utm_unw.tif │ │ │ ├── 20060828_slc.par │ │ │ ├── 20061002-20070219_base.par │ │ │ ├── 20061002-20070219_utm.unw │ │ │ ├── 20061002-20070219_utm_unw.tif │ │ │ ├── 20061002-20070430_base.par │ │ │ ├── 20061002-20070430_utm.unw │ │ │ ├── 20061002-20070430_utm_unw.tif │ │ │ ├── 20061002_slc.par │ │ │ ├── 20061106-20061211_base.par │ │ │ ├── 20061106-20061211_utm.unw │ │ │ ├── 20061106-20061211_utm_unw.tif │ │ │ ├── 20061106-20070115_base.par │ │ │ ├── 20061106-20070115_utm.unw │ │ │ ├── 20061106-20070115_utm_unw.tif │ │ │ ├── 20061106-20070326_base.par │ │ │ ├── 20061106-20070326_utm.unw │ │ │ ├── 20061106-20070326_utm_unw.tif │ │ │ ├── 20061106_slc.par │ │ │ ├── 20061211-20070709_base.par │ │ │ ├── 20061211-20070709_utm.unw │ │ │ ├── 20061211-20070709_utm_unw.tif │ │ │ ├── 20061211-20070813_base.par │ │ │ ├── 20061211-20070813_utm.unw │ │ │ ├── 20061211-20070813_utm_unw.tif │ │ │ ├── 20061211_slc.par │ │ │ ├── 20070115-20070326_base.par │ │ │ ├── 20070115-20070326_utm.unw │ │ │ ├── 20070115-20070326_utm_unw.tif │ │ │ ├── 20070115-20070917_base.par │ │ │ ├── 20070115-20070917_utm.unw │ │ │ ├── 20070115-20070917_utm_unw.tif │ │ │ ├── 20070115_slc.par │ │ │ ├── 20070219-20070430_base.par │ │ │ ├── 20070219-20070430_utm.unw │ │ │ ├── 20070219-20070430_utm_unw.tif │ │ │ ├── 20070219-20070604_base.par │ │ │ ├── 20070219-20070604_utm.unw │ │ │ ├── 20070219-20070604_utm_unw.tif │ │ │ ├── 20070219_slc.par │ │ │ ├── 20070326-20070917_base.par │ │ │ ├── 20070326-20070917_utm.unw │ │ │ ├── 20070326-20070917_utm_unw.tif │ │ │ ├── 20070326_slc.par │ │ │ ├── 20070430-20070604_base.par │ │ │ ├── 20070430-20070604_utm.unw │ │ │ ├── 20070430-20070604_utm_unw.tif │ │ │ ├── 20070430_slc.par │ │ │ ├── 20070604-20070709_base.par │ │ │ ├── 20070604-20070709_utm.unw │ │ │ ├── 20070604-20070709_utm_unw.tif │ │ │ ├── 20070604_slc.par │ │ │ ├── 20070709-20070813_base.par │ │ │ ├── 20070709-20070813_utm.unw │ │ │ ├── 20070709-20070813_utm_unw.tif │ │ │ ├── 20070709_slc.par │ │ │ ├── 20070813_slc.par │ │ │ ├── 20070917_slc.par │ │ │ ├── baseline_list.txt │ │ │ ├── cropped_lookup_table.lt │ │ │ ├── header_list.txt │ │ │ ├── input_parameters.conf │ │ │ └── interferogram_list.txt │ │ ├── geotiff/ │ │ │ ├── 20060619-20061002_base.par │ │ │ ├── 20060619-20061002_utm_unw.tif │ │ │ ├── 20060619_slc.par │ │ │ ├── 20060619_utm_dem.par │ │ │ ├── 20060619_utm_dem.tif │ │ │ ├── 20060828-20061211_base.par │ │ │ ├── 20060828-20061211_utm_unw.tif │ │ │ ├── 20060828_slc.par │ │ │ ├── 20061002-20070219_base.par │ │ │ ├── 20061002-20070219_utm_unw.tif │ │ │ ├── 20061002-20070430_base.par │ │ │ ├── 20061002-20070430_utm_unw.tif │ │ │ ├── 20061002_slc.par │ │ │ ├── 20061106-20061211_base.par │ │ │ ├── 20061106-20061211_utm_unw.tif │ │ │ ├── 20061106-20070115_base.par │ │ │ ├── 20061106-20070115_utm_unw.tif │ │ │ ├── 20061106-20070326_base.par │ │ │ ├── 20061106-20070326_utm_unw.tif │ │ │ ├── 20061106_slc.par │ │ │ ├── 20061211-20070709_base.par │ │ │ ├── 20061211-20070709_utm_unw.tif │ │ │ ├── 20061211-20070813_base.par │ │ │ ├── 20061211-20070813_utm_unw.tif │ │ │ ├── 20061211_slc.par │ │ │ ├── 20070115-20070326_base.par │ │ │ ├── 20070115-20070326_utm_unw.tif │ │ │ ├── 20070115-20070917_base.par │ │ │ ├── 20070115-20070917_utm_unw.tif │ │ │ ├── 20070115_slc.par │ │ │ ├── 20070219-20070430_base.par │ │ │ ├── 20070219-20070430_utm_unw.tif │ │ │ ├── 20070219-20070604_base.par │ │ │ ├── 20070219-20070604_utm_unw.tif │ │ │ ├── 20070219_slc.par │ │ │ ├── 20070326-20070917_base.par │ │ │ ├── 20070326-20070917_utm_unw.tif │ │ │ ├── 20070326_slc.par │ │ │ ├── 20070430-20070604_base.par │ │ │ ├── 20070430-20070604_utm_unw.tif │ │ │ ├── 20070430_slc.par │ │ │ ├── 20070604-20070709_base.par │ │ │ ├── 20070604-20070709_utm_unw.tif │ │ │ ├── 20070604_slc.par │ │ │ ├── 20070709-20070813_base.par │ │ │ ├── 20070709-20070813_utm_unw.tif │ │ │ ├── 20070709_slc.par │ │ │ ├── 20070813_slc.par │ │ │ ├── 20070917_slc.par │ │ │ ├── baseline_list.txt │ │ │ ├── header_list.txt │ │ │ ├── input_parameters.conf │ │ │ └── interferogram_list.txt │ │ └── roipac/ │ │ ├── dem/ │ │ │ ├── roipac_test_trimmed.dem │ │ │ └── roipac_test_trimmed.dem.rsc │ │ ├── header_list.txt │ │ ├── headers/ │ │ │ ├── geo_060619-061002.unw.rsc │ │ │ ├── geo_060828-061211.unw.rsc │ │ │ ├── geo_061002-070219.unw.rsc │ │ │ ├── geo_061002-070430.unw.rsc │ │ │ ├── geo_061106-061211.unw.rsc │ │ │ ├── geo_061106-070115.unw.rsc │ │ │ ├── geo_061106-070326.unw.rsc │ │ │ ├── geo_061211-070709.unw.rsc │ │ │ ├── geo_061211-070813.unw.rsc │ │ │ ├── geo_070115-070326.unw.rsc │ │ │ ├── geo_070115-070917.unw.rsc │ │ │ ├── geo_070219-070430.unw.rsc │ │ │ ├── geo_070219-070604.unw.rsc │ │ │ ├── geo_070326-070917.unw.rsc │ │ │ ├── geo_070430-070604.unw.rsc │ │ │ ├── geo_070604-070709.unw.rsc │ │ │ └── geo_070709-070813.unw.rsc │ │ ├── input_parameters.conf │ │ ├── interferogram_list.txt │ │ └── interferograms/ │ │ ├── geo_060619-061002.unw │ │ ├── geo_060619-061002.unw.rsc │ │ ├── geo_060619-061002_unw.tif │ │ ├── geo_060828-061211.unw │ │ ├── geo_060828-061211.unw.rsc │ │ ├── geo_060828-061211_unw.tif │ │ ├── geo_061002-070219.unw │ │ ├── geo_061002-070219.unw.rsc │ │ ├── geo_061002-070219_unw.tif │ │ ├── geo_061002-070430.unw │ │ ├── geo_061002-070430.unw.rsc │ │ ├── geo_061002-070430_unw.tif │ │ ├── geo_061106-061211.unw │ │ ├── geo_061106-061211.unw.rsc │ │ ├── geo_061106-061211_unw.tif │ │ ├── geo_061106-070115.unw │ │ ├── geo_061106-070115.unw.rsc │ │ ├── geo_061106-070115_unw.tif │ │ ├── geo_061106-070326.unw │ │ ├── geo_061106-070326.unw.rsc │ │ ├── geo_061106-070326_unw.tif │ │ ├── geo_061211-070709.unw │ │ ├── geo_061211-070709.unw.rsc │ │ ├── geo_061211-070709_unw.tif │ │ ├── geo_061211-070813.unw │ │ ├── geo_061211-070813.unw.rsc │ │ ├── geo_061211-070813_unw.tif │ │ ├── geo_070115-070326.unw │ │ ├── geo_070115-070326.unw.rsc │ │ ├── geo_070115-070326_unw.tif │ │ ├── geo_070115-070917.unw │ │ ├── geo_070115-070917.unw.rsc │ │ ├── geo_070115-070917_unw.tif │ │ ├── geo_070219-070430.unw │ │ ├── geo_070219-070430.unw.rsc │ │ ├── geo_070219-070430_unw.tif │ │ ├── geo_070219-070604.unw │ │ ├── geo_070219-070604.unw.rsc │ │ ├── geo_070219-070604_unw.tif │ │ ├── geo_070326-070917.unw │ │ ├── geo_070326-070917.unw.rsc │ │ ├── geo_070326-070917_unw.tif │ │ ├── geo_070430-070604.unw │ │ ├── geo_070430-070604.unw.rsc │ │ ├── geo_070430-070604_unw.tif │ │ ├── geo_070604-070709.unw │ │ ├── geo_070604-070709.unw.rsc │ │ ├── geo_070604-070709_unw.tif │ │ ├── geo_070709-070813.unw │ │ ├── geo_070709-070813.unw.rsc │ │ ├── geo_070709-070813_unw.tif │ │ └── roipac_test_trimmed_dem.tif │ ├── test_dem_error.py │ ├── test_gamma.py │ ├── test_gamma_vs_roipac.py │ ├── test_gdal_python.py │ ├── test_geometry.py │ ├── test_merge.py │ ├── test_mpi.py │ ├── test_mpi_vs_multiprocess_vs_single_process.py │ ├── test_mst.py │ ├── test_orbital.py │ ├── test_prepifg.py │ ├── test_prepifg_system_vs_python.py │ ├── test_pyrate.py │ ├── test_ref_phs_est.py │ ├── test_refpixel.py │ ├── test_roipac.py │ ├── test_shared.py │ ├── test_stackrate.py │ ├── test_system.py │ └── test_timeseries.py └── utils/ ├── .coveragerc ├── __init__.py ├── create_lv_theta.py ├── crop_ifgs.py ├── developer_hints.txt ├── docker_install.txt ├── gdaldem.py ├── list_creator.sh ├── make_tscuml_animation.py ├── plot_correction_files.py ├── plot_linear_rate_profile.py ├── plot_sbas_network.py ├── plot_time_series.py └── pyrate_pycallgraph.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .coveragerc ================================================ # .coveragerc to control coverage.py [run] branch = True source = pyrate #omit = # omit everything in # pyrate/tasks/* # omit these files # pyrate/pyratelog.py # pyrate/scripts/main.py # pyrate/pyaps.py # pyrate/compat.py [report] # Regexes for lines to exclude from consideration exclude_lines = # Have to re-enable the standard pragma pragma: no cover # Don't complain about missing debug-only code: def __repr__ def __str__ if self\.debug # Don't complain if tests don't hit defensive assertion code: raise AssertionError raise NotImplementedError raise ValueError raise RuntimeError raise IOError except OSError except ValueError except IndexError raise ImportError except ImportError raise ConfigException raise ReferencePhaseError raise RefPixelError raise RoipacException raise GeotiffException raise RasterException raise IfgException raise GammaException raise PreprocessError raise TimeSeriesError raise OrbitalError # Don't complain if non-runnable code isn't run: if 0: if __name__ == .__main__.: ignore_errors = True [html] directory = coverage_html_report ================================================ FILE: .github/ISSUE_TEMPLATE/bug_report.md ================================================ --- name: Bug report about: Create a report to help us improve title: '' labels: '' assignees: '' --- **Describe the bug** A clear and concise description of what the bug is. **To Reproduce** Steps to reproduce the behavior: 1. Go to '...' 2. Click on '....' 3. Scroll down to '....' 4. See error **Expected behavior** A clear and concise description of what you expected to happen. **Screenshots** If applicable, add screenshots to help explain your problem. **Desktop (please complete the following information):** - OS: [e.g. iOS] - Browser [e.g. chrome, safari] - Version [e.g. 22] **Smartphone (please complete the following information):** - Device: [e.g. iPhone6] - OS: [e.g. iOS8.1] - Browser [e.g. stock browser, safari] - Version [e.g. 22] **Additional context** Add any other context about the problem here. ================================================ FILE: .github/ISSUE_TEMPLATE/feature_request.md ================================================ --- name: Feature request about: Suggest an idea for this project title: '' labels: '' assignees: '' --- **Is your feature request related to a problem? Please describe.** A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] **Describe the solution you'd like** A clear and concise description of what you want to happen. **Describe alternatives you've considered** A clear and concise description of any alternative solutions or features you've considered. **Additional context** Add any other context or screenshots about the feature request here. ================================================ FILE: .github/workflows/build.yml ================================================ name: PyRate CI on: pull_request: branches: - 'master' push: branches: - '**' jobs: build: if: ${{ ! (contains(github.event.head_commit.message, 'ci skip') || contains(github.event.head_commit.message, 'skip ci'))}} runs-on: ubuntu-18.04 name: Python ${{ matrix.python }} strategy: matrix: include: - build: 1 python-version: "3.7" GDALVERSION: "3.0.2" PROJVERSION: "6.2.1" - build: 2 python-version: "3.7" GDALVERSION: "3.0.4" PROJVERSION: "6.1.1" - build: 3 python-version: "3.8" GDALVERSION: "3.0.4" PROJVERSION: "6.1.1" - build: 4 python-version: "3.9" GDALVERSION: "3.0.4" PROJVERSION: "6.3.2" env: PIP_WHEEL_DIR: "/home/runner/.cache/pip/wheels" PIP_FIND_LINKS: "file:///home/runner/.cache/pip/wheels" GDALINST: "/home/runner/gdalinstall" GDALBUILD: "/home/runner/gdalbuild" GDALVERSION: ${{ matrix.GDALVERSION }} PROJINST: "/home/runner/gdalinstall" PROJBUILD: "/home/runner/projbuild" PROJVERSION: ${{ matrix.PROJVERSION }} PYTHONVERSION: ${{ matrix.python-version }} steps: - uses: actions/checkout@v2 - name: Cache multiple paths uses: actions/cache@v2 with: path: | /home/runner/gdalinstall key: ${{ runner.os }}-cache-GDAL-${{ matrix.GDALVERSION }}-proj-${{ matrix.PROJVERSION }} - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v2 with: python-version: ${{ matrix.python-version }} - name: Common set up ${{ matrix.python-version }} run: | sudo apt update sudo apt upgrade python -m pip install -U pip wheel setuptools==58.0 - name: Install packages including openmpi if: env.PYTHONVERSION != '3.9' run: sudo apt install libhdf5-serial-dev libnetcdf13 libatlas-base-dev gfortran openmpi-bin libopenmpi-dev - name: Install packages except openmpi libraries if: env.PYTHONVERSION == '3.9' run: sudo apt install libhdf5-serial-dev libnetcdf13 libatlas-base-dev gfortran - name: Install proj ${{matrix.PROJVERSION}} run: | echo "PATH=$GDALINST/gdal-$GDALVERSION/bin:$PATH" >> $GITHUB_ENV echo "LD_LIBRARY_PATH=$GDALINST/gdal-$GDALVERSION/lib:$LD_LIBRARY_PATH" >> $GITHUB_ENV source ./scripts/ci_proj_install.sh - name: Install GDAL ${{matrix.GDALVERSION}} run: | echo "GDAL_DATA=$GDALINST/gdal-$GDALVERSION/share/gdal" >> $GITHUB_ENV echo "PROJ_LIB=$GDALINST/gdal-$GDALVERSION/share/proj" >> $GITHUB_ENV echo "LDFLAGS=-L$GDALINST/gdal-$GDALVERSION/lib -Wl,-rpath,$GDALINST/gdal-$GDALVERSION/lib" >> $GITHUB_ENV source ./scripts/ci_gdal_install.sh - name: Python ${{ matrix.python-version }} with MPI if: env.PYTHONVERSION != '3.9' run: | python setup.py install rm -rf Py_Rate.egg-info # remove the local egg echo "PYTHONPATH=$(pwd):$PYTHONPATH" >> $GITHUB_ENV chmod 444 tests/test_data/small_test/tif/geo_070709-070813_unw.tif # makes the file readonly, used in a test pip install -r requirements-dev.txt -r requirements-test.txt - name: Python ${{ matrix.python-version }} without MPI if: env.PYTHONVERSION == '3.9' run: | python setup.py install rm -rf Py_Rate.egg-info # remove the local egg echo "PYTHONPATH=$(pwd):$PYTHONPATH" >> $GITHUB_ENV chmod 444 tests/test_data/small_test/tif/geo_070709-070813_unw.tif # makes the file readonly, used in a test pip install -r requirements-test.txt - name: Test Pyrate in Python ${{ matrix.python-version }} with MPI if: env.PYTHONVERSION != '3.9' run: | pytest tests/ -m "slow" mpirun -n 3 pytest tests/test_shared.py -k test_tiles_split -s pytest --cov-config=.coveragerc --cov-report term-missing:skip-covered --cov=pyrate tests/ -m "not slow" env: OMP_NUM_THREADS: 1 - name: Test Pyrate in Python ${{ matrix.python-version }} without MPI if: env.PYTHONVERSION == '3.9' run: | pytest tests/ -m "not mpi and slow" pytest --cov-config=.coveragerc --cov-report term-missing:skip-covered --cov=pyrate tests/ -m "not slow and not mpi" env: OMP_NUM_THREADS: 1 ================================================ FILE: .gitignore ================================================ !*docs/_build/html/404.html !*tests/test_data/merge/linrate.tif !*tests/test_data/small_test/gamma_obs/20060619-20061002_utm_unw.tif *data/* *out* *.pyo *.pyc .project .pydevproject /tmp /.idea .spyderworkspace doc .eggs/ .tox/ .coverage.* .vscode/ *.egg-info/ .installed.cfg *.egg *__pycache__/ # files generated by doc build docs/_build/ # files generated by setup .cache/ build/ dist/ # out Py_Rate.egg-info # files generated during tests *tests/test_data/prepifg/tif/0_1rlks_4cr.tif *tests/test_data/prepifg/tif/0_2rlks_4cr.tif *tests/test_data/prepifg/tif/1_1rlks_4cr.tif *tests/test_data/prepifg/tif/1_2rlks_4cr.tif *tests/test_data/merge/colormap.txt *tests/test_data/merge/linrate.kml *tests/test_data/merge/linrate.png *tests/test_data/merge/linrate.png.aux.xml *tests/test_data/merge/linrate.tif.aux.xml *tests/test_data/small_test/gamma_obs/*.tif ================================================ FILE: .pylintrc ================================================ [MASTER] # Specify a configuration file. #rcfile= # Python code to execute, usually for sys.path manipulation such as # pygtk.require(). #init-hook= # Add files or directories to the blacklist. They should be base names, not # paths. ignore=CVS # Add files or directories matching the regex patterns to the blacklist. The # regex matches against base names, not paths. ignore-patterns= # Pickle collected data for later comparisons. persistent=yes # List of plugins (as comma separated values of python modules names) to load, # usually to register additional checkers. load-plugins= # Use multiple processes to speed up Pylint. jobs=1 # Allow loading of arbitrary C extensions. Extensions are imported into the # active Python interpreter and may run arbitrary code. unsafe-load-any-extension=no # A comma-separated list of package or module names from where C extensions may # be loaded. Extensions are loading into the active Python interpreter and may # run arbitrary code extension-pkg-whitelist= # Allow optimization of some AST trees. This will activate a peephole AST # optimizer, which will apply various small optimizations. For instance, it can # be used to obtain the result of joining multiple strings with the addition # operator. Joining a lot of strings can lead to a maximum recursion error in # Pylint and this flag can prevent that. It has one side effect, the resulting # AST will be different than the one from reality. This option is deprecated # and it will be removed in Pylint 2.0. optimize-ast=no [MESSAGES CONTROL] # Only show warnings with the listed confidence levels. Leave empty to show # all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED confidence= # Enable the message, report, category or checker with the given id(s). You can # either give multiple identifier separated by comma (,) or put this option # multiple time (only on the command line, not in the configuration file where # it should appear only once). See also the "--disable" option for examples. #enable= # Disable the message, report, category or checker with the given id(s). You # can either give multiple identifiers separated by comma (,) or put this # option multiple times (only on the command line, not in the configuration # file where it should appear only once).You can also use "--disable=all" to # disable everything first and then reenable specific checks. For example, if # you want to run only the similarities checker, you can use "--disable=all # --enable=similarities". If you want to run only the classes checker, but have # no Warning level messages displayed, use"--disable=all --enable=classes # --disable=W" disable=logging-format-interpolation,too-few-public-methods,import-star-module-level,old-octal-literal,oct-method,print-statement,unpacking-in-except,parameter-unpacking,backtick,old-raise-syntax,old-ne-operator,long-suffix,dict-view-method,dict-iter-method,metaclass-assignment,next-method-called,raising-string,indexing-exception,raw_input-builtin,long-builtin,file-builtin,execfile-builtin,coerce-builtin,cmp-builtin,buffer-builtin,basestring-builtin,apply-builtin,filter-builtin-not-iterating,using-cmp-argument,useless-suppression,range-builtin-not-iterating,suppressed-message,no-absolute-import,old-division,cmp-method,reload-builtin,zip-builtin-not-iterating,intern-builtin,unichr-builtin,reduce-builtin,standarderror-builtin,unicode-builtin,xrange-builtin,coerce-method,delslice-method,getslice-method,setslice-method,input-builtin,round-builtin,hex-method,nonzero-method,map-builtin-not-iterating,fixme [REPORTS] # Set the output format. Available formats are text, parseable, colorized, msvs # (visual studio) and html. You can also give a reporter class, eg # mypackage.mymodule.MyReporterClass. output-format=text # Put messages in a separate file for each module / package specified on the # command line instead of printing them on stdout. Reports (if any) will be # written in a file name "pylint_global.[txt|html]". This option is deprecated # and it will be removed in Pylint 2.0. files-output=no # Tells whether to display a full report or only the messages reports=yes # Python expression which should return a note less than 10 (10 is the highest # note). You have access to the variables errors warning, statement which # respectively contain the number of errors / warnings messages and the total # number of statements analyzed. This is used by the global evaluation report # (RP0004). evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10) # Template used to display messages. This is a python new-style format string # used to format the message information. See doc for all details #msg-template= [MISCELLANEOUS] # List of note tags to take in consideration, separated by a comma. notes=FIXME,XXX,TODO [LOGGING] # Logging modules to check that the string format arguments are in logging # function parameter format logging-modules=logging [SPELLING] # Spelling dictionary name. Available dictionaries: none. To make it working # install python-enchant package. spelling-dict= # List of comma separated words that should not be checked. spelling-ignore-words= # A path to a file that contains private dictionary; one word per line. spelling-private-dict-file= # Tells whether to store unknown words to indicated private dictionary in # --spelling-private-dict-file option instead of raising a message. spelling-store-unknown-words=no [TYPECHECK] # Tells whether missing members accessed in mixin class should be ignored. A # mixin class is detected if its name ends with "mixin" (case insensitive). ignore-mixin-members=yes # List of module names for which member attributes should not be checked # (useful for modules/projects where namespaces are manipulated during runtime # and thus existing member attributes cannot be deduced by static analysis. It # supports qualified module names, as well as Unix pattern matching. ignored-modules=numpy # List of class names for which member attributes should not be checked (useful # for classes with dynamically set attributes). This supports the use of # qualified names. ignored-classes=optparse.Values,thread._local,_thread._local # List of members which are set dynamically and missed by pylint inference # system, and so shouldn't trigger E1101 when accessed. Python regular # expressions are accepted. generated-members= # List of decorators that produce context managers, such as # contextlib.contextmanager. 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A callback # name must start or end with one of those strings. callbacks=cb_,_cb # List of qualified module names which can have objects that can redefine # builtins. redefining-builtins-modules=six.moves,future.builtins [SIMILARITIES] # Minimum lines number of a similarity. min-similarity-lines=4 # Ignore comments when computing similarities. ignore-comments=yes # Ignore docstrings when computing similarities. ignore-docstrings=yes # Ignore imports when computing similarities. ignore-imports=no [FORMAT] # Maximum number of characters on a single line. max-line-length=100 # Regexp for a line that is allowed to be longer than the limit. ignore-long-lines=^\s*(# )??$ # Allow the body of an if to be on the same line as the test if there is no # else. single-line-if-stmt=no # List of optional constructs for which whitespace checking is disabled. `dict- # separator` is used to allow tabulation in dicts, etc.: {1 : 1,\n222: 2}. # `trailing-comma` allows a space between comma and closing bracket: (a, ). # `empty-line` allows space-only lines. no-space-check=trailing-comma,dict-separator # Maximum number of lines in a module max-module-lines=1000 # String used as indentation unit. This is usually " " (4 spaces) or "\t" (1 # tab). indent-string=' ' # Number of spaces of indent required inside a hanging or continued line. indent-after-paren=4 # Expected format of line ending, e.g. empty (any line ending), LF or CRLF. expected-line-ending-format= [BASIC] # Good variable names which should always be accepted, separated by a comma good-names=ts,i,j,k,ex,Run,_,x,w,f,n,v,t,r,d,c,cd,g,r,q,V,m,A,b,log,y,id,ds,gt,md # Bad variable names which should always be refused, separated by a comma bad-names=foo,bar,baz,toto,tutu,tata # Colon-delimited sets of names that determine each other's naming style when # the name regexes allow several styles. name-group= # Include a hint for the correct naming format with invalid-name include-naming-hint=no # List of decorators that produce properties, such as abc.abstractproperty. Add # to this list to register other decorators that produce valid properties. property-classes=abc.abstractproperty # Regular expression matching correct function names function-rgx=[a-z_][a-z0-9_]{2,30}$ # Naming hint for function names function-name-hint=[a-z_][a-z0-9_]{2,30}$ # Regular expression matching correct variable names variable-rgx=[a-z_][a-z0-9_]{2,30}$ # Naming hint for variable names variable-name-hint=[a-z_][a-z0-9_]{2,30}$ # Regular expression matching correct constant names const-rgx=(([A-Z_][A-Z0-9_]*)|(__.*__))$ # Naming hint for constant names const-name-hint=(([A-Z_][A-Z0-9_]*)|(__.*__))$ # Regular expression matching correct attribute names attr-rgx=[a-z_][a-z0-9_]{2,30}$ # Naming hint for attribute names attr-name-hint=[a-z_][a-z0-9_]{2,30}$ # Regular expression matching correct argument names argument-rgx=[a-z_][a-z0-9_]{2,30}$ # Naming hint for argument names argument-name-hint=[a-z_][a-z0-9_]{2,30}$ # Regular expression matching correct class attribute names class-attribute-rgx=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$ # Naming hint for class attribute names class-attribute-name-hint=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$ # Regular expression matching correct inline iteration names inlinevar-rgx=[A-Za-z_][A-Za-z0-9_]*$ # Naming hint for inline iteration names inlinevar-name-hint=[A-Za-z_][A-Za-z0-9_]*$ # Regular expression matching correct class names class-rgx=[A-Z_][a-zA-Z0-9]+$ # Naming hint for class names class-name-hint=[A-Z_][a-zA-Z0-9]+$ # Regular expression matching correct module names module-rgx=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$ # Naming hint for module names module-name-hint=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$ # Regular expression matching correct method names method-rgx=[a-z_][a-z0-9_]{2,30}$ # Naming hint for method names method-name-hint=[a-z_][a-z0-9_]{2,30}$ # Regular expression which should only match function or class names that do # not require a docstring. no-docstring-rgx=^_ # Minimum line length for functions/classes that require docstrings, shorter # ones are exempt. docstring-min-length=-1 [ELIF] # Maximum number of nested blocks for function / method body max-nested-blocks=5 [DESIGN] # Maximum number of arguments for function / method max-args=5 # Argument names that match this expression will be ignored. Default to name # with leading underscore ignored-argument-names=_.* # Maximum number of locals for function / method body max-locals=15 # Maximum number of return / yield for function / method body max-returns=6 # Maximum number of branch for function / method body max-branches=12 # Maximum number of statements in function / method body max-statements=50 # Maximum number of parents for a class (see R0901). max-parents=7 # Maximum number of attributes for a class (see R0902). max-attributes=7 # Minimum number of public methods for a class (see R0903). min-public-methods=2 # Maximum number of public methods for a class (see R0904). max-public-methods=20 # Maximum number of boolean expressions in a if statement max-bool-expr=5 [IMPORTS] # Deprecated modules which should not be used, separated by a comma deprecated-modules=regsub,TERMIOS,Bastion,rexec # Create a graph of every (i.e. internal and external) dependencies in the # given file (report RP0402 must not be disabled) import-graph= # Create a graph of external dependencies in the given file (report RP0402 must # not be disabled) ext-import-graph= # Create a graph of internal dependencies in the given file (report RP0402 must # not be disabled) int-import-graph= # Force import order to recognize a module as part of the standard # compatibility libraries. known-standard-library= # Force import order to recognize a module as part of a third party library. known-third-party=enchant # Analyse import fallback blocks. This can be used to support both Python 2 and # 3 compatible code, which means that the block might have code that exists # only in one or another interpreter, leading to false positives when analysed. analyse-fallback-blocks=no [CLASSES] # List of method names used to declare (i.e. assign) instance attributes. defining-attr-methods=__init__,__new__,setUp # List of valid names for the first argument in a class method. valid-classmethod-first-arg=cls # List of valid names for the first argument in a metaclass class method. valid-metaclass-classmethod-first-arg=mcs # List of member names, which should be excluded from the protected access # warning. exclude-protected=_asdict,_fields,_replace,_source,_make [EXCEPTIONS] # Exceptions that will emit a warning when being caught. Defaults to # "Exception" overgeneral-exceptions=Exception ================================================ FILE: Dockerfile ================================================ FROM ubuntu:16.04 ENV LANG C.UTF-8 ENV LANG=en_AU.UTF-8 ENV WORKON_HOME=$HOME/venvs ENV GDALINST=$HOME/gdalinstall ENV GDALBUILD=$HOME/gdalbuild ENV PROJINST=$HOME/gdalinstall ENV GDALVERSION="3.0.2" ENV PROJVERSION="6.1.1" ENV PROJOPT="--with-proj=$GDALINST/gdal-$GDALVERSION" ENV PATH=$GDALINST/gdal-$GDALVERSION/bin:$PATH ENV LD_LIBRARY_PATH=$GDALINST/gdal-$GDALVERSION/lib:$LD_LIBRARY_PATH ENV PROJ_LIB=$GDALINST/gdal-$GDALVERSION/share/proj ENV GDAL_DATA=$GDALINST/gdal-$GDALVERSION/share/gdal RUN apt-get update \ && apt-get install -y build-essential checkinstall libreadline-gplv2-dev \ libncursesw5-dev libssl-dev libsqlite3-dev tk-dev libgdbm-dev libc6-dev \ libbz2-dev openssl curl libffi-dev RUN mkdir -p $HOME/opt RUN cd $HOME/opt \ && curl -O https://www.python.org/ftp/python/3.7.7/Python-3.7.7.tgz \ && tar -xzf Python-3.7.7.tgz \ && cd Python-3.7.7 \ && ./configure --enable-shared --enable-optimizations --prefix=/usr/local LDFLAGS="-Wl,--rpath=/usr/local/lib" \ && make altinstall RUN apt-get install -y build-essential python3-pip \ apt-utils git libgdal-dev libatlas-base-dev openmpi-bin \ libopenmpi-dev gfortran wget libhdf5-serial-dev sqlite3 vim # update pip RUN python3.7 -m pip install pip --upgrade RUN python3.7 -m pip install wheel RUN pip3 install --upgrade setuptools RUN mkdir -p $GDALBUILD $GDALINST $PROJBUILD $PROJINST ENV GDALOPTS=" --with-geos \ --with-expat \ --without-libtool \ --with-libz=internal \ --with-libtiff=internal \ --with-geotiff=internal \ --without-gif \ --without-pg \ --without-grass \ --without-libgrass \ --without-cfitsio \ --without-pcraster \ --with-netcdf \ --with-png=internal \ --with-jpeg=internal \ --without-gif \ --without-ogdi \ --without-fme \ --without-hdf4 \ --with-hdf5 \ --without-jasper \ --without-ecw \ --without-kakadu \ --without-mrsid \ --without-jp2mrsid \ --without-mysql \ --without-ingres \ --without-xerces \ --without-odbc \ --with-curl \ --without-sqlite3 \ --without-idb \ --without-sde \ --without-perl \ --without-python" RUN cd $PROJBUILD && wget -q https://download.osgeo.org/proj/proj-$PROJVERSION.tar.gz \ && tar -xzf proj-$PROJVERSION.tar.gz \ && cd proj-$PROJVERSION \ && ./configure --prefix=$PROJINST/gdal-$GDALVERSION \ && make -s -j 2 \ && make install RUN cd $GDALBUILD \ && wget -q http://download.osgeo.org/gdal/$GDALVERSION/gdal-$GDALVERSION.tar.gz \ && tar -xzf gdal-$GDALVERSION.tar.gz RUN cd $GDALBUILD/gdal-$GDALVERSION \ && ./configure --prefix=$GDALINST/gdal-$GDALVERSION $GDALOPTS $PROJOPT RUN cd $GDALBUILD/gdal-$GDALVERSION && make RUN cd $GDALBUILD/gdal-$GDALVERSION && make install RUN pip install virtualenv virtualenvwrapper ENV VIRTUALENVWRAPPER_PYTHON=/usr/local/bin/python3.7 ADD . / PyRate/ SHELL ["/bin/bash", "-c"] RUN source /usr/local/bin/virtualenvwrapper.sh \ && mkvirtualenv -p python3.7 pyrate \ && cd PyRate \ && sed -i '/^GDAL/d' requirements.txt \ && workon pyrate \ && pip install -r requirements.txt -r requirements-dev.txt -r requirements-test.txt \ && pip install GDAL==$(gdal-config --version) \ && python setup.py install ================================================ FILE: LICENSE ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: README.rst ================================================ .. image:: docs/PyRate_logo_50.png :alt: PyRate logo Python tool for InSAR Rate and Time-series Estimation ===================================================== .. image:: https://github.com/GeoscienceAustralia/PyRate/workflows/PyRate%20CI/badge.svg?branch=master :target: https://github.com/GeoscienceAustralia/PyRate/actions .. image:: https://codecov.io/gh/GeoscienceAustralia/PyRate/branch/master/graph/badge.svg :target: https://codecov.io/gh/GeoscienceAustralia/PyRate .. image:: https://img.shields.io/badge/License-Apache%202.0-blue.svg :target: https://opensource.org/licenses/Apache-2.0 .. image:: https://img.shields.io/pypi/pyversions/Py-Rate :target: https://pypi.org/project/Py-Rate/ PyRate is a Python tool for estimating the average displacement rate (velocity) and cumulative displacement time-series of surface movements for every pixel in a stack of geocoded unwrapped interferograms generated by Interferometric Synthetic Aperture Radar (InSAR) processing. PyRate uses a "Small Baseline Subset" (SBAS) processing strategy and currently supports input data in the GAMMA or ROI_PAC software formats. Additionally, the European Space Agency `SNAP software `_ version 8 has a "PyRate export" capability that prepares SNAP output data in the GAMMA format for use with PyRate. The PyRate project started in 2012 as a partial Python translation of "Pirate", a Matlab tool developed by the University of Leeds and the Guangdong University of Technology. The full PyRate documentation is available at http://geoscienceaustralia.github.io/PyRate Dependencies ------------ The following system dependencies are required by PyRate: - `Python `_, versions 3.7, 3.8 or 3.9. - `GDAL `_, versions 3.0.2 or 3.0.4 The following optional dependency is required for MPI processing capability: - `Open MPI `_, versions 2.1.6, 3.0.4, 3.1.4 or 4.0.2 The versions of each package stated above have been tested to work using `GitHub Actions `_ continuous integration testing. Python dependencies for PyRate are:: joblib==1.0.0 mpi4py==3.0.3 networkx==2.5 numpy==1.19.4 pyproj==3.0.0 scipy==1.5.4 numexpr==2.7.2 nptyping==1.4.0 Install ------- Details of all install options are given in the `PyRate documentation `_. PyRate and its Python dependencies can be installed directly from the `Python Package Index (PyPI) `_:: pip install Py-Rate Alternatively, to install from source and create an executable program in Linux, enter these commands in a terminal:: cd ~ git clone https://github.com/GeoscienceAustralia/PyRate.git python3 -m venv ~/PyRateVenv source ~/PyRateVenv/bin/activate cd ~/PyRate python3 setup.py install This will install the above-listed Python dependencies and compile the executable program ``pyrate``. To learn more about using PyRate, type ``pyrate`` command in the terminal:: >> pyrate --help usage: pyrate [-h] [-v {DEBUG,INFO,WARNING,ERROR}] {conv2tif,prepifg,correct,timeseries,stack,merge,workflow} ... PyRate workflow: Step 1: conv2tif Step 2: prepifg Step 3: correct Step 4: timeseries Step 5: stack Step 6: merge Refer to https://geoscienceaustralia.github.io/PyRate/usage.html for more details. positional arguments: {conv2tif,prepifg,correct,timeseries,stack,merge,workflow} conv2tif Convert interferograms to geotiff. prepifg Perform multilooking, cropping and coherence masking to interferogram geotiffs. correct Calculate and apply corrections to interferogram phase data. timeseries Timeseries inversion of interferogram phase data. stack Stacking of interferogram phase data. merge Reassemble computed tiles and save as geotiffs. workflow Sequentially run all the PyRate processing steps. optional arguments: -h, --help show this help message and exit -v {DEBUG,INFO,WARNING,ERROR}, --verbosity {DEBUG,INFO,WARNING,ERROR} Increase output verbosity Test ---- To run the test suite, enter these commands in the terminal:: pip install -r requirements-test.txt python3 -m pytest -m "not slow" tests/ To run the tests for a single module (e.g. test_timeseries.py), use this command:: python3 -m pytest tests/test_timeseries.py ================================================ FILE: __init__.py ================================================ ================================================ FILE: docs/Makefile ================================================ # Minimal makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build SPHINXPROJ = PyRate SOURCEDIR = . BUILDDIR = _build # Put it first so that "make" without argument is like "make help". help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) .PHONY: help Makefile # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) ================================================ FILE: docs/algorithm.rst ================================================ Algorithm Module ================ .. automodule:: pyrate.core.algorithm :members: ================================================ FILE: docs/aps.rst ================================================ Atmospheric Phase Screen Module =============================== .. automodule:: pyrate.core.aps :members: ================================================ FILE: docs/authors.rst ================================================ ======= Credits ======= Development Lead ---------------- `PyRate` has been developed by `Geoscience Australia `__ with some initial assistance from the `National Computational Infrastructure `__. Contact: `Geoscience Australia InSAR team `__ Contributors ------------ Thanks to `all the contributors`_ who helped to build the ship and raise the sail, Arrrr! .. _`all the contributors`: https://github.com/GeoscienceAustralia/PyRate/graphs/contributors * Sudipta Basak (GA) * Ben Davies (NCI) * Alistair Deane (GA) * Thomas Fuhrmann (GA) * Syed Sheece Raza Gardezi (GA) * Matt Garthwaite (GA) * Simon Knapp (GA) * Sarah Lawrie (GA) * Jonathan Mettes (GA) * Negin Moghaddam (GA) * Brenainn Moushall (GA) * Vanessa Newey (GA) * Chandrakanta Ojha (GA) * Garrick Paskos (GA) * Nahidul Samrat (GA) * Richard Taylor (GA) ================================================ FILE: docs/conf.py ================================================ #!/usr/bin/env python3 # -*- coding: utf-8 -*- import os import datetime import sphinx_rtd_theme __version__ = "0.6.0" # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. # # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = ['sphinx.ext.autodoc', 'sphinx.ext.imgmath', 'sphinx.ext.viewcode', 'sphinx.ext.githubpages', 'sphinx.ext.mathjax', 'sphinx.ext.autosummary', 'sphinx.ext.napoleon', 'matplotlib.sphinxext.plot_directive', 'IPython.sphinxext.ipython_console_highlighting', 'IPython.sphinxext.ipython_directive', 'sphinxcontrib.programoutput', 'm2r2' ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # from recommonmark.parser import CommonMarkParser source_parsers = {'.md': CommonMarkParser} source_suffix = ['.rst', '.md'] # source_suffix = '.rst' # The main toctree document. main_doc = 'index' # General information about the project. project = 'PyRate' copyright = str(datetime.datetime.now().year)+', Geoscience Australia' author = 'Geoscience Australia InSAR Team' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = __version__ # The full version, including alpha/beta/rc tags. release = __version__ # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = 'en' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = "sphinx_rtd_theme" html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] html_logo = "PyRate_logo.png" html_show_sourcelink = False # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # # html_theme_options = {} html_theme_options = { 'collapse_navigation': False, 'sticky_navigation': True, 'display_version': False, # 'titles_only': True, 'navigation_depth': 2, 'logo_only': True, 'prev_next_buttons_location': 'top', 'style_nav_header_background': 'white' } # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = [os.path.join('_build', 'html', '_static')] # -- Options for HTMLHelp output ------------------------------------------ # Output file base name for HTML help builder. htmlhelp_basename = 'PyRatedoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (main_doc, 'PyRate.tex', 'PyRate Documentation', 'Geoscience Australia InSAR team', 'manual'), ] # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (main_doc, 'pyrate', 'PyRate Documentation', [author], 1) ] # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (main_doc, 'PyRate', 'PyRate Documentation', author, 'PyRate', 'One line description of project.', 'Miscellaneous'), ] # -- Options for Epub output ---------------------------------------------- # Bibliographic Dublin Core info. epub_title = project epub_author = author epub_publisher = author epub_copyright = copyright # The unique identifier of the text. This can be a ISBN number # or the project homepage. # # epub_identifier = '' # A unique identification for the text. # # epub_uid = '' # A list of files that should not be packed into the epub file. epub_exclude_files = ['search.html'] # link check ignore linkcheck_ignore = [r'http://localhost:\d+/', 'https://github.com/Nekroze/PyRate/fork'] def setup(app): app.add_stylesheet('css/custom.css') ================================================ FILE: docs/configuration.rst ================================================ Configuration Module ==================== .. automodule:: pyrate.configuration :members: ================================================ FILE: docs/contributing.rst ================================================ ============ Contributing ============ Contributions to `PyRate` are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given. Types of Contributions ---------------------- You can contribute to `PyRate` in many ways: Report Bugs ^^^^^^^^^^^ Report bugs on the `Github issues`_ page. If you are reporting a bug, please include: .. _`Github issues`: https://github.com/GeoscienceAustralia/PyRate/issues * Your operating system name and version. * Any details about your local setup that might be helpful in troubleshooting. * Detailed steps to reproduce the bug. For an example of how to report a bug please see: https://github.com/GeoscienceAustralia/PyRate/issues/146 Fix Bugs ^^^^^^^^ Look for any `GitHub issues`_ with the "bug" label. Implement Features ^^^^^^^^^^^^^^^^^^ Look for any `GitHub issues`_ with the "enhancement" label. Write Documentation ^^^^^^^^^^^^^^^^^^^ `PyRate` could always use more documentation, whether as part of the official documentation, in docstrings, or even on the web in blog posts, articles etc. Submit Feedback ^^^^^^^^^^^^^^^ General feedback or questions about `PyRate` can be emailed to the `Geoscience Australia InSAR Team`_. .. _`Geoscience Australia InSAR Team`: mailto:insar@ga.gov.au The best way to send feedback about a bug in the software is to lodge an Issue_. .. _Issue: https://github.com/GeoscienceAustralia/PyRate/issues If you are proposing a feature: * Explain in detail how it would work. * Keep the scope as narrow as possible, to make it easier to implement. * Remember that this is a volunteer-driven project, and that contributions are welcome :) Get Started! ------------ Ready to contribute? Here's how to set up `PyRate` for local development. 1. Fork_ the `PyRate` repository on GitHub. 2. Clone your fork locally: :: cd ~ git clone git@github.com:/PyRate.git 3. Create a branch for local development :: git checkout -b name-of-your-bugfix-or-feature Now you can make your changes locally. 4. When you have finished making changes, check that your changes pass against unit tests. A suite of tests have been developed for use in testing `PyRate` functionality and for further code development. The tests use `pytest `__ and can be found in the ``PyRate/tests/`` directory. A small test dataset used by the test suite is included in the repository in the ``PyRate/tests/test_data/small_test`` directory. :: cd ~/PyRate # 'not slow' avoids running those tests marked as 'slow' pytest tests/ -m "not slow" 5. If the tests pass, commit your changes and push your branch to GitHub: :: git add . git commit -m "Short description of your changes." git push origin name-of-your-bugfix-or-feature 6. Submit a pull request through the GitHub website. .. _Fork: https://help.github.com/articles/fork-a-repo/ Pull Request Guidelines ----------------------- Before you submit a pull request, check that it meets these guidelines: 1. The pull request should include tests that cover new functionality. 2. If the pull request adds functionality, the documentation should be updated. Put your new functionality into a function with a docstring. 3. The pull request should work for Python 3.7+. Check https://github.com/GeoscienceAustralia/PyRate/actions for active pull requests or run the ``tox`` command and make sure that the tests pass for all supported Python versions. ================================================ FILE: docs/covariance.rst ================================================ Covariance Module ===================== .. automodule:: pyrate.core.covariance :members: ================================================ FILE: docs/demerror.rst ================================================ DEM Error Correction Module =========================== .. automodule:: pyrate.core.dem_error :members: ================================================ FILE: docs/dependencies.rst ================================================ Dependencies ------------ The following dependencies need to be on your system (or in your working environment) prior to `PyRate` installation: - Python_, versions 3.7, 3.8 or 3.9. - GDAL_, versions 3.0.2 or 3.0.4 The following optional dependency should be on your system if you want to make use of MPI processing: - `Open MPI`_, versions 2.1.6, 3.0.4, 3.1.4 or 4.0.2 The versions of each package stated above have been tested to work using `GitHub Actions`_ continuous integration testing. .. _Python: https://www.python.org/downloads/ .. _GDAL: https://gdal.org/download.html .. _`Open MPI`: https://www.open-mpi.org/software/ompi/v4.0/ .. _`GitHub Actions`: https://github.com/GeoscienceAustralia/PyRate/actions Other Python dependencies that will be installed are listed in ``PyRate/requirements.txt``. ================================================ FILE: docs/docker.rst ================================================ Docker ^^^^^^ Docker can be used to run `PyRate` on Linux, Windows and MacOS systems. .. note:: - Docker is the recommended method to use `PyRate` under Windows. The only system pre-requisites for using docker are Git and Docker Desktop (e.g. for Windows https://docs.docker.com/docker-for-windows/ ). The other system dependencies required by PyRate are installed in to the docker container itself during the build operation. Once Git and Docker Desktop are installed, and Docker Desktop is running, execute the following at a command line prompt (e.g. PowerShell in Windows 10):: git clone git@github.com:GeoscienceAustralia/PyRate.git cd PyRate docker build -t pyrate-image . docker run -it pyrate-image .. note:: - The image name “pyrate-image” is not mandatory. You are free to name the docker image whatever you choose. The ``docker build`` command builds the docker container described in the ``Dockerfile`` in the `PyRate` repo. It first starts by setting up an Ubuntu linux system image, and then installs the required system and python dependencies. Once the docker container is running (you will see a different-looking command prompt in the PowerShell), execute the following commands:: source /usr/local/bin/virtualenvwrapper.sh workon pyrate cd PyRate python3 setup.py install pyrate --help pyrate workflow -f input_parameters.conf The ``pyrate`` executable program is built as part of the ``docker build`` step. If the ``pyrate`` executable is reported as "not found", re-run the compilation:: cd PyRate python3 setup.py install ================================================ FILE: docs/gamma.rst ================================================ GAMMA Module ============ .. automodule:: pyrate.core.gamma :members: ================================================ FILE: docs/gdal_python.rst ================================================ GDAL-Python Bindings Module =========================== .. automodule:: pyrate.core.gdal_python :members: ================================================ FILE: docs/geometry.rst ================================================ Geometry Module =============== .. automodule:: pyrate.core.geometry :members: ================================================ FILE: docs/history.rst ================================================ .. :changelog: Release History =============== 0.6.1 (2022-02-18) ------------------ Added +++++ - List generator in ``utils/listcreator.sh`` for easier input generation from GAMMA output to PyRate. Fixed +++++ - Fix wrong sign in Y-intercept output file. - Fix and simplify how user supplied reference pixel is validated and cropping issue. - Add metadata for reference pixel latitude and longitude to output files. 0.6.0 (2021-10-18) ------------------ Added +++++ - Geometry and baseline calculations, making use of GAMMA software MLI metadata and baseline files. - DEM error estimation and correction functionality (untested prototype). - Unwrapping error detection and masking functionality, making use of phase closure loops. - Tests to check that the independent and network orbital methods are both able to recover the input parameters in synthetic examples. - Tests for temporal and spatial gaussian filter options. Compare PyRate code against ``scipy.ndimage`` equivalents. - More output visualisation scripts in the ``utils/`` directory, including an interferogram plotting script. - New "Crop A" unit test dataset derived from Sentinel-1 data over Mexico City. - Scaling factor parameter ``velerror_nsig`` for uncertainty/error output products. - Calculate and output coherence statistics files during ``prepifg``. - Add line-of-sight projection functionality for output products. - Add signal polarity switching functionality for output products. - Error handling for more than two MLI.par files in the header list matching an interferograms date-pair. Fixed +++++ - Fix bugs in the application of NaN masking and radian-to-millimetre conversion of interferogram data in ``correct`` step. - Fix bugs in the implementation of the orbital error network and independent methods. - Fix bugs in the implementation of spatial and temporal filters in the APS module. - Fix bug in the application of reference phase subtraction that resulted in NaNs in the reference window. - Fix file handling behaviour in ``prepifg`` so that a large number of files are not left open. Changed +++++++ - Enabled multi-looking and parallel processing for the independent orbital method. - Simplify output filenames produced by PyRate. - Move output files to dedicated named sub-directories. - Use of the ``offset`` parameter in orbital module; now renamed to ``intercept``. - Input rasters to ``conv2tif`` can now have un-equal X and Y pixel resolutions. - Change units of temporal filter from years to days. - Moved Continuous Integration from Travis service to GitHub Actions. - Made MPI an optional system dependency for PyRate (previously required). Removed +++++++ - Remove unused cython, glob2 and pillow dependencies. - Remove support for Python 3.6. - Remove support for filter types other than Gaussian in the APS module, and associated config options ``slpfmethod``, ``slpforder``, ``tlpfmethod``. - ``config`` module deprecated; functionality moved to ``configuration``, ``shared`` and ``constants`` modules. - Deprecated ``obs_dir``, ``slc_dir`` and ``coh_file_dir`` configuration parameters. 0.5.0 (2020-09-08) ------------------ Added +++++ - New functionality "``linear_rate``" to calculate linear regression of cumulative displacement time series for every pixel as part of the ``timeseries`` step. - Script for plotting ``timeseries`` and ``linear_rate`` output geotiff products using Matplotlib. To use, additional dependencies listed in ``requirements-plot.txt`` are required. - Correction data (except ``maxvar`` and ``vcmt``) applied to the ifg data is saved to disk and re-used on subsequent repeat runs. Corrections are only re-calculated if config parameters change between runs. - MPI parallelisation of APS spatio-temporal filter correction. - Unit test coverage for refpixel lat/lon to x/y conversion and ``aps`` module. Fixed +++++ - Re-enable ``ifglksx`` and ``ifglksy`` to be different values, resulting in different resolutions in x and y dimensions in multi-looked interferograms. - Re-enable ``orbfitlksx`` and ``orbfitlksy`` to be different values, resulting in different resolutions in x and y dimensions during network orbit correction. - Screen messages from main process only during MPI runs. Changed +++++++ - ``process`` step has been renamed ``correct``. Stacking and timeseries have been removed from this step and are now invoked by separate ``timeseries`` and ``stack`` command line options. - Processing of coherence files by ``conv2tif`` and ``prepifg`` is now triggered by the presence of ``cohfilelist`` in the config file. If the list is present, multilooked/cropped coherence files are saved to disk, regardless of whether ``cohmask`` is 0 or 1. - Parallelisation capability is refactored - MPI and multiprocessing both now use a common tiling framework for ``stack``, ``timeseries`` and ``mst`` algorithms. - Introduced a simplified and standardised file naming format for files produced by the ``prepifg`` step. Information previously saved in the filename (e.g. ``ifglksx``, ``ifglksy``, and ``ifgcropopt`` values) is now saved to the geotiff header instead. Removed +++++++ - Redundant ``tscal`` config parameter was deprecated - not needed now there is a ``timeseries`` step invokable on the command line. - Unused ``Pillow``, ``cython`` and ``glob2`` dependencies. - Deprecated function ``pyrate.prepifg_helper.prepare_ifgs``, which is no longer needed. 0.4.3 (2020-08-04) ------------------ Added +++++ - Ability to define the order of steps in the ``process`` workflow (default order unchanged). Fixed +++++ - Nil Changed +++++++ - ``prepifg`` output interferograms are saved as read-only files. - ``process`` makes a writable copy of the ``prepifg`` output data at the beginning of each run. - The selected reference pixel is saved to disk and re-used on subsequent ``process`` runs. - Saving of incremental time series (``tsincr``) products is optional, controlled by the ``savetsincr`` configuration parameter (default is on). Removed +++++++ - Removed obsolete InSAR terminology from code, docs and test data (changed to `first` and `second` images). - Stopped using ``unittest`` unit test framework in favour of exclusively using ``pytest``. 0.4.2 (2020-06-26) ------------------ Added +++++ - Save full-res coherence files to disk in ``conv2tif`` step if ``cohmask = 1``. - Save multi-looked coherence files to disk in ``prepifg`` step if ``cohmask = 1``. - Additional ``DATA_TYPE`` geotiff header metadata for above coherence files. - ``conv2tif`` and ``prepifg`` output files have a tag applied to filename dependent on data type, i.e. ``_ifg.tif``, ``_coh.tif``, ``_dem.tif``. - Metadata about used reference pixel is added to interferogram geotiff headers: lat/lon and x/y values; mean and standard deviation of reference window samples. - Quicklook PNG and KML files are generated for the ``Stack Rate`` error map by default. Fixed +++++ - Ensure ``prepifg`` treats input data files as `read only`. - Fix the way that the reference phase is subtracted from interferograms during ``process`` step. - Manual entry of ``refx/y`` converted to type ``int``. Changed +++++++ - User supplies latitude and longitude values when specifying a reference pixel in the config file. Pixel x/y values are calculated and used internally. - Move ``Stack Rate`` masking to a standalone function ``pyrate.core.stack.mask_rate``, applied during the ``merge`` step and add unit tests. - Skip ``Stack Rate`` masking if threshold parameter ``maxsig = 0``. - Provide log message indicating the percentage of pixels masked by ``pyrate.core.stack.mask_rate``. - Refactor ``pyrate.core.stack`` module; expose two functions in documentation: i) single pixel stacking algorithm, and ii) loop function for processing full ifg array. - Refactor ``pyrate.merge`` script; remove duplicated code and create reusable generic functions. - Colourmap used to render quicklook PNG images is calculated from min/max values of the geotiff band. - Updated ``test`` and ``dev`` requirements. Removed +++++++ - Deprecate unused functions in ``pyrate.core.config`` and corresponding tests. - Static colourmap ``utils/colourmap.txt`` that was previously used to render quicklook PNG images is removed. 0.4.1 (2020-05-19) ------------------ Added +++++ - Python 3.8 support. - Algorithm to automatically calculate rows and columns for tiling. User no longer specifies these as part of the CLI, but can optionally specify ``rows`` and ``cols`` in the configuration file. - Improvements to the test suite, including systems-wide tests. - Improved logging. Fixed +++++ - Fixed bug in resampling/multi-looking when coherence masking is used. This bugfix will result in significantly fewer ``nan`` pixels in the outputs. - Fixed a bug in how NaNs are handled during coherence masking and multi-looking. Output rasters will contain ``nan`` as the nodata value. Changed +++++++ - ``Linear Rate`` algorithm has been renamed ``Stack Rate``. - User supplies full paths to input files in respective file lists. - All files generated by `PyRate` saved to user-defined ``outdir`` directory. - Renamed ``slcfilelist`` parameter to ``hdrfilelist``. - Log files are generated in the ``outdir`` and every `PyRate` step produces independent log files. Removed +++++++ - Deprecate the use of ``obsdir``, ``slcfiledir`` and ``cohdir`` configuration variables. - Deprecate ``parallel = 2`` option; splitting image via rows for parallelisation. 0.4.0 (2019-10-31) ------------------ Added +++++ - Python 3.7 support. - Optional ``conv2tif`` step. - Building of docs integrated with Travis CI. - Coherence masking, view coherence masking section in ``input_parameters.conf`` for options. - Input parameter validation. - SLC and coherence file lists for file discovery. - Create quick view png for rate map product. - Add support for reading interferogram in Geotiff format. - Add detailed validation and hints for configuration parameters - Add system tests for all 3 types of input formats Changed +++++++ - ``linrate`` step has been renamed to ``process``. - ``postprocess`` step has been renamed to ``merge``. - ``converttogeotiff`` step has been renamed to ``conv2tif``. - CLI structure: config files now need to be provided with ``-f`` flag. - Reduced console output, default verbosity setting is now ``INFO``. - Restructure of code layout, src modules now in ``PyRate/pyrate/core`` directory and scripts at ``PyRate/scripts``. - Reference pixel values are expected to be in latitude and longitude values. Removed +++++++ - Unused luigi code. - References to Matlab. - Unused tests for legacy api. 0.3.0 (2019-07-26) ------------------ Added +++++ - ``utils/apt_install.sh`` script that lists Ubuntu/apt package requirements. - ``utils/load_modules.sh`` script that sets up NCI Raijin HPC environment. Fixed +++++ - Errors being caused by newer version of ``networkx``; v2.3 now supported. Removed +++++++ - Unused Python and OS packages. - environment.yml - conda env will now be installed using ``requirements.txt``. - HPC directory - hpc README.rst moved to docs. - setup.cfg - no longer needed. - Luigi functionality - hasn't been operational and is reported as vulnerable. Single machine parallelism is achieved with joblib. Changed +++++++ - Requirements now managed by ``requirements.txt`` file, parsed by ``setup.py``. - Requirements now split across base ``requirements.txt`` and separate files for dev (``requirements-dev.txt``) and testing (``requirements-test.txt``). - Moved default config files to top level source directory. - Pinned Python dependencies to specific versions. - Travis build now installs GDAL from apt. - Travis only builds on master, develop and \*-travis branches. - Consolidated documentation into ``PyRate/docs``. - Updated install instructions for Ubuntu and NCI. 0.2.0 (2017-05-22) ------------------ - Stable beta release. 0.1.0 (2017-01-31) ------------------ - First release on PyPI. ================================================ FILE: docs/hpc.rst ================================================ HPC ^^^ These instructions have been written for the Gadi supercomputer of the `National Computational Infrastructure (NCI)`_. The process for other HPC platforms may differ. .. _`National Computational Infrastructure (NCI)`: https://nci.org.au/ Login to Gadi and clone the `PyRate` repository:: ssh @gadi.nci.org.au cd ~ git clone https://github.com/GeoscienceAustralia/PyRate.git Load the required Gadi modules (this will also remove the default NCI GDAL Python bindings so we can build and use our own):: source ~/PyRate/scripts/nci_load_modules.sh Create a Python virtual environment:: python3 -m venv ~/PyRateVenv source ~/PyRateVenv/bin/activate Install `PyRate`:: cd ~/PyRate python3 setup.py install Following this, `PyRate` will be available for PBS jobs. To verify the installation, first run an interactive session:: qsub -I -q express -l walltime=01:00:00,mem=16Gb,ncpus=4,wd Once the session has started, you will need to reactivate your virtual environment and reload the required modules. There is a call to activate the virtual environment built into the script below so this can be completed with a single command:: source ~/PyRate/scripts/nci_load_modules.sh ================================================ FILE: docs/ifgconstants.rst ================================================ Ifgconstants Module =================== .. automodule:: pyrate.core.ifgconstants :members: ================================================ FILE: docs/index.rst ================================================ PyRate documentation ==================== This is the documentation for the `PyRate` software. `PyRate` is a Python tool for estimating the velocity and cumulative displacement time-series of surface movements for every pixel in a stack of geocoded unwrapped interferograms generated by Interferometric Synthetic Aperture Radar (InSAR) processing. `PyRate` exploits distributed scatterers using a "Small Baseline Subset" (SBAS) processing strategy and currently supports input data in the `GAMMA` or `ROI\_PAC` software formats. Additionally, the European Space Agency SNAP_ software version 8 has a "PyRate export" capability that prepares `SNAP` output data in the `GAMMA` format for use with `PyRate`. .. _`SNAP`: https://step.esa.int/main/download/snap-download/ .. note:: - Support and development of `ROI\_PAC` has been discontinued. - `ROI\_PAC` support in `PyRate` will be deprecated in a future release. The `PyRate` project started in 2012 as a partial Python translation of "Pirate", a Matlab tool developed by the University of Leeds and the Guangdong University of Technology. * Link to `Github Repository`_ * Link to `Issue tracking page`_ .. _`Github Repository`: https://github.com/GeoscienceAustralia/PyRate .. _`Issue tracking page`: https://github.com/GeoscienceAustralia/PyRate/issues Feedback -------- If you have any suggestions or questions about `PyRate` please email the `Geoscience Australia InSAR team`_. .. _`Geoscience Australia InSAR team`: mailto:insar@ga.gov.au If you encounter any errors or problems with `PyRate`, please let us know! Open an Issue_ in the GitHub repository. .. _Issue: https://github.com/GeoscienceAustralia/PyRate/issues Contents: --------- .. toctree:: :maxdepth: 3 installation usage scripts modules troubleshooting contributing authors history .. _Home: https://geoscienceaustralia.github.io/PyRate/ Indices and tables ================== * :ref:`modindex` * :ref:`genindex` ================================================ FILE: docs/installation.rst ================================================ Installation ============ This is an installation guide to get `PyRate` running on various platforms. Follow the instructions to install `PyRate` and run a small toy example using the test dataset included in the repository. .. include:: dependencies.rst Platforms --------- PyPI ^^^^ `PyRate` and its Python dependencies can be installed directly from the Python Package Index (PyPI_):: pip install Py-Rate .. _PyPI: https://pypi.org/project/Py-Rate/ .. include:: ubuntu.rst .. include:: docker.rst .. include:: hpc.rst Verify Installation ------------------- To verify `PyRate` has been successfully installed, run the full processing workflow with the example config file and the small dataset included in the repository. If you compiled the ``pyrate`` executable program:: pyrate workflow -f input_parameters.conf If you installed from the Python Package Index (PyPI_), you won't have a ``pyrate`` executable program. Instead use:: python3 pyrate/main.py workflow -f input_parameters.conf If the installation has been successful, this workflow will complete without errors and output geotiff files will be generated in:: ~/PyRate/out ================================================ FILE: docs/logger.rst ================================================ Logging Module ============== .. automodule:: pyrate.core.logger :members: ================================================ FILE: docs/make.bat ================================================ @ECHO OFF pushd %~dp0 REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set SOURCEDIR=. set BUILDDIR=_build set SPHINXPROJ=PyRate if "%1" == "" goto help %SPHINXBUILD% >NUL 2>NUL if errorlevel 9009 ( echo. echo.The 'sphinx-build' command was not found. Make sure you have Sphinx echo.installed, then set the SPHINXBUILD environment variable to point echo.to the full path of the 'sphinx-build' executable. Alternatively you echo.may add the Sphinx directory to PATH. echo. echo.If you don't have Sphinx installed, grab it from echo.http://sphinx-doc.org/ exit /b 1 ) %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% goto end :help %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% :end popd ================================================ FILE: docs/modules.rst ================================================ PyRate Modules ============== .. toctree:: :maxdepth: 4 algorithm aps configuration covariance demerror gamma geometry gdal_python ifgconstants logger mpiops mst orbital phase_closure prepifg_helper ref_phs_est refpixel roipac shared stack timeseries ================================================ FILE: docs/mpiops.rst ================================================ MPI operations Module ===================== .. automodule:: pyrate.core.mpiops :members: ================================================ FILE: docs/mst.rst ================================================ Minimum Spanning Tree Module ============================ .. automodule:: pyrate.core.mst :members: ================================================ FILE: docs/orbital.rst ================================================ Orbital Error Correction Module =============================== .. automodule:: pyrate.core.orbital :members: ================================================ FILE: docs/phase_closure.rst ================================================ Phase Closure Module ==================== The phase closure functionality in PyRate is designed to identify and mitigate the effects of spatial unwrapping errors in the input interferograms (ifgs). Identification is by forming "closure loops" between sets of ifgs and then summing the phase in this closed loop for each pixel. Theoretically, the closure phase should be equal to zero, though in reality, close to zero is good enough. An unwrapping error in one of the loop edges will manifest as a closure phase of n\ *2*\ pi radians. By forming many loops amongst a network of ifgs, we attempt to identify the individual ifgs that are contributing the unwrapping error. Once identified, the unwrapping errors are mitigated by masking those pixels in the necessary (but not all) ifgs. By default, PyRate will do the phase closure step after orbital error, reference phase and DEM error correction steps. If a user wants to change the order of the corrections during PyRate's ``correct`` step, copy and paste the following code-block in to the PyRate configuration file and re-order the steps. This will over-ride the default behaviour. .. code-block:: [correct] steps = orbfit refphase demerror phase_closure mst apscorrect maxvar Five parameters control the phase closure functionality in PyRate. These are described in the example PyRate configuration file _input\ *parameters.conf* as follows: .. code-block:: #------------------------------------ # Phase closure correction parameters # closure_thr: Closure threshold for each pixel in multiples of pi, e.g. 0.5 = pi/2, 1 = pi. # ifg_drop_thr: Ifgs with more than this fraction of pixels above the closure threshold in all # loops it participates in, will be dropped entirely. # min_loops_per_ifg: Ifgs are dropped entirely if they do not participate in at least this many closure loops. # max_loop_length: Closure loops with up to this many edges will be used. # max_loop_redundancy: A closure loop will be discarded if all constituent ifgs in that loop have # already contributed to a number of loops equal to this parameter. closure_thr: 0.5 ifg_drop_thr: 0.05 min_loops_per_ifg: 2 max_loop_length: 4 max_loop_redundancy: 2 The PyRate *phase closure* algorithm proceeds as follows: #. Find the closed loops within the given ifg network having edges less than or equal to the parameter ``max_loop_length``. This is done in the function ``mst_closure.sort_loops_based_on_weights_and_date``. We perform several steps in this stage: * Find closed loops with edges numbering between 3 and ``max_loop_length``. (function ``mst_closure.__find_closed_loops``\ ) * Sort ifgs within each loop based on first date (earlier date first). In case of a tie, we sort based on the second date. We then compute weight of each ifg as the temporal baseline in days. Then we sum the ifg weights in a loop to give the weight of each closure loop (function ``mst_closure.__add_signs_and_weights_to_loops``\ ). * Sort the loops based on their weights, and in case of ties, further sort by primary date, and secondary date. (function ``mst_closure.sort_loops_based_on_weights_and_date``\ ) #. Discard closure loops when all of the constituent ifgs have already contributed to a number of loops equalling the ``max_loop_redundancy`` parameter. (function ``closure_check.discard_loops_containing_max_ifg_count``\ ) #. Drop ifgs from further PyRate processing when they are found to not form part of any closed loop. (function ``closure_check.__drop_ifgs_if_not_part_of_any_loop``\ ) #. Compute phase closure sums for each pixel (in radians) in all the chosen loops and flag when the resultant absolute sum exceeds the quantity <\ ``closure_thr`` * pi>. The median closure sum across all pixels is subtracted from the closure phase (optional parameter ``subtract_median``\ , which is on by default). (function ``sum_closure.__compute_ifgs_breach_count``\ ) #. Next, ifgs are dropped (removed from the processing list) if the fraction of constituent pixels breaching the ``closure_thr`` parameter in all loops the ifg participates in exceeds the parameter ``ifg_drop_thr``\ , or the ifg does not contribute to a number of loops at least equal to the parameter ``min_loops_per_ifg``. (function ``closure_check.__drop_ifgs_exceeding_threshold``\ ) #. Steps 1-5 are repeated iteratively until a stable list of ifgs is returned. The iteration is orchestrated by the function ``closure_check.iterative_closure_check``. #. Once a stable list of ifgs is found, a new ifglist is written in the working directory, and used for further PyRate processing. #. The final step involves finding pixels in the ifg phase data that breach the closure threshold defined by the ``closure_thr`` parameter. Those pixels in breach are masked (changed to NaN value) for those pixels in those ifgs. (function ``closure_check.mask_pixels_with_unwrapping_errors``\ ) Example usage ------------- To illustrate the PyRate phase closure functionality, we use a small example dataset of 8 Sentinel-1 ifgs which connect 5 common acquisition dates: .. code-block:: 20160314-20160326 20160314-20160407 20160314-20160501 20160326-20160407 20160326-20160513 20160407-20160501 20160407-20160513 20160501-20160513 The below plots show the ifgs following ``prepifg``\ : .. image:: ./phase_closure_images/ifg-phase-plot-1-before.png :target: ./phase_closure_images/ifg-phase-plot-1-before.png :alt: Ifgs before phase closure correction Circled in black are some visually obvious unwrapping errors. In this example, we run the ``correct`` step with orbital correction and phase closure correction enabled, and the following parameters: .. code-block:: orbfitmethod: 2 orbfitdegrees: 1 orbfitlksx: 10 orbfitlksy: 10 closure_thr: 0.5 ifg_drop_thr: 0.1 min_loops_per_ifg: 2 max_loop_length: 4 max_loop_redundancy: 2 We get the following log information from iteration #1: .. code-block:: 16:40:45 closure_check:126 421776 INFO 0/7 Closure check iteration #1: working on 8 ifgs 16:40:45 closure_check:187 421776 INFO 0/7 Total number of selected closed loops with up to MAX_LOOP_LENGTH = 4 edges is 9 16:40:45 closure_check:202 421776 INFO 0/7 After applying MAX_LOOP_REDUNDANCY = 2 criteria, 8 loops are retained 16:40:52 plot_closure:76 421776 INFO 0/7 8 closure loops plotted in out/phase_closure_dir/closure_loops_iteration_1_fig_0.png .. image:: ./phase_closure_images/closure_loops_iteration_1_fig_0.png :target: ./phase_closure_images/closure_loops_iteration_1_fig_0.png :alt: Iteration #1 closure loops The 8 plotted closure loops from iteration #1 show areas where the ``closure_thr`` threshold has been breached as either dark red or dark blue. The previously circled unwrapping errors show up as breached areas in several closure loops. The ``ifg_drop_thr`` parameter is set to 10% in this example. This is enough to detect the largest mis-closed area, which amounts to around 25% of the phase data area spatially. The ifg introducing this mis-closure (20160407-20160513) is dropped and iteration #2 continues: .. code-block:: 16:40:52 closure_check:126 421776 INFO 0/7 Closure check iteration #2: working on 7 ifgs 16:40:52 closure_check:187 421776 INFO 0/7 Total number of selected closed loops with up to MAX_LOOP_LENGTH = 4 edges is 5 16:40:52 closure_check:202 421776 INFO 0/7 After applying MAX_LOOP_REDUNDANCY = 2 criteria, 5 loops are retained 16:40:58 plot_closure:76 421776 INFO 0/7 5 closure loops plotted in out/phase_closure_dir/closure_loops_iteration_2_fig_0.png .. image:: ./phase_closure_images/closure_loops_iteration_2_fig_0.png :target: ./phase_closure_images/closure_loops_iteration_2_fig_0.png :alt: Iteration #2 closure loops Now the ifg network is smaller (7 ifgs) and less closure loops (5) are retained. Three smaller breached areas are evident: in the top left, centre bottom and bottom right of the image. The average breached area is now not greater than 10% so no further ifgs are fully dropped and no further iteration is required. Finally, pixels found to be breaching the ``closure_thr`` are masked in ifgs: .. image:: ./phase_closure_images/ifg-phase-plot-1-after.png :target: ./phase_closure_images/ifg-phase-plot-1-after.png :alt: Ifgs after phase closure correction The previously circled unwrapping error in ifg 20160314-20160501 has now been masked, but this spatial area has not been masked in other ifgs. In this case, the algorithm has been successfully able to attribute the source of the unwrapping error to this single ifg. The ability to do this depends on the parameter settings chosen. .. automodule:: pyrate.core.phase_closure.closure_check :members: .. automodule:: pyrate.core.phase_closure.collect_loops :members: .. automodule:: pyrate.core.phase_closure.correct_phase :members: .. automodule:: pyrate.core.phase_closure.mst_closure :members: .. automodule:: pyrate.core.phase_closure.plot_closure :members: .. automodule:: pyrate.core.phase_closure.sum_closure :members: ================================================ FILE: docs/prepifg_helper.rst ================================================ Prepifg Helper Module ===================== .. automodule:: pyrate.core.prepifg_helper :members: ================================================ FILE: docs/pyrate.conv2tif.rst ================================================ PyRate Conv2tif Script ====================== .. automodule:: pyrate.conv2tif :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/pyrate.correct.rst ================================================ PyRate Correct Script ======================== .. automodule:: pyrate.correct :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/pyrate.main.rst ================================================ PyRate Main Script ================== .. automodule:: pyrate.main .. rubric:: Functions .. autosummary:: conv2tif prepifg correct timeseries stack merge ================================================ FILE: docs/pyrate.merge.rst ================================================ PyRate Merge Script =================== .. automodule:: pyrate.merge :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/pyrate.prepifg.rst ================================================ PyRate Prepifg Script ========================= .. automodule:: pyrate.prepifg :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/ref_phs_est.rst ================================================ Reference Phase Calculation Module ================================== .. automodule:: pyrate.core.ref_phs_est :members: ================================================ FILE: docs/refpixel.rst ================================================ Reference Pixel Calculation Module ================================== .. automodule:: pyrate.core.refpixel :members: ================================================ FILE: docs/roipac.rst ================================================ ROI_PAC Module ============== .. automodule:: pyrate.core.roipac :members: ================================================ FILE: docs/scripts.rst ================================================ PyRate Scripts ============== .. toctree:: :maxdepth: 4 pyrate.conv2tif pyrate.prepifg pyrate.correct pyrate.merge pyrate.main ================================================ FILE: docs/shared.rst ================================================ Shared Module ============= .. automodule:: pyrate.core.shared :members: ================================================ FILE: docs/stack.rst ================================================ Stacking Module =============== .. automodule:: pyrate.core.stack :members: ================================================ FILE: docs/timeseries.rst ================================================ Time Series Module ================== .. automodule:: pyrate.core.timeseries :members: ================================================ FILE: docs/troubleshooting.rst ================================================ Troubleshooting =============== Some common/known issues with `PyRate` that users may encounter are described below. If your issue is not covered below, please contact the `Geoscience Australia InSAR Team`_ for help. .. _`Geoscience Australia InSAR Team`: mailto:insar@ga.gov.au ValueError: too many values to unpack (expected 2) -------------------------------------------------- **Problem**: During ``prepifg`` step, the following error is encountered: :: Traceback (most recent call last): File "pyrate/main.py", line 131, in main() File "pyrate/main.py", line 103, in main prepifg.main(params) File "~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/pyrate/prepifg.py", line 57, in main do_prepifg(gtiff_paths, params) File "~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/pyrate/prepifg.py", line 88, in do_prepifg _prepifg_multiprocessing(gtiff_path, xlooks, ylooks, exts, thresh, crop, params) File "~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/pyrate/prepifg.py", line 112, in _prepifg_multiprocessing coherence_path=coherence_path, coherence_thresh=coherence_thresh) File "~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/pyrate/core/prepifg_helper.py", line 187, in prepare_ifg op = output_tiff_filename(raster.data_path, out_path) File "~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/pyrate/core/shared.py", line 1222, in output_tiff_filename fname, ext = os.path.basename(inpath).split('.') ValueError: too many values to unpack (expected 2) **Reason**: This is caused by multiple double dots being used in the file names. **Solution**: Ensure only a single dot is given in the file names, delimiting the file extension. For example, rename ``20151219-20160112.unw.tif`` to ``20151219-20160112_unw.tif``. Stack Rate map appears to be blank/empty ---------------------------------------- **Problem**: Output of Stack Rate algorithm contains NaN values causing “merge“ to fail: :: Traceback (most recent call last): File "~/PyRateVenv/bin/pyrate", line 11, in load_entry_point('Py-Rate==0.4.0', 'console_scripts', 'pyrate')() File "~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/main.py", line 154, in main merge_handler(args.config_file) File "~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/main.py", line 87, in merge_handler merge.main(config.__dict__) File "~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/merge.py", line 64, in main create_png_from_tif(output_folder_path) File "~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/merge.py", line 130, in create_png_from_tif minimum, maximum, mean, stddev = srcband.GetStatistics(True, True) File "/apps/gdal/3.0.2/lib/python3.7/site-packages/osgeo/gdal.py", line 2610, in GetStatistics return _gdal.Band_GetStatistics(self, *args) RuntimeError: ~/out/stack_rate.tif, band 1: Failed to compute statistics, no valid pixels found in sampling. Traceback (most recent call last): File "~/PyRateVenv/bin/pyrate", line 11, in load_entry_point('Py-Rate==0.4.0', 'console_scripts', 'pyrate')() File "~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/main.py", line 154, in main merge_handler(args.config_file) File "~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/main.py", line 87, in merge_handler merge.main(config.__dict__) File "~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/merge.py", line 64, in main create_png_from_tif(output_folder_path) File "~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/merge.py", line 130, in create_png_from_tif minimum, maximum, mean, stddev = srcband.GetStatistics(True, True) File "/apps/gdal/3.0.2/lib/python3.7/site-packages/osgeo/gdal.py", line 2610, in GetStatistics return _gdal.Band_GetStatistics(self, *args) RuntimeError: ~/out/stack_rate.tif, band 1: Failed to compute statistics, no valid pixels found in sampling. Traceback (most recent call last): File "~/PyRateVenv/bin/pyrate", line 11, in load_entry_point('Py-Rate==0.4.0', 'console_scripts', 'pyrate')() File "~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/main.py", line 154, in main merge_handler(args.config_file) File "~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/main.py", line 87, in merge_handler merge.main(config.__dict__) File "~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/merge.py", line 64, in main create_png_from_tif(output_folder_path) File "~/PyRateVenv/lib/python3.7/site-packages/Py_Rate-0.4.0-py3.7.egg/merge.py", line 130, in create_png_from_tif minimum, maximum, mean, stddev = srcband.GetStatistics(True, True) File "/apps/gdal/3.0.2/lib/python3.7/site-packages/osgeo/gdal.py", line 2610, in GetStatistics return _gdal.Band_GetStatistics(self, *args) RuntimeError: ~/out/stack_rate.tif, band 1: Failed to compute statistics, no valid pixels found in sampling. **Reason**: The ``maxsig`` parameter is too low, resulting in stack rate values being replaced by NaNs. ``maxsig`` is a threshold for masking stack rate pixels according to the corresponding stack error estimate saved in ``out/tmpdir/stack_error_*.npy``. **Solution**: Increase ``maxsig``, then re-run the ``merge`` step. Maximum permittable value for ``maxsig`` is 1000 mm. Failure of APS spatial low pass filter --------------------------------------- **Problem**: Atmospheric corrections during ``correct`` step fails due to the interpolated grid used for correction being empty: :: +8s pyrate.aps:INFO Applying spatial low pass filter Traceback (most recent call last): File "~/PyRateVenv/bin/pyrate", line 11, in load_entry_point('Py-Rate==0.3.0.post3', 'console_scripts', 'pyrate')() File "~/PyRateVenv/lib/python3.6/site-packages/Click-7.0-py3.6.egg/click/core.py", line 764, in __call__ return self.main(*args, **kwargs) File "~/PyRateVenv/lib/python3.6/site-packages/Click-7.0-py3.6.egg/click/core.py", line 717, in main rv = self.invoke(ctx) File "~/PyRateVenv/lib/python3.6/site-packages/Click-7.0-py3.6.egg/click/core.py", line 1137, in invoke return _process_result(sub_ctx.command.invoke(sub_ctx)) File "~/PyRateVenv/lib/python3.6/site-packages/Click-7.0-py3.6.egg/click/core.py", line 956, in invoke return ctx.invoke(self.callback, **ctx.params) File "~/PyRateVenv/lib/python3.6/site-packages/Click-7.0-py3.6.egg/click/core.py", line 555, in invoke return callback(*args, **kwargs) File "~/PyRateVenv/lib/python3.6/site-packages/Py_Rate-0.3.0.post3-py3.6.egg/pyrate/scripts/main.py", line 69, in linrate run_pyrate.process_ifgs(sorted(dest_paths), params, rows, cols) File "~/PyRateVenv/lib/python3.6/site-packages/Py_Rate-0.3.0.post3-py3.6.egg/pyrate/scripts/run_pyrate.py", line 391, in process_ifgs _wrap_spatio_temporal_filter(ifg_paths, params, tiles, preread_ifgs) File "~/PyRateVenv/lib/python3.6/site-packages/Py_Rate-0.3.0.post3-py3.6.egg/pyrate/aps.py", line 63, in _wrap_spatio_temporal_filter spatio_temporal_filter(tsincr, ifg, params, preread_ifgs) File "~/PyRateVenv/lib/python3.6/site-packages/Py_Rate-0.3.0.post3-py3.6.egg/pyrate/aps.py", line 86, in spatio_temporal_filter ts_aps = mpiops.run_once(spatial_low_pass_filter, ts_hp, ifg, params) File "~/PyRateVenv/lib/python3.6/site-packages/Py_Rate-0.3.0.post3-py3.6.egg/pyrate/mpiops.py", line 54, in run_once f_result = f(*args, **kwargs) File "~/PyRateVenv/lib/python3.6/site-packages/Py_Rate-0.3.0.post3-py3.6.egg/pyrate/aps.py", line 192, in spatial_low_pass_filter _interpolate_nans(ts_lp, params[cf.SLPF_NANFILL_METHOD]) File "~/PyRateVenv/lib/python3.6/site-packages/Py_Rate-0.3.0.post3-py3.6.egg/pyrate/aps.py", line 208, in _interpolate_nans _interpolate_nans_2d(a, rows, cols, method) File "~/PyRateVenv/lib/python3.6/site-packages/Py_Rate-0.3.0.post3-py3.6.egg/pyrate/aps.py", line 224, in _interpolate_nans_2d method=method File "~/PyRateVenv/lib/python3.6/site-packages/scipy-1.3.0-py3.6-linux-x86_64.egg/scipy/interpolate/ndgriddata.py", line 226, in griddata rescale=rescale) File "interpnd.pyx", line 846, in scipy.interpolate.interpnd.CloughTocher2DInterpolator.__init__ File "qhull.pyx", line 1836, in scipy.spatial.qhull.Delaunay.__init__ File "qhull.pyx", line 276, in scipy.spatial.qhull._Qhull.__init__ ValueError: No points given **Solution**: Use more interferograms as input and/or reduce the threshold parameters ``ts_pthr``, ``pthr``, ``tlpfpthr`` in the configuration file. In general, users are advised to input a network of small-baseline interferograms that has at least 2 interferometric connections per SAR image epoch. Furthermore, make sure that ``ts_pthr``, ``pthr`` and ``tlpfpthr`` are smaller than the number of image epochs. To check that the spatio-temporal filters worked correctly, users can check that the numpy arrays saved at ``//aps_error/*aps_error.npy`` contain numeric values and not NaNs. Out of memory errors -------------------- **Problem**: `PyRate` is memory intensive. You may receive various out of memory errors if there is not enough memory to accommodate the images being processed. :: joblib.externals.loky.process_executor.TerminatedWorkerError: A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive memory usage causing the Operating System to kill the worker. The exit codes of the workers are {EXIT(1), EXIT(1), EXIT(1)} **Solution**: Increase the amount of memory available. On HPC systems this can be done by increasing the value provided to the ``mem`` argument when submitting a PBS job, e.g.:: mem=32Gb If no more memory can be called upon, users can try running the job in serial, or reducing the size of the interferograms by increasing the multi-looking factors applied during ``prepifg`` (parameters ``ifglksx`` and ``ifglksy``). Incorrect modules loaded on Gadi ---------------------------------- **Problem**: `PyRate` requires certain versions of Python, GDAL and Open MPI to be loaded on Gadi and other HPC systems. While sourcing the `PyRate/scripts/nci_load_modules.sh` script will load the correct modules, you may need to unload previously unloaded modules. Example of errors caused by module conflicts:: ERROR:150: Module 'python3/3.7.2' conflicts with the currently loaded module(s) 'python3/3.4.3-matplotlib' ERROR:150: Module 'gdal/2.2.2' conflicts with the currently loaded module(s) 'gdal/2.0.0' **Solution**: Purge the loaded modules and source the ``nci_load_modules.sh`` script: :: module purge source ~/PyRate/scripts/nci_load_modules.sh ================================================ FILE: docs/ubuntu.rst ================================================ Linux ^^^^^ These instructions have been tested using Ubuntu 18.04. If using another Linux distribution, you will have to install packages equivalent to those listed in ``PyRate/scripts/apt_install.sh``. Clone the repository, install required packages and install `PyRate`: :: git clone git@github.com:GeoscienceAustralia/PyRate.git ./PyRate/scripts/apt_install.sh # Add GDAL includes to C/CPLUS paths before building Python GDAL bindings. export C_INCLUDE_PATH=$C_INCLUDE_PATH:/usr/include/gdal export CPLUS_INCLUDE_PATH=$CPLUS_INCLUDE_PATH:/usr/include/gdal python3 setup.py install These commands will compile the executable program ``pyrate``. ================================================ FILE: docs/usage.rst ================================================ Usage ===== Configuration ------------- Various parameters are controlled by values given in a text-based configuration file. The user can choose any filename for this configuration file. Example parameters for running `PyRate` with `GAMMA`-format interferograms are contained in the example configuration file ``PyRate/input_parameters.conf``. Workflow -------- File Discovery ^^^^^^^^^^^^^^ To allow flexibility in the file types that can be processed, `PyRate` requires file lists to be provided. This allows `PyRate` to identify files of each type without relying on file extensions. The file path to these lists are provided under ``ifgfilelist``, ``hdrfilelist``, ``cohfilelist`` and ``basefilelist`` keywords in the configuration file. These lists can be created manually or generated using the script ``list_creator.sh`` located in the ``utils`` folder. This script may need to be slightly modified depending on the output paths of your input data. .. note:: - Filenames should be provided in these lists with their absolute system path, with each name on a separate line. - Filenames should only have a single period (".") delimiting the file extension. Multiple periods in a filename will raise an error during runtime. The ``ifgfilelist`` gives the list of interferograms to be processed. Example of an interferogram file list for `GAMMA` flat-binary files:: /absolute/path/to/20150702-20150920.unw /absolute/path/to/20151219-20160109.unw /absolute/path/to/20160202-20160415.unw .. note:: - Interferogram filenames must contain a pair of date epochs. Any naming convention is appropriate as long as an epoch pair of format ``YYYYMMDD-YYYYMMDD`` or ``YYMMDD-YYMMDD`` exists in the filename. The ``hdrfilelist`` gives the list of `GAMMA` Multi-Look Intensity (MLI) header text files. Example of a `GAMMA` MLI header file list:: /absolute/path/to/20150702_mli.par /absolute/path/to/20150920_mli.par /absolute/path/to/20151219_mli.par /absolute/path/to/20160109_mli.par /absolute/path/to/20160202_mli.par /absolute/path/to/20160415_mli.par .. note:: - MLI header filenames must contain a single date epoch in the format ``YYYYMMDD`` or ``YYMMDD``. - `GAMMA` Single-Look Complex (SLC) header files could be used instead, but the geometry calculations in ``prepifg`` will not work, and subsequently the DEM Error correction step in ``correct`` will not work. Both require parameters valid for the multi-looked radar-coded interferograms. The ``cohfilelist`` is an optional list which contains the pool of all available coherence files to be used for optional coherence masking. Example of a coherence file list for `GAMMA` flat-binary files:: /absolute/path/to/20150702-20150920.coh /absolute/path/to/20151219-20160109.coh /absolute/path/to/20160202-20160415.coh .. note:: - Like interferograms, coherence filenames must contain a pair of epochs. The epoch pair must be in the format ``YYYYMMDD-YYYYMMDD`` or ``YYMMDD-YYMMDD``. Otherwise, any naming convention is appropriate. The ``basefilelist`` is an optional list which contains the pool of all available GAMMA baseline vector files. These are only needed if the user wants to use the DEM error correction functionality. Example of a baseline file list for `GAMMA` baseline vector files:: /absolute/path/to/20150702-20150920_base.par /absolute/path/to/20151219-20160109_base.par /absolute/path/to/20160202-20160415_base.par The date epochs in filenames are used to match the corresponding MLI header or coherence files to each interferogram. It is recommended to provide a complete list of available headers/coherence/baseline files for a stack in their respective lists, since only the necessary files will be used. This allows you to process a subset of interferograms by removing entries in ``ifgfilelist`` without needing to modify all four lists. `PyRate` Workflow ^^^^^^^^^^^^^^^^^ After `Installation `__, an executable program ``pyrate`` is created in the system path:: >> pyrate --help usage: pyrate [-h] [-v {DEBUG,INFO,WARNING,ERROR}] {conv2tif,prepifg,correct,timeseries,stack,merge,workflow} ... PyRate workflow: Step 1: conv2tif Step 2: prepifg Step 3: correct Step 4: timeseries Step 5: stack Step 6: merge Refer to https://geoscienceaustralia.github.io/PyRate/usage.html for more details. positional arguments: {conv2tif,prepifg,correct,timeseries,stack,merge,workflow} conv2tif Convert interferograms to geotiff. prepifg Perform multilooking, cropping and coherence masking to interferogram geotiffs. correct Calculate and apply corrections to interferogram phase data. timeseries Timeseries inversion of interferogram phase data. stack Stacking of interferogram phase data. merge Reassemble computed tiles and save as geotiffs. workflow Sequentially run all the PyRate processing steps. optional arguments: -h, --help show this help message and exit -v {DEBUG,INFO,WARNING,ERROR}, --verbosity {DEBUG,INFO,WARNING,ERROR} Increase output verbosity .. note:: - If running on NCI, be sure to first load the correct modules and virtual environment: ``source ~/PyRate/scripts/nci_load_modules.sh`` The ``pyrate`` program has six command line options corresponding to different steps in the `PyRate` workflow: 1. ``conv2tif`` (optional) 2. ``prepifg`` 3. ``correct`` 4. ``timeseries`` (optional) 5. ``stack`` (optional) 6. ``merge`` Not all steps are required as indicated above. A seventh option, ``workflow``, is available that will run all six of the above steps in the order shown. Command line arguments for each step can be found using (e.g. for ``conv2tif``):: >> pyrate conv2tif --help usage: pyrate conv2tif [-h] -f CONFIG_FILE optional arguments: -h, --help show this help message and exit -f CONFIG_FILE, --config_file CONFIG_FILE Pass configuration file Each step can be run on the command line in one of the following two ways (e.g. for ``conv2tif``):: >> pyrate conv2tif -f /path/to/config_file or:: >> python3 pyrate/main.py conv2tif -f /path/to/config_file In the following sub-sections we describe each of the available steps. ``conv2tif``: Converting flat-binary files to Geotiff format ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Before `PyRate` can process interferograms that are in flat-binary file format, they need to be converted into geotiff format using the optional ``conv2tif`` step. .. note:: - Users of the `GAMMA` software can skip the ``conv2tif`` step if they have generated geotiffs using the `GAMMA` program ``data2geotiff``, which is included in all `GAMMA` software distributions. - In this case, ``ifgfilelist`` and ``cohfilelist`` would contain the absolute paths to these geotiff files. Even when using geotiff files, the MLI header files are still required by ``prepifg``. - If a DEM is to be processed by ``prepifg``, it's file format should match the input interferograms (e.g. geotiff or flat-binary files). Upon completion of ``conv2tif`` geotiff formatted copies of the input files will be placed in the ```` directory defined in the configuration file. .. note:: - ``conv2tif`` will not perform the conversion if geotiffs for the provided input files already exist. ``prepifg``: Preparing input interferograms ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ``prepifg`` is the second step of `PyRate`, which applys optional multi-looking (sub-sampling), cropping and coherence masking operations to the geotiff-format input interferograms. This is a required step, which formats the input data in a way expected by the rest of the `PyRate` workflow. **Coherence masking** ``prepifg`` will perform optional coherence masking on the interferograms before multi-looking and cropping is performed. This requires corresponding coherence images for each interferogram. The purpose of coherence masking is to remove poor quality phase observations and leave a set of high-quality pixels for analysis. Coherence masking is enabled by setting ``cohmask: 1`` in the configuration file. A threshold, ``cohthresh`` needs to be provided. Pixels with coherence values below ``cohthresh`` will be set to Not-a-Number (NaN). .. note:: - The number of pixels with numeric phase values (i.e. pixels not equal to NaN) will be different in each interferogram after coherence masking. The available coherence files need to be specified in a list file as described above and defined in the ``cohfilelist`` parameter. .. note:: - Multi-looked and cropped versions of those coherence images found that match the epochs of the input interferograms will be saved to disk in geotiff format, even if coherence masking is not applied (i.e. ``cohmask: 0``). - Additionally, the mean, median and standard deviation of the coherence for each pixel is calculated and saved as part of ``prepifg``. - All coherence files are saved to the ``\coherence_dir`` directory. **Multi-looking** The ``prepifg`` step will perform optional multi-looking (image sub-sampling) of the input interferograms in geotiff format. The purpose of multi-looking is twofold: - Reduce the spatial resolution of the interferograms in order to improve the computational efficiency of `PyRate` analysis. - Reduce the general phase noise in the interferograms in order to enhance the signal-to-noise ratio in the output products. To multi-look, set ``ifglksx`` and ``ifglksy`` to an integer subsampling factor greater than one in the x (easting) and y (northing) dimensions respectively. Separate parameters for x and y gives flexibility for users in case they want to achieve different spatial resolution in each dimension. .. note:: - For example, a value of ``2`` will reduce the resolution by half. A value of ``1`` will keep the resolution the same as the input interferograms (i.e. no multi-looking). - It is recommended to try a large multi-look factor to start with (e.g. ``10`` or greater), and subsequently reduce the multi-looking factor once the user has experience with processing a particular dataset. **Cropping** The ``prepifg`` step will perform optional spatial cropping of the input interferograms. This is useful if you are focussing on a specific area of interest within the full extent of the input interferograms. The advantage of cropping is that `PyRate` analysis will be computationally more efficient. To crop, set ``ifgcropopt`` to ``3`` and provide the geographic latitude and longitude bounds in the ``ifgxfirst`` (west), ``ifgxlast`` (east), ``ifgyfirst`` (north), and ``ifgylast`` (south) parameters. **Geometry calculations** During the ``prepifg`` step, the radar viewing geometry for every pixel is calculated using metadata from the `GAMMA` MLI parameter files. .. note:: - Geometry calculations are only implemented for `GAMMA` format input data. The output arrays are saved to ``/geometry_dir`` and contain as follows: - ``rdc_azimuth.tif``: azimuth coordinate in range-doppler system; - ``rdc_range.tif``: range coordinate in range-doppler system; - ``look_angle.tif``: look angle (vector between line-of-sight and satellite nadir); - ``incidence_angle.tif``: incidence angle (vector between line-of-sight and vector perpendicular to local ground surface); - ``azimuth_angle.tif``: azimuth angle (projection of line-of-sight on the surface); - ``range_dist.tif``: satellite to ground range distance; - ``dem.tif``: digital elevation model. Upon completion, ``prepifg`` will save a new set of interferogram files in the ``\interferogram_dir`` (``*_ifg.tif``). If provided as input, coherence files will be saved to ``\coherence_dir`` (``*_coh.tif``). ``correct``: Compute and apply interferometric phase corrections ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ``correct`` is the third step in the `PyRate` processing workflow. This step will perform a series of optional corrections to the interferogram phase data and apply a number of prepatory steps required prior to data inversion. The sub-steps are performed in the following default order: - Search for a suitable reference pixel; - Residual Orbit error correction (optional, controlled by ``orbfit`` parameter); - Correction of reference phase in interferograms; - Residual DEM error correction (optional, controlled by ``demerror`` parameter); - Unwrapping error detection and masking (optional, controlled by ``phase_closure`` parameter); - Minimum Spanning Tree matrix formation; - Spatio-temporal filtering of the interferograms ((optional, controlled by ``apsest`` parameter); - Calculation of interferogram spatial covariance functions; - Assembly of the variance-covariance matrix. This default order of steps can be modified by the user by copying the following code block in to the configuration file and switching the order of steps as required:: [correct] steps = orbfit refphase demerror phase_closure mst apscorrect maxvar The corrected interferogram phase is saved to copies of the ``prepifg`` interferograms in the directory ``/temp_mlooked_dir/`` (the original output from ``prepifg`` is retained as a read-only interferogram dataset in the ``/interferogram_dir``). Additionally, copies of the phase corrections subtracted from interferograms are saved to disk as numpy array files (``*.npy``) for use in post-processing. These can be found in labelled sub-directories in the ````. ``timeseries``: Compute the displacement time series ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ``timeseries`` is the optional fourth step in the `PyRate` processing workflow. This step will perform a time series inversion to derive the cumulative displacement time series from the stack of corrected interferograms. The cumulative displacement time series (``tscuml*``) is saved by default. Users can optionally save the incremental displacement time series (``tsincr*``) by setting parameter ``savetsincr: 1``. A linear regression of the cumulative displacement time series is also computed as part of the ``timeseries`` step. The resulting linear rate (velocity), standard error, R-squared and y-intercept terms are all saved to disk. ``stack``: Compute the average velocity via stacking ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ``stack`` is the optional fifth step in the `PyRate` processing workflow. This step will perform an iterative stacking of the phase data to derive a robust velocity estimate for each pixel in the interferograms. The velocity from stacking (``stack_rate*``) is saved by default. .. note:: - Both ``timeseries`` and ``stack`` are optional and independent steps that can be computed in either order. ``merge``: Reassemble the tiles ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ``merge`` is the sixth and final step of the `PyRate` workflow, which produces geotiff files containing the final time series, linear rate and stacking products. ``merge`` will also re-assemble tiles that were generated during the previous steps. Tiling is discussed in the :ref:`parallel_label` section below. The final outputs contain signals in the line-of-sight (LOS) of the satellite. Using the ``los_projection`` option, the user can project those signals to either the pseudo-vertical (set to 1) or pseudo-horizontal (set to 2). This projection makes use of the per-pixel incidence angle image generated during ``prepifg``. .. note:: - Users should be aware that the pseudo-vertical and pseudo-horizontal signal projections do not necessarily represent the **true** vertical or horizontal ground movement signal. The true signals cannot be recovered with InSAR data from a single LOS viewing geometry. - If the user has InSAR data from multiple viewing geometries (e.g. from both ascending and descending orbits), signal decomposition can be carried out after PyRate analysis as a post-processing step. If necessary, the user can switch the polarity of the final output products by setting ``signal_polarity: -1``. The error products produced by `PyRate` can be scaled by n-sigma using the parameter ``velerror_nsig``. After running the ``merge`` step, several geotiff products will appear in the ``/velocity_dir`` and ``/timeseries_dir`` directories. ``workflow``: Run the full PyRate workflow ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ``workflow`` is an additional option that will run all the above six steps in order as a single job. This could be useful for batch processing runs. Input Files ----------- `PyRate` currently supports input files generated by the `GAMMA`, `ROI\_PAC` and `SNAP` interferometry softwares. `PyRate` will determine the input format from the ``processor:`` parameter in the configuration file (``0``: `ROI\_PAC`; ``1``: `GAMMA` (and `SNAP`)). `GAMMA` ^^^^^^^ Each `GAMMA` geocoded unwrapped interferogram requires three header files to extract metadata required for data formatting: a geocoded DEM header file (``demHeaderFile`` keyword in the configuration file) and the relevant MLI image header files (``*mli.par``) found in the ``hdrfilelist``. The header files for the first and second MLI images used in the formation of a particular interferogram are found automatically by date-string pattern matching based on date epochs given in the filenames. A DEM with matching size and geometry to the interferograms can also be processed. The DEM absolute path and filename are set with the ``demfile`` parameter. `SNAP` ^^^^^^ The European Space Agency SNAP_ software has supported PyRate since version 8.0.0. Users can convert their output data in to `GAMMA` format by using the "PyRate export" function in `SNAP`. Then users should follow the `PyRate` instructions for `GAMMA`-formatted data. .. note:: - The capability to generate SBAS interferogram networks that are needed for time series analysis of large InSAR stacks in `PyRate` is planned for a future version of `SNAP`. .. _SNAP: https://step.esa.int/main/download/snap-download/ `ROI\_PAC` ^^^^^^^^^^ Each `ROI\_PAC` geocoded unwrapped interferogram requires its own header/resource file (``*.rsc``). These header files need to be listed in the defined ``hdrfilelist``. In addition, the geocoded DEM header file is required and its path and name are specified in the config file under ``demHeaderFile``. The geographic projection in the parameter ``DATUM:`` is extracted from the DEM header file. A DEM with matching size and geometry to the interferograms can also be processed. The DEM absolute path and filename are set with the ``demfile`` parameter. .. note:: - Support and development of `ROI\_PAC` has been discontinued. - `ROI\_PAC` support in `PyRate` will be deprecated in a future release. .. _parallel_label: Parallel Processing ------------------- By their very nature, interferograms are large files. This is particularly the case for `Sentinel-1`_, which has an image swath of 250 km and a pixel resolution on the order of tens of metres in IW-mode. Consequently, InSAR processing can be computationally expensive and time consuming. It therefore makes sense to parallelise processing operations wherever possible. .. _`Sentinel-1`: https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar `PyRate` can be run in parallel using standard multi-threading simply by turning ``parallel: 1`` in the configuration file to take advantage of multiple cores on a single machine. The parameter ``processes`` sets the number of threads. Alternatively, `PyRate` can be parallelised on a system with an installed MPI library by using ``mpirun``:: # Modify '-n' based on the number of processors available. mpirun -n 4 pyrate conv2tif -f path/to/config_file mpirun -n 4 pyrate prepifg -f path/to/config_file mpirun -n 4 pyrate correct -f path/to/config_file mpirun -n 4 pyrate timeseries -f input_parameters.conf mpirun -n 4 pyrate stack -f input_parameters.conf mpirun -n 4 pyrate merge -f path/to/config_file .. note:: - In the case that `PyRate` is run using ``mpirun``, standard multi-threading is automatically disabled (i.e. equivalent to setting ``parallel: 0``). During ``conv2tif`` and ``prepifg``, parallelism is achieved by sending sub-lists of input files to each process. Parallelism in the ``correct``, ``timeseries`` and ``stack`` steps is achieved by splitting the images in to a grid of tiles, where the number of tiles equals the number of processes passed with the ``-n`` argument to ``mpirun``, or the ``processes`` parameter for multi-threading. The number of tiles in x and y dimension are automatically calculated by `PyRate`, ensuring a roughly equivalent number in both dimensions. One of the functions of the ``merge`` step is to reassemble these tiles in to the full image for each output product. Results Visualisation --------------------- Several plotting scripts are included in the ``utils/`` directory to help the user visually inspect the output products of `PyRate`: - ``make_tscuml_animation.py``: Make an animated gif from cumulative time series data; - ``plot_linear_rate_profile.py``: Plot a profile through a linear rate map; - ``plot_time_series.py``: Map and graph view of cumulative time series data; - ``plot_correction_files.py``: Before and after viewing of interferogram corrections; - ``plot_sbas_network.py``: Baseline-time plot for the interferogram network. Example usage of ``plot_time_series.py`` with the included test data:: cd PyRate source ~/PyRateVenv/bin/activate pyrate workflow -f input_parameters.conf pip install -r requirements-plot.txt python3 utils/plot_time_series.py out/ .. image:: PyRate_plot_screenshot.png :alt: Screenshot of PyRate plotting tool :scale: 30 % ================================================ FILE: input_parameters.conf ================================================ # PyRate configuration file for GAMMA-format interferograms # #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # Optional Correction ON/OFF switches - ON = 1; OFF = 0 # Coherence masking (PREPIFG) cohmask: 0 # Orbital error correction (CORRECT) orbfit: 0 # DEM error (residual topography) correction (CORRECT) demerror: 0 # Phase Closure correction (CORRECT) phase_closure: 1 # APS correction using spatio-temporal filter (CORRECT) apsest: 0 #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # Integer parameters # LOS Projection of output products (MERGE) # Converts slanted (native) LOS signals to either "pseudo-vertical" or "pseudo-horizontal", # by dividing by the cosine or sine of the incidence angle for each pixel, respectively. # los_projection: 0 = LOS (no conversion); 1 = pseudo-vertical; 2 = pseudo-horizontal. los_projection: 0 # Sign convention for phase data (MERGE) # signal_polarity: 1 = retain sign convention of input interferograms # signal_polarity: -1 = reverse sign convention of input interferograms (default) signal_polarity: -1 # Number of sigma to report velocity error. Positive integer. Default: 2 (TIMESERIES/STACK) velerror_nsig: 2 # Optional save of numpy array files for output products (MERGE) savenpy: 0 # Optional save of incremental time series products (TIMESERIES/MERGE) savetsincr: 0 #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # Multi-threading parameters (CORRECT/TIMESERIES/STACK) # parallel: 1 = parallel, 0 = serial parallel: 0 # number of processes processes: 8 #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # Input/Output file locations # # File containing the list of interferograms to use. ifgfilelist: tests/test_data/cropA/ifg_30 # The DEM file used in the InSAR processing demfile: tests/test_data/cropA/geotiffs/cropA_T005A_dem.tif # The DEM header file from GAMMA (*.par) or ROI_PAC (*.rsc). demHeaderFile: tests/test_data/cropA/headers/cropA_20180106_VV_8rlks_eqa_dem.par # File listing the pool of available header files (GAMMA: *mli.par, ROI_PAC: *.rsc) hdrfilelist: tests/test_data/cropA/headers_13 # File listing the pool of available coherence files. cohfilelist: tests/test_data/cropA/coherence_30 # File listing the pool of available baseline files (GAMMA). basefilelist: tests/test_data/cropA/baseline_30 # Look-up table containing radar-coded row and column for lat/lon pixels (GAMMA) ltfile: tests/test_data/cropA/geometry/20180106_VV_8rlks_eqa_to_rdc.lt # Directory to write the outputs to outdir: out/ #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # PREPIFG parameters #------------------------------------ # Input data format: ROI_PAC = 0, GAMMA = 1 processor: 1 # Coherence threshold value for masking, between 0 and 1 cohthresh: 0.3 # Multi-look/subsampling factor in east (x) and north (y) dimension ifglksx: 1 ifglksy: 1 # Cropping options # ifgcropopt: 1 = minimum extent 2 = maximum extent 3 = crop 4 = no cropping # ifgxfirst,ifgyfirst: longitude (x) and latitude (y) of north-west corner # ifgxlast,ifgylast: longitude (x) and latitude (y) of south-east corner ifgcropopt: 4 ifgxfirst: 150.92 ifgyfirst: -34.18 ifgxlast: 150.94 ifgylast: -34.22 # No-data averaging threshold (0 = 0%; 1 = 100%) noDataAveragingThreshold: 0.5 # The No-data value used in the interferogram files noDataValue: 0.0 # Nan conversion flag. Set to 1 if missing No-data values are to be converted to NaN nan_conversion: 1 #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # CORRECT parameters #------------------------------------ # Reference pixel search parameters # refx/y: Lon/Lat coordinate of reference pixel. If left blank then search for best pixel will be performed # refnx/y: number of search grid points in x/y image dimensions # refchipsize: size of the data window at each search grid point # refminfrac: minimum fraction of valid (non-NaN) pixels in the data window refx: -99.18 refy: 19.44 refnx: 5 refny: 5 refchipsize: 7 refminfrac: 0.01 #------------------------------------ # Reference phase correction method # refest: 1 = median of the whole interferogram # refest: 2 = median within the window surrounding the chosen reference pixel refest: 2 #------------------------------------ # Orbital error correction parameters # orbfitmethod = 1: interferograms corrected independently; 2: network method # orbfitdegrees: Degree of polynomial surface to fit (1 = planar; 2 = quadratic; 3 = part-cubic) # orbfitlksx/y: additional multi-look factor for network orbital correction orbfitmethod: 1 orbfitdegrees: 1 orbfitlksx: 5 orbfitlksy: 5 #------------------------------------ # Phase closure correction parameters # closure_thr: Closure threshold for each pixel in multiples of pi, e.g. 0.5 = pi/2, 1 = pi. # ifg_drop_thr: Ifgs with more than this fraction of pixels above the closure threshold in all # loops it participates in, will be dropped entirely. # min_loops_per_ifg: Ifgs are dropped entirely if they do not participate in at least this many closure loops. # max_loop_length: Closure loops with up to this many edges will be used. # max_loop_redundancy: A closure loop will be discarded if all constituent ifgs in that loop have # already contributed to a number of loops equal to this parameter. closure_thr: 0.5 ifg_drop_thr: 0.5 min_loops_per_ifg: 2 max_loop_length: 4 max_loop_redundancy: 2 #------------------------------------ # APS filter parameters # tlpfcutoff: cutoff t0 for temporal high-pass Gaussian filter in days (int); # tlpfpthr: valid pixel threshold; # slpfcutoff: spatial low-pass Gaussian filter cutoff in km (greater than zero). # slpfcutoff=0 triggers cutoff estimation from exponential covariance function tlpfcutoff: 30 tlpfpthr: 1 slpfcutoff: 1 #------------------------------------ # DEM error (residual topography) correction parameters # de_pthr: valid observations threshold; de_pthr: 20 #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # TIMESERIES parameters #------------------------------------ # tsmethod: Method for time series inversion (1 = Laplacian Smoothing; 2 = SVD) # smorder: order of Laplacian smoothing operator (1 = first-order difference; 2 = second-order difference) # smfactor: smoothing factor for Laplacian smoothing (value provided is converted as 10**smfactor) # ts_pthr: valid observations threshold for time series inversion tsmethod: 2 smorder: 2 smfactor: -0.25 ts_pthr: 10 #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # STACK parameters #------------------------------------ # pthr: threshold for minimum number of ifg observations for each pixel # nsig: threshold for iterative removal of observations # maxsig: maximum sigma (std dev; millimetres) used as an output masking threshold applied in Merge step. 0 = OFF. pthr: 5 nsig: 3 maxsig: 1000 ================================================ FILE: pyrate/__init__.py ================================================ ================================================ FILE: pyrate/configuration.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains utilities to validate user input parameters parsed in a PyRate configuration file. """ import re from configparser import ConfigParser from pathlib import Path, PurePath from typing import Union import pyrate.constants as C from pyrate.constants import NO_OF_PARALLEL_PROCESSES, sixteen_digits_pattern, twelve_digits_pattern, ORB_ERROR_DIR, \ DEM_ERROR_DIR, TEMP_MLOOKED_DIR from pyrate.core import ifgconstants as ifg from pyrate.default_parameters import PYRATE_DEFAULT_CONFIGURATION from pyrate.core.algorithm import factorise_integer from pyrate.core.shared import extract_epochs_from_filename, InputTypes, get_tiles def set_parameter_value(data_type, input_value, default_value, required, input_name): if len(input_value) < 1: input_value = None if required: # pragma: no cover raise ValueError("A required parameter is missing value in input configuration file: " + str(input_name)) if input_value is not None: if str(data_type) in "path": return Path(input_value) return data_type(input_value) return default_value def validate_parameter_value(input_name, input_value, min_value=None, max_value=None, possible_values=None): if isinstance(input_value, PurePath): if not Path.exists(input_value): # pragma: no cover raise ValueError("Given path: " + str(input_value) + " does not exist.") if input_value is not None: if min_value is not None: if input_value < min_value: # pragma: no cover raise ValueError( "Invalid value for " + str(input_name) + " supplied: " + str( input_value) + ". Provide a value greater than or equal to " + str(min_value) + ".") if input_value is not None: if max_value is not None: if input_value > max_value: # pragma: no cover raise ValueError( "Invalid value for " + str(input_name) + " supplied: " + str( input_value) + ". Provide a value less than or equal to " + str(max_value) + ".") if possible_values is not None: if input_value not in possible_values: # pragma: no cover raise ValueError( "Invalid value for " + str(input_name) + " supplied: " + str( input_value) + ". Provide one of these values: " + str(possible_values) + ".") return True def validate_file_list_values(file_list, no_of_epochs): if file_list is None: # pragma: no cover raise ValueError("No value supplied for input file list: " + str(file_list)) files = parse_namelist(file_list) for f in files: if not Path(f).exists(): # pragma: no cover raise ConfigException(f"{f} does not exist") else: matches = extract_epochs_from_filename(filename_with_epochs=f) if len(matches) < no_of_epochs: # pragma: no cover raise ConfigException(f"the number of epochs in {f} names are less the required number: {no_of_epochs}") class MultiplePaths: def __init__(self, file_name: str, params: dict, input_type: InputTypes = InputTypes.IFG): self.input_type = input_type out_dir = params[C.OUT_DIR] tempdir = params[C.TEMP_MLOOKED_DIR] if isinstance(tempdir, str): tempdir = Path(tempdir) b = Path(file_name) if input_type in [InputTypes.IFG, InputTypes.COH]: d = re.search(sixteen_digits_pattern, b.stem) if d is None: # could be 6 digit epoch dates d = re.search(twelve_digits_pattern, b.stem) if d is None: raise ValueError(f"{input_type.value} filename does not contain two 8- or 6-digit date strings") filestr = d.group() + '_' else: filestr = '' dir_exists = input_type.value in InputTypes.dir_map.value.keys() anchoring_dir = Path(out_dir).joinpath(InputTypes.dir_map.value[input_type.value]) \ if dir_exists else Path(out_dir) if b.suffix == ".tif": self.unwrapped_path = None converted_path = b # original file self.sampled_path = anchoring_dir.joinpath(filestr + input_type.value + '.tif') else: self.unwrapped_path = b.as_posix() converted_path = anchoring_dir.joinpath(b.stem.split('.')[0] + '_' + b.suffix[1:]).with_suffix('.tif') self.sampled_path = converted_path.with_name(filestr + input_type.value + '.tif') # tmp_sampled_paths are used after prepifg, during correct steps self.tmp_sampled_path = tempdir.joinpath(self.sampled_path.name).as_posix() self.converted_path = converted_path.as_posix() self.sampled_path = self.sampled_path.as_posix() @staticmethod def orb_error_path(ifg_path: Union[str, Path], params) -> Path: if isinstance(ifg_path, str): ifg_path = Path(ifg_path) return Path(params[C.OUT_DIR], C.ORB_ERROR_DIR, ifg_path.stem + '_' + '_'.join([str(params[C.ORBITAL_FIT_METHOD]), str(params[C.ORBITAL_FIT_DEGREE]), str(params[C.ORBITAL_FIT_LOOKS_X]), str(params[C.ORBITAL_FIT_LOOKS_Y])]) + '_orbfit.npy') @staticmethod def dem_error_path(ifg_path: Union[str, Path], params) -> Path: if isinstance(ifg_path, str): ifg_path = Path(ifg_path) return Path(params[C.OUT_DIR], C.DEM_ERROR_DIR, ifg_path.stem + '_' + str(params[C.DE_PTHR]) + '_dem_error.npy') @staticmethod def aps_error_path(ifg_path: Union[str, Path], params) -> Path: if isinstance(ifg_path, str): ifg_path = Path(ifg_path) return Path(params[C.OUT_DIR], C.APS_ERROR_DIR, ifg_path.stem + '_' + '_'.join([str(x) for x in [ params[C.SLPF_CUTOFF], params[C.SLPF_NANFILL], params[C.SLPF_NANFILL_METHOD], params[C.TLPF_CUTOFF], params[C.TLPF_PTHR] ] ]) + '_aps_error.npy') def __str__(self): # pragma: no cover st = "" if self.unwrapped_path is not None: st += """\nunwrapped_path = """ + self.unwrapped_path else: st += """\nunwrapped_path = None""" st += """ converted_path = """ + self.converted_path + """ sampled_path = """ + self.sampled_path + """ tmp_sampled_path = """ + self.tmp_sampled_path + """ """ return st class Configuration: def __init__(self, config_file_path): parser = ConfigParser() parser.optionxform = str # mimic header to fulfil the requirement for configparser with open(config_file_path) as stream: parser.read_string("[root]\n" + stream.read()) for key, value in parser["root"].items(): self.__dict__[key] = value # make output path, if not provided will error Path(self.outdir).mkdir(exist_ok=True, parents=True) # custom correct sequence if 'correct' section is provided in config if 'correct' in parser and 'steps' in parser['correct']: self.__dict__['correct'] = list(filter(None, parser['correct'].get('steps').splitlines())) else: self.__dict__['correct'] = [ 'orbfit', 'refphase', 'demerror', 'phase_closure', 'mst', 'apscorrect', 'maxvar', ] # Validate required parameters exist. required = {k for k, v in PYRATE_DEFAULT_CONFIGURATION.items() if v['Required']} if not required.issubset(self.__dict__): # pragma: no cover raise ValueError("Required configuration parameters: " + str( required.difference(self.__dict__)) + " are missing from input config file.") # handle control parameters for parameter_name in PYRATE_DEFAULT_CONFIGURATION: param_value = self.__dict__[parameter_name] if parameter_name in required or \ parameter_name in self.__dict__ else '' self.__dict__[parameter_name] = set_parameter_value( PYRATE_DEFAULT_CONFIGURATION[parameter_name]["DataType"], param_value, PYRATE_DEFAULT_CONFIGURATION[parameter_name]["DefaultValue"], PYRATE_DEFAULT_CONFIGURATION[parameter_name]["Required"], parameter_name) validate_parameter_value(parameter_name, self.__dict__[parameter_name], PYRATE_DEFAULT_CONFIGURATION[parameter_name]["MinValue"], PYRATE_DEFAULT_CONFIGURATION[parameter_name]["MaxValue"], PYRATE_DEFAULT_CONFIGURATION[parameter_name]["PossibleValues"]) # bespoke parameter validation if self.refchipsize % 2 != 1: # pragma: no cover if self.refchipsize - 1 > 1: # Configuration parameters refchipsize must be odd # values too large (>101) will slow down the process without significant gains in results. self.refchipsize = self.refchipsize - 1 # calculate rows and cols if not supplied if hasattr(self, 'rows') and hasattr(self, 'cols'): self.rows, self.cols = int(self.rows), int(self.cols) else: if NO_OF_PARALLEL_PROCESSES > 1: # i.e. mpirun self.rows, self.cols = [int(num) for num in factorise_integer(NO_OF_PARALLEL_PROCESSES)] else: if self.parallel: # i.e. joblib parallelism self.rows, self.cols = [int(num) for num in factorise_integer(self.processes)] else: # i.e. serial self.rows, self.cols = 1, 1 # create a temporary directory if not supplied if not hasattr(self, 'tmpdir'): self.tmpdir = Path(self.outdir).joinpath("tmpdir") else: self.tmpdir = Path(self.tmpdir) self.tmpdir.mkdir(parents=True, exist_ok=True) # create orbfit error dir self.orb_error_dir = Path(self.outdir).joinpath(ORB_ERROR_DIR) self.orb_error_dir.mkdir(parents=True, exist_ok=True) self.interferogram_dir = Path(self.outdir).joinpath(C.INTERFEROGRAM_DIR) self.interferogram_dir.mkdir(parents=True, exist_ok=True) # create DEM error dir self.dem_error_dir = Path(self.outdir).joinpath(DEM_ERROR_DIR) self.dem_error_dir.mkdir(parents=True, exist_ok=True) # create aps error dir self.aps_error_dir = Path(self.outdir).joinpath(C.APS_ERROR_DIR) self.aps_error_dir.mkdir(parents=True, exist_ok=True) # create mst dir self.mst_dir = Path(self.outdir).joinpath(C.MST_DIR) self.mst_dir.mkdir(parents=True, exist_ok=True) self.phase_closure_dir = Path(self.outdir).joinpath(C.PHASE_CLOSURE_DIR) self.phase_closure_dir.mkdir(parents=True, exist_ok=True) self.coherence_dir = Path(self.outdir).joinpath(C.COHERENCE_DIR) self.coherence_dir.mkdir(parents=True, exist_ok=True) self.geometry_dir = Path(self.outdir).joinpath(C.GEOMETRY_DIR) self.geometry_dir.mkdir(parents=True, exist_ok=True) self.timeseries_dir = Path(self.outdir).joinpath(C.TIMESERIES_DIR) self.timeseries_dir.mkdir(parents=True, exist_ok=True) self.velocity_dir = Path(self.outdir).joinpath(C.VELOCITY_DIR) self.velocity_dir.mkdir(parents=True, exist_ok=True) # create temp multilooked files dir self.temp_mlooked_dir = Path(self.outdir).joinpath(TEMP_MLOOKED_DIR) self.temp_mlooked_dir.mkdir(parents=True, exist_ok=True) self.ref_pixel_file = self.ref_pixel_path(self.__dict__) # var no longer used self.APS_ELEVATION_EXT = None self.APS_INCIDENCE_EXT = None self.apscorrect = 0 self.apsmethod = 0 self.elevationmap = None self.incidencemap = None # define parallel processes that will run self.NUMEXPR_MAX_THREADS = str(NO_OF_PARALLEL_PROCESSES) # Validate file names supplied in list exist and contain correct epochs in file names if self.cohfilelist is not None: # if self.processor != 0: # not roipac validate_file_list_values(self.cohfilelist, 1) self.coherence_file_paths = self.__get_files_from_attr('cohfilelist', input_type=InputTypes.COH) if self.basefilelist is not None: # if self.processor != 0: # not roipac validate_file_list_values(self.basefilelist, 1) self.baseline_file_paths = self.__get_files_from_attr('basefilelist', input_type=InputTypes.BASE) self.header_file_paths = self.__get_files_from_attr('hdrfilelist', input_type=InputTypes.HEADER) self.interferogram_files = self.__get_files_from_attr('ifgfilelist') self.dem_file = MultiplePaths(self.demfile, self.__dict__, input_type=InputTypes.DEM) # backward compatibility for string paths for key in self.__dict__: if isinstance(self.__dict__[key], PurePath): self.__dict__[key] = str(self.__dict__[key]) @staticmethod def ref_pixel_path(params): return Path(params[C.OUT_DIR]).joinpath( '_'.join( [str(x) for x in [ 'ref_pixel', params[C.REFX], params[C.REFY], params[ C.REFNX], params[C.REFNY], params[C.REF_CHIP_SIZE], params[C.REF_MIN_FRAC], '.npy' ] ] ) ) @staticmethod def mst_path(params, index) -> Path: return Path(params[C.OUT_DIR], C.MST_DIR).joinpath(f'mst_mat_{index}.npy') @staticmethod def preread_ifgs(params: dict) -> Path: return Path(params[C.TMPDIR], 'preread_ifgs.pk') @staticmethod def vcmt_path(params): return Path(params[C.OUT_DIR], C.VCMT).with_suffix('.npy') @staticmethod def phase_closure_filtered_ifgs_list(params): return Path(params[C.TEMP_MLOOKED_DIR]).joinpath('phase_closure_filtered_ifgs_list') def refresh_ifg_list(self, params): # update params dict filtered_ifgs_list = self.phase_closure_filtered_ifgs_list(params) files = parse_namelist(filtered_ifgs_list.as_posix()) params[C.INTERFEROGRAM_FILES] = [MultiplePaths(p, self.__dict__, input_type=InputTypes.IFG) for p in files] return params @staticmethod def ref_phs_file(params): ref_pixel_path = Configuration.ref_pixel_path(params) # add ref pixel path as when ref pixel changes - ref phs path should also change return Path(params[C.OUT_DIR]).joinpath( ref_pixel_path.stem + '_' + '_'.join(['ref_phs', str(params[C.REF_EST_METHOD]), '.npy']) ) @staticmethod def get_tiles(params): ifg_path = params[C.INTERFEROGRAM_FILES][0].sampled_path rows, cols = params['rows'], params['cols'] return get_tiles(ifg_path, rows, cols) def __get_files_from_attr(self, attr, input_type=InputTypes.IFG): val = self.__getattribute__(attr) files = parse_namelist(val) return [MultiplePaths(p, self.__dict__, input_type=input_type) for p in files] def closure(self): closure_d = Path(self.phase_closure_dir) class Closure: def __init__(self): self.closure = closure_d.joinpath('closure.npy') self.ifgs_breach_count = closure_d.joinpath('ifgs_breach_count.npy') self.num_occurences_each_ifg = closure_d.joinpath('num_occurrences_each_ifg.npy') self.loops = closure_d.joinpath('loops.npy') return Closure() @staticmethod def coherence_stats(params): coh_d = Path(params[C.COHERENCE_DIR]) return {k: coh_d.joinpath(k.lower() + '.tif').as_posix() for k in [ifg.COH_MEDIAN, ifg.COH_MEAN, ifg.COH_STD]} @staticmethod def geometry_files(params): geom_dir = Path(params[C.GEOMETRY_DIR]) return {k: geom_dir.joinpath(k.lower() + '.tif').as_posix() for k in C.GEOMETRY_OUTPUT_TYPES} def write_config_parser_file(conf: ConfigParser, output_conf_file: Union[str, Path]): """replacement function for write_config_file which uses dict instead of a ConfigParser instance""" with open(output_conf_file, 'w') as configfile: conf.write(configfile) def write_config_file(params, output_conf_file): """ Takes a param object and writes the config file. Reverse of get_conf_params. :param dict params: parameter dictionary :param str output_conf_file: output file name :return: config file :rtype: list """ with open(output_conf_file, 'w') as f: for k, v in params.items(): if v is not None: if k == 'correct': f.write(''.join(['[', k, ']' ':\t', '', '\n'])) f.write(''.join(['steps = ', '\n'])) for vv in v: f.write(''.join(['\t' + str(vv), '\n'])) elif isinstance(v, list): continue else: if isinstance(v, MultiplePaths): if v.unwrapped_path is None: vv = v.converted_path else: vv = v.unwrapped_path else: vv = v f.write(''.join([k, ':\t', str(vv), '\n'])) else: f.write(''.join([k, ':\t', '', '\n'])) def parse_namelist(nml): """ Parses name list file into array of paths :param str nml: interferogram file list :return: list of interferogram file names :rtype: list """ with open(nml) as f_in: lines = [line.rstrip() for line in f_in] return filter(None, lines) class ConfigException(Exception): """ Default exception class for configuration errors. """ ================================================ FILE: pyrate/constants.py ================================================ import os import re from pathlib import Path import numpy as np PYRATEPATH = Path(__file__).parent.parent __version__ = "0.6.0" CLI_DESCRIPTION = """ PyRate workflow: Step 1: conv2tif Step 2: prepifg Step 3: correct Step 4: timeseries Step 5: stack Step 6: merge Refer to https://geoscienceaustralia.github.io/PyRate/usage.html for more details. """ from pyrate.core.mpiops import comm NO_OF_PARALLEL_PROCESSES = comm.Get_size() CONV2TIF = 'conv2tif' PREPIFG = 'prepifg' CORRECT = 'correct' TIMESERIES = 'timeseries' STACK = 'stack' MERGE = 'merge' # distance division factor of 1000 converts to km and is needed to match legacy output DISTFACT = 1000 # mappings for metadata in header for interferogram GAMMA_DATE = 'date' GAMMA_TIME = 'center_time' GAMMA_WIDTH = 'width' GAMMA_NROWS = 'nlines' GAMMA_CORNER_LAT = 'corner_lat' GAMMA_CORNER_LONG = 'corner_lon' GAMMA_Y_STEP = 'post_lat' GAMMA_X_STEP = 'post_lon' GAMMA_DATUM = 'ellipsoid_name' GAMMA_FREQUENCY = 'radar_frequency' GAMMA_INCIDENCE = 'incidence_angle' GAMMA_HEADING = 'heading' GAMMA_AZIMUTH = 'azimuth_angle' GAMMA_RANGE_PIX = 'range_pixel_spacing' GAMMA_RANGE_N = 'range_samples' GAMMA_AZIMUTH_PIX = 'azimuth_pixel_spacing' GAMMA_AZIMUTH_N = 'azimuth_lines' GAMMA_AZIMUTH_LOOKS = 'azimuth_looks' GAMMA_PRF = 'prf' GAMMA_NEAR_RANGE = 'near_range_slc' GAMMA_SAR_EARTH = 'sar_to_earth_center' GAMMA_SEMI_MAJOR_AXIS = 'earth_semi_major_axis' GAMMA_SEMI_MINOR_AXIS = 'earth_semi_minor_axis' GAMMA_PRECISION_BASELINE = 'precision_baseline(TCN)' GAMMA_PRECISION_BASELINE_RATE = 'precision_baseline_rate' # RADIANS = 'RADIANS' # GAMMA = 'GAMMA' # value assigned to no-data-value LOW_FLOAT32 = np.finfo(np.float32).min*1e-10 SIXTEEN_DIGIT_EPOCH_PAIR = r'\d{8}-\d{8}' sixteen_digits_pattern = re.compile(SIXTEEN_DIGIT_EPOCH_PAIR) TWELVE_DIGIT_EPOCH_PAIR = r'\d{6}-\d{6}' twelve_digits_pattern = re.compile(TWELVE_DIGIT_EPOCH_PAIR) # general constants NO_MULTILOOKING = 1 ROIPAC = 0 GAMMA = 1 LOG_LEVEL = 'INFO' # constants for lookups #: STR; Name of input interferogram list file IFG_FILE_LIST = 'ifgfilelist' #: (0/1/2); The interferogram processor used (0==ROIPAC, 1==GAMMA, 2: GEOTIF) PROCESSOR = 'processor' #: STR; Name of directory for saving output products OUT_DIR = 'outdir' #: STR; Name of Digital Elevation Model file DEM_FILE = 'demfile' #: STR; Name of the DEM header file DEM_HEADER_FILE = 'demHeaderFile' #: STR; Name of the file list containing the pool of available header files HDR_FILE_LIST = 'hdrfilelist' INTERFEROGRAM_FILES = 'interferogram_files' HEADER_FILE_PATHS = 'header_file_paths' COHERENCE_FILE_PATHS = 'coherence_file_paths' BASELINE_FILE_PATHS = 'baseline_file_paths' DEM_FILE_PATH = 'dem_file' # STR; The projection of the input interferograms. # TODO: only used in tests; deprecate? INPUT_IFG_PROJECTION = 'projection' #: FLOAT; The no data value in the interferogram files. NO_DATA_VALUE = 'noDataValue' #: FLOAT; No data averaging threshold for prepifg NO_DATA_AVERAGING_THRESHOLD = 'noDataAveragingThreshold' # BOOL (1/2/3); Re-project data from Line of sight, 1 = vertical, 2 = horizontal, 3 = no conversion # REPROJECTION = 'prjflag' # NOT CURRENTLY USED #: BOOL (0/1): Convert no data values to Nan NAN_CONVERSION = 'nan_conversion' # Prepifg parameters #: BOOL (1/2/3/4); Method for cropping interferograms, 1 = minimum overlapping area (intersection), 2 = maximum area (union), 3 = customised area, 4 = all ifgs already same size IFG_CROP_OPT = 'ifgcropopt' #: INT; Multi look factor for interferogram preparation in x dimension IFG_LKSX = 'ifglksx' #: INT; Multi look factor for interferogram preparation in y dimension IFG_LKSY = 'ifglksy' #: FLOAT; Minimum longitude for cropping with method 3 IFG_XFIRST = 'ifgxfirst' #: FLOAT; Maximum longitude for cropping with method 3 IFG_XLAST = 'ifgxlast' #: FLOAT; Minimum latitude for cropping with method 3 IFG_YFIRST = 'ifgyfirst' #: FLOAT; Maximum latitude for cropping with method 3 IFG_YLAST = 'ifgylast' # reference pixel parameters #: INT; Longitude (decimal degrees) of reference pixel, or if left blank a search will be performed REFX = 'refx' REFX_FOUND = 'refxfound' #: INT; Latitude (decimal degrees) of reference pixel, or if left blank a search will be performed REFY = 'refy' REFY_FOUND = 'refyfound' #: INT; Number of reference pixel grid search nodes in x dimension REFNX = "refnx" #: INT; Number of reference pixel grid search nodes in y dimension REFNY = "refny" #: INT; Dimension of reference pixel search window (in number of pixels) REF_CHIP_SIZE = 'refchipsize' #: FLOAT; Minimum fraction of observations required in search window for pixel to be a viable reference pixel REF_MIN_FRAC = 'refminfrac' #: BOOL (1/2); Reference phase estimation method (1: median of the whole interferogram, 2: median within the window surrounding the reference pixel) REF_EST_METHOD = 'refest' MAXVAR = 'maxvar' VCMT = 'vcmt' PREREAD_IFGS = 'preread_ifgs' TILES = 'tiles' # coherence masking parameters #: BOOL (0/1); Perform coherence masking (1: yes, 0: no) COH_MASK = 'cohmask' #: FLOAT; Coherence threshold for masking COH_THRESH = 'cohthresh' #: STR; Name of the file list containing the pool of available coherence files COH_FILE_LIST = 'cohfilelist' # baseline parameters #: STR; Directory containing baseline files BASE_FILE_DIR = 'basefiledir' #: STR; Name of the file list containing the pool of available baseline files BASE_FILE_LIST = 'basefilelist' #: STR; Name of the file containing the GAMMA lookup table between lat/lon and radar coordinates (row/col) LT_FILE = 'ltfile' # atmospheric error correction parameters NOT CURRENTLY USED APS_CORRECTION = 'apscorrect' APS_METHOD = 'apsmethod' APS_INCIDENCE_MAP = 'incidencemap' APS_INCIDENCE_EXT = 'APS_INCIDENCE_EXT' APS_ELEVATION_MAP = 'elevationmap' APS_ELEVATION_EXT = 'APS_ELEVATION_EXT' # phase closure PHASE_CLOSURE = 'phase_closure' CLOSURE_THR = 'closure_thr' IFG_DROP_THR = 'ifg_drop_thr' MIN_LOOPS_PER_IFG = 'min_loops_per_ifg' MAX_LOOP_LENGTH = 'max_loop_length' MAX_LOOP_REDUNDANCY = 'max_loop_redundancy' SUBTRACT_MEDIAN = 'subtract_median' # orbital error correction/parameters #: BOOL (1/0); Perform orbital error correction (1: yes, 0: no) ORBITAL_FIT = 'orbfit' #: BOOL (1/2); Method for orbital error correction (1: independent, 2: network) ORBITAL_FIT_METHOD = 'orbfitmethod' #: BOOL (1/2/3) Polynomial order of orbital error model (1: planar in x and y - 2 parameter model, 2: quadratic in x and y - 5 parameter model, 3: quadratic in x and cubic in y - part-cubic 6 parameter model) ORBITAL_FIT_DEGREE = 'orbfitdegrees' #: INT; Multi look factor for orbital error calculation in x dimension ORBITAL_FIT_LOOKS_X = 'orbfitlksx' #: INT; Multi look factor for orbital error calculation in y dimension ORBITAL_FIT_LOOKS_Y = 'orbfitlksy' #: BOOL (1/0); Estimate interferogram offsets during orbit correction design matrix (1: yes, 0: no) ORBFIT_OFFSET = 'orbfitoffset' #: FLOAT; Scaling parameter for orbital correction design matrix ORBFIT_SCALE = 'orbfitscale' ORBFIT_INTERCEPT = 'orbfitintercept' # Stacking parameters #: FLOAT; Threshold ratio between 'model minus observation' residuals and a-priori observation standard deviations for stacking estimate acceptance (otherwise remove furthest outlier and re-iterate) LR_NSIG = 'nsig' #: INT; Number of required observations per pixel for stacking to occur LR_PTHRESH = 'pthr' #: FLOAT; Maximum allowable standard error for pixels in stacking LR_MAXSIG = 'maxsig' # atmospheric delay errors fitting parameters NOT CURRENTLY USED # atmfitmethod = 1: interferogram by interferogram; atmfitmethod = 2, epoch by epoch # ATM_FIT = 'atmfit' # ATM_FIT_METHOD = 'atmfitmethod' # APS correction parameters #: BOOL (0/1) Perform APS correction (1: yes, 0: no) APSEST = 'apsest' # temporal low-pass filter parameters #: FLOAT; Cutoff time for gaussian filter in days; TLPF_CUTOFF = 'tlpfcutoff' #: INT; Number of required input observations per pixel for temporal filtering TLPF_PTHR = 'tlpfpthr' # spatially correlated noise low-pass filter parameters #: FLOAT; Cutoff value for both butterworth and gaussian filters in km SLPF_CUTOFF = 'slpfcutoff' #: INT (1/0); Do spatial interpolation at NaN locations (1 for interpolation, 0 for zero fill) SLPF_NANFILL = 'slpnanfill' #: #: STR; Method for spatial interpolation (one of: linear, nearest, cubic), only used when slpnanfill=1 SLPF_NANFILL_METHOD = 'slpnanfill_method' # DEM error correction parameters #: BOOL (0/1) Perform DEM error correction (1: yes, 0: no) DEMERROR = 'demerror' #: INT; Number of required input observations per pixel for DEM error estimation DE_PTHR = 'de_pthr' # Time series parameters #: INT (1/2); Method for time series inversion (1: Laplacian Smoothing; 2: SVD) TIME_SERIES_METHOD = 'tsmethod' #: INT; Number of required input observations per pixel for time series inversion TIME_SERIES_PTHRESH = 'ts_pthr' #: INT (1/2); Order of Laplacian smoothing operator, first or second order TIME_SERIES_SM_ORDER = 'smorder' #: FLOAT; Laplacian smoothing factor (values used is 10**smfactor) TIME_SERIES_SM_FACTOR = 'smfactor' # tsinterp is automatically assigned in the code; not needed in conf file # TIME_SERIES_INTERP = 'tsinterp' #: BOOL (0/1/2); Use parallelisation/Multi-threading (0: in serial, 1: in parallel by rows, 2: in parallel by pixel) PARALLEL = 'parallel' #: INT; Number of processes for multi-threading PROCESSES = 'processes' LARGE_TIFS = 'largetifs' # Orbital error correction constants for conversion to readable strings INDEPENDENT_METHOD = 1 NETWORK_METHOD = 2 PLANAR = 1 QUADRATIC = 2 PART_CUBIC = 3 # Orbital error name look up for logging ORB_METHOD_NAMES = {INDEPENDENT_METHOD: 'INDEPENDENT', NETWORK_METHOD: 'NETWORK'} ORB_DEGREE_NAMES = {PLANAR: 'PLANAR', QUADRATIC: 'QUADRATIC', PART_CUBIC: 'PART CUBIC'} # geometry outputs GEOMETRY_OUTPUT_TYPES = ['rdc_azimuth', 'rdc_range', 'look_angle', 'incidence_angle', 'azimuth_angle', 'range_dist'] # sign convention for phase data SIGNAL_POLARITY = 'signal_polarity' # LOS projection LOS_PROJECTION = 'los_projection' # Number of sigma to report velocity error VELERROR_NSIG = 'velerror_nsig' # dir for temp files TMPDIR = 'tmpdir' # Lookup to help convert args to correct type/defaults # format is key : (conversion, default value) # None = no conversion # filenames reused in many parts of the program REF_PIXEL_FILE = 'ref_pixel_file' ORB_ERROR_DIR = 'orb_error_dir' DEM_ERROR_DIR = 'dem_error_dir' APS_ERROR_DIR = 'aps_error_dir' PHASE_CLOSURE_DIR = 'phase_closure_dir' MST_DIR = 'mst_dir' TEMP_MLOOKED_DIR = 'temp_mlooked_dir' COHERENCE_DIR = 'coherence_dir' INTERFEROGRAM_DIR = 'interferogram_dir' GEOMETRY_DIR = 'geometry_dir' TIMESERIES_DIR = 'timeseries_dir' VELOCITY_DIR = 'velocity_dir' ================================================ FILE: pyrate/conv2tif.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python script converts ROI_PAC or GAMMA format input interferograms into geotiff format files """ # -*- coding: utf-8 -*- import os from typing import Tuple, List from joblib import Parallel, delayed import numpy as np from pathlib import Path import pyrate.constants as C from pyrate.core.prepifg_helper import PreprocessError from pyrate.core import shared, mpiops, gamma, roipac from pyrate.core import ifgconstants as ifc from pyrate.core.logger import pyratelogger as log from pyrate.configuration import MultiplePaths from pyrate.core.shared import mpi_vs_multiprocess_logging GAMMA = 1 ROIPAC = 0 GEOTIF = 2 def main(params): """ Parse parameters and prepare files for conversion. :param dict params: Parameters dictionary read in from the config file """ # TODO: looks like base_ifg_paths are ordered according to ifg list # This probably won't be a problem because input list won't be reordered # and the original gamma generated list is ordered) this may not affect # the important pyrate stuff anyway, but might affect gen_thumbs.py. # Going to assume base_ifg_paths is ordered correcly # pylint: disable=too-many-branches if params[C.PROCESSOR] == 2: # if geotif log.warning("'conv2tif' step not required for geotiff!") return mpi_vs_multiprocess_logging("conv2tif", params) base_ifg_paths = params[C.INTERFEROGRAM_FILES] if params[C.COH_FILE_LIST] is not None: base_ifg_paths.extend(params[C.COHERENCE_FILE_PATHS]) if params[C.DEM_FILE] is not None: # optional DEM conversion base_ifg_paths.append(params[C.DEM_FILE_PATH]) process_base_ifgs_paths = np.array_split(base_ifg_paths, mpiops.size)[mpiops.rank] gtiff_paths = do_geotiff(process_base_ifgs_paths, params) mpiops.comm.barrier() log.info("Finished 'conv2tif' step") return gtiff_paths def do_geotiff(unw_paths: List[MultiplePaths], params: dict) -> List[str]: """ Convert input interferograms to geotiff format. """ # pylint: disable=expression-not-assigned log.info("Converting input interferograms to geotiff") parallel = params[C.PARALLEL] if parallel: log.info("Running geotiff conversion in parallel with {} processes".format(params[C.PROCESSES])) dest_base_ifgs = Parallel(n_jobs=params[C.PROCESSES], verbose=shared.joblib_log_level( C.LOG_LEVEL))( delayed(_geotiff_multiprocessing)(p, params) for p in unw_paths) else: log.info("Running geotiff conversion in serial") dest_base_ifgs = [_geotiff_multiprocessing(b, params) for b in unw_paths] return dest_base_ifgs def _geotiff_multiprocessing(unw_path: MultiplePaths, params: dict) -> Tuple[str, bool]: """ Multiprocessing wrapper for full-res geotiff conversion """ # TODO: Need a more robust method for identifying coherence files. dest = unw_path.converted_path processor = params[C.PROCESSOR] # roipac or gamma # Create full-res geotiff if not already on disk if not os.path.exists(dest): if processor == GAMMA: header = gamma.gamma_header(unw_path.unwrapped_path, params) elif processor == ROIPAC: log.info("Warning: ROI_PAC support will be deprecated in a future PyRate release") header = roipac.roipac_header(unw_path.unwrapped_path, params) else: raise PreprocessError('Processor must be ROI_PAC (0) or GAMMA (1)') header[ifc.INPUT_TYPE] = unw_path.input_type shared.write_fullres_geotiff(header, unw_path.unwrapped_path, dest, nodata=params[ C.NO_DATA_VALUE]) Path(dest).chmod(0o444) # readonly output return dest, True else: log.warning(f"Full-res geotiff already exists in {dest}! Returning existing geotiff!") return dest, False ================================================ FILE: pyrate/core/.coveragerc ================================================ [run] plugins = Cython.Coverage source = pyrate omit = aps.py branch = True [report] exclude_lines = pragma: no cover def __repr__ raise AssertionError raise NotImplementedError if __name__ == .__main__.: ================================================ FILE: pyrate/core/__init__.py ================================================ ================================================ FILE: pyrate/core/algorithm.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains a collection of generic algorithms used in PyRate """ from typing import Union, Iterable, Dict, Tuple from numpy import sin, cos, unique, histogram, diag, dot from scipy.linalg import qr, solve, lstsq from pyrate.core.shared import EpochList, IfgException, PrereadIfg from pyrate.core.ifgconstants import DAYS_PER_YEAR def is_square(arr): """ Determines whether an array is square or not. :param ndarray arr: numpy array :return: condition :rtype: bool """ shape = arr.shape if len(shape) == 2 and (shape[0] == shape[1]): return True return False def least_squares_covariance(A, b, v): """ Least squares solution in the presence of known covariance. :param ndarray A: Design matrix :param ndarray b: Observations (vector of phase values) :param ndarray v: Covariances (vector of weights) :return: solution :rtype: ndarray """ # pylint: disable=too-many-locals # X = LSCOV(A,b,V) returns the vector X that minimizes # (A*X-b)'*inv(V)*(A*X-b) for the case in which length(b) > length(X). # This is the over-determined least squares problem with covariance V. # The solution is found without needing to invert V which is a square # symmetric matrix with dimensions equal to length(b). # # The classical linear algebra solution to this problem is: # # x = inv(A'*inv(V)*A)*A'*inv(V)*b # # Reference: # G. Strang, "Introduction to Applied Mathematics", # Wellesley-Cambridge, p. 398, 1986. # L. S hure 3-31-89 # convert vectors to 2D singleton array if len(A.shape) != 2: raise ValueError('') m, n = A.shape if m <= n: raise ValueError('Problem must be over-determined') V = diag(1.0 / v.squeeze()) q, r = qr(A) # Orthogonal-triangular Decomposition efg = dot(q.T, dot(V, q)) # TODO: round it?? g = efg[n:, n:] # modified to 0 indexing cd = dot(q.T, b) # q.T * b f = efg[:n, n:] # TODO: check +1/indexing c = cd[:n] # modified to 0 indexing d = cd[n:] # modified to 0 indexing r = r[:n, :n] # modified to 0 indexing func = solve if is_square(g) else lstsq tmp = func(g, d) func = solve if is_square(r) else lstsq return func(r, (c-f * tmp)) def los_conversion(phase_data, unit_vec): """ Converts phase from line-of-sight (LOS) to horizontal/vertical components. :param ndarray phase_data: Phase band data array (eg. ifg.phase_data) :param tuple unit_vec: 3 component unit vector e.g. (EW, NS, vertical) :return: converted_phase :rtype: ndarray """ # NB: currently not tested as implementation is too simple return phase_data * unit_vec def unit_vector(incidence, azimuth): """ Returns unit vector tuple (east_west, north_south, vertical). :param float incidence: incidence angle w.r.t. nadir :param float azimuth: azimuth of looking vector :return: Unit vector (EW, NS, vertical). :rtype: tuple """ vertical = cos(incidence) north_south = sin(incidence) * cos(azimuth) east_west = sin(incidence) * sin(azimuth) return east_west, north_south, vertical def ifg_date_lookup(ifgs, date_pair): """ Returns the Interferogram which has the first and second dates given in 'date_pair'. :param list ifgs: List of interferogram objects to search :param tuple date_pair: A (datetime.date, datetime.date) :return: interferogram list :rtype: list """ if len(date_pair) != 2: msg = "Need (datetime.date, datetime.date) first/second image pair" raise IfgException(msg) # check first/second dates are in order try: # TODO: Clarify: Is the comparison here for a different date? # Then it should be written in a more pythonic way # The if below is always true as long as the dates are different # and not in order if date_pair[0] > date_pair[1]: date_pair = date_pair[1], date_pair[0] except: raise ValueError("Bad date_pair arg to ifg_date_lookup()") for i in ifgs: if date_pair == (i.first, i.second): return i raise ValueError("Cannot find Ifg with first/second" "image dates of %s" % str(date_pair)) def ifg_date_index_lookup(ifgs, date_pair): """ Returns the Interferogram index which has the first and second image dates given in 'date_pair'. :param list ifgs: List of interferogram objects to search :param tuple date_pair: A (datetime.date, datetime.date) :return: interferogram index :rtype: int """ if len(date_pair) != 2: msg = "Need (datetime.date, datetime.date) first/second image date pair" raise IfgException(msg) # check first/second image dates are in order try: if date_pair[0] > date_pair[1]: date_pair = date_pair[1], date_pair[0] except: raise ValueError("Bad date_pair arg to ifg_date_lookup()") for i, _ in enumerate(ifgs): if date_pair == (ifgs[i].first, ifgs[i].second): return i raise ValueError("Cannot find Ifg with first/second image dates of %s" % str(date_pair)) def get_epochs(ifgs: Union[Iterable, Dict]) -> Tuple[EpochList, int]: """ Returns an EpochList derived from the given interferograms. :param ifgs: List of interferogram objects :return: EpochList :rtype: list """ if isinstance(ifgs, dict): ifgs = [v for v in ifgs.values() if isinstance(v, PrereadIfg)] combined = get_all_epochs(ifgs) dates, n = unique(combined, False, True) repeat, _ = histogram(n, bins=len(set(n))) # absolute span for each date from the zero/start point span = [(dates[i] - dates[0]).days / DAYS_PER_YEAR for i in range(len(dates))] return EpochList(dates, repeat, span), n def get_all_epochs(ifgs): """ Returns a sequence of all image dates used to form given interferograms. :param list ifgs: List of interferogram objects :return: list of all image dates :rtype: list """ return [ifg.first for ifg in ifgs] + [ifg.second for ifg in ifgs] def first_second_ids(dates): """ Returns a dictionary of 'date:unique ID' for each date in 'dates'. IDs are ordered from oldest to newest, starting at 0. :param list dates: List of dates :return: unique dates IDs :rtype: dict """ dset = sorted(set(dates)) return dict([(date_, i) for i, date_ in enumerate(dset)]) def factorise_integer(n, memo={}, left=2): """ Returns two factors a and b of a supplied number n such that a * b = n. The two factors are evaluated to be as close to each other in size as possible :param int n: Number to factorise :param dict memo: dictionary of candidate factors :param int left: operation flag (default = 2) :return: a, factor one :rtype: int :return: b, factor two :rtype: int """ n = int(n) if (n, left) in memo: return memo[(n, left)] if left == 1: return n, [n] i = 2 best = n bestTuple = [n] while i * i <= n: if n % i == 0: rem = factorise_integer(n / i, memo, left - 1) if rem[0] + i < best: best = rem[0] + i bestTuple = [i] + rem[1] i += 1 # handle edge case when only one processor is available if bestTuple == [4]: return 2, 2 if len(bestTuple) == 1: bestTuple.append(1) return int(bestTuple[0]), int(bestTuple[1]) ================================================ FILE: pyrate/core/aps.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module implements a spatio-temporal filter method for correcting interferograms for atmospheric phase screen (APS) signals. """ # pylint: disable=invalid-name, too-many-locals, too-many-arguments import os from copy import deepcopy from collections import OrderedDict from typing import List import numpy as np from numpy import isnan from scipy.fftpack import fft2, ifft2, fftshift, ifftshift from scipy.interpolate import griddata import pyrate.constants as C from pyrate.core.logger import pyratelogger as log from pyrate.core import shared, ifgconstants as ifc, mpiops from pyrate.core.covariance import cvd_from_phase, RDist from pyrate.core.algorithm import get_epochs from pyrate.core.shared import Ifg, Tile, EpochList, nan_and_mm_convert from pyrate.core.timeseries import time_series from pyrate.merge import assemble_tiles from pyrate.configuration import MultiplePaths, Configuration def spatio_temporal_filter(params: dict) -> None: """ Applies a spatio-temporal filter to remove the atmospheric phase screen (APS) and saves the corrected interferograms. Firstly the incremental time series is computed using the SVD method, before a cascade of temporal then spatial Gaussian filters is applied. The resulting APS corrections are saved to disc before being subtracted from each interferogram. :param params: Dictionary of PyRate configuration parameters. """ if params[C.APSEST]: log.info('Doing APS spatio-temporal filtering') else: log.info('APS spatio-temporal filtering not required') return tiles = params[C.TILES] preread_ifgs = params[C.PREREAD_IFGS] ifg_paths = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]] # perform some checks on existing ifgs log.debug('Checking APS correction status') if mpiops.run_once(shared.check_correction_status, ifg_paths, ifc.PYRATE_APS_ERROR): log.debug('Finished APS correction') return # return if True condition returned aps_paths = [MultiplePaths.aps_error_path(i, params) for i in ifg_paths] if all(a.exists() for a in aps_paths): log.warning('Reusing APS errors from previous run') _apply_aps_correction(ifg_paths, aps_paths, params) return # obtain the incremental time series using SVD tsincr = _calc_svd_time_series(ifg_paths, params, preread_ifgs, tiles) mpiops.comm.barrier() # get lists of epochs and ifgs ifgs = list(OrderedDict(sorted(preread_ifgs.items())).values()) epochlist = mpiops.run_once(get_epochs, ifgs)[0] # first perform temporal high pass filter ts_hp = temporal_high_pass_filter(tsincr, epochlist, params) # second perform spatial low pass filter to obtain APS correction in ts domain ifg = Ifg(ifg_paths[0]) # just grab any for parameters in slpfilter ifg.open() ts_aps = spatial_low_pass_filter(ts_hp, ifg, params) ifg.close() # construct APS corrections for each ifg _make_aps_corrections(ts_aps, ifgs, params) # apply correction to ifgs and save ifgs to disc. _apply_aps_correction(ifg_paths, aps_paths, params) # update/save the phase_data in the tiled numpy files shared.save_numpy_phase(ifg_paths, params) def _calc_svd_time_series(ifg_paths: List[str], params: dict, preread_ifgs: dict, tiles: List[Tile]) -> np.ndarray: """ Helper function to obtain time series for spatio-temporal filter using SVD method """ # Is there other existing functions that can perform this same job? log.info('Calculating incremental time series via SVD method for APS ' 'correction') # copy params temporarily new_params = deepcopy(params) new_params[C.TIME_SERIES_METHOD] = 2 # use SVD method process_tiles = mpiops.array_split(tiles) nvels = None for t in process_tiles: log.debug(f'Calculating time series for tile {t.index} during APS ' f'correction') ifgp = [shared.IfgPart(p, t, preread_ifgs, params) for p in ifg_paths] mst_tile = np.load(Configuration.mst_path(params, t.index)) tsincr = time_series(ifgp, new_params, vcmt=None, mst=mst_tile)[0] np.save(file=os.path.join(params[C.TMPDIR], f'tsincr_aps_{t.index}.npy'), arr=tsincr) nvels = tsincr.shape[2] nvels = mpiops.comm.bcast(nvels, root=0) mpiops.comm.barrier() # need to assemble tsincr from all processes tsincr_g = _assemble_tsincr(ifg_paths, params, preread_ifgs, tiles, nvels) log.debug('Finished calculating time series for spatio-temporal filter') return tsincr_g def _assemble_tsincr(ifg_paths: List[str], params: dict, preread_ifgs: dict, tiles: List[Tile], nvels: np.float32) -> np.ndarray: """ Helper function to reconstruct time series images from tiles """ # pre-allocate dest 3D array shape = preread_ifgs[ifg_paths[0]].shape tsincr_p = {} process_nvels = mpiops.array_split(range(nvels)) for i in process_nvels: tsincr_p[i] = assemble_tiles(shape, params[C.TMPDIR], tiles, out_type='tsincr_aps', index=i) tsincr_g = shared.join_dicts(mpiops.comm.allgather(tsincr_p)) return np.dstack([v[1] for v in sorted(tsincr_g.items())]) def _make_aps_corrections(ts_aps: np.ndarray, ifgs: List[Ifg], params: dict) -> None: """ Function to convert the time series APS filter output into interferometric phase corrections and save them to disc. :param ts_aps: Incremental APS time series array. :param ifgs: List of Ifg class objects. :param params: Dictionary of PyRate configuration parameters. """ log.debug('Reconstructing interferometric observations from time series') # get first and second image indices _ , n = mpiops.run_once(get_epochs, ifgs) index_first, index_second = n[:len(ifgs)], n[len(ifgs):] num_ifgs_tuples = mpiops.array_split(list(enumerate(ifgs))) for i, ifg in [(int(num), ifg) for num, ifg in num_ifgs_tuples]: # sum time slice data from first to second epoch ifg_aps = np.sum(ts_aps[:, :, index_first[i]: index_second[i]], axis=2) aps_error_on_disc = MultiplePaths.aps_error_path(ifg.tmp_path, params) np.save(file=aps_error_on_disc, arr=ifg_aps) # save APS as numpy array mpiops.comm.barrier() def _apply_aps_correction(ifg_paths: List[str], aps_paths: List[str], params: dict) -> None: """ Function to read and apply (subtract) APS corrections from interferogram data. """ for ifg_path, aps_path in mpiops.array_split(list(zip(ifg_paths, aps_paths))): # read the APS correction from numpy array aps_corr = np.load(aps_path) # open the Ifg object ifg = Ifg(ifg_path) ifg.open(readonly=False) # convert NaNs and convert to mm nan_and_mm_convert(ifg, params) # subtract the correction from the ifg phase data ifg.phase_data[~np.isnan(ifg.phase_data)] -= aps_corr[~np.isnan(ifg.phase_data)] # set meta-data tags after aps error correction ifg.dataset.SetMetadataItem(ifc.PYRATE_APS_ERROR, ifc.APS_REMOVED) # write phase data to disc and close ifg. ifg.write_modified_phase() ifg.close() def spatial_low_pass_filter(ts_hp: np.ndarray, ifg: Ifg, params: dict) -> np.ndarray: """ Filter time series data spatially using a Gaussian low-pass filter defined by a cut-off distance. If the cut-off distance is defined as zero in the parameters dictionary then it is calculated for each time step using the pyrate.covariance.cvd_from_phase method. :param ts_hp: Array of temporal high-pass time series data, shape (ifg.shape, n_epochs) :param ifg: pyrate.core.shared.Ifg Class object. :param params: Dictionary of PyRate configuration parameters. :return: ts_lp: Low-pass filtered time series data of shape (ifg.shape, n_epochs). """ log.info('Applying spatial low-pass filter') nvels = ts_hp.shape[2] cutoff = params[C.SLPF_CUTOFF] # nanfill = params[cf.SLPF_NANFILL] # fillmethod = params[cf.SLPF_NANFILL_METHOD] if cutoff == 0: r_dist = RDist(ifg)() # only needed for cvd_for_phase else: r_dist = None log.info(f'Gaussian spatial filter cutoff is {cutoff:.3f} km for all ' f'{nvels} time-series images') process_nvel = mpiops.array_split(range(nvels)) process_ts_lp = {} for i in process_nvel: process_ts_lp[i] = _slpfilter(ts_hp[:, :, i], ifg, r_dist, params) ts_lp_d = shared.join_dicts(mpiops.comm.allgather(process_ts_lp)) ts_lp = np.dstack([v[1] for v in sorted(ts_lp_d.items())]) log.debug('Finished applying spatial low pass filter') return ts_lp def _interpolate_nans_2d(arr: np.ndarray, method: str) -> None: """ In-place array interpolation and NaN-fill using scipy.interpolation.griddata. :param arr: 2D ndarray to be interpolated. :param method: Method; one of 'nearest', 'linear', and 'cubic'. """ log.debug(f'Interpolating array with "{method}" method') r, c = np.indices(arr.shape) arr[np.isnan(arr)] = griddata( (r[~np.isnan(arr)], c[~np.isnan(arr)]), # points we know arr[~np.isnan(arr)], # values we know (r[np.isnan(arr)], c[np.isnan(arr)]), # points to interpolate method=method, fill_value=0) def _slpfilter(phase: np.ndarray, ifg: Ifg, r_dist: float, params: dict) -> np.ndarray: """ Wrapper function for spatial low pass filter """ cutoff = params[C.SLPF_CUTOFF] nanfill = params[C.SLPF_NANFILL] fillmethod = params[C.SLPF_NANFILL_METHOD] if np.all(np.isnan(phase)): # return for nan matrix return phase if cutoff == 0: _, alpha = cvd_from_phase(phase, ifg, r_dist, calc_alpha=True) cutoff = 1.0 / alpha log.info(f'Gaussian spatial filter cutoff is {cutoff:.3f} km') return gaussian_spatial_filter(phase, cutoff, ifg.x_size, ifg.y_size, nanfill, fillmethod) def gaussian_spatial_filter(image: np.ndarray, cutoff: float, x_size: float, y_size: float, nanfill: bool = True, fillmethod: str = 'nearest') -> np.ndarray: """ Function to apply a Gaussian spatial low-pass filter to a 2D image with unequal pixel resolution in x and y dimensions. Performs filtering in the Fourier domain. Any NaNs in the image are interpolated prior to Fourier transformation, with NaNs being replaced in to the filtered output image. :param image: 2D image to be filtered :param cutoff: filter cutoff in kilometres :param x_size: pixel size in x dimension, in metres :param y_size: pixel size in y dimension, in metres :param nanfill: interpolate image to fill NaNs :param fillmethod: interpolation method ('nearest', 'cubic', or 'linear') :return: filt: Gaussian low-pass filtered 2D image """ # create NaN mask of image mask = np.isnan(image) # in-place nearest-neighbour interpolation to fill NaNs # nearest neighbour will fill values outside the convex hull if nanfill: _interpolate_nans_2d(image, fillmethod) rows, cols = image.shape pad = 4096 # pad the image to a large square array. # TODO: implement variable padding dependent on image size im = np.pad(image, ((0, pad - rows), (0, pad - cols)), 'constant') # fast fourier transform of the input image imf = fftshift(fft2(im)) # calculate centre coords of image cx = np.floor(pad / 2) cy = np.floor(pad / 2) # calculate distance array [xx, yy] = np.meshgrid(range(pad), range(pad)) xx = (xx - cx) * x_size # these are in meters as x_size in metres yy = (yy - cy) * y_size dist = np.sqrt(xx ** 2 + yy ** 2) / ifc.METRE_PER_KM # change m to km # Estimate sigma value for Gaussian kernel function in spectral domain # by converting cutoff distance to wavenumber and applying a scaling # factor based on fixed kernel window size. sigma = np.std(dist) * (1 / cutoff) # Calculate kernel weights wgt = _kernel(dist, sigma) # Apply Gaussian smoothing kernel outf = imf * wgt # Inverse Fourier transform out = np.real(ifft2(ifftshift(outf))) filt = out[:rows, :cols] # grab non-padded part filt[mask] = np.nan # re-insert nans in output image return filt # TODO: use tiles here and distribute amongst processes def temporal_high_pass_filter(tsincr: np.ndarray, epochlist: EpochList, params: dict) -> np.ndarray: """ Isolate high-frequency components of time series data by subtracting low-pass components obtained using a Gaussian filter defined by a cut-off time period (in days). :param tsincr: Array of incremental time series data of shape (ifg.shape, n_epochs). :param epochlist: A pyrate.core.shared.EpochList Class instance. :param params: Dictionary of PyRate configuration parameters. :return: ts_hp: Filtered high frequency time series data; shape (ifg.shape, nepochs). """ log.info('Applying temporal high-pass filter') threshold = params[C.TLPF_PTHR] cutoff_day = params[C.TLPF_CUTOFF] if cutoff_day < 1 or type(cutoff_day) != int: raise ValueError(f'tlpf_cutoff must be an integer greater than or ' f'equal to 1 day. Value provided = {cutoff_day}') # convert cutoff in days to years cutoff_yr = cutoff_day / ifc.DAYS_PER_YEAR log.info(f'Gaussian temporal filter cutoff is {cutoff_day} days ' f'({cutoff_yr:.4f} years)') intv = np.diff(epochlist.spans) # time interval for the neighboring epochs span = epochlist.spans[: tsincr.shape[2]] + intv / 2 # accumulated time rows, cols = tsincr.shape[:2] tsfilt_row = {} process_rows = mpiops.array_split(list(range(rows))) for r in process_rows: tsfilt_row[r] = np.empty(tsincr.shape[1:], dtype=np.float32) * np.nan for j in range(cols): # Result of gaussian filter is low frequency time series tsfilt_row[r][j, :] = gaussian_temporal_filter(tsincr[r, j, :], cutoff_yr, span, threshold) tsfilt_combined = shared.join_dicts(mpiops.comm.allgather(tsfilt_row)) tsfilt = np.array([v[1] for v in tsfilt_combined.items()]) log.debug("Finished applying temporal high-pass filter") # Return the high-pass time series by subtracting low-pass result from input return tsincr - tsfilt def gaussian_temporal_filter(tsincr: np.ndarray, cutoff: float, span: np.ndarray, thr: int) -> np.ndarray: """ Function to apply a Gaussian temporal low-pass filter to a 1D time-series vector for one pixel with irregular temporal sampling. :param tsincr: 1D time-series vector to be filtered. :param cutoff: filter cutoff in years. :param span: 1D vector of cumulative time spans, in years. :param thr: threshold for non-NaN values in tsincr. :return: ts_lp: Low-pass filtered time series vector. """ nanmat = ~isnan(tsincr) sel = np.nonzero(nanmat)[0] # don't select if nan ts_lp = np.empty(tsincr.shape, dtype=np.float32) * np.nan m = len(sel) if m >= thr: for k in range(m): yr = span[sel] - span[sel[k]] # apply Gaussian smoothing kernel wgt = _kernel(yr, cutoff) wgt /= np.sum(wgt) ts_lp[sel[k]] = np.sum(tsincr[sel] * wgt) return ts_lp def _kernel(x: np.ndarray, sigma: float) -> np.ndarray: """ Gaussian low-pass filter kernel """ return np.exp(-0.5 * (x / sigma) ** 2) ================================================ FILE: pyrate/core/covariance.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module implements covariance calculation and Variance/Covariance matrix functionality. """ # coding: utf-8 from os.path import basename, join from collections import OrderedDict from numpy import array, where, isnan, real, imag, sqrt, meshgrid from numpy import zeros, vstack, ceil, mean, exp, reshape from numpy.linalg import norm import numpy as np from scipy.fftpack import fft2, ifft2, fftshift from scipy.optimize import fmin import pyrate.constants as C from pyrate.core import shared, ifgconstants as ifc, mpiops from pyrate.core.shared import PrereadIfg, Ifg from pyrate.core.algorithm import first_second_ids from pyrate.core.logger import pyratelogger as log from pyrate.configuration import Configuration # pylint: disable=too-many-arguments MAIN_PROCESS = 0 def _pendiffexp(alphamod, cvdav): """ Fits an exponential model to data. :param float alphamod: Exponential decay exponent. :param ndarray cvdav: Function magnitude at 0 radius (2 col array of radius, variance) """ # pylint: disable=invalid-name # maxvar usually at zero lag mx = cvdav[1, 0] return norm(cvdav[1, :] - (mx * exp(-alphamod * cvdav[0, :]))) # this is not used any more def _unique_points(points): # pragma: no cover """ Returns unique points from a list of coordinates. :param points: Sequence of (y,x) or (x,y) tuples. """ return vstack([array(u) for u in set(points)]) def cvd(ifg_path, params, r_dist, calc_alpha=False, write_vals=False, save_acg=False): """ Calculate the 1D covariance function of an entire interferogram as the radial average of its 2D autocorrelation. :param str ifg_path: An interferogram file path. OR :param dict params: Dictionary of configuration parameters :param ndarray r_dist: Array of distance values from the image centre (See Rdist class for more details) :param bool calc_alpha: If True calculate alpha :param bool write_vals: If True write maxvar and alpha values to interferogram metadata :param bool save_acg: If True write autocorrelation and radial distance data to numpy array file on disk :return: maxvar: The maximum variance (at zero lag) :rtype: float :return: alpha: the exponential length-scale of decay factor :rtype: float """ ifg = shared.Ifg(ifg_path) ifg.open() shared.nan_and_mm_convert(ifg, params) # calculate 2D auto-correlation of image using the # spectral method (Wiener-Khinchin theorem) if ifg.nan_converted: # if nancoverted earlier, convert nans back to 0's phase = where(isnan(ifg.phase_data), 0, ifg.phase_data) else: phase = ifg.phase_data maxvar, alpha = cvd_from_phase(phase, ifg, r_dist, calc_alpha, save_acg=save_acg, params=params) if write_vals: ifg.add_metadata(**{ ifc.PYRATE_MAXVAR: str(maxvar), ifc.PYRATE_ALPHA: str(alpha) }) ifg.close() return maxvar, alpha def _save_cvd_data(acg, r_dist, ifg_path, outdir): """ Function to save numpy array of autocorrelation data to disk """ data = np.column_stack((acg, r_dist)) data_file = join(outdir, 'cvd_data_{b}.npy'.format(b=basename(ifg_path).split('.')[0])) np.save(file=data_file, arr=data) def cvd_from_phase(phase, ifg, r_dist, calc_alpha, save_acg=False, params=None): """ A convenience function used to compute radial autocovariance from phase data :param ndarray phase: An array of interferogram phase data :param Ifg class ifg: A pyrate.shared.Ifg class instance :param ndarray r_dist: Array of distance values from the image centre (See Rdist class for more details) :param bool calc_alpha: If True calculate alpha :param bool save_acg: If True write autocorrelation and radial distance data to numpy array file on disk :param dict params: [optional] Dictionary of configuration parameters; Must be provided if save_acg=True :return: maxvar: The maximum variance (at zero lag) :rtype: float :return: alpha: the exponential length-scale of decay factor :rtype: float """ # pylint: disable=invalid-name # pylint: disable=too-many-locals autocorr_grid = _get_autogrid(phase) acg = reshape(autocorr_grid, phase.size, order='F') # Symmetry in image; keep only unique points # tmp = _unique_points(zip(acg, r_dist)) # Sudipta: Unlikely, as unique_point is a search/comparison, # whereas keeping 1st half is just numpy indexing. # If it is not faster, why was this done differently here? # r_dist = r_dist[:int(ceil(phase.size / 2.0)) + nrows] acg = acg[:len(r_dist)] # Alternative method to remove duplicate cells # r_dist = r_dist[:ceil(len(r_dist)/2)+nlines] # Reason for '+nlines' term unknown # eg. array([x for x in set([(1,1), (2,2), (1,1)])]) # the above shortens r_dist by some number of cells # pick the smallest axis to determine circle search radius if (ifg.x_centre * ifg.x_size) < (ifg.y_centre * ifg.y_size): maxdist = (ifg.x_centre+1) * ifg.x_size / ifc.METRE_PER_KM else: maxdist = (ifg.y_centre+1) * ifg.y_size / ifc.METRE_PER_KM # filter out data where the of lag distance is greater than maxdist # r_dist = array([e for e in rorig if e <= maxdist]) # # MG: prefers to use all the data # acg = array([e for e in rorig if e <= maxdist]) indices_to_keep = r_dist < maxdist acg = acg[indices_to_keep] # optionally save acg vs dist observations to disk if save_acg: _save_cvd_data(acg, r_dist[indices_to_keep], ifg.data_path, params[C.TMPDIR]) if calc_alpha: # bin width for collecting data bin_width = max(ifg.x_size, ifg.y_size) * 2 / ifc.METRE_PER_KM # km r_dist = r_dist[indices_to_keep] # km # classify values of r_dist according to bin number rbin = ceil(r_dist / bin_width).astype(int) maxbin = max(rbin) - 1 # consistent with Legacy data cvdav = zeros(shape=(2, maxbin + 1)) # the following stays in numpy land # distance instead of bin number cvdav[0, :] = np.multiply(range(maxbin + 1), bin_width) # mean variance for the bins cvdav[1, :] = [mean(acg[rbin == b]) for b in range(maxbin + 1)] # calculate best fit function maxvar*exp(-alpha*r_dist) alphaguess = 2 / (maxbin * bin_width) alpha = fmin(_pendiffexp, x0=alphaguess, args=(cvdav,), disp=False, xtol=1e-6, ftol=1e-6) log.debug("1st guess alpha {}, converged " "alpha: {}".format(alphaguess, alpha)) # maximum variance usually at the zero lag: max(acg[:len(r_dist)]) return np.max(acg), alpha[0] # alpha unit 1/km else: return np.max(acg), None class RDist(): """ RDist class used for caching r_dist during maxvar/alpha computation """ # pylint: disable=invalid-name def __init__(self, ifg): self.r_dist = None self.ifg = ifg self.nrows, self.ncols = ifg.shape def __call__(self): if self.r_dist is None: size = self.nrows * self.ncols # pixel distances from pixel at zero lag (image centre). xx, yy = meshgrid(range(self.ncols), range(self.nrows)) # r_dist is distance from the center # doing np.divide and np.sqrt will improve performance as it keeps # calculations in the numpy land self.r_dist = np.divide(np.sqrt(((xx - self.ifg.x_centre) * self.ifg.x_size) ** 2 + ((yy - self.ifg.y_centre) * self.ifg.y_size) ** 2), ifc.METRE_PER_KM) # km self.r_dist = reshape(self.r_dist, size, order='F') self.r_dist = self.r_dist[:int(ceil(size / 2.0)) + self.nrows] return self.r_dist def _get_autogrid(phase): """ Helper function to assist with memory re-allocation during FFT calculation """ autocorr_grid = _calc_autoc_grid(phase) nzc = np.sum(np.sum(phase != 0)) autocorr_grid = fftshift(real(autocorr_grid)) / nzc return autocorr_grid def _calc_autoc_grid(phase): """ Helper function to assist with memory re-allocation during FFT calculation """ pspec = _calc_power_spectrum(phase) autocorr_grid = ifft2(pspec) return autocorr_grid.astype(dtype=np.complex64) def _calc_power_spectrum(phase): """ Helper function to assist with memory re-allocation during FFT calculation """ fft_phase = fft2(phase) pspec = real(fft_phase) ** 2 + imag(fft_phase) ** 2 return pspec.astype(dtype=np.float32) def get_vcmt(ifgs, maxvar): """ Assembles a temporal variance/covariance matrix using the method described by Biggs et al., Geophys. J. Int, 2007. Matrix elements are evaluated according to sig_i * sig_j * C_ij where i and j are two interferograms and C is a matrix of coefficients: C = 1 if the first and second epochs of i and j are equal C = 0.5 if have i and j share either a common first or second epoch C = -0.5 if the first of i or j equals the second of the other C = 0 otherwise :param list ifgs: A list of pyrate.shared.Ifg class objects. :param ndarray maxvar: numpy array of maximum variance values for the interferograms. :return: vcm_t: temporal variance-covariance matrix :rtype: ndarray """ # pylint: disable=too-many-locals # c=0.5 for common first or second; c=-0.5 if first # of one matches second of another if isinstance(ifgs, dict): ifgs = {k: v for k, v in ifgs.items() if isinstance(v, PrereadIfg)} ifgs = OrderedDict(sorted(ifgs.items())) # pylint: disable=redefined-variable-type ifgs = ifgs.values() nifgs = len(ifgs) vcm_pat = zeros((nifgs, nifgs)) dates = [ifg.first for ifg in ifgs] + [ifg.second for ifg in ifgs] ids = first_second_ids(dates) for i, ifg in enumerate(ifgs): mas1, slv1 = ids[ifg.first], ids[ifg.second] for j, ifg2 in enumerate(ifgs): mas2, slv2 = ids[ifg2.first], ids[ifg2.second] if mas1 == mas2 or slv1 == slv2: vcm_pat[i, j] = 0.5 if mas1 == slv2 or slv1 == mas2: vcm_pat[i, j] = -0.5 if mas1 == mas2 and slv1 == slv2: vcm_pat[i, j] = 1.0 # diagonal elements # make covariance matrix in time domain std = sqrt(maxvar).reshape((nifgs, 1)) vcm_t = std * std.transpose() return vcm_t * vcm_pat def maxvar_vcm_calc_wrapper(params): """ MPI wrapper for maxvar and vcmt computation """ preread_ifgs = params[C.PREREAD_IFGS] ifg_paths = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]] log.info('Calculating the temporal variance-covariance matrix') def _get_r_dist(ifg_path): """ Get RDIst class object """ ifg = Ifg(ifg_path) ifg.open() r_dist = RDist(ifg)() ifg.close() return r_dist r_dist = mpiops.run_once(_get_r_dist, ifg_paths[0]) prcs_ifgs = mpiops.array_split(list(enumerate(ifg_paths))) process_maxvar = {} for n, i in prcs_ifgs: log.debug(f'Calculating maxvar for {n} of process ifgs {len(prcs_ifgs)} of total {len(ifg_paths)}') process_maxvar[int(n)] = cvd(i, params, r_dist, calc_alpha=True, write_vals=True, save_acg=True)[0] maxvar_d = shared.join_dicts(mpiops.comm.allgather(process_maxvar)) maxvar = [v[1] for v in sorted(maxvar_d.items(), key=lambda s: s[0])] vcmt = mpiops.run_once(get_vcmt, preread_ifgs, maxvar) log.debug("Finished maxvar and vcm calc!") params[C.MAXVAR], params[C.VCMT] = maxvar, vcmt np.save(Configuration.vcmt_path(params), arr=vcmt) return maxvar, vcmt ================================================ FILE: pyrate/core/dem_error.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module implements the calculation of correction for residual topographic effects (a.k.a. as DEM errors). """ # pylint: disable=invalid-name, too-many-locals, too-many-arguments import os import numpy as np from typing import Tuple, Optional from os.path import join from pathlib import Path import pyrate.constants as C from pyrate.core import geometry, shared, mpiops, ifgconstants as ifc from pyrate.core.logger import pyratelogger as log from pyrate.core.shared import Ifg, Geometry, DEM, Tile, tiles_split from pyrate.core.timeseries import TimeSeriesError from pyrate.configuration import MultiplePaths, Configuration from pyrate.merge import assemble_tiles def dem_error_calc_wrapper(params: dict) -> None: """ MPI wrapper for DEM error correction :param params: Dictionary of PyRate configuration parameters. """ if not params[C.DEMERROR]: log.info("DEM error correction not required") return # geometry information needed to calculate Bperp for each pixel using first IFG in list ifg_paths = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]] # check if DEM error correction is already available if mpiops.run_once(__check_and_apply_demerrors_found_on_disc, ifg_paths, params): log.warning("Reusing DEM error correction from previous run!!!") else: log.info("Calculating DEM error correction") # read and open the first IFG in list ifg0_path = ifg_paths[0] ifg0 = Ifg(ifg0_path) ifg0.open(readonly=True) # not currently implemented for ROIPAC data which breaks some tests # if statement can be deleted once ROIPAC is deprecated from PyRate if ifg0.meta_data[ifc.PYRATE_INSAR_PROCESSOR] == 'ROIPAC': log.warning("Geometry calculations are not implemented for ROI_PAC") return if params[C.BASE_FILE_LIST] is None: log.warning("No baseline files supplied: DEM error has not been computed") return # todo: subtract other corrections (e.g. orbital) from displacement phase before estimating the DEM error # the following code is a quick way to do the bperp calculation, but is not identical to the GAMMA output # where the near range of the first SLC is used for each pair. # calculate look angle for interferograms (using the Near Range of the first SLC) # look_angle = geometry.calc_local_geometry(ifg0, None, rg, lon, lat, params) # bperp = geometry.calc_local_baseline(ifg0, az, look_angle) # split full arrays in to tiles for parallel processing tiles_split(_process_dem_error_per_tile, params) preread_ifgs = params[C.PREREAD_IFGS] # write dem error and correction values to file mpiops.run_once(_write_dem_errors, ifg_paths, params, preread_ifgs) shared.save_numpy_phase(ifg_paths, params) log.debug('Finished DEM error correction step') def _process_dem_error_per_tile(tile: Tile, params: dict) -> None: """ Convenience function for processing DEM error in tiles :param tile: pyrate.core.shared.Tile Class object. :param params: Dictionary of PyRate configuration parameters. """ ifg_paths = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]] ifg0_path = ifg_paths[0] ifg0 = Ifg(ifg0_path) ifg0.open(readonly=True) # read lon and lat values of multi-looked ifg (first ifg only) lon, lat = geometry.get_lonlat_coords(ifg0) # read azimuth and range coords and DEM from tif files generated in prepifg geom_files = Configuration.geometry_files(params) rdc_az_file = geom_files['rdc_azimuth'] geom_az = Geometry(rdc_az_file) rdc_rg_file = geom_files['rdc_range'] geom_rg = Geometry(rdc_rg_file) dem_file = params[C.DEM_FILE_PATH].sampled_path dem = DEM(dem_file) preread_ifgs = params[C.PREREAD_IFGS] threshold = params[C.DE_PTHR] ifg_parts = [shared.IfgPart(p, tile, preread_ifgs, params) for p in ifg_paths] lon_parts = lon(tile) lat_parts = lat(tile) az_parts = geom_az(tile) rg_parts = geom_rg(tile) dem_parts = dem(tile) log.debug(f"Calculating per-pixel baseline for tile {tile.index} during DEM error correction") bperp, look_angle, range_dist = _calculate_bperp_wrapper(ifg_paths, az_parts, rg_parts, lat_parts, lon_parts, dem_parts) log.debug(f"Calculating DEM error for tile {tile.index} during DEM error correction") # mst_tile = np.load(Configuration.mst_path(params, tile.index)) # calculate the DEM error estimate and the correction values for each IFG # current implementation uses the look angle and range distance matrix of the primary SLC in the last IFG # todo: check the impact of using the same information from another SLC dem_error, dem_error_correction, _ = calc_dem_errors(ifg_parts, bperp, look_angle, range_dist, threshold) # dem_error contains the estimated DEM error for each pixel (i.e. the topographic change relative to the DEM) # size [row, col] # dem_error_correction contains the correction value for each interferogram # size [num_ifg, row, col] # save tiled data in tmpdir np.save(file=os.path.join(params[C.TMPDIR], 'dem_error_{}.npy'.format(tile.index)), arr=dem_error) # swap the axes of 3D array to fit the style used in function assemble_tiles tmp_array = np.moveaxis(dem_error_correction, 0, -1) # new dimension is [row, col, num_ifg] # save tiled data into tmpdir np.save(file=os.path.join(params[C.TMPDIR], 'dem_error_correction_{}.npy'.format(tile.index)), arr=tmp_array) # Calculate and save the average perpendicular baseline for the tile bperp_avg = np.nanmean(bperp, axis=(1, 2), dtype=np.float64) np.save(file=os.path.join(params[C.TMPDIR], 'bperp_avg_{}.npy'.format(tile.index)), arr=bperp_avg) def _calculate_bperp_wrapper(ifg_paths: list, az_parts: np.ndarray, rg_parts: np.ndarray, lat_parts: np.ndarray, lon_parts: np.ndarray, dem_parts: np.ndarray, ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: """ Wrapper function to calculate the perpendicular baseline for each pixel in each interferogram. :param ifg_paths: list of pyrate.core.shared.Ifg Class objects. :param az_parts: azimuth coordinate (i.e. line) for each pixel. :param rg_parts: range coordinate (i.e. column) for each pixel. :param lat_parts: latitude for each pixel. :param lon_parts: longitude for each pixel. :param dem_parts: DEM height for each pixel. :return: bperp: perpendicular baseline for each pixel and interferogram. :return: look_angle: look angle for each pixel. :return: range_dist: range distance measurement for each pixel. """ nifgs = len(ifg_paths) bperp = np.empty((nifgs, lon_parts.shape[0], lon_parts.shape[1])) * np.nan # calculate per-pixel perpendicular baseline for each IFG # loop could be avoided by approximating the look angle for the first Ifg for ifg_num, ifg_path in enumerate(ifg_paths): ifg = Ifg(ifg_path) ifg.open(readonly=True) # calculate look angle for interferograms (using the Near Range of the primary SLC) look_angle, _, _, range_dist = geometry.calc_pixel_geometry(ifg, rg_parts, lon_parts, lat_parts, dem_parts) bperp[ifg_num, :, :] = geometry.calc_local_baseline(ifg, az_parts, look_angle) return bperp, look_angle, range_dist def calc_dem_errors(ifgs: list, bperp: np.ndarray, look_angle: np.ndarray, range_dist: np.ndarray, threshold: int) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: """ Function to calculate the DEM error for each pixel using least-squares adjustment of phase data and perpendicular baseline. The least-squares adjustment co-estimates the velocities. - *nrows* is the number of rows in the ifgs, and - *ncols* is the number of columns in the ifgs. :param ifgs: list of interferogram class objects. :param bperp: Per-pixel perpendicular baseline for each interferogram :param look_angle: Per-pixel look angle :param range_dist: Per-pixel range distance measurement :param threshold: minimum number of redundant phase values at pixel (config parameter de_pthr) :return: dem_error: estimated per-pixel dem error (nrows x ncols) :return: dem_error_correction: DEM error correction for each pixel and interferogram (nifgs x nrows x ncols) :return: vel: velocity estimate for each pixel (nrows x ncols) """ ifg_data, mst, ncols, nrows, ifg_time_span = _per_tile_setup(ifgs) if threshold < 4: msg = f"pixel threshold too low (i.e. <4) resulting in non-redundant DEM error estimation" raise DEMError(msg) # pixel-by-pixel calculation # preallocate empty arrays for results dem_error = np.empty([nrows, ncols], dtype=np.float32) * np.nan vel = np.empty([nrows, ncols], dtype=np.float32) * np.nan # nested loops to loop over the 2 image dimensions for row in range(nrows): for col in range(ncols): # calc DEM error for each pixel with valid Bperp and ifg phase data # check pixel for non-redundant ifgs sel = np.nonzero(mst[:, row, col])[0] # trues in mst are chosen if len(sel) >= threshold: # given threshold for number of valid pixels in time series # phase observations (in mm) y = ifg_data[sel, row, col] bperp_pix = bperp[sel, row, col] # If NaN: skip pixel if np.isnan(y).any() or np.isnan(bperp_pix).any(): continue # using the actual geometry of a particular IFG would be possible but is likely not signif. different # geom = bperp_pix / (range_dist[row, col] * np.sin(look_angle[row, col])) time_span = ifg_time_span[sel] # new covariance matrix using actual number of observations m = len(sel) # Design matrix of least-squares system, velocities are co-estimated as done in StaMPS or MintPy A = np.column_stack((np.ones(m), bperp_pix, time_span)) # solve ordinary least-squares system using numpy.linalg.lstsq function xhat, res, rnk, s = np.linalg.lstsq(A, y, rcond=None) # dem error estimate for the pixel is the second parameter (cf. design matrix) dem_error[row][col] = xhat[1] # velocity estimate for the pixel is the third parameter (cf. design matrix) vel[row][col] = xhat[2] # calculate correction value for each IFG by multiplying the least-squares estimate with the Bperp value dem_error_correction = np.multiply(dem_error, bperp) # calculate metric difference to DEM by multiplying the estimate with the per-pixel geometry # (i.e. range distance and look angle, see Eq. (2.4.12) in Hanssen (2001)) # also scale by -0.001 since the phase observations are in mm with positive values away from the sensor dem_error = np.multiply(dem_error, np.multiply(range_dist, np.sin(look_angle))) * (-0.001) return dem_error, dem_error_correction, vel def _per_tile_setup(ifgs: list) -> Tuple[np.ndarray, np.ndarray, int, int, np.ndarray]: """ Convenience function for setting up DEM error computation parameters :param ifgs: list of interferogram class objects. :return: ifg_data: phase observations for each pixel and interferogram :return: mst: an array of booleans representing valid ifg connections (i.e. the minimum spanning tree) :return: ncols: number of columns :return: nrows: number of rows :return: ifg_time_span: date difference for each interferogram """ if len(ifgs) < 1: msg = 'Time series requires 2+ interferograms' raise TimeSeriesError(msg) nrows = ifgs[0].nrows ncols = ifgs[0].ncols nifgs = len(ifgs) ifg_data = np.zeros((nifgs, nrows, ncols), dtype=np.float32) for ifg_num in range(nifgs): ifg_data[ifg_num] = ifgs[ifg_num].phase_data # phase data is read from numpy files mst = ~np.isnan(ifg_data) ifg_time_span = np.zeros((nifgs)) for ifg_num in range(nifgs): ifg_time_span[ifg_num] = ifgs[ifg_num].time_span return ifg_data, mst, ncols, nrows, ifg_time_span def _write_dem_errors(ifg_paths: list, params: dict, preread_ifgs: dict) -> None: """ Convenience function for writing DEM error (one file) and DEM error correction for each IFG to disc :param ifg_paths: List of interferogram class objects. :param params: Dictionary of PyRate configuration parameters. :param preread_ifgs: Dictionary of interferogram metadata. """ tiles = params[C.TILES] # re-assemble tiles and save into dem_error dir shape = preread_ifgs[ifg_paths[0]].shape # save dem error as geotiff file in out directory gt, md, wkt = shared.get_geotiff_header_info(ifg_paths[0]) md[ifc.DATA_TYPE] = ifc.DEM_ERROR dem_error = assemble_tiles(shape, params[C.TMPDIR], tiles, out_type='dem_error') dem_error_file = os.path.join(params[C.DEM_ERROR_DIR], 'dem_error.tif') shared.remove_file_if_exists(dem_error_file) shared.write_output_geotiff(md, gt, wkt, dem_error, dem_error_file, np.nan) # read the average bperp vals for each ifg and each tile bperp = np.empty(shape=(len(tiles), len(ifg_paths)), dtype=np.float64) for t in tiles: bperp_file = Path(join(params[C.TMPDIR], 'bperp_avg_' + str(t.index) + '.npy')) arr = np.load(file=bperp_file) bperp[t.index, :] = arr # loop over all ifgs idx = 0 for ifg_path in ifg_paths: ifg = Ifg(ifg_path) ifg.open() shared.nan_and_mm_convert(ifg, params) # ensure we have phase data in mm # read dem error correction file from tmpdir dem_error_correction_ifg = assemble_tiles(shape, params[C.TMPDIR], tiles, out_type='dem_error_correction', index=idx) # calculate average bperp value across all tiles for the ifg bperp_val = np.nanmean(bperp[:, idx]) dem_error_correction_on_disc = MultiplePaths.dem_error_path(ifg.data_path, params) np.save(file=dem_error_correction_on_disc, arr=dem_error_correction_ifg) idx += 1 # subtract DEM error from the ifg ifg.phase_data -= dem_error_correction_ifg _save_dem_error_corrected_phase(ifg, bperp_val) def __check_and_apply_demerrors_found_on_disc(ifg_paths: list, params: dict) -> bool: """ Convenience function to check if DEM error correction files have already been produced in a previous run :param ifg_paths: List of interferogram class objects. :param params: Dictionary of PyRate configuration parameters. :return: bool value: True if dem error files found on disc, otherwise False. """ saved_dem_err_paths = [MultiplePaths.dem_error_path(ifg_path, params) for ifg_path in ifg_paths] for d, i in zip(saved_dem_err_paths, ifg_paths): if d.exists(): dem_corr = np.load(d) if isinstance(i, str): # are paths ifg = Ifg(i) ifg.open() else: ifg = i ifg.phase_data -= dem_corr # set geotiff meta tag and save phase to file # TODO: calculate avg bperp and add to metadata even for reused DEM error correction _save_dem_error_corrected_phase(ifg) return all(d.exists() for d in saved_dem_err_paths) def _save_dem_error_corrected_phase(ifg: Ifg, bperp: Optional[np.float64] = None) -> None: """ Convenience function to update metadata and save latest phase after DEM error correction :param ifg: pyrate.core.shared.Ifg Class object :param bperp: perpendicular baseline value for adding to geotiff metadata. """ # update geotiff tags after DEM error correction ifg.dataset.SetMetadataItem(ifc.PYRATE_DEM_ERROR, ifc.DEM_ERROR_REMOVED) if bperp is not None: ifg.dataset.SetMetadataItem(ifc.PYRATE_BPERP, str(bperp)) ifg.write_modified_phase() ifg.close() class DEMError(Exception): """ Generic exception for DEM errors. """ ================================================ FILE: pyrate/core/gamma.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains tools for reading GAMMA format input data. """ # coding: utf-8 import re import os from os.path import split from pathlib import Path from datetime import date, time, timedelta import numpy as np import pyrate.constants as C from pyrate.configuration import ConfigException, parse_namelist import pyrate.core.ifgconstants as ifc from pyrate.constants import sixteen_digits_pattern, BASELINE_FILE_PATHS, BASE_FILE_DIR from pyrate.core.shared import extract_epochs_from_filename, data_format from pyrate.core.logger import pyratelogger as log import struct # constants GAMMA_DATE = 'date' GAMMA_TIME = 'center_time' GAMMA_WIDTH = 'width' GAMMA_NROWS = 'nlines' GAMMA_CORNER_LAT = 'corner_lat' GAMMA_CORNER_LONG = 'corner_lon' GAMMA_Y_STEP = 'post_lat' GAMMA_X_STEP = 'post_lon' GAMMA_DATUM = 'ellipsoid_name' GAMMA_FREQUENCY = 'radar_frequency' GAMMA_INCIDENCE = 'incidence_angle' GAMMA_HEADING = 'heading' GAMMA_AZIMUTH = 'azimuth_angle' GAMMA_RANGE_PIX = 'range_pixel_spacing' GAMMA_RANGE_N = 'range_samples' GAMMA_RANGE_LOOKS = 'range_looks' GAMMA_AZIMUTH_PIX = 'azimuth_pixel_spacing' GAMMA_AZIMUTH_N = 'azimuth_lines' GAMMA_AZIMUTH_LOOKS = 'azimuth_looks' GAMMA_PRF = 'prf' GAMMA_NEAR_RANGE = 'near_range_slc' GAMMA_SAR_EARTH = 'sar_to_earth_center' GAMMA_SEMI_MAJOR_AXIS = 'earth_semi_major_axis' GAMMA_SEMI_MINOR_AXIS = 'earth_semi_minor_axis' GAMMA_INITIAL_BASELINE = 'initial_baseline(TCN)' GAMMA_INITIAL_BASELINE_RATE = 'initial_baseline_rate' GAMMA_PRECISION_BASELINE = 'precision_baseline(TCN)' GAMMA_PRECISION_BASELINE_RATE = 'precision_baseline_rate' RADIANS = 'RADIANS' GAMMA = 'GAMMA' def _parse_header(path): """Parses all GAMMA header file fields into a dictionary""" with open(path) as f: text = f.read().splitlines() raw_segs = [line.split() for line in text if ':' in line] # convert the content into a giant dict of all key, values return dict((i[0][:-1], i[1:]) for i in raw_segs) def parse_epoch_header(path): """ Returns dictionary of epoch metadata required for PyRate :param str path: Full path to GAMMA mli.par file. Note that the mli.par is required as input since the baseline calculations require the input values valid for the GAMMA multi-looked products and also the GAMMA lookup table gives radar coordinates for the multi-looked geometry. :return: subset: subset of full metadata :rtype: dict """ lookup = _parse_header(path) subset = _parse_date_time(lookup) # handle conversion of radar frequency to wavelength freq, unit = lookup[GAMMA_FREQUENCY] if unit != "Hz": # pragma: no cover msg = 'Unrecognised unit field for radar_frequency: %s' raise GammaException(msg % unit) subset[ifc.PYRATE_WAVELENGTH_METRES] = _frequency_to_wavelength(float(freq)) incidence, unit = lookup[GAMMA_INCIDENCE] if unit != "degrees": # pragma: no cover msg = 'Unrecognised unit field for incidence_angle: %s' raise GammaException(msg % unit) subset[ifc.PYRATE_INCIDENCE_DEGREES] = float(incidence) sat_heading, unit = lookup[GAMMA_HEADING] if unit != "degrees": # pragma: no cover msg = 'Unrecognised unit field for heading: %s' raise GammaException(msg % unit) subset[ifc.PYRATE_HEADING_DEGREES] = float(sat_heading) sat_azimuth, unit = lookup[GAMMA_AZIMUTH] if unit != "degrees": # pragma: no cover msg = 'Unrecognised unit field for azimuth_angle: %s' raise GammaException(msg % unit) subset[ifc.PYRATE_AZIMUTH_DEGREES] = float(sat_azimuth) range_pix, unit = lookup[GAMMA_RANGE_PIX] if unit != "m": # pragma: no cover msg = 'Unrecognised unit field for range_pixel_spacing: %s' raise GammaException(msg % unit) subset[ifc.PYRATE_RANGE_PIX_METRES] = float(range_pix) range_n = lookup[GAMMA_RANGE_N] # number without a unit in .par file subset[ifc.PYRATE_RANGE_N] = int(range_n[0]) range_looks = lookup[GAMMA_RANGE_LOOKS] # number without a unit in .par file subset[ifc.PYRATE_RANGE_LOOKS] = int(range_looks[0]) azimuth_pix, unit = lookup[GAMMA_AZIMUTH_PIX] if unit != "m": # pragma: no cover msg = 'Unrecognised unit field for azimuth_pixel_spacing: %s' raise GammaException(msg % unit) subset[ifc.PYRATE_AZIMUTH_PIX_METRES] = float(azimuth_pix) azimuth_n = lookup[GAMMA_AZIMUTH_N] # number without a unit in .par file subset[ifc.PYRATE_AZIMUTH_N] = int(azimuth_n[0]) azimuth_looks = lookup[GAMMA_AZIMUTH_LOOKS] # number without a unit in .par file subset[ifc.PYRATE_AZIMUTH_LOOKS] = int(azimuth_looks[0]) pulse_rep_freq, unit = lookup[GAMMA_PRF] if unit != "Hz": # pragma: no cover msg = 'Unrecognised unit field for prf: %s' raise GammaException(msg % unit) subset[ifc.PYRATE_PRF_HERTZ] = float(pulse_rep_freq) near_range, unit = lookup[GAMMA_NEAR_RANGE] if unit != "m": # pragma: no cover msg = 'Unrecognised unit field for near_range_slc: %s' raise GammaException(msg % unit) subset[ifc.PYRATE_NEAR_RANGE_METRES] = float(near_range) sar_to_earth, unit = lookup[GAMMA_SAR_EARTH] if unit != "m": # pragma: no cover msg = 'Unrecognised unit field for sar_to_earth_center: %s' raise GammaException(msg % unit) subset[ifc.PYRATE_SAR_EARTH_METRES] = float(sar_to_earth) semi_major_axis, unit = lookup[GAMMA_SEMI_MAJOR_AXIS] if unit != "m": # pragma: no cover msg = 'Unrecognised unit field for earth_semi_major_axis: %s' raise GammaException(msg % unit) subset[ifc.PYRATE_SEMI_MAJOR_AXIS_METRES] = float(semi_major_axis) semi_minor_axis, unit = lookup[GAMMA_SEMI_MINOR_AXIS] if unit != "m": # pragma: no cover msg = 'Unrecognised unit field for earth_semi_minor_axis: %s' raise GammaException(msg % unit) subset[ifc.PYRATE_SEMI_MINOR_AXIS_METRES] = float(semi_minor_axis) return subset def _parse_date_time(lookup): """Grab date and time metadata and convert to datetime objects""" subset = {} if len(lookup[GAMMA_DATE]) == 3: # pragma: no cover year, month, day, = [int(float(i)) for i in lookup[GAMMA_DATE][:3]] if lookup.get(GAMMA_TIME) is not None: t = lookup[GAMMA_TIME][0] h, m, s = str(timedelta(seconds=float(t))).split(":") hour = int(h) min = int(m) sec = int(s.split(".")[0]) else: # Occasionally GAMMA header has no time information - default to midnight hour, min, sec = 0, 0, 0 elif len(lookup[GAMMA_DATE]) == 6: year, month, day, hour, min, sec = [int(float(i)) for i in lookup[GAMMA_DATE][:6]] else: # pragma: no cover msg = "Date and time information not complete in GAMMA headers" raise GammaException(msg) subset[ifc.FIRST_DATE] = date(year, month, day) subset[ifc.FIRST_TIME] = time(hour, min, sec) return subset def parse_dem_header(path): """ Returns dictionary of DEM metadata required for PyRate :param str path: `Full path to Gamma *dem.par file` :return: subset: subset of full metadata :rtype: dict """ lookup = _parse_header(path) # NB: many lookup fields have multiple elements, eg ['1000', 'Hz'] subset = {ifc.PYRATE_NCOLS: int(lookup[GAMMA_WIDTH][0]), ifc.PYRATE_NROWS: int(lookup[GAMMA_NROWS][0])} expected = ['decimal', 'degrees'] for k in [GAMMA_CORNER_LAT, GAMMA_CORNER_LONG, GAMMA_X_STEP, GAMMA_Y_STEP]: units = lookup[GAMMA_CORNER_LAT][1:] if units != expected: # pragma: no cover msg = "Unrecognised units for GAMMA %s field\n. Got %s, expected %s" raise GammaException(msg % (k, units, expected)) subset[ifc.PYRATE_LAT] = float(lookup[GAMMA_CORNER_LAT][0]) subset[ifc.PYRATE_LONG] = float(lookup[GAMMA_CORNER_LONG][0]) subset[ifc.PYRATE_Y_STEP] = float(lookup[GAMMA_Y_STEP][0]) subset[ifc.PYRATE_X_STEP] = float(lookup[GAMMA_X_STEP][0]) subset[ifc.PYRATE_DATUM] = "".join(lookup[GAMMA_DATUM]) subset[ifc.PYRATE_INSAR_PROCESSOR] = GAMMA return subset def parse_baseline_header(path: str) -> dict: """ Returns dictionary of Baseline metadata required for PyRate. Will read the Precise baseline estimate, if available, otherwise will read the Initial baseline estimate. :param path: Full path to GAMMA base.par file :return: bdict: Dictionary of baseline values """ lookup = _parse_header(path) # read file contents in to a dict # split the initial and precise baselines initial = lookup[GAMMA_INITIAL_BASELINE] initial_rate = lookup[GAMMA_INITIAL_BASELINE_RATE] precise = lookup[GAMMA_PRECISION_BASELINE] precise_rate = lookup[GAMMA_PRECISION_BASELINE_RATE] # read the initial baseline if all precise components are zero # (indicates that the precise baseline estimation was not ran in GAMMA workflow) if float(precise[0]) == 0.0 and float(precise[1]) == 0.0 and float(precise[2]) == 0.0: log.debug('Reading Initial GAMMA baseline values') baseline, baseline_rate = initial, initial_rate else: log.debug('Reading Precise GAMMA baseline values') baseline, baseline_rate = precise, precise_rate # Extract and return a dict of baseline values bdict = {} # baseline vector (along Track, aCross track, Normal to the track) bdict[ifc.PYRATE_BASELINE_T] = float(baseline[0]) bdict[ifc.PYRATE_BASELINE_C] = float(baseline[1]) bdict[ifc.PYRATE_BASELINE_N] = float(baseline[2]) bdict[ifc.PYRATE_BASELINE_RATE_T] = float(baseline_rate[0]) bdict[ifc.PYRATE_BASELINE_RATE_C] = float(baseline_rate[1]) bdict[ifc.PYRATE_BASELINE_RATE_N] = float(baseline_rate[2]) return bdict def _frequency_to_wavelength(freq): """ Convert radar frequency to wavelength """ return ifc.SPEED_OF_LIGHT_METRES_PER_SECOND / freq def combine_headers(hdr0, hdr1, dem_hdr, base_hdr=None): """ Combines metadata for first and second image epochs, DEM and baselines into a single dictionary for an interferogram. :param dict hdr0: Metadata for the first image :param dict hdr1: Metadata for the second image :param dict dem_hdr: Metadata for the DEM :param dict base_hdr: Metadata for baselines (if available) :return: chdr: combined metadata :rtype: dict """ if not all([isinstance(a, dict) for a in [hdr0, hdr1, dem_hdr]]): raise GammaException('Header args need to be dicts') if base_hdr and not isinstance(base_hdr, dict): raise GammaException('Header args need to be dicts') date0, date1 = hdr0[ifc.FIRST_DATE], hdr1[ifc.FIRST_DATE] if date0 == date1: raise GammaException("Can't combine headers for the same day") elif date1 < date0: raise GammaException("Wrong date order") chdr = {ifc.PYRATE_TIME_SPAN: (date1 - date0).days / ifc.DAYS_PER_YEAR, ifc.FIRST_DATE: date0, ifc.FIRST_TIME: hdr0[ifc.FIRST_TIME], ifc.SECOND_DATE: date1, ifc.SECOND_TIME: hdr1[ifc.FIRST_TIME], ifc.DATA_UNITS: RADIANS, ifc.PYRATE_INSAR_PROCESSOR: GAMMA} # set incidence angle to mean of first amd second image values inc_ang = hdr0[ifc.PYRATE_INCIDENCE_DEGREES] if np.isclose(inc_ang, hdr1[ifc.PYRATE_INCIDENCE_DEGREES], atol=1e-1): chdr[ifc.PYRATE_INCIDENCE_DEGREES] = (hdr0[ifc.PYRATE_INCIDENCE_DEGREES] + hdr1[ ifc.PYRATE_INCIDENCE_DEGREES]) / 2 else: msg = "Incidence angles differ by more than 0.1 degrees" raise GammaException(msg) wavelen = hdr0[ifc.PYRATE_WAVELENGTH_METRES] if np.isclose(wavelen, hdr1[ifc.PYRATE_WAVELENGTH_METRES], atol=1e-6): chdr[ifc.PYRATE_WAVELENGTH_METRES] = wavelen else: args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE]) msg = "Wavelength mismatch, check both header files for %s & %s" raise GammaException(msg % args) # use parameter of first image (as done by GAMMA during interferometric processing) heading_ang = hdr0[ifc.PYRATE_HEADING_DEGREES] if np.isclose(heading_ang, hdr1[ifc.PYRATE_HEADING_DEGREES], atol=5e-1): chdr[ifc.PYRATE_HEADING_DEGREES] = heading_ang else: args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE]) msg = "Satellite heading angles differ by more than 0.5 degrees" raise GammaException(msg % args) azimuth_ang = hdr0[ifc.PYRATE_AZIMUTH_DEGREES] if np.isclose(azimuth_ang, hdr1[ifc.PYRATE_AZIMUTH_DEGREES], atol=1e-1): chdr[ifc.PYRATE_AZIMUTH_DEGREES] = azimuth_ang else: args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE]) msg = "Satellite azimuth angles differ by more than 0.1 degrees" raise GammaException(msg % args) range_pix = hdr0[ifc.PYRATE_RANGE_PIX_METRES] if np.isclose(range_pix, hdr1[ifc.PYRATE_RANGE_PIX_METRES], atol=1e-1): chdr[ifc.PYRATE_RANGE_PIX_METRES] = range_pix else: args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE]) msg = "Range pixel spacing differs by more than 0.001 metres" raise GammaException(msg % args) range_n = hdr0[ifc.PYRATE_RANGE_N] if range_n == hdr1[ifc.PYRATE_RANGE_N]: chdr[ifc.PYRATE_RANGE_N] = range_n else: args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE]) msg = "Number of range pixels mismatch, check both header files for %s & %s" raise GammaException(msg % args) range_looks = hdr0[ifc.PYRATE_RANGE_LOOKS] if range_looks == hdr1[ifc.PYRATE_RANGE_LOOKS]: chdr[ifc.PYRATE_RANGE_LOOKS] = range_looks else: args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE]) msg = "Number of range looks mismatch, check both header files for %s & %s" raise GammaException(msg % args) azimuth_pix = hdr0[ifc.PYRATE_AZIMUTH_PIX_METRES] if np.isclose(azimuth_pix, hdr1[ifc.PYRATE_AZIMUTH_PIX_METRES], atol=1e-1): chdr[ifc.PYRATE_AZIMUTH_PIX_METRES] = azimuth_pix else: args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE]) msg = "Azimuth pixel spacing differs by more than 0.001 metres" raise GammaException(msg % args) azimuth_n = hdr0[ifc.PYRATE_AZIMUTH_N] if azimuth_n == hdr1[ifc.PYRATE_AZIMUTH_N]: chdr[ifc.PYRATE_AZIMUTH_N] = azimuth_n else: args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE]) msg = "Number of azimuth pixels mismatch, check both header files for %s & %s" raise GammaException(msg % args) azimuth_looks = hdr0[ifc.PYRATE_AZIMUTH_LOOKS] if azimuth_looks == hdr1[ifc.PYRATE_AZIMUTH_LOOKS]: chdr[ifc.PYRATE_AZIMUTH_LOOKS] = azimuth_looks else: args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE]) msg = "Number of azimuth looks mismatch, check both header files for %s & %s" raise GammaException(msg % args) prf_hertz = hdr0[ifc.PYRATE_PRF_HERTZ] if np.isclose(prf_hertz, hdr1[ifc.PYRATE_PRF_HERTZ], atol=1e-6): chdr[ifc.PYRATE_PRF_HERTZ] = prf_hertz else: args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE]) msg = "Pulse repetition frequency mismatch, check both header files for %s & %s" raise GammaException(msg % args) near_range = hdr0[ifc.PYRATE_NEAR_RANGE_METRES] if np.isclose(near_range, hdr1[ifc.PYRATE_NEAR_RANGE_METRES], atol=1e3): chdr[ifc.PYRATE_NEAR_RANGE_METRES] = near_range else: args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE]) msg = "Near range differs by more than 1000 metres" raise GammaException(msg % args) sar_earth = hdr0[ifc.PYRATE_SAR_EARTH_METRES] if np.isclose(sar_earth, hdr1[ifc.PYRATE_SAR_EARTH_METRES], atol=1e3): chdr[ifc.PYRATE_SAR_EARTH_METRES] = sar_earth else: args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE]) msg = "SAR to Earth Center differs by more than 1000 metres" raise GammaException(msg % args) semi_major_axis = hdr0[ifc.PYRATE_SEMI_MAJOR_AXIS_METRES] if np.isclose(semi_major_axis, hdr1[ifc.PYRATE_SEMI_MAJOR_AXIS_METRES], atol=1e-4): chdr[ifc.PYRATE_SEMI_MAJOR_AXIS_METRES] = semi_major_axis else: args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE]) msg = "Earth semi major axis differs by more than 0.0001 metres" raise GammaException(msg % args) semi_minor_axis = hdr0[ifc.PYRATE_SEMI_MINOR_AXIS_METRES] if np.isclose(semi_minor_axis, hdr1[ifc.PYRATE_SEMI_MINOR_AXIS_METRES], atol=1e-4): chdr[ifc.PYRATE_SEMI_MINOR_AXIS_METRES] = semi_minor_axis else: args = (chdr[ifc.FIRST_DATE], chdr[ifc.SECOND_DATE]) msg = "Earth semi minor axis differs by more than 0.0001 metres" raise GammaException(msg % args) # non-cropped, non-multilooked geotif process step information added chdr[ifc.DATA_TYPE] = ifc.ORIG chdr.update(dem_hdr) # add geographic data if base_hdr: chdr.update(base_hdr) # add baseline information return chdr def manage_headers(dem_header_file, header_paths, baseline_paths=None): """ Manage and combine header files for GAMMA interferograms, DEM and incidence files :param str dem_header_file: DEM header path :param list header_paths: List of first/second image header paths :return: combined_header: Combined metadata dictionary :rtype: dict """ dem_header = parse_dem_header(dem_header_file) # find param files containing filename dates if len(header_paths) == 2: hdrs = [parse_epoch_header(hp) for hp in header_paths] if baseline_paths is not None: baseline_header = parse_baseline_header(baseline_paths) combined_header = combine_headers(hdrs[0], hdrs[1], dem_header, baseline_header) else: combined_header = combine_headers(hdrs[0], hdrs[1], dem_header) elif len(header_paths) > 2: msg = f'There are too many parameter files for one interferogram; there should only be two. {len(header_paths)} parameter files have been given: {header_paths}.' raise GammaException(msg) else: # probably have DEM or incidence file combined_header = dem_header combined_header[ifc.DATA_TYPE] = ifc.DEM return combined_header def get_header_paths(input_file, slc_file_list): """ Function that matches input GAMMA file names with GAMMA header file names :param str input_file: input GAMMA image file. :param slc_file_list: file listing the pool of available header files (GAMMA: slc.par, ROI_PAC: .rsc) :return: list of matching header files :rtype: list """ f = Path(input_file) epochs = extract_epochs_from_filename(f.name) header_names = parse_namelist(slc_file_list) matches = [hdr for hdr in header_names if any(e in hdr for e in epochs)] return matches def gamma_header(ifg_file_path, params): """ Function to obtain combined Gamma headers for image file Args: ifg_file_path: Path to interferogram file to find headers for. params: PyRate parameters dictionary. Returns: A combined header dictionary containing metadata from matching gamma headers and DEM header. """ dem_hdr_path = params[C.DEM_HEADER_FILE] header_paths = get_header_paths(ifg_file_path, params[C.HDR_FILE_LIST]) if len(header_paths) == 2 and params[C.BASE_FILE_LIST] is not None: baseline_path = baseline_paths_for(ifg_file_path, params) else: baseline_path = None # don't read baseline files for DEM combined_headers = manage_headers(dem_hdr_path, header_paths, baseline_path) if os.path.basename(ifg_file_path).split('.')[1] == \ (params[C.APS_INCIDENCE_EXT] or params[C.APS_ELEVATION_EXT]): # TODO: implement incidence class here combined_headers['FILE_TYPE'] = 'Incidence' return combined_headers def read_lookup_table(head, data_path, xlooks, ylooks, xmin, xmax, ymin, ymax): # pylint: disable = too - many - statements """ Creates a copy of input lookup table file in a numpy array and applies the ifg ML factors :param IFG object head: first IFG in the list to read metadata :param str data_path: Input file :param int xlooks: multi-looking factor in x :param int ylooks: multi-looking factor in y :param int xmin: start pixel of cropped extent in x :param int xmax: end pixel of cropped extent in x :param int ymin: start pixel of cropped extent in y :param int ymax: end pixel of cropped extent in y :return: np-array lt_data_az: azimuth (i.e. row) of radar-coded MLI :return: np-array lt_data_rg: range (i.e. column) of radar-coded MLI """ # pylint: disable=too-many-branches # pylint: disable=too-many-locals # read relevant metadata parameters nrows_lt = int(head.meta_data[ifc.PYRATE_NROWS]) # number of rows of original geotiff files ncols_lt = int(head.meta_data[ifc.PYRATE_NCOLS]) # number of columns of original geotiff files nrows = head.nrows # number of rows in multi-looked and cropped data sets ncols = head.ncols # number of columns in multi-looked and cropped data sets ifg_proc = head.meta_data[ifc.PYRATE_INSAR_PROCESSOR] # get dimensions of lookup table file bytes_per_col, fmtstr = data_format(ifg_proc, True, ncols_lt*2) # float complex data set containing value tupels # check if lookup table has the correct size small_size = _check_raw_data(bytes_per_col * 2, data_path, ncols_lt, nrows_lt) # todo: delete the following if condition once a suitable test data set has been included if small_size: # this is a test data set without a corresponding lt-file lt_data_az = np.empty((nrows, ncols)) * np.nan # nan array with size of input data set lt_data_rg = np.empty((nrows, ncols)) * np.nan # nan array with size of input data set else: # this is a real data set with an lt-file of correct size row_bytes = ncols_lt * 2 * bytes_per_col lt_data_az = np.empty((0, ncols)) # empty array with correct number of columns lt_data_rg = np.empty((0, ncols)) # empty array with correct number of column # for indexing: lookup table file contains value pairs (i.e. range, azimuth) # value pair 0 would be index 0 and 1, value pair 1 would be index 2 and 3, and so on # example: for a multi-looking factor of 10 we want value pair 4, 14, 24, ... # this would be index 8 and 9, index 28 and 29, 48 and 49, ... # start column needs to be added in case cropping is applied if (xlooks % 2) == 0: # for even ml factors idx_start = xmin + int(xlooks / 2) - 1 else: # for odd ml factors idx_start = xmin + int((xlooks - 1) / 2) # indices of range info in lookup table for the cropped and multi-looked data set idx_rg = np.arange(2 * idx_start, xmax * 2, 2 * xlooks) # first value idx_az = np.arange(2 * idx_start + 1, xmax * 2, 2 * xlooks) # second value # set up row idx, e.g. for ml=10 (without cropping): 4, 14, 24, ... if (ylooks % 2) == 0: # for even ml factors idx_start = ymin + int(ylooks / 2) - 1 else: # for odd ml factors idx_start = ymin + int((ylooks - 1) / 2) row_idx = np.arange(idx_start, ymax, ylooks) # read the binary lookup table file and save the range/azimuth value pair for each position in the cropped and # multi-looked data set log.debug(f"Reading lookup table file {data_path}") with open(data_path, 'rb') as f: for y in range(nrows_lt): # loop through all lines in file # this could potentially be made quicker by skipping unwanted bytes in the f.read command? data = struct.unpack(fmtstr, f.read(row_bytes)) # but only read data from lines in row index: if y in row_idx: row_data = np.array(data) row_data_ml_az = row_data[idx_az] # azimuth for PyRate row_data_ml_rg = row_data[idx_rg] # range for PyRate lt_data_az = np.append(lt_data_az, [row_data_ml_az], axis=0) lt_data_rg = np.append(lt_data_rg, [row_data_ml_rg], axis=0) return lt_data_az, lt_data_rg def _check_raw_data(bytes_per_col, data_path, ncols, nrows): """ Convenience function to check the file size is as expected """ size = ncols * nrows * bytes_per_col act_size = os.stat(data_path).st_size if act_size != size: msg = '%s should have size %s, not %s. Is the correct file being used?' if size < 28000: # test data set doesn't currently fit the lookup table size, stop further calculation # todo: delete this if statement once a new test data set has been introduced return True else: raise GammaException(msg % (data_path, size, act_size)) class GammaException(Exception): """Gamma generic exception class""" def baseline_paths_for(path: str, params: dict) -> str: """ Returns path to baseline file for given interferogram. Pattern matches based on epoch in filename. Example: '20151025-20160501_base.par' Date pair is the epoch. Args: path: Path to intergerogram to find baseline file for. params: Parameter dictionary. tif: Find converted tif if True (_cc.tif), else find .cc file. Returns: Path to baseline file. """ _, filename = split(path) try: epoch = re.search(sixteen_digits_pattern, filename).group(0) except: # catch cases where filename does not have two epochs, e.g. DEM file return None base_file_paths = [f.unwrapped_path for f in params[BASELINE_FILE_PATHS] if epoch in f.unwrapped_path] if len(base_file_paths) > 1: raise ConfigException(f"'{BASE_FILE_DIR}': found more than one baseline " f"file for '{path}'. There must be only one " f"baseline file per interferogram. Found {base_file_paths}.") return base_file_paths[0] ================================================ FILE: pyrate/core/gdal_python.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains bindings for the GDAL library """ # pylint: disable=too-many-arguments,R0914 from typing import Union, List, Tuple from osgeo import gdal, gdalconst from osgeo.gdal import Dataset import numpy as np import numexpr as ne from pyrate.core import shared, ifgconstants as ifc from pyrate.core.logger import pyratelogger as log gdal.SetCacheMax(2**15) GDAL_WARP_MEMORY_LIMIT = 2**10 LOW_FLOAT32 = np.finfo(np.float32).min*1e-10 all_mlooked_types = [ifc.MLOOKED_COH_MASKED_IFG, ifc.MULTILOOKED, ifc.MULTILOOKED_COH, ifc.MLOOKED_DEM] def coherence_masking(input_gdal_dataset: Dataset, coh_file_path: str, coh_thr: float) -> None: """ Perform coherence masking on raster in-place. Based on gdal_calc formula provided by Nahidul: gdal_calc.py -A 20151127-20151209_VV_8rlks_flat_eqa.cc.tif -B 20151127-20151209_VV_8rlks_eqa.unw.tif --outfile=test_v1.tif --calc="B*(A>=0.8)-999*(A<0.8)" --NoDataValue=-999 """ coherence_ds = gdal.Open(coh_file_path, gdalconst.GA_ReadOnly) coherence_band = coherence_ds.GetRasterBand(1) src_band = input_gdal_dataset.GetRasterBand(1) ndv = np.nan coherence = coherence_band.ReadAsArray() src = src_band.ReadAsArray() var = {"coh": coherence, "src": src, "t": coh_thr, "ndv": ndv} formula = "where(coh>=t, src, ndv)" res = ne.evaluate(formula, local_dict=var) src_band.WriteArray(res) # update metadata input_gdal_dataset.GetRasterBand(1).SetNoDataValue(ndv) input_gdal_dataset.FlushCache() # write on the disc log.info(f"Masking ifg using file {coh_file_path} and coherence threshold: {coh_thr}") def world_to_pixel(geo_transform, x, y): """ Uses a gdal geomatrix (gdal.GetGeoTransform()) to calculate the pixel location of a geospatial coordinate; see: http://pcjericks.github.io/py-gdalogr-cookbook/raster_layers.html :param list geo_transform: Affine transformation coefficients :param float x: longitude coordinate :param float y: latitude coordinate :return: col: pixel column number :rtype: int :return: line: pixel line number :rtype: int """ ul_x = geo_transform[0] ul_y = geo_transform[3] xres = geo_transform[1] yres = geo_transform[5] col = int(np.round((x - ul_x) / xres)) line = int(np.round((ul_y - y) / abs(yres))) # yres has negative size return col, line def resample_nearest_neighbour(input_tif, extents, new_res, output_file): """ Nearest neighbor resampling and cropping of an image. :param str input_tif: input geotiff file path :param list extents: new extents for cropping :param list[float] new_res: new resolution for resampling :param str output_file: output geotiff file path :return: dst: resampled image :rtype: ndarray """ dst, resampled_proj, src, _ = _crop_resample_setup(extents, input_tif, new_res, output_file) # Do the work gdal.ReprojectImage(src, dst, '', resampled_proj, gdalconst.GRA_NearestNeighbour) return dst.ReadAsArray() def _crop_resample_setup(extents, input_tif, new_res, output_file, dst_driver_type='GTiff', out_bands=2): """ Convenience function for crop/resample setup """ # Source src_ds = gdal.Open(input_tif, gdalconst.GA_ReadOnly) src_proj = src_ds.GetProjection() # source metadata to be copied into the output meta_data = src_ds.GetMetadata() # get the image extents min_x, min_y, max_x, max_y = extents geo_transform = src_ds.GetGeoTransform() # tuple of 6 numbers # Create a new geotransform for the image gt2 = list(geo_transform) gt2[0] = min_x gt2[3] = max_y # We want a section of source that matches this: resampled_proj = src_proj if new_res[0]: # if new_res is not None, it can't be zero either resampled_geotrans = gt2[:1] + [new_res[0]] + gt2[2:-1] + [new_res[1]] else: resampled_geotrans = gt2 px_height, px_width = _gdalwarp_width_and_height(max_x, max_y, min_x, min_y, resampled_geotrans) # Output / destination dst = gdal.GetDriverByName(dst_driver_type).Create( output_file, px_width, px_height, out_bands, gdalconst.GDT_Float32) dst.SetGeoTransform(resampled_geotrans) dst.SetProjection(resampled_proj) for k, v in meta_data.items(): dst.SetMetadataItem(k, v) return dst, resampled_proj, src_ds, src_proj def _gdalwarp_width_and_height(max_x, max_y, min_x, min_y, geo_trans): """ Modify pixel height and width """ # modified image extents ul_x, ul_y = world_to_pixel(geo_trans, min_x, max_y) lr_x, lr_y = world_to_pixel(geo_trans, max_x, min_y) # Calculate the pixel size of the new image px_width = int(lr_x - ul_x) px_height = int(lr_y - ul_y) return px_height, px_width # this is the same as `gdalwarp` def crop_resample_average( input_tif, extents: Union[List, Tuple], new_res, output_file, thresh, hdr, out_driver_type='GTiff', match_pyrate=False, coherence_path=None, coherence_thresh=None): """ Crop, resample, and average a geotiff image. :param str input_tif: Path to input geotiff to resample/crop :param tuple extents: Cropping extents (xfirst, yfirst, xlast, ylast) :param list new_res: [xres, yres] resolution of output image :param str output_file: Path to output resampled/cropped geotiff :param float thresh: NaN fraction threshold :param str out_driver_type: The output driver; `MEM` or `GTiff` (optional) :param bool match_pyrate: Match Legacy output (optional) :param dict hdr: dictionary of metadata :return: resampled_average: output cropped and resampled image :rtype: ndarray :return: out_ds: destination gdal dataset object :rtype: gdal.Dataset """ dst_ds, _, _, _ = _crop_resample_setup(extents, input_tif, new_res, output_file, out_bands=2, dst_driver_type='MEM') # make a temporary copy of the dst_ds for PyRate style prepifg if (match_pyrate and new_res[0]): tmp_ds = gdal.GetDriverByName('MEM').CreateCopy('', dst_ds) else: tmp_ds = None src_ds, src_ds_mem = _setup_source(input_tif) if coherence_path and coherence_thresh: coherence_masking(src_ds_mem, coherence_path, coherence_thresh) elif coherence_path and not coherence_thresh: raise ValueError("Coherence file provided without a coherence " "threshold. Please ensure you provide 'cohthresh' " "in your config if coherence masking is enabled.") resampled_average, src_ds_mem = gdal_average(dst_ds, src_ds, src_ds_mem, thresh) src_ds = None src_dtype = src_ds_mem.GetRasterBand(1).DataType src_gt = src_ds_mem.GetGeoTransform() # required to match Legacy output if tmp_ds: _alignment(input_tif, new_res, resampled_average, src_ds_mem, src_gt, tmp_ds) # grab metadata from existing geotiff gt = dst_ds.GetGeoTransform() wkt = dst_ds.GetProjection() # insert metadata from the header md = shared.collate_metadata(hdr) # update metadata for output # TODO: Metadata should be updated immediately as a prepifg/process step is applied # move this into the respective steps for k, v in md.items(): if k == ifc.DATA_TYPE: # update data type metadata if (v == ifc.ORIG) and (coherence_path is not None): md.update({ifc.DATA_TYPE: ifc.MLOOKED_COH_MASKED_IFG}) elif (v == ifc.ORIG) and (coherence_path is None): md.update({ifc.DATA_TYPE: ifc.MULTILOOKED}) elif v == ifc.COH: md.update({ifc.DATA_TYPE: ifc.MULTILOOKED_COH}) elif v == ifc.DEM: md.update({ifc.DATA_TYPE: ifc.MLOOKED_DEM}) else: raise TypeError(f'Data Type metadata {v} not recognised') _add_looks_and_crop_from_header(hdr, md, coherence_thresh) # In-memory GDAL driver doesn't support compression so turn it off. creation_opts = ['compress=packbits'] if out_driver_type != 'MEM' else [] out_ds = shared.gdal_dataset(output_file, dst_ds.RasterXSize, dst_ds.RasterYSize, driver=out_driver_type, bands=1, dtype=src_dtype, metadata=md, crs=wkt, geotransform=gt, creation_opts=creation_opts) if out_driver_type != 'MEM': log.info(f"Writing geotiff: {output_file}") shared.write_geotiff(resampled_average, out_ds, np.nan) else: out_ds.GetRasterBand(1).WriteArray(resampled_average) return resampled_average, out_ds def _add_looks_and_crop_from_header(hdr, md, coh_thr): """ function to add prepfig options to geotiff metadata """ # insert prepifg mlook and crop params as metadata if any(m in md.values() for m in all_mlooked_types): if ifc.IFG_LKSX in hdr: md[ifc.IFG_LKSX] = hdr[ifc.IFG_LKSX] if ifc.IFG_LKSY in hdr: md[ifc.IFG_LKSY] = hdr[ifc.IFG_LKSY] if ifc.IFG_CROP in hdr: md[ifc.IFG_CROP] = hdr[ifc.IFG_CROP] # add coherence threshold to metadata, if used for masking ifgs if md[ifc.DATA_TYPE] == ifc.MLOOKED_COH_MASKED_IFG: md[ifc.COH_THRESH] = coh_thr def _alignment(input_tif, new_res, resampled_average, src_ds_mem, src_gt, tmp_ds): """ Correction step to match python multi-look/crop output to match that of Legacy data. Modifies the resampled_average array in place. """ src_ds = gdal.Open(input_tif) data = src_ds.GetRasterBand(1).ReadAsArray() xlooks = ylooks = int(new_res[0] / src_gt[1]) xres, yres = _get_resampled_data_size(xlooks, ylooks, data) nrows, ncols = resampled_average.shape # Legacy nearest neighbor resampling for the last # [yres:nrows, xres:ncols] cells without nan_conversion # turn off nan-conversion src_ds_mem.GetRasterBand(1).SetNoDataValue(LOW_FLOAT32) # nearest neighbor resapling gdal.ReprojectImage(src_ds_mem, tmp_ds, '', '', gdal.GRA_NearestNeighbour) # only take the [yres:nrows, xres:ncols] slice if nrows > yres or ncols > xres: resampled_nearest_neighbor = tmp_ds.GetRasterBand(1).ReadAsArray() resampled_average[yres - nrows:, xres - ncols:] = \ resampled_nearest_neighbor[yres - nrows:, xres - ncols:] def gdal_average(dst_ds, src_ds, src_ds_mem, thresh): """ Perform subsampling of an image by averaging values :param gdal.Dataset dst_ds: Destination gdal dataset object :param str input_tif: Input geotif :param float thresh: NaN fraction threshold :return resampled_average: resampled image data :rtype: ndarray :return src_ds_mem: Modified in memory src_ds with nan_fraction in Band2. The nan_fraction is computed efficiently here in gdal in the same step as the that of the resampled average (band 1). This results is huge memory and computational efficiency :rtype: gdal.Dataset """ src_gt = src_ds.GetGeoTransform() src_ds_mem.SetGeoTransform(src_gt) data = src_ds_mem.GetRasterBand(1).ReadAsArray() # update nan_matrix # if data==nan, then 1, else 0 nan_matrix = np.isnan(data) # all nans due to phase data + coh masking if used src_ds_mem.GetRasterBand(2).WriteArray(nan_matrix) gdal.ReprojectImage(src_ds_mem, dst_ds, '', '', gdal.GRA_Average) # dst_ds band2 average is our nan_fraction matrix nan_frac = dst_ds.GetRasterBand(2).ReadAsArray() resampled_average = dst_ds.GetRasterBand(1).ReadAsArray() resampled_average[nan_frac >= thresh] = np.nan return resampled_average, src_ds_mem def _setup_source(input_tif): """convenience setup function for gdal_average""" src_ds = gdal.Open(input_tif) data = src_ds.GetRasterBand(1).ReadAsArray() src_dtype = src_ds.GetRasterBand(1).DataType mem_driver = gdal.GetDriverByName('MEM') src_ds_mem = mem_driver.Create('', src_ds.RasterXSize, src_ds.RasterYSize, 2, src_dtype) if isinstance(shared.dem_or_ifg(data_path=input_tif), shared.Ifg): data[np.isclose(data, 0, atol=1e-6)] = np.nan # nan conversion of phase data src_ds_mem.GetRasterBand(1).WriteArray(data) src_ds_mem.GetRasterBand(1).SetNoDataValue(np.nan) src_ds_mem.GetRasterBand(2).SetNoDataValue(np.nan) src_ds_mem.SetGeoTransform(src_ds.GetGeoTransform()) return src_ds, src_ds_mem def _get_resampled_data_size(xscale, yscale, data): """convenience function mimicking the Legacy output size""" xscale = int(xscale) yscale = int(yscale) ysize, xsize = data.shape xres, yres = int(xsize / xscale), int(ysize / yscale) return xres, yres ================================================ FILE: pyrate/core/geometry.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module implements the calculation and output of the per-pixel vector of the radar viewing geometry (i.e. local angles, incidence angles and azimuth angles) as well as the calculation of per-pixel baseline values used for correcting interferograms for residual topographic effects (a.k.a. DEM errors). """ # pylint: disable=invalid-name, too-many-locals, too-many-arguments import numpy as np from typing import Tuple, Union import pyrate.constants as C from pyrate.core import ifgconstants as ifc from pyrate.core.gamma import read_lookup_table from pyrate.core.shared import Ifg, IfgPart, MemGeometry def get_lonlat_coords(ifg: Ifg) -> Tuple[MemGeometry, MemGeometry]: """ Function to get longitude and latitude coordinates for each pixel in the multi-looked. interferogram dataset. Coordinates are identical for each interferogram in the stack. :param ifg: pyrate.core.shared.Ifg Class object. :return: lon: Longitude for each pixel (decimal degrees) :return: lat: Latitude for each pixel (decimal degrees) """ # assume all interferograms have same projection and will share the same transform transform = ifg.dataset.GetGeoTransform() # number of rows and columns in dataset nrows, ncols = ifg.shape yOrigin = transform[3] pixelHeight = -transform[5] xOrigin = transform[0] pixelWidth = transform[1] lons = np.arange(0, ncols) * pixelWidth + xOrigin lats = yOrigin - np.arange(0, nrows) * pixelHeight lon, lat = np.meshgrid(lons, lats) return MemGeometry(lon), MemGeometry(lat) def calc_radar_coords(ifg: Ifg, params: dict, xmin: int, xmax: int, ymin: int, ymax: int) -> Tuple[np.ndarray, np.ndarray]: """ Function to calculate radar coordinates for each pixel in the multi-looked interferogram dataset. Radar coordinates are identical for each interferogram in the stack. Uses the Gamma lookup table defined in the configuration file. :param ifg: pyrate.core.shared.Ifg Class object. :param params: Dictionary of PyRate configuration parameters. :param xmin: Minimum longitude of cropped image (decimal degrees). :param xmax: Maximum longitude of cropped image (decimal degrees) :param ymin: Minimum latitude of cropped image (decimal degrees). :param ymax: Maximum latitude of cropped image (decimal degrees) :return: lt_az: Radar geometry azimuth coordinate for each pixel. :return: lt_rg: Radar geometry range coordinate for each pixel. """ # lookup table file: lookup_table = params[C.LT_FILE] if lookup_table is None: msg = f"No lookup table file supplied: Geometry cannot be computed" raise FileNotFoundError(msg) # PyRate IFG multi-looking factors ifglksx = params[C.IFG_LKSX] ifglksy = params[C.IFG_LKSY] # transform float lookup table file to np array, min/max pixel coordinates are required for cropping lt_az, lt_rg = read_lookup_table(ifg, lookup_table, ifglksx, ifglksy, xmin, xmax, ymin, ymax) # replace 0.0 with NaN lt_az[lt_az == 0.0] = np.nan lt_rg[lt_rg == 0.0] = np.nan return lt_az, lt_rg def get_sat_positions(lat: np.ndarray, lon: np.ndarray, look_angle: np.ndarray, inc_angle: np.ndarray, heading: np.float64, look_dir: np.float64) -> Tuple[np.ndarray, np.ndarray]: """ Function to calculate the lon/lat position of the satellite for each pixel. :param lat: Ground latitude for each pixel (decimal degrees). :param lon: Ground point longitude for each pixel (decimal degrees). :param look_angle: Look angle (between nadir and look vector) for each pixel (radians). :param inc_angle: Local incidence angle (between vertical and look vector) for each pixel (radians). :param heading: Satellite flight heading (radians). :param look_dir: Look direction w.r.t. satellite heading; +ve = right looking (radians). :return: sat_lat: Satellite position latitude for each pixel (decimal degrees). :return: sat_lon: Satellite position longitude for each pixel (decimal degrees). """ # note that the accuracy of satellite lat/lon positions code could be improved by calculating satellite positions # for each azimuth row from orbital state vectors given in .par file, using the following workflow: # 1. read orbital state vectors and start/stop times from mli.par # 2. for each pixel get the corresponding radar row (from matrix az) # 3. get the corresponding radar time for that row (using linear interpolation) # 4. calculate the satellite XYZ position for that time by interpolating the time and velocity state vectors # angle at the Earth's center between se and re epsilon = np.pi - look_angle - (np.pi - inc_angle) # azimuth of satellite look vector (satellite heading + look direction (+90 deg for right-looking SAR) sat_azi = heading + look_dir # the following equations are adapted from Section 4.4 (page 4-16) in EARTH-REFERENCED AIRCRAFT NAVIGATION AND # SURVEILLANCE ANALYSIS (https://ntlrepository.blob.core.windows.net/lib/59000/59300/59358/DOT-VNTSC-FAA-16-12.pdf) sat_lon = np.divide(np.arcsin(-(np.multiply(np.sin(epsilon), np.sin(sat_azi)))), np.cos(lat)) + lon # Eq. 103 temp = np.multiply(np.divide(np.cos(0.5 * (sat_azi + sat_lon - lon)), np.cos(0.5 * (sat_azi - sat_lon + lon))), \ np.tan(0.5 * (np.pi / 2 + lat - epsilon))) # Eq. 104 sat_lat = -np.pi / 2 + 2 * np.arctan(temp) return np.real(sat_lat), np.real(sat_lon) def calc_pixel_geometry(ifg: Union[Ifg, IfgPart], rg: np.ndarray, lon: np.ndarray, lat: np.ndarray, dem_height: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: """ Function to calculate angular satellite to ground geometries and distance for each pixel. :param ifg: pyrate.core.shared.Ifg Class object. :param rg: Range image coordinate for each pixel. :param lon: Longitude for each pixel (decimal degrees). :param lat: Latitude for each pixel (decimal degrees). :param dem_height: Height from DEM for each pixel (metres). :return: look_angle: Look angle (between nadir and look vector) for each pixel (radians). :return: incidence_angle: Local incidence angle (between vertical and look vector) for each pixel (radians). :return: azimuth_angle: Geodetic azimuth for each pixel (radians). :return: range_dist: Distance from satellite to ground for each pixel (metres). """ # read relevant metadata from first IFG a = float(ifg.meta_data[ifc.PYRATE_SEMI_MAJOR_AXIS_METRES]) b = float(ifg.meta_data[ifc.PYRATE_SEMI_MINOR_AXIS_METRES]) se = float(ifg.meta_data[ifc.PYRATE_SAR_EARTH_METRES]) # near range of primary image is stored in the interferogram metadata near_range = float(ifg.meta_data[ifc.PYRATE_NEAR_RANGE_METRES]) rps = float(ifg.meta_data[ifc.PYRATE_RANGE_PIX_METRES]) heading = float(ifg.meta_data[ifc.PYRATE_HEADING_DEGREES]) # direction of look vector w.r.t. satellite heading. # Gamma convention: +ve = right; -ve = left. look_dir = float(ifg.meta_data[ifc.PYRATE_AZIMUTH_DEGREES]) # convert to radians lon = np.radians(lon) lat = np.radians(lat) heading = np.radians(heading) look_dir = np.radians(look_dir) # Earth radius at given latitude re = np.sqrt(np.divide(np.square(a ** 2 * np.cos(lat)) + np.square(b ** 2 * np.sin(lat)), np.square(a * np.cos(lat)) + np.square(b * np.sin(lat)))) # range measurement at pixel ij range_dist = near_range + rps * rg # look angle at pixel ij -> law of cosines in "satellite - Earth centre - ground pixel" triangle # see e.g. Section 2 in https://www.cs.uaf.edu/~olawlor/ref/asf/sar_equations_2006_08_17.pdf look_angle = np.arccos(np.divide(se ** 2 + np.square(range_dist) - np.square(re), 2 * se * range_dist)) # add per-pixel height to the earth radius to obtain a more accurate ground pixel position for # incidence angle calculation re = re + dem_height # incidence angle at pixel ij -> law of cosines in "satellite - Earth centre - ground pixel" triangle # see e.g. Section 2 in https://www.cs.uaf.edu/~olawlor/ref/asf/sar_equations_2006_08_17.pdf incidence_angle = np.pi - np.arccos(np.divide(np.square(range_dist) + np.square(re) - se ** 2, 2 * np.multiply(range_dist, re))) # calculate satellite positions for each pixel sat_lat, sat_lon = get_sat_positions(lat, lon, look_angle, incidence_angle, heading, look_dir) # # calc azimuth angle using Vincenty's equations azimuth_angle = vincinv(lat, lon, sat_lat, sat_lon, a, b) return look_angle, incidence_angle, azimuth_angle, range_dist def calc_local_baseline(ifg: Ifg, az: np.ndarray, look_angle: np.ndarray) -> np.ndarray: """ Function to calculate perpendicular baseline values for each pixel. :param ifg: pyrate.core.shared.Ifg Class object :param az: Azimuth image coordinate for each pixel :param look_angle: Look angle for each pixel (radians) :return: bperp: Perpendicular baseline for each pixel (metres) """ # open ifg object if not ifg.is_open: ifg.open() # read relevant metadata from IFG prf = float(ifg.meta_data[ifc.PYRATE_PRF_HERTZ]) az_looks = int(ifg.meta_data[ifc.PYRATE_AZIMUTH_LOOKS]) az_n = int(ifg.meta_data[ifc.PYRATE_AZIMUTH_N]) base_C = float(ifg.meta_data[ifc.PYRATE_BASELINE_C]) base_N = float(ifg.meta_data[ifc.PYRATE_BASELINE_N]) baserate_C = float(ifg.meta_data[ifc.PYRATE_BASELINE_RATE_C]) baserate_N = float(ifg.meta_data[ifc.PYRATE_BASELINE_RATE_N]) # calculate per pixel baseline vectors across track (C) and normal to the track (N) mean_az = az_n / 2 - 0.5 # mean azimuth line prf = prf / az_looks # Pulse Repetition Frequency needs to be adjusted according to GAMMA azimuth looks base_C_local = base_C + baserate_C * (az - mean_az) / prf base_N_local = base_N + baserate_N * (az - mean_az) / prf # calculate the per-pixel perpendicular baseline (see Eq. 3.5 in Baehr, 2012 available here: # http://www.dgk.badw.de/fileadmin/user_upload/Files/DGK/docs/c-719.pdf) bperp = np.multiply(base_C_local, np.cos(look_angle)) - np.multiply(base_N_local, np.sin(look_angle)) return bperp def vincinv(lat1: np.ndarray, lon1: np.ndarray, lat2: np.ndarray, lon2: np.ndarray, semimaj: float, semimin: float) -> np.ndarray: """ Vincenty's Inverse Formula, adapted from GeodePy function vincinv (see https://github.com/GeoscienceAustralia/GeodePy/blob/master/geodepy/geodesy.py) - only relevant parts of the Geodepy to retrieve the azimuth angle have been used - vectorised the function for use with numpy arrays :param lat1: Latitude of Point 1 (radians) :param lon1: Longitude of Point 1 (radians) :param lat2: Latitude of Point 2 (radians) :param lon2: Longitude of Point 2 (radians) :param semimaj: semi-major axis of ellipsoid :param semimin: semi-minor axis of ellipsoid :return: azimuth1to2: Azimuth from Point 1 to 2 (Decimal Degrees) """ # Exit if any of the two sets of input points are the same if np.any(lat1 == lat2) and np.any(lon1 == lon2): return 0 # calculate flattening f = (semimaj - semimin) / semimaj # Equation numbering is from the GDA2020 Tech Manual v1.0 # Eq. 71 u1 = np.arctan((1 - f) * np.tan(lat1)) # Eq. 72 u2 = np.arctan((1 - f) * np.tan(lat2)) # Eq. 73; initial approximation lon = lon2 - lon1 omega = lon # Iterate until the change in lambda, lambda_sigma, is insignificant # (< 1e-12) or after 1000 iterations have been completed for i in range(1000): # Eq. 74 sin_sigma = np.sqrt( (np.cos(u2) * np.sin(lon)) ** 2 + (np.cos(u1) * np.sin(u2) - np.sin(u1) * np.cos(u2) * np.cos(lon)) ** 2) # Eq. 75 cos_sigma = np.sin(u1) * np.sin(u2) + np.cos(u1) * np.cos(u2) * np.cos(lon) # Eq. 76 sigma = np.arctan2(sin_sigma, cos_sigma) # Eq. 77 alpha = np.arcsin((np.cos(u1) * np.cos(u2) * np.sin(lon)) / sin_sigma) # Eq. 78 cos_two_sigma_m = np.cos(sigma) - (2 * np.sin(u1) * np.sin(u2) / np.cos(alpha) ** 2) # Eq. 79 c = (f / 16) * np.cos(alpha) ** 2 * (4 + f * (4 - 3 * np.cos(alpha) ** 2)) # Eq. 80 new_lon = omega + (1 - c) * f * np.sin(alpha) * ( sigma + c * np.sin(sigma) * (cos_two_sigma_m + c * np.cos(sigma) * (-1 + 2 * (cos_two_sigma_m ** 2))) ) delta_lon = new_lon - lon lon = new_lon if np.all(np.absolute(delta_lon) < 1e-12): break # Calculate the azimuth from point 1 to point 2 azimuth1to2 = np.arctan2((np.cos(u2) * np.sin(lon)), (np.cos(u1) * np.sin(u2) - np.sin(u1) * np.cos(u2) * np.cos(lon))) # add 2 pi in case an angle is below zero for azi in np.nditer(azimuth1to2, op_flags=['readwrite']): if azi < 0: azi[...] = azi + 2 * np.pi return np.round(azimuth1to2, 9) ================================================ FILE: pyrate/core/ifgconstants.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains a collection of constants used in various components of the PyRate software """ # lookup keys for the metadata fields in PyRate GeoTIFF files PYRATE_NCOLS = 'NCOLS' PYRATE_NROWS = 'NROWS' PYRATE_X_STEP = 'X_STEP' PYRATE_Y_STEP = 'Y_STEP' PYRATE_LAT = 'LAT' PYRATE_LONG = 'LONG' FIRST_DATE = 'FIRST_DATE' FIRST_TIME = 'FIRST_TIME' SECOND_DATE = 'SECOND_DATE' SECOND_TIME = 'SECOND_TIME' EPOCH_DATE = 'EPOCH_DATE' PYRATE_DATUM = 'DATUM' PYRATE_TIME_SPAN = 'TIME_SPAN_YEAR' PYRATE_WAVELENGTH_METRES = 'WAVELENGTH_METRES' PYRATE_INCIDENCE_DEGREES = 'INCIDENCE_DEGREES' PYRATE_HEADING_DEGREES = 'HEADING_DEGREES' PYRATE_AZIMUTH_DEGREES = 'AZIMUTH_DEGREES' PYRATE_RANGE_PIX_METRES = 'RANGE_PIX_METRES' PYRATE_RANGE_N = 'RANGE_N' PYRATE_RANGE_LOOKS = 'RANGE_LOOKS' PYRATE_AZIMUTH_PIX_METRES = 'AZIMUTH_PIX_METRES' PYRATE_AZIMUTH_N = 'AZIMUTH_N' PYRATE_AZIMUTH_LOOKS = 'AZIMUTH_LOOKS' PYRATE_PRF_HERTZ = 'PRF_HERTZ' PYRATE_NEAR_RANGE_METRES = 'NEAR_RANGE_METRES' PYRATE_SAR_EARTH_METRES = 'SAR_EARTH_METRES' PYRATE_SEMI_MAJOR_AXIS_METRES = 'SEMI_MAJOR_AXIS_METRES' PYRATE_SEMI_MINOR_AXIS_METRES = 'SEMI_MINOR_AXIS_METRES' PYRATE_BASELINE_T = 'BASELINE_T' PYRATE_BASELINE_C = 'BASELINE_C' PYRATE_BASELINE_N = 'BASELINE_N' PYRATE_BASELINE_RATE_T = 'BASELINE_RATE_T' PYRATE_BASELINE_RATE_C = 'BASELINE_RATE_C' PYRATE_BASELINE_RATE_N = 'BASELINE_RATE_N' PYRATE_BPERP = 'BASELINE_PERP_METRES' PYRATE_INSAR_PROCESSOR = 'INSAR_PROCESSOR' PYRATE_WEATHER_ERROR = 'WEATHER_ERROR' PYRATE_APS_ERROR = 'APS_ERROR' PYRATE_DEM_ERROR = 'DEM_ERROR' PYRATE_MAXVAR = 'CVD_MAXVAR' PYRATE_ALPHA = 'CVD_ALPHA' IFG_LKSX = 'IFG_MULTILOOK_X' IFG_LKSY = 'IFG_MULTILOOK_Y' IFG_CROP = 'IFG_CROP_OPT' COH_THRESH = 'COH_MASK_THRESHOLD' MLOOKED_COH_MASKED_IFG = 'COHERENCE_MASKED_MULTILOOKED_IFG' MULTILOOKED = 'MULTILOOKED_IFG' MULTILOOKED_COH = 'MULTILOOKED_COH' ORIG = 'ORIGINAL_IFG' COH = 'ORIGINAL_COH' DEM = 'ORIGINAL_DEM' MLOOKED_DEM = 'MULTILOOKED_DEM' RDC_AZIMUTH = 'RDC_AZIMUTH_MAP' RDC_RANGE = 'RDC_RANGE_MAP' LOOK = 'LOOK_ANGLE_MAP' INCIDENCE = 'INCIDENCE_ANGLE_MAP' AZIMUTH = 'AZIMUTH_ANGLE_MAP' RANGE_DIST = 'RANGE_DIST_MAP' # coherence stats COH_MEDIAN = 'COH_MEDIAN' COH_MEAN = 'COH_MEAN' COH_STD = 'COH_STD' BPERP = 'PERPENDICULAR_BASELINE_MAP' DEM_ERROR = 'DEM_ERROR_MAP' INCR = 'INCREMENTAL_TIME_SLICE' CUML = 'CUMULATIVE_TIME_SLICE' STACKRATE = 'STACKED_RATE_MAP' STACKERROR = 'STACKED_RATE_ERROR' STACKSAMP = 'STACKED_RATE_SAMPLES' LINRATE = 'LINEAR_RATE_MAP' LINERROR = 'LINEAR_RATE_ERROR' LINSAMP = 'LINEAR_RATE_SAMPLES' LINRSQ = 'LINEAR_RATE_RSQUARED' LINICPT = 'LINEAR_RATE_INTERCEPT' PYRATE_ORBITAL_ERROR = 'ORBITAL_ERROR' ORB_REMOVED = 'REMOVED' APS_REMOVED = 'REMOVED' DEM_ERROR_REMOVED = 'REMOVED' PYRATE_REF_PHASE = 'REFERENCE_PHASE' REF_PHASE_REMOVED = 'REMOVED' NAN_STATUS = 'NAN_STATUS' NAN_CONVERTED = 'CONVERTED' DATA_TYPE = 'DATA_TYPE' DATA_UNITS = 'DATA_UNITS' INPUT_TYPE = 'INPUT_TYPE' PYRATE_REFPIX_X = 'REF_PIX_X' PYRATE_REFPIX_Y = 'REF_PIX_Y' PYRATE_REFPIX_LAT = 'REF_PIX_LAT' PYRATE_REFPIX_LON = 'REF_PIX_LON' PYRATE_MEAN_REF_AREA = 'REF_AREA_MEAN' PYRATE_STDDEV_REF_AREA = 'REF_AREA_STDDEV' SEQUENCE_POSITION = 'SEQUENCE_POSITION' PYRATE_ORB_METHOD = 'ORB_METHOD' PYRATE_ORB_DEG = 'ORB_DEGREES' PYRATE_ORB_XLOOKS = 'ORB_MULTILOOK_X' PYRATE_ORB_YLOOKS = 'ORB_MULTILOOK_Y' PYRATE_ORB_PLANAR = 'PLANAR' PYRATE_ORB_QUADRATIC = 'QUADRATIC' PYRATE_ORB_PART_CUBIC = 'PART-CUBIC' PYRATE_ORB_INDEPENDENT = 'INDEPENDENT' PYRATE_ORB_NETWORK = 'NETWORK' DAYS_PER_YEAR = 365.25 # span of year, not a calendar year YEARS_PER_DAY = 1 / DAYS_PER_YEAR SPEED_OF_LIGHT_METRES_PER_SECOND = 3e8 MM_PER_METRE = 1000 METRE_PER_KM = 1000 LINE_OF_SIGHT = 0 PSEUDO_VERTICAL = 1 PSEUDO_HORIZONTAL = 2 LOS_PROJECTION_OPTION = { LINE_OF_SIGHT: 'LINE-OF-SIGHT', PSEUDO_VERTICAL: 'PSEUDO-VERTICAL', PSEUDO_HORIZONTAL: 'PSEUDO-HORIZONTAL' } ================================================ FILE: pyrate/core/logger.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains functions to control PyRate log outputs """ import sys import logging import warnings import traceback from datetime import datetime from pyrate.core.mpiops import size, rank, run_once pyratelogger = logging.getLogger(__name__) formatter = logging.Formatter("%(asctime)s %(module)s:%(lineno)d %(process)d %(levelname)s " + str(rank) + "/" + str( size-1)+" %(message)s", "%H:%M:%S") def configure_stage_log(verbosity, step_name, log_file_name='pyrate.log.'): log_file_name = run_once(str.__add__, log_file_name, step_name + '.' + datetime.now().isoformat()) ch = MPIStreamHandler() ch.setLevel(verbosity) ch.setFormatter(formatter) fh = logging.FileHandler(log_file_name) fh.setLevel(verbosity) fh.setFormatter(formatter) pyratelogger.addHandler(ch) pyratelogger.addHandler(fh) def warn_with_traceback(message, category, filename, lineno, line=None): """ copied from: http://stackoverflow.com/questions/22373927/get-traceback-of-warnings """ traceback.print_stack() log = sys.stderr log.write(warnings.formatwarning(message, category, filename, lineno, line)) class MPIStreamHandler(logging.StreamHandler): """ Only logs messages from Node 0 """ def emit(self, record): if rank == 0: super().emit(record) ================================================ FILE: pyrate/core/mpiops.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains MPI convenience functions for PyRate """ # pylint: disable=no-member # pylint: disable=invalid-name import logging import pickle from typing import Callable, Any, Iterable import numpy as np """For MPI compatibility""" try: from mpi4py import MPI MPI_INSTALLED = True # # We're having trouble with the MPI pickling and 64bit integers # MPI.pickle.__init__(pickle.dumps, pickle.loads) # module-level MPI 'world' object representing all connected nodes comm = MPI.COMM_WORLD # int: the total number of nodes in the MPI world size = comm.Get_size() # int: the index (from zero) of this node in the MPI world. Also known as # the rank of the node. rank = comm.Get_rank() except ImportError: MPI_INSTALLED = False size = 1 rank = 0 class MPI: SUM = np.sum class comm: """ the mpi simulators that are used in a non-mpi environment """ @staticmethod def barrier(): pass @staticmethod def reduce(arr, op, root=0): return op(arr) @staticmethod def Get_size(): return 1 @staticmethod def allgather(* args): return args @staticmethod def gather(* args, **kwargs): return args @staticmethod def allreduce(arr, op): return op(arr) @staticmethod def Bcast(arr, root=0): return @staticmethod def bcast(arr, root=0): return arr class MPIException(Exception): pass def validate_mpi(): if not MPI_INSTALLED: raise MPIException("MPI needs to be installed in order to use this module") def run_once(f: Callable, *args, **kwargs) -> Any: """ Run a function on one node and then broadcast result to all. :param Callable f: The function to be evaluated. Can take arbitrary arguments and return anything or nothing :param list args: Other positional arguments to pass on to f (optional) :param dict kwargs: Other named arguments to pass on to f (optional) :return: The value returned by f. :rtype: unknown """ if MPI_INSTALLED: if rank == 0: # without the try except, any error in this step hangs the other mpi threads # and these processes never exit, i.e., this MPI call hangs in case process 0 errors. try: f_result = f(*args, **kwargs) except Exception as e: f_result = e else: f_result = None result = comm.bcast(f_result, root=0) if isinstance(result, Exception): raise result else: return result else: return f(*args, **kwargs) def array_split(arr: Iterable, process: int = None) -> Iterable: """ Convenience function for splitting array elements across MPI processes :param ndarray arr: Numpy array :param int process: Process for which array members are required. If None, MPI.comm.rank is used instead. (optional) :return List corresponding to array members in a process. :rtype: list """ if MPI_INSTALLED: r = process if process else rank return np.array_split(np.array(arr, dtype=object), size)[r] else: return np.array(arr) def sum_vars(x, y, dtype): s = np.sum([x, y], axis=0) return s def sum_axis_0(x, y, dtype): s = np.sum(np.stack((x, y)), axis=0) return s if MPI_INSTALLED: sum_op = MPI.Op.Create(sum_vars, commute=True) sum0_op = MPI.Op.Create(sum_axis_0, commute=True) else: sum_op = lambda arr: np.sum(np.expand_dims(arr, 0), axis=0) sum0_op = lambda arr: np.sum(np.stack(np.expand_dims(arr, 0), axis=0), axis=0) ================================================ FILE: pyrate/core/mst.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # coding: utf-8 """ This Python module implements the minimum spanning tree functionality for selecting interferometric observations. """ # pylint: disable=invalid-name from pathlib import Path from itertools import product from numpy import array, nan, isnan, float32, empty, sum as nsum import numpy as np from networkx.classes.reportviews import EdgeView import networkx as nx from joblib import Parallel, delayed import pyrate.constants as C from pyrate.core.algorithm import ifg_date_lookup from pyrate.core.algorithm import ifg_date_index_lookup from pyrate.core.shared import IfgPart, create_tiles, tiles_split from pyrate.core.shared import joblib_log_level, Tile from pyrate.core.logger import pyratelogger as log from pyrate.configuration import Configuration np.seterr(invalid='ignore') # stops RuntimeWarning in nan conversion # TODO: may need to implement memory saving row-by-row access # TODO: document weighting by either Nan fraction OR variance def mst_from_ifgs(ifgs): """ Returns a Minimum Spanning Tree (MST) network from the given interferograms using Kruskal's algorithm. The MST is calculated using a weighting based on the number of NaN cells in the phase band. :param list ifgs: List of interferogram objects (Ifg class) :return: edges: The number of network connections :rtype: int :return: is_tree: A boolean that is True if network is a tree :rtype: bool :return: ntrees: The number of disconnected trees :rtype: int :return: mst_ifgs: Minimum Spanning Tree network of interferograms :rtype: list """ edges_with_weights_for_networkx = [(i.first, i.second, i.nan_fraction) for i in ifgs] g_nx = _build_graph_networkx(edges_with_weights_for_networkx) mst = nx.minimum_spanning_tree(g_nx) # mst_edges, is tree?, number of trees edges = mst.edges() ifg_sub = [ifg_date_index_lookup(ifgs, d) for d in edges] mst_ifgs = [i for k, i in enumerate(ifgs) if k in ifg_sub] return mst.edges(), nx.is_tree(mst), nx.number_connected_components(mst), mst_ifgs def mst_parallel(ifgs, params): """ Wrapper function for calculating MST matrix in non-MPI runs. :param list ifgs: List of interferogram objects (Ifg class) :param dict params: Dictionary of parameters :return: result: 3-dimensional Minimum Spanning Tree matrix :rtype: ndarray """ log.info('Calculating MST in tiles') ncpus = params[C.PROCESSES] no_ifgs = len(ifgs) no_y, no_x = ifgs[0].phase_data.shape tiles = create_tiles(ifgs[0].shape) no_tiles = len(tiles) # need to break up the ifg class as multiprocessing does not allow pickling # don't read in all the phase data at once # use data paths and locally read in each core/process, # gdal read is very fast ifg_paths = [i.data_path for i in ifgs] result = empty(shape=(no_ifgs, no_y, no_x), dtype=np.bool) if params[C.PARALLEL]: log.info('Calculating MST using {} tiles in parallel using {} ' \ 'processes'.format(no_tiles, ncpus)) t_msts = Parallel(n_jobs=params[C.PROCESSES], verbose=joblib_log_level(C.LOG_LEVEL))( delayed(mst_multiprocessing)(t, ifg_paths, params=params) for t in tiles ) for k, tile in enumerate(tiles): result[:, tile.top_left_y:tile.bottom_right_y, tile.top_left_x: tile.bottom_right_x] = t_msts[k] else: log.info('Calculating MST using {} tiles in serial'.format(no_tiles)) for k, tile in enumerate(tiles): result[:, tile.top_left_y:tile.bottom_right_y, tile.top_left_x: tile.bottom_right_x] = \ mst_multiprocessing(tile, ifg_paths, params=params) return result def mst_multiprocessing(tile, ifgs_or_paths, preread_ifgs=None, params=None): """ Wrapper function for calculating MST matrix for a tile :param ndarray tile: Tile class instance :param list ifgs_or_paths: All interferograms paths of the problem. List of strings :param dict preread_ifgs: Dictionary of interferogram metadata :return: mst_tile: MST matrix tile. An array of booleans representing valid ifg connections :rtype: ndarray """ # The memory requirement during MPI MST computation is determined by the # number of interferograms times size of IfgPart. Note that we need all # interferogram header information (like first/second image dates) for MST # computation. To manage memory we need smaller tiles (IfgPart) as number # of interferograms increases ifg_parts = [IfgPart(p, tile, preread_ifgs, params) for p in ifgs_or_paths] return mst_boolean_array(ifg_parts) def _build_graph_networkx(edges_with_weights): """ Convenience graph builder function: returns a new graph object. """ g = nx.Graph() g.add_weighted_edges_from(edges_with_weights) return g def mst_boolean_array(ifgs): """ Returns a 3D array of booleans constituting valid interferogram connections in the Minimum Spanning Tree matrix. :param list ifgs: Sequence of interferogram objects :return: result: Array of booleans representing valid ifg connections :rtype: ndarray """ # The MSTs are stripped of connecting edge info, leaving just the ifgs. nifgs = len(ifgs) ny, nx = ifgs[0].phase_data.shape result = empty(shape=(nifgs, ny, nx), dtype=np.bool) for y, x, mst in mst_matrix_networkx(ifgs): # mst is a list of datetime.date tuples if isinstance(mst, EdgeView): ifg_sub = [ifg_date_index_lookup(ifgs, d) for d in mst] ifg_sub_bool = [True if i in ifg_sub else False for i in range(nifgs)] # boolean conversion result[:, y, x] = np.array(ifg_sub_bool) else: result[:, y, x] = np.zeros(nifgs, dtype=bool) return result def _mst_matrix_ifgs_only(ifgs): """ Alternative method for producing 3D MST array """ # Currently not used # The MSTs are stripped of connecting edge info, leaving just the ifgs. result = empty(shape=ifgs[0].phase_data.shape, dtype=object) for y, x, mst in mst_matrix_networkx(ifgs): if isinstance(mst, EdgeView): ifg_sub = [ifg_date_lookup(ifgs, d) for d in mst] result[(y, x)] = tuple(ifg_sub) else: result[(y, x)] = mst # usually NaN return result def _mst_matrix_as_array(ifgs): """ Alternative method for producing 3D MST array """ # Currently not used # Each pixel contains an MST (with connecting edges etc). mst_result = empty(shape=ifgs[0].phase_data.shape, dtype=object) for y, x, mst in mst_matrix_networkx(ifgs): mst_result[y, x] = mst return mst_result # TODO: custom edge weighting could be included with an additional # 'weights' arg if some other weighting criterion is required later def mst_matrix_networkx(ifgs): """ Generates MST network for a single pixel for the given ifgs using NetworkX-package algorithms. :param list ifgs: Sequence of interferogram objects :return: y: pixel y coordinate :rtype: int :return: x: pixel x coordinate :rtype: int :return: mst: list of tuples for edges in the minimum spanning tree :rtype: list """ # make default MST to optimise result when no Ifg cells in a stack are nans edges_with_weights = [(i.first, i.second, i.nan_fraction) for i in ifgs] edges, g_nx = _minimum_spanning_edges_from_mst(edges_with_weights) # TODO: memory efficiencies can be achieved here with tiling list_of_phase_data = [i.phase_data for i in ifgs] data_stack = array(list_of_phase_data, dtype=float32) # create MSTs for each pixel in the ifg data stack nifgs = len(ifgs) for y, x in product(range(ifgs[0].nrows), range(ifgs[0].ncols)): values = data_stack[:, y, x] # vertical stack of ifg values for a pixel nan_count = nsum(isnan(values)) # optimisations: use pre-created results for all nans/no nans if nan_count == 0: yield y, x, edges continue elif nan_count == nifgs: yield y, x, nan continue # dynamically modify graph to reuse a single graph: this should avoid # repeatedly creating new graph objs & reduce RAM use ebunch_add = [] ebunch_delete = [] for value, edge in zip(values, edges_with_weights): if not isnan(value): if not g_nx.has_edge(edge[0], edge[1]): ebunch_add.append(edge) else: if g_nx.has_edge(edge[0], edge[1]): ebunch_delete.append(edge) if ebunch_add: g_nx.add_weighted_edges_from(ebunch_add) if ebunch_delete: g_nx.remove_edges_from(ebunch_delete) yield y, x, nx.minimum_spanning_tree(g_nx).edges() def _minimum_spanning_edges_from_mst(edges): """ Convenience function to determine MST edges """ g_nx = _build_graph_networkx(edges) T = nx.minimum_spanning_tree(g_nx) # step ifglist_mst in make_mstmat.m edges = T.edges() return edges, g_nx def mst_calc_wrapper(params): """ MPI wrapper function for MST calculation """ log.info('Calculating minimum spanning tree matrix') def _save_mst_tile(tile: Tile, params: dict) -> None: """ Convenient inner loop for mst tile saving """ preread_ifgs = params[C.PREREAD_IFGS] dest_tifs = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]] mst_file_process_n = Configuration.mst_path(params, index=tile.index) if mst_file_process_n.exists(): return mst_tile = mst_multiprocessing(tile, dest_tifs, preread_ifgs, params) # locally save the mst_mat np.save(file=mst_file_process_n, arr=mst_tile) tiles_split(_save_mst_tile, params) log.debug('Finished minimum spanning tree calculation') ================================================ FILE: pyrate/core/orbital.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module implements residual orbital corrections for interferograms. """ # pylint: disable=invalid-name import tempfile from typing import Optional, List, Dict, Iterable from collections import OrderedDict from pathlib import Path from numpy import empty, isnan, reshape, float32, squeeze from numpy import dot, vstack, zeros, meshgrid import numpy as np from numpy.linalg import pinv, cond import pyrate.constants as C from pyrate.core.algorithm import first_second_ids, get_all_epochs from pyrate.core import shared, ifgconstants as ifc, prepifg_helper, mst, mpiops from pyrate.core.shared import nanmedian, Ifg, InputTypes, iterable_split from pyrate.core.logger import pyratelogger as log from pyrate.prepifg import find_header from pyrate.configuration import MultiplePaths # Orbital correction tasks # # TODO: options for multilooking # 1) do the 2nd stage mlook at prepifg.py/generate up front, then delete in # workflow afterward # 2) refactor prep_ifgs() call to take input filenames and params & generate # mlooked versions from that # this needs to be more generic to call at any point in the runtime. # Design notes: # The orbital correction code includes several enhancements. PyRate creates # sparse arrays for the linear inversion, which contain many empty cells. # This is unnecessary for the independent method, and temporarily wastes # potentially a lot of memory. # # For the independent method, PyRate makes individual small design matrices and # corrects the Ifgs one by one. If required in the correction, the intercept # option adds an extra column of ones to include in the inversion. # # Network method design matrices are mostly empty, and intercept terms are handled # differently. Individual design matrices (== independent method DMs) are # placed in the sparse network design matrix. Intercept terms are not included in the # smaller DMs to prevent unwanted cols being inserted. This is why some funcs # appear to ignore the intercept parameter in the networked method. Network DM # intercept terms are cols of 1s in a diagonal line on the RHS of the sparse array. MAIN_PROCESS = 0 # ORBITAL ERROR correction constants INDEPENDENT_METHOD = C.INDEPENDENT_METHOD NETWORK_METHOD = C.NETWORK_METHOD PLANAR = C.PLANAR QUADRATIC = C.QUADRATIC PART_CUBIC = C.PART_CUBIC def remove_orbital_error(ifgs: List, params: dict) -> None: """ Wrapper function for PyRate orbital error removal functionality. NB: the ifg data is modified in situ, rather than create intermediate files. The network method assumes the given ifgs have already been reduced to a minimum spanning tree network. """ ifg_paths = [i.data_path for i in ifgs] if isinstance(ifgs[0], Ifg) else ifgs degree = params[C.ORBITAL_FIT_DEGREE] method = params[C.ORBITAL_FIT_METHOD] orbfitlksx = params[C.ORBITAL_FIT_LOOKS_X] orbfitlksy = params[C.ORBITAL_FIT_LOOKS_Y] # Sanity check of the orbital params if type(orbfitlksx) != int or type(orbfitlksy) != int: msg = f"Multi-look factors for orbital correction should be of type: int" raise OrbitalError(msg) if degree not in [PLANAR, QUADRATIC, PART_CUBIC]: msg = "Invalid degree of %s for orbital correction" % C.ORB_DEGREE_NAMES.get(degree) raise OrbitalError(msg) if method not in [NETWORK_METHOD, INDEPENDENT_METHOD]: msg = "Invalid method of %s for orbital correction" % C.ORB_METHOD_NAMES.get(method) raise OrbitalError(msg) # Give informative log messages based on selected options log.info(f'Calculating {__degrees_as_string(degree)} orbital correction using ' f'{__methods_as_string(method)} method') if orbfitlksx > 1 or orbfitlksy > 1: log.info(f'Multi-looking interferograms for orbital correction with ' f'factors of X = {orbfitlksx} and Y = {orbfitlksy}') if method == INDEPENDENT_METHOD: iterable_split(independent_orbital_correction, ifg_paths, params) elif method == NETWORK_METHOD: # Here we do all the multilooking in one process, but in memory # could use multiple processes if we write data to disc during # remove_orbital_error step # TODO: performance comparison of saving multilooked files on # disc vs in-memory single-process multilooking # # The gdal swig bindings prevent us from doing multi-looking in parallel # when using multiprocessing because the multilooked ifgs are held in # memory using in-memory tifs. Parallelism using MPI is possible. # TODO: Use a flag to select mpi parallel vs multiprocessing in the # iterable_split function, which will use mpi but can fall back on # single process based on the flag for the multiprocessing side. if mpiops.rank == MAIN_PROCESS: mlooked = __create_multilooked_datasets(params) _validate_mlooked(mlooked, ifg_paths) network_orbital_correction(ifg_paths, params, mlooked) else: raise OrbitalError("Unrecognised orbital correction method") def __create_multilooked_datasets(params): exts, ifg_paths, multi_paths = __extents_from_params(params) mlooked_datasets = [_create_mlooked_dataset(m, i, exts, params) for m, i in zip(multi_paths, ifg_paths)] mlooked = [Ifg(m) for m in mlooked_datasets] for m in mlooked: m.initialize() shared.nan_and_mm_convert(m, params) return mlooked def __extents_from_params(params): multi_paths = params[C.INTERFEROGRAM_FILES] ifg_paths = [p.tmp_sampled_path for p in multi_paths] rasters = [shared.dem_or_ifg(r) for r in ifg_paths] crop_opt = prepifg_helper.ALREADY_SAME_SIZE xlooks = params[C.ORBITAL_FIT_LOOKS_X] ylooks = params[C.ORBITAL_FIT_LOOKS_Y] exts = prepifg_helper.get_analysis_extent(crop_opt, rasters, xlooks, ylooks, None) return exts, ifg_paths, multi_paths def _create_mlooked_dataset(multi_path, ifg_path, exts, params): ''' Wrapper to generate a multi-looked dataset for a single ifg ''' header = find_header(multi_path, params) thresh = params[C.NO_DATA_AVERAGING_THRESHOLD] crop_opt = prepifg_helper.ALREADY_SAME_SIZE xlooks = params[C.ORBITAL_FIT_LOOKS_X] ylooks = params[C.ORBITAL_FIT_LOOKS_Y] out_path = tempfile.mktemp() log.debug(f'Multi-looking {ifg_path} with factors X = {xlooks} and Y = {ylooks} for orbital correction') resampled_data, out_ds = prepifg_helper.prepare_ifg( ifg_path, xlooks, ylooks, exts, thresh, crop_opt, header, False, out_path ) return out_ds def _validate_mlooked(mlooked, ifgs): ''' Basic sanity checking of the multilooked ifgs. ''' if len(mlooked) != len(ifgs): msg = "Mismatching # ifgs and # multilooked ifgs" raise OrbitalError(msg) if not all([hasattr(i, 'phase_data') for i in mlooked]): msg = "Mismatching types in multilooked ifgs arg:\n%s" % mlooked raise OrbitalError(msg) def _get_num_params(degree, intercept: Optional[bool] = False): ''' Returns number of model parameters from string parameter ''' if degree == PLANAR: nparams = 2 elif degree == QUADRATIC: nparams = 5 elif degree == PART_CUBIC: nparams = 6 else: msg = "Invalid orbital model degree: %s" \ % C.ORB_DEGREE_NAMES.get(degree) raise OrbitalError(msg) # NB: independent method only, network method handles intercept terms differently if intercept: nparams += 1 # c in y = mx + c return nparams def independent_orbital_correction(ifg_path, params): """ Calculates and removes an orbital error surface from a single independent interferogram. Warning: This will write orbital error corrected phase_data to the ifg. :param Ifg class instance ifg: the interferogram to be corrected :param dict params: dictionary of configuration parameters :return: None - interferogram phase data is updated and saved to disk """ log.debug(f"Orbital correction of {ifg_path}") degree = params[C.ORBITAL_FIT_DEGREE] offset = params[C.ORBFIT_OFFSET] intercept = params[C.ORBFIT_INTERCEPT] xlooks = params[C.ORBITAL_FIT_LOOKS_X] ylooks = params[C.ORBITAL_FIT_LOOKS_Y] ifg0 = shared.Ifg(ifg_path) if isinstance(ifg_path, str) else ifg_path # get full-resolution design matrix fullres_dm = get_design_matrix(ifg0, degree, intercept=intercept) ifg = shared.dem_or_ifg(ifg_path) if isinstance(ifg_path, str) else ifg_path multi_path = MultiplePaths(ifg.data_path, params) orb_on_disc = MultiplePaths.orb_error_path(ifg.data_path, params) if not ifg.is_open: ifg.open() shared.nan_and_mm_convert(ifg, params) fullres_ifg = ifg # keep a backup fullres_phase = fullres_ifg.phase_data if orb_on_disc.exists(): log.info(f'Reusing already computed orbital fit correction: {orb_on_disc}') orbital_correction = np.load(file=orb_on_disc) else: # Multi-look the ifg data if either X or Y is greater than 1 if (xlooks > 1) or (ylooks > 1): exts, _, _ = __extents_from_params(params) mlooked = _create_mlooked_dataset(multi_path, ifg.data_path, exts, params) ifg = Ifg(mlooked) # multi-looked Ifg object ifg.initialize() shared.nan_and_mm_convert(ifg, params) # vectorise phase data, keeping NODATA vphase = reshape(ifg.phase_data, ifg.num_cells) # compute design matrix for multi-looked data mlooked_dm = get_design_matrix(ifg, degree, intercept=intercept) # invert to obtain the correction image (forward model) at full-res orbital_correction = __orb_correction(fullres_dm, mlooked_dm, fullres_phase, vphase, offset=offset) # save correction to disc if not orb_on_disc.parent.exists(): shared.mkdir_p(orb_on_disc.parent) np.save(file=orb_on_disc, arr=orbital_correction) # subtract orbital correction from the full-res ifg fullres_ifg.phase_data -= orbital_correction # set orbfit meta tag and save phase to file _save_orbital_error_corrected_phase(fullres_ifg, params) def __orb_correction(fullres_dm, mlooked_dm, fullres_phase, mlooked_phase, offset=False): """ Function to perform the inversion to obtain orbital model parameters and return the orbital correction as the full resolution forward model. """ # perform inversion using pseudoinverse of DM orbparams = __orb_inversion(mlooked_dm, mlooked_phase) # compute forward model at full resolution fullorb = reshape(dot(fullres_dm, orbparams), fullres_phase.shape) # Estimate the offset of the interferogram as the median of ifg minus model # Only needed if reference phase correction has already been applied? if offset: offset_removal = nanmedian(np.ravel(fullres_phase - fullorb)) else: offset_removal = 0 orbital_correction = fullorb - offset_removal return orbital_correction def __orb_inversion(design_matrix, data): """Inversion using pseudoinverse of design matrix""" # remove NaN elements before inverting to get the model B = design_matrix[~isnan(data)] d = data[~isnan(data)] return pinv(B) @ d def network_orbital_correction(ifg_paths, params, m_ifgs: Optional[List] = None): """ This algorithm implements a network inversion to determine orbital corrections for a set of interferograms forming a connected network. Warning: This will write orbital error corrected phase_data to the ifgs. :param list ifg_paths: List of Ifg class objects reduced to a minimum spanning tree network :param dict params: dictionary of configuration parameters :param list m_ifgs: list of multilooked Ifg class objects (sequence must be multilooked versions of 'ifgs' arg) :return: None - interferogram phase data is updated and saved to disk """ # pylint: disable=too-many-locals, too-many-arguments offset = params[C.ORBFIT_OFFSET] degree = params[C.ORBITAL_FIT_DEGREE] preread_ifgs = params[C.PREREAD_IFGS] intercept = params[C.ORBFIT_INTERCEPT] scale = params[C.ORBFIT_SCALE] # all orbit corrections available? if isinstance(ifg_paths[0], str): if __check_and_apply_orberrors_found_on_disc(ifg_paths, params): log.warning("Reusing orbfit errors from previous run!!!") return # all corrections are available in numpy files already saved - return ifgs = [shared.Ifg(i) for i in ifg_paths] else: # alternate test paths # TODO: improve ifgs = ifg_paths src_ifgs = ifgs if m_ifgs is None else m_ifgs src_ifgs = mst.mst_from_ifgs(src_ifgs)[3] # use networkx mst if preread_ifgs: temp_ifgs = OrderedDict(sorted(preread_ifgs.items())).values() ids = first_second_ids(get_all_epochs(temp_ifgs)) else: ids = first_second_ids(get_all_epochs(ifgs)) nepochs = len(set(ids)) # call the actual inversion routine coefs = calc_network_orb_correction(src_ifgs, degree, scale, nepochs, intercept=intercept) # create full res DM to expand determined coefficients into full res # orbital correction (eg. expand coarser model to full size) if preread_ifgs: temp_ifg = Ifg(ifg_paths[0]) # ifgs here are paths temp_ifg.open() dm = get_design_matrix(temp_ifg, degree, intercept=intercept, scale=scale) temp_ifg.close() else: ifg = ifgs[0] dm = get_design_matrix(ifg, degree, intercept=intercept, scale=scale) for i in ifg_paths: # open if not Ifg instance if isinstance(i, str): # pragma: no cover # are paths i = Ifg(i) i.open(readonly=False) shared.nan_and_mm_convert(i, params) _remove_network_orb_error(coefs, dm, i, ids, offset, params) def calc_network_orb_correction(src_ifgs, degree, scale, nepochs, intercept=False): """ Calculate and return coefficients for the network orbital correction model given a set of ifgs: :param list src_ifgs: iterable of Ifg objects :param str degree: the degree of the orbital fit (planar, quadratic or part-cubic) :param int scale: Scale factor for design matrix to improve inversion robustness :param int nepochs: The number of epochs in the network :param intercept: whether to include a constant offset to fit to each ifg. This intercept is discarded and not returned. :return coefs: a list of coefficient lists, indexed by epoch. The coefficient lists are in the following order: PLANAR - x, y QUADRATIC - x^2, y^2, x*y, x, y PART_CUBIC - x*y^2, x^2, y^2, x*y, x, y """ vphase = vstack([i.phase_data.reshape((i.num_cells, 1)) for i in src_ifgs]) vphase = squeeze(vphase) B = get_network_design_matrix(src_ifgs, degree, scale, intercept=intercept) orbparams = __orb_inversion(B, vphase) ncoef = _get_num_params(degree) + intercept # extract all params except intercept terms coefs = [orbparams[i:i+ncoef] for i in range(0, nepochs * ncoef, ncoef)] return coefs def __check_and_apply_orberrors_found_on_disc(ifg_paths, params): saved_orb_err_paths = [MultiplePaths.orb_error_path(ifg_path, params) for ifg_path in ifg_paths] for p, i in zip(saved_orb_err_paths, ifg_paths): if p.exists(): orb = np.load(p) if isinstance(i, str): # are paths ifg = Ifg(i) ifg.open(readonly=False) shared.nan_and_mm_convert(ifg, params) else: ifg = i ifg.phase_data -= orb # set orbfit meta tag and save phase to file _save_orbital_error_corrected_phase(ifg, params) return all(p.exists() for p in saved_orb_err_paths) def _remove_network_orb_error(coefs, dm, ifg, ids, offset, params): """ remove network orbital error from input interferograms """ saved_orb_err_path = MultiplePaths.orb_error_path(ifg.data_path, params) orb = dm.dot(coefs[ids[ifg.second]] - coefs[ids[ifg.first]]) orb = orb.reshape(ifg.shape) # Estimate the offset of the interferogram as the median of ifg minus model # Only needed if reference phase correction has already been applied? if offset: # brings all ifgs to same reference level orb -= nanmedian(np.ravel(ifg.phase_data - orb)) # subtract orbital error from the ifg ifg.phase_data -= orb # save orb error on disc np.save(file=saved_orb_err_path, arr=orb) # set orbfit meta tag and save phase to file _save_orbital_error_corrected_phase(ifg, params) def _save_orbital_error_corrected_phase(ifg, params): """ Convenience function to update metadata and save latest phase after orbital fit correction """ # set orbfit tags after orbital error correction ifg.dataset.SetMetadataItem(ifc.PYRATE_ORB_METHOD, __methods_as_string(params[C.ORBITAL_FIT_METHOD])) ifg.dataset.SetMetadataItem(ifc.PYRATE_ORB_DEG, __degrees_as_string(params[C.ORBITAL_FIT_DEGREE])) ifg.dataset.SetMetadataItem(ifc.PYRATE_ORB_XLOOKS, str(params[C.ORBITAL_FIT_LOOKS_X])) ifg.dataset.SetMetadataItem(ifc.PYRATE_ORB_YLOOKS, str(params[C.ORBITAL_FIT_LOOKS_Y])) ifg.dataset.SetMetadataItem(ifc.PYRATE_ORBITAL_ERROR, ifc.ORB_REMOVED) ifg.write_modified_phase() ifg.close() def __methods_as_string(method): """Look up table to get orbital method string names""" meth = {1:ifc.PYRATE_ORB_INDEPENDENT, 2:ifc.PYRATE_ORB_NETWORK} return str(meth[method]) def __degrees_as_string(degree): """Look up table to get orbital degree string names""" deg = {1: ifc.PYRATE_ORB_PLANAR, 2: ifc.PYRATE_ORB_QUADRATIC, 3: ifc.PYRATE_ORB_PART_CUBIC} return str(deg[degree]) # TODO: subtract reference pixel coordinate from x and y def get_design_matrix(ifg, degree, intercept: Optional[bool] = True, scale: Optional[int] = 1): """ Returns orbital error design matrix with columns for model parameters. :param Ifg class instance ifg: interferogram to get design matrix for :param str degree: model to fit (PLANAR / QUADRATIC / PART_CUBIC) :param bool intercept: whether to include column for the intercept term. :param int scale: Scale factor for design matrix to improve inversion robustness :return: dm: design matrix :rtype: ndarray """ if not ifg.is_open: ifg.open() if degree not in [PLANAR, QUADRATIC, PART_CUBIC]: raise OrbitalError("Invalid degree argument") if scale < 1: raise OrbitalError("Scale argument must be greater or equal to 1") # scaling required with higher degree models to help with param estimation xsize = ifg.x_size / scale if scale else ifg.x_size ysize = ifg.y_size / scale if scale else ifg.y_size # mesh needs to start at 1, otherwise first cell resolves to 0 and ignored xg, yg = [g+1 for g in meshgrid(range(ifg.ncols), range(ifg.nrows))] x = xg.reshape(ifg.num_cells) * xsize y = yg.reshape(ifg.num_cells) * ysize # TODO: performance test this vs np.concatenate (n by 1 cols)?? dm = empty((ifg.num_cells, _get_num_params(degree, intercept)), dtype=float32) # apply positional parameter values, multiply pixel coordinate by cell size # to get distance (a coord by itself doesn't tell us distance from origin) if degree == PLANAR: dm[:, 0] = x dm[:, 1] = y elif degree == QUADRATIC: dm[:, 0] = x**2 dm[:, 1] = y**2 dm[:, 2] = x * y dm[:, 3] = x dm[:, 4] = y elif degree == PART_CUBIC: dm[:, 0] = x * (y**2) dm[:, 1] = x**2 dm[:, 2] = y**2 dm[:, 3] = x * y dm[:, 4] = x dm[:, 5] = y if intercept: dm[:, -1] = np.ones(ifg.num_cells) # estimate the intercept term # report condition number of the design matrix - L2-norm computed using SVD log.debug(f'The condition number of the design matrix is {cond(dm)}') return dm def get_network_design_matrix(ifgs, degree, scale, intercept=True): # pylint: disable=too-many-locals """ Returns larger-format design matrix for network error correction. The network design matrix includes rows which relate to those of NaN cells. :param list ifgs: List of Ifg class objects :param str degree: model to fit (PLANAR / QUADRATIC / PART_CUBIC) :param int scale: Scale factor for design matrix to improve inversion robustness :param bool intercept: whether to include columns for intercept estimation. :return: netdm: network design matrix :rtype: ndarray """ if degree not in [PLANAR, QUADRATIC, PART_CUBIC]: raise OrbitalError("Invalid degree argument") if scale < 1: raise OrbitalError("Scale argument must be greater or equal to 1") nifgs = len(ifgs) if nifgs < 1: # can feasibly do correction on a single Ifg/2 epochs raise OrbitalError("Invalid number of Ifgs: %s" % nifgs) # init sparse network design matrix nepochs = len(set(get_all_epochs(ifgs))) # no intercepts here; they are included separately below ncoef = _get_num_params(degree) shape = [ifgs[0].num_cells * nifgs, ncoef * nepochs] if intercept: shape[1] += nepochs # add extra space for intercepts netdm = zeros(shape, dtype=float32) # calc location for individual design matrices dates = [ifg.first for ifg in ifgs] + [ifg.second for ifg in ifgs] ids = first_second_ids(dates) tmpdm = get_design_matrix(ifgs[0], degree, intercept=intercept, scale=scale) # iteratively build up sparse matrix for i, ifg in enumerate(ifgs): rs = i * ifg.num_cells # starting row m = ids[ifg.first] * (ncoef + intercept) # start col for first s = ids[ifg.second] * (ncoef + intercept) # start col for second netdm[rs:rs + ifg.num_cells, m:m + ncoef + intercept] = -tmpdm netdm[rs:rs + ifg.num_cells, s:s + ncoef + intercept] = tmpdm return netdm class OrbitalError(Exception): """ Generic class for errors in orbital correction. """ def orb_fit_calc_wrapper(params: dict) -> None: """ MPI wrapper for orbital fit correction """ multi_paths = params[C.INTERFEROGRAM_FILES] if not params[C.ORBITAL_FIT]: log.info('Orbital correction not required!') return ifg_paths = [p.tmp_sampled_path for p in multi_paths] remove_orbital_error(ifg_paths, params) mpiops.comm.barrier() # update phase_data saved in tiled numpy files shared.save_numpy_phase(ifg_paths, params) log.debug('Finished Orbital error correction') ================================================ FILE: pyrate/core/phase_closure/__init__.py ================================================ ================================================ FILE: pyrate/core/phase_closure/closure_check.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from collections import defaultdict from typing import List, Dict, Tuple, Any from nptyping import NDArray, UInt16, Float32 import numpy as np import pyrate.constants as C from pyrate.core import mpiops from pyrate.core.phase_closure.mst_closure import sort_loops_based_on_weights_and_date, WeightedLoop, Edge from pyrate.configuration import Configuration, MultiplePaths from pyrate.core.phase_closure.sum_closure import sum_phase_closures from pyrate.core.phase_closure.plot_closure import plot_closure from pyrate.core.shared import Ifg, nan_and_mm_convert from pyrate.core.logger import pyratelogger as log def mask_pixels_with_unwrapping_errors(ifgs_breach_count: NDArray[(Any, Any, Any), UInt16], num_occurrences_each_ifg: NDArray[(Any,), UInt16], params: dict) -> None: """ Find pixels in the phase data that breach closure_thr, and mask (assign NaNs) to those pixels in those ifgs. :param ifgs_breach_count: unwrapping issues at pixels in all loops :param num_occurrences_each_ifg: frequency of ifgs appearing in all loops :param params: params dict """ log.debug("Masking phase data of retained ifgs") for i, m_p in enumerate(params[C.INTERFEROGRAM_FILES]): pix_index = ifgs_breach_count[:, :, i] == num_occurrences_each_ifg[i] ifg = Ifg(m_p.tmp_sampled_path) ifg.open() nan_and_mm_convert(ifg, params) ifg.phase_data[pix_index] = np.nan ifg.write_modified_phase() log.info(f"Masked phase data of {i + 1} retained ifgs after phase closure") return None def __drop_ifgs_if_not_part_of_any_loop(ifg_files: List[str], loops: List[WeightedLoop], params: dict) -> List[str]: """ Check if an ifg is part of any of the loops, otherwise drop it from the list of interferograms for further PyRate processing. """ loop_ifgs = set() for weighted_loop in loops: for edge in weighted_loop.loop: loop_ifgs.add(Edge(edge.first, edge.second)) ifgs = [Ifg(i) for i in ifg_files] for i in ifgs: i.open() i.nodata_value = params[C.NO_DATA_VALUE] selected_ifg_files = [] for i, f in zip(ifgs, ifg_files): if Edge(i.first, i.second) in loop_ifgs: selected_ifg_files.append(f) if len(ifg_files) != len(selected_ifg_files): log.info(f'Only {len(selected_ifg_files)} (out of {len(ifg_files)}) ifgs participate in ' f'one or more closure loops, and are selected for further PyRate analysis') return selected_ifg_files def __drop_ifgs_exceeding_threshold(orig_ifg_files: List[str], ifgs_breach_count, num_occurences_each_ifg, params): """ Function to identify and drop ifgs, based on two thresholds. We demand two thresholds to be breached before an ifg is dropped: 1. min_loops_per_ifg: the basic ifg loop participation count check: does the ifg participate in enough loops to accurately check for unwrapping errors? 2. The second threshold is an average check of pixels breached taking all loops into account. It is evaluated as follows: (i) ifgs_breach_count contains the number of loops where this pixel in this ifg had a closure exceeding closure_thr. (b) sum(ifgs_breach_count[:, :, i]) is the number of pixels in ifg exceeding closure_thr over all loops (c) divide by loop_count_of_this_ifg and num of cells (nrows x ncols) for a normalised measure of threshold. """ orig_ifg_files.sort() nrows, ncols, n_ifgs = ifgs_breach_count.shape selected_ifg_files = [] for i, ifg_file in enumerate(orig_ifg_files): loop_count_of_this_ifg = num_occurences_each_ifg[i] if loop_count_of_this_ifg: # if the ifg participated in at least one loop ifg_remove_threshold_breached = \ np.sum(ifgs_breach_count[:, :, i] == loop_count_of_this_ifg) / (nrows * ncols) > params[ C.IFG_DROP_THR] if not ( # min loops count # check 1 (num_occurences_each_ifg[i] > params[C.MIN_LOOPS_PER_IFG]) and ifg_remove_threshold_breached # and breached threshold ): selected_ifg_files.append(ifg_file) return selected_ifg_files def iterative_closure_check(config, interactive_plot=True) -> \ Tuple[List[str], NDArray[(Any, Any, Any), UInt16], NDArray[(Any,), UInt16]]: """ This function iterates the closure check until a stable list of interferogram files is returned. :param config: Configuration class instance :param interactive_plot: bool, whether to plot sum closures of loops :return: stable list of ifg files, their ifgs_breach_count, and number of occurrences of ifgs in loops """ params = config.__dict__ ifg_files = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]] i = 1 # iteration counter while True: # iterate till ifgs/loops are stable log.info(f"Closure check iteration #{i}: working on {len(ifg_files)} ifgs") rets = __wrap_closure_check(config) if rets is None: return new_ifg_files, closure, ifgs_breach_count, num_occurences_each_ifg, loops = rets if interactive_plot: if mpiops.rank == 0: plot_closure(closure=closure, loops=loops, config=config, thr=params[C.CLOSURE_THR], iteration=i) if len(ifg_files) == len(new_ifg_files): break else: i += 1 ifg_files = new_ifg_files # exit condition could be some other check like number_of_loops mpiops.comm.barrier() log.info(f"Stable list of ifgs achieved after iteration #{i}. {len(ifg_files)} ifgs are retained") return ifg_files, ifgs_breach_count, num_occurences_each_ifg def discard_loops_containing_max_ifg_count(loops: List[WeightedLoop], params) -> List[WeightedLoop]: """ This function will discard loops when each ifg participating in a loop has met the max loop count criteria. :param loops: list of loops :param params: params dict :return: selected loops satisfying MAX_LOOP_REDUNDANCY criteria """ selected_loops = [] ifg_counter = defaultdict(int) for loop in loops: edge_appearances = np.array([ifg_counter[e] for e in loop.edges]) if not np.all(edge_appearances > params[C.MAX_LOOP_REDUNDANCY]): selected_loops.append(loop) for e in loop.edges: ifg_counter[e] += 1 else: log.debug(f"Loop {loop.loop} ignored: all constituent ifgs have been in a loop " f"{params[C.MAX_LOOP_REDUNDANCY]} times or more") return selected_loops def __wrap_closure_check(config: Configuration) -> \ Tuple[ List[str], NDArray[(Any, Any), Float32], NDArray[(Any, Any, Any), UInt16], NDArray[(Any,), UInt16], List[WeightedLoop]]: """ This wrapper function returns the closure check outputs for a single iteration of closure check. :param config: Configuration class instance For return variables see docstring in `sum_phase_closures`. """ params = config.__dict__ ifg_files = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]] ifg_files.sort() log.debug(f"The number of ifgs in the list is {len(ifg_files)}") sorted_signed_loops = mpiops.run_once(sort_loops_based_on_weights_and_date, params) log.info(f"Total number of selected closed loops with up to MAX_LOOP_LENGTH = " f"{params[C.MAX_LOOP_LENGTH]} edges is {len(sorted_signed_loops)}") if len(sorted_signed_loops) < 1: return None retained_loops = mpiops.run_once(discard_loops_containing_max_ifg_count, sorted_signed_loops, params) ifgs_with_loops = mpiops.run_once(__drop_ifgs_if_not_part_of_any_loop, ifg_files, retained_loops, params) msg = f"After applying MAX_LOOP_REDUNDANCY = {params[C.MAX_LOOP_REDUNDANCY]} criteria, " \ f"{len(retained_loops)} loops are retained" if len(retained_loops) < 1: return None else: log.info(msg) closure, ifgs_breach_count, num_occurences_each_ifg = sum_phase_closures(ifgs_with_loops, retained_loops, params) if mpiops.rank == 0: closure_ins = config.closure() np.save(closure_ins.closure, closure) np.save(closure_ins.ifgs_breach_count, ifgs_breach_count) np.save(closure_ins.num_occurences_each_ifg, num_occurences_each_ifg) np.save(closure_ins.loops, retained_loops, allow_pickle=True) selected_ifg_files = mpiops.run_once(__drop_ifgs_exceeding_threshold, ifgs_with_loops, ifgs_breach_count, num_occurences_each_ifg, params) # update the ifg list in the parameters dictionary params[C.INTERFEROGRAM_FILES] = \ mpiops.run_once(update_ifg_list, selected_ifg_files, params[C.INTERFEROGRAM_FILES]) return selected_ifg_files, closure, ifgs_breach_count, num_occurences_each_ifg, retained_loops # TODO: Consider whether this helper function is better homed in a generic module and used more widely def update_ifg_list(ifg_files: List[str], multi_paths: List[MultiplePaths]) -> List[MultiplePaths]: """ Function to extract full paths for a subsetted list of interferograms :param ifg_files: list of interferograms to subset :param multi_paths: list of full paths for list of original interferograms :return: filtered_multi_paths: list of full paths for the subset """ filtered_multi_paths = [] for m_p in multi_paths: if m_p.tmp_sampled_path in ifg_files: filtered_multi_paths.append(m_p) return filtered_multi_paths ================================================ FILE: pyrate/core/phase_closure/collect_loops.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import List from pyrate.core.logger import pyratelogger as log def dfs(graph, marked, n, vert, start, count, loop, all_loops): """ Depth First Search algorithm to count loops of length n in a given graph. Adapted from https://www.geeksforgeeks.org/print-all-the-cycles-in-an-undirected-graph/ """ # Number of vertices V = graph.shape[0] # mark the vertex vert as visited marked[vert] = True # if the path of length (n-1) is found if n == 0: # mark vert as un-visited to make it usable again. marked[vert] = False # Check if vertex vert can end with vertex start if graph[vert][start] == 1: count += 1 all_loops.append(loop) return count else: return count # For searching every possible path of length (n-1) for i in range(V): if (not marked[i]) and (graph[vert][i] == 1): next_path = loop[:] next_path.append(i) # DFS for searching path by decreasing length by 1 count = dfs(graph, marked, n - 1, i, start, count, next_path, all_loops) # marking vert as unvisited to make it usable again. marked[vert] = False return count def find_loops(graph, loop_length): """Counts and collects loops of a defined length in an undirected and connected graph""" V = graph.shape[0] all_loops = [] # all vertex are marked un-visited initially. marked = [False] * V # Searching for loop by using v-n+1 vertices count = 0 for i in range(V - (loop_length - 1)): count = dfs(graph, marked, loop_length - 1, i, i, count, [i], all_loops) # ith vertex is marked as visited and will not be visited again. marked[i] = True log.debug(f"Total possible number of loops of length {loop_length} is {count}") return count, all_loops def dedupe_loops(loops: List[List]) -> List: """ Deduplication of loops with same members. For example: for nodes 1, 2, 3 in the graph below [ [0, 1, 1], [1, 0, 1], [1, 1, 0], ] Loops of length 3 are 0->1->2 and 0->2->1. We only retain 1 of these loops after dedupe. Example 2: For the following graph: [ [0, 1, 1, 1], [1, 0, 1, 1], [1, 1, 0, 1], [1, 1, 1, 0], ] Pictorially this is the network 1 ---------2 | * * | | * | | * * | 4----------3 Loops of length 4 are: [[0, 1, 2, 3], [0, 1, 3, 2], [0, 2, 1, 3], [0, 2, 3, 1], [0, 3, 1, 2], [0, 3, 2, 1]] After deduplication we will only retain [0, 1, 2, 3] """ seen_sets = set() filtered = [] for l in loops: loop = l[:] l.sort() l = tuple(l) if l not in seen_sets: seen_sets.add(l) filtered.append(loop) return filtered ================================================ FILE: pyrate/core/phase_closure/correct_phase.py ================================================ ================================================ FILE: pyrate/core/phase_closure/mst_closure.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from collections import namedtuple from typing import List, Union from datetime import date import numpy as np import networkx as nx from pyrate.core.shared import dem_or_ifg import pyrate.constants as C from pyrate.core.phase_closure.collect_loops import find_loops, dedupe_loops from pyrate.core.logger import pyratelogger as log Edge = namedtuple('Edge', ['first', 'second']) class SignedEdge: def __init__(self, edge: Edge, sign: int): self.edge = edge self.sign = sign self.first = self.edge.first self.second = self.edge.second def __repr__(self): return f'({self.first}, {self.second})' class SignedWeightedEdge(SignedEdge): def __init__(self, signed_edge: SignedEdge, weight: int): super().__init__(signed_edge.edge, sign=signed_edge.sign) self.signed_edge = signed_edge self.weight = weight class WeightedLoop: """ Loop with weight equal to the sum of the edge weights, where edge weights are the ifg duration in days """ def __init__(self, loop: List[SignedWeightedEdge]): self.loop = loop @property def weight(self): return sum([swe.weight for swe in self.loop]) @property def earliest_date(self): return min({swe.first for swe in self.loop}) @property def primary_dates(self): first_dates = [swe.first for swe in self.loop] first_dates.sort() st = ''.join([str(d) for d in first_dates]) return st @property def secondary_dates(self): first_dates = [swe.second for swe in self.loop] first_dates.sort() st = ''.join([str(d) for d in first_dates]) return st def __len__(self): return len(self.loop) @property def edges(self): return [Edge(swe.first, swe.edge.second) for swe in self.loop] def __find_closed_loops(edges: List[Edge], max_loop_length: int) -> List[List[date]]: g = nx.Graph() edges = [(we.first, we.second) for we in edges] g.add_edges_from(edges) A = nx.adjacency_matrix(g) graph = np.asarray(A.todense()) loops = [] for n in range(3, max_loop_length + 1): log.debug(f"Searching for loops of length {n} using Depth First Search") _, all_loops = find_loops(graph=graph, loop_length=n) loops_ = dedupe_loops(all_loops) log.debug(f"Selected number of loops of length {n} after deduplication is {len(loops_)}") loops.extend(loops_) node_list = g.nodes() node_list_dict = {i: n for i, n in enumerate(node_list)} loop_subset = [] for l in loops: loop = [] for ll in l: loop.append(node_list_dict[ll]) loop_subset.append(loop) log.debug(f"Total number of loops is {len(loop_subset)}") return loop_subset def __add_signs_and_weights_to_loops(loops: List[List[date]], available_edges: List[Edge]) -> List[WeightedLoop]: """ add signs and weights to loops. Additionally, sort the loops (change order of ifgs appearing in loop) by weight and date """ weighted_signed_loops = [] available_edges = set(available_edges) # hash it once for O(1) lookup for i, l in enumerate(loops): weighted_signed_loop = [] l.append(l[0]) # add the closure loop for ii, ll in enumerate(l[:-1]): if l[ii+1] > ll: edge = Edge(ll, l[ii+1]) assert edge in available_edges signed_edge = SignedEdge(edge, 1) # opposite direction of ifg else: edge = Edge(l[ii+1], ll) assert edge in available_edges signed_edge = SignedEdge(edge, -1) # in direction of ifg weighted_signed_edge = SignedWeightedEdge( signed_edge, (signed_edge.second - signed_edge.first).days # weight in days ) weighted_signed_loop.append(weighted_signed_edge) # sort the loops by minimum first date, and then second date if there are ties weighted_signed_loop.sort(key=lambda x: (x.first, x.second, x.sign)) weighted_loop = WeightedLoop(weighted_signed_loop) weighted_signed_loops.append(weighted_loop) return weighted_signed_loops def __setup_edges(ifg_files: List[str]) -> List[Edge]: ifg_files.sort() ifgs = [dem_or_ifg(i) for i in ifg_files] for i in ifgs: i.open() i.nodata_value = 0 return [Edge(i.first, i.second) for i in ifgs] def __find_signed_closed_loops(params: dict) -> List[WeightedLoop]: ifg_files = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]] ifg_files.sort() log.debug(f"The number of ifgs in the list is {len(ifg_files)}") available_edges = __setup_edges(ifg_files) all_loops = __find_closed_loops(available_edges, max_loop_length=params[C.MAX_LOOP_LENGTH]) # find loops with weights signed_weighted_loops = __add_signs_and_weights_to_loops(all_loops, available_edges) return signed_weighted_loops def sort_loops_based_on_weights_and_date(params: dict) -> List[WeightedLoop]: """ :param params: dict of params :return: list of sorted, signed, and weighted loops """ signed_weighted_loops = __find_signed_closed_loops(params) # sort based on weights and dates signed_weighted_loops.sort(key=lambda x: [x.weight, x.primary_dates, x.secondary_dates]) return signed_weighted_loops ================================================ FILE: pyrate/core/phase_closure/plot_closure.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import List import numpy as np from pathlib import Path from pyrate.core.phase_closure.mst_closure import WeightedLoop from pyrate.core.logger import pyratelogger as log from pyrate.configuration import Configuration def plot_closure(closure: np.ndarray, loops: List[WeightedLoop], config: Configuration, thr: float, iteration: int): thr = thr * np.pi try: import matplotlib.pyplot as plt import matplotlib as mpl from mpl_toolkits.axes_grid1 import make_axes_locatable cmap = mpl.cm.Spectral except ImportError as e: log.warn(ImportError(e)) log.warn("Required plotting packages are not found in environment. " "Closure loop plot will not be generated!!!") return nrows, ncols, n_loops = closure.shape # 49 ifgs per fig plt_rows = 7 plt_cols = 7 plots_per_fig = plt_rows * plt_cols n_figs = n_loops // plots_per_fig + (n_loops % plots_per_fig > 0) all_fig_plots = 1 for fig_i in range(n_figs): fig = plt.figure(figsize=(9*plt_rows, 6*plt_cols)) this_fig_plots = 0 for p_r in range(plt_rows): for p_c in range(plt_cols): if all_fig_plots == n_loops + 1: break ax = fig.add_subplot(plt_rows, plt_cols, plt_cols * p_r + p_c + 1) data = closure[:, :, plt_cols * p_r + p_c + fig_i * plots_per_fig] loop = loops[plt_cols * p_r + p_c + fig_i * plots_per_fig] title = ',\n'.join([repr(l) for l in loop.loop]) im = ax.imshow(data, vmin=-thr, vmax=thr, cmap=cmap) plt.tick_params(axis='both', which='major', labelsize=12) text = ax.set_title(title) text.set_fontsize(16) #text.set_fontsize(min(20, int(n_loops/3))) divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) plt.colorbar(im, cax=cax) this_fig_plots += 1 all_fig_plots += 1 fig.tight_layout() closure_plot_file = Path(config.phase_closure_dir).joinpath(f'closure_loops_iteration_{iteration}_fig_{fig_i}.png') plt.savefig(closure_plot_file, dpi=100) plt.close(fig) log.info(f'{this_fig_plots} closure loops plotted in {closure_plot_file}') ================================================ FILE: pyrate/core/phase_closure/sum_closure.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import resource from collections import namedtuple from typing import List, Dict, Tuple, Any from nptyping import NDArray, Float32, UInt16 import numpy as np import pyrate.constants as C from pyrate.core import mpiops from pyrate.core.shared import Ifg, join_dicts from pyrate.core.phase_closure.mst_closure import Edge, WeightedLoop from pyrate.core.logger import pyratelogger as log IndexedIfg = namedtuple('IndexedIfg', ['index', 'IfgPhase']) class IfgPhase: """ workaround class to only hold phase data for mpi SwigPyObject pickle error """ def __init__(self, phase_data): self.phase_data = phase_data def __create_ifg_edge_dict(ifg_files: List[str], params: dict) -> Dict[Edge, IndexedIfg]: """Returns a dictionary of indexed ifg 'edges'""" ifg_files.sort() ifgs = [Ifg(i) for i in ifg_files] def _func(ifg, index): ifg.open() ifg.nodata_value = params[C.NO_DATA_VALUE] ifg.convert_to_nans() ifg.convert_to_radians() idx_ifg = IndexedIfg(index, IfgPhase(ifg.phase_data)) return idx_ifg process_ifgs = mpiops.array_split(list(enumerate(ifgs))) ret_combined = {} for idx, _ifg in process_ifgs: ret_combined[Edge(_ifg.first, _ifg.second)] = _func(_ifg, idx) _ifg.close() ret_combined = join_dicts(mpiops.comm.allgather(ret_combined)) return ret_combined def sum_phase_closures(ifg_files: List[str], loops: List[WeightedLoop], params: dict) -> \ Tuple[NDArray[(Any, Any, Any), Float32], NDArray[(Any, Any, Any), UInt16], NDArray[(Any,), UInt16]]: """ Compute the closure sum for each pixel in each loop, and count the number of times a pixel contributes to a failed closure loop (where the summed closure is above/below the CLOSURE_THR threshold). :param ifg_files: list of ifg files :param loops: list of loops :param params: params dict :return: Tuple of closure, ifgs_breach_count, num_occurrences_each_ifg closure: summed closure for each loop. ifgs_breach_count: shape=(ifg.shape, n_ifgs) number of times a pixel in an ifg fails the closure check (i.e., has unwrapping error) in all loops under investigation. num_occurrences_each_ifg: frequency of ifg appearance in all loops. """ edge_to_indexed_ifgs = __create_ifg_edge_dict(ifg_files, params) ifgs = [v.IfgPhase for v in edge_to_indexed_ifgs.values()] n_ifgs = len(ifgs) if params[C.PARALLEL]: # rets = Parallel(n_jobs=params[cf.PROCESSES], verbose=joblib_log_level(cf.LOG_LEVEL))( # delayed(__compute_ifgs_breach_count)(ifg0, n_ifgs, weighted_loop, edge_to_indexed_ifgs, params) # for weighted_loop in loops # ) # for k, r in enumerate(rets): # closure_dict[k], ifgs_breach_count_dict[k] = r # TODO: enable multiprocessing - needs pickle error workaround closure = np.zeros(shape=(* ifgs[0].phase_data.shape, len(loops)), dtype=np.float32) ifgs_breach_count = np.zeros(shape=(ifgs[0].phase_data.shape + (n_ifgs,)), dtype=np.uint16) for k, weighted_loop in enumerate(loops): closure[:, :, k], ifgs_breach_count_l = __compute_ifgs_breach_count(weighted_loop, edge_to_indexed_ifgs, params) ifgs_breach_count += ifgs_breach_count_l else: process_loops = mpiops.array_split(loops) closure_process = np.zeros(shape=(* ifgs[0].phase_data.shape, len(process_loops)), dtype=np.float32) ifgs_breach_count_process = np.zeros(shape=(ifgs[0].phase_data.shape + (n_ifgs,)), dtype=np.uint16) for k, weighted_loop in enumerate(process_loops): closure_process[:, :, k], ifgs_breach_count_l = \ __compute_ifgs_breach_count(weighted_loop, edge_to_indexed_ifgs, params) ifgs_breach_count_process += ifgs_breach_count_l # process total_gb = mpiops.comm.allreduce(ifgs_breach_count_process.nbytes / 1e9, op=mpiops.MPI.SUM) log.debug(f"Memory usage to compute ifgs_breach_count_process was {total_gb} GB") log.debug(f"shape of ifgs_breach_count_process is {ifgs_breach_count_process.shape}") log.debug(f"dtype of ifgs_breach_count_process is {ifgs_breach_count_process.dtype}") total_gb = mpiops.comm.allreduce(closure_process.nbytes / 1e9, op=mpiops.MPI.SUM) log.debug(f"Memory usage to compute closure_process was {total_gb} GB") if mpiops.rank == 0: ifgs_breach_count = np.zeros(shape=(ifgs[0].phase_data.shape + (n_ifgs,)), dtype=np.uint16) # closure closure = np.zeros(shape=(* ifgs[0].phase_data.shape, len(loops)), dtype=np.float32) main_process_indices = mpiops.array_split(range(len(loops))).astype(np.uint16) closure[:, :, main_process_indices] = closure_process for rank in range(1, mpiops.size): rank_indices = mpiops.array_split(range(len(loops)), rank).astype(np.uint16) this_rank_closure = np.zeros(shape=(* ifgs[0].phase_data.shape, len(rank_indices)), dtype=np.float32) mpiops.comm.Recv(this_rank_closure, source=rank, tag=rank) closure[:, :, rank_indices] = this_rank_closure else: closure = None ifgs_breach_count = None mpiops.comm.Send(closure_process, dest=0, tag=mpiops.rank) if mpiops.MPI_INSTALLED: mpiops.comm.Reduce([ifgs_breach_count_process, mpiops.MPI.UINT16_T], [ifgs_breach_count, mpiops.MPI.UINT16_T], op=mpiops.MPI.SUM, root=0) # global else: ifgs_breach_count = mpiops.comm.reduce(ifgs_breach_count_process, op=mpiops.sum0_op, root=0) log.debug(f"successfully summed phase closure breach array") num_occurrences_each_ifg = None if mpiops.rank == 0: num_occurrences_each_ifg = _find_num_occurrences_each_ifg(loops, edge_to_indexed_ifgs, n_ifgs) return closure, ifgs_breach_count, num_occurrences_each_ifg def _find_num_occurrences_each_ifg(loops: List[WeightedLoop], edge_to_indexed_ifgs: Dict[Edge, IndexedIfg], n_ifgs: int) -> NDArray[(Any,), UInt16]: """find how many times each ifg appears in total in all loops""" num_occurrences_each_ifg = np.zeros(shape=n_ifgs, dtype=np.uint16) for weighted_loop in loops: for signed_edge in weighted_loop.loop: indexed_ifg = edge_to_indexed_ifgs[signed_edge.edge] ifg_index = indexed_ifg.index num_occurrences_each_ifg[ifg_index] += 1 return num_occurrences_each_ifg def __compute_ifgs_breach_count(weighted_loop: WeightedLoop, edge_to_indexed_ifgs: Dict[Edge, IndexedIfg], params: dict) \ -> Tuple[NDArray[(Any, Any), Float32], NDArray[(Any, Any, Any), UInt16]]: """Compute summed `closure` of each loop, and compute `ifgs_breach_count` for each pixel.""" n_ifgs = len(edge_to_indexed_ifgs) indexed_ifg = list(edge_to_indexed_ifgs.values())[0] ifg = indexed_ifg.IfgPhase closure_thr = params[C.CLOSURE_THR] * np.pi use_median = params[C.SUBTRACT_MEDIAN] closure = np.zeros(shape=ifg.phase_data.shape, dtype=np.float32) # initiate variable for check of unwrapping issues at the same pixels in all loops ifgs_breach_count = np.zeros(shape=(ifg.phase_data.shape + (n_ifgs,)), dtype=np.uint16) for signed_edge in weighted_loop.loop: indexed_ifg = edge_to_indexed_ifgs[signed_edge.edge] ifg = indexed_ifg.IfgPhase closure += signed_edge.sign * ifg.phase_data if use_median: closure -= np.nanmedian(closure) # optionally subtract the median closure phase # this will deal with nans in `closure`, i.e., nans are not selected in indices_breaching_threshold indices_breaching_threshold = np.absolute(closure) > closure_thr for signed_edge in weighted_loop.loop: ifg_index = edge_to_indexed_ifgs[signed_edge.edge].index # the variable 'ifgs_breach_count' is increased by 1 for that pixel # make sure we are not incrementing the nan positions in the closure # as we don't know the phase of these pixels and also they were converted to zero before closure check # Therefore, we leave them out of ifgs_breach_count, i.e., we don't increment their ifgs_breach_count values ifgs_breach_count[indices_breaching_threshold, ifg_index] += 1 return closure, ifgs_breach_count ================================================ FILE: pyrate/core/prepifg_helper.py ================================================ # This Python module is part of the PyRate software package # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module converts interferogram input files to a common geotiff format with PyRate specific metadata headers. The module also implements multilooking/downsampling and cropping operations to reduce the size of the computational problem. """ # pylint: disable=too-many-arguments,invalid-name import re from collections import namedtuple from os.path import split from math import modf from numbers import Number from decimal import Decimal from typing import List, Tuple, Union, Callable from numpy import array, nan, isnan, nanmean, float32, zeros, sum as nsum from pyrate.constants import sixteen_digits_pattern, COHERENCE_FILE_PATHS, IFG_CROP_OPT, IFG_LKSX, IFG_LKSY from pyrate.configuration import ConfigException from pyrate.core.gdal_python import crop_resample_average from pyrate.core.shared import dem_or_ifg, Ifg, DEM from pyrate.core.logger import pyratelogger as log CustomExts = namedtuple('CustExtents', ['xfirst', 'yfirst', 'xlast', 'ylast']) # Constants MINIMUM_CROP = 1 MAXIMUM_CROP = 2 CUSTOM_CROP = 3 ALREADY_SAME_SIZE = 4 CROP_OPTIONS = [MINIMUM_CROP, MAXIMUM_CROP, CUSTOM_CROP, ALREADY_SAME_SIZE] GRID_TOL = 1e-6 def get_analysis_extent(crop_opt: int, rasters: List[Union[Ifg, DEM]], xlooks: int, ylooks: int, user_exts: Tuple[float, float, float, float]) -> Tuple[float, float, float, float]: """ Function checks prepifg parameters and returns extents/bounding box. :param int crop_opt: Cropping option :param list rasters: List of either Ifg or DEM class objects :param int xlooks: Number of multi-looks in x :param int ylooks: Number of multi-looks in y :param tuple user_exts: Tuple of user defined cropping coordinates :return: extents: tuple of four bounding coordinates :rtype: tuple """ if crop_opt not in CROP_OPTIONS: raise PreprocessError("Unrecognised crop option: %s" % crop_opt) if crop_opt == CUSTOM_CROP: if not user_exts: raise PreprocessError('No custom cropping extents specified') elif len(user_exts) != 4: # check for required numbers raise PreprocessError('Custom extents must have all 4 values') elif len(user_exts) == 4: # check for non floats if not all([_is_number(z) for z in user_exts]): raise PreprocessError('Custom extents must be 4 numbers') _check_looks(xlooks, ylooks) _check_resolution(rasters) return _get_extents(rasters, crop_opt, user_exts) def _is_number(s): """ Check whether string can be converted to float """ try: float(s) return True except TypeError: # for example if 'None' is sent return False except ValueError: # for example if a 'string' is sent return False def _check_looks(xlooks, ylooks): """ Convenience function to verify that looks parameters are valid. """ if not (isinstance(xlooks, Number) and isinstance(ylooks, Number)): # pragma: no cover msg = "Non-numeric looks parameter(s), x: %s, y: %s" % (xlooks, ylooks) raise PreprocessError(msg) if not (xlooks > 0 and ylooks > 0): # pragma: no cover msg = "Invalid looks parameter(s), x: %s, y: %s. " \ "Looks must be an integer greater than zero" % (xlooks, ylooks) raise PreprocessError(msg) if xlooks != ylooks: log.warning('X and Y multi-look factors are not equal') def _check_resolution(ifgs: List[Union[Ifg, DEM]]): """ Convenience function to verify Ifg resolutions are equal. """ for var in ['x_step', 'y_step']: values = [] for i in ifgs: i.open() values.append(getattr(i, var)) i.close() values = array(values) if not (values == values[0]).all(): # pragma: no cover msg = "Grid resolution does not match for %s" % var raise PreprocessError(msg) def _get_extents(ifgs, crop_opt, user_exts=None): """ Convenience function that returns extents/bounding box. MINIMUM_CROP = 1 MAXIMUM_CROP = 2 CUSTOM_CROP = 3 ALREADY_SAME_SIZE = 4 """ if crop_opt == MINIMUM_CROP: extents = __bounds(ifgs, min) elif crop_opt == MAXIMUM_CROP: extents = __bounds(ifgs, max) elif crop_opt == CUSTOM_CROP: extents = _custom_bounds(ifgs, *user_exts) # only need to check crop coords when custom bounds are supplied _check_crop_coords(ifgs, *extents) else: extents = _get_same_bounds(ifgs) return extents def prepare_ifg(raster_path, xlooks, ylooks, exts, thresh, crop_opt, header, write_to_disk=True, out_path=None, coherence_path=None, coherence_thresh=None): """ Open, resample, crop and optionally save to disk an interferogram or DEM. Returns are only given if write_to_disk=False :param str raster_path: Input raster file path name :param int xlooks: Number of multi-looks in x; 5 is 5 times smaller, 1 is no change :param int ylooks: Number of multi-looks in y :param tuple exts: Tuple of user defined georeferenced extents for new file: (xfirst, yfirst, xlast, ylast)cropping coordinates :param float thresh: NaN fraction threshold below which resampled_data is assigned NaNs :param int crop_opt: Crop option :param bool write_to_disk: Write new data to disk :param str out_path: Path for output file :param dict header: dictionary of metadata from header file :return: resampled_data: output cropped and resampled image :rtype: ndarray :return: out_ds: destination gdal dataset object :rtype: gdal.Dataset """ do_multilook = xlooks > 1 or ylooks > 1 # resolution=None completes faster for non-multilooked layers in gdalwarp resolution = [None, None] raster = dem_or_ifg(raster_path) if not raster.is_open: raster.open() if do_multilook: resolution = [xlooks * raster.x_step, ylooks * raster.y_step] # # Add missing/updated metadata to resampled ifg/DEM # new_lyr = type(ifg)(looks_path) # new_lyr.open(readonly=True) # # for non-DEMs, phase bands need extra metadata & conversions # if hasattr(new_lyr, "phase_band"): # # TODO: LOS conversion to vertical/horizontal (projection) # # TODO: push out to workflow # #if params.has_key(REPROJECTION_FLAG): # # reproject() driver_type = 'GTiff' if write_to_disk else 'MEM' resampled_data, out_ds = crop_resample_average( input_tif=raster.data_path, extents=exts, new_res=resolution, output_file=out_path, thresh=thresh, out_driver_type=driver_type, hdr=header, coherence_path=coherence_path, coherence_thresh=coherence_thresh ) return resampled_data, out_ds # TODO: Not being used. Remove in future? def _resample(data, xscale, yscale, thresh): """ Resamples/averages 'data' to return an array from the averaging of blocks of several tiles in 'data'. NB: Assumes incoherent cells are NaNs. :param data: source array to resample to different size :param xscale: number of cells to average along X axis :param yscale: number of Y axis cells to average :param thresh: minimum allowable proportion of NaN cells (range from 0.0-1.0), eg. 0.25 = 1/4 or more as NaNs results in a NaN value for the output cell. """ # TODO: make more efficient if thresh < 0 or thresh > 1: raise ValueError("threshold must be >= 0 and <= 1") xscale = int(xscale) yscale = int(yscale) ysize, xsize = data.shape xres, yres = int(xsize / xscale), int(ysize / yscale) dest = zeros((yres, xres), dtype=float32) * nan tile_cell_count = xscale * yscale # calc mean without nans (fractional threshold ignores tiles # with excess NaNs) for x in range(xres): for y in range(yres): tile = data[y * yscale: (y + 1) * yscale, x * xscale: (x + 1) * xscale] nan_fraction = nsum(isnan(tile)) / float(tile_cell_count) if nan_fraction < thresh or (nan_fraction == 0 and thresh == 0): dest[y, x] = nanmean(tile) return dest def __bounds(ifgs: List[Ifg], func: Callable) -> Tuple[float, float, float, float]: """ Returns bounds for the total area covered by the given interferograms. """ assert func in [min, max] # no other func allowed opposite_func = min if func is max else max x_firsts = [] y_firsts = [] x_lasts = [] y_lasts = [] for i in ifgs: i.open() x_firsts.append(i.x_first) y_firsts.append(i.y_first) x_lasts.append(i.x_last) y_lasts.append(i.y_last) i.close() xmin = opposite_func(x_firsts) ymax = func(y_firsts) xmax = func(x_lasts) ymin = opposite_func(y_lasts) return xmin, ymin, xmax, ymax def _get_same_bounds(ifgs: List[Ifg]) -> Tuple[float, float, float, float]: """ Check and return bounding box for ALREADY_SAME_SIZE option. """ tfs = [] for i in ifgs: i.open() tfs.append(i.dataset.GetGeoTransform()) i.close() equal = [] for t in tfs[1:]: for i, tf in enumerate(tfs[0]): if round(Decimal(tf), 4) == round(Decimal(t[i]), 4): equal.append(True) else: equal.append(False) if not all(equal): msg = 'Ifgs do not have the same bounding box for crop option: %s' raise PreprocessError(msg % ALREADY_SAME_SIZE) ifg = ifgs[0] ifg.open() xmin, xmax = ifg.x_first, ifg.x_last ymin, ymax = ifg.y_first, ifg.y_last ifg.close() # swap y_first & y_last when using southern hemisphere -ve coords if ymin > ymax: ymin, ymax = ymax, ymin return xmin, ymin, xmax, ymax def _custom_bounds(ifgs, xw, ytop, xe, ybot): """ Check and modify input custom crop bounds to line up with grid interval """ # pylint: disable=too-many-locals # pylint: disable=too-many-branches msg = 'Cropped image bounds are outside the original image bounds' i = ifgs[0] i.open() if ytop < ybot: raise PreprocessError('ERROR Custom crop bounds: ' 'ifgyfirst must be greater than ifgylast') if xe < xw: raise PreprocessError('ERROR Custom crop bounds: ' 'ifgxfirst must be greater than ifgxlast') for par, crop, orig, step in zip(['x_first', 'x_last', 'y_first', 'y_last'], [xw, xe, ytop, ybot], [i.x_first, i.x_last, i.y_first, i.y_last], [i.x_step, i.x_step, i.y_step, i.y_step]): diff = crop - orig nint = round(diff / step) if par == 'x_first': if diff < 0: raise PreprocessError(msg) xmin = orig + (nint * step) elif par == 'x_last': if diff > 0: raise PreprocessError(msg) xmax = orig + (nint * step) elif par == 'y_first': if diff > 0: raise PreprocessError(msg) y1 = orig + (nint * step) elif par == 'y_last': if diff < 0: raise PreprocessError(msg) y2 = orig + (nint * step) else: raise ValueError('Value error in supplied custom bounds') i.close() if y2 > y1: ymin = y1 ymax = y2 else: ymin = y2 ymax = y1 return xmin, ymin, xmax, ymax def _check_crop_coords(ifgs, xmin, ymin, xmax, ymax): """ Ensures cropping coords line up with grid system within tolerance. """ # NB: assumption is the first Ifg is correct, so only test against it i = ifgs[0] i.open() for par, crop, step in zip(['x_first', 'x_last', 'y_first', 'y_last'], [xmin, xmax, ymax, ymin], [i.x_step, i.x_step, i.y_step, i.y_step]): # is diff of the given extent from grid a multiple of X|Y_STEP ? param = getattr(i, par) diff = abs(crop - param) remainder = abs(modf(diff / step)[0]) # handle cases where division gives remainder near zero, or just < 1 if (remainder > GRID_TOL) and (remainder < (1 - GRID_TOL)): # pragma: no cover msg = "%s crop extent not within %s of grid coordinate" raise PreprocessError(msg % (par, GRID_TOL)) i.close() class PreprocessError(Exception): """ Preprocess exception """ def transform_params(params): """ Returns subset of all parameters for cropping and multilooking. :param dict params: Parameter dictionary :return: xlooks, ylooks, crop :rtype: int """ t_params = [IFG_LKSX, IFG_LKSY, IFG_CROP_OPT] xlooks, ylooks, crop = [params[k] for k in t_params] return xlooks, ylooks, crop def coherence_paths_for(path: str, params: dict, tif=False) -> str: """ Returns path to coherence file for given interferogram. Pattern matches based on epoch in filename. Example: '20151025-20160501_eqa_filt.cc' Date pair is the epoch. Args: path: Path to intergerogram to find coherence file for. params: Parameter dictionary. tif: Find converted tif if True (_cc.tif), else find .cc file. Returns: Path to coherence file. """ _, filename = split(path) epoch = re.search(sixteen_digits_pattern, filename).group(0) if tif: coh_file_paths = [f.converted_path for f in params[COHERENCE_FILE_PATHS] if epoch in f.converted_path] else: coh_file_paths = [f.unwrapped_path for f in params[COHERENCE_FILE_PATHS] if epoch in f.unwrapped_path] if len(coh_file_paths) > 1: raise ConfigException(f"found more than one coherence " f"file for '{path}'. There must be only one " f"coherence file per interferogram. Found {coh_file_paths}.") return coh_file_paths[0] ================================================ FILE: pyrate/core/ref_phs_est.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # coding: utf-8 """ This Python module implements a reference phase estimation algorithm. """ from pathlib import Path from typing import List from joblib import Parallel, delayed import numpy as np import pyrate.constants as C from pyrate.core import ifgconstants as ifc, shared from pyrate.core.shared import joblib_log_level, nanmedian, Ifg, nan_and_mm_convert from pyrate.core import mpiops from pyrate.configuration import Configuration from pyrate.core.logger import pyratelogger as log MAIN_PROCESS = 0 def est_ref_phase_patch_median(ifg_paths, params, refpx, refpy): """ Reference phase estimation, calculated as the median within a patch around the supplied reference pixel. :param list ifg_paths: List of interferogram paths or objects. :param dict params: Dictionary of configuration parameters :param int refpx: Reference pixel X found by ref pixel method :param int refpy: Reference pixel Y found by ref pixel method :return: ref_phs: Numpy array of reference phase values of size (nifgs, 1) :rtype: ndarray :return: ifgs: Reference phase data is removed interferograms in place """ half_chip_size = int(np.floor(params[C.REF_CHIP_SIZE] / 2.0)) chipsize = 2 * half_chip_size + 1 thresh = chipsize * chipsize * params[C.REF_MIN_FRAC] def _inner(ifg_paths): if isinstance(ifg_paths[0], Ifg): ifgs = ifg_paths else: ifgs = [Ifg(ifg_path) for ifg_path in ifg_paths] for ifg in ifgs: if not ifg.is_open: ifg.open(readonly=False) phase_data = [i.phase_data for i in ifgs] if params[C.PARALLEL]: ref_phs = Parallel(n_jobs=params[C.PROCESSES], verbose=joblib_log_level(C.LOG_LEVEL))( delayed(_est_ref_phs_patch_median)(p, half_chip_size, refpx, refpy, thresh) for p in phase_data) else: ref_phs = np.zeros(len(ifgs)) for n, ifg in enumerate(ifgs): ref_phs[n] = _est_ref_phs_patch_median(phase_data[n], half_chip_size, refpx, refpy, thresh) return ref_phs process_ifgs_paths = mpiops.array_split(ifg_paths) ref_phs = _inner(process_ifgs_paths) return ref_phs def _est_ref_phs_patch_median(phase_data, half_chip_size, refpx, refpy, thresh): """ Convenience function for ref phs estimate method 2 parallelisation """ patch = phase_data[refpy - half_chip_size: refpy + half_chip_size + 1, refpx - half_chip_size: refpx + half_chip_size + 1] patch = np.reshape(patch, newshape=(-1, 1), order='F') nanfrac = np.sum(~np.isnan(patch)) if nanfrac < thresh: raise ReferencePhaseError('The data window at the reference pixel ' 'does not have enough valid observations. ' 'Actual = {}, Threshold = {}.'.format( nanfrac, thresh)) ref_ph = nanmedian(patch) return ref_ph def est_ref_phase_ifg_median(ifg_paths, params): """ Reference phase estimation, calculated as the median of the whole interferogram image. :param list ifg_paths: List of interferogram paths or objects :param dict params: Dictionary of configuration parameters :return: ref_phs: Numpy array of reference phase values of size (nifgs, 1) :rtype: ndarray :return: ifgs: Reference phase data is removed interferograms in place """ def _process_phase_sum(ifg_paths): if isinstance(ifg_paths[0], Ifg): proc_ifgs = ifg_paths else: proc_ifgs = [Ifg(ifg_path) for ifg_path in ifg_paths] for ifg in proc_ifgs: if not ifg.is_open: ifg.open(readonly=False) ifg_phase_data_sum = np.zeros(proc_ifgs[0].shape, dtype=np.float32) for ifg in proc_ifgs: ifg_phase_data_sum += ifg.phase_data return ifg_phase_data_sum def _inner(proc_ifgs, phase_data_sum): if isinstance(proc_ifgs[0], Ifg): proc_ifgs = proc_ifgs else: proc_ifgs = [Ifg(ifg_path) for ifg_path in proc_ifgs] for ifg in proc_ifgs: if not ifg.is_open: ifg.open(readonly=False) comp = np.isnan(phase_data_sum) comp = np.ravel(comp, order='F') if params[C.PARALLEL]: log.debug("Calculating ref phase using multiprocessing") ref_phs = Parallel(n_jobs=params[C.PROCESSES], verbose=joblib_log_level(C.LOG_LEVEL))( delayed(_est_ref_phs_ifg_median)(p.phase_data, comp) for p in proc_ifgs ) else: log.debug("Calculating ref phase") ref_phs = np.zeros(len(proc_ifgs)) for n, ifg in enumerate(proc_ifgs): ref_phs[n] = _est_ref_phs_ifg_median(ifg.phase_data, comp) return ref_phs process_ifg_paths = mpiops.array_split(ifg_paths) ifg_phase_data_sum = mpiops.comm.allreduce(_process_phase_sum(process_ifg_paths), mpiops.sum_op) ref_phs = _inner(process_ifg_paths, ifg_phase_data_sum) return ref_phs def _est_ref_phs_ifg_median(phase_data, comp): """ Convenience function for ref phs estimate method 1 parallelisation """ ifgv = np.ravel(phase_data, order='F') ifgv[comp == 1] = np.nan return nanmedian(ifgv) def _update_phase_and_metadata(ifgs, ref_phs, params): """ Function that applies the reference phase correction and updates ifg metadata """ def __inner(ifg, ref_ph): ifg.open() # nan-convert and mm-convert before subtracting ref phase nan_and_mm_convert(ifg, params) # add 1e-20 to avoid 0.0 values being converted to NaN downstream (Github issue #310) # TODO: implement a more robust way of avoiding this issue, e.g. using numpy masked # arrays to mark invalid pixel values rather than directly changing values to NaN ifg.phase_data -= ref_ph + 1e-20 ifg.meta_data[ifc.PYRATE_REF_PHASE] = ifc.REF_PHASE_REMOVED ifg.write_modified_phase() log.debug(f"Reference phase corrected for {ifg.data_path}") ifg.close() log.info("Correcting ifgs by subtracting reference phase") for i, rp in zip(mpiops.array_split(ifgs), mpiops.array_split(ref_phs)): __inner(i, rp) class ReferencePhaseError(Exception): """ Generic class for errors in reference phase estimation. """ pass def ref_phase_est_wrapper(params): """ Wrapper for reference phase estimation. """ ifg_paths = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]] refpx, refpy = params[C.REFX_FOUND], params[C.REFY_FOUND] if len(ifg_paths) < 2: raise ReferencePhaseError( "At least two interferograms required for reference phase correction ({len_ifg_paths} " "provided).".format(len_ifg_paths=len(ifg_paths)) ) # this is not going to be true as we now start with fresh multilooked ifg copies - remove? if mpiops.run_once(shared.check_correction_status, ifg_paths, ifc.PYRATE_REF_PHASE): log.warning('Reference phase correction already applied to ifgs; returning') return ifgs = [Ifg(ifg_path) for ifg_path in ifg_paths] # Save reference phase numpy arrays to disk. ref_phs_file = Configuration.ref_phs_file(params) # If ref phase file exists on disk, then reuse - subtract ref_phase from ifgs and return if ref_phs_file.exists(): ref_phs = np.load(ref_phs_file) _update_phase_and_metadata(ifgs, ref_phs, params) shared.save_numpy_phase(ifg_paths, params) return ref_phs, ifgs # determine the reference phase for each ifg if params[C.REF_EST_METHOD] == 1: log.info("Calculating reference phase as median of interferogram") ref_phs = est_ref_phase_ifg_median(ifg_paths, params) elif params[C.REF_EST_METHOD] == 2: log.info('Calculating reference phase in a patch surrounding pixel (x, y): ({}, {})'.format(refpx, refpy)) ref_phs = est_ref_phase_patch_median(ifg_paths, params, refpx, refpy) else: raise ReferencePhaseError("No such option, set parameter 'refest' to '1' or '2'.") # gather all reference phases from distributed processes and save to disk if mpiops.rank == MAIN_PROCESS: collected_ref_phs = np.zeros(len(ifg_paths), dtype=np.float64) process_indices = mpiops.array_split(range(len(ifg_paths))).astype(np.uint16) collected_ref_phs[process_indices] = ref_phs for r in range(1, mpiops.size): process_indices = mpiops.array_split(range(len(ifg_paths)), r).astype(np.uint16) this_process_ref_phs = np.zeros(shape=len(process_indices), dtype=np.float64) mpiops.comm.Recv(this_process_ref_phs, source=r, tag=r) collected_ref_phs[process_indices] = this_process_ref_phs np.save(file=ref_phs_file, arr=collected_ref_phs) else: collected_ref_phs = np.empty(len(ifg_paths), dtype=np.float64) mpiops.comm.Send(ref_phs, dest=MAIN_PROCESS, tag=mpiops.rank) mpiops.comm.Bcast(collected_ref_phs, root=0) # subtract ref_phase from ifgs _update_phase_and_metadata(ifgs, collected_ref_phs, params) mpiops.comm.barrier() shared.save_numpy_phase(ifg_paths, params) log.debug("Finished reference phase correction") # Preserve old return value so tests don't break. return ref_phs, ifgs ================================================ FILE: pyrate/core/refpixel.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module implements an algorithm to search for the location of the interferometric reference pixel """ import os from os.path import join from typing import Tuple from itertools import product import numpy as np from numpy import isnan, std, mean, sum as nsum from joblib import Parallel, delayed import pyrate.constants as C from pyrate.core import ifgconstants as ifc from pyrate.core import mpiops from pyrate.core.shared import Ifg, nan_and_mm_convert from pyrate.core.shared import joblib_log_level from pyrate.core.logger import pyratelogger as log from pyrate.core import prepifg_helper from pyrate.configuration import Configuration, ConfigException MAIN_PROCESS = 0 def update_refpix_metadata(ifg_paths, refx, refy, transform, params): """ Function that adds metadata about the chosen reference pixel to each interferogram. """ pyrate_refpix_lon, pyrate_refpix_lat = mpiops.run_once(convert_pixel_value_to_geographic_coordinate, refx, refy, transform) process_ifgs_paths = mpiops.array_split(ifg_paths) for ifg_file in process_ifgs_paths: log.debug("Updating metadata for: "+ifg_file) ifg = Ifg(ifg_file) log.debug("Open dataset") ifg.open(readonly=True) nan_and_mm_convert(ifg, params) half_patch_size = params["refchipsize"] // 2 x, y = refx, refy log.debug("Extract reference pixel windows") data = ifg.phase_data[y - half_patch_size: y + half_patch_size + 1, x - half_patch_size: x + half_patch_size + 1] log.debug("Calculate standard deviation for reference window") stddev_ref_area = np.nanstd(data) log.debug("Calculate mean for reference window") mean_ref_area = np.nanmean(data) ifg.add_metadata(**{ ifc.PYRATE_REFPIX_X: str(refx), ifc.PYRATE_REFPIX_Y: str(refy), ifc.PYRATE_REFPIX_LAT: str(pyrate_refpix_lat), ifc.PYRATE_REFPIX_LON: str(pyrate_refpix_lon), ifc.PYRATE_MEAN_REF_AREA: str(mean_ref_area), ifc.PYRATE_STDDEV_REF_AREA: str(stddev_ref_area) }) ifg.write_modified_phase() ifg.close() def convert_pixel_value_to_geographic_coordinate(refx, refy, transform): """ Converts a pixel coordinate to a latitude/longitude coordinate given the geotransform of the image. Args: refx: The pixel x coordinate. refy: The pixel ye coordinate. transform: The geotransform array of the image. Returns: Tuple of lon, lat geographic coordinate. """ lon = lon_from_pixel_coordinate(refx, transform) lat = lat_from_pixel_coordinate(refy, transform) return lon, lat def lat_from_pixel_coordinate(refy, transform): yOrigin = transform[3] pixelHeight = -transform[5] lat = yOrigin - refy * pixelHeight return lat def lon_from_pixel_coordinate(refx, transform): xOrigin = transform[0] pixelWidth = transform[1] lon = refx * pixelWidth + xOrigin return lon def convert_geographic_coordinate_to_pixel_value(lon, lat, transform): """ Converts a latitude/longitude coordinate to a pixel coordinate given the geotransform of the image. Args: lon: Pixel longitude. lat: Pixel latitude. transform: The geotransform array of the image. Returns: Tuple of refx, refy pixel coordinates. """ xOrigin = transform[0] yOrigin = transform[3] pixelWidth = transform[1] pixelHeight = -transform[5] refx = round((lon - xOrigin) / pixelWidth) refy = round((yOrigin - lat) / pixelHeight) return int(refx), int(refy) # TODO: move error checking to config step (for fail fast) # TODO: this function is not used. Plan removal def ref_pixel(ifgs, params): """ Determines the most appropriate reference pixel coordinate by conducting a grid search and calculating the mean standard deviation with patches around candidate pixels from the given interferograms. If the config file REFX or REFY values are empty or negative, the search for the reference pixel is performed. If the REFX|Y values are within the bounds of the raster, a search is not performed. REFX|Y values outside the upper bounds cause an exception. :param list ifgs: List of interferogram objects :param dict params: Dictionary of configuration parameters :return: tuple of best REFX and REFY coordinates :rtype: tuple """ half_patch_size, thresh, grid = ref_pixel_setup(ifgs, params) parallel = params[C.PARALLEL] if parallel: phase_data = [i.phase_data for i in ifgs] mean_sds = Parallel(n_jobs=params[C.PROCESSES], verbose=joblib_log_level(C.LOG_LEVEL))( delayed(_ref_pixel_multi)(g, half_patch_size, phase_data, thresh, params) for g in grid) refxy = find_min_mean(mean_sds, grid) else: phase_data = [i.phase_data for i in ifgs] mean_sds = [] for g in grid: mean_sds.append(_ref_pixel_multi(g, half_patch_size, phase_data, thresh, params)) refxy = find_min_mean(mean_sds, grid) if isinstance(refxy, RefPixelError): raise RefPixelError('Refpixel calculation not possible!') refy, refx = refxy if refy and refx: return refy, refx raise RefPixelError("Could not find a reference pixel") def find_min_mean(mean_sds, grid): """ Determine the ref pixel block with minimum mean value :param list mean_sds: List of mean standard deviations from each reference pixel grid :param list grid: List of ref pixel coordinates tuples :return: Tuple of (refy, refx) with minimum mean :rtype: tuple """ log.debug('Ranking ref pixel candidates based on mean values') try: refp_index = np.nanargmin(mean_sds) return grid[refp_index] except RefPixelError as v: log.error(v) return v def ref_pixel_setup(ifgs_or_paths, params): """ Sets up the grid for reference pixel computation and saves numpy files to disk for later use during ref pixel computation. :param list ifgs_or_paths: List of interferogram filenames or Ifg objects :param dict params: Dictionary of configuration parameters :return: half_patch_size: size of patch :rtype: float :return: thresh :rtype: float :return: list(product(ysteps, xsteps)) :rtype: list """ log.debug('Setting up ref pixel computation') refnx, refny, chipsize, min_frac = params[C.REFNX], \ params[C.REFNY], \ params[C.REF_CHIP_SIZE], \ params[C.REF_MIN_FRAC] if len(ifgs_or_paths) < 1: msg = 'Reference pixel search requires 2+ interferograms' raise RefPixelError(msg) if isinstance(ifgs_or_paths[0], str): head = Ifg(ifgs_or_paths[0]) head.open(readonly=True) else: head = ifgs_or_paths[0] # sanity check inputs _validate_chipsize(chipsize, head) _validate_minimum_fraction(min_frac) _validate_search_win(refnx, refny, chipsize, head) # pre-calculate useful amounts half_patch_size = chipsize // 2 chipsize = half_patch_size * 2 + 1 thresh = min_frac * chipsize * chipsize # do window searches across dataset, central pixel of stack with smallest # mean is the reference pixel rows, cols = head.shape ysteps = _step(rows, refny, half_patch_size) xsteps = _step(cols, refnx, half_patch_size) log.debug('Ref pixel setup finished') return half_patch_size, thresh, list(product(ysteps, xsteps)) def save_ref_pixel_blocks(grid, half_patch_size, ifg_paths, params): """ Save reference pixel grid blocks to numpy array files on disk :param list grid: List of tuples (y, x) corresponding to ref pixel grids :param int half_patch_size: patch size in pixels :param list ifg_paths: list of interferogram paths :param dict params: Dictionary of configuration parameters :return: None, file saved to disk """ log.debug('Saving ref pixel blocks') outdir = params[C.TMPDIR] for pth in ifg_paths: ifg = Ifg(pth) ifg.open(readonly=True) ifg.nodata_value = params[C.NO_DATA_VALUE] ifg.convert_to_nans() ifg.convert_to_mm() for y, x in grid: data = ifg.phase_data[y - half_patch_size:y + half_patch_size + 1, x - half_patch_size:x + half_patch_size + 1] data_file = join(outdir, 'ref_phase_data_{b}_{y}_{x}.npy'.format( b=os.path.basename(pth).split('.')[0], y=y, x=x)) np.save(file=data_file, arr=data) ifg.close() log.debug('Saved ref pixel blocks') def _ref_pixel_mpi(process_grid, half_patch_size, ifgs, thresh, params): """ Convenience function for MPI-enabled ref pixel calculation """ log.debug('Ref pixel calculation started') mean_sds = [] for g in process_grid: mean_sds.append(_ref_pixel_multi(g, half_patch_size, ifgs, thresh, params)) return mean_sds def _ref_pixel_multi(g, half_patch_size, phase_data_or_ifg_paths, thresh, params): """ Convenience function for ref pixel optimisation """ # pylint: disable=invalid-name # phase_data_or_ifg is list of ifgs y, x, = g if isinstance(phase_data_or_ifg_paths[0], str): # this consumes a lot less memory # one ifg.phase_data in memory at any time data = [] output_dir = params[C.TMPDIR] for p in phase_data_or_ifg_paths: data_file = os.path.join(output_dir, 'ref_phase_data_{b}_{y}_{x}.npy'.format( b=os.path.basename(p).split('.')[0], y=y, x=x)) data.append(np.load(file=data_file)) else: # phase_data_or_ifg is phase_data list data = [p[y - half_patch_size:y + half_patch_size + 1, x - half_patch_size:x + half_patch_size + 1] for p in phase_data_or_ifg_paths] valid = [nsum(~isnan(d)) > thresh for d in data] if all(valid): # ignore if 1+ ifgs have too many incoherent cells sd = [std(i[~isnan(i)]) for i in data] return mean(sd) else: return np.nan def _step(dim, ref, radius): """ Helper: returns range object of axis indices for a search window. :param int dim: Total length of the grid dimension :param int ref: The desired number of steps :param int radius: The number of cells from the centre of the chip eg. (chipsize / 2) :return: range object of axis indices :rtype: range """ # if ref == 1: # # centre a single search step # return xrange(dim // 2, dim, dim) # fake step to ensure single xrange value # if ref == 2: # handle 2 search windows, method below doesn't cover the case # return [radius, dim-radius-1] # max_dim = dim - (2*radius) # max possible number for refn(x|y) # step = max_dim // (ref-1) step_size = dim // ref return range(radius, dim-radius, step_size) def _validate_chipsize(chipsize, head): """ Sanity check min chipsize """ if chipsize is None: raise ConfigException('Chipsize is None') if chipsize < 3 or chipsize > head.ncols or (chipsize % 2 == 0): msg = "Chipsize setting must be >=3 and at least <= grid width" raise RefPixelError(msg) log.debug('Chipsize validation successful') def _validate_minimum_fraction(min_frac): """ Sanity check min fraction """ if min_frac is None: raise ConfigException('Minimum fraction is None') if min_frac < 0.0 or min_frac > 1.0: raise RefPixelError("Minimum fraction setting must be >= 0.0 and <= 1.0 ") def _validate_search_win(refnx, refny, chipsize, head): """ Sanity check X|Y steps """ if refnx is None: raise ConfigException('refnx is None') max_width = (head.ncols - (chipsize-1)) if refnx < 1 or refnx > max_width: msg = "Invalid refnx setting, must be > 0 and <= %s" raise RefPixelError(msg % max_width) if refny is None: raise ConfigException('refny is None') max_rows = (head.nrows - (chipsize-1)) if refny < 1 or refny > max_rows: msg = "Invalid refny setting, must be > 0 and <= %s" raise RefPixelError(msg % max_rows) def __validate_supplied_lat_lon(params: dict) -> None: """ Function to validate that the user supplied lat/lon values sit within image bounds """ lon, lat = params[C.REFX], params[C.REFY] if lon == -1 or lat == -1: return xmin, ymin, xmax, ymax = prepifg_helper.get_analysis_extent( crop_opt=params[C.IFG_CROP_OPT], rasters=[prepifg_helper.dem_or_ifg(p.sampled_path) for p in params[C.INTERFEROGRAM_FILES]], xlooks=params[C.IFG_LKSX], ylooks=params[C.IFG_LKSY], user_exts=(params[C.IFG_XFIRST], params[C.IFG_YFIRST], params[ C.IFG_XLAST], params[C.IFG_YLAST]) ) msg = "Supplied {} value is outside the bounds of the interferogram data" lat_lon_txt = '' if (lon < xmin) or (lon > xmax): lat_lon_txt += 'longitude' if (lat < ymin) or (lat > ymax): lat_lon_txt += ' and latitude' if lat_lon_txt else 'latitude' if lat_lon_txt: raise RefPixelError(msg.format(lat_lon_txt)) class RefPixelError(Exception): """ Generic exception for reference pixel errors. """ def ref_pixel_calc_wrapper(params: dict) -> Tuple[int, int]: """ Wrapper for reference pixel calculation """ __validate_supplied_lat_lon(params) ifg_paths = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]] lon = params[C.REFX] lat = params[C.REFY] ifg = Ifg(ifg_paths[0]) ifg.open(readonly=True) # assume all interferograms have same projection and will share the same transform transform = ifg.dataset.GetGeoTransform() ref_pixel_file = Configuration.ref_pixel_path(params) def __reuse_ref_pixel_file_if_exists(): if ref_pixel_file.exists(): refx, refy = np.load(ref_pixel_file) log.info('Reusing pre-calculated ref-pixel values: ({}, {}) from file {}'.format( refx, refy, ref_pixel_file.as_posix())) log.warning("Reusing ref-pixel values from previous run!!!") params[C.REFX_FOUND], params[C.REFY_FOUND] = int(refx), int(refy) return int(refx), int(refy) else: return None, None # read and return refx, refy = mpiops.run_once(__reuse_ref_pixel_file_if_exists) if (refx is not None) and (refy is not None): update_refpix_metadata(ifg_paths, int(refx), int(refy), transform, params) return refx, refy if lon == -1 or lat == -1: log.info('Searching for best reference pixel location') half_patch_size, thresh, grid = ref_pixel_setup(ifg_paths, params) process_grid = mpiops.array_split(grid) save_ref_pixel_blocks(process_grid, half_patch_size, ifg_paths, params) mean_sds = _ref_pixel_mpi(process_grid, half_patch_size, ifg_paths, thresh, params) mean_sds = mpiops.comm.gather(mean_sds, root=0) if mpiops.rank == MAIN_PROCESS: mean_sds = np.hstack(mean_sds) refpixel_returned = mpiops.run_once(find_min_mean, mean_sds, grid) if isinstance(refpixel_returned, ValueError): raise RefPixelError( "Reference pixel calculation returned an all nan slice!\n" "Cannot continue downstream computation. Please change reference pixel algorithm used before " "continuing.") refy, refx = refpixel_returned # row first means first value is latitude log.info('Selected reference pixel coordinate (x, y): ({}, {})'.format(refx, refy)) lon, lat = convert_pixel_value_to_geographic_coordinate(refx, refy, transform) log.info('Selected reference pixel coordinate (lon, lat): ({}, {})'.format(lon, lat)) else: log.info('Using reference pixel from config file (lon, lat): ({}, {})'.format(lon, lat)) log.warning("Ensure user supplied reference pixel values are in lon/lat") refx, refy = convert_geographic_coordinate_to_pixel_value(lon, lat, transform) log.info('Converted reference pixel coordinate (x, y): ({}, {})'.format(refx, refy)) np.save(file=ref_pixel_file, arr=[int(refx), int(refy)]) update_refpix_metadata(ifg_paths, refx, refy, transform, params) log.debug("refpx, refpy: "+str(refx) + " " + str(refy)) ifg.close() params[C.REFX_FOUND], params[C.REFY_FOUND] = int(refx), int(refy) return int(refx), int(refy) ================================================ FILE: pyrate/core/roipac.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains tools for reading ROI_PAC format input data. """ import os from pathlib import Path import re import datetime import pyrate.constants as C import pyrate.core.ifgconstants as ifc from pyrate.core.shared import extract_epochs_from_filename # ROIPAC RSC header file constants WIDTH = "WIDTH" FILE_LENGTH = "FILE_LENGTH" XMIN = "XMIN" XMAX = "XMAX" YMIN = "YMIN" YMAX = "YMAX" X_FIRST = "X_FIRST" X_STEP = "X_STEP" X_UNIT = "X_UNIT" Y_FIRST = "Y_FIRST" Y_STEP = "Y_STEP" Y_UNIT = "Y_UNIT" TIME_SPAN_YEAR = "TIME_SPAN_YEAR" # Old ROIPAC headers (may not be needed) ORBIT_NUMBER = "ORBIT_NUMBER" VELOCITY = "VELOCITY" HEIGHT = "HEIGHT" EARTH_RADIUS = "EARTH_RADIUS" WAVELENGTH = "WAVELENGTH" DATE = "DATE" DATE12 = "DATE12" HEADING_DEG = "HEADING_DEG" # DEM specific Z_OFFSET = "Z_OFFSET" Z_SCALE = "Z_SCALE" PROJECTION = "PROJECTION" DATUM = "DATUM" # custom header aliases FIRST = "FIRST" SECOND = "SECOND" X_LAST = "X_LAST" Y_LAST = "Y_LAST" RADIANS = "RADIANS" ROIPAC = "ROIPAC" # store type for each of the header items INT_HEADERS = [WIDTH, FILE_LENGTH, XMIN, XMAX, YMIN, YMAX, Z_OFFSET, Z_SCALE] STR_HEADERS = [X_UNIT, Y_UNIT, ORBIT_NUMBER, DATUM, PROJECTION] FLOAT_HEADERS = [X_FIRST, X_STEP, Y_FIRST, Y_STEP, TIME_SPAN_YEAR, VELOCITY, HEIGHT, EARTH_RADIUS, WAVELENGTH, HEADING_DEG] DATE_HEADERS = [DATE, DATE12] ROIPAC_HEADER_LEFT_JUSTIFY = 18 ROI_PAC_HEADER_FILE_EXT = ".rsc" def parse_date(dstr): """ Parses ROI_PAC 'yymmdd' or 'yymmdd-yymmdd' format string to datetime. :param str dstr: 'date' or 'date1-date2' string :return: dstr: datetime string or tuple :rtype: str or tuple """ def to_date(date_str): """convert string to datetime""" year, month, day = [int(date_str[i:i+2]) for i in range(0, 6, 2)] year += 1900 if ((year <= 99) and (year >= 50)) else 2000 return datetime.date(year, month, day) if "-" in dstr: # ranged date return tuple([to_date(d) for d in dstr.split("-")]) else: return to_date(dstr) def parse_header(hdr_file): """ Parses ROI_PAC header file metadata to a dictionary. :param str hdr_file: `path to ROI_PAC *.rsc file` :return: subset: subset of metadata :rtype: dict """ with open(hdr_file, encoding="utf8", errors='ignore') as f: text = f.read() try: lines = [e.split() for e in text.split("\n") if e != ""] headers = dict(lines) is_dem = True if DATUM in headers or Z_SCALE in headers \ or PROJECTION in headers else False if is_dem and DATUM not in headers: msg = 'No "DATUM" parameter in DEM header/resource file' raise RoipacException(msg) except ValueError: msg = "Unable to parse content of %s. Is it a ROIPAC header file?" raise RoipacException(msg % hdr_file) for k in headers.keys(): if k in INT_HEADERS: headers[k] = int(headers[k]) elif k in STR_HEADERS: headers[k] = str(headers[k]) elif k in FLOAT_HEADERS: headers[k] = float(headers[k]) elif k in DATE_HEADERS: headers[k] = parse_date(headers[k]) else: # pragma: no cover pass # ignore other headers # grab a subset for GeoTIFF conversion subset = {ifc.PYRATE_NCOLS: headers[WIDTH], ifc.PYRATE_NROWS: headers[FILE_LENGTH], ifc.PYRATE_LAT: headers[Y_FIRST], ifc.PYRATE_LONG: headers[X_FIRST], ifc.PYRATE_X_STEP: headers[X_STEP], ifc.PYRATE_Y_STEP: headers[Y_STEP]} if is_dem: subset[ifc.PYRATE_DATUM] = headers[DATUM] else: subset[ifc.PYRATE_WAVELENGTH_METRES] = headers[WAVELENGTH] # grab first/second dates from header, or the filename has_dates = True if DATE in headers and DATE12 in headers else False dates = headers[DATE12] if has_dates else _parse_dates_from(hdr_file) subset[ifc.FIRST_DATE], subset[ifc.SECOND_DATE] = dates # replace time span as ROIPAC is ~4 hours different to (second minus first) timespan = (subset[ifc.SECOND_DATE] - subset[ifc.FIRST_DATE]).days / ifc.DAYS_PER_YEAR subset[ifc.PYRATE_TIME_SPAN] = timespan # Add data units of interferogram subset[ifc.DATA_UNITS] = RADIANS # Add InSAR processor flag subset[ifc.PYRATE_INSAR_PROCESSOR] = ROIPAC # add custom X|Y_LAST for convenience subset[X_LAST] = headers[X_FIRST] + (headers[X_STEP] * (headers[WIDTH])) subset[Y_LAST] = headers[Y_FIRST] + (headers[Y_STEP] * (headers[FILE_LENGTH])) return subset def _parse_dates_from(filename): """Determine dates from file name""" # pylint: disable=invalid-name # process dates from filename if rsc file doesn't have them (skip for DEMs) p = re.compile(r'\d{6}-\d{6}') # match 2 sets of 6 digits separated by '-' m = p.search(filename) if m: s = m.group() min_date_len = 13 # assumes "nnnnnn-nnnnnn" format if len(s) == min_date_len: return parse_date(s) else: # pragma: no cover msg = "Filename does not include first/second image dates: %s" raise RoipacException(msg % filename) def manage_header(header_file, projection): """ Manage header files for ROI_PAC interferograms and DEM files. NB: projection = roipac.parse_header(dem_file)[ifc.PYRATE_DATUM] :param str header_file: `ROI_PAC *.rsc header file path` :param projection: Projection obtained from dem header. :return: combined_header: Combined metadata dictionary :rtype: dict """ header = parse_header(header_file) if ifc.PYRATE_DATUM not in header: # DEM already has DATUM header[ifc.PYRATE_DATUM] = projection header[ifc.DATA_TYPE] = ifc.ORIG # non-cropped, non-multilooked geotiff return header def roipac_header(file_path, params): """ Function to obtain a header for roipac interferogram file or converted geotiff. """ rsc_file = params[C.DEM_HEADER_FILE] p = Path(file_path) if rsc_file is not None: projection = parse_header(rsc_file)[ifc.PYRATE_DATUM] else: raise RoipacException('No DEM resource/header file is provided') if file_path.endswith('_dem.tif'): header_file = os.path.join(params[C.DEM_HEADER_FILE]) elif file_path.endswith('unw_ifg.tif') or file_path.endswith('unw.tif'): # TODO: improve this interferogram_epoches = extract_epochs_from_filename(p.name) for header_path in params[C.HEADER_FILE_PATHS]: h = Path(header_path.unwrapped_path) header_epochs = extract_epochs_from_filename(h.name) if set(header_epochs).__eq__(set(interferogram_epoches)): header_file = header_path.unwrapped_path break else: header_file = "%s%s" % (file_path, ROI_PAC_HEADER_FILE_EXT) header = manage_header(header_file, projection) return header class RoipacException(Exception): """ Convenience class for throwing exception """ ================================================ FILE: pyrate/core/shared.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # coding: utf-8 """ This Python module contains utilities and classes shared by all other PyRate modules """ # pylint: disable=too-many-lines import re from typing import List, Union, Optional, Iterable, Callable, Dict import errno import math from joblib import Parallel, delayed from math import floor import os from os.path import basename, join from pathlib import Path import struct from datetime import date from itertools import product from enum import Enum import numpy as np from numpy import where, nan, isnan, sum as nsum, isclose import pyproj import pkg_resources import pyrate.constants as C from osgeo import osr, gdal from osgeo.gdalconst import GA_Update, GA_ReadOnly from pyrate.core import ifgconstants as ifc, mpiops from pyrate.core.logger import pyratelogger as log gdal.UseExceptions() VERBOSE = True # Constants PHASE_BAND = 1 RADIANS = 'RADIANS' MILLIMETRES = 'MILLIMETRES' GAMMA = 'GAMMA' ROIPAC = 'ROIPAC' # GDAL projection list GDAL_X_CELLSIZE = 1 GDAL_Y_CELLSIZE = 5 GDAL_X_FIRST = 0 GDAL_Y_FIRST = 3 class InputTypes(Enum): IFG = 'ifg' COH = 'coh' BASE = 'base' LT = 'lt' DEM = 'dem' HEADER = 'header' dir_map = { IFG: C.INTERFEROGRAM_DIR, COH: C.COHERENCE_DIR, DEM: C.GEOMETRY_DIR, } def joblib_log_level(level: str) -> int: """ Convert python log level to joblib int verbosity. """ if level == 'INFO': return 0 else: return 60 def mkdir_p(path): """ Make new directory and create parent directories as necessary. Copied from stackoverflow: http://stackoverflow.com/questions/600268/mkdir-p-functionality-in-python :param str path: Path name for new directory """ try: os.makedirs(path) except OSError as exc: # Python >2.5 if exc.errno == errno.EEXIST and os.path.isdir(path): pass else: raise class RasterBase(object): """ Base class for PyRate GeoTIFF based raster datasets. """ # pylint: disable=missing-docstring # pylint: disable=too-many-instance-attributes def __init__(self, path: Union[gdal.Dataset, str, Path]): if isinstance(path, gdal.Dataset): self.dataset = path # path will be Dataset in this case self.data_path = self.dataset # data_path dummy self.add_geographic_data() else: path = path.as_posix() if isinstance(path, Path) else path self.data_path = path self.dataset = None # for GDAL dataset obj self._readonly = not os.access(path, os.R_OK | os.W_OK) if self._readonly is None: raise NotImplementedError # os.access() has failed? def __str__(self): name = self.__class__.__name__ return "%s('%s')" % (name, self.data_path) def __repr__(self): name = self.__class__.__name__ return "%s('%s')" % (name, self.data_path) def open(self, readonly=None): """ Opens generic raster dataset. """ if self.dataset is not None: msg = "open() already called for %s" % self raise RasterException(msg) if not os.path.exists(self.data_path): raise IOError('The file {path} does not exist. Consider first running prepifg'.format(path=self.data_path)) # unless read only, by default open files as writeable if readonly not in [True, False, None]: raise ValueError("readonly must be True, False or None") if readonly is False and self._readonly is True: raise IOError("Cannot open write protected file for writing") flag = GA_ReadOnly if self._readonly else GA_Update self.dataset = gdal.Open(self.data_path, flag) if self.dataset is None: raise RasterException("Error opening %s" % self.data_path) self.add_geographic_data() def add_geographic_data(self): """ Determine and add geographic data to object """ # add some geographic data self.x_centre = int(self.ncols / 2) self.y_centre = int(self.nrows / 2) self.lat_centre = self.y_first + (self.y_step * self.y_centre) self.long_centre = self.x_first + (self.x_step * self.x_centre) # use cell size from centre of scene self.x_size, self.y_size = cell_size(self.lat_centre, self.long_centre, self.x_step, self.y_step) @property def ncols(self): """ Number of raster columns """ return self.dataset.RasterXSize @property def nrows(self): """ Number of raster rows """ return self.dataset.RasterYSize @property def x_step(self): """ Raster pixel size in X (easting) dimension """ return float(self.dataset.GetGeoTransform()[GDAL_X_CELLSIZE]) @property def y_step(self): """ Raster pixel size in Y (northing) dimension """ return float(self.dataset.GetGeoTransform()[GDAL_Y_CELLSIZE]) @property def x_first(self): """ Raster western bounding coordinate """ return float(self.dataset.GetGeoTransform()[GDAL_X_FIRST]) @property def x_last(self): """ Raster eastern bounding coordinate """ return self.x_first + (self.x_step * self.ncols) @property def y_first(self): """ Raster northern bounding coordinate """ return float(self.dataset.GetGeoTransform()[GDAL_Y_FIRST]) @property def y_last(self): """ Raster southern bounding coordinate """ return self.y_first + (self.y_step * self.nrows) @property def shape(self): """ Returns tuple of (Y,X) shape of the raster (as per numpy.shape). """ return self.dataset.RasterYSize, self.dataset.RasterXSize @property def num_cells(self): """ Total number of pixels in raster dataset """ if self.is_open: return self.dataset.RasterXSize * self.dataset.RasterYSize else: raise RasterException('Dataset not open') @property def is_open(self): """ Returns True if the underlying dataset has been opened by GDAL. """ return self.dataset is not None def close(self): """ Explicitly closes file opened by gdal.Open() This is required in windows, otherwise opened files can not be removed, because windows locks open files. """ if self.is_open: self.dataset = None @property def is_read_only(self): """ Determines file permissions """ return self._readonly def _get_band(self, band): """ Wrapper (with error checking) for GDAL's Band.GetRasterBand() method. :param int band: number of band, starting at 1 """ if self.dataset is not None: return self.dataset.GetRasterBand(band) else: raise RasterException("Raster %s has not been opened" % self.data_path) class Ifg(RasterBase): """ Interferogram (Ifg) class objects; double as a container for interferometric phase raster band data and related data. """ # pylint: disable=too-many-instance-attributes def __init__(self, path: Union[str, Path, gdal.Dataset]): """ Interferogram constructor, for 2-band Ifg raster datasets. :param str path: Path to interferogram file """ RasterBase.__init__(self, path) self._phase_band = None self._phase_data = None self.first = None self.second = None self.nan_converted = False self.mm_converted = False self.meta_data = None self.wavelength = None self._nodata_value = None self.time_span = None def open(self, readonly=None): """ Open raster file. :param bool readonly: True/False, or None to open as underlying file setting """ RasterBase.open(self, readonly) self.initialize() def initialize(self): """ Read basic interferogram properties upon opening interferogram. """ self._init_dates() md = self.dataset.GetMetadata() self.wavelength = float(md[ifc.PYRATE_WAVELENGTH_METRES]) self.meta_data = md self.nan_converted = False # This flag set True after NaN conversion def _init_dates(self): """ Determine first and second image dates, and interferogram timespan """ def _to_date(datestr): year, month, day = [int(i) for i in datestr.split('-')] return date(year, month, day) md = self.dataset.GetMetadata() datestrs = [md[k] for k in [ifc.FIRST_DATE, ifc.SECOND_DATE]] if all(datestrs): self.first, self.second = [_to_date(s) for s in datestrs] self.time_span = (self.second - self.first).days/ifc.DAYS_PER_YEAR else: msg = 'Missing first and/or second date in %s' % self.data_path raise IfgException(msg) def convert_to_nans(self): """ Convert phase data of given value to NaN """ if (self._nodata_value is None) \ or (self.dataset is None): # pragma: no cover msg = 'nodata value needs to be set for nan conversion.' \ 'Use ifg.nodata_value = NoDataValue to set nodata_value' log.warning(msg) raise RasterException(msg) if ((self.dataset.GetMetadataItem(ifc.NAN_STATUS) == ifc.NAN_CONVERTED) or self.nan_converted): self.phase_data = self.phase_data self.nan_converted = True msg = '{}: ignored as previous nan ' \ 'conversion detected'.format(self.data_path) log.debug(msg) return else: self.phase_data = where( isclose(self.phase_data, self._nodata_value, atol=1e-6), nan, self.phase_data) self.meta_data[ifc.NAN_STATUS] = ifc.NAN_CONVERTED self.nan_converted = True # self.write_modified_phase(self.phase_data) @property def phase_band(self): """ Returns a GDAL Band object for the phase band. """ if self._phase_band is None: self._phase_band = self._get_band(PHASE_BAND) return self._phase_band @property def nodata_value(self): """ Determine the no-data value in phase band. """ return self._nodata_value @nodata_value.setter def nodata_value(self, val): """ Set the no-data value for phase band. """ self._nodata_value = val @property def phase_data(self): """ Returns phase band as an array. """ # TODO: enhance this to use x/y offset and size if self._phase_data is None: self._phase_data = self.phase_band.ReadAsArray() return self._phase_data def convert_to_mm(self): """ Convert phase_data units from radians to millimetres. Note: converted phase_data held in memory and not written to disc (see shared.write_modified_phase) """ if self.dataset.GetMetadataItem(ifc.DATA_UNITS) == MILLIMETRES: self.mm_converted = True msg = '{}: ignored as phase units are already ' \ 'millimetres'.format(self.data_path) log.debug(msg) return elif self.dataset.GetMetadataItem(ifc.DATA_UNITS) == RADIANS: self.phase_data = convert_radians_to_mm(self.phase_data, self.wavelength) self.meta_data[ifc.DATA_UNITS] = MILLIMETRES self.mm_converted = True # self.write_modified_phase() # otherwise NaN's don't write to bytecode properly # and numpy complains # self.dataset.FlushCache() msg = '{}: converted phase units to millimetres'.format(self.data_path) log.debug(msg) return else: # pragma: no cover msg = 'Phase units are not millimetres or radians' raise IfgException(msg) def convert_to_radians(self): """ Convert phase_data units from millimetres to radians. Note: converted phase_data held in memory and not written to disc (see shared.write_modified_phase) """ if self.meta_data[ifc.DATA_UNITS] == MILLIMETRES: self.phase_data = convert_mm_to_radians(self.phase_data, wavelength=self.wavelength) self.meta_data[ifc.DATA_UNITS] = RADIANS self.mm_converted = False msg = '{}: converted phase units to radians'.format(self.data_path) log.debug(msg) return elif self.meta_data[ifc.DATA_UNITS] == RADIANS: self.mm_converted = False msg = '{}: ignored as phase units are already ' \ 'radians'.format(self.data_path) log.debug(msg) return else: # pragma: no cover msg = 'Phase units are not millimetres or radians' raise IfgException(msg) @phase_data.setter def phase_data(self, data): """ Set phase data value """ self._phase_data = data @property def phase_rows(self): """ Generator returning each row of the phase data. """ for y in range(self.nrows): r = self.phase_band.ReadAsArray(yoff=y, win_xsize=self.ncols, win_ysize=1) yield r[0] # squeezes row from (1, WIDTH) to 1D array @property def nan_count(self): """ Returns total number of NaN cells in the phase data. """ return nsum(isnan(self.phase_data)) @property def nan_fraction(self): """ Returns decimal fraction of NaN cells in the phase band. """ if (self._nodata_value is None) or (self.dataset is None): msg = 'nodata_value needs to be set for nan fraction calc.' \ 'Use ifg.nondata = NoDataValue to set nodata' raise RasterException(msg) # don't cache nan_count as client code may modify phase data nan_count = self.nan_count # handle datasets with no 0 -> NaN replacement if not self.nan_converted and (nan_count == 0): nan_count = nsum(np.isclose(self.phase_data, self._nodata_value, atol=1e-6)) return nan_count / float(self.num_cells) def write_modified_phase(self, data=None): """ Write updated phase data to file on disk. """ if self.is_read_only: raise IOError("Cannot write to read only Ifg") # keep this block # if new_data_path is None: # self.dataset = gdal.Open(self.data_path, GA_Update) # else: # self.dataset = gdal.Open(new_data_path, GA_Update) # self._phase_band = None if data is not None: assert isinstance(data, np.ndarray) data_r, data_c = data.shape assert data_r == self.nrows and data_c == self.ncols self.phase_data = data self.phase_band.WriteArray(self.phase_data) for k, v in self.meta_data.items(): self.dataset.SetMetadataItem(k, v) self.dataset.FlushCache() def add_metadata(self, **kwargs): if (not self.is_open) or self.is_read_only: raise IOError("Ifg not open or readonly. Cannot write!") for k, v in kwargs.items(): self.dataset.SetMetadataItem(k, v) self.dataset.FlushCache() # write to disc class Tile: """ Tile class for containing a sub-part of an interferogram """ def __init__(self, index, top_left, bottom_right): """ Parameters ---------- index: int identifying index of a tile top_left: tuple ifg index of top left of tile bottom_right: tuple ifg index of bottom right of tile """ self.index = index self.top_left = top_left self.bottom_right = bottom_right self.top_left_y, self.top_left_x = top_left self.bottom_right_y, self.bottom_right_x = bottom_right def __str__(self): return "Convenience Tile class containing tile co-ordinates" class IfgPart(object): """ Create a tile (subset) of an Ifg data object """ def __init__(self, ifg_or_path, tile: Tile, ifg_dict=None, params=None): """ Interferogram tile constructor. :param str path: Path to interferogram file """ self.tile = tile self.r_start = self.tile.top_left_y self.r_end = self.tile.bottom_right_y self.c_start = self.tile.top_left_x self.c_end = self.tile.bottom_right_x if ifg_dict is not None: # should be used with MPI ifg = ifg_dict[ifg_or_path] self.nan_fraction = ifg.nan_fraction self.first = ifg.first self.second = ifg.second self.time_span = ifg.time_span phase_file = 'phase_data_{}_{}.npy'.format(basename(ifg_or_path).split('.')[0], tile.index) self.phase_data = np.load(join(params[C.TMPDIR], phase_file)) else: # check if Ifg was sent. if isinstance(ifg_or_path, Ifg): ifg = ifg_or_path else: self.data_path = ifg_or_path ifg = Ifg(ifg_or_path) self.phase_data = None self.nan_fraction = None self.first = None self.second = None self.time_span = None if isinstance(ifg, Ifg): self.read_tile(ifg) def read_tile(self, ifg: Ifg): """ Read interferogram file if not already open. """ if not ifg.is_open: ifg.open(readonly=True) ifg.nodata_value = 0 self.phase_data = ifg.phase_data[self.r_start:self.r_end, self.c_start:self.c_end] self.nan_fraction = ifg.nan_fraction self.first = ifg.first self.second = ifg.second self.time_span = ifg.time_span ifg.phase_data = None ifg.close() # close base ifg @property def nrows(self): """ Determine number of rows in tile. """ return self.r_end - self.r_start @property def ncols(self): """ Determine number of columns in tile. """ return self.c_end - self.c_start class Incidence(RasterBase): # pragma: no cover """ Class for storing viewing geometry data. e.g. incidence and azimuth raster values """ def __init__(self, path): """ Incidence object constructor. """ RasterBase.__init__(self, path) self._incidence_band = None self._azimuth_band = None self._incidence_data = None self._azimuth_data = None @property def incidence_band(self): """ Returns the GDALBand for the incidence angle layer. """ if self._incidence_band is None: self._incidence_band = self._get_band(1) return self._incidence_band @property def incidence_data(self): """ Returns the incidence band as an array. """ if self._incidence_data is None: self._incidence_data = self.incidence_band.ReadAsArray() return self._incidence_data @property def azimuth_band(self): """ Returns the GDALBand for the azimuth layer. """ if self._azimuth_band is None: self._azimuth_band = self._get_band(2) return self._azimuth_band @property def azimuth_data(self): """ Returns the azimuth band as an array. """ if self._azimuth_data is None: self._azimuth_data = self.azimuth_band.ReadAsArray() return self._azimuth_data class TileMixin: def __call__(self, tile: Tile): t = tile return self.data[t.top_left_y:t.bottom_right_y, t.top_left_x:t.bottom_right_x] class DEM(RasterBase, TileMixin): """ Generic raster class for single band DEM/Geometry files. """ def __init__(self, path): """ DEM constructor. """ RasterBase.__init__(self, path) self._band = None self._data = None @property def band(self): """ Returns the GDALBand for the elevation layer. """ if not self.is_open: self.open() if self._band is None: self._band = self._get_band(1) return self._band @property def data(self): """ Returns the geometry band as an array. """ if self._data is None: self._data = self.band.ReadAsArray() return self._data class MemGeometry(TileMixin): def __init__(self, data): """ Set phase data value """ self._data = data @property def data(self): return self._data Geometry = DEM class IfgException(Exception): """ Generic exception class for interferogram errors. """ class RasterException(Exception): """ Generic exception for raster errors. """ class EpochList(object): """ Metadata container for epoch related information. """ def __init__(self, dates=None, repeat=None, spans=None): """ Construct epochlist object """ self.dates = dates # list of unique dates from all the ifgs self.repeat = repeat self.spans = spans # time span from earliest ifg def __str__(self): return "EpochList: %s" % str(self.dates) def __repr__(self): return "EpochList: %s" % repr(self.dates) def convert_radians_to_mm(data, wavelength): """ Function to translates phase in units of radians to units in millimetres. :param ndarray data: Interferogram phase data array :param float wavelength: Radar wavelength in metres :return: data: converted phase data :rtype: ndarray """ return data * ifc.MM_PER_METRE * (wavelength / (4 * math.pi)) def convert_mm_to_radians(data, wavelength): """ Function to translates phase in units of radians to units in millimetres. :param ndarray data: Interferogram phase data array :param float wavelength: Radar wavelength in metres :return: data: converted phase data :rtype: ndarray """ return data / ifc.MM_PER_METRE * ((4 * math.pi) / wavelength) def nanmedian(x): """ Determine the median of values excluding nan values. Use different numpy algorithm dependent on numpy version. :param ndarray x: array of numeric data. :return: y: median value :rtype: float """ # pylint: disable=no-member version = [int(i) for i in pkg_resources.get_distribution("numpy").version.split('.')[:2]] if version[0] == 1 and version[1] > 9: return np.nanmedian(x) else: # pragma: no cover return np.median(x[~np.isnan(x)]) def _is_interferogram(hdr): """ Convenience function to determine if file is interferogram """ return (ifc.PYRATE_WAVELENGTH_METRES in hdr) and \ (hdr[ifc.INPUT_TYPE] == InputTypes.IFG if ifc.INPUT_TYPE in hdr else True) def _is_coherence(hdr): """ Convenience function to determine if file is interferogram """ return (ifc.PYRATE_WAVELENGTH_METRES in hdr) and \ (hdr[ifc.INPUT_TYPE] == InputTypes.COH if ifc.INPUT_TYPE in hdr else False) def _is_baseline(hdr): """ Convenience function to determine if file is baseline file """ return (ifc.PYRATE_WAVELENGTH_METRES in hdr) and \ (hdr[ifc.INPUT_TYPE] == InputTypes.BASE if ifc.INPUT_TYPE in hdr else False) def _is_lookuptable(hdr): """ Convenience function to determine if file is lookup table file """ return (ifc.PYRATE_WAVELENGTH_METRES in hdr) and \ (hdr[ifc.INPUT_TYPE] == InputTypes.LT if ifc.INPUT_TYPE in hdr else False) def _is_incidence(hdr): """ Convenience function to determine if incidence file """ return 'FILE_TYPE' in hdr def write_fullres_geotiff(header, data_path, dest, nodata): # pylint: disable=too-many-statements """ Creates a copy of input image data (interferograms, DEM, incidence maps etc) in GeoTIFF format with PyRate metadata. :param dict header: Interferogram metadata dictionary :param str data_path: Input file :param str dest: Output destination file :param float nodata: No-data value :return: None, file saved to disk """ # pylint: disable=too-many-branches # pylint: disable=too-many-locals ifg_proc = header[ifc.PYRATE_INSAR_PROCESSOR] ncols = header[ifc.PYRATE_NCOLS] nrows = header[ifc.PYRATE_NROWS] bytes_per_col, fmtstr = data_format(ifg_proc, _is_interferogram(header), ncols) if _is_interferogram(header) and ifg_proc == ROIPAC: # roipac ifg has 2 bands _check_raw_data(bytes_per_col*2, data_path, ncols, nrows) else: _check_raw_data(bytes_per_col, data_path, ncols, nrows) # position and projection data gt = [header[ifc.PYRATE_LONG], header[ifc.PYRATE_X_STEP], 0, header[ifc.PYRATE_LAT], 0, header[ifc.PYRATE_Y_STEP]] srs = osr.SpatialReference() res = srs.SetWellKnownGeogCS(header[ifc.PYRATE_DATUM]) if res: msg = 'Unrecognised projection: %s' % header[ifc.PYRATE_DATUM] raise GeotiffException(msg) wkt = srs.ExportToWkt() dtype = 'float32' if (_is_interferogram(header) or _is_incidence(header) or _is_coherence(header) or \ _is_baseline(header) or _is_lookuptable(header)) else 'int16' # get subset of metadata relevant to PyRate md = collate_metadata(header) # create GDAL object ds = gdal_dataset( dest, ncols, nrows, driver="GTiff", bands=1, dtype=dtype, metadata=md, crs=wkt, geotransform=gt, creation_opts=["compress=packbits"] ) # copy data from the binary file band = ds.GetRasterBand(1) band.SetNoDataValue(nodata) row_bytes = ncols * bytes_per_col with open(data_path, 'rb') as f: for y in range(nrows): if ifg_proc == ROIPAC: if _is_interferogram(header): f.seek(row_bytes, 1) # skip interleaved band 1 data = struct.unpack(fmtstr, f.read(row_bytes)) band.WriteArray(np.array(data).reshape(1, ncols), yoff=y) ds = None # manual close del ds def gdal_dataset(out_fname, columns, rows, driver="GTiff", bands=1, dtype='float32', metadata=None, crs=None, geotransform=None, creation_opts=None): """ Initialises a py-GDAL dataset object for writing image data. """ if dtype == 'float32': gdal_dtype = gdal.GDT_Float32 elif dtype == 'int16': gdal_dtype = gdal.GDT_Int16 else: # assume gdal.GDT val is passed to function gdal_dtype = dtype # create output dataset driver = gdal.GetDriverByName(driver) outds = driver.Create(out_fname, columns, rows, bands, gdal_dtype, options=creation_opts) # geospatial info outds.SetGeoTransform(geotransform) outds.SetProjection(crs) # add metadata if metadata is not None: for k, v in metadata.items(): outds.SetMetadataItem(k, str(v)) return outds def collate_metadata(header): """ Grab metadata relevant to PyRate from input metadata :param dict header: Input file metadata dictionary :return: dict of relevant metadata for PyRate """ md = dict() def __common_ifg_coh_update(header, md): for k in [ifc.PYRATE_WAVELENGTH_METRES, ifc.PYRATE_TIME_SPAN, ifc.PYRATE_INSAR_PROCESSOR, ifc.FIRST_DATE, ifc.SECOND_DATE, ifc.DATA_UNITS]: md.update({k: str(header[k])}) if header[ifc.PYRATE_INSAR_PROCESSOR] == GAMMA: for k in [ifc.FIRST_TIME, ifc.SECOND_TIME, ifc.PYRATE_NROWS, ifc.PYRATE_NCOLS, ifc.PYRATE_INCIDENCE_DEGREES, ifc.PYRATE_HEADING_DEGREES, ifc.PYRATE_AZIMUTH_DEGREES, ifc.PYRATE_RANGE_PIX_METRES, ifc.PYRATE_RANGE_N, ifc.PYRATE_RANGE_LOOKS, ifc.PYRATE_AZIMUTH_PIX_METRES, ifc.PYRATE_AZIMUTH_N, ifc.PYRATE_AZIMUTH_LOOKS, ifc.PYRATE_PRF_HERTZ, ifc.PYRATE_NEAR_RANGE_METRES, ifc.PYRATE_SAR_EARTH_METRES, ifc.PYRATE_SEMI_MAJOR_AXIS_METRES, ifc.PYRATE_SEMI_MINOR_AXIS_METRES]: md.update({k: str(header[k])}) if ifc.PYRATE_BASELINE_T in header: for k in [ifc.PYRATE_BASELINE_T, ifc.PYRATE_BASELINE_C, ifc.PYRATE_BASELINE_N, ifc.PYRATE_BASELINE_RATE_T, ifc.PYRATE_BASELINE_RATE_C, ifc.PYRATE_BASELINE_RATE_N]: md.update({k: str(header[k])}) if _is_coherence(header): __common_ifg_coh_update(header, md) md.update({ifc.DATA_TYPE: ifc.COH}) elif _is_baseline(header): __common_ifg_coh_update(header, md) md.update({ifc.DATA_TYPE: ifc.BASE}) elif _is_interferogram(header): __common_ifg_coh_update(header, md) md.update({ifc.DATA_TYPE: ifc.ORIG}) elif _is_lookuptable(header): md.update({ifc.DATA_TYPE: ifc.LT}) elif _is_incidence(header): md.update({ifc.DATA_TYPE: ifc.INCIDENCE}) else: # must be dem md.update({ifc.DATA_TYPE: ifc.DEM}) return md def data_format(ifg_proc, is_ifg, ncols): """ Convenience function to determine the bytesize and format of input files """ if ifg_proc == GAMMA: fmtstr = '!' + ('f' * ncols) # data format is big endian float32s bytes_per_col = 4 elif ifg_proc == ROIPAC: if is_ifg: fmtstr = '<' + ('f' * ncols) # roipac ifgs are little endian float32s bytes_per_col = 4 else: fmtstr = '<' + ('h' * ncols) # roipac DEM is little endian signed int16 bytes_per_col = 2 else: # pragma: no cover msg = 'Unrecognised InSAR Processor: %s' % ifg_proc raise GeotiffException(msg) return bytes_per_col, fmtstr def _check_raw_data(bytes_per_col, data_path, ncols, nrows): """ Convenience function to check the file size is as expected """ size = ncols * nrows * bytes_per_col act_size = os.stat(data_path).st_size if act_size != size: msg = '%s should have size %s, not %s. Is the correct file being used?' raise GeotiffException(msg % (data_path, size, act_size)) def write_unw_from_data_or_geotiff(geotif_or_data, dest_unw, ifg_proc): """ Function to write numpy array data or a geotiff to a GAMMA-format big-endian float32 .unw file :param str geotif_or_data: path name of geotiff file to convert OR :param ndarray geotif_or_data: numpy array of data to convert :param str dest_unw: destination unw file :param int ifg_proc: processor type, GAMMA=1, ROIPAC=0 :return: None, file saved to disk """ if ifg_proc != 1: raise NotImplementedError('only supports GAMMA format for now') if isinstance(geotif_or_data, str): assert os.path.exists(geotif_or_data), 'make sure geotif exists' ds = gdal.Open(geotif_or_data) data = ds.ReadAsArray() ds = None else: data = geotif_or_data nrows, ncols = data.shape fmtstr = '!' + ('f' * ncols) # data format is big endian float32s with open(dest_unw, 'wb') as f: for y in range(nrows): col_data = struct.pack(fmtstr, *data[y, :]) f.write(col_data) def write_output_geotiff(md, gt, wkt, data, dest, nodata): # pylint: disable=too-many-arguments """ Writes PyRate output data to a GeoTIFF file. :param dict md: Dictionary containing PyRate metadata :param list gt: GDAL geotransform for the data :param list wkt: GDAL projection information for the data :param ndarray data: Output data array to save :param str dest: Destination file name :param float nodata: No data value of data :return None, file saved to disk """ driver = gdal.GetDriverByName("GTiff") nrows, ncols = data.shape ds = driver.Create(dest, ncols, nrows, 1, gdal.GDT_Float32, options=['compress=packbits']) # set spatial reference for geotiff ds.SetGeoTransform(gt) ds.SetProjection(wkt) # set data type metadata ds.SetMetadataItem(ifc.DATA_TYPE, str(md[ifc.DATA_TYPE])) # set other metadata for k in [ifc.SEQUENCE_POSITION, ifc.PYRATE_REFPIX_X, ifc.PYRATE_REFPIX_Y, ifc.PYRATE_REFPIX_LAT, ifc.PYRATE_REFPIX_LON, ifc.PYRATE_MEAN_REF_AREA, ifc.PYRATE_STDDEV_REF_AREA, ifc.EPOCH_DATE, C.LOS_PROJECTION.upper(), C.SIGNAL_POLARITY.upper(), C.VELERROR_NSIG.upper()]: if k in md: ds.SetMetadataItem(k, str(md[k])) # write data to geotiff band = ds.GetRasterBand(1) band.SetNoDataValue(nodata) band.WriteArray(data, 0, 0) del ds def write_geotiff(data, outds, nodata): # pylint: disable=too-many-arguments """ A generic routine for writing a NumPy array to a geotiff. :param ndarray data: Output data array to save :param obj outds: GDAL destination object :param float nodata: No data value of data :return None, file saved to disk """ # only support "2 <= dims <= 3" if data.ndim == 3: count, height, width = data.shape elif data.ndim == 2: height, width = data.shape else: msg = "Only support dimensions of '2 <= dims <= 3'." raise GeotiffException(msg) # write data to geotiff band = outds.GetRasterBand(1) band.SetNoDataValue(nodata) band.WriteArray(data, 0, 0) outds = None band = None del outds del band class GeotiffException(Exception): """ Geotiff exception class """ def create_tiles(shape, nrows=2, ncols=2): """ Return a list of tiles containing nrows x ncols with each tile preserving the physical layout of original array. The number of rows can be changed (increased) such that the resulting tiles with float32's do not exceed 500MB in memory. When the array shape (rows, columns) are not divisible by (nrows, ncols) then some of the array dimensions can change according to numpy.array_split. :param tuple shape: Shape tuple (2-element) of interferogram. :param int nrows: Number of rows of tiles :param int ncols: Number of columns of tiles :return: List of Tile class instances. :rtype: list """ if len(shape) != 2: raise ValueError('shape must be a length 2 tuple') no_y, no_x = shape if ncols > no_x or nrows > no_y: raise ValueError('nrows/cols must be greater than ifg dimensions') col_arr = np.array_split(range(no_x), ncols) row_arr = np.array_split(range(no_y), nrows) return [Tile(i, (r[0], c[0]), (r[-1]+1, c[-1]+1)) for i, (r, c) in enumerate(product(row_arr, col_arr))] def get_tiles(ifg_path, rows, cols) -> List[Tile]: """ Break up the interferograms into smaller tiles based on user supplied rows and columns. :param list ifg_path: List of destination geotiff file names :param int rows: Number of rows to break each interferogram into :param int cols: Number of columns to break each interferogram into :return: tiles: List of shared.Tile instances :rtype: list """ ifg = Ifg(ifg_path) ifg.open(readonly=True) tiles = create_tiles(ifg.shape, nrows=rows, ncols=cols) ifg.close() return tiles def nan_and_mm_convert(ifg, params): """ Perform millimetre and nan conversion on interferogram data :param Ifg instance ifg: Interferogram class instance :param dict params: Dictionary of parameters :return: None, data modified internally """ nan_conversion = params[C.NAN_CONVERSION] if nan_conversion: # nan conversion happens here in networkx mst # if not ifg.nan_converted: ifg.nodata_value = params[C.NO_DATA_VALUE] ifg.convert_to_nans() if not ifg.mm_converted: ifg.convert_to_mm() def cell_size(lat, lon, x_step, y_step): # pylint: disable=invalid-name """ Converts X|Y_STEP in degrees to X & Y cell size in metres. This function depends on PyProj/PROJ4 to implement the function :param float lat: Latitude in degrees :param float lon: Longitude in degrees :param float x_step: Horizontal step size in degrees :param float y_step: Vertical step size in degrees :return: tuple of X and Y cell size floats :rtype: tuple """ if lat > 84.0 or lat < -80: msg = "No UTM zone for polar region: > 84 degrees N or < 80 degrees S. " \ "Provided values are lat: "+str(lat) +" long: " +str(lon) raise ValueError(msg) zone = _utm_zone(lon) p0 = pyproj.Proj(proj='latlong', ellps='WGS84') p1 = pyproj.Proj(proj='utm', zone=zone, ellps='WGS84') x0, y0 = pyproj.transform(p0, p1, lon, lat, errcheck=True) x1, y1 = pyproj.transform(p0, p1, lon + x_step, lat + y_step, errcheck=True) return tuple(abs(e) for e in (x1 - x0, y1 - y0)) def _utm_zone(longitude): """ Returns basic UTM zone for given longitude in degrees. Currently does NOT handle the sub-zoning around Scandanavian countries. See http://www.dmap.co.uk/utmworld.htm """ if longitude == 180: return 60.0 return floor((longitude + 180) / 6.0) + 1 class PrereadIfg: """ Convenience class for handling pre-calculated ifg params """ # pylint: disable=too-many-arguments # pylint: disable=too-many-instance-attributes def __init__(self, path, tmp_path, nan_fraction, first, second, time_span, nrows, ncols, metadata): self.path = path self.tmp_path = tmp_path self.nan_fraction = nan_fraction self.first = first self.second = second self.time_span = time_span self.nrows = nrows self.ncols = ncols self.shape = (nrows, ncols) self.metadata = metadata def save_numpy_phase(ifg_paths, params): """ Split interferogram phase data in to tiles (if they exist in the params dict) and save as numpy array files on disk. :param list ifg_paths: List of strings for interferogram paths :param dict params: Dictionary of configuration parameters :return: None, numpy file saved to disk """ tiles = params['tiles'] outdir = params[C.TMPDIR] if not os.path.exists(outdir): mkdir_p(outdir) for ifg_path in mpiops.array_split(ifg_paths): ifg = Ifg(ifg_path) ifg.open() phase_data = ifg.phase_data bname = basename(ifg_path).split('.')[0] for t in tiles: p_data = phase_data[t.top_left_y:t.bottom_right_y, t.top_left_x:t.bottom_right_x] phase_file = 'phase_data_{}_{}.npy'.format(bname, t.index) np.save(file=join(outdir, phase_file), arr=p_data) ifg.close() mpiops.comm.barrier() log.debug(f'Finished writing phase_data to numpy files in {outdir}') def get_geotiff_header_info(ifg_path): """ Return information from a geotiff interferogram header using GDAL methods. :param str ifg_path: path to interferogram geotiff file :return: md: PyRate metadata :rtype: list :return: gt: GDAL geotransform for the data :rtype: list :return: wkt: GDAL projection information for the data :rtype: list """ ds = gdal.Open(ifg_path) md = ds.GetMetadata() # get metadata for writing on output tifs gt = ds.GetGeoTransform() # get geographical bounds of data wkt = ds.GetProjection() # get projection of data ds = None # close dataset return gt, md, wkt def warp_required(xlooks, ylooks, crop): """ Check if a crop or multi-look operation is required. :param int xlooks: Resampling/multi-looking in x dir :param int ylooks: Resampling/multilooking in y dir :param int crop: Interferogram crop option :return: True if params show rasters need to be cropped and/or resized :rtype: bool """ if xlooks > 1 or ylooks > 1: return True if crop is None: return False return True def check_correction_status(ifgs, meta): # pragma: no cover """ Generic function for checking if a correction has already been performed in a previous run by interrogating PyRate meta data entries :param preread_ifgs: Dictionary of pre-read interferogram information :param str meta: Meta data flag to check for :return: True if correction has been performed, otherwise False :rtype: bool """ def close_all(ifgs): for ifg in ifgs: ifg.close() if not isinstance(ifgs[0], Ifg): ifgs = [Ifg(ifg_path) for ifg_path in ifgs] for ifg in ifgs: if not ifg.is_open: ifg.open() flags = [meta in ifg.meta_data for ifg in ifgs] if all(flags): log.info('Skipped: interferograms already corrected') return True elif not all(flags) and any(flags): log.debug('Detected mix of corrected and uncorrected interferograms') for i, flag in zip(ifgs, flags): if flag: msg = '{}: correction detected'.format(i.data_path) else: msg = '{}: correction NOT detected'.format(i.data_path) log.debug(msg) close_all(ifgs) raise CorrectionStatusError(msg) else: log.debug('Calculating corrections') close_all(ifgs) return False class CorrectionStatusError(Exception): """ Generic class for correction status errors. """ def extract_epochs_from_filename(filename_with_epochs: str) -> List[str]: src_epochs = re.findall(r"(\d{8})", str(filename_with_epochs)) if not len(src_epochs) > 0: src_epochs = re.findall(r"(\d{6})", str(filename_with_epochs)) return src_epochs def mpi_vs_multiprocess_logging(step, params): if mpiops.size > 1: # Over-ride input options if this is an MPI job log.info(f"Running '{step}' step with MPI using {mpiops.size} processes") log.warning("Disabling joblib parallel processing (setting parallel = 0)") params[C.PARALLEL] = 0 else: if params[C.PARALLEL] == 1: log.info(f"Running '{step}' step in parallel using {params[C.PROCESSES]} processes") else: log.info(f"Running '{step}' step in serial") def dem_or_ifg(data_path: str) -> Union[Ifg, DEM]: """ Returns an Ifg or DEM class object from input geotiff file. :param data_path: file path name :return: Interferogram or DEM object from input file :rtype: Ifg or DEM class object """ ds = gdal.Open(data_path) md = ds.GetMetadata() if ifc.FIRST_DATE in md: # ifg return Ifg(data_path) else: return DEM(data_path) ds = None # close the dataset def join_dicts(dicts: List[dict]) -> dict: """ Function to concatenate a list of dictionaries of distinct keys. """ if dicts is None: # pragma: no cover return {} assembled_dict = {k: v for D in dicts for k, v in D.items()} return assembled_dict def iterable_split(func: Callable, iterable: Iterable, params: dict, *args, **kwargs) -> np.ndarray: """ # TODO: a faster version using buffer-provider objects via the uppercase communication method A faster version of iterable/tiles_split is possible when the return values from each process is of the same size and will be addressed in future. In this case a buffer-provider object can be sent between processes using the uppercase communication (like Gather instead of gather) methods which can be significantly faster. """ if params[C.PARALLEL]: ret_combined = {} rets = Parallel( n_jobs=params[C.PROCESSES], verbose=joblib_log_level(C.LOG_LEVEL) )(delayed(func)(t, params, *args, **kwargs) for t in iterable) for i, r in enumerate(rets): ret_combined[i] = r else: iterable_with_index = list(enumerate(iterable)) process_iterables = mpiops.array_split(iterable_with_index) ret_combined = {} for i, t in process_iterables: ret_combined[i] = func(t, params, *args, **kwargs) ret_combined = join_dicts(mpiops.comm.allgather(ret_combined)) ret = np.array([v[1] for v in ret_combined.items()], dtype=object) mpiops.comm.barrier() return ret def tiles_split(func: Callable, params: dict, *args, **kwargs) -> np.ndarray: """ Function to pass tiles of a full array to an array processing function call. :param func: Name of function to pass tiles to. :param params: Dictionary of PyRate configuration parameters; must contain a 'tiles' list """ tiles = params[C.TILES] return iterable_split(func, tiles, params, *args, **kwargs) def output_tiff_filename(inpath: str, outpath: str) -> str: """ Output geotiff filename for a given input filename. :param inpath: Path of input file location. :param outpath: Path of output file location. :return: name: Geotiff filename for the given file. """ fname, ext = os.path.basename(inpath).split('.') outpath = os.path.dirname(inpath) if outpath is None else outpath if ext == 'tif': name = os.path.join(outpath, fname + '.tif') else: name = os.path.join(outpath, fname + '_' + ext + '.tif') return name def remove_file_if_exists(filename: str) -> None: """ Function to remove a file if it already exists. :param filename: Name of file to be removed. """ try: os.remove(filename) except OSError: pass ================================================ FILE: pyrate/core/stack.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module implements pixel-by-pixel rate (velocity) estimation using an iterative weighted least-squares stacking method. """ import os import pickle as cp from scipy.linalg import solve, cholesky, qr, inv from numpy import nan, isnan, sqrt, diag, delete, array, float32, size import numpy as np import pyrate.constants as C from pyrate.core import shared from pyrate.core.shared import tiles_split from pyrate.core.logger import pyratelogger as log from pyrate.configuration import Configuration def stack_rate_array(ifgs, params, vcmt, mst=None): """ This function loops over all interferogram pixels in a 3-dimensional array and estimates the pixel rate (velocity) by applying the iterative weighted least-squares stacking algorithm 'pyrate.core.stack.stack_rate_pixel'. :param Ifg.object ifgs: Sequence of objects containing the interferometric observations :param dict params: Configuration parameters :param ndarray vcmt: Derived positive definite temporal variance covariance matrix :param ndarray mst: Pixel-wise matrix describing the minimum spanning tree network :return: rate: Rate (velocity) map :rtype: ndarray :return: error: Standard deviation of the rate map :rtype: ndarray :return: samples: Number of observations used in rate calculation for each pixel :rtype: ndarray """ nsig, pthresh, cols, error, mst, obs, rate, rows, samples, span = _stack_setup(ifgs, mst, params) # pixel-by-pixel calculation. # nested loops to loop over the 2 image dimensions for i in range(rows): for j in range(cols): rate[i, j], error[i, j], samples[i, j] = stack_rate_pixel(obs[:, i, j], mst[:, i, j], vcmt, span, nsig, pthresh) return rate, params[C.VELERROR_NSIG]*error, samples def mask_rate(rate, error, maxsig): """ Function to mask pixels in the rate and error arrays when the error is greater than the error threshold 'maxsig'. :param ndarray rate: array of pixel rates derived by stacking :param ndarray error: array of errors for the pixel rates :param int maxsig: error threshold for masking (in millimetres). :return: rate: Masked rate (velocity) map :rtype: ndarray :return: error: Masked error (standard deviation) map :rtype: ndarray """ # initialise mask array with existing NaNs mask = ~isnan(error) # original Nan count orig = np.count_nonzero(mask) # determine where error is larger than the maximum sigma threshold mask[mask] &= error[mask] > maxsig # replace values with NaNs rate[mask] = nan error[mask] = nan # calculate percentage of masked pixels nummasked = int(np.count_nonzero(mask)/orig*100) log.info('Percentage of pixels masked = {}%'.format(nummasked)) return rate, error def stack_rate_pixel(obs, mst, vcmt, span, nsig, pthresh): """ Algorithm to estimate the rate (velocity) for a single pixel using iterative weighted least-squares stacking method. :param ndarray obs: Vector of interferometric phase observations for the pixel :param ndarray mst: Vector describing the minimum spanning tree network for the pixel :param ndarray vcmt: Derived positive definite temporal variance covariance matrix :param ndarray span: Vector of interferometric time spans :param int nsig: Threshold for iterative removal of interferometric observations :param int pthresh: Threshold for minimum number of observations for the pixel :return: rate: Estimated rate (velocity) for the pixel :rtype: float64 :return: error: Standard deviation of the observations with respect to the estimated rate :rtype: float64 :return: samples: Number of observations used in the rate estimation for the pixel :rtype: int """ # find the indices of independent ifgs from MST ind = np.nonzero(mst)[0] # only True's in mst are chosen # iterative loop to calculate 'robust' velocity for pixel default_no_samples = len(ind) while len(ind) >= pthresh: # select ifg observations ifgv = obs[ind] # form design matrix from appropriate ifg time spans B = span[:, ind] # Subset of full VCM matrix for selected observations vcm_temp = vcmt[ind, np.vstack(ind)] # Get the lower triangle cholesky decomposition. # V must be positive definite (symmetrical and square) T = cholesky(vcm_temp, 1) # Incorporate inverse of VCM into the design matrix # and observations vector A = solve(T, B.transpose()) b = solve(T, ifgv.transpose()) # Factor the design matrix, incorporate covariances or weights into the # system of equations, and transform the response vector. Q, R, _ = qr(A, mode='economic', pivoting=True) z = Q.conj().transpose().dot(b) # Compute the Lstsq coefficient for the velocity v = solve(R, z) # Compute the model errors err1 = inv(vcm_temp).dot(B.conj().transpose()) err2 = B.dot(err1) err = sqrt(diag(inv(err2))) # Compute the residuals (model minus observations) r = (B * v) - ifgv # determine the ratio of residuals and apriori variances w = cholesky(inv(vcm_temp)) wr = abs(np.dot(w, r.transpose())) # test if maximum ratio is greater than user threshold. max_val = wr.max() if max_val > nsig: # if yes, discard and re-do the calculation. ind = delete(ind, wr.argmax()) else: # if no, save estimate, exit the while loop and go to next pixel return v[0], err[0], ifgv.shape[0] # dummy return for no change return np.nan, np.nan, default_no_samples def _stack_setup(ifgs, mst, params): """ Convenience function for stack rate setup """ # stack rate parameters from config file # n-sigma ratio used to threshold 'model minus observation' residuals nsig = params[C.LR_NSIG] # Pixel threshold; minimum number of observations for a pixel pthresh = params[C.LR_PTHRESH] rows, cols = ifgs[0].phase_data.shape # make 3D block of observations obs = array([np.where(isnan(x.phase_data), 0, x.phase_data) for x in ifgs]) span = array([[x.time_span for x in ifgs]]) # Update MST in case additional NaNs generated by APS filtering if mst is None: # dummy mst if none is passed in mst = ~isnan(obs) else: mst[isnan(obs)] = 0 # preallocate empty arrays (no need to preallocate NaNs) error = np.empty([rows, cols], dtype=float32) rate = np.empty([rows, cols], dtype=float32) samples = np.empty([rows, cols], dtype=np.float32) return nsig, pthresh, cols, error, mst, obs, rate, rows, samples, span def stack_calc_wrapper(params): """ Wrapper for stacking on a set of tiles. """ log.info('Calculating rate map via stacking') if not Configuration.vcmt_path(params).exists(): raise FileNotFoundError("VCMT is not found on disc. Have you run the 'correct' step?") params[C.PREREAD_IFGS] = cp.load(open(Configuration.preread_ifgs(params), 'rb')) params[C.VCMT] = np.load(Configuration.vcmt_path(params)) params[C.TILES] = Configuration.get_tiles(params) tiles_split(_stacking_for_tile, params) log.debug("Finished stacking calc!") def _stacking_for_tile(tile, params): """ Wrapper for stacking calculation on a single tile """ preread_ifgs = params[C.PREREAD_IFGS] vcmt = params[C.VCMT] ifg_paths = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]] output_dir = params[C.TMPDIR] log.debug(f"Stacking of tile {tile.index}") ifg_parts = [shared.IfgPart(p, tile, preread_ifgs, params) for p in ifg_paths] mst_tile = np.load(Configuration.mst_path(params, tile.index)) rate, error, samples = stack_rate_array(ifg_parts, params, vcmt, mst_tile) np.save(file=os.path.join(output_dir, 'stack_rate_{}.npy'.format(tile.index)), arr=rate) np.save(file=os.path.join(output_dir, 'stack_error_{}.npy'.format(tile.index)), arr=error) np.save(file=os.path.join(output_dir, 'stack_samples_{}.npy'.format(tile.index)), arr=samples) ================================================ FILE: pyrate/core/timeseries.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains functions for computing a time series inversion in PyRate. """ # pylint: disable=too-many-locals # pylint: disable=too-many-arguments import os import pickle as cp from numpy import (where, isnan, nan, diff, zeros, float32, cumsum, dot, delete, asarray) from numpy.linalg import matrix_rank, pinv, cholesky import numpy as np from scipy.linalg import qr from scipy.stats import linregress import pyrate.constants as C from pyrate.core.shared import tiles_split from pyrate.core.algorithm import first_second_ids, get_epochs from pyrate.core import mst as mst_module, shared from pyrate.core.logger import pyratelogger as log from pyrate.configuration import Configuration, ConfigException def _time_series_setup(ifgs, params, mst=None): """ Convenience function for setting up time series computation parameters """ if len(ifgs) < 1: msg = 'Time series requires 2+ interferograms' raise TimeSeriesError(msg) # if mst is not a single tree then do interpolation interp = 0 if mst_module.mst_from_ifgs(ifgs)[1] else 1 # Time Series parameters tsmethod = params[C.TIME_SERIES_METHOD] pthresh, smfactor, smorder = _validate_params(params, tsmethod) epochlist = get_epochs(ifgs)[0] nrows = ifgs[0].nrows ncols = ifgs[0].ncols nifgs = len(ifgs) span = diff(epochlist.spans) nepoch = len(epochlist.dates) # epoch number nvelpar = nepoch - 1 # velocity parameters number # nlap = nvelpar - smorder # Laplacian observations number mast_second_ids = first_second_ids(epochlist.dates) ifirst = [mast_second_ids[ifg.first] for ifg in ifgs] isecond = [mast_second_ids[ifg.second] for ifg in ifgs] ifirst = min(ifirst, isecond) isecond = max(ifirst, isecond) b0_mat = zeros((nifgs, nvelpar)) for i in range(nifgs): b0_mat[i, ifirst[i]:isecond[i]] = span[ifirst[i]:isecond[i]] # change the sign if second is earlier than first isign = where(np.atleast_1d(ifirst) > np.atleast_1d(isecond)) b0_mat[isign[0], :] = -b0_mat[isign[0], :] tsvel_matrix = np.empty(shape=(nrows, ncols, nvelpar), dtype=float32) ifg_data = np.zeros((nifgs, nrows, ncols), dtype=float32) for ifg_num in range(nifgs): ifg_data[ifg_num] = ifgs[ifg_num].phase_data if mst is None: mst = ~isnan(ifg_data) return b0_mat, interp, pthresh, smfactor, smorder, tsmethod, ifg_data, \ mst, ncols, nrows, nvelpar, span, tsvel_matrix def _validate_params(params, tsmethod): """ Helper function to validate supplied time series parameters """ if tsmethod == 1 and params[C.TIME_SERIES_SM_ORDER] is None: _missing_option_error(C.TIME_SERIES_SM_ORDER) else: smorder = params[C.TIME_SERIES_SM_ORDER] if tsmethod == 1 and params[C.TIME_SERIES_SM_FACTOR] is None: _missing_option_error(C.TIME_SERIES_SM_FACTOR) else: smfactor = np.power(10, params[C.TIME_SERIES_SM_FACTOR]) if params[C.TIME_SERIES_PTHRESH] is None: _missing_option_error(C.TIME_SERIES_PTHRESH) else: pthresh = params[C.TIME_SERIES_PTHRESH] if pthresh < 0.0 or pthresh > 1000: raise TimeSeriesError( "minimum number of coherent observations for a pixel" " TIME_SERIES_PTHRESH setting must be >= 0.0 and <= 1000") return pthresh, smfactor, smorder def time_series(ifgs, params, vcmt=None, mst=None): """ Calculates the displacement time series from the given interferogram network. Solves the linear least squares system using either the SVD method (similar to the SBAS method implemented by Berardino et al. 2002) or a Finite Difference method using a Laplacian Smoothing operator (similar to the method implemented by Schmidt and Burgmann 2003). The returned outputs are multi-dimensional arrays of size(nrows, ncols, nepochs-1), where: - *nrows* is the number of rows in the ifgs, - *ncols* is the number of columns in the ifgs, and - *nepochs* is the number of unique epochs (dates) :param list ifgs: list of interferogram class objects. :param dict params: Dictionary of configuration parameters :param ndarray vcmt: Positive definite temporal variance covariance matrix :param ndarray mst: [optional] Minimum spanning tree array. :return: tsincr: incremental displacement time series. :rtype: ndarray :return: tscuml: cumulative displacement time series. :rtype: ndarray :return: tsvel_matrix: velocity for each epoch interval. :rtype: ndarray """ b0_mat, interp, p_thresh, sm_factor, sm_order, ts_method, ifg_data, mst, \ ncols, nrows, nvelpar, span, tsvel_matrix = \ _time_series_setup(ifgs, params, mst) # pixel-by-pixel calculation. # nested loops to loop over the 2 image dimensions for row in range(nrows): for col in range(ncols): tsvel_matrix[row, col] = _time_series_pixel( row, col, b0_mat, sm_factor, sm_order, ifg_data, mst, nvelpar, p_thresh, interp, vcmt, ts_method) tsvel_matrix = where(tsvel_matrix == 0, nan, tsvel_matrix) # SB: do the span multiplication as a numpy linalg operation, MUCH faster # not even this is necessary here, perform late for performance tsincr = tsvel_matrix * span tscuml = cumsum(tsincr, 2) # SB: perform this after tsvel_matrix has been nan converted, # saves the step of comparing a large matrix (tsincr) to zero. # tscuml = where(tscuml == 0, nan, tscuml) return tsincr, tscuml, tsvel_matrix def _remove_rank_def_rows(b_mat, nvelpar, ifgv, sel): """ Remove rank deficient rows of design matrix """ _, _, e_var = qr(b_mat, mode='economic', pivoting=True) licols = e_var[matrix_rank(b_mat):nvelpar] [rmrow, _] = where(b_mat[:, licols] != 0) b_mat = delete(b_mat, rmrow, axis=0) ifgv = delete(ifgv, rmrow) sel = delete(sel, rmrow) return b_mat, ifgv, sel, rmrow def _time_series_pixel(row, col, b0_mat, sm_factor, sm_order, ifg_data, mst, nvelpar, p_thresh, interp, vcmt, method): """ Wrapper function to compute time series for single pixel. """ # check pixel for non-redundant ifgs sel = np.nonzero(mst[:, row, col])[0] # trues in mst are chosen if len(sel) >= p_thresh: ifgv = ifg_data[sel, row, col] # make design matrix, b_mat b_mat = b0_mat[sel, :] if interp == 0: # remove rank deficient rows rmrow = asarray([0]) # dummy while len(rmrow) > 0: # if b_mat.shape[0] <=1 then we return nans if b_mat.shape[0] > 1: b_mat, ifgv, sel, rmrow = _remove_rank_def_rows( b_mat, nvelpar, ifgv, sel) else: return np.empty(nvelpar) * np.nan # Some epochs have been deleted; get valid epoch indices velflag = sum(abs(b_mat), 0) # remove corresponding columns in design matrix b_mat = b_mat[:, ~np.isclose(velflag, 0.0)] else: velflag = np.ones(nvelpar) if method == 1: # Use Laplacian smoothing method tsvel = _solve_ts_lap(nvelpar, velflag, ifgv, b_mat, sm_order, sm_factor, sel, vcmt) elif method == 2: # Use SVD method tsvel = _solve_ts_svd(nvelpar, velflag, ifgv, b_mat) else: raise TimeSeriesError("Unrecognised time series method") return tsvel else: return np.empty(nvelpar) * np.nan def _solve_ts_svd(nvelpar, velflag, ifgv, b_mat): """ Solve the linear least squares system using the SVD method. """ # pre-allocate the velocity matrix tsvel = np.empty(nvelpar, dtype=float32) * np.nan # solve least squares equation using Moore-Penrose pseudoinverse tsvel[velflag != 0] = dot(pinv(b_mat), ifgv) return tsvel def _solve_ts_lap(nvelpar, velflag, ifgv, mat_b, smorder, smfactor, sel, vcmt): """ Solve the linear least squares system using the Finite Difference method using a Laplacian Smoothing operator. """ # pylint: disable=invalid-name # Laplacian observations number nlap = nvelpar - smorder # Laplacian smoothing coefficient matrix b_lap0 = np.zeros(shape=(nlap, nvelpar)) for i in range(nlap): if smorder == 1: b_lap0[i, i:i+2] = [-1, 1] else: b_lap0[i, i:i+3] = [1, -2, 1] # Scale the coefficients by Laplacian smoothing factor b_lap0 *= smfactor # Laplacian smoothing design matrix nvelleft = np.count_nonzero(velflag) nlap = nvelleft - smorder # constrain for the first and the last incremental b_lap1 = - np.divide(np.ones(shape=nvelleft), nvelleft - 1) b_lap1[0] = 1.0 b_lapn = - np.divide(np.ones(shape=nvelleft), nvelleft - 1) b_lapn[-1] = 1.0 b_lap = np.empty(shape=(nlap + 2, nvelleft)) b_lap[0, :] = b_lap1 b_lap[1:nlap + 1, :] = b_lap0[0:nlap, 0:nvelleft] b_lap[-1, :] = b_lapn nlap += 2 # add laplacian design matrix to existing design matrix mat_b = np.concatenate((mat_b, b_lap), axis=0) # combine ifg and Laplacian smooth vector v_lap = np.zeros(nlap) obsv = np.concatenate((ifgv, v_lap), axis=0) # make variance-covariance matrix # new covariance matrix, adding the laplacian equations m = len(sel) nobs = m + nlap vcm_tmp = np.eye(nobs) vcm_tmp[:m, :m] = vcmt[sel, np.vstack(sel)] # solve the equation by least-squares # calculate velocities # we get the lower triangle in numpy w = cholesky(pinv(vcm_tmp)).T wb = dot(w, mat_b) wl = dot(w, obsv) x = dot(pinv(wb, rcond=1e-8), wl) # TODO: implement residuals and roughness calculations tsvel = np.empty(nvelpar, dtype=float32) * np.nan tsvel[~np.isclose(velflag, 0.0, atol=1e-8)] = x[:nvelleft] # TODO: implement uncertainty estimates (tserror) return tsvel def linear_rate_pixel(y, t): """ Calculate best fitting linear rate to cumulative displacement time series for one pixel using linear regression. :param ndarray y: 1-dimensional vector of cumulative displacement time series :param ndarray t: 1-dimensional vector of cumulative time at each epoch :return: linrate: Linear rate; gradient of fitted line. :rtype: float :return intercept: Y-axis intercept of fitted line at t = 0 :rtype: float :return: rsquared: R-squared value of the linear regression :rtype: float :return: error: Standard error of the linear regression :rtype: float :return: samples: Number of observations used in linear regression :rtype: int """ # Mask to exclude nan elements mask = ~isnan(y) # remove nan elements from both arrays y = y[mask] try: t = t[mask] except IndexError: raise TimeSeriesError("linear_rate_pixel: y and t are not equal length") # break out of func if not enough time series obs for line fitting nsamp = len(y) if nsamp < 2: return nan, nan, nan, nan, nan # compute linear regression of tscuml linrate, intercept, r_value, p_value, std_err = linregress(t, y) return linrate, intercept, r_value**2, std_err, int(nsamp) def linear_rate_array(tscuml, ifgs, params): """ This function loops over all pixels in a 3-dimensional cumulative time series array and calculates the linear rate (line of best fit) for each pixel using linear regression. :param ndarray tscuml: 3-dimensional cumulative time series array :param list ifgs: list of interferogram class objects. :param dict params: Configuration parameters :return: linrate: Linear rate map from linear regression :rtype: ndarray :return: intercept: Array of Y-axis intercepts from linear regression :rtype: ndarray :return: rsquared: Array of R-squared value of the linear regression :rtype: ndarray :return: error: Array of standard errors of the linear regression :rtype: ndarray :return: samples: Number of observations used in linear regression for each pixel :rtype: ndarray """ b0_mat, interp, p_thresh, sm_factor, sm_order, ts_method, ifg_data, mst, \ ncols, nrows, nvelpar, span, tsvel_matrix = \ _time_series_setup(ifgs, params) epochlist = get_epochs(ifgs)[0] # get cumulative time per epoch t = asarray(epochlist.spans) # test for equal length of input vectors if tscuml.shape[2] != len(t): raise TimeSeriesError("linear_rate_array: tscuml and nepochs are not equal length") # preallocate empty arrays for results linrate = np.empty([nrows, ncols], dtype=float32) rsquared = np.empty([nrows, ncols], dtype=float32) error = np.empty([nrows, ncols], dtype=float32) intercept = np.empty([nrows, ncols], dtype=np.float32) samples = np.empty([nrows, ncols], dtype=np.float32) # pixel-by-pixel calculation. # nested loops to loop over the 2 image dimensions for i in range(nrows): for j in range(ncols): linrate[i, j], intercept[i, j], rsquared[i, j], error[i, j], samples[i, j] = \ linear_rate_pixel(tscuml[i, j, :], t) return linrate, intercept, rsquared, params[C.VELERROR_NSIG]*error, samples def _missing_option_error(option): """ Convenience function for raising similar missing option errors. """ msg = "Missing '%s' option in config file" % option raise ConfigException(msg) class TimeSeriesError(Exception): """ Generic exception for time series errors. """ def timeseries_calc_wrapper(params): """ Wrapper for time series calculation on a set of tiles. """ if params[C.TIME_SERIES_METHOD] == 1: log.info('Calculating time series using Laplacian Smoothing method') elif params[C.TIME_SERIES_METHOD] == 2: log.info('Calculating time series using SVD method') if not Configuration.vcmt_path(params).exists(): raise FileNotFoundError("VCMT is not found on disc. Have you run the 'correct' step?") params[C.PREREAD_IFGS] = cp.load(open(Configuration.preread_ifgs(params), 'rb')) params[C.VCMT] = np.load(Configuration.vcmt_path(params)) params[C.TILES] = Configuration.get_tiles(params) tiles_split(__calc_time_series_for_tile, params) log.debug("Finished timeseries calc!") def __calc_time_series_for_tile(tile, params): """ Wrapper for time series calculation on a single tile """ preread_ifgs = params[C.PREREAD_IFGS] vcmt = params[C.VCMT] ifg_paths = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]] output_dir = params[C.TMPDIR] log.debug(f"Calculating time series for tile {tile.index}") ifg_parts = [shared.IfgPart(p, tile, preread_ifgs, params) for p in ifg_paths] mst_tile = np.load(Configuration.mst_path(params, tile.index)) tsincr, tscuml, _ = time_series(ifg_parts, params, vcmt, mst_tile) np.save(file=os.path.join(output_dir, 'tscuml_{}.npy'.format(tile.index)), arr=tscuml) # optional save of tsincr npy tiles if params["savetsincr"] == 1: np.save(file=os.path.join(output_dir, 'tsincr_{}.npy'.format(tile.index)), arr=tsincr) tscuml = np.insert(tscuml, 0, 0, axis=2) # add zero epoch to tscuml 3D array log.info('Calculating linear regression of cumulative time series') linrate, intercept, r_squared, std_err, samples = linear_rate_array(tscuml, ifg_parts, params) np.save(file=os.path.join(output_dir, 'linear_rate_{}.npy'.format(tile.index)), arr=linrate) np.save(file=os.path.join(output_dir, 'linear_intercept_{}.npy'.format(tile.index)), arr=intercept) np.save(file=os.path.join(output_dir, 'linear_rsquared_{}.npy'.format(tile.index)), arr=r_squared) np.save(file=os.path.join(output_dir, 'linear_error_{}.npy'.format(tile.index)), arr=std_err) np.save(file=os.path.join(output_dir, 'linear_samples_{}.npy'.format(tile.index)), arr=samples) ================================================ FILE: pyrate/correct.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # coding: utf-8 """ This Python module runs the main PyRate correction workflow """ import shutil import os from pathlib import Path import pickle as cp from typing import List import sys import pyrate.constants as C from pyrate.core import (shared, algorithm, mpiops) from pyrate.core.aps import spatio_temporal_filter from pyrate.core.covariance import maxvar_vcm_calc_wrapper from pyrate.core.mst import mst_calc_wrapper from pyrate.core.orbital import orb_fit_calc_wrapper from pyrate.core.dem_error import dem_error_calc_wrapper from pyrate.core.phase_closure.closure_check import iterative_closure_check, mask_pixels_with_unwrapping_errors, \ update_ifg_list from pyrate.core.ref_phs_est import ref_phase_est_wrapper from pyrate.core.refpixel import ref_pixel_calc_wrapper from pyrate.core.shared import PrereadIfg, Ifg, get_tiles, mpi_vs_multiprocess_logging, join_dicts, \ nan_and_mm_convert, save_numpy_phase from pyrate.core.logger import pyratelogger as log from pyrate.configuration import Configuration, MultiplePaths, ConfigException MAIN_PROCESS = 0 def _create_ifg_dict(params): """ Save the preread_ifgs dict with information about the ifgs that are later used for fast loading of Ifg files in IfgPart class :param list dest_tifs: List of destination tifs :param dict params: Config dictionary :param list tiles: List of all Tile instances :return: preread_ifgs: Dictionary containing information regarding interferograms that are used later in workflow :rtype: dict """ dest_tifs = [ifg_path for ifg_path in params[C.INTERFEROGRAM_FILES]] ifgs_dict = {} process_tifs = mpiops.array_split(dest_tifs) for d in process_tifs: ifg = Ifg(d.tmp_sampled_path) # get the writable copy ifg.open() nan_and_mm_convert(ifg, params) ifgs_dict[d.tmp_sampled_path] = PrereadIfg( path=d.sampled_path, tmp_path=d.tmp_sampled_path, nan_fraction=ifg.nan_fraction, first=ifg.first, second=ifg.second, time_span=ifg.time_span, nrows=ifg.nrows, ncols=ifg.ncols, metadata=ifg.meta_data ) ifg.write_modified_phase() # update phase converted to mm ifg.close() ifgs_dict = join_dicts(mpiops.comm.allgather(ifgs_dict)) ifgs_dict = mpiops.run_once(__save_ifgs_dict_with_headers_and_epochs, dest_tifs, ifgs_dict, params, process_tifs) params[C.PREREAD_IFGS] = ifgs_dict return ifgs_dict def __save_ifgs_dict_with_headers_and_epochs(dest_tifs, ifgs_dict, params, process_tifs): tmpdir = params[C.TMPDIR] if not os.path.exists(tmpdir): shared.mkdir_p(tmpdir) preread_ifgs_file = Configuration.preread_ifgs(params) nifgs = len(dest_tifs) # add some extra information that's also useful later gt, md, wkt = shared.get_geotiff_header_info(process_tifs[0].tmp_sampled_path) epochlist = algorithm.get_epochs(ifgs_dict)[0] log.info('Found {} unique epochs in the {} interferogram network'.format(len(epochlist.dates), nifgs)) ifgs_dict['epochlist'] = epochlist ifgs_dict['gt'] = gt ifgs_dict['md'] = md ifgs_dict['wkt'] = wkt # dump ifgs_dict file for later use cp.dump(ifgs_dict, open(preread_ifgs_file, 'wb')) for k in ['gt', 'epochlist', 'md', 'wkt']: ifgs_dict.pop(k) return ifgs_dict def _copy_mlooked(params): """ Make a copy of the multi-looked files in the 'tmp_sampled_path' for manipulation during correct steps """ log.info("Copying input files into tempdir for manipulation during 'correct' steps") mpaths = params[C.INTERFEROGRAM_FILES] process_mpaths = mpiops.array_split(mpaths) for p in process_mpaths: shutil.copy(p.sampled_path, p.tmp_sampled_path) Path(p.tmp_sampled_path).chmod(0o664) # assign write permission as prepifg output is readonly def main(config): """ Top level function to perform PyRate workflow on given interferograms :param dict params: Dictionary of configuration parameters :return: refpt: tuple of reference pixel x and y position :rtype: tuple :return: maxvar: array of maximum variance values of interferograms :rtype: ndarray :return: vcmt: Variance-covariance matrix array :rtype: ndarray """ params = config.__dict__ mpi_vs_multiprocess_logging("correct", params) _copy_mlooked(params) return correct_ifgs(config) def _update_params_with_tiles(params: dict) -> None: ifg_path = params[C.INTERFEROGRAM_FILES][0].tmp_sampled_path rows, cols = params["rows"], params["cols"] tiles = mpiops.run_once(get_tiles, ifg_path, rows, cols) # add tiles to params params[C.TILES] = tiles def phase_closure_wrapper(params: dict, config: Configuration) -> dict: """ This wrapper will run the iterative phase closure check to return a stable list of checked interferograms, and then mask pixels in interferograms that exceed the unwrapping error threshold. :param params: Dictionary of PyRate configuration parameters. :param config: Configuration class instance. :return: params: Updated dictionary of PyRate configuration parameters. """ if not params[C.PHASE_CLOSURE]: log.info("Phase closure correction is not required!") return rets = iterative_closure_check(config) if rets is None: log.info("Zero loops are returned from the iterative closure check.") log.warning("Abandoning phase closure correction without modifying the interferograms.") return ifg_files, ifgs_breach_count, num_occurences_each_ifg = rets # update params with closure checked ifg list params[C.INTERFEROGRAM_FILES] = \ mpiops.run_once(update_ifg_list, ifg_files, params[C.INTERFEROGRAM_FILES]) if mpiops.rank == 0: with open(config.phase_closure_filtered_ifgs_list(params), 'w') as f: lines = [p.converted_path + '\n' for p in params[C.INTERFEROGRAM_FILES]] f.writelines(lines) # mask ifgs with nans where phase unwrap threshold is breached if mpiops.rank == 0: mask_pixels_with_unwrapping_errors(ifgs_breach_count, num_occurences_each_ifg, params) _create_ifg_dict(params) # update the preread_ifgs dict ifg_paths = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]] # update/save the phase_data in the tiled numpy files save_numpy_phase(ifg_paths, params) return params correct_steps = { 'orbfit': orb_fit_calc_wrapper, 'refphase': ref_phase_est_wrapper, 'phase_closure': phase_closure_wrapper, 'demerror': dem_error_calc_wrapper, 'mst': mst_calc_wrapper, 'apscorrect': spatio_temporal_filter, 'maxvar': maxvar_vcm_calc_wrapper, } def correct_ifgs(config: Configuration) -> None: """ Top level function to perform PyRate workflow on given interferograms """ params = config.__dict__ __validate_correct_steps(params) # work out the tiling and add to params dict _update_params_with_tiles(params) # create the preread_ifgs dict for use with tiled data _create_ifg_dict(params) ifg_paths = [ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]] # create initial tiled phase_data numpy files on disc save_numpy_phase(ifg_paths, params) params[C.REFX_FOUND], params[C.REFY_FOUND] = ref_pixel_calc_wrapper(params) # run through the correct steps in user specified sequence for step in params['correct']: if step == 'phase_closure': correct_steps[step](params, config) else: correct_steps[step](params) log.info("Finished 'correct' step") def __validate_correct_steps(params): for step in params['correct']: if step not in correct_steps.keys(): raise ConfigException(f"{step} is not a supported 'correct' step. \n" f"Supported steps are {correct_steps.keys()}") ================================================ FILE: pyrate/default_parameters.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, # Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, # either express or implied. # See the License for the specific language governing permissions and # limitations under the License. PYRATE_DEFAULT_CONFIGURATION = { "ifgfilelist": { "DataType": "path", "DefaultValue": None, "MinValue": None, "MaxValue": None, "PossibleValues": None, "Required": True }, "demfile": { "DataType": "path", "DefaultValue": None, "MinValue": None, "MaxValue": None, "PossibleValues": None, "Required": True }, "demHeaderFile": { "DataType": "path", "DefaultValue": None, "MinValue": None, "MaxValue": None, "PossibleValues": None, "Required": True }, "hdrfilelist": { "DataType": "path", "DefaultValue": None, "MinValue": None, "MaxValue": None, "PossibleValues": None, "Required": True }, "cohfilelist": { "DataType": "path", "DefaultValue": None, "MinValue": None, "MaxValue": None, "PossibleValues": None, "Required": False }, "basefilelist": { "DataType": "path", "DefaultValue": None, "MinValue": None, "MaxValue": None, "PossibleValues": None, "Required": False }, "ltfile": { "DataType": "path", "DefaultValue": None, "MinValue": None, "MaxValue": None, "PossibleValues": None, "Required": False }, "outdir": { "DataType": "path", "DefaultValue": None, "MinValue": None, "MaxValue": None, "PossibleValues": None, "Required": True }, "processor": { "DataType": int, "DefaultValue": None, "MinValue": None, "MaxValue": None, "PossibleValues": [0, 1, 2], "Required": False }, "noDataAveragingThreshold": { "DataType": float, "DefaultValue": 0.0, "MinValue": 0, "MaxValue": None, "PossibleValues": None, "Required": False }, "noDataValue": { "DataType": float, "DefaultValue": 0.0, "MinValue": None, "MaxValue": None, "PossibleValues": None, "Required": False }, "nan_conversion": { "DataType": int, "DefaultValue": 0, "MinValue": None, "MaxValue": None, "PossibleValues": [0, 1], "Required": False }, "largetifs": { "DataType": int, "DefaultValue": 0, "MinValue": None, "MaxValue": None, "PossibleValues": [0, 1], "Required": False }, "parallel": { "DataType": int, "DefaultValue": 0, "MinValue": None, "MaxValue": None, "PossibleValues": [0, 1], "Required": False }, "processes": { "DataType": int, "DefaultValue": 8, "MinValue": 1, "MaxValue": None, "PossibleValues": None, "Required": False }, "cohmask": { "DataType": int, "DefaultValue": 0, "MinValue": None, "MaxValue": None, "PossibleValues": [0, 1], "Required": False }, "cohthresh": { "DataType": float, "DefaultValue": 0.3, "MinValue": 0.0, "MaxValue": 1.0, "PossibleValues": None, "Required": False }, "ifgcropopt": { "DataType": int, "DefaultValue": 4, "MinValue": 1, "MaxValue": 4, "PossibleValues": [1, 2, 3, 4], "Required": False }, "ifglksx": { "DataType": int, "DefaultValue": 1, "MinValue": 1, "MaxValue": None, "PossibleValues": None, "Required": False }, "ifglksy": { "DataType": int, "DefaultValue": 1, "MinValue": 1, "MaxValue": None, "PossibleValues": None, "Required": False }, "ifgxfirst": { "DataType": float, "DefaultValue": None, "MinValue": None, "MaxValue": None, "PossibleValues": None, "Required": False }, "ifgxlast": { "DataType": float, "DefaultValue": None, "MinValue": None, "MaxValue": None, "PossibleValues": None, "Required": False }, "ifgyfirst": { "DataType": float, "DefaultValue": None, "MinValue": None, "MaxValue": None, "PossibleValues": None, "Required": False }, "ifgylast": { "DataType": float, "DefaultValue": None, "MinValue": None, "MaxValue": None, "PossibleValues": None, "Required": False }, "refx": { "DataType": float, "DefaultValue": -1, "MinValue": None, "MaxValue": None, "PossibleValues": None, "Required": False }, "refy": { "DataType": float, "DefaultValue": -1, "MinValue": None, "MaxValue": None, "PossibleValues": None, "Required": False }, "refnx": { "DataType": int, "DefaultValue": 10, "MinValue": 1, "MaxValue": 50, "PossibleValues": None, "Required": False }, "refny": { "DataType": int, "DefaultValue": 10, "MinValue": 1, "MaxValue": 50, "PossibleValues": None, "Required": False }, "refchipsize": { "DataType": int, "DefaultValue": 21, "MinValue": 1, "MaxValue": 101, "PossibleValues": None, "Note": "Must be an odd number.", "Required": False }, "refminfrac": { "DataType": float, "DefaultValue": 0.5, "MinValue": 0.0, "MaxValue": 1.0, "PossibleValues": None, "Required": False }, "refest": { "DataType": int, "DefaultValue": 1, "MinValue": None, "MaxValue": None, "PossibleValues": [1, 2], "Required": False }, "orbfit": { "DataType": int, "DefaultValue": 0, "MinValue": None, "MaxValue": None, "PossibleValues": [0, 1], "Required": False }, "orbfitmethod": { "DataType": int, "DefaultValue": 2, "MinValue": None, "MaxValue": None, "PossibleValues": [1, 2], "Required": False }, "orbfitdegrees": { "DataType": int, "DefaultValue": 1, "MinValue": None, "MaxValue": None, "PossibleValues": [1, 2, 3], "Required": False }, "orbfitlksx": { "DataType": int, "DefaultValue": 10, "MinValue": 1, "MaxValue": None, "PossibleValues": None, "Required": False }, "orbfitlksy": { "DataType": int, "DefaultValue": 10, "MinValue": 1, "MaxValue": None, "PossibleValues": None, "Required": False }, "orbfitintercept": { "DataType": int, "DefaultValue": 1, "MinValue": None, "MaxValue": None, "PossibleValues": [0, 1], "Required": False }, "orbfitoffset": { "DataType": int, "DefaultValue": 0, "MinValue": None, "MaxValue": None, "PossibleValues": [0, 1], "Required": False }, "orbfitscale": { "DataType": int, "DefaultValue": 100, "MinValue": 1, "MaxValue": None, "PossibleValues": None, "Required": False }, "apsest": { "DataType": int, "DefaultValue": 0, "MinValue": None, "MaxValue": None, "PossibleValues": [0, 1], "Required": False }, "slpfcutoff": { "DataType": float, "DefaultValue": 1.0, "MinValue": 0, "MaxValue": None, "PossibleValues": None, "Required": False }, "slpnanfill": { "DataType": int, "DefaultValue": 1, "MinValue": None, "MaxValue": None, "PossibleValues": [0, 1], "Required": False }, "slpnanfill_method": { "DataType": str, "DefaultValue": "nearest", "MinValue": None, "MaxValue": None, "PossibleValues": ["linear", "nearest", "cubic"], "Required": False }, "tlpfcutoff": { "DataType": int, "DefaultValue": 30, "MinValue": 1, "MaxValue": None, "PossibleValues": None, "Required": False }, "tlpfpthr": { "DataType": int, "DefaultValue": 1, "MinValue": 1, "MaxValue": None, "PossibleValues": None, "Required": False }, "phase_closure": { "DataType": int, "DefaultValue": 0, "MinValue": None, "MaxValue": None, "PossibleValues": [0, 1], "Required": False }, "closure_thr": { "DataType": float, "DefaultValue": 0.5, "MinValue": 0.01, "MaxValue": None, "PossibleValues": None, "Required": False }, "ifg_drop_thr": { "DataType": float, "DefaultValue": 0.5, "MinValue": 0.01, "MaxValue": 1.0, "PossibleValues": None, "Required": False }, "min_loops_per_ifg": { "DataType": int, "DefaultValue": 2, "MinValue": 1, "MaxValue": None, "PossibleValues": None, "Required": False }, "max_loop_length": { "DataType": int, "DefaultValue": 4, "MinValue": 3, "MaxValue": None, "PossibleValues": None, "Required": False }, "max_loop_redundancy": { "DataType": int, "DefaultValue": 2, "MinValue": 1, "MaxValue": None, "PossibleValues": None, "Required": False }, "subtract_median": { "DataType": int, "DefaultValue": 1, "MinValue": None, "MaxValue": None, "PossibleValues": [0, 1], "Required": False }, "demerror": { "DataType": int, "DefaultValue": 0, "MinValue": None, "MaxValue": None, "PossibleValues": [0, 1], "Required": False }, "de_pthr": { "DataType": int, "DefaultValue": 10, "MinValue": 4, "MaxValue": None, "PossibleValues": None, "Required": False }, "tsmethod": { "DataType": int, "DefaultValue": 2, "MinValue": None, "MaxValue": None, "PossibleValues": [1, 2], "Required": False }, "smorder": { "DataType": int, "DefaultValue": None, "MinValue": None, "MaxValue": None, "PossibleValues": [1, 2], "Required": False }, "smfactor": { "DataType": float, "DefaultValue": -1.0, "MinValue": -5.0, "MaxValue": 0, "PossibleValues": None, "Required": False }, "ts_pthr": { "DataType": int, "DefaultValue": 3, "MinValue": 1, "MaxValue": 1000, "PossibleValues": None, "Required": False }, "nsig": { "DataType": int, "DefaultValue": 2, "MinValue": 1, "MaxValue": 10, "PossibleValues": None, "Required": False }, "pthr": { "DataType": int, "DefaultValue": 3, "MinValue": 1, "MaxValue": None, "PossibleValues": None, "Required": False }, "maxsig": { "DataType": int, "DefaultValue": 1000, "MinValue": 0, "MaxValue": 1000, "PossibleValues": None, "Required": False }, "savenpy": { "DataType": int, "DefaultValue": 0, "MinValue": 0, "MaxValue": 1, "PossibleValues": [1, 0], "Required": False }, "savetsincr": { "DataType": int, "DefaultValue": 1, "MinValue": 0, "MaxValue": 1, "PossibleValues": [1, 0], "Required": False }, "los_projection": { "DataType": int, "DefaultValue": 0, "MinValue": 0, "MaxValue": 2, "PossibleValues": [2, 1, 0], "Required": False }, "signal_polarity": { "DataType": int, "DefaultValue": -1, "MinValue": -1, "MaxValue": 1, "PossibleValues": [1, -1], "Required": False }, "velerror_nsig": { "DataType": int, "DefaultValue": 2, "MinValue": 1, "MaxValue": None, "PossibleValues": None, "Required": False }, "correct": { "DataType": list, "DefaultValue": ['orbfit', 'refphase', 'demerror', 'mst', 'apscorrect', 'maxvar', 'timeseries', 'stack'], "MinValue": None, "MaxValue": None, "PossibleValues": None, "Required": False } } ================================================ FILE: pyrate/main.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module defines executable run configuration for the PyRate software """ import os import argparse from argparse import RawTextHelpFormatter import time from pathlib import Path import pyrate.constants as C from pyrate.constants import CLI_DESCRIPTION from pyrate import conv2tif, prepifg, correct, merge from pyrate.core.logger import pyratelogger as log, configure_stage_log from pyrate.core import mpiops from pyrate.configuration import Configuration from pyrate.core.shared import mpi_vs_multiprocess_logging from pyrate.core.stack import stack_calc_wrapper from pyrate.core.timeseries import timeseries_calc_wrapper def _params_from_conf(config_file): config_file = os.path.abspath(config_file) config = Configuration(config_file) params = config.__dict__ return params def update_params_due_to_ifg_selection(config): params = config.__dict__ if config.phase_closure_filtered_ifgs_list(params).exists(): params = config.refresh_ifg_list(params) correct._create_ifg_dict(params) correct._update_params_with_tiles(params) return params def main(): start_time = time.time() parser = argparse.ArgumentParser(prog='pyrate', description=CLI_DESCRIPTION, add_help=True, formatter_class=RawTextHelpFormatter) parser.add_argument('-v', '--verbosity', type=str, default='INFO', choices=['DEBUG', 'INFO', 'WARNING', 'ERROR'], help="Increase output verbosity") subparsers = parser.add_subparsers(dest='command') subparsers.required = True parser_conv2tif = subparsers.add_parser('conv2tif', help=' Convert interferograms to geotiff.', add_help=True) parser_prepifg = subparsers.add_parser( 'prepifg', help='Perform multilooking, cropping and coherence masking to interferogram geotiffs.', add_help=True) parser_correct = subparsers.add_parser( 'correct', help='Calculate and apply corrections to interferogram phase data.', add_help=True) parser_ts = subparsers.add_parser( 'timeseries', help=' Timeseries inversion of interferogram phase data.', add_help=True ) parser_stack = subparsers.add_parser('stack', help=' Stacking of interferogram phase data.', add_help=True) parser_merge = subparsers.add_parser( 'merge', help="Reassemble computed tiles and save as geotiffs.", add_help=True) parser_workflow = subparsers.add_parser( 'workflow', help=" Sequentially run all the PyRate processing steps.", add_help=True) for p in [parser_conv2tif, parser_prepifg, parser_correct, parser_merge, parser_ts, parser_stack, parser_workflow]: p.add_argument('-f', '--config_file', action="store", type=str, default=None, help="Pass configuration file", required=False) args = parser.parse_args() params = mpiops.run_once(_params_from_conf, args.config_file) configure_stage_log(args.verbosity, args.command, Path(params[C.OUT_DIR]).joinpath('pyrate.log.').as_posix()) log.debug("Starting PyRate") log.debug("Arguments supplied at command line: ") log.debug(args) if args.verbosity: log.setLevel(args.verbosity) log.info("Verbosity set to " + str(args.verbosity) + ".") if args.command == "conv2tif": conv2tif.main(params) if args.command == "prepifg": prepifg.main(params) if args.command == "correct": config_file = os.path.abspath(args.config_file) config = Configuration(config_file) correct.main(config) if args.command == "timeseries": config_file = os.path.abspath(args.config_file) config = Configuration(config_file) timeseries(config) if args.command == "stack": config_file = os.path.abspath(args.config_file) config = Configuration(config_file) stack(config) if args.command == "merge": merge.main(params) if args.command == "workflow": log.info("***********CONV2TIF**************") conv2tif.main(params) log.info("***********PREPIFG**************") params = mpiops.run_once(_params_from_conf, args.config_file) prepifg.main(params) log.info("***********CORRECT**************") # reset params as prepifg modifies params config_file = os.path.abspath(args.config_file) config = Configuration(config_file) correct.main(config) log.info("***********TIMESERIES**************") config = Configuration(config_file) timeseries(config) log.info("***********STACK**************") config = Configuration(config_file) stack(config) log.info("***********MERGE**************") params = mpiops.run_once(_params_from_conf, args.config_file) merge.main(params) log.info("--- Runtime = %s seconds ---" % (time.time() - start_time)) def timeseries(config: Configuration) -> None: params = config.__dict__ mpi_vs_multiprocess_logging("timeseries", params) params = update_params_due_to_ifg_selection(config=config) timeseries_calc_wrapper(params) def stack(config: Configuration) -> None: params = config.__dict__ mpi_vs_multiprocess_logging("stack", params) params = update_params_due_to_ifg_selection(config=config) stack_calc_wrapper(params) if __name__ == "__main__": main() ================================================ FILE: pyrate/merge.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module does post-processing steps to assemble the stack rate and time series outputs and save as geotiff files """ from os.path import join, isfile, exists import pickle import numpy as np from osgeo import gdal import subprocess from pathlib import Path from typing import Optional, Tuple import pyrate.constants as C from pyrate.core import shared, stack, ifgconstants as ifc, mpiops from pyrate.core.logger import pyratelogger as log from pyrate.configuration import Configuration from pyrate.core.shared import Tile gdal.SetCacheMax(64) def main(params: dict) -> None: """ PyRate merge main function. Assembles product tiles in to single geotiff files """ if params[C.SIGNAL_POLARITY] == 1: log.info(f"Saving output products with the same sign convention as input data") elif params[C.SIGNAL_POLARITY] == -1: log.info(f"Saving output products with reversed sign convention compared to the input data") else: log.warning(f"Check the value of signal_polarity parameter") out_types = [] tsfile = join(params[C.TMPDIR], 'tscuml_0.npy') if exists(tsfile): _merge_timeseries(params, 'tscuml') _merge_linrate(params) out_types += ['linear_rate', 'linear_error', 'linear_rsquared'] # optional save of merged tsincr products if params["savetsincr"] == 1: _merge_timeseries(params, 'tsincr') else: log.warning('Not merging time series products; {} does not exist'.format(tsfile)) stfile = join(params[C.TMPDIR], 'stack_rate_0.npy') if exists(stfile): # setup paths mpiops.run_once(_merge_stack, params) out_types += ['stack_rate', 'stack_error'] else: log.warning('Not merging stack products; {} does not exist'.format(stfile)) if len(out_types) > 0: process_out_types = mpiops.array_split(out_types) for out_type in process_out_types: create_png_and_kml_from_tif(params[C.VELOCITY_DIR], output_type=out_type) else: log.warning('Exiting: no products to merge') def _merge_stack(params: dict) -> None: """ Merge stacking outputs """ shape, tiles, ifgs_dict = __merge_setup(params) log.info('Merging and writing Stack Rate product geotiffs') # read and assemble tile outputs rate = assemble_tiles(shape, params[C.TMPDIR], tiles, out_type='stack_rate') error = assemble_tiles(shape, params[C.TMPDIR], tiles, out_type='stack_error') samples = assemble_tiles(shape, params[C.TMPDIR], tiles, out_type='stack_samples') # mask pixels according to threshold if params[C.LR_MAXSIG] > 0: log.info(f"Masking stack_rate and stack_error maps where error is greater than {params[C.LR_MAXSIG]} millimetres") rate, error = stack.mask_rate(rate, error, params[C.LR_MAXSIG]) else: log.info('Skipping stack product masking (maxsig = 0)') # save geotiff and numpy array files for out, ot in zip([rate, error, samples], ['stack_rate', 'stack_error', 'stack_samples']): __save_merged_files(ifgs_dict, params, out, ot, savenpy=params["savenpy"]) def _merge_linrate(params: dict) -> None: """ Merge linear rate outputs """ shape, tiles, ifgs_dict = mpiops.run_once(__merge_setup, params) log.info('Merging and writing Linear Rate product geotiffs') # read and assemble tile outputs out_types = ['linear_' + x for x in ['rate', 'rsquared', 'error', 'intercept', 'samples']] process_out_types = mpiops.array_split(out_types) for p_out_type in process_out_types: out = assemble_tiles(shape, params[C.TMPDIR], tiles, out_type=p_out_type) __save_merged_files(ifgs_dict, params, out, p_out_type, savenpy=params["savenpy"]) mpiops.comm.barrier() def _merge_timeseries(params: dict, tstype: str) -> None: """ Merge tiled time series outputs """ log.info('Merging {} time series outputs'.format(tstype)) shape, tiles, ifgs_dict = __merge_setup(params) # load the first time series file to determine the number of time series tifs ts_file = join(params[C.TMPDIR], tstype + '_0.npy') ts = np.load(file=ts_file) # pylint: disable=no-member no_ts_tifs = ts.shape[2] process_tifs = mpiops.array_split(range(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 {} writing {} {} time series tifs of ' 'total {}'.format(mpiops.rank, len(process_tifs), tstype, no_ts_tifs)) for i in process_tifs: ts_arr = assemble_tiles(shape, params[C.TMPDIR], tiles, out_type=tstype, index=i) __save_merged_files(ifgs_dict, params, ts_arr, out_type=tstype, index=i, savenpy=params["savenpy"]) mpiops.comm.barrier() log.debug('Process {} finished writing {} {} time series tifs of ' 'total {}'.format(mpiops.rank, len(process_tifs), tstype, no_ts_tifs)) def create_png_and_kml_from_tif(output_folder_path: str, output_type: str) -> None: """ Function to create a preview PNG format image from a geotiff, and a KML file """ log.info(f'Creating quicklook image for {output_type}') # open raster and choose band to find min, max raster_path = join(output_folder_path, f"{output_type}.tif") if not isfile(raster_path): raise Exception(f"{output_type}.tif file not found at: " + raster_path) gtif = gdal.Open(raster_path) # find bounds of image west, north, east, south = "", "", "", "" for line in gdal.Info(gtif).split('\n'): if "Upper Left" in line: west, north = line.split(")")[0].split("(")[1].split(",") if "Lower Right" in line: east, south = line.split(")")[0].split("(")[1].split(",") # write KML file kml_file_path = join(output_folder_path, f"{output_type}.kml") kml_file_content = f""" {output_type}.kml {output_type}.png {output_type}.png """ + north + """ """ + south + """ """ + east + """ """ + west + """ """ with open(kml_file_path, "w") as f: f.write(kml_file_content) # Get raster statistics srcband = gtif.GetRasterBand(1) minimum, maximum, _, _ = srcband.GetStatistics(True, True) del gtif # close geotiff (used to calculate statistics) # steps used for the colourmap, must be even (currently hard-coded to 254 resulting in 255 values) no_of_steps = 254 # slightly different code required for rate map and rate error map if output_type in ('stack_rate', 'linear_rate'): # minimum value might be negative maximum = max(abs(minimum), abs(maximum)) minimum = -1 * maximum # colours: blue -> white -> red (white==0) # note that an extra value will be added for zero (i.e. white: 255 255 255) # generate a colourmap for odd number of values (currently hard-coded to 255) mid = int(no_of_steps * 0.5) # allocate RGB values to three numpy arrays r, g, b r = np.arange(0, mid) / mid g = r r = np.concatenate((r, np.ones(mid + 1))) g = np.concatenate((g, np.array([1]), np.flipud(g))) b = np.flipud(r) # change direction of colours (blue: positve, red: negative) r = np.flipud(r) * 255 g = np.flipud(g) * 255 b = np.flipud(b) * 255 if output_type in ('stack_error', 'linear_error', 'linear_rsquared'): # colours: white -> red (minimum error -> maximum error # allocate RGB values to three numpy arrays r, g, b r = np.ones(no_of_steps + 1) * 255 g = np.arange(0, no_of_steps + 1) / (no_of_steps) g = np.flipud(g) * 255 b = g # generate the colourmap file in the output folder color_map_path = join(output_folder_path, f"colourmap_{output_type}.txt") log.info('Saving colour map to file {}; min/max values: {:.2f}/{:.2f}'.format( color_map_path, minimum, maximum)) # write colourmap as text file with open(color_map_path, "w") as f: f.write("nan 0 0 0 0\n") for i, value in enumerate(np.linspace(minimum, maximum, no_of_steps + 1)): f.write("%f %f %f %f 255\n" % (value, r[i], g[i], b[i])) # create PNG image file input_tif_path = join(output_folder_path, f"{output_type}.tif") output_png_path = join(output_folder_path, f"{output_type}.png") subprocess.check_call(["gdaldem", "color-relief", "-of", "PNG", input_tif_path, "-alpha", color_map_path, output_png_path, "-nearest_color_entry"]) log.debug(f'Finished creating quicklook image for {output_type}') def assemble_tiles(s: Tuple, dir: str, tiles: Tile, out_type: str, index: Optional[int] = None) -> np.ndarray: """ Function to reassemble tiles from numpy files in to a merged array :param s: shape for merged array. :param dir: path to directory containing numpy tile files. :param tiles: pyrate.core.shared.Tile Class object. :param out_type: product type string, used to construct numpy tile file name. :param index: array third dimension index to extract from 3D time series array tiles. :return: merged_array: array assembled from all tiles. """ log.debug('Re-assembling tiles for {}'.format(out_type)) # pre-allocate dest array merged_array = np.empty(shape=s, dtype=np.float32) # loop over each tile, load and slot in to correct spot for t in tiles: tile_file = Path(join(dir, out_type + '_' + str(t.index) + '.npy')) tile = np.load(file=tile_file) if index is None: # 2D array merged_array[t.top_left_y:t.bottom_right_y, t.top_left_x:t.bottom_right_x] = tile else: # 3D array merged_array[t.top_left_y:t.bottom_right_y, t.top_left_x:t.bottom_right_x] = tile[:, :, index] log.debug('Finished assembling tiles for {}'.format(out_type)) return merged_array out_type_md_dict = { 'stack_rate': ifc.STACKRATE, 'stack_error': ifc.STACKERROR, 'stack_samples': ifc.STACKSAMP, 'linear_rate': ifc.LINRATE, 'linear_error': ifc.LINERROR, 'linear_samples': ifc.LINSAMP, 'linear_intercept': ifc.LINICPT, 'linear_rsquared': ifc.LINRSQ, 'tsincr': ifc.INCR, 'tscuml': ifc.CUML } error_out_types = {'linear_error', 'stack_error'} los_projection_out_types = {'tsincr', 'tscuml', 'linear_rate', 'stack_rate'} los_projection_divisors = { ifc.LINE_OF_SIGHT: lambda data: 1, ifc.PSEUDO_VERTICAL: lambda data: np.cos(data), ifc.PSEUDO_HORIZONTAL: lambda data: np.sin(data) } def __save_merged_files(ifgs_dict, params, array, out_type, index=None, savenpy=None): """ Convenience function to save PyRate geotiff and numpy array files """ outdir = params[C.OUT_DIR] ts_dir = params[C.TIMESERIES_DIR] vel_dir = params[C.VELOCITY_DIR] log.debug('Saving PyRate outputs {}'.format(out_type)) gt, md, wkt = ifgs_dict['gt'], ifgs_dict['md'], ifgs_dict['wkt'] epochlist = ifgs_dict['epochlist'] if out_type in ('tsincr', 'tscuml'): epoch = epochlist.dates[index + 1] dest = join(ts_dir, out_type + "_" + str(epoch) + ".tif") npy_file = join(ts_dir, out_type + "_" + str(epoch) + ".npy") # sequence position; first time slice is #0 md['SEQUENCE_POSITION'] = index + 1 md[ifc.EPOCH_DATE] = epoch else: dest = join(vel_dir, out_type + ".tif") npy_file = join(vel_dir, out_type + '.npy') md[ifc.EPOCH_DATE] = [d.strftime('%Y-%m-%d') for d in epochlist.dates] md[ifc.DATA_TYPE] = out_type_md_dict[out_type] if out_type in los_projection_out_types: # apply LOS projection and sign conversion for these outputs incidence_path = Path(Configuration.geometry_files(params)['incidence_angle']) if incidence_path.exists(): # We can do LOS projection if params[C.LOS_PROJECTION] == ifc.LINE_OF_SIGHT: log.info(f"Retaining Line-of-sight signal projection for file {dest}") elif params[C.LOS_PROJECTION] != ifc.LINE_OF_SIGHT: log.info(f"Projecting Line-of-sight signal into " f"{ifc.LOS_PROJECTION_OPTION[params[C.LOS_PROJECTION]]} for file {dest}") incidence = shared.Geometry(incidence_path) incidence.open() array /= los_projection_divisors[params[C.LOS_PROJECTION]](incidence.data) * params[C.SIGNAL_POLARITY] md[C.LOS_PROJECTION.upper()] = ifc.LOS_PROJECTION_OPTION[params[C.LOS_PROJECTION]] md[C.SIGNAL_POLARITY.upper()] = params[C.SIGNAL_POLARITY] if out_type in error_out_types: # add n-sigma value to error file metadata # TODO: move the multiplication of nsigma and error data here? md[C.VELERROR_NSIG.upper()] = params[C.VELERROR_NSIG] log.info(f"Saving file {dest} with {params[C.VELERROR_NSIG]}-sigma uncertainties") shared.write_output_geotiff(md, gt, wkt, array, dest, np.nan) # clear the extra metadata so it does not mess up other images if C.LOS_PROJECTION.upper() in md: md.pop(C.LOS_PROJECTION.upper()) if C.SIGNAL_POLARITY.upper() in md: md.pop(C.SIGNAL_POLARITY.upper()) if C.VELERROR_NSIG.upper() in md: md.pop(C.VELERROR_NSIG.upper()) if savenpy: np.save(file=npy_file, arr=array) log.debug('Finished saving {}'.format(out_type)) def __merge_setup(params): """ Convenience function for Merge set up steps """ # load previously saved preread_ifgs dict preread_ifgs_file = Configuration.preread_ifgs(params) ifgs_dict = pickle.load(open(preread_ifgs_file, 'rb')) ifgs = [v for v in ifgs_dict.values() if isinstance(v, shared.PrereadIfg)] shape = ifgs[0].shape tiles = Configuration.get_tiles(params) return shape, tiles, ifgs_dict ================================================ FILE: pyrate/prepifg.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python script applies optional multilooking and cropping to input interferogram geotiff files. There are two modes of running prepifg using pyrate: 1. A python/numpy version, which is also the default version, and 2. a `largetifs` option which can be activated using the config option largetifs: 1 The python/numpy version is recommended when the both the input interferogram and multilooked interferogram can fit into the memory allocated to the process. When dealing with relatively large (compared to memory available to the process) interferogram, the largetif option can be used. This option uses a slightly modified version of the `gdal_calc.py` included with pyrate. Arbitrarily large interferograms can be multilooked using largetifs. The `largetifs` option uses system calls to gdal utilities and avoid loading large chunks of memory. Our tests (tests/test_prepifg_largetifs_vs_python.py::test_prepifg_largetifs_vs_python) indicate that for two different small datasets included in our test suite, the `largetifs` option exactly match the output of the numpy version. However, on large practical datasets we have observed numerical differences in the multilooked output in the 3rd and 4th decimal places (in sub mm range). """ # -*- coding: utf-8 -*- import os from subprocess import check_call import warnings from typing import List, Tuple, Dict from pathlib import Path from joblib import Parallel, delayed import numpy as np from osgeo import gdal import pyrate.constants as C from pyrate.core import shared, geometry, mpiops, prepifg_helper, gamma, roipac, ifgconstants as ifc from pyrate.core.prepifg_helper import PreprocessError, coherence_paths_for, transform_params from pyrate.core.logger import pyratelogger as log from pyrate.configuration import MultiplePaths, Configuration from pyrate.core.shared import Ifg, DEM from pyrate.core.refpixel import convert_geographic_coordinate_to_pixel_value from pyrate.core import ifgconstants as ifg GAMMA = 1 ROIPAC = 0 GEOTIF = 2 def main(params): """ Main workflow function for preparing interferograms for PyRate. :param dict params: Parameters dictionary read in from the config file """ # TODO: looks like ifg_paths are ordered according to ifg list # This probably won't be a problem because input list won't be reordered # and the original gamma generated list is ordered) this may not affect # the important pyrate stuff anyway, but might affect gen_thumbs.py. # Going to assume ifg_paths is ordered correcly # pylint: disable=too-many-branches shared.mpi_vs_multiprocess_logging("prepifg", params) ifg_paths = params[C.INTERFEROGRAM_FILES] if params[C.DEM_FILE] is not None: # optional DEM conversion ifg_paths.append(params[C.DEM_FILE_PATH]) if params[C.COH_FILE_LIST] is not None: ifg_paths.extend(params[C.COHERENCE_FILE_PATHS]) if params[C.COH_FILE_LIST] is None and params[C.COH_MASK]: raise FileNotFoundError("Cannot apply coherence masking: no coherence file list " "supplied (parameter 'cohfilelist')") shared.mkdir_p(params[C.OUT_DIR]) # create output dir user_exts = (params[C.IFG_XFIRST], params[C.IFG_YFIRST], params[ C.IFG_XLAST], params[C.IFG_YLAST]) xlooks, ylooks, crop = transform_params(params) ifgs = [prepifg_helper.dem_or_ifg(p.converted_path) for p in ifg_paths] exts = prepifg_helper.get_analysis_extent(crop, ifgs, xlooks, ylooks, user_exts=user_exts) ifg0 = ifgs[0] ifg0.open() transform = ifg0.dataset.GetGeoTransform() process_ifgs_paths = np.array_split(ifg_paths, mpiops.size)[mpiops.rank] do_prepifg(process_ifgs_paths, exts, params) if params[C.LT_FILE] is not None: log.info("Calculating and writing geometry files") __write_geometry_files(params, exts, transform, ifg_paths[0].sampled_path) else: log.info("Skipping geometry calculations: Lookup table not provided") if params[C.COH_FILE_LIST] is not None: log.info("Calculating and writing coherence statistics") mpiops.run_once(__calc_coherence_stats, params, ifg_paths[0].sampled_path) else: log.info("Skipping coherence file statistics computation.") log.info("Finished 'prepifg' step") ifg0.close() def __calc_coherence_stats(params, ifg_path): coherence_files_multi_paths = params[C.COHERENCE_FILE_PATHS] sampled_paths = [c.sampled_path for c in coherence_files_multi_paths] ifgs = [Ifg(s) for s in sampled_paths] for i in ifgs: i.open() phase_data = np.stack([i.phase_data for i in ifgs]) coh_stats = Configuration.coherence_stats(params) for stat_func, out_type in zip([np.nanmedian, np.nanmean, np.nanstd], [ifg.COH_MEDIAN, ifg.COH_MEAN, ifg.COH_STD]): with warnings.catch_warnings(): warnings.simplefilter("ignore", category=RuntimeWarning) arr = stat_func(phase_data, axis=0) arr[arr==0.0] = np.nan # convert exact zeros (no-data) to NaN dest = coh_stats[out_type] __save_geom_files(ifg_path, dest, arr, out_type) def do_prepifg(multi_paths: List[MultiplePaths], exts: Tuple[float, float, float, float], params: dict) -> None: """ Prepare interferograms by applying multilooking/cropping operations. """ # pylint: disable=expression-not-assigned parallel = params[C.PARALLEL] if mpiops.size > 1: parallel = False for f in multi_paths: if not os.path.isfile(f.converted_path): raise FileNotFoundError("Can not find geotiff: " + str(f) + ". Ensure you have converted your " "interferograms to geotiffs.") if params[C.LARGE_TIFS]: log.info("Using gdal system calls to execute 'prepifg' step") ifg = prepifg_helper.dem_or_ifg(multi_paths[0].converted_path) ifg.open() xlooks, ylooks = params[C.IFG_LKSX], params[C.IFG_LKSY] res_str = [xlooks * ifg.x_step, ylooks * ifg.y_step] res_str = ' '.join([str(e) for e in res_str]) if parallel: Parallel(n_jobs=params[C.PROCESSES], verbose=50)( delayed(__prepifg_system)(exts, gtiff_path, params, res_str) for gtiff_path in multi_paths) else: for m_path in multi_paths: __prepifg_system(exts, m_path, params, res_str) else: if parallel: Parallel(n_jobs=params[C.PROCESSES], verbose=50)( delayed(_prepifg_multiprocessing)(p, exts, params) for p in multi_paths ) else: for m_path in multi_paths: _prepifg_multiprocessing(m_path, exts, params) mpiops.comm.barrier() COMMON_OPTIONS = "-co BLOCKXSIZE=256 -co BLOCKYSIZE=256 -co TILED=YES --config GDAL_CACHEMAX=64 -q" COMMON_OPTIONS2 = "--co BLOCKXSIZE=256 --co BLOCKYSIZE=256 --co TILED=YES --quiet" GDAL_CALC = 'gdal_calc_local.py' def __prepifg_system(exts, gtiff, params, res): thresh = params[C.NO_DATA_AVERAGING_THRESHOLD] p, c, l = _prepifg_multiprocessing(gtiff, exts, params) log.info("Multilooking {p} into {l}".format(p=p, l=l)) extents = ' '.join([str(e) for e in exts]) if isinstance(prepifg_helper.dem_or_ifg(p), shared.DEM): check_call('gdalwarp {co} -te\t{extents}\t-tr\t{res}\t-r\taverage \t{p}\t{l}\n'.format( co=COMMON_OPTIONS, extents=extents, res=res, p=p, l=l), shell=True) __update_meta_data(p, c, l, params) return p_unset = Path(params[C.OUT_DIR]).joinpath(Path(p).name).with_suffix('.unset.tif') # change nodataval from zero, also leave input geotifs unchanged if one supplies conv2tif output/geotifs check_call('gdal_translate {co} -a_nodata nan\t{p}\t{q}'.format(co=COMMON_OPTIONS, p=p, q=p_unset), shell=True) # calculate nan-fraction # TODO: use output options and datatypes to reduce size of the next two tifs nan_frac = Path(l).with_suffix('.nanfrac.tif') nan_frac_avg = Path(l).with_suffix('.nanfrac.avg.tif') corrected_p = Path(p_unset).with_suffix('.corrected.tif') if c is not None: # find all the nans log.info(f"applying coherence + nodata masking on {p}") check_call(f'{GDAL_CALC} {COMMON_OPTIONS2} -A {p_unset} -B {c} --outfile={nan_frac}\t' f'--calc=\"logical_or((B<{params[C.COH_THRESH]}), isclose(A,0,atol=0.000001))\"\t' f'--NoDataValue=nan', shell=True) # coh masking check_call(f'{GDAL_CALC} {COMMON_OPTIONS2} --overwrite -A {p_unset} -B {c}\t' f'--calc=\"A*(B>={params[C.COH_THRESH]}) - ' f'99999*logical_or((B<{params[C.COH_THRESH]}), isclose(A,0,atol=0.000001))\"\t' f'--outfile={corrected_p}\t' f'--NoDataValue=nan', shell=True) else: log.info(f"applying nodata masking on {p}") check_call(f'{GDAL_CALC} {COMMON_OPTIONS2} --overwrite -A {p_unset}\t' f'--calc=\"isclose(A,0,atol=0.000001)\"\t' f'--outfile={nan_frac}\t' f'--NoDataValue=nan', shell=True) check_call(f'{GDAL_CALC} {COMMON_OPTIONS2} --overwrite -A {p_unset}\t' f'--calc=\"A - 99999*isclose(A, 0, atol=0.000001)\"\t' f'--outfile={corrected_p}\t' f'--NoDataValue=nan', shell=True) # crop resample/average multilooking of nan-fraction check_call('gdalwarp {co} -te\t{extents}\t-tr\t{res}\t-r\taverage\t{p}\t{out_file}'.format( co=COMMON_OPTIONS, extents=extents, res=res, p=nan_frac, out_file=nan_frac_avg), shell=True) # crop resample/average multilooking of raster check_call('gdalwarp {co} -te\t{extents}\t-tr\t{res}\t-r\taverage \t{p}\t{l}'.format( co=COMMON_OPTIONS, extents=extents, res=res, p=corrected_p, l=l), shell=True) check_call(f'{GDAL_CALC} {COMMON_OPTIONS2} --overwrite -A {nan_frac_avg}\t-B {l}\t' f'--calc=\"B*(A < {thresh}) -99999*(A >= {thresh})\"\t' f'--outfile={l}\t' f'--NoDataValue=nan', shell=True) __update_meta_data(p_unset.as_posix(), c, l, params) # clean up nan_frac_avg.unlink() nan_frac.unlink() corrected_p.unlink() p_unset.unlink() def __update_meta_data(p_unset, c, l, params): # update metadata ds = gdal.Open(p_unset) md = ds.GetMetadata() # remove data type v = md.pop(ifc.DATA_TYPE) md[ifc.IFG_LKSX] = str(params[C.IFG_LKSX]) md[ifc.IFG_LKSY] = str(params[C.IFG_LKSY]) md[ifc.IFG_CROP] = str(params[C.IFG_CROP_OPT]) # update data type if c is not None: # it's a interferogram when COH_MASK=1 md_str = '-mo {k}={v}'.format(k=ifc.DATA_TYPE, v=ifc.MLOOKED_COH_MASKED_IFG) md[ifc.COH_THRESH] = str(params[C.COH_THRESH]) else: if v == ifc.DEM: # it's a dem md_str = '-mo {k}={v}'.format(k=ifc.DATA_TYPE, v=ifc.MLOOKED_DEM) elif v == ifc.COH: md_str = '-mo {k}={v}'.format(k=ifc.DATA_TYPE, v=ifc.MULTILOOKED_COH) else: # it's an ifg md_str = '-mo {k}={v}'.format(k=ifc.DATA_TYPE, v=ifc.MULTILOOKED) for k, v in md.items(): md_str += ' -mo {k}={v}'.format(k=k.replace(' ', '_'), v=v.replace(' ', '_')) check_call('gdal_edit.py -unsetmd {md} {f}'.format(md=md_str, f=l), shell=True) ds = None # make prepifg output readonly Path(l).chmod(0o444) # readonly output def _prepifg_multiprocessing(m_path: MultiplePaths, exts: Tuple[float, float, float, float], params: dict): """ Multiprocessing wrapper for prepifg """ thresh = params[C.NO_DATA_AVERAGING_THRESHOLD] hdr = find_header(m_path, params) hdr[ifc.INPUT_TYPE] = m_path.input_type xlooks, ylooks, crop = transform_params(params) hdr[ifc.IFG_LKSX] = xlooks hdr[ifc.IFG_LKSY] = ylooks hdr[ifc.IFG_CROP] = crop # If we're performing coherence masking, find the coherence file for this IFG. if params[C.COH_MASK] and shared._is_interferogram(hdr): coherence_path = coherence_paths_for(m_path.converted_path, params, tif=True) coherence_thresh = params[C.COH_THRESH] else: coherence_path = None coherence_thresh = None if params[C.LARGE_TIFS]: return m_path.converted_path, coherence_path, m_path.sampled_path else: prepifg_helper.prepare_ifg(m_path.converted_path, xlooks, ylooks, exts, thresh, crop, out_path=m_path.sampled_path, header=hdr, coherence_path=coherence_path, coherence_thresh=coherence_thresh) Path(m_path.sampled_path).chmod(0o444) # readonly output def find_header(path: MultiplePaths, params: dict) -> Dict[str, str]: processor = params[C.PROCESSOR] # roipac, gamma or geotif tif_path = path.converted_path if (processor == GAMMA) or (processor == GEOTIF): header = gamma.gamma_header(tif_path, params) elif processor == ROIPAC: import warnings warnings.warn("Warning: ROI_PAC support will be deprecated in a future PyRate release", category=DeprecationWarning) header = roipac.roipac_header(tif_path, params) else: raise PreprocessError('Processor must be ROI_PAC (0) or GAMMA (1)') header[ifc.INPUT_TYPE] = path.input_type return header def __write_geometry_files(params: dict, exts: Tuple[float, float, float, float], transform, ifg_path: str) -> None: """ Calculate geometry and save to geotiff files using the information in the first interferogram in the stack, i.e.: - rdc_azimuth.tif (azimuth radar coordinate at each pixel) - rdc_range.tif (range radar coordinate at each pixel) - azimuth_angle.tif (satellite azimuth angle at each pixel) - incidence_angle.tif (incidence angle at each pixel) - look_angle.tif (look angle at each pixel) """ ifg = Ifg(ifg_path) ifg.open(readonly=True) # calculate per-pixel lon/lat lon, lat = geometry.get_lonlat_coords(ifg) # not currently implemented for ROIPAC data which breaks some tests # if statement can be deleted once ROIPAC is deprecated from PyRate if ifg.meta_data[ifc.PYRATE_INSAR_PROCESSOR] == 'ROIPAC': log.warning("Geometry calculations are not implemented for ROI_PAC") return # get geometry information and save radar coordinates and angles to tif files # using metadata of the first image in the stack # get pixel values of crop (needed to crop lookup table file) # pixel extent of cropped area (original IFG input) xmin, ymax = convert_geographic_coordinate_to_pixel_value(exts[0], exts[1], transform) xmax, ymin = convert_geographic_coordinate_to_pixel_value(exts[2], exts[3], transform) # xmin, xmax: columns of crop # ymin, ymax: rows of crop # calculate per-pixel radar coordinates az, rg = geometry.calc_radar_coords(ifg, params, xmin, xmax, ymin, ymax) # Read height data from DEM dem_file = params[C.DEM_FILE_PATH].sampled_path dem = DEM(dem_file) # calculate per-pixel look angle (also calculates and saves incidence and azimuth angles) lk_ang, inc_ang, az_ang, rg_dist = geometry.calc_pixel_geometry(ifg, rg, lon.data, lat.data, dem.data) # save radar coordinates and angles to geotiff files combinations = zip([az, rg, lk_ang, inc_ang, az_ang, rg_dist], C.GEOMETRY_OUTPUT_TYPES) shared.iterable_split(__parallelly_write, combinations, params, ifg_path) def __parallelly_write(tup, params, ifg_path): array, output_type = tup dest = Configuration.geometry_files(params)[output_type] if mpiops.size > 0: log.debug(f"Writing {dest} using process {mpiops.rank}") __save_geom_files(ifg_path, dest, array, output_type) out_type_md_dict = { 'rdc_azimuth': ifc.RDC_AZIMUTH, 'rdc_range': ifc.RDC_RANGE, 'look_angle': ifc.LOOK, 'incidence_angle': ifc.INCIDENCE, 'azimuth_angle': ifc.AZIMUTH, 'range_dist': ifc.RANGE_DIST, ifc.COH_MEAN: ifc.COH_MEAN, ifc.COH_MEDIAN: ifc.COH_MEDIAN, ifc.COH_STD: ifc.COH_STD + '_1SIGMA' } def __save_geom_files(ifg_path, dest, array, out_type): """ Convenience function to save geometry geotiff files """ log.debug('Saving PyRate outputs {}'.format(out_type)) gt, md, wkt = shared.get_geotiff_header_info(ifg_path) md[ifc.DATA_TYPE] = out_type_md_dict[out_type] shared.remove_file_if_exists(dest) log.info(f"Writing geotiff: {dest}") shared.write_output_geotiff(md, gt, wkt, array, dest, np.nan) ================================================ FILE: pytest.ini ================================================ [pytest] filterwarnings = ignore::DeprecationWarning markers = slow: marks tests as slow (deselect with '-m "not slow"') mpi: marks tests that require mpi (deselect with '-m "not mpi"') ================================================ FILE: requirements-dev.txt ================================================ ghp-import==0.5.5 sphinx==3.0.3 sphinx_rtd_theme==0.4.3 sphinxcontrib-programoutput==0.16 recommonmark==0.6.0 m2r2 mpi4py==3.0.3 matplotlib ipython setuptools twine wheel pylint pytest ================================================ FILE: requirements-plot.txt ================================================ rasterio matplotlib statsmodels xarray pylab ================================================ FILE: requirements-test.txt ================================================ pytest-cov==2.8.1 coverage==5.1 codecov==2.1.0 tox==3.15.0 pytest==5.4.2 ================================================ FILE: requirements.txt ================================================ GDAL joblib==1.0.0 mpi4py==3.0.3 networkx==2.5 numpy==1.19.4 pyproj==3.0.0 scipy==1.5.4 numexpr==2.7.2 nptyping==1.4.0 ================================================ FILE: scripts/apt_install.sh ================================================ echo "This script will install packages required by PyRate. Continue?" select yn in "Yes" "No"; do case $yn in Yes ) break;; No ) exit;; esac done # OS package requirements for Ubuntu 18.04 sudo apt-get update sudo apt-get -y install \ gdal-bin \ libgdal-dev \ openmpi-bin \ libopenmpi-dev ================================================ FILE: scripts/ci_gdal_install.sh ================================================ #!/bin/bash # # originally contributed by @rbuffat to Toblerity/Fiona set -e GDALOPTS=" --with-geos \ --with-expat \ --without-libtool \ --with-libz=internal \ --with-libtiff=internal \ --with-geotiff=internal \ --without-gif \ --without-pg \ --without-grass \ --without-libgrass \ --without-cfitsio \ --without-pcraster \ --with-netcdf \ --with-png=internal \ --with-jpeg=internal \ --without-gif \ --without-ogdi \ --without-fme \ --without-hdf4 \ --with-hdf5 \ --without-jasper \ --without-ecw \ --without-kakadu \ --without-mrsid \ --without-jp2mrsid \ --without-mysql \ --without-ingres \ --without-xerces \ --without-odbc \ --with-curl \ --without-sqlite3 \ --without-idb \ --without-sde \ --without-perl \ --without-python" # Create build dir if not exists if [ ! -d "$GDALBUILD" ]; then mkdir $GDALBUILD; fi if [ ! -d "$GDALINST" ]; then mkdir $GDALINST; fi ls -l $GDALINST if [ "$GDALVERSION" = "master" ]; then PROJOPT="--with-proj=$GDALINST/gdal-$GDALVERSION" cd $GDALBUILD git clone --depth 1 https://github.com/OSGeo/gdal gdal-$GDALVERSION cd gdal-$GDALVERSION/gdal git rev-parse HEAD > newrev.txt BUILD=no # Only build if nothing cached or if the GDAL revision changed if test ! -f $GDALINST/gdal-$GDALVERSION/rev.txt; then BUILD=yes elif ! diff newrev.txt $GDALINST/gdal-$GDALVERSION/rev.txt >/dev/null; then BUILD=yes fi if test "$BUILD" = "yes"; then mkdir -p $GDALINST/gdal-$GDALVERSION cp newrev.txt $GDALINST/gdal-$GDALVERSION/rev.txt ./configure --prefix=$GDALINST/gdal-$GDALVERSION $GDALOPTS $PROJOPT make -s -j 2 make install fi else case "$GDALVERSION" in 3*) PROJOPT="--with-proj=$GDALINST/gdal-$GDALVERSION" ;; 2.4*) PROJOPT="--with-proj=$GDALINST/gdal-$GDALVERSION" ;; 2.3*) PROJOPT="--with-proj=$GDALINST/gdal-$GDALVERSION" ;; 2.2*) PROJOPT="--with-static-proj4=$GDALINST/gdal-$GDALVERSION" ;; 2.1*) PROJOPT="--with-static-proj4=$GDALINST/gdal-$GDALVERSION" ;; 2.0*) PROJOPT="--with-static-proj4=$GDALINST/gdal-$GDALVERSION" ;; 1*) PROJOPT="--with-static-proj4=$GDALINST/gdal-$GDALVERSION" ;; esac if [ ! -d "$GDALINST/gdal-$GDALVERSION/share/gdal" ]; then cd $GDALBUILD gdalver=$(expr "$GDALVERSION" : '\([0-9]*.[0-9]*.[0-9]*\)') wget -q http://download.osgeo.org/gdal/$gdalver/gdal-$GDALVERSION.tar.gz tar -xzf gdal-$GDALVERSION.tar.gz cd gdal-$gdalver ./configure --prefix=$GDALINST/gdal-$GDALVERSION $GDALOPTS $PROJOPT make -s -j 2 make install fi fi ================================================ FILE: scripts/ci_proj_install.sh ================================================ #!/bin/sh set -e # Create build dir if not exists if [ ! -d "$PROJBUILD" ]; then mkdir $PROJBUILD; fi if [ ! -d "$PROJINST" ]; then mkdir $PROJINST; fi ls -l $PROJINST echo "PROJ VERSION: $PROJVERSION" if [ ! -d "$PROJINST/gdal-$GDALVERSION/share/proj" ]; then cd $PROJBUILD wget -q https://download.osgeo.org/proj/proj-$PROJVERSION.tar.gz tar -xzf proj-$PROJVERSION.tar.gz cd proj-$PROJVERSION ./configure --prefix=$PROJINST/gdal-$GDALVERSION make -s -j 2 make install fi ================================================ FILE: scripts/create_html_documentation.sh ================================================ #!/bin/bash module purge module load python3/3.7.4 module load gdal/3.0.2 module load openmpi/4.0.2 export PYTHONPATH=/apps/gdal/3.0.2/lib64/python3.7/site-packages:$PYTHONPATH source ~/PyRateVenv/bin/activate cd ~/PyRate/docs make html echo "Use following cmd to copy file to local machine:" cd ~ echo "scp -r `whoami`@gadi.nci.org.au:`pwd ~`/PyRate/docs/_build/html C:/preview" ================================================ FILE: scripts/gdal_calc_local.py ================================================ #! /usr/bin/python # -*- coding: utf-8 -*- #****************************************************************************** # # Project: GDAL # Purpose: Command line raster calculator with numpy syntax # Author: Chris Yesson, chris.yesson@ioz.ac.uk # #****************************************************************************** # Copyright (c) 2010, Chris Yesson # Copyright (c) 2010-2011, Even Rouault # Copyright (c) 2016, Piers Titus van der Torren # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the "Software"), # to deal in the Software without restriction, including without limitation # the rights to use, copy, modify, merge, publish, distribute, sublicense, # and/or sell copies of the Software, and to permit persons to whom the # Software is furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included # in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS # OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL # THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER # DEALINGS IN THE SOFTWARE. #****************************************************************************** ################################################################ # Command line raster calculator with numpy syntax. Use any basic arithmetic supported by numpy arrays such as +-*\ along with logical operators such as >. Note that all files must have the same dimensions, but no projection checking is performed. Use gdal_calc.py --help for list of options. # This script is a copy of gdal_calc.py included in the GDAL library, modified for use with PyRate. ################################################################ from optparse import OptionParser, Values import os import sys import numpy from osgeo import gdal from osgeo import gdalnumeric # create alphabetic list for storing input layers AlphaList=["A","B","C","D","E","F","G","H","I","J","K","L","M", "N","O","P","Q","R","S","T","U","V","W","X","Y","Z"] # set up some default nodatavalues for each datatype DefaultNDVLookup={'Byte':255, 'UInt16':65535, 'Int16':-32767, 'UInt32':4294967293, 'Int32':-2147483647, 'Float32':3.402823466E+38, 'Float64':1.7976931348623158E+308} ################################################################ def doit(opts, args): if opts.debug: print("gdal_calc.py starting calculation %s" %(opts.calc)) # set up global namespace for eval with all functions of gdalnumeric global_namespace = dict([(key, getattr(gdalnumeric, key)) for key in dir(gdalnumeric) if not key.startswith('__')]) if not opts.calc: raise Exception("No calculation provided.") elif not opts.outF: raise Exception("No output file provided.") ################################################################ # fetch details of input layers ################################################################ # set up some lists to store data for each band myFiles=[] myBands=[] myAlphaList=[] myDataType=[] myDataTypeNum=[] myNDV=[] DimensionsCheck=None # loop through input files - checking dimensions for myI, myF in opts.input_files.items(): if not myI.endswith("_band"): # check if we have asked for a specific band... if "%s_band" % myI in opts.input_files: myBand = opts.input_files["%s_band" % myI] else: myBand = 1 myFile = gdal.Open(myF, gdal.GA_ReadOnly) if not myFile: raise IOError("No such file or directory: '%s'" % myF) myFiles.append(myFile) myBands.append(myBand) myAlphaList.append(myI) myDataType.append(gdal.GetDataTypeName(myFile.GetRasterBand(myBand).DataType)) myDataTypeNum.append(myFile.GetRasterBand(myBand).DataType) myNDV.append(myFile.GetRasterBand(myBand).GetNoDataValue()) # check that the dimensions of each layer are the same if DimensionsCheck: if DimensionsCheck != [myFile.RasterXSize, myFile.RasterYSize]: raise Exception("Error! Dimensions of file %s (%i, %i) are different from other files (%i, %i). Cannot proceed" % \ (myF, myFile.RasterXSize, myFile.RasterYSize, DimensionsCheck[0], DimensionsCheck[1])) else: DimensionsCheck = [myFile.RasterXSize, myFile.RasterYSize] if opts.debug: print("file %s: %s, dimensions: %s, %s, type: %s" %(myI,myF,DimensionsCheck[0],DimensionsCheck[1],myDataType[-1])) # process allBands option allBandsIndex=None allBandsCount=1 if opts.allBands: try: allBandsIndex=myAlphaList.index(opts.allBands) except ValueError: raise Exception("Error! allBands option was given but Band %s not found. Cannot proceed" % (opts.allBands)) allBandsCount=myFiles[allBandsIndex].RasterCount if allBandsCount <= 1: allBandsIndex=None ################################################################ # set up output file ################################################################ # open output file exists if os.path.isfile(opts.outF) and not opts.overwrite: if allBandsIndex is not None: raise Exception("Error! allBands option was given but Output file exists, must use --overwrite option!") if opts.debug: print("Output file %s exists - filling in results into file" %(opts.outF)) myOut=gdal.Open(opts.outF, gdal.GA_Update) if [myOut.RasterXSize,myOut.RasterYSize] != DimensionsCheck: raise Exception("Error! Output exists, but is the wrong size. Use the --overwrite option to automatically overwrite the existing file") myOutB=myOut.GetRasterBand(1) myOutNDV=myOutB.GetNoDataValue() myOutType=gdal.GetDataTypeName(myOutB.DataType) else: # remove existing file and regenerate if os.path.isfile(opts.outF): os.remove(opts.outF) # create a new file if opts.debug: print("Generating output file %s" %(opts.outF)) # find data type to use if not opts.type: # use the largest type of the input files myOutType=gdal.GetDataTypeName(max(myDataTypeNum)) else: myOutType=opts.type # create file myOutDrv = gdal.GetDriverByName(opts.format) myOut = myOutDrv.Create( opts.outF, DimensionsCheck[0], DimensionsCheck[1], allBandsCount, gdal.GetDataTypeByName(myOutType), opts.creation_options) # set output geo info based on first input layer myOut.SetGeoTransform(myFiles[0].GetGeoTransform()) myOut.SetProjection(myFiles[0].GetProjection()) if opts.NoDataValue!=None: myOutNDV=opts.NoDataValue else: myOutNDV=DefaultNDVLookup[myOutType] for i in range(1,allBandsCount+1): myOutB=myOut.GetRasterBand(i) myOutB.SetNoDataValue(myOutNDV) # write to band myOutB=None if opts.debug: print("output file: %s, dimensions: %s, %s, type: %s" %(opts.outF,myOut.RasterXSize,myOut.RasterYSize,myOutType)) ################################################################ # find block size to chop grids into bite-sized chunks ################################################################ # use the block size of the first layer to read efficiently myBlockSize=myFiles[0].GetRasterBand(myBands[0]).GetBlockSize(); # store these numbers in variables that may change later nXValid = myBlockSize[0] nYValid = myBlockSize[1] # find total x and y blocks to be read nXBlocks = (int)((DimensionsCheck[0] + myBlockSize[0] - 1) / myBlockSize[0]); nYBlocks = (int)((DimensionsCheck[1] + myBlockSize[1] - 1) / myBlockSize[1]); myBufSize = myBlockSize[0]*myBlockSize[1] if opts.debug: print("using blocksize %s x %s" %(myBlockSize[0], myBlockSize[1])) # variables for displaying progress ProgressCt=-1 ProgressMk=-1 ProgressEnd=nXBlocks*nYBlocks*allBandsCount ################################################################ # start looping through each band in allBandsCount ################################################################ for bandNo in range(1,allBandsCount+1): ################################################################ # start looping through blocks of data ################################################################ # loop through X-lines for X in range(0,nXBlocks): # in the rare (impossible?) case that the blocks don't fit perfectly # change the block size of the final piece if X==nXBlocks-1: nXValid = DimensionsCheck[0] - X * myBlockSize[0] myBufSize = nXValid*nYValid # find X offset myX=X*myBlockSize[0] # reset buffer size for start of Y loop nYValid = myBlockSize[1] myBufSize = nXValid*nYValid # loop through Y lines for Y in range(0,nYBlocks): ProgressCt+=1 if 10*ProgressCt/ProgressEnd%10!=ProgressMk and not opts.quiet: ProgressMk=10*ProgressCt/ProgressEnd%10 from sys import version_info if version_info >= (3,0,0): exec('print("%d.." % (10*ProgressMk), end=" ")') else: exec('print 10*ProgressMk, "..",') # change the block size of the final piece if Y==nYBlocks-1: nYValid = DimensionsCheck[1] - Y * myBlockSize[1] myBufSize = nXValid*nYValid # find Y offset myY=Y*myBlockSize[1] # create empty buffer to mark where nodata occurs myNDVs = None # make local namespace for calculation local_namespace = {} # fetch data for each input layer for i, Alpha in enumerate(myAlphaList): # populate lettered arrays with values if allBandsIndex is not None and allBandsIndex==i: myBandNo=bandNo else: myBandNo=myBands[i] myval=gdalnumeric.BandReadAsArray(myFiles[i].GetRasterBand(myBandNo), xoff=myX, yoff=myY, win_xsize=nXValid, win_ysize=nYValid) # fill in nodata values if myNDV[i] is not None: if myNDVs is None: myNDVs = numpy.zeros(myBufSize) myNDVs.shape=(nYValid,nXValid) myNDVs=1*numpy.logical_or(myNDVs==1, myval==myNDV[i]) # add an array of values for this block to the eval namespace local_namespace[Alpha] = myval myval=None # try the calculation on the array blocks try: myResult = eval(opts.calc, global_namespace, local_namespace) except: print("evaluation of calculation %s failed" %(opts.calc)) raise if myNDVs is not None: myResult[numpy.isclose(myResult, -99999, atol=1e-5)] = myOutNDV elif not isinstance(myResult, numpy.ndarray): myResult = numpy.ones( (nYValid,nXValid) ) * myResult # write data block to the output file myOutB=myOut.GetRasterBand(bandNo) gdalnumeric.BandWriteArray(myOutB, myResult, xoff=myX, yoff=myY) if not opts.quiet: print("100 - Done") #print("Finished - Results written to %s" %opts.outF) return ################################################################ def Calc(calc, outfile, NoDataValue=None, type=None, format='GTiff', creation_options=[], allBands='', overwrite=False, debug=False, quiet=False, **input_files): """ Perform raster calculations with numpy syntax. Use any basic arithmetic supported by numpy arrays such as +-*\ along with logical operators such as >. Note that all files must have the same dimensions, but no projection checking is performed. Keyword arguments: [A-Z]: input files [A_band - Z_band]: band to use for respective input file Examples: add two files together: Calc("A+B", A="input1.tif", B="input2.tif", outfile="result.tif") average of two layers: Calc(calc="(A+B)/2", A="input1.tif", B="input2.tif", outfile="result.tif") set values of zero and below to null: Calc(calc="A*(A>0)", A="input.tif", A_Band=2, outfile="result.tif", NoDataValue=0) """ opts = Values() opts.input_files = input_files opts.calc = calc opts.outF = outfile opts.NoDataValue = NoDataValue opts.type = type opts.format = format opts.creation_options = creation_options opts.allBands = allBands opts.overwrite = overwrite opts.debug = debug opts.quiet = quiet doit(opts, None) def store_input_file(option, opt_str, value, parser): if not hasattr(parser.values, 'input_files'): parser.values.input_files = {} parser.values.input_files[opt_str.lstrip('-')] = value def main(): usage = """usage: %prog --calc=expression --outfile=out_filename [-A filename] [--A_band=n] [-B...-Z filename] [other_options]""" parser = OptionParser(usage) # define options parser.add_option("--calc", dest="calc", help="calculation in gdalnumeric syntax using +-/* or any numpy array functions (i.e. log10())", metavar="expression") # limit the input file options to the ones in the argument list given_args = set([a[1] for a in sys.argv if a[1:2] in AlphaList] + ['A']) for myAlpha in given_args: parser.add_option("-%s" % myAlpha, action="callback", callback=store_input_file, type=str, help="input gdal raster file, you can use any letter (A-Z)", metavar='filename') parser.add_option("--%s_band" % myAlpha, action="callback", callback=store_input_file, type=int, help="number of raster band for file %s (default 1)" % myAlpha, metavar='n') parser.add_option("--outfile", dest="outF", help="output file to generate or fill", metavar="filename") parser.add_option("--NoDataValue", dest="NoDataValue", type=float, help="output nodata value (default datatype specific value)", metavar="value") parser.add_option("--type", dest="type", help="output datatype, must be one of %s" % list(DefaultNDVLookup.keys()), metavar="datatype") parser.add_option("--format", dest="format", default="GTiff", help="GDAL format for output file (default 'GTiff')", metavar="gdal_format") parser.add_option( "--creation-option", "--co", dest="creation_options", default=[], action="append", help="Passes a creation option to the output format driver. Multiple " "options may be listed. See format specific documentation for legal " "creation options for each format.", metavar="option") parser.add_option("--allBands", dest="allBands", default="", help="process all bands of given raster (A-Z)", metavar="[A-Z]") parser.add_option("--overwrite", dest="overwrite", action="store_true", help="overwrite output file if it already exists") parser.add_option("--debug", dest="debug", action="store_true", help="print debugging information") parser.add_option("--quiet", dest="quiet", action="store_true", help="suppress progress messages") (opts, args) = parser.parse_args() if not hasattr(opts, "input_files"): opts.input_files = {} if len(sys.argv) == 1: parser.print_help() sys.exit(1) elif not opts.calc: print("No calculation provided. Nothing to do!") parser.print_help() sys.exit(1) elif not opts.outF: print("No output file provided. Cannot proceed.") parser.print_help() sys.exit(1) else: try: doit(opts, args) except IOError as e: print(e) sys.exit(1) if __name__ == "__main__": main() ================================================ FILE: scripts/nci_install.sh ================================================ #!/bin/bash rm -rf ~/PyRateVenv rm -rf ~/PyRate git clone https://github.com/GeoscienceAustralia/PyRate.git module purge module load python3/3.7.4 module load gdal/3.0.2 module load openmpi/4.0.2 export PYTHONPATH=/apps/gdal/3.0.2/lib64/python3.7/site-packages:$PYTHONPATH python3 -m venv ~/PyRateVenv source ~/PyRateVenv/bin/activate cd ~/PyRate python setup.py install ================================================ FILE: scripts/nci_load_modules.sh ================================================ #!bin/bash module purge module load python3/3.7.4 module load gdal/3.0.2 module load openmpi/4.0.2 # Remove the prebuilt GDAL Python bindings that get added by the NCI module. export PYTHONPATH=/apps/gdal/3.0.2/lib64/python3.7/site-packages:$PYTHONPATH # Required by Click export LC_ALL=en_AU.UTF-8 export LANG=en_AU.UTF-8 source ${PYRATE_VENV:-${HOME}/PyRateVenv}/bin/activate ================================================ FILE: scripts/nci_run_tests.sh ================================================ #!/bin/bash module purge module load python3/3.7.4 module load gdal/3.0.2 module load openmpi/4.0.2 export PYTHONPATH=/apps/gdal/3.0.2/lib64/python3.7/site-packages:$PYTHONPATH source ~/PyRateVenv/bin/activate pip install -U pytest cd ~/PyRate pytest tests/ ================================================ FILE: scripts/plot_ifgs.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module plots the input interferograms to the PyRate software """ import argparse from argparse import RawTextHelpFormatter from pathlib import Path import math import numpy as np import pyrate.constants as C from pyrate.core.logger import pyratelogger as log, configure_stage_log from pyrate.core.shared import Ifg, InputTypes, nan_and_mm_convert from pyrate.main import _params_from_conf try: import matplotlib.pyplot as plt import matplotlib as mpl from mpl_toolkits.axes_grid1 import make_axes_locatable cmap = mpl.cm.Spectral_r cmap.set_bad(color='grey') except ImportError as e: log.warn(ImportError(e)) log.warn("Required plotting packages are not found in environment. " "Please install matplotlib in your environment to continue plotting!!!") raise ImportError(e) def main(): parser = argparse.ArgumentParser(prog='plot_ifgs', description="Python script to plot interferograms", add_help=True, formatter_class=RawTextHelpFormatter) parser.add_argument('-v', '--verbosity', type=str, default='INFO', choices=['DEBUG', 'INFO', 'WARNING', 'ERROR'], help="Increase output verbosity") parser.add_argument('-d', '--directory', action="store", type=str, default=None, help="Pass path to directory containing ifgs", required=True) parser.add_argument('-f', '--config_file', action="store", type=str, default=None, help="Pass configuration file", required=True) parser.add_argument('-n', '--ifgs_per_plot', type=int, default=50, help='number of ifgs per plot', required=False) args = parser.parse_args() params = _params_from_conf(args.config_file) configure_stage_log(args.verbosity, 'plot_ifgs', Path(params[C.OUT_DIR]).joinpath('pyrate.log.').as_posix()) if args.verbosity: log.setLevel(args.verbosity) log.info("Verbosity set to " + str(args.verbosity) + ".") log.debug("Arguments supplied at command line: ") log.debug(args) ifgs = sorted(list(Path(args.directory).glob('*_ifg.tif'))) num_ifgs = len(ifgs) if num_ifgs == 0: log.warning(f'No interferograms with extension *_ifg.tif were found in {args.directory}') return log.info(f'Plotting {num_ifgs} interferograms found in {args.directory}') ifgs_per_plot = args.ifgs_per_plot plt_rows = np.int(np.sqrt(ifgs_per_plot)) plt_cols = ifgs_per_plot//plt_rows if ifgs_per_plot % plt_rows: plt_cols += 1 tot_plots = 0 num_of_figs = math.ceil(num_ifgs / ifgs_per_plot) fig_no = 0 for i in range(num_of_figs): fig_no += 1 fig = plt.figure(figsize=(12*plt_rows, 8*plt_cols)) fig_plots = 0 for p_r in range(plt_rows): for p_c in range(plt_cols): ax = fig.add_subplot(plt_rows, plt_cols, fig_plots + 1) ifg_num = plt_cols * p_r + p_c + ifgs_per_plot * i file = ifgs[ifg_num] log.info(f'Plotting {file}') __plot_ifg(file, cmap, ax, num_ifgs, params) tot_plots += 1 fig_plots += 1 log.debug(f'Plotted interferogram #{tot_plots}') if (fig_plots == ifgs_per_plot) or (tot_plots == num_ifgs): f_name = Path(args.directory).joinpath('ifg-phase-plot-' + str(fig_no) + '.png').as_posix() plt.savefig(f_name, dpi=50) log.info(f'{fig_plots} interferograms plotted in {f_name}') plt.close(fig) break if tot_plots == num_ifgs: break def __plot_ifg(file, cmap, ax, num_ifgs, params): try: ifg = Ifg(file) ifg.open() except: raise AttributeError(f'Cannot open interferogram geotiff: {file}') # change nodata values to NaN for display; convert units to mm (if in radians) nan_and_mm_convert(ifg, params) im = ax.imshow(ifg.phase_data, cmap=cmap) text = ax.set_title(Path(ifg.data_path).stem) text.set_fontsize(20) # text.set_fontsize(min(20, int(num_ifgs / 2))) divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) plt.colorbar(im, cax=cax, label='mm') ifg.close() if __name__ == "__main__": main() ================================================ FILE: scripts/prepifg.sh ================================================ #!/usr/bin/env bash # inputs are extents, thresh, coherence file, coherence_threshold # coherence masking # gdal average # gdal average has two steps # create a raster with the input raster nan_matrix, then compute the nan_frac of the input raster # take average of the input raster, and set nan_frac > thresh = nan inputdir=/home/sudipta/repos/PyRate/out/gamma/out outdir_crop=/home/sudipta/repos/PyRate/out/gamma/out/cropped outdir_coh=/home/sudipta/repos/PyRate/out/gamma/out/coherence mkdir -p ${outdir_crop} mkdir -p ${outdir_coh} function crop_resample_average { f=$1 echo ${f} gdalwarp -overwrite ${f} } function nan_matrix { f=$1 echo ${f} python gdal_calc_local.py --overwrite ${f} } function resampled_average { f=$1 echo ${f} python gdal_calc_local.py --overwrite ${f} } export -f crop_resample_average #export -f mask #export -f multilook export -f nan_matrix export -f resampled_average #ls ${inputdir}/*.tif | parallel crop ${outdir_crop} # while IFS= read -r lineA && IFS= read -r lineB <&3; do # echo "$lineA"; echo "$lineB" # done %s' % (self.first, self.second) def open(self): # TODO: could move some of the init code here to mimic Ifgs pass # can't actually open anything! @property def nan_count(self): return nsum(isnan(self.phase_data)) @property def shape(self): return self.nrows, self.ncols def write_modified_phase(self): #dummy pass def close(self): # dummy pass def reconstruct_stack_rate(shape, tiles, output_dir, out_type): rate = np.zeros(shape=shape, dtype=np.float32) for t in tiles: rate_file = os.path.join(output_dir, 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 return rate def reconstruct_mst(shape, tiles, output_dir): mst_file_0 = os.path.join(output_dir, C.MST_DIR, 'mst_mat_{}.npy'.format(0)) shape0 = np.load(mst_file_0).shape[0] mst = np.empty(shape=((shape0,) + shape), dtype=np.float32) for i, t in enumerate(tiles): mst_file_n = os.path.join(output_dir, C.MST_DIR, 'mst_mat_{}.npy'.format(i)) mst[:, t.top_left_y:t.bottom_right_y, t.top_left_x: t.bottom_right_x] = np.load(mst_file_n) return mst def move_files(source_dir, dest_dir, file_type='*.tif', copy=False): for filename in glob.glob(os.path.join(source_dir, file_type)): if copy: shutil.copy(filename, dest_dir) else: shutil.move(filename, dest_dir) def assert_ifg_phase_equal(ifg_path1, ifg_path2): ds1 = gdal.Open(ifg_path1) ds2 = gdal.Open(ifg_path2) np.testing.assert_array_almost_equal(ds1.ReadAsArray(), ds2.ReadAsArray()) ds1 = None ds2 = None def prepare_ifgs_without_phase(ifg_paths, params): ifgs = [Ifg(p) for p in ifg_paths] for i in ifgs: i.open(readonly=False) nan_conversion = params[C.NAN_CONVERSION] if nan_conversion: # nan conversion happens here in networkx mst # if not ifg.nan_converted: i.nodata_value = params[C.NO_DATA_VALUE] i.convert_to_nans() return ifgs 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) # write mst output to a file mst_mat_binary_file = join(params[C.OUT_DIR], 'mst_mat') np.save(file=mst_mat_binary_file, arr=mst_grid) for i in ifgs: i.close() return mst_grid return None def get_nml(ifg_list_instance, nodata_value, nan_conversion=False): """ :param xxx(eg str, tuple, int, float...) ifg_list_instance: xxxx :param float nodata_value: No data value in image :param bool nan_conversion: Convert NaNs :return: ifg_list_instance: replaces in place :rtype: list :return: _epoch_list: list of epochs :rtype: list """ _epoch_list, n = algorithm.get_epochs(ifg_list_instance.ifgs) ifg_list_instance.reshape_n(n) if nan_conversion: ifg_list_instance.update_nan_frac(nodata_value) # turn on for nan conversion ifg_list_instance.convert_nans(nan_conversion=nan_conversion) ifg_list_instance.make_data_stack() return ifg_list_instance, _epoch_list def compute_time_series(ifgs, mst_grid, params, vcmt): # Calculate time series tsincr, tscum, tsvel = calculate_time_series( ifgs, params, vcmt=vcmt, mst=mst_grid) # tsvel_file = join(params[cf.OUT_DIR], 'tsvel.npy') tsincr_file = join(params[C.OUT_DIR], 'tsincr.npy') tscum_file = join(params[C.OUT_DIR], 'tscum.npy') np.save(file=tsincr_file, arr=tsincr) np.save(file=tscum_file, arr=tscum) # np.save(file=tsvel_file, arr=tsvel) # TODO: write tests for these functions write_timeseries_geotiff(ifgs, params, tsincr, pr_type='tsincr') write_timeseries_geotiff(ifgs, params, tscum, pr_type='tscuml') # write_timeseries_geotiff(ifgs, params, tsvel, pr_type='tsvel') return tsincr, tscum, tsvel def calculate_time_series(ifgs, params, vcmt, mst): res = timeseries.time_series(ifgs, params, vcmt, mst) for r in res: if len(r.shape) != 3: raise timeseries.TimeSeriesError tsincr, tscum, tsvel = res return tsincr, tscum, tsvel 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[C.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 calculate_stack_rate(ifgs, params, vcmt, mst_mat=None): # log.info('Calculating stacked rate') res = stack.stack_rate_array(ifgs, params, vcmt, mst_mat) for r in res: if r is None: raise ValueError('TODO: bad value') r, e, samples = res rate, error = stack.mask_rate(r, e, params['maxsig']) write_stackrate_tifs(ifgs, params, res) # log.info('Stacked rate calculated') return rate, error, samples def write_stackrate_tifs(ifgs, params, res): rate, error, samples = res gt, md, wkt = get_geotiff_header_info(ifgs[0].data_path) epochlist = algorithm.get_epochs(ifgs)[0] dest = join(params[C.OUT_DIR], "stack_rate.tif") md[ifc.EPOCH_DATE] = epochlist.dates md[ifc.DATA_TYPE] = ifc.STACKRATE write_output_geotiff(md, gt, wkt, rate, dest, np.nan) dest = join(params[C.OUT_DIR], "stack_error.tif") md[ifc.DATA_TYPE] = ifc.STACKERROR write_output_geotiff(md, gt, wkt, error, dest, np.nan) dest = join(params[C.OUT_DIR], "stack_samples.tif") md[ifc.DATA_TYPE] = ifc.STACKSAMP write_output_geotiff(md, gt, wkt, samples, dest, np.nan) write_stackrate_numpy_files(error, rate, samples, params) def write_stackrate_numpy_files(error, rate, samples, params): rate_file = join(params[C.OUT_DIR], 'rate.npy') error_file = join(params[C.OUT_DIR], 'error.npy') samples_file = join(params[C.OUT_DIR], 'samples.npy') np.save(file=rate_file, arr=rate) np.save(file=error_file, arr=error) np.save(file=samples_file, arr=samples) def copytree(src: Union[str, bytes, os.PathLike], dst: Union[str, bytes, os.PathLike], symlinks=False, ignore=None): # pylint: disable=line-too-long """ Copy entire contents of src directory into dst directory. See: http://stackoverflow.com/questions/1868714/how-do-i-copy-an-entire-directory-of-files-into-an-existing-directory-using-pyth?lq=1 :param str src: source directory path :param str dst: destination directory path (created if does not exist) :param bool symlinks: Whether to copy symlink or not :param bool ignore: """ # pylint: disable=invalid-name if not os.path.exists(dst): # pragma: no cover os.makedirs(dst) shutil.copystat(src, dst) lst = os.listdir(src) if ignore: excl = ignore(src, lst) lst = [x for x in lst if x not in excl] for item in lst: s = os.path.join(src, item) d = os.path.join(dst, item) if symlinks and os.path.islink(s): # pragma: no cover if os.path.lexists(d): os.remove(d) os.symlink(os.readlink(s), d) try: st = os.lstat(s) mode = stat.S_IMODE(st.st_mode) os.lchmod(d, mode) except AttributeError: pass # lchmod not available elif os.path.isdir(s): # pragma: no cover copytree(s, d, symlinks, ignore) else: shutil.copy2(s, d) def repair_params_for_correct_tests(out_dir, params): base_ifg_paths = [d.unwrapped_path for d in params[C.INTERFEROGRAM_FILES]] headers = [roipac.roipac_header(i, params) for i in base_ifg_paths] params[C.INTERFEROGRAM_FILES] = params[C.INTERFEROGRAM_FILES][:-2] dest_paths = [Path(out_dir).joinpath(Path(d.sampled_path).name).as_posix() for d in params[C.INTERFEROGRAM_FILES]] for p, d in zip(params[C.INTERFEROGRAM_FILES], dest_paths): # hack p.sampled_path = d return dest_paths, headers def pre_prepare_ifgs(ifg_paths, params): """ nan and mm convert ifgs """ ifgs = [Ifg(p) for p in ifg_paths] for i in ifgs: i.open(readonly=False) nan_and_mm_convert(i, params) return ifgs def assert_two_dirs_equal(dir1, dir2, ext, num_files=None): if not isinstance(ext, list): ext = [ext] dir1_files = list(itertools.chain(* [list(Path(dir1).glob(ex)) for ex in ext])) dir2_files = list(itertools.chain(* [list(Path(dir2).glob(ex)) for ex in ext])) dir1_files.sort() dir2_files.sort() # 17 unwrapped geotifs # 17 cropped multilooked tifs + 1 dem if num_files is not None: assert len(dir1_files) == num_files assert len(dir2_files) == num_files else: assert len(dir1_files) == len(dir2_files) if dir1_files[0].suffix == '.tif': for m_f, s_f in zip(dir1_files, dir2_files): assert m_f.name == s_f.name assert_tifs_equal(m_f.as_posix(), s_f.as_posix()) elif dir1_files[0].suffix == '.npy': for m_f, s_f in zip(dir1_files, dir2_files): assert m_f.name == s_f.name np.testing.assert_array_almost_equal(np.load(m_f), np.load(s_f), decimal=3) elif dir1_files[0].suffix in {'.kml', '.png'}: return else: raise def assert_same_files_produced(dir1, dir2, dir3, ext, num_files=None): assert_two_dirs_equal(dir1, dir2, ext, num_files) assert_two_dirs_equal(dir1, dir3, ext, num_files) working_dirs = { GAMMA_SYSTEM_CONF: GAMMA_SYSTEM_FILES, ROIPAC_SYSTEM_CONF: ROIPAC_SYSTEM_FILES, GEOTIF_SYSTEM_CONF: GEOTIF_SYSTEM_FILES, Path(TEST_CONF_ROIPAC).name: ROIPAC_SML_TEST_DIR, Path(TEST_CONF_GAMMA).name: GAMMA_SML_TEST_DIR } def manipulate_test_conf(conf_file, work_dir: Path): params = Configuration(conf_file).__dict__ if conf_file == MEXICO_CROPA_CONF: copytree(MEXICO_CROPA_DIR, work_dir) copytree(MEXICO_CROPA_DIR_HEADERS, work_dir) copytree(MEXICO_CROPA_DIR_GEOMETRY, work_dir) copytree(MEXICO_CROPA_DIR_DEM_ERROR, work_dir) shutil.copy2(params[C.IFG_FILE_LIST], work_dir) shutil.copy2(params[C.HDR_FILE_LIST], work_dir) shutil.copy2(params[C.COH_FILE_LIST], work_dir) shutil.copy2(params[C.BASE_FILE_LIST], work_dir) for m_path in params[C.INTERFEROGRAM_FILES]: m_path.converted_path = work_dir.joinpath(Path(m_path.converted_path).name).as_posix() else: # legacy unit test data params[WORKING_DIR] = working_dirs[Path(conf_file).name] copytree(params[WORKING_DIR], work_dir) params[WORKING_DIR] = work_dir.as_posix() # manipulate params outdir = work_dir.joinpath('out') outdir.mkdir(exist_ok=True) params[C.OUT_DIR] = outdir.as_posix() params[C.TEMP_MLOOKED_DIR] = outdir.joinpath(C.TEMP_MLOOKED_DIR).as_posix() params[C.DEM_FILE] = work_dir.joinpath(Path(params[C.DEM_FILE]).name).as_posix() params[C.DEM_HEADER_FILE] = work_dir.joinpath(Path(params[C.DEM_HEADER_FILE]).name).as_posix() params[C.HDR_FILE_LIST] = work_dir.joinpath(Path(params[C.HDR_FILE_LIST]).name).as_posix() params[C.IFG_FILE_LIST] = work_dir.joinpath(Path(params[C.IFG_FILE_LIST]).name).as_posix() params[C.TMPDIR] = outdir.joinpath(C.TMPDIR).as_posix() params[C.COHERENCE_DIR] = outdir.joinpath(C.COHERENCE_DIR).as_posix() params[C.GEOMETRY_DIR] = outdir.joinpath(C.GEOMETRY_DIR).as_posix() params[C.APS_ERROR_DIR] = outdir.joinpath(C.APS_ERROR_DIR).as_posix() params[C.MST_DIR] = outdir.joinpath(C.MST_DIR).as_posix() params[C.ORB_ERROR_DIR] = outdir.joinpath(C.ORB_ERROR_DIR).as_posix() params[C.PHASE_CLOSURE_DIR] = outdir.joinpath(C.PHASE_CLOSURE_DIR).as_posix() params[C.DEM_ERROR_DIR] = outdir.joinpath(C.DEM_ERROR_DIR).as_posix() params[C.INTERFEROGRAM_DIR] = outdir.joinpath(C.INTERFEROGRAM_DIR).as_posix() params[C.VELOCITY_DIR] = outdir.joinpath(C.VELOCITY_DIR).as_posix() params[C.TIMESERIES_DIR] = outdir.joinpath(C.TIMESERIES_DIR).as_posix() return params class UnitTestAdaptation: @staticmethod def assertEqual(arg1, arg2): assert arg1 == arg2 @staticmethod def assertTrue(arg, msg=''): assert arg, msg @staticmethod def assertFalse(arg, msg=''): assert ~ arg, msg @staticmethod def assertIsNotNone(arg, msg=''): assert arg is not None, msg @staticmethod def assertIsNone(arg, msg=''): assert arg is None, msg @staticmethod def assertDictEqual(d1: dict, d2: dict): assert d1 == d2 @staticmethod def assertRaises(excpt: Exception, func, *args, **kwargs): with pytest.raises(excpt): func(*args, **kwargs) @staticmethod def assertIn(item, s: Iterable): assert item in s @staticmethod def assertAlmostEqual(arg1, arg2, places=7): places *= -1 num = Decimal((0, (1, ), places)) assert arg1 == pytest.approx(arg2, abs=num) def min_params(out_dir): params = {} params[C.OUT_DIR] = out_dir params[C.IFG_LKSX] = 1 params[C.IFG_LKSY] = 1 params[C.IFG_CROP_OPT] = 4 params[C.TEMP_MLOOKED_DIR] = Path(tempfile.mkdtemp()) params[C.ORBFIT_OFFSET] = 1 params[C.ORBITAL_FIT_METHOD] = 1 params[C.ORBITAL_FIT_DEGREE] = 2 params[C.ORBITAL_FIT_LOOKS_X] = 1 params[C.ORBITAL_FIT_LOOKS_Y] = 1 return params def sub_process_run(cmd, *args, **kwargs): return run(cmd, *args, shell=True, check=True, **kwargs) def original_ifg_paths(ifglist_path, working_dir): """ Returns sequence of paths to files in given ifglist file. Args: ifglist_path: Absolute path to interferogram file list. working_dir: Absolute path to observations directory. Returns: list: List of full paths to interferogram files. """ ifglist = parse_namelist(ifglist_path) return [os.path.join(working_dir, p) for p in ifglist] ================================================ FILE: tests/conftest.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains fixtures for use in the PyRate test suite. """ import shutil import random import string import tempfile import pytest import pyrate.constants as C from pyrate.constants import PYRATEPATH from pyrate.core import mpiops, shared from pyrate.configuration import Configuration from tests.common import TEST_CONF_ROIPAC, TEST_CONF_GAMMA, SML_TEST_DEM_TIF, MEXICO_CROPA_CONF from tests.common import ROIPAC_SYSTEM_CONF, GAMMA_SYSTEM_CONF, GEOTIF_SYSTEM_CONF, SML_TEST_COH_LIST @pytest.fixture def tempdir(): """ tempdir for tests """ def tmpdir(): return tempfile.mkdtemp() return tmpdir @pytest.fixture(params=[ROIPAC_SYSTEM_CONF, GEOTIF_SYSTEM_CONF, GAMMA_SYSTEM_CONF]) def system_conf(request): params = Configuration(request.param).__dict__ yield request.param shutil.rmtree(params[C.OUT_DIR], ignore_errors=True) @pytest.fixture def random_filename(tmp_path_factory): def make_random_filename(ext=''): fname = ''.join(random.choice(string.ascii_lowercase) for _ in range(10)) return tmp_path_factory.mktemp(basename='pyrate-').joinpath(fname + ext) return make_random_filename # Make sure all MPI tests use this fixure @pytest.fixture() def mpisync(request): mpiops.comm.barrier() def fin(): mpiops.comm.barrier() request.addfinalizer(fin) return mpiops.comm @pytest.fixture(params=[0, 1]) def coh_mask(request): return request.param @pytest.fixture(params=[1, 2]) def ref_est_method(request): return request.param @pytest.fixture(params=[(-1, -1), (150.941666654, -34.218333314)]) def ref_pixel(request): return request.param @pytest.fixture(params=[1, 3]) def orbfit_lks(request): return request.param @pytest.fixture(params=C.ORB_METHOD_NAMES.keys()) def orbfit_method(request): return request.param @pytest.fixture(params=C.ORB_DEGREE_NAMES.keys()) def orbfit_degrees(request): return request.param @pytest.fixture(params=[1, 3]) def get_lks(request): return request.param @pytest.fixture(params=[1, 4]) def get_crop(request): return request.param @pytest.fixture() def get_config(): def params(conf_file): params_ = Configuration(conf_file).__dict__ return params_ return params @pytest.fixture def gamma_params(): params = Configuration(TEST_CONF_GAMMA).__dict__ shared.mkdir_p(params[C.OUT_DIR]) yield params shutil.rmtree(params[C.OUT_DIR], ignore_errors=True) @pytest.fixture def roipac_params(): params = Configuration(TEST_CONF_ROIPAC).__dict__ yield params shutil.rmtree(params[C.OUT_DIR], ignore_errors=True) @pytest.fixture def mexico_cropa_params(): params = Configuration(MEXICO_CROPA_CONF).__dict__ yield params shutil.rmtree(params[C.OUT_DIR], ignore_errors=True) @pytest.fixture(params=[TEST_CONF_GAMMA, TEST_CONF_ROIPAC]) def roipac_or_gamma_conf(request): return request.param @pytest.fixture(params=[TEST_CONF_GAMMA], scope='session') def gamma_conf(request): params = Configuration(TEST_CONF_GAMMA).__dict__ yield request.param shutil.rmtree(params[C.OUT_DIR], ignore_errors=True) @pytest.fixture def coh_list_file(): return SML_TEST_COH_LIST @pytest.fixture def dem(): d = shared.dem_or_ifg(SML_TEST_DEM_TIF) d.open() return d @pytest.fixture(params=[TEST_CONF_GAMMA, MEXICO_CROPA_CONF], scope='session') def gamma_or_mexicoa_conf(request): params = Configuration(request.param).__dict__ yield request.param shutil.rmtree(params[C.OUT_DIR], ignore_errors=True) @pytest.fixture(params=range(5)) def run_number(request): return request.param GEOTIFF = PYRATEPATH.joinpath('tests', 'test_data', 'geotiffs') @pytest.fixture def geotiffs(): tifs = [u.as_posix() for u in GEOTIFF.glob('*_unw.tif')] tifs.sort() return tifs @pytest.fixture def ten_geotiffs(): tifs = [u.as_posix() for u in GEOTIFF.glob('*_unw.tif')] tifs.sort() return tifs[:10] @pytest.fixture def cropa_geotifs(mexico_cropa_params): m_paths = mexico_cropa_params[C.INTERFEROGRAM_FILES] return [m.converted_path for m in m_paths] ================================================ FILE: tests/phase_closure/__init__.py ================================================ ================================================ FILE: tests/phase_closure/common.py ================================================ class IfgDummy: def __init__(self, ifg_path): self.tmp_sampled_path = ifg_path ================================================ FILE: tests/phase_closure/conftest.py ================================================ from pathlib import Path import pytest from pyrate import constants as C from tests.phase_closure.common import IfgDummy from tests.common import MEXICO_CROPA_DIR @pytest.fixture() def closure_params(geotiffs): ifg_files = [IfgDummy(ifg_path) for ifg_path in geotiffs] params = { C.INTERFEROGRAM_FILES: ifg_files, C.MAX_LOOP_LENGTH: 100, 'geotiffs': geotiffs } return params @pytest.fixture(scope='module') def cropa_geotifs(): tifs = [u.as_posix() for u in Path(MEXICO_CROPA_DIR).glob('*_unw.tif')] tifs.sort() return tifs ================================================ FILE: tests/phase_closure/test_closure_check.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np import pyrate.constants as C from pyrate.core.phase_closure.mst_closure import ( sort_loops_based_on_weights_and_date, ) from pyrate.core.phase_closure.closure_check import ( discard_loops_containing_max_ifg_count, __drop_ifgs_if_not_part_of_any_loop ) def test_discard_loops_containing_max_ifg_count(closure_params): params = closure_params loops1 = retain_loops(params) loops2 = retain_loops(params) m_weights = [m.weight for m in loops1] s_weights = [m.weight for m in loops2] np.testing.assert_array_equal(m_weights, s_weights) def retain_loops(params): sorted_loops = sort_loops_based_on_weights_and_date(params) params[C.MAX_LOOP_REDUNDANCY] = 2 params[C.MAX_LOOP_LENGTH] = 3 retained_loops_meeting_max_loop_criteria = [sl for sl in sorted_loops if len(sl) <= params[C.MAX_LOOP_LENGTH]] msg = f"After applying MAX_LOOP_LENGTH={params[C.MAX_LOOP_LENGTH]} criteria, " \ f"{len(retained_loops_meeting_max_loop_criteria)} loops are retained" print(msg) retained_loops = discard_loops_containing_max_ifg_count(retained_loops_meeting_max_loop_criteria, params) msg = f"After applying MAX_LOOP_REDUNDANCY={params[C.MAX_LOOP_REDUNDANCY]} criteria, " \ f"{len(retained_loops)} loops are retained" print(msg) return retained_loops def test_drop_ifgs_if_not_part_of_any_loop(closure_params): params = closure_params params[C.NO_DATA_VALUE] = 0.0 geotiffs = params['geotiffs'] loops1 = retain_loops(params) selected_tifs1 = __drop_ifgs_if_not_part_of_any_loop(geotiffs, loops1, params) loops2 = retain_loops(params) selected_tifs2 = __drop_ifgs_if_not_part_of_any_loop(geotiffs, loops2, params) assert all([a == b for a, b in zip(selected_tifs1, selected_tifs2)]) ================================================ FILE: tests/phase_closure/test_collect_loops.py ================================================ from typing import List from datetime import date import pytest import numpy as np import networkx as nx from pyrate.core.phase_closure.mst_closure import Edge, __setup_edges, __find_closed_loops from pyrate.core.phase_closure.collect_loops import dedupe_loops, find_loops def test_collect_loops(): graph = np.array( [ [0, 1, 0, 1, 0], [1, 0, 1, 0, 1], [0, 1, 0, 1, 0], [1, 0, 1, 0, 1], [0, 1, 0, 1, 0] ] ) n = 4 count, all_loops = find_loops(graph, n) assert count == 6 deduped_loops = dedupe_loops(all_loops) np.testing.assert_array_equal(deduped_loops, [[0, 1, 2, 3], [0, 1, 4, 3], [1, 2, 3, 4]]) def test_count_loops(): graph = np.array( [ [0, 1, 1, 1], [1, 0, 1, 1], [1, 1, 0, 1], [1, 1, 1, 0], ] ) n = 4 count, all_loops = find_loops(graph, n) assert len(all_loops) == 6 np.testing.assert_array_equal(all_loops, [[0, 1, 2, 3], [0, 1, 3, 2], [0, 2, 1, 3], [0, 2, 3, 1], [0, 3, 1, 2], [0, 3, 2, 1]]) deduped_loops = dedupe_loops(all_loops) np.testing.assert_array_equal(deduped_loops, [all_loops[0]]) def __find_closed_loops_nx(edges: List[Edge], max_loop_length: int) -> List[List[date]]: g = nx.Graph() edges = [(we.first, we.second) for we in edges] g.add_edges_from(edges) dg = nx.DiGraph(g) print(f"Evaluating all possible loops using NetworkX simple_cycles function") simple_cycles = list(nx.simple_cycles(dg)) print(f"Total number of possible loops is {len(simple_cycles)}") print(f"Discarding loops with less than 3 edges and more than {max_loop_length} edges") loop_subset = [scc for scc in simple_cycles if (len(scc) > 2) # three or more edges reqd for closed loop and (len(scc) <= max_loop_length) # discard loops exceeding max loop length ] print(f"Number of remaining loops is {len(loop_subset)}") # also discard loops when the loop members are the same return dedupe_loops(loop_subset) @pytest.fixture(scope='module') def available_edges(cropa_geotifs): available_edges = __setup_edges(cropa_geotifs) return available_edges def max_loop_length(available_edges): all_possible_loops = __find_closed_loops_nx(available_edges, max_loop_length=100) max_length = max([len(l) for l in all_possible_loops]) return max_length def test_find_closed_loops_vs_networkx(available_edges): max_length = max_loop_length(available_edges) for n in range(max_length + 1): print(f'====checking for max_loop_length {n}=====') networkx_loops = __find_closed_loops_nx(available_edges, max_loop_length=n) networkx_loops = [sorted(l) for l in networkx_loops] networkx_set = {tuple(l) for l in networkx_loops} our_loops = __find_closed_loops(available_edges, max_loop_length=n) our_loops = [sorted(l) for l in our_loops] our_set = {tuple(l) for l in our_loops} assert networkx_set == our_set ================================================ FILE: tests/phase_closure/test_mst_closure.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from datetime import date import numpy as np import pytest from pyrate.constants import PYRATEPATH from pyrate.core.phase_closure.mst_closure import ( __find_closed_loops, Edge, SignedWeightedEdge, SignedEdge, __setup_edges, __add_signs_and_weights_to_loops, sort_loops_based_on_weights_and_date, WeightedLoop, __find_signed_closed_loops ) import pyrate.constants as C from tests.phase_closure.common import IfgDummy GEOTIFF = PYRATEPATH.joinpath('tests', 'test_data', 'geotiffs') @pytest.fixture def geotiffs(): tifs = [u.as_posix() for u in GEOTIFF.glob('*_unw.tif')] tifs.sort() return tifs @pytest.fixture def all_loops(geotiffs): edges = __setup_edges(geotiffs) loops = __find_closed_loops(edges, max_loop_length=100) assert len(loops) == 541 return loops @pytest.fixture def edges(geotiffs): all_edges = __setup_edges(geotiffs) return all_edges @pytest.fixture def signed_loops(all_loops, edges): loops = __add_signs_and_weights_to_loops(all_loops, edges) return loops @pytest.fixture(params=[True, False]) def weight(request): return request.param def test_setup_edges(geotiffs): edges = __setup_edges(geotiffs) assert len(edges) == len(geotiffs) == 30 assert isinstance(edges[0], Edge) def test_associate_ifgs_with_loops(signed_loops, geotiffs): assert len(geotiffs) == 30 assert len(signed_loops) == 541 assert isinstance(signed_loops[0], WeightedLoop) swe = signed_loops[0].loop[0] assert isinstance(swe, SignedWeightedEdge) assert isinstance(swe.signed_edge, SignedEdge) assert isinstance(swe.edge, Edge) assert isinstance(swe.first, date) assert isinstance(swe.second, date) def test_sort_loops_based_on_weights_and_date(geotiffs): ifg_files = [IfgDummy(ifg_path) for ifg_path in geotiffs] params = { C.INTERFEROGRAM_FILES: ifg_files, C.MAX_LOOP_LENGTH: 100 } weighted_loops = sort_loops_based_on_weights_and_date(params) assert len(weighted_loops) == 541 # order weights = [w.weight for w in weighted_loops] earliest_dates = [w.earliest_date for w in weighted_loops] assert np.all(np.diff(weights) >= 0) for i, (w, d, wl) in enumerate(zip(weights[:-1], earliest_dates[:-1], weighted_loops[:-1])): sub_list = [d] if w == weights[i+1]: sub_list.append(earliest_dates[i+1]) if len(sub_list) > 1: tds = np.array([td.days for td in np.diff(sub_list)]) assert np.all(tds >= 0) # assert all dates are increasing for same weights def test_add_signs_and_weights_to_loops(closure_params): geotiffs = closure_params["geotiffs"] """also tests find_signed_closed_loops""" all_edges = __setup_edges(geotiffs) all_loops = __find_closed_loops(all_edges, max_loop_length=closure_params[C.MAX_LOOP_LENGTH]) loops1 = __add_signs_and_weights_to_loops(all_loops, all_edges) all_edges = __setup_edges(geotiffs) all_loops = __find_closed_loops(all_edges, closure_params[C.MAX_LOOP_LENGTH]) loops2 = __add_signs_and_weights_to_loops(all_loops, all_edges) compare_loops(loops1, loops2) def compare_loops(loops1, loops2): for i, (l1, l2) in enumerate(zip(loops1, loops2)): assert l1.weight == l2.weight for ll1, ll2 in zip(l1.loop, l2.loop): assert ll1.weight == ll2.weight assert ll1.first == ll2.first assert ll1.second == ll2.second assert ll1.sign == ll2.sign assert i == len(loops1) - 1 assert i == 540 def test_find_signed_closed_loops(closure_params): loops1 = __find_signed_closed_loops(closure_params) loops2 = __find_signed_closed_loops(closure_params) compare_loops(loops1, loops2) def test_sort_loops_based_on_weights_and_date_2(closure_params): sorted_loops1 = sort_loops_based_on_weights_and_date(closure_params) sorted_loops2 = sort_loops_based_on_weights_and_date(closure_params) compare_loops(sorted_loops1, sorted_loops2) ================================================ FILE: tests/phase_closure/test_plot_closure.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import shutil from pathlib import Path from subprocess import check_call import pytest import pyrate.constants as C from pyrate import correct from pyrate.configuration import Configuration from tests.common import MEXICO_CROPA_CONF try: import matplotlib.pyplot as plt import matplotlib as mpl from mpl_toolkits.axes_grid1 import make_axes_locatable cmap = mpl.cm.Spectral PLOT = True except ImportError as e: PLOT = False steps = ['orbfit', 'refphase', 'phase_closure'] @pytest.mark.mpi @pytest.mark.slow @pytest.mark.skipif((not PLOT), reason='skipped as plotting packages are missing') def test_plot_closure(mexico_cropa_params): config = Configuration(MEXICO_CROPA_CONF) params = config.__dict__ check_call(f"mpirun -n 3 pyrate prepifg -f {MEXICO_CROPA_CONF}", shell=True) correct._copy_mlooked(params) correct.__validate_correct_steps(params) # house keeping correct._update_params_with_tiles(params) correct._create_ifg_dict(params) params[C.REFX_FOUND], params[C.REFY_FOUND] = correct.ref_pixel_calc_wrapper(params) # run through the correct steps in user specified sequence for step in steps: if step == 'phase_closure': correct.correct_steps[step](params, config) else: correct.correct_steps[step](params) closure_plot_file = Path(config.phase_closure_dir).joinpath(f'closure_loops_iteration_1_fig_0.png') assert closure_plot_file.exists() shutil.rmtree(params[C.OUT_DIR], ignore_errors=True) ================================================ FILE: tests/phase_closure/test_sum_closure.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pytest from pathlib import Path import numpy as np import pyrate.constants as C from pyrate.configuration import Configuration, write_config_file from tests.common import MEXICO_CROPA_CONF, manipulate_test_conf, PY37GDAL302, sub_process_run @pytest.fixture def modified_config(tempdir, get_lks=1, get_crop=1, orbfit_lks=2, orbfit_method=1, orbfit_degrees=1, ref_est_method=1): def modify_params(conf_file, parallel_vs_serial, output_conf_file): tdir = Path(tempdir()) params = manipulate_test_conf(conf_file, tdir) if params[C.PROCESSOR] == 1: # turn on coherence for gamma params[C.COH_MASK] = 1 params[C.PARALLEL] = parallel_vs_serial params[C.PROCESSES] = 4 params[C.APSEST] = 1 params[C.IFG_LKSX], params[C.IFG_LKSY] = get_lks, get_lks params[C.REFNX], params[C.REFNY] = 2, 2 params[C.IFG_CROP_OPT] = get_crop params[C.ORBITAL_FIT_LOOKS_X], params[ C.ORBITAL_FIT_LOOKS_Y] = orbfit_lks, orbfit_lks params[C.ORBITAL_FIT] = 1 params[C.ORBITAL_FIT_METHOD] = orbfit_method params[C.ORBITAL_FIT_DEGREE] = orbfit_degrees params[C.REF_EST_METHOD] = ref_est_method params[C.MAX_LOOP_LENGTH] = 3 params["rows"], params["cols"] = 3, 2 params["savenpy"] = 1 params["notiles"] = params["rows"] * params["cols"] # number of tiles # write new temp config output_conf = tdir.joinpath(output_conf_file) write_config_file(params=params, output_conf_file=output_conf) return output_conf, params return modify_params @pytest.mark.mpi @pytest.mark.slow @pytest.mark.skipif((not PY37GDAL302), reason="Only run in one CI env") def test_mpi_vs_single_process(modified_config): mpi_conf, m_params = modified_config(MEXICO_CROPA_CONF, 0, 'mpi_conf.conf') sub_process_run(f"mpirun -n 3 pyrate conv2tif -f {mpi_conf}") sub_process_run(f"mpirun -n 3 pyrate prepifg -f {mpi_conf}") sub_process_run(f"mpirun -n 3 pyrate correct -f {mpi_conf}") serial_conf, s_params = modified_config(MEXICO_CROPA_CONF, 0, 'single_conf.conf') sub_process_run(f"pyrate conv2tif -f {serial_conf}") sub_process_run(f"pyrate prepifg -f {serial_conf}") sub_process_run(f"pyrate correct -f {serial_conf}") parallel_conf, p_params = modified_config(MEXICO_CROPA_CONF, 1, 'parallel_conf.conf') sub_process_run(f"pyrate conv2tif -f {parallel_conf}") sub_process_run(f"pyrate prepifg -f {parallel_conf}") sub_process_run(f"pyrate correct -f {parallel_conf}") m_config = Configuration(mpi_conf) s_config = Configuration(serial_conf) p_config = Configuration(parallel_conf) m_closure = np.load(m_config.closure().closure) s_closure = np.load(s_config.closure().closure) p_closure = np.load(p_config.closure().closure) # loops m_loops = np.load(m_config.closure().loops, allow_pickle=True) s_loops = np.load(s_config.closure().loops, allow_pickle=True) p_loops = np.load(p_config.closure().loops, allow_pickle=True) m_weights = [m.weight for m in m_loops] s_weights = [m.weight for m in s_loops] p_weights = [m.weight for m in p_loops] np.testing.assert_array_equal(m_weights, s_weights) np.testing.assert_array_equal(m_weights, p_weights) for i, (m, s) in enumerate(zip(m_loops, s_loops)): assert all(m_e == s_e for m_e, s_e in zip(m.edges, s.edges)) # closure np.testing.assert_array_almost_equal(np.abs(m_closure), np.abs(s_closure), decimal=4) np.testing.assert_array_almost_equal(np.abs(m_closure), np.abs(p_closure), decimal=4) # num_occurrences_each_ifg m_num_occurrences_each_ifg = np.load(m_config.closure().num_occurences_each_ifg, allow_pickle=True) s_num_occurrences_each_ifg = np.load(s_config.closure().num_occurences_each_ifg, allow_pickle=True) p_num_occurrences_each_ifg = np.load(p_config.closure().num_occurences_each_ifg, allow_pickle=True) np.testing.assert_array_equal(m_num_occurrences_each_ifg, s_num_occurrences_each_ifg) np.testing.assert_array_equal(m_num_occurrences_each_ifg, p_num_occurrences_each_ifg) # check ps m_ifgs_breach_count = np.load(m_config.closure().ifgs_breach_count) s_ifgs_breach_count = np.load(s_config.closure().ifgs_breach_count) p_ifgs_breach_count = np.load(p_config.closure().ifgs_breach_count) np.testing.assert_array_equal(m_ifgs_breach_count, s_ifgs_breach_count) np.testing.assert_array_equal(m_ifgs_breach_count, p_ifgs_breach_count) ================================================ FILE: tests/test_algorithm.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains tests for the algorithm.py PyRate module. """ from datetime import date from math import pi, cos, sin, radians from numpy import array, reshape, squeeze from os.path import join import numpy as np from numpy.testing import assert_array_almost_equal, assert_allclose from pyrate.core.algorithm import (least_squares_covariance, is_square, unit_vector, ifg_date_lookup, get_all_epochs, get_epochs, first_second_ids, factorise_integer, ) from pyrate.configuration import parse_namelist from pyrate.core.shared import Ifg, convert_radians_to_mm from tests.common import small5_mock_ifgs, SML_TEST_TIF, UnitTestAdaptation class TestLeastSquaresTests(UnitTestAdaptation): """ Unit tests for the PyRate least_squares_covariance() implementation. """ @staticmethod def test_least_squares_covariance(): b = array([[13, 7.2, 5.7]]).T A = array([[1, 0.4, 0.3], [1, 1, 1]]).T v = array([[1, 1, 1]]).T r = least_squares_covariance(A, b, v) exp = [10.1628, 2.8744] assert_array_almost_equal(r.T.squeeze(), exp, decimal=4) def test_least_squares_covariance_overdetermined(self): # must be overdetermined, ie. more observations than params b = array([[10]]).T A = array([[1]]).T v = array([[1]]).T self.assertRaises(ValueError, least_squares_covariance, A, b, v) # try non transposed style b = array([[10]]) A = array([[1]]) v = array([[1]]) self.assertRaises(ValueError, least_squares_covariance, A, b, v) class TestAlgorithmTests(UnitTestAdaptation): """ Misc unittests for functions in the algorithm module. """ def test_factorise(self): self.assertEqual(factorise_integer(1), (1, 1)) self.assertEqual(factorise_integer(2), (2, 1)) self.assertEqual(factorise_integer(4), (2, 2)) self.assertEqual(factorise_integer(9), (3, 3)) self.assertEqual(factorise_integer(76), (4, 19)) self.assertEqual(factorise_integer(76.5), (4, 19)) a, b = factorise_integer(12) self.assertEqual(type(a), int) self.assertEqual(type(b), int) def test_is_square(self): self.assertTrue(is_square(np.empty((2, 2)))) def test_is_not_square(self): for shape in [(3, 2), (2, 3)]: self.assertFalse(is_square(np.empty(shape))) @staticmethod def test_phase_conversion(): # ROIPAC interferograms in units of radians, verify conversion to mm xs, ys = 5, 7 data = (np.arange(xs * ys) - 1.7) * 0.1 # fake a range of values data = np.where(data == 0, np.nan, data) wavelen = 0.0562356424 exp = (data * wavelen * 1000) / (4 * pi) act = convert_radians_to_mm(data, wavelen) assert_allclose(exp, act) def test_unit_vector(self): # last values here simulate a descending pass incidence = [radians(x) for x in (34.3, 39.3, 29.3, 34.3)] azimuth = [radians(x) for x in (77.8, 77.9, 80.0, 282.2)] vert, ns, ew = [], [], [] for i, a in zip(incidence, azimuth): vert.append(cos(i)) ns.append(sin(i) * cos(a)) ew.append(sin(i) * sin(a)) sh = 4 unitv = [array(ew), array(ns), array(vert)] unitv = [a.reshape(sh) for a in unitv] # NB: assumes radian inputs act = unit_vector(reshape(incidence, sh), reshape(azimuth, sh)) for a, e in zip(act, unitv): assert_array_almost_equal(squeeze(a), e) # check unit vec components have correct signs E, N, V = act # test E/W component of ascending is +ve self.assertTrue((E[:-2]).all() > 0) self.assertTrue(E[-1] < 0) # test E/W component of descending is -ve self.assertTrue((N > 0).all()) # ensure all north values are positive # check unit vec components have correct magnitudes self.assertTrue((abs(V) > abs(E)).all()) self.assertTrue((abs(V) > abs(N)).all()) self.assertTrue((abs(E) > abs(N)).all()) class TestDateLookup(UnitTestAdaptation): """ Tests for the algorithm.ifg_date_lookup() function. """ @classmethod def setup_class(cls): cls.ifgs = small5_mock_ifgs() def test_ifg_date_lookup(self): # check reverse lookup of ifg given a first and second date tuple date_pair = (date(2006, 8, 28), date(2006, 12, 11)) i = ifg_date_lookup(self.ifgs, date_pair) self.assertEqual(self.ifgs[0], i) # test with reversed date tuple, should reorder it according to age date_pair = (date(2006, 12, 11), date(2006, 11, 6)) i = ifg_date_lookup(self.ifgs, date_pair) self.assertEqual(self.ifgs[1], i) def test_ifg_date_lookup_failure(self): # error when lookup cannot find an ifg given a date pair dates = (date(2006, 12, 11), date(2007, 3, 26)) self.assertRaises(ValueError, ifg_date_lookup, self.ifgs, dates) def test_date_lookup_bad_inputs(self): # test some bad inputs to date lookup inputs = [(None, None), (1, 10), (34.56, 345.93), (date(2007, 3, 26), ""), (date(2007, 3, 26), None)] for d in inputs: self.assertRaises(ValueError, ifg_date_lookup, self.ifgs, d) # TODO: InitialModelTests #class InitialModelTests(unittest.TestCase): # def test_initial_model(self): # 1. fake an RSC file with coords # 2. fake a ones(shape) # could also make a ramp etc # data is single band of DISPLACEMENT #raise NotImplementedError class TestEpochs(UnitTestAdaptation): """ Unittests for the EpochList class. """ def test_get_epochs(self): def str2date(s): segs = s[:4], s[4:6], s[6:] # year, month, day return date(*[int(sg) for sg in segs]) raw_date = ['20060619', '20060828', '20061002', '20061106', '20061211', '20070115', '20070219', '20070326', '20070430', '20070604', '20070709', '20070813', '20070917'] exp_dates = [str2date(d) for d in raw_date] exp_repeat = [1, 1, 3, 3, 4, 3, 3, 3, 3, 3, 3, 2, 2] exp_spans = [0, 0.1916, 0.2875, 0.3833, 0.4791, 0.5749, 0.6708, 0.7666, 0.8624, 0.9582, 1.0541, 1.1499, 1.2457] ifms = join(SML_TEST_TIF, "ifms_17") ifgs = [Ifg(join(SML_TEST_TIF, p)) for p in parse_namelist(ifms)] for i in ifgs: i.open() epochs = get_epochs(ifgs)[0] self.assertTrue((exp_dates == epochs.dates).all()) self.assertTrue((exp_repeat == epochs.repeat).all()) assert_array_almost_equal(exp_spans, epochs.spans, decimal=4) def test_get_all_epochs(self): # test function to extract all dates from sequence of ifgs ifgs = small5_mock_ifgs() for i in ifgs: i.nodata_value = 0 dates = [date(2006, 8, 28), date(2006, 11, 6), date(2006, 12, 11), date(2007, 1, 15), date(2007, 3, 26), date(2007, 9, 17)] self.assertEqual(dates, sorted(set(get_all_epochs(ifgs)))) def test_get_epoch_count(self): self.assertEqual(6, len(set(get_all_epochs(small5_mock_ifgs())))) def test_first_second_ids(self): d0 = date(2006, 6, 19) d1 = date(2006, 8, 28) d2 = date(2006, 10, 2) d3 = date(2006, 11, 6) exp = {d0: 0, d1: 1, d2: 2, d3: 3} # test unordered and with duplicates self.assertEqual(exp, first_second_ids([d3, d0, d2, d1])) self.assertEqual(exp, first_second_ids([d3, d0, d2, d1, d3, d0])) ================================================ FILE: tests/test_aps.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains tests for the APS PyRate module. """ import os import shutil import pytest import numpy as np from os.path import join from scipy.ndimage import gaussian_filter1d, gaussian_filter from numpy.testing import assert_array_almost_equal import pyrate.constants as C from pyrate import conv2tif, prepifg, correct from pyrate.configuration import Configuration, MultiplePaths from pyrate.core import shared from pyrate.core.aps import spatio_temporal_filter, _interpolate_nans_2d, _kernel from pyrate.core.aps import gaussian_temporal_filter as tlpfilter, gaussian_spatial_filter as slpfilter from pyrate.core.shared import Ifg from pyrate.core.ifgconstants import DAYS_PER_YEAR from tests import common from tests.common import BASE_TEST, PY37GDAL304 @pytest.fixture(params=["linear", "nearest", "cubic"]) def slpnanfill_method(request): return request.param @pytest.fixture(params=[0.1, 0.5, 1, 5]) def slpfcutoff_method(request): return request.param @pytest.fixture(params=[0.001, 0.01]) def slpfcutoff(request): return request.param def test_interpolate_nans_2d(slpnanfill_method): arr = np.random.rand(200, 100) arr[arr < 0.1] = np.nan # insert some nans assert np.sum(np.isnan(arr)) != 0 # some nans present _interpolate_nans_2d(arr, method=slpnanfill_method) assert np.sum(np.isnan(arr)) == 0 # should not be any nans class TestSpatialFilter: """ Test the implementation of Gaussian spatial filter """ def setup_method(self): ifg_path = join(str(BASE_TEST), 'cropB', '20180106-20180130_ifg.tif') ifg = Ifg(ifg_path) ifg.open() p = ifg.phase_data self.x_size = ifg.x_size self.y_size = ifg.y_size # convert zeros to NaNs p[p == 0] = np.nan self.phase = p def test_gaussian_filter(self, slpfcutoff_method): """ Compare against scipy.ndimage.gaussian_filter, that operates in the spatial domain on data with equal resolution in x and y """ e = gaussian_filter(self.phase, sigma=slpfcutoff_method) r = slpfilter(self.phase, cutoff=slpfcutoff_method, x_size=self.x_size, y_size=self.y_size) # pull data from middle of images; away from potential edge effects exp = e[50:150, 50:150] res = r[50:150, 50:150] assert_array_almost_equal(res, exp, 0) def test_gaussian_kernel(): """ Test the Gaussian smoothing kernel """ x = np.arange(1, 10, 2) res = _kernel(x, 3) exp = np.array([0.94595947, 0.60653066, 0.24935221, 0.06572853, 0.011109]) np.testing.assert_array_almost_equal(res, exp, decimal=6) res = _kernel(x, 4) exp = np.array([0.96923323, 0.7548396, 0.45783336, 0.21626517, 0.07955951]) np.testing.assert_array_almost_equal(res, exp, decimal=6) res = _kernel(x, 0.001) exp = np.array([0, 0, 0, 0, 0]) np.testing.assert_array_equal(res, exp) class TestTemporalFilter: """ Tests for the temporal filter with synthetic data for a single pixel """ def setup_method(self): self.thr = 1 # no nans in these test cases, threshold = 1 # instance of normally distributed noise n = np.array([-0.36427456, 0.69539061, 0.42181139, -2.56306134, 0.55844095, -0.65562626, 0.65607911, 1.19431637, -1.43837395, -0.91656358]) # synthetic incremental displacement d = np.array([1., 1., 0.7, 0.3, 0., 0.1, 0.2, 0.6, 1., 1.]) # incremental displacement + noise self.tsincr = d * 2 + n # regular time series, every 12 days self.interval = 12 / DAYS_PER_YEAR # 0.03285 years intv = np.ones(d.shape, dtype=np.float32) * self.interval self.span = np.cumsum(intv) def test_tlpfilter_repeatability(self): """ TEST 1: check for repeatability against expected result from tlpfilter. Cutoff equal to sampling interval (sigma=1) """ res = tlpfilter(self.tsincr, self.interval, self.span, self.thr) exp = np.array([1.9936507, 1.9208364, 1.0252733, -0.07402889, -0.1842336, 0.24325351, 0.94737214, 1.3890865, 1.1903466, 1.0036403]) np.testing.assert_array_almost_equal(res, exp, decimal=6) def test_tlpfilter_scipy_sig1(self): """ TEST 2: compare tlpfilter to scipy.ndimage.gaussian_filter1d. Works for regularly sampled data. Cutoff equal to sampling interval (sigma=1) """ res = tlpfilter(self.tsincr, self.interval, self.span, self.thr) exp = gaussian_filter1d(self.tsincr, sigma=1) np.testing.assert_array_almost_equal(res, exp, decimal=1) def test_tlpfilter_scipy_sig2(self): """ TEST 3: compare tlpfilter to scipy.ndimage.gaussian_filter1d. Works for regularly sampled data. Cutoff equal to twice the sampling interval (sigma=2) """ res = tlpfilter(self.tsincr, self.interval * 2, self.span, self.thr) exp = gaussian_filter1d(self.tsincr, sigma=2) np.testing.assert_array_almost_equal(res, exp, decimal=1) def test_tlpfilter_scipy_sig05(self): """ TEST 4: compare tlpfilter to scipy.ndimage.gaussian_filter1d. Works for regularly sampled data. Cutoff equal to half the sampling interval (sigma=0.5) """ res = tlpfilter(self.tsincr, self.interval * 0.5, self.span, self.thr) exp = gaussian_filter1d(self.tsincr, sigma=0.5) np.testing.assert_array_almost_equal(res, exp, decimal=2) @pytest.mark.slow @pytest.mark.skipif(not PY37GDAL304, reason="Only run in one CI env") class TestAPSErrorCorrectionsOnDiscReused: @classmethod def setup_method(cls): cls.conf = common.TEST_CONF_GAMMA params = Configuration(cls.conf).__dict__ conv2tif.main(params) params = Configuration(cls.conf).__dict__ prepifg.main(params) cls.params = Configuration(cls.conf).__dict__ correct._copy_mlooked(cls.params) correct._update_params_with_tiles(cls.params) correct._create_ifg_dict(cls.params) multi_paths = cls.params[C.INTERFEROGRAM_FILES] cls.ifg_paths = [p.tmp_sampled_path for p in multi_paths] cls.ifgs = [shared.Ifg(i) for i in cls.ifg_paths] for i in cls.ifgs: i.open() shared.save_numpy_phase(cls.ifg_paths, cls.params) correct.mst_calc_wrapper(cls.params) @classmethod def teardown_method(cls): shutil.rmtree(cls.params[C.OUT_DIR]) def test_aps_error_files_on_disc(self, slpnanfill_method, slpfcutoff): self.params[C.SLPF_NANFILL_METHOD] = slpnanfill_method self.params[C.SLPF_CUTOFF] = slpfcutoff spatio_temporal_filter(self.params) # test_orb_errors_written aps_error_files = [MultiplePaths.aps_error_path(i, self.params) for i in self.ifg_paths] assert all(p.exists() for p in aps_error_files) last_mod_times = [os.stat(o).st_mtime for o in aps_error_files] phase_prev = [i.phase_data for i in self.ifgs] # run aps error removal again spatio_temporal_filter(self.params) aps_error_files2 = [MultiplePaths.aps_error_path(i, self.params) for i in self.ifg_paths] # if files are written again - times will change last_mod_times_2 = [os.stat(o).st_mtime for o in aps_error_files2] # test_aps_error_reused_if_params_unchanged assert all(a == b for a, b in zip(last_mod_times, last_mod_times_2)) phase_now = [i.phase_data for i in self.ifgs] # run aps error correction once mroe spatio_temporal_filter(self.params) aps_error_files3 = [MultiplePaths.aps_error_path(i, self.params) for i in self.ifg_paths] last_mod_times_3 = [os.stat(o).st_mtime for o in aps_error_files3] assert all(a == b for a, b in zip(last_mod_times, last_mod_times_3)) phase_again = [i.phase_data for i in self.ifgs] np.testing.assert_array_equal(phase_prev, phase_now) np.testing.assert_array_equal(phase_prev, phase_again) ================================================ FILE: tests/test_constants.py ================================================ import re from pathlib import Path import pytest from pyrate.constants import twelve_digits_pattern, sixteen_digits_pattern from pyrate.configuration import parse_namelist from tests.common import IFMS16, GAMMA_SML_TEST_DIR @pytest.fixture def six_digits_filenames(): return IFMS16 @pytest.fixture def eight_digits_filenames(): files = list(parse_namelist(Path(GAMMA_SML_TEST_DIR).joinpath('ifms_17').as_posix())) return files @pytest.mark.parametrize( "regex_pattern,extracted_string_length", [ (twelve_digits_pattern, 12+1), # +1 due to the internal joining `-` (sixteen_digits_pattern, 16+1) ] ) def test_file_patterns(regex_pattern, extracted_string_length, six_digits_filenames, eight_digits_filenames): if regex_pattern == twelve_digits_pattern: for f in six_digits_filenames: m = re.search(twelve_digits_pattern, Path(f).stem).group() assert len(m) == extracted_string_length if regex_pattern == sixteen_digits_pattern: for f in eight_digits_filenames: m = re.search(sixteen_digits_pattern, Path(f).stem).group() assert len(m) == extracted_string_length ================================================ FILE: tests/test_conv2tif.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains tests for the PyRate conv2tif script. """ import os import shutil import pytest import glob import copy import itertools from pathlib import Path import pyrate.configuration import pyrate.constants as C from pyrate.core.shared import Ifg, DEM from pyrate.core import ifgconstants as ifc from pyrate import conv2tif, prepifg, configuration from tests.common import manipulate_test_conf, GAMMA_SML_TEST_DIR def test_dem_and_incidence_not_converted(gamma_params): gp_copy = copy.deepcopy(gamma_params) gp_copy[C.DEM_FILE] = None gp_copy[C.APS_INCIDENCE_MAP] = None conv2tif.main(gp_copy) inc_tif = glob.glob(os.path.join(GAMMA_SML_TEST_DIR, '*inc.tif')) assert len(inc_tif) == 0 dem_tif = glob.glob(os.path.join(GAMMA_SML_TEST_DIR, '*dem.tif')) assert len(dem_tif) == 0 def test_conv2tif_file_types(tempdir, gamma_conf): tdir = Path(tempdir()) params = manipulate_test_conf(gamma_conf, tdir) params[C.COH_MASK] = 1 output_conf_file = 'conf.conf' output_conf = tdir.joinpath(output_conf_file) pyrate.configuration.write_config_file(params=params, output_conf_file=output_conf) params_s = configuration.Configuration(output_conf).__dict__ conv2tif.main(params_s) ifg_files = list(Path(tdir.joinpath(params_s[C.OUT_DIR])).glob('*_ifg.tif')) coh_files = list(Path(tdir.joinpath(params_s[C.OUT_DIR])).glob('*_coh.tif')) dem_file = list(Path(tdir.joinpath(params_s[C.GEOMETRY_DIR])).glob('*_dem.tif'))[0] # assert coherence and ifgs have correct metadata for i in itertools.chain(*[ifg_files, coh_files]): ifg = Ifg(i) ifg.open() md = ifg.meta_data if i.name.endswith('_ifg.tif'): assert md[ifc.DATA_TYPE] == ifc.ORIG continue if i.name.endswith('_coh.tif'): assert md[ifc.DATA_TYPE] == ifc.COH continue # assert dem has correct metadata dem = DEM(dem_file.as_posix()) dem.open() md = dem.dataset.GetMetadata() assert md[ifc.DATA_TYPE] == ifc.DEM shutil.rmtree(tdir) def test_tifs_placed_in_out_dir(gamma_params): # Test no tifs in obs dir tifs = glob.glob(os.path.join(gamma_params[C.INTERFEROGRAM_DIR], '*.tif')) assert len(tifs) == 0 # Test tifs in obs dir conv2tif.main(gamma_params) tifs = glob.glob(os.path.join(gamma_params[C.INTERFEROGRAM_DIR], '*.tif')) + \ glob.glob(os.path.join(gamma_params[C.COHERENCE_DIR], '*.tif')) + \ glob.glob(os.path.join(gamma_params[C.GEOMETRY_DIR], '*.tif')) \ assert len(tifs) == 35 def test_num_gamma_tifs_equals_num_unws(gamma_params): gtifs = conv2tif.main(gamma_params) # 17 unws + dem + 17 cohfiles assert len(gtifs) == 35 for g, _ in gtifs: # assert all output from conv2tfi are readonly assert Path(g).stat().st_mode == 33060 def test_num_roipac_tifs_equals_num_unws(roipac_params): gtifs = conv2tif.main(roipac_params) # 17 unws + dem assert len(gtifs) == 18 def test_conversion_not_recomputed(gamma_params): """ If a gtif already exists, conv2tif will return None instead of path to the gtif. """ conv2tif.main(gamma_params) gtifs_and_conversion_bool = conv2tif.main(gamma_params) assert all([not b for gt, b in gtifs_and_conversion_bool]) def test_no_tifs_exits(gamma_params): with pytest.raises(Exception): prepifg.main(gamma_params) def test_tifs_succeeds(gamma_params): conv2tif.main(gamma_params) prepifg.main(gamma_params) ================================================ FILE: tests/test_correct.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains tests for the correct.py PyRate module. """ import shutil from pathlib import Path import pytest import pyrate.constants as C from pyrate.configuration import ConfigException, Configuration, write_config_file from pyrate import correct, prepifg, conv2tif from tests import common def test_unsupported_process_steps_raises(gamma_conf): config = Configuration(gamma_conf) gamma_params = config.__dict__ gamma_params['correct'] = ['orbfit2', 'something_other_step'] with pytest.raises(ConfigException): correct.correct_ifgs(config) def test_supported_process_steps_dont_raise(gamma_params): supported_stpes = ['orbfit', 'refphase', 'mst', 'apscorrect', 'maxvar', 'demerror', 'phase_closure'] assert all([s in gamma_params['correct'] for s in supported_stpes]) correct.__validate_correct_steps(params=gamma_params) @pytest.mark.slow @pytest.mark.skipif(not common.PYTHON3P9, reason="Only run in one CI env") def test_process_treats_prepif_outputs_readonly(gamma_conf, tempdir, coh_mask): from pyrate.configuration import Configuration tdir = Path(tempdir()) params = common.manipulate_test_conf(gamma_conf, tdir) params[C.COH_MASK] = coh_mask params[C.PARALLEL] = 0 output_conf = tdir.joinpath('conf.cfg') write_config_file(params=params, output_conf_file=output_conf) params = Configuration(output_conf).__dict__ conv2tif.main(params) tifs = list(Path(params[C.INTERFEROGRAM_DIR]).glob('*_unw.tif')) assert len(tifs) == 17 if params[C.COH_FILE_LIST] is not None: coh_tifs = list(Path(params[C.COHERENCE_DIR]).glob('*_cc.tif')) assert len(coh_tifs) == 17 params = Configuration(output_conf).__dict__ prepifg.main(params) cropped_coh = list(Path(params[C.COHERENCE_DIR]).glob('*_coh.tif')) cropped_ifgs = list(Path(params[C.INTERFEROGRAM_DIR]).glob('*_ifg.tif')) dem_ifgs = list(Path(params[C.GEOMETRY_DIR]).glob('*_dem.tif')) if params[C.COH_FILE_LIST] is not None: # 17 + 1 dem + 17 coh files assert len(cropped_coh) + len(cropped_ifgs) + len(dem_ifgs) == 35 else: # 17 + 1 dem assert len(cropped_coh) + len(cropped_ifgs) + len(dem_ifgs) == 18 # check all tifs from conv2tif are still readonly for t in tifs: assert t.stat().st_mode == 33060 # check all prepifg outputs are readonly for c in cropped_coh + cropped_ifgs: assert c.stat().st_mode == 33060 config = Configuration(output_conf) correct.main(config) # check all after correct steps multilooked files are still readonly for c in cropped_coh + cropped_ifgs + dem_ifgs: assert c.stat().st_mode == 33060 shutil.rmtree(params[C.OUT_DIR]) ================================================ FILE: tests/test_covariance.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains tests for the covariance.py PyRate module. """ import os import shutil from pathlib import Path from numpy import array import numpy as np from numpy.testing import assert_array_almost_equal import pyrate.constants as C import pyrate.core.ref_phs_est import pyrate.core.refpixel from pyrate.core import shared, ifgconstants as ifc from pyrate import correct, prepifg, conv2tif from pyrate.core.covariance import cvd, get_vcmt, RDist from pyrate.configuration import Configuration, MultiplePaths import pyrate.core.orbital from tests import common from tests.common import ( small5_mock_ifgs, small5_ifg_paths, TEST_CONF_ROIPAC, small_data_setup, prepare_ifgs_without_phase ) class TestCovariance: @classmethod def setup_class(cls): cls.ifgs = small_data_setup() for i in cls.ifgs: i.mm_converted = True params = dict() params[C.NO_DATA_VALUE] = 0 params[C.NAN_CONVERSION] = True cls.params = params cls.r_dist = RDist(cls.ifgs[0])() def test_covariance_basic(self): for i in small5_ifg_paths(): maxvar, alpha = cvd(i, self.params, self.r_dist, calc_alpha=True) assert maxvar is not None assert alpha is not None print("maxvar: %s, alpha: %s" % (maxvar, alpha)) def test_covariance_17ifgs(self): # After raw data import # (no reference pixel correction and units in radians) exp_maxvar = [5.6149, 8.7710, 2.9373, 0.3114, 12.9931, 2.0459, 0.4236, 2.1243, 0.4745, 0.6725, 0.8333, 3.8232, 3.3052, 2.4925, 16.0159, 2.8025, 1.4345] exp_alpha = [0.0356, 0.1601, 0.5128, 0.5736, 0.0691, 0.1337, 0.2333, 0.3202, 1.2338, 0.4273, 0.9024, 0.1280, 0.3585, 0.1599, 0.0110, 0.1287, 0.0676] act_maxvar = [] act_alpha = [] for i in self.ifgs: maxvar, alpha = cvd(i.data_path, self.params, self.r_dist, calc_alpha=True) assert maxvar is not None assert alpha is not None act_maxvar.append(maxvar / 20.02) # rough conversion factor back to radians act_alpha.append(alpha) # below tests fails at 3 d.p. on account of above conversion factor not being accurate enough assert_array_almost_equal(act_maxvar, exp_maxvar, decimal=2) # This test fails for greater than 1 decimal place. # Discrepancies observed in distance calculations. assert_array_almost_equal(act_alpha, exp_alpha, decimal=1) class TestVCMT: def setup_class(cls): cls.ifgs = small_data_setup() def test_vcm_basic(self): ifgs = small5_mock_ifgs(5, 9) maxvar = [8.486, 12.925, 6.313, 0.788, 0.649] exp = array([[8.486, 5.2364, 0.0, 0.0, 0.0], [5.2364, 12.925, 4.5165, 1.5957, 0.0], [0.0, 4.5165, 6.313, 1.1152, 0.0], [0.0, 1.5957, 1.1152, 0.788, -0.3576], [0.0, 0.0, 0.0, -0.3576, 0.649]]) act = get_vcmt(ifgs, maxvar) assert_array_almost_equal(act, exp, decimal=3) def test_vcm_17ifgs(self): # TODO: maxvar should be calculated by vcm.cvd maxvar = [2.879, 4.729, 22.891, 4.604, 3.290, 6.923, 2.519, 13.177, 7.548, 6.190, 12.565, 9.822, 18.484, 7.776, 2.734, 6.411, 4.754] exp = array([ [2.879, 0.0, -4.059, -1.820, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 4.729, 0.0, 0.0, 1.972, 0.0, 0.0, -3.947, -2.987, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [-4.059, 0.0, 22.891, 5.133, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -7.497, -10.285, 0.0, 0.0, 0.0, 0.0], [-1.820, 0.0, 5.133, 4.604, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.362, 0.0, 0.0, -1.774, 0.0, 0.0], [0.0, 1.972, 0.0, 0.0, 3.290, 2.386, 1.439, -3.292, -2.492, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 2.386, 6.923, 2.088, 0.0, 0.0, -3.273, -4.663, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.439, 2.088, 2.519, 0.0, 0.0, 1.974, 0.0, 0.0, 0.0, -2.213, 0.0, 0.0, 0.0], [0.0, -3.947, 0.0, 0.0, -3.292, 0.0, 0.0, 13.177, 4.986, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.596, -3.957], [0.0, -2.987, 0.0, 0.0, -2.492, 0.0, 0.0, 4.986, 7.548, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.995], [0.0, 0.0, 0.0, 0.0, 0.0, -3.273, 1.974, 0.0, 0.0, 6.190, 4.410, 0.0, 0.0, -3.469, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, -4.663, 0.0, 0.0, 0.0, 4.410, 12.565, 0.0, 0.0, 4.942, 0.0, 0.0, 0.0], [0.0, 0.0, -7.497, 3.362, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 9.8221, 6.737, 0.0, -2.591, 0.0, 0.0], [0.0, 0.0, -10.285, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 6.737, 18.484, 0.0, 3.554, -5.443, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.213, 0.0, 0.0, -3.469, 4.942, 0.0, 0.0, 7.776, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, -1.774, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.591, 3.554, 0.0, 2.734, -2.093, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.596, 0.0, 0.0, 0.0, 0.0, -5.443, 0.0, -2.093, 6.411, -2.760], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -3.957, 2.995, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.760, 4.754] ]) act = get_vcmt(self.ifgs, maxvar) assert_array_almost_equal(act, exp, decimal=3) legacy_maxvar = [15.4156637191772, 2.85829424858093, 34.3486289978027, 2.59190344810486, 3.18510007858276, 3.61054635047913, 1.64398515224457, 14.9226036071777, 5.13451862335205, 6.82901763916016, 10.9644861221313, 14.5026779174805, 29.3710079193115, 8.00364685058594, 2.06328082084656, 5.66661834716797, 5.62802362442017] class TestLegacyEquality: @classmethod def setup_class(cls): roipac_params = Configuration(TEST_CONF_ROIPAC).__dict__ from copy import deepcopy params = deepcopy(roipac_params) shared.mkdir_p(params[C.TMPDIR]) params[C.REF_EST_METHOD] = 2 conv2tif.main(params) params = deepcopy(roipac_params) prepifg.main(params) params = deepcopy(roipac_params) base_ifg_paths = [c.unwrapped_path for c in params[C.INTERFEROGRAM_FILES]] dest_paths = [c.converted_path for c in params[C.INTERFEROGRAM_FILES]] params[C.INTERFEROGRAM_FILES] = [MultiplePaths(d, params) for d in dest_paths] for p in params[C.INTERFEROGRAM_FILES]: # hack p.sampled_path = p.converted_path for i in dest_paths: Path(i).chmod(0o664) # assign write permission as conv2tif output is readonly ifgs = common.pre_prepare_ifgs(dest_paths, params) correct._copy_mlooked(params) correct._update_params_with_tiles(params) correct._create_ifg_dict(params) pyrate.core.refpixel.ref_pixel_calc_wrapper(params) params[C.ORBFIT_OFFSET] = True pyrate.core.orbital.remove_orbital_error(ifgs, params) ifgs = prepare_ifgs_without_phase(dest_paths, params) for ifg in ifgs: ifg.close() for p in params[C.INTERFEROGRAM_FILES]: # hack p.tmp_sampled_path = p.sampled_path _, cls.ifgs = pyrate.core.ref_phs_est.ref_phase_est_wrapper(params) ifgs[0].open() r_dist = RDist(ifgs[0])() ifgs[0].close() # Calculate interferogram noise cls.maxvar = [cvd(i, params, r_dist, calc_alpha=True, save_acg=True, write_vals=True)[0] for i in dest_paths] cls.vcmt = get_vcmt(ifgs, cls.maxvar) for ifg in ifgs: ifg.close() cls.params = params @classmethod def teardown_class(cls): shutil.rmtree(cls.params[C.OUT_DIR]) def test_legacy_maxvar_equality_small_test_files(self): np.testing.assert_array_almost_equal(self.maxvar, legacy_maxvar, decimal=3) def test_legacy_vcmt_equality_small_test_files(self): from tests.common import SML_TEST_DIR LEGACY_VCM_DIR = os.path.join(SML_TEST_DIR, 'vcm') legacy_vcm = np.genfromtxt(os.path.join(LEGACY_VCM_DIR, 'vcmt.csv'), delimiter=',') np.testing.assert_array_almost_equal(legacy_vcm, self.vcmt, decimal=3) def test_metadata(self): for ifg in self.ifgs: if not ifg.is_open: ifg.open() assert ifc.PYRATE_MAXVAR in ifg.meta_data assert ifc.PYRATE_ALPHA in ifg.meta_data def test_save_cvd_data(self): from os.path import join, basename, isfile for ifg in self.ifgs: if not ifg.is_open: ifg.open() data_file = join(self.params[C.TMPDIR], 'cvd_data_{b}.npy'.format(b=basename(ifg.data_path).split('.')[0])) assert isfile(data_file) ================================================ FILE: tests/test_data/cropA/baseline_30 ================================================ tests/test_data/cropA/geometry/20180106-20180130_VV_8rlks_base.par tests/test_data/cropA/geometry/20180106-20180319_VV_8rlks_base.par tests/test_data/cropA/geometry/20180106-20180412_VV_8rlks_base.par tests/test_data/cropA/geometry/20180106-20180518_VV_8rlks_base.par tests/test_data/cropA/geometry/20180130-20180307_VV_8rlks_base.par tests/test_data/cropA/geometry/20180130-20180412_VV_8rlks_base.par tests/test_data/cropA/geometry/20180307-20180319_VV_8rlks_base.par tests/test_data/cropA/geometry/20180307-20180331_VV_8rlks_base.par tests/test_data/cropA/geometry/20180307-20180506_VV_8rlks_base.par tests/test_data/cropA/geometry/20180307-20180530_VV_8rlks_base.par tests/test_data/cropA/geometry/20180307-20180611_VV_8rlks_base.par tests/test_data/cropA/geometry/20180319-20180331_VV_8rlks_base.par tests/test_data/cropA/geometry/20180319-20180506_VV_8rlks_base.par tests/test_data/cropA/geometry/20180319-20180518_VV_8rlks_base.par tests/test_data/cropA/geometry/20180319-20180530_VV_8rlks_base.par tests/test_data/cropA/geometry/20180319-20180623_VV_8rlks_base.par tests/test_data/cropA/geometry/20180331-20180412_VV_8rlks_base.par tests/test_data/cropA/geometry/20180331-20180506_VV_8rlks_base.par tests/test_data/cropA/geometry/20180331-20180518_VV_8rlks_base.par tests/test_data/cropA/geometry/20180331-20180530_VV_8rlks_base.par tests/test_data/cropA/geometry/20180331-20180623_VV_8rlks_base.par tests/test_data/cropA/geometry/20180331-20180717_VV_8rlks_base.par tests/test_data/cropA/geometry/20180412-20180506_VV_8rlks_base.par tests/test_data/cropA/geometry/20180412-20180518_VV_8rlks_base.par tests/test_data/cropA/geometry/20180506-20180518_VV_8rlks_base.par tests/test_data/cropA/geometry/20180506-20180530_VV_8rlks_base.par tests/test_data/cropA/geometry/20180506-20180611_VV_8rlks_base.par tests/test_data/cropA/geometry/20180506-20180623_VV_8rlks_base.par tests/test_data/cropA/geometry/20180506-20180705_VV_8rlks_base.par tests/test_data/cropA/geometry/20180506-20180717_VV_8rlks_base.par ================================================ FILE: tests/test_data/cropA/coherence_30 ================================================ tests/test_data/cropA/geotiffs/cropA_20180106-20180130_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180106-20180319_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180106-20180412_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180106-20180518_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180130-20180307_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180130-20180412_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180307-20180319_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180307-20180331_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180307-20180506_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180307-20180530_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180307-20180611_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180319-20180331_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180319-20180506_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180319-20180518_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180319-20180530_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180319-20180623_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180331-20180412_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180331-20180506_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180331-20180518_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180331-20180530_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180331-20180623_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180331-20180717_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180412-20180506_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180412-20180518_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180506-20180518_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180506-20180530_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180506-20180611_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180506-20180623_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180506-20180705_VV_8rlks_flat_eqa_cc.tif tests/test_data/cropA/geotiffs/cropA_20180506-20180717_VV_8rlks_flat_eqa_cc.tif ================================================ FILE: tests/test_data/cropA/geometry/20180106-20180130_VV_8rlks_base.par ================================================ initial_baseline(TCN): -0.1536945 40.1145103 4.5090025 m m m initial_baseline_rate: 0.0000000 0.0636107 0.0126167 m/s m/s m/s precision_baseline(TCN): 0.0000000 40.1010426 4.5164084 m m m precision_baseline_rate: 0.0000000 0.0703755 0.0082572 m/s m/s m/s unwrap_phase_constant: -0.00001 radians ================================================ FILE: tests/test_data/cropA/geometry/20180106-20180130_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): -0.154 40.115 4.509 orbit baseline rate (TCN) (m/s): 0.000e+00 6.361e-02 1.262e-02 baseline vector (TCN) (m): 0.000 40.101 4.516 baseline rate (TCN) (m/s): 0.000e+00 7.038e-02 8.257e-03 SLC-1 center baseline length (m): 40.3546 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 0.0000 39.4441 4.4393 27.496918 22.1492 32.9386 39.6932 0 200 0.0000 39.4441 4.4393 27.939152 22.4028 32.7667 39.6932 0 400 0.0000 39.4441 4.4393 28.370753 22.6490 32.5970 39.6932 0 600 0.0000 39.4441 4.4393 28.792243 22.8881 32.4295 39.6932 0 800 0.0000 39.4441 4.4393 29.204102 23.1207 32.2642 39.6932 0 1000 0.0000 39.4441 4.4393 29.606772 23.3468 32.1009 39.6932 0 1200 0.0000 39.4441 4.4393 30.000665 23.5670 31.9396 39.6932 0 1400 0.0000 39.4441 4.4393 30.386160 23.7813 31.7803 39.6932 0 1600 0.0000 39.4441 4.4393 30.763612 23.9902 31.6230 39.6932 0 1800 0.0000 39.4441 4.4393 31.133349 24.1937 31.4675 39.6932 0 2000 0.0000 39.4441 4.4393 31.495679 24.3923 31.3139 39.6932 0 2200 0.0000 39.4441 4.4393 31.850890 24.5859 31.1621 39.6932 0 2400 0.0000 39.4441 4.4393 32.199251 24.7749 31.0120 39.6932 0 2600 0.0000 39.4441 4.4393 32.541017 24.9595 30.8637 39.6932 0 2800 0.0000 39.4441 4.4393 32.876424 25.1397 30.7170 39.6932 0 3000 0.0000 39.4441 4.4393 33.205696 25.3158 30.5721 39.6932 0 3200 0.0000 39.4441 4.4393 33.529047 25.4880 30.4287 39.6932 0 3400 0.0000 39.4441 4.4393 33.846674 25.6563 30.2869 39.6932 0 3600 0.0000 39.4441 4.4393 34.158768 25.8209 30.1467 39.6932 0 3800 0.0000 39.4441 4.4393 34.465506 25.9819 30.0081 39.6932 0 4000 0.0000 39.4441 4.4393 34.767059 26.1395 29.8709 39.6932 0 4200 0.0000 39.4441 4.4393 35.063588 26.2937 29.7352 39.6932 0 4400 0.0000 39.4441 4.4393 35.355245 26.4447 29.6010 39.6932 0 4600 0.0000 39.4441 4.4393 35.642177 26.5926 29.4682 39.6932 0 4800 0.0000 39.4441 4.4393 35.924522 26.7375 29.3368 39.6932 0 5000 0.0000 39.4441 4.4393 36.202412 26.8795 29.2068 39.6932 0 5200 0.0000 39.4441 4.4393 36.475974 27.0186 29.0781 39.6932 0 5400 0.0000 39.4441 4.4393 36.745327 27.1550 28.9508 39.6932 0 5600 0.0000 39.4441 4.4393 37.010588 27.2888 28.8247 39.6932 0 5800 0.0000 39.4441 4.4393 37.271866 27.4199 28.7000 39.6932 0 6000 0.0000 39.4441 4.4393 37.529266 27.5486 28.5765 39.6932 0 6200 0.0000 39.4441 4.4393 37.782890 27.6748 28.4543 39.6932 0 6400 0.0000 39.4441 4.4393 38.032835 27.7987 28.3333 39.6932 0 6600 0.0000 39.4441 4.4393 38.279194 27.9203 28.2135 39.6932 0 6800 0.0000 39.4441 4.4393 38.522056 28.0396 28.0949 39.6932 0 7000 0.0000 39.4441 4.4393 38.761507 28.1568 27.9775 39.6932 0 7200 0.0000 39.4441 4.4393 38.997629 28.2718 27.8612 39.6932 0 7400 0.0000 39.4441 4.4393 39.230502 28.3848 27.7461 39.6932 0 7600 0.0000 39.4441 4.4393 39.460203 28.4958 27.6321 39.6932 0 7800 0.0000 39.4441 4.4393 39.686804 28.6049 27.5192 39.6932 0 8000 0.0000 39.4441 4.4393 39.910378 28.7121 27.4073 39.6932 0 8200 0.0000 39.4441 4.4393 40.130991 28.8174 27.2966 39.6932 0 8400 0.0000 39.4441 4.4393 40.348712 28.9209 27.1869 39.6932 500 0 0.0000 39.5888 4.4563 27.495955 22.2305 33.0595 39.8388 500 200 0.0000 39.5888 4.4563 27.938206 22.4850 32.8869 39.8388 500 400 0.0000 39.5888 4.4563 28.369822 22.7321 32.7166 39.8388 500 600 0.0000 39.5888 4.4563 28.791327 22.9722 32.5485 39.8388 500 800 0.0000 39.5888 4.4563 29.203199 23.2056 32.3825 39.8388 500 1000 0.0000 39.5888 4.4563 29.605883 23.4326 32.2186 39.8388 500 1200 0.0000 39.5888 4.4563 29.999788 23.6535 32.0568 39.8388 500 1400 0.0000 39.5888 4.4563 30.385296 23.8687 31.8969 39.8388 500 1600 0.0000 39.5888 4.4563 30.762759 24.0783 31.7390 39.8388 500 1800 0.0000 39.5888 4.4563 31.132507 24.2826 31.5829 39.8388 500 2000 0.0000 39.5888 4.4563 31.494848 24.4819 31.4287 39.8388 500 2200 0.0000 39.5888 4.4563 31.850069 24.6762 31.2763 39.8388 500 2400 0.0000 39.5888 4.4563 32.198441 24.8659 31.1257 39.8388 500 2600 0.0000 39.5888 4.4563 32.540215 25.0512 30.9768 39.8388 500 2800 0.0000 39.5888 4.4563 32.875632 25.2321 30.8297 39.8388 500 3000 0.0000 39.5888 4.4563 33.204913 25.4088 30.6841 39.8388 500 3200 0.0000 39.5888 4.4563 33.528272 25.5816 30.5403 39.8388 500 3400 0.0000 39.5888 4.4563 33.845908 25.7505 30.3980 39.8388 500 3600 0.0000 39.5888 4.4563 34.158010 25.9157 30.2573 39.8388 500 3800 0.0000 39.5888 4.4563 34.464756 26.0773 30.1181 39.8388 500 4000 0.0000 39.5888 4.4563 34.766316 26.2355 29.9804 39.8388 500 4200 0.0000 39.5888 4.4563 35.062852 26.3903 29.8442 39.8388 500 4400 0.0000 39.5888 4.4563 35.354517 26.5419 29.7095 39.8388 500 4600 0.0000 39.5888 4.4563 35.641456 26.6903 29.5762 39.8388 500 4800 0.0000 39.5888 4.4563 35.923807 26.8358 29.4443 39.8388 500 5000 0.0000 39.5888 4.4563 36.201704 26.9783 29.3138 39.8388 500 5200 0.0000 39.5888 4.4563 36.475272 27.1179 29.1847 39.8388 500 5400 0.0000 39.5888 4.4563 36.744632 27.2548 29.0569 39.8388 500 5600 0.0000 39.5888 4.4563 37.009898 27.3891 28.9304 39.8388 500 5800 0.0000 39.5888 4.4563 37.271182 27.5207 28.8052 39.8388 500 6000 0.0000 39.5888 4.4563 37.528588 27.6498 28.6812 39.8388 500 6200 0.0000 39.5888 4.4563 37.782218 27.7765 28.5586 39.8388 500 6400 0.0000 39.5888 4.4563 38.032168 27.9009 28.4371 39.8388 500 6600 0.0000 39.5888 4.4563 38.278532 28.0229 28.3169 39.8388 500 6800 0.0000 39.5888 4.4563 38.521399 28.1427 28.1979 39.8388 500 7000 0.0000 39.5888 4.4563 38.760855 28.2603 28.0800 39.8388 500 7200 0.0000 39.5888 4.4563 38.996982 28.3757 27.9633 39.8388 500 7400 0.0000 39.5888 4.4563 39.229860 28.4892 27.8477 39.8388 500 7600 0.0000 39.5888 4.4563 39.459566 28.6006 27.7333 39.8388 500 7800 0.0000 39.5888 4.4563 39.686172 28.7100 27.6200 39.8388 500 8000 0.0000 39.5888 4.4563 39.909750 28.8176 27.5077 39.8388 500 8200 0.0000 39.5888 4.4563 40.130368 28.9233 27.3965 39.8388 500 8400 0.0000 39.5888 4.4563 40.348092 29.0272 27.2864 39.8388 1000 0 0.0000 39.7335 4.4733 27.494972 22.3118 33.1803 39.9845 1000 200 0.0000 39.7335 4.4733 27.937239 22.5672 33.0071 39.9845 1000 400 0.0000 39.7335 4.4733 28.368871 22.8152 32.8362 39.9845 1000 600 0.0000 39.7335 4.4733 28.790391 23.0562 32.6675 39.9845 1000 800 0.0000 39.7335 4.4733 29.202277 23.2904 32.5009 39.9845 1000 1000 0.0000 39.7335 4.4733 29.604975 23.5183 32.3364 39.9845 1000 1200 0.0000 39.7335 4.4733 29.998893 23.7400 32.1739 39.9845 1000 1400 0.0000 39.7335 4.4733 30.384413 23.9560 32.0135 39.9845 1000 1600 0.0000 39.7335 4.4733 30.761888 24.1664 31.8550 39.9845 1000 1800 0.0000 39.7335 4.4733 31.131648 24.3715 31.6983 39.9845 1000 2000 0.0000 39.7335 4.4733 31.494000 24.5714 31.5436 39.9845 1000 2200 0.0000 39.7335 4.4733 31.849231 24.7665 31.3906 39.9845 1000 2400 0.0000 39.7335 4.4733 32.197613 24.9569 31.2395 39.9845 1000 2600 0.0000 39.7335 4.4733 32.539398 25.1428 31.0900 39.9845 1000 2800 0.0000 39.7335 4.4733 32.874823 25.3244 30.9423 39.9845 1000 3000 0.0000 39.7335 4.4733 33.204114 25.5018 30.7962 39.9845 1000 3200 0.0000 39.7335 4.4733 33.527482 25.6752 30.6518 39.9845 1000 3400 0.0000 39.7335 4.4733 33.845126 25.8448 30.5090 39.9845 1000 3600 0.0000 39.7335 4.4733 34.157236 26.0106 30.3678 39.9845 1000 3800 0.0000 39.7335 4.4733 34.463990 26.1728 30.2281 39.9845 1000 4000 0.0000 39.7335 4.4733 34.765558 26.3315 30.0899 39.9845 1000 4200 0.0000 39.7335 4.4733 35.062102 26.4869 29.9532 39.9845 1000 4400 0.0000 39.7335 4.4733 35.353774 26.6391 29.8180 39.9845 1000 4600 0.0000 39.7335 4.4733 35.640720 26.7881 29.6842 39.9845 1000 4800 0.0000 39.7335 4.4733 35.923078 26.9340 29.5519 39.9845 1000 5000 0.0000 39.7335 4.4733 36.200982 27.0770 29.4209 39.9845 1000 5200 0.0000 39.7335 4.4733 36.474556 27.2172 29.2912 39.9845 1000 5400 0.0000 39.7335 4.4733 36.743922 27.3546 29.1630 39.9845 1000 5600 0.0000 39.7335 4.4733 37.009195 27.4893 29.0360 39.9845 1000 5800 0.0000 39.7335 4.4733 37.270484 27.6215 28.9103 39.9845 1000 6000 0.0000 39.7335 4.4733 37.527896 27.7511 28.7860 39.9845 1000 6200 0.0000 39.7335 4.4733 37.781532 27.8782 28.6628 39.9845 1000 6400 0.0000 39.7335 4.4733 38.031488 28.0030 28.5409 39.9845 1000 6600 0.0000 39.7335 4.4733 38.277857 28.1255 28.4203 39.9845 1000 6800 0.0000 39.7335 4.4733 38.520730 28.2457 28.3008 39.9845 1000 7000 0.0000 39.7335 4.4733 38.760191 28.3637 28.1825 39.9845 1000 7200 0.0000 39.7335 4.4733 38.996323 28.4796 28.0654 39.9845 1000 7400 0.0000 39.7335 4.4733 39.229206 28.5935 27.9494 39.9845 1000 7600 0.0000 39.7335 4.4733 39.458916 28.7053 27.8345 39.9845 1000 7800 0.0000 39.7335 4.4733 39.685527 28.8152 27.7208 39.9845 1000 8000 0.0000 39.7335 4.4733 39.909109 28.9231 27.6081 39.9845 1000 8200 0.0000 39.7335 4.4733 40.129732 29.0292 27.4965 39.9845 1000 8400 0.0000 39.7335 4.4733 40.347461 29.1335 27.3860 39.9845 1500 0 0.0000 39.8781 4.4903 27.493968 22.3930 33.3012 40.1301 1500 200 0.0000 39.8781 4.4903 27.936252 22.6494 33.1274 40.1301 1500 400 0.0000 39.8781 4.4903 28.367900 22.8983 32.9558 40.1301 1500 600 0.0000 39.8781 4.4903 28.789435 23.1402 32.7865 40.1301 1500 800 0.0000 39.8781 4.4903 29.201336 23.3753 32.6193 40.1301 1500 1000 0.0000 39.8781 4.4903 29.604048 23.6040 32.4542 40.1301 1500 1200 0.0000 39.8781 4.4903 29.997980 23.8266 32.2911 40.1301 1500 1400 0.0000 39.8781 4.4903 30.383512 24.0433 32.1301 40.1301 1500 1600 0.0000 39.8781 4.4903 30.760999 24.2545 31.9710 40.1301 1500 1800 0.0000 39.8781 4.4903 31.130771 24.4603 31.8138 40.1301 1500 2000 0.0000 39.8781 4.4903 31.493134 24.6610 31.6584 40.1301 1500 2200 0.0000 39.8781 4.4903 31.848377 24.8568 31.5049 40.1301 1500 2400 0.0000 39.8781 4.4903 32.196769 25.0479 31.3532 40.1301 1500 2600 0.0000 39.8781 4.4903 32.538563 25.2345 31.2032 40.1301 1500 2800 0.0000 39.8781 4.4903 32.873999 25.4168 31.0550 40.1301 1500 3000 0.0000 39.8781 4.4903 33.203299 25.5948 30.9084 40.1301 1500 3200 0.0000 39.8781 4.4903 33.526676 25.7689 30.7634 40.1301 1500 3400 0.0000 39.8781 4.4903 33.844329 25.9390 30.6201 40.1301 1500 3600 0.0000 39.8781 4.4903 34.156447 26.1054 30.4783 40.1301 1500 3800 0.0000 39.8781 4.4903 34.463209 26.2682 30.3381 40.1301 1500 4000 0.0000 39.8781 4.4903 34.764785 26.4276 30.1994 40.1301 1500 4200 0.0000 39.8781 4.4903 35.061337 26.5835 30.0622 40.1301 1500 4400 0.0000 39.8781 4.4903 35.353016 26.7362 29.9265 40.1301 1500 4600 0.0000 39.8781 4.4903 35.639969 26.8858 29.7923 40.1301 1500 4800 0.0000 39.8781 4.4903 35.922335 27.0322 29.6594 40.1301 1500 5000 0.0000 39.8781 4.4903 36.200245 27.1758 29.5279 40.1301 1500 5200 0.0000 39.8781 4.4903 36.473826 27.3165 29.3978 40.1301 1500 5400 0.0000 39.8781 4.4903 36.743198 27.4544 29.2691 40.1301 1500 5600 0.0000 39.8781 4.4903 37.008477 27.5896 29.1417 40.1301 1500 5800 0.0000 39.8781 4.4903 37.269773 27.7222 29.0155 40.1301 1500 6000 0.0000 39.8781 4.4903 37.527191 27.8523 28.8907 40.1301 1500 6200 0.0000 39.8781 4.4903 37.780832 27.9799 28.7671 40.1301 1500 6400 0.0000 39.8781 4.4903 38.030794 28.1052 28.6448 40.1301 1500 6600 0.0000 39.8781 4.4903 38.277169 28.2281 28.5237 40.1301 1500 6800 0.0000 39.8781 4.4903 38.520047 28.3487 28.4037 40.1301 1500 7000 0.0000 39.8781 4.4903 38.759513 28.4672 28.2850 40.1301 1500 7200 0.0000 39.8781 4.4903 38.995651 28.5835 28.1674 40.1301 1500 7400 0.0000 39.8781 4.4903 39.228539 28.6978 28.0510 40.1301 1500 7600 0.0000 39.8781 4.4903 39.458254 28.8100 27.9357 40.1301 1500 7800 0.0000 39.8781 4.4903 39.684869 28.9203 27.8216 40.1301 1500 8000 0.0000 39.8781 4.4903 39.908457 29.0286 27.7085 40.1301 1500 8200 0.0000 39.8781 4.4903 40.129084 29.1351 27.5965 40.1301 1500 8400 0.0000 39.8781 4.4903 40.346818 29.2398 27.4856 40.1301 2000 0 0.0000 40.0228 4.5072 27.492944 22.4743 33.4221 40.2758 2000 200 0.0000 40.0228 4.5072 27.935245 22.7316 33.2476 40.2758 2000 400 0.0000 40.0228 4.5072 28.366910 22.9814 33.0754 40.2758 2000 600 0.0000 40.0228 4.5072 28.788460 23.2242 32.9055 40.2758 2000 800 0.0000 40.0228 4.5072 29.200376 23.4601 32.7376 40.2758 2000 1000 0.0000 40.0228 4.5072 29.603102 23.6897 32.5719 40.2758 2000 1200 0.0000 40.0228 4.5072 29.997048 23.9131 32.4083 40.2758 2000 1400 0.0000 40.0228 4.5072 30.382593 24.1306 32.2466 40.2758 2000 1600 0.0000 40.0228 4.5072 30.760093 24.3425 32.0870 40.2758 2000 1800 0.0000 40.0228 4.5072 31.129876 24.5491 31.9292 40.2758 2000 2000 0.0000 40.0228 4.5072 31.492251 24.7506 31.7733 40.2758 2000 2200 0.0000 40.0228 4.5072 31.847505 24.9471 31.6192 40.2758 2000 2400 0.0000 40.0228 4.5072 32.195907 25.1389 31.4669 40.2758 2000 2600 0.0000 40.0228 4.5072 32.537712 25.3262 31.3164 40.2758 2000 2800 0.0000 40.0228 4.5072 32.873158 25.5091 31.1676 40.2758 2000 3000 0.0000 40.0228 4.5072 33.202467 25.6878 31.0205 40.2758 2000 3200 0.0000 40.0228 4.5072 33.525853 25.8625 30.8750 40.2758 2000 3400 0.0000 40.0228 4.5072 33.843515 26.0332 30.7311 40.2758 2000 3600 0.0000 40.0228 4.5072 34.155642 26.2003 30.5889 40.2758 2000 3800 0.0000 40.0228 4.5072 34.462413 26.3637 30.4482 40.2758 2000 4000 0.0000 40.0228 4.5072 34.763997 26.5236 30.3090 40.2758 2000 4200 0.0000 40.0228 4.5072 35.060556 26.6801 30.1713 40.2758 2000 4400 0.0000 40.0228 4.5072 35.352243 26.8333 30.0351 40.2758 2000 4600 0.0000 40.0228 4.5072 35.639204 26.9834 29.9003 40.2758 2000 4800 0.0000 40.0228 4.5072 35.921577 27.1305 29.7669 40.2758 2000 5000 0.0000 40.0228 4.5072 36.199494 27.2745 29.6350 40.2758 2000 5200 0.0000 40.0228 4.5072 36.473082 27.4157 29.5044 40.2758 2000 5400 0.0000 40.0228 4.5072 36.742461 27.5541 29.3752 40.2758 2000 5600 0.0000 40.0228 4.5072 37.007746 27.6899 29.2473 40.2758 2000 5800 0.0000 40.0228 4.5072 37.269048 27.8230 29.1207 40.2758 2000 6000 0.0000 40.0228 4.5072 37.526472 27.9535 28.9954 40.2758 2000 6200 0.0000 40.0228 4.5072 37.780120 28.0816 28.8714 40.2758 2000 6400 0.0000 40.0228 4.5072 38.030087 28.2073 28.7486 40.2758 2000 6600 0.0000 40.0228 4.5072 38.276468 28.3307 28.6270 40.2758 2000 6800 0.0000 40.0228 4.5072 38.519351 28.4518 28.5067 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40.4568 4.5581 36.740165 27.8534 29.6936 40.7127 3500 5600 0.0000 40.4568 4.5581 37.005471 27.9906 29.5643 40.7127 3500 5800 0.0000 40.4568 4.5581 37.266792 28.1251 29.4364 40.7127 3500 6000 0.0000 40.4568 4.5581 37.524236 28.2571 29.3097 40.7127 3500 6200 0.0000 40.4568 4.5581 37.777902 28.3866 29.1843 40.7127 3500 6400 0.0000 40.4568 4.5581 38.027888 28.5137 29.0602 40.7127 3500 6600 0.0000 40.4568 4.5581 38.274286 28.6384 28.9373 40.7127 3500 6800 0.0000 40.4568 4.5581 38.517187 28.7608 28.8156 40.7127 3500 7000 0.0000 40.4568 4.5581 38.756676 28.8810 28.6951 40.7127 3500 7200 0.0000 40.4568 4.5581 38.992835 28.9990 28.5759 40.7127 3500 7400 0.0000 40.4568 4.5581 39.225745 29.1149 28.4577 40.7127 3500 7600 0.0000 40.4568 4.5581 39.455480 29.2288 28.3408 40.7127 3500 7800 0.0000 40.4568 4.5581 39.682117 29.3407 28.2249 40.7127 3500 8000 0.0000 40.4568 4.5581 39.905724 29.4506 28.1102 40.7127 3500 8200 0.0000 40.4568 4.5581 40.126371 29.5586 27.9966 40.7127 3500 8400 0.0000 40.4568 4.5581 40.344124 29.6648 27.8841 40.7127 4000 0 0.0000 40.6014 4.5751 27.488641 22.7991 33.9058 40.8584 4000 200 0.0000 40.6014 4.5751 27.931016 23.0602 33.7287 40.8584 4000 400 0.0000 40.6014 4.5751 28.362751 23.3137 33.5540 40.8584 4000 600 0.0000 40.6014 4.5751 28.784368 23.5599 33.3816 40.8584 4000 800 0.0000 40.6014 4.5751 29.196347 23.7994 33.2113 40.8584 4000 1000 0.0000 40.6014 4.5751 29.599133 24.0322 33.0432 40.8584 4000 1200 0.0000 40.6014 4.5751 29.993137 24.2589 32.8771 40.8584 4000 1400 0.0000 40.6014 4.5751 30.378737 24.4796 32.7131 40.8584 4000 1600 0.0000 40.6014 4.5751 30.756290 24.6946 32.5511 40.8584 4000 1800 0.0000 40.6014 4.5751 31.126124 24.9042 32.3910 40.8584 4000 2000 0.0000 40.6014 4.5751 31.488547 25.1086 32.2329 40.8584 4000 2200 0.0000 40.6014 4.5751 31.843848 25.3080 32.0765 40.8584 4000 2400 0.0000 40.6014 4.5751 32.192296 25.5026 31.9220 40.8584 4000 2600 0.0000 40.6014 4.5751 32.534144 25.6926 31.7693 40.8584 4000 2800 0.0000 40.6014 4.5751 32.869631 25.8782 31.6183 40.8584 4000 3000 0.0000 40.6014 4.5751 33.198982 26.0595 31.4691 40.8584 4000 3200 0.0000 40.6014 4.5751 33.522407 26.2368 31.3215 40.8584 4000 3400 0.0000 40.6014 4.5751 33.840106 26.4100 31.1755 40.8584 4000 3600 0.0000 40.6014 4.5751 34.152270 26.5795 31.0311 40.8584 4000 3800 0.0000 40.6014 4.5751 34.459076 26.7453 30.8884 40.8584 4000 4000 0.0000 40.6014 4.5751 34.760695 26.9075 30.7472 40.8584 4000 4200 0.0000 40.6014 4.5751 35.057287 27.0663 30.6075 40.8584 4000 4400 0.0000 40.6014 4.5751 35.349006 27.2218 30.4693 40.8584 4000 4600 0.0000 40.6014 4.5751 35.635998 27.3741 30.3325 40.8584 4000 4800 0.0000 40.6014 4.5751 35.918401 27.5233 30.1972 40.8584 4000 5000 0.0000 40.6014 4.5751 36.196348 27.6694 30.0634 40.8584 4000 5200 0.0000 40.6014 4.5751 36.469965 27.8127 29.9309 40.8584 4000 5400 0.0000 40.6014 4.5751 36.739372 27.9531 29.7998 40.8584 4000 5600 0.0000 40.6014 4.5751 37.004685 28.0908 29.6700 40.8584 4000 5800 0.0000 40.6014 4.5751 37.266013 28.2258 29.5416 40.8584 4000 6000 0.0000 40.6014 4.5751 37.523463 28.3583 29.4145 40.8584 4000 6200 0.0000 40.6014 4.5751 37.777136 28.4882 29.2886 40.8584 4000 6400 0.0000 40.6014 4.5751 38.027128 28.6158 29.1640 40.8584 4000 6600 0.0000 40.6014 4.5751 38.273533 28.7409 29.0407 40.8584 4000 6800 0.0000 40.6014 4.5751 38.516440 28.8638 28.9186 40.8584 4000 7000 0.0000 40.6014 4.5751 38.755934 28.9844 28.7977 40.8584 4000 7200 0.0000 40.6014 4.5751 38.992100 29.1029 28.6780 40.8584 4000 7400 0.0000 40.6014 4.5751 39.225015 29.2192 28.5594 40.8584 4000 7600 0.0000 40.6014 4.5751 39.454756 29.3335 28.4421 40.8584 4000 7800 0.0000 40.6014 4.5751 39.681398 29.4458 28.3258 40.8584 4000 8000 0.0000 40.6014 4.5751 39.905010 29.5561 28.2107 40.8584 4000 8200 0.0000 40.6014 4.5751 40.125663 29.6645 28.0966 40.8584 4000 8400 0.0000 40.6014 4.5751 40.343421 29.7711 27.9837 40.8584 4500 0 0.0000 40.7461 4.5921 27.487515 22.8802 34.0267 41.0040 4500 200 0.0000 40.7461 4.5921 27.929909 23.1423 33.8490 41.0040 4500 400 0.0000 40.7461 4.5921 28.361662 23.3967 33.6737 41.0040 4500 600 0.0000 40.7461 4.5921 28.783297 23.6438 33.5006 41.0040 4500 800 0.0000 40.7461 4.5921 29.195293 23.8841 33.3297 41.0040 4500 1000 0.0000 40.7461 4.5921 29.598095 24.1179 33.1610 41.0040 4500 1200 0.0000 40.7461 4.5921 29.992113 24.3453 32.9944 41.0040 4500 1400 0.0000 40.7461 4.5921 30.377729 24.5668 32.8298 41.0040 4500 1600 0.0000 40.7461 4.5921 30.755295 24.7826 32.6672 41.0040 4500 1800 0.0000 40.7461 4.5921 31.125143 24.9930 32.5065 41.0040 4500 2000 0.0000 40.7461 4.5921 31.487579 25.1981 32.3478 41.0040 4500 2200 0.0000 40.7461 4.5921 31.842892 25.3982 32.1909 41.0040 4500 2400 0.0000 40.7461 4.5921 32.191352 25.5935 32.0358 41.0040 4500 2600 0.0000 40.7461 4.5921 32.533211 25.7842 31.8826 41.0040 4500 2800 0.0000 40.7461 4.5921 32.868710 25.9705 31.7310 41.0040 4500 3000 0.0000 40.7461 4.5921 33.198070 26.1525 31.5812 41.0040 4500 3200 0.0000 40.7461 4.5921 33.521506 26.3303 31.4331 41.0040 4500 3400 0.0000 40.7461 4.5921 33.839215 26.5042 31.2866 41.0040 4500 3600 0.0000 40.7461 4.5921 34.151388 26.6743 31.1417 41.0040 4500 3800 0.0000 40.7461 4.5921 34.458204 26.8407 30.9985 41.0040 4500 4000 0.0000 40.7461 4.5921 34.759832 27.0035 30.8567 41.0040 4500 4200 0.0000 40.7461 4.5921 35.056433 27.1628 30.7165 41.0040 4500 4400 0.0000 40.7461 4.5921 35.348161 27.3189 30.5778 41.0040 4500 4600 0.0000 40.7461 4.5921 35.635161 27.4717 30.4406 41.0040 4500 4800 0.0000 40.7461 4.5921 35.917572 27.6214 30.3048 41.0040 4500 5000 0.0000 40.7461 4.5921 36.195527 27.7681 30.1705 41.0040 4500 5200 0.0000 40.7461 4.5921 36.469151 27.9119 30.0375 41.0040 4500 5400 0.0000 40.7461 4.5921 36.738565 28.0528 29.9059 41.0040 4500 5600 0.0000 40.7461 4.5921 37.003885 28.1910 29.7757 41.0040 4500 5800 0.0000 40.7461 4.5921 37.265221 28.3265 29.6468 41.0040 4500 6000 0.0000 40.7461 4.5921 37.522678 28.4594 29.5192 41.0040 4500 6200 0.0000 40.7461 4.5921 37.776357 28.5899 29.3930 41.0040 4500 6400 0.0000 40.7461 4.5921 38.026356 28.7178 29.2679 41.0040 4500 6600 0.0000 40.7461 4.5921 38.272767 28.8435 29.1442 41.0040 4500 6800 0.0000 40.7461 4.5921 38.515680 28.9668 29.0216 41.0040 4500 7000 0.0000 40.7461 4.5921 38.755181 29.0878 28.9003 41.0040 4500 7200 0.0000 40.7461 4.5921 38.991352 29.2067 28.7801 41.0040 4500 7400 0.0000 40.7461 4.5921 39.224272 29.3234 28.6612 41.0040 4500 7600 0.0000 40.7461 4.5921 39.454020 29.4381 28.5433 41.0040 4500 7800 0.0000 40.7461 4.5921 39.680667 29.5508 28.4267 41.0040 4500 8000 0.0000 40.7461 4.5921 39.904285 29.6615 28.3111 41.0040 4500 8200 0.0000 40.7461 4.5921 40.124942 29.7704 28.1967 41.0040 4500 8400 0.0000 40.7461 4.5921 40.342705 29.8773 28.0833 41.0040 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.010 ================================================ FILE: tests/test_data/cropA/geometry/20180106-20180319_VV_8rlks_base.par ================================================ initial_baseline(TCN): -0.0425811 3.6031274 -0.5126463 m m m initial_baseline_rate: 0.0000000 0.0521367 0.0726505 m/s m/s m/s precision_baseline(TCN): -0.0425811 3.6031274 -0.5126463 m m m precision_baseline_rate: 0.0000000 0.0521367 0.0726505 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/cropA/geometry/20180106-20180319_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): -0.043 3.603 -0.513 orbit baseline rate (TCN) (m/s): 0.000e+00 5.214e-02 7.265e-02 baseline vector (TCN) (m): -0.043 3.603 -0.513 baseline rate (TCN) (m/s): 0.000e+00 5.214e-02 7.265e-02 SLC-1 center baseline length (m): 3.6397 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 -0.0426 3.1165 -1.1908 27.496918 0.3826 3.3143 3.3365 0 200 -0.0426 3.1165 -1.1908 27.939152 0.4082 3.3112 3.3365 0 400 -0.0426 3.1165 -1.1908 28.370753 0.4331 3.3081 3.3365 0 600 -0.0426 3.1165 -1.1908 28.792243 0.4575 3.3048 3.3365 0 800 -0.0426 3.1165 -1.1908 29.204102 0.4812 3.3014 3.3365 0 1000 -0.0426 3.1165 -1.1908 29.606772 0.5044 3.2979 3.3365 0 1200 -0.0426 3.1165 -1.1908 30.000665 0.5271 3.2944 3.3365 0 1400 -0.0426 3.1165 -1.1908 30.386160 0.5492 3.2908 3.3365 0 1600 -0.0426 3.1165 -1.1908 30.763612 0.5709 3.2871 3.3365 0 1800 -0.0426 3.1165 -1.1908 31.133349 0.5921 3.2833 3.3365 0 2000 -0.0426 3.1165 -1.1908 31.495679 0.6128 3.2795 3.3365 0 2200 -0.0426 3.1165 -1.1908 31.850890 0.6332 3.2757 3.3365 0 2400 -0.0426 3.1165 -1.1908 32.199251 0.6531 3.2717 3.3365 0 2600 -0.0426 3.1165 -1.1908 32.541017 0.6726 3.2678 3.3365 0 2800 -0.0426 3.1165 -1.1908 32.876424 0.6917 3.2638 3.3365 0 3000 -0.0426 3.1165 -1.1908 33.205696 0.7104 3.2598 3.3365 0 3200 -0.0426 3.1165 -1.1908 33.529047 0.7288 3.2557 3.3365 0 3400 -0.0426 3.1165 -1.1908 33.846674 0.7468 3.2516 3.3365 0 3600 -0.0426 3.1165 -1.1908 34.158768 0.7645 3.2475 3.3365 0 3800 -0.0426 3.1165 -1.1908 34.465506 0.7819 3.2434 3.3365 0 4000 -0.0426 3.1165 -1.1908 34.767059 0.7990 3.2392 3.3365 0 4200 -0.0426 3.1165 -1.1908 35.063588 0.8157 3.2350 3.3365 0 4400 -0.0426 3.1165 -1.1908 35.355245 0.8322 3.2308 3.3365 0 4600 -0.0426 3.1165 -1.1908 35.642177 0.8484 3.2266 3.3365 0 4800 -0.0426 3.1165 -1.1908 35.924522 0.8642 3.2224 3.3365 0 5000 -0.0426 3.1165 -1.1908 36.202412 0.8799 3.2182 3.3365 0 5200 -0.0426 3.1165 -1.1908 36.475974 0.8952 3.2139 3.3365 0 5400 -0.0426 3.1165 -1.1908 36.745327 0.9103 3.2097 3.3365 0 5600 -0.0426 3.1165 -1.1908 37.010588 0.9252 3.2054 3.3365 0 5800 -0.0426 3.1165 -1.1908 37.271866 0.9398 3.2012 3.3365 0 6000 -0.0426 3.1165 -1.1908 37.529266 0.9541 3.1969 3.3365 0 6200 -0.0426 3.1165 -1.1908 37.782890 0.9683 3.1927 3.3365 0 6400 -0.0426 3.1165 -1.1908 38.032835 0.9822 3.1884 3.3365 0 6600 -0.0426 3.1165 -1.1908 38.279194 0.9959 3.1842 3.3365 0 6800 -0.0426 3.1165 -1.1908 38.522056 1.0094 3.1799 3.3365 0 7000 -0.0426 3.1165 -1.1908 38.761507 1.0227 3.1757 3.3365 0 7200 -0.0426 3.1165 -1.1908 38.997629 1.0358 3.1714 3.3365 0 7400 -0.0426 3.1165 -1.1908 39.230502 1.0486 3.1672 3.3365 0 7600 -0.0426 3.1165 -1.1908 39.460203 1.0613 3.1630 3.3365 0 7800 -0.0426 3.1165 -1.1908 39.686804 1.0738 3.1587 3.3365 0 8000 -0.0426 3.1165 -1.1908 39.910378 1.0861 3.1545 3.3365 0 8200 -0.0426 3.1165 -1.1908 40.130991 1.0983 3.1503 3.3365 0 8400 -0.0426 3.1165 -1.1908 40.348712 1.1102 3.1461 3.3365 500 0 -0.0426 3.2236 -1.0414 27.495955 0.5645 3.3404 3.3880 500 200 -0.0426 3.2236 -1.0414 27.938206 0.5903 3.3359 3.3880 500 400 -0.0426 3.2236 -1.0414 28.369822 0.6154 3.3314 3.3880 500 600 -0.0426 3.2236 -1.0414 28.791327 0.6399 3.3268 3.3880 500 800 -0.0426 3.2236 -1.0414 29.203199 0.6638 3.3221 3.3880 500 1000 -0.0426 3.2236 -1.0414 29.605883 0.6871 3.3174 3.3880 500 1200 -0.0426 3.2236 -1.0414 29.999788 0.7099 3.3125 3.3880 500 1400 -0.0426 3.2236 -1.0414 30.385296 0.7322 3.3077 3.3880 500 1600 -0.0426 3.2236 -1.0414 30.762759 0.7540 3.3028 3.3880 500 1800 -0.0426 3.2236 -1.0414 31.132507 0.7753 3.2979 3.3880 500 2000 -0.0426 3.2236 -1.0414 31.494848 0.7961 3.2929 3.3880 500 2200 -0.0426 3.2236 -1.0414 31.850069 0.8165 3.2879 3.3880 500 2400 -0.0426 3.2236 -1.0414 32.198441 0.8365 3.2829 3.3880 500 2600 -0.0426 3.2236 -1.0414 32.540215 0.8561 3.2778 3.3880 500 2800 -0.0426 3.2236 -1.0414 32.875632 0.8752 3.2728 3.3880 500 3000 -0.0426 3.2236 -1.0414 33.204913 0.8940 3.2677 3.3880 500 3200 -0.0426 3.2236 -1.0414 33.528272 0.9124 3.2626 3.3880 500 3400 -0.0426 3.2236 -1.0414 33.845908 0.9305 3.2575 3.3880 500 3600 -0.0426 3.2236 -1.0414 34.158010 0.9482 3.2523 3.3880 500 3800 -0.0426 3.2236 -1.0414 34.464756 0.9656 3.2472 3.3880 500 4000 -0.0426 3.2236 -1.0414 34.766316 0.9827 3.2421 3.3880 500 4200 -0.0426 3.2236 -1.0414 35.062852 0.9995 3.2370 3.3880 500 4400 -0.0426 3.2236 -1.0414 35.354517 1.0160 3.2318 3.3880 500 4600 -0.0426 3.2236 -1.0414 35.641456 1.0321 3.2267 3.3880 500 4800 -0.0426 3.2236 -1.0414 35.923807 1.0480 3.2216 3.3880 500 5000 -0.0426 3.2236 -1.0414 36.201704 1.0636 3.2165 3.3880 500 5200 -0.0426 3.2236 -1.0414 36.475272 1.0790 3.2113 3.3880 500 5400 -0.0426 3.2236 -1.0414 36.744632 1.0941 3.2062 3.3880 500 5600 -0.0426 3.2236 -1.0414 37.009898 1.1089 3.2011 3.3880 500 5800 -0.0426 3.2236 -1.0414 37.271182 1.1235 3.1960 3.3880 500 6000 -0.0426 3.2236 -1.0414 37.528588 1.1378 3.1910 3.3880 500 6200 -0.0426 3.2236 -1.0414 37.782218 1.1519 3.1859 3.3880 500 6400 -0.0426 3.2236 -1.0414 38.032168 1.1658 3.1808 3.3880 500 6600 -0.0426 3.2236 -1.0414 38.278532 1.1795 3.1758 3.3880 500 6800 -0.0426 3.2236 -1.0414 38.521399 1.1929 3.1708 3.3880 500 7000 -0.0426 3.2236 -1.0414 38.760855 1.2062 3.1658 3.3880 500 7200 -0.0426 3.2236 -1.0414 38.996982 1.2192 3.1608 3.3880 500 7400 -0.0426 3.2236 -1.0414 39.229860 1.2321 3.1558 3.3880 500 7600 -0.0426 3.2236 -1.0414 39.459566 1.2447 3.1508 3.3880 500 7800 -0.0426 3.2236 -1.0414 39.686172 1.2571 3.1459 3.3880 500 8000 -0.0426 3.2236 -1.0414 39.909750 1.2694 3.1409 3.3880 500 8200 -0.0426 3.2236 -1.0414 40.130368 1.2815 3.1360 3.3880 500 8400 -0.0426 3.2236 -1.0414 40.348092 1.2934 3.1311 3.3880 1000 0 -0.0426 3.3308 -0.8921 27.494972 0.7464 3.3665 3.4485 1000 200 -0.0426 3.3308 -0.8921 27.937239 0.7724 3.3607 3.4485 1000 400 -0.0426 3.3308 -0.8921 28.368871 0.7977 3.3548 3.4485 1000 600 -0.0426 3.3308 -0.8921 28.790391 0.8223 3.3488 3.4485 1000 800 -0.0426 3.3308 -0.8921 29.202277 0.8464 3.3428 3.4485 1000 1000 -0.0426 3.3308 -0.8921 29.604975 0.8699 3.3368 3.4485 1000 1200 -0.0426 3.3308 -0.8921 29.998893 0.8928 3.3307 3.4485 1000 1400 -0.0426 3.3308 -0.8921 30.384413 0.9152 3.3246 3.4485 1000 1600 -0.0426 3.3308 -0.8921 30.761888 0.9371 3.3185 3.4485 1000 1800 -0.0426 3.3308 -0.8921 31.131648 0.9585 3.3124 3.4485 1000 2000 -0.0426 3.3308 -0.8921 31.494000 0.9794 3.3063 3.4485 1000 2200 -0.0426 3.3308 -0.8921 31.849231 0.9999 3.3001 3.4485 1000 2400 -0.0426 3.3308 -0.8921 32.197613 1.0199 3.2940 3.4485 1000 2600 -0.0426 3.3308 -0.8921 32.539398 1.0395 3.2879 3.4485 1000 2800 -0.0426 3.3308 -0.8921 32.874823 1.0588 3.2817 3.4485 1000 3000 -0.0426 3.3308 -0.8921 33.204114 1.0776 3.2756 3.4485 1000 3200 -0.0426 3.3308 -0.8921 33.527482 1.0961 3.2694 3.4485 1000 3400 -0.0426 3.3308 -0.8921 33.845126 1.1142 3.2633 3.4485 1000 3600 -0.0426 3.3308 -0.8921 34.157236 1.1320 3.2572 3.4485 1000 3800 -0.0426 3.3308 -0.8921 34.463990 1.1494 3.2511 3.4485 1000 4000 -0.0426 3.3308 -0.8921 34.765558 1.1665 3.2450 3.4485 1000 4200 -0.0426 3.3308 -0.8921 35.062102 1.1832 3.2389 3.4485 1000 4400 -0.0426 3.3308 -0.8921 35.353774 1.1997 3.2328 3.4485 1000 4600 -0.0426 3.3308 -0.8921 35.640720 1.2159 3.2268 3.4485 1000 4800 -0.0426 3.3308 -0.8921 35.923078 1.2318 3.2208 3.4485 1000 5000 -0.0426 3.3308 -0.8921 36.200982 1.2474 3.2147 3.4485 1000 5200 -0.0426 3.3308 -0.8921 36.474556 1.2627 3.2088 3.4485 1000 5400 -0.0426 3.3308 -0.8921 36.743922 1.2778 3.2028 3.4485 1000 5600 -0.0426 3.3308 -0.8921 37.009195 1.2926 3.1968 3.4485 1000 5800 -0.0426 3.3308 -0.8921 37.270484 1.3072 3.1909 3.4485 1000 6000 -0.0426 3.3308 -0.8921 37.527896 1.3215 3.1850 3.4485 1000 6200 -0.0426 3.3308 -0.8921 37.781532 1.3356 3.1791 3.4485 1000 6400 -0.0426 3.3308 -0.8921 38.031488 1.3494 3.1733 3.4485 1000 6600 -0.0426 3.3308 -0.8921 38.277857 1.3631 3.1674 3.4485 1000 6800 -0.0426 3.3308 -0.8921 38.520730 1.3765 3.1616 3.4485 1000 7000 -0.0426 3.3308 -0.8921 38.760191 1.3897 3.1558 3.4485 1000 7200 -0.0426 3.3308 -0.8921 38.996323 1.4027 3.1501 3.4485 1000 7400 -0.0426 3.3308 -0.8921 39.229206 1.4155 3.1444 3.4485 1000 7600 -0.0426 3.3308 -0.8921 39.458916 1.4281 3.1387 3.4485 1000 7800 -0.0426 3.3308 -0.8921 39.685527 1.4405 3.1330 3.4485 1000 8000 -0.0426 3.3308 -0.8921 39.909109 1.4527 3.1273 3.4485 1000 8200 -0.0426 3.3308 -0.8921 40.129732 1.4647 3.1217 3.4485 1000 8400 -0.0426 3.3308 -0.8921 40.347461 1.4766 3.1161 3.4485 1500 0 -0.0426 3.4380 -0.7428 27.493968 0.9283 3.3927 3.5176 1500 200 -0.0426 3.4380 -0.7428 27.936252 0.9545 3.3854 3.5176 1500 400 -0.0426 3.4380 -0.7428 28.367900 0.9800 3.3781 3.5176 1500 600 -0.0426 3.4380 -0.7428 28.789435 1.0048 3.3708 3.5176 1500 800 -0.0426 3.4380 -0.7428 29.201336 1.0290 3.3635 3.5176 1500 1000 -0.0426 3.4380 -0.7428 29.604048 1.0526 3.3562 3.5176 1500 1200 -0.0426 3.4380 -0.7428 29.997980 1.0756 3.3489 3.5176 1500 1400 -0.0426 3.4380 -0.7428 30.383512 1.0982 3.3416 3.5176 1500 1600 -0.0426 3.4380 -0.7428 30.760999 1.1201 3.3342 3.5176 1500 1800 -0.0426 3.4380 -0.7428 31.130771 1.1416 3.3269 3.5176 1500 2000 -0.0426 3.4380 -0.7428 31.493134 1.1627 3.3197 3.5176 1500 2200 -0.0426 3.4380 -0.7428 31.848377 1.1832 3.3124 3.5176 1500 2400 -0.0426 3.4380 -0.7428 32.196769 1.2033 3.3051 3.5176 1500 2600 -0.0426 3.4380 -0.7428 32.538563 1.2230 3.2979 3.5176 1500 2800 -0.0426 3.4380 -0.7428 32.873999 1.2423 3.2907 3.5176 1500 3000 -0.0426 3.4380 -0.7428 33.203299 1.2612 3.2835 3.5176 1500 3200 -0.0426 3.4380 -0.7428 33.526676 1.2797 3.2763 3.5176 1500 3400 -0.0426 3.4380 -0.7428 33.844329 1.2979 3.2692 3.5176 1500 3600 -0.0426 3.4380 -0.7428 34.156447 1.3157 3.2620 3.5176 1500 3800 -0.0426 3.4380 -0.7428 34.463209 1.3331 3.2550 3.5176 1500 4000 -0.0426 3.4380 -0.7428 34.764785 1.3502 3.2479 3.5176 1500 4200 -0.0426 3.4380 -0.7428 35.061337 1.3670 3.2409 3.5176 1500 4400 -0.0426 3.4380 -0.7428 35.353016 1.3835 3.2339 3.5176 1500 4600 -0.0426 3.4380 -0.7428 35.639969 1.3997 3.2269 3.5176 1500 4800 -0.0426 3.4380 -0.7428 35.922335 1.4156 3.2200 3.5176 1500 5000 -0.0426 3.4380 -0.7428 36.200245 1.4312 3.2130 3.5176 1500 5200 -0.0426 3.4380 -0.7428 36.473826 1.4465 3.2062 3.5176 1500 5400 -0.0426 3.4380 -0.7428 36.743198 1.4615 3.1993 3.5176 1500 5600 -0.0426 3.4380 -0.7428 37.008477 1.4763 3.1925 3.5176 1500 5800 -0.0426 3.4380 -0.7428 37.269773 1.4909 3.1858 3.5176 1500 6000 -0.0426 3.4380 -0.7428 37.527191 1.5052 3.1790 3.5176 1500 6200 -0.0426 3.4380 -0.7428 37.780832 1.5192 3.1723 3.5176 1500 6400 -0.0426 3.4380 -0.7428 38.030794 1.5331 3.1657 3.5176 1500 6600 -0.0426 3.4380 -0.7428 38.277169 1.5467 3.1591 3.5176 1500 6800 -0.0426 3.4380 -0.7428 38.520047 1.5600 3.1525 3.5176 1500 7000 -0.0426 3.4380 -0.7428 38.759513 1.5732 3.1459 3.5176 1500 7200 -0.0426 3.4380 -0.7428 38.995651 1.5861 3.1394 3.5176 1500 7400 -0.0426 3.4380 -0.7428 39.228539 1.5989 3.1329 3.5176 1500 7600 -0.0426 3.4380 -0.7428 39.458254 1.6114 3.1265 3.5176 1500 7800 -0.0426 3.4380 -0.7428 39.684869 1.6238 3.1201 3.5176 1500 8000 -0.0426 3.4380 -0.7428 39.908457 1.6360 3.1138 3.5176 1500 8200 -0.0426 3.4380 -0.7428 40.129084 1.6479 3.1074 3.5176 1500 8400 -0.0426 3.4380 -0.7428 40.346818 1.6597 3.1011 3.5176 2000 0 -0.0426 3.5451 -0.5934 27.492944 1.1102 3.4188 3.5947 2000 200 -0.0426 3.5451 -0.5934 27.935245 1.1366 3.4101 3.5947 2000 400 -0.0426 3.5451 -0.5934 28.366910 1.1622 3.4015 3.5947 2000 600 -0.0426 3.5451 -0.5934 28.788460 1.1872 3.3928 3.5947 2000 800 -0.0426 3.5451 -0.5934 29.200376 1.2116 3.3842 3.5947 2000 1000 -0.0426 3.5451 -0.5934 29.603102 1.2353 3.3756 3.5947 2000 1200 -0.0426 3.5451 -0.5934 29.997048 1.2585 3.3670 3.5947 2000 1400 -0.0426 3.5451 -0.5934 30.382593 1.2811 3.3585 3.5947 2000 1600 -0.0426 3.5451 -0.5934 30.760093 1.3032 3.3500 3.5947 2000 1800 -0.0426 3.5451 -0.5934 31.129876 1.3248 3.3415 3.5947 2000 2000 -0.0426 3.5451 -0.5934 31.492251 1.3459 3.3331 3.5947 2000 2200 -0.0426 3.5451 -0.5934 31.847505 1.3666 3.3246 3.5947 2000 2400 -0.0426 3.5451 -0.5934 32.195907 1.3868 3.3163 3.5947 2000 2600 -0.0426 3.5451 -0.5934 32.537712 1.4065 3.3079 3.5947 2000 2800 -0.0426 3.5451 -0.5934 32.873158 1.4259 3.2996 3.5947 2000 3000 -0.0426 3.5451 -0.5934 33.202467 1.4448 3.2914 3.5947 2000 3200 -0.0426 3.5451 -0.5934 33.525853 1.4634 3.2832 3.5947 2000 3400 -0.0426 3.5451 -0.5934 33.843515 1.4815 3.2750 3.5947 2000 3600 -0.0426 3.5451 -0.5934 34.155642 1.4994 3.2669 3.5947 2000 3800 -0.0426 3.5451 -0.5934 34.462413 1.5168 3.2588 3.5947 2000 4000 -0.0426 3.5451 -0.5934 34.763997 1.5340 3.2508 3.5947 2000 4200 -0.0426 3.5451 -0.5934 35.060556 1.5508 3.2428 3.5947 2000 4400 -0.0426 3.5451 -0.5934 35.352243 1.5673 3.2349 3.5947 2000 4600 -0.0426 3.5451 -0.5934 35.639204 1.5834 3.2270 3.5947 2000 4800 -0.0426 3.5451 -0.5934 35.921577 1.5993 3.2191 3.5947 2000 5000 -0.0426 3.5451 -0.5934 36.199494 1.6149 3.2114 3.5947 2000 5200 -0.0426 3.5451 -0.5934 36.473082 1.6302 3.2036 3.5947 2000 5400 -0.0426 3.5451 -0.5934 36.742461 1.6453 3.1959 3.5947 2000 5600 -0.0426 3.5451 -0.5934 37.007746 1.6601 3.1882 3.5947 2000 5800 -0.0426 3.5451 -0.5934 37.269048 1.6746 3.1806 3.5947 2000 6000 -0.0426 3.5451 -0.5934 37.526472 1.6888 3.1731 3.5947 2000 6200 -0.0426 3.5451 -0.5934 37.780120 1.7029 3.1656 3.5947 2000 6400 -0.0426 3.5451 -0.5934 38.030087 1.7167 3.1581 3.5947 2000 6600 -0.0426 3.5451 -0.5934 38.276468 1.7302 3.1507 3.5947 2000 6800 -0.0426 3.5451 -0.5934 38.519351 1.7436 3.1433 3.5947 2000 7000 -0.0426 3.5451 -0.5934 38.758823 1.7567 3.1360 3.5947 2000 7200 -0.0426 3.5451 -0.5934 38.994966 1.7696 3.1288 3.5947 2000 7400 -0.0426 3.5451 -0.5934 39.227859 1.7823 3.1215 3.5947 2000 7600 -0.0426 3.5451 -0.5934 39.457579 1.7948 3.1144 3.5947 2000 7800 -0.0426 3.5451 -0.5934 39.684200 1.8071 3.1072 3.5947 2000 8000 -0.0426 3.5451 -0.5934 39.907792 1.8192 3.1002 3.5947 2000 8200 -0.0426 3.5451 -0.5934 40.128424 1.8312 3.0931 3.5947 2000 8400 -0.0426 3.5451 -0.5934 40.346162 1.8429 3.0862 3.5947 2500 0 -0.0426 3.6523 -0.4441 27.491899 1.2921 3.4450 3.6795 2500 200 -0.0426 3.6523 -0.4441 27.934218 1.3186 3.4349 3.6795 2500 400 -0.0426 3.6523 -0.4441 28.365900 1.3445 3.4249 3.6795 2500 600 -0.0426 3.6523 -0.4441 28.787466 1.3696 3.4149 3.6795 2500 800 -0.0426 3.6523 -0.4441 29.199398 1.3942 3.4049 3.6795 2500 1000 -0.0426 3.6523 -0.4441 29.602138 1.4181 3.3950 3.6795 2500 1200 -0.0426 3.6523 -0.4441 29.996097 1.4414 3.3852 3.6795 2500 1400 -0.0426 3.6523 -0.4441 30.381656 1.4641 3.3754 3.6795 2500 1600 -0.0426 3.6523 -0.4441 30.759169 1.4863 3.3657 3.6795 2500 1800 -0.0426 3.6523 -0.4441 31.128964 1.5080 3.3561 3.6795 2500 2000 -0.0426 3.6523 -0.4441 31.491351 1.5292 3.3465 3.6795 2500 2200 -0.0426 3.6523 -0.4441 31.846616 1.5499 3.3369 3.6795 2500 2400 -0.0426 3.6523 -0.4441 32.195030 1.5702 3.3274 3.6795 2500 2600 -0.0426 3.6523 -0.4441 32.536845 1.5900 3.3180 3.6795 2500 2800 -0.0426 3.6523 -0.4441 32.872300 1.6094 3.3086 3.6795 2500 3000 -0.0426 3.6523 -0.4441 33.201620 1.6284 3.2993 3.6795 2500 3200 -0.0426 3.6523 -0.4441 33.525015 1.6470 3.2901 3.6795 2500 3400 -0.0426 3.6523 -0.4441 33.842686 1.6652 3.2809 3.6795 2500 3600 -0.0426 3.6523 -0.4441 34.154822 1.6831 3.2718 3.6795 2500 3800 -0.0426 3.6523 -0.4441 34.461601 1.7006 3.2627 3.6795 2500 4000 -0.0426 3.6523 -0.4441 34.763194 1.7177 3.2537 3.6795 2500 4200 -0.0426 3.6523 -0.4441 35.059761 1.7345 3.2448 3.6795 2500 4400 -0.0426 3.6523 -0.4441 35.351456 1.7510 3.2359 3.6795 2500 4600 -0.0426 3.6523 -0.4441 35.638424 1.7672 3.2271 3.6795 2500 4800 -0.0426 3.6523 -0.4441 35.920804 1.7831 3.2183 3.6795 2500 5000 -0.0426 3.6523 -0.4441 36.198729 1.7987 3.2097 3.6795 2500 5200 -0.0426 3.6523 -0.4441 36.472323 1.8140 3.2010 3.6795 2500 5400 -0.0426 3.6523 -0.4441 36.741709 1.8290 3.1925 3.6795 2500 5600 -0.0426 3.6523 -0.4441 37.007001 1.8438 3.1840 3.6795 2500 5800 -0.0426 3.6523 -0.4441 37.268310 1.8583 3.1755 3.6795 2500 6000 -0.0426 3.6523 -0.4441 37.525740 1.8725 3.1671 3.6795 2500 6200 -0.0426 3.6523 -0.4441 37.779394 1.8865 3.1588 3.6795 2500 6400 -0.0426 3.6523 -0.4441 38.029367 1.9003 3.1506 3.6795 2500 6600 -0.0426 3.6523 -0.4441 38.275754 1.9138 3.1424 3.6795 2500 6800 -0.0426 3.6523 -0.4441 38.518643 1.9271 3.1342 3.6795 2500 7000 -0.0426 3.6523 -0.4441 38.758120 1.9402 3.1261 3.6795 2500 7200 -0.0426 3.6523 -0.4441 38.994268 1.9531 3.1181 3.6795 2500 7400 -0.0426 3.6523 -0.4441 39.227167 1.9657 3.1101 3.6795 2500 7600 -0.0426 3.6523 -0.4441 39.456892 1.9782 3.1022 3.6795 2500 7800 -0.0426 3.6523 -0.4441 39.683518 1.9904 3.0944 3.6795 2500 8000 -0.0426 3.6523 -0.4441 39.907115 2.0025 3.0866 3.6795 2500 8200 -0.0426 3.6523 -0.4441 40.127752 2.0144 3.0789 3.6795 2500 8400 -0.0426 3.6523 -0.4441 40.345495 2.0261 3.0712 3.6795 3000 0 -0.0426 3.7595 -0.2948 27.490833 1.4740 3.4711 3.7713 3000 200 -0.0426 3.7595 -0.2948 27.933171 1.5007 3.4596 3.7713 3000 400 -0.0426 3.7595 -0.2948 28.364870 1.5267 3.4482 3.7713 3000 600 -0.0426 3.7595 -0.2948 28.786452 1.5521 3.4369 3.7713 3000 800 -0.0426 3.7595 -0.2948 29.198400 1.5767 3.4257 3.7713 3000 1000 -0.0426 3.7595 -0.2948 29.601155 1.6008 3.4145 3.7713 3000 1200 -0.0426 3.7595 -0.2948 29.995129 1.6242 3.4034 3.7713 3000 1400 -0.0426 3.7595 -0.2948 30.380701 1.6471 3.3924 3.7713 3000 1600 -0.0426 3.7595 -0.2948 30.758227 1.6694 3.3815 3.7713 3000 1800 -0.0426 3.7595 -0.2948 31.128035 1.6912 3.3706 3.7713 3000 2000 -0.0426 3.7595 -0.2948 31.490433 1.7125 3.3599 3.7713 3000 2200 -0.0426 3.7595 -0.2948 31.845710 1.7333 3.3492 3.7713 3000 2400 -0.0426 3.7595 -0.2948 32.194135 1.7536 3.3386 3.7713 3000 2600 -0.0426 3.7595 -0.2948 32.535961 1.7735 3.3280 3.7713 3000 2800 -0.0426 3.7595 -0.2948 32.871427 1.7930 3.3176 3.7713 3000 3000 -0.0426 3.7595 -0.2948 33.200756 1.8120 3.3072 3.7713 3000 3200 -0.0426 3.7595 -0.2948 33.524162 1.8306 3.2970 3.7713 3000 3400 -0.0426 3.7595 -0.2948 33.841842 1.8489 3.2868 3.7713 3000 3600 -0.0426 3.7595 -0.2948 34.153987 1.8668 3.2766 3.7713 3000 3800 -0.0426 3.7595 -0.2948 34.460775 1.8843 3.2666 3.7713 3000 4000 -0.0426 3.7595 -0.2948 34.762376 1.9014 3.2566 3.7713 3000 4200 -0.0426 3.7595 -0.2948 35.058951 1.9183 3.2467 3.7713 3000 4400 -0.0426 3.7595 -0.2948 35.350654 1.9348 3.2369 3.7713 3000 4600 -0.0426 3.7595 -0.2948 35.637630 1.9510 3.2272 3.7713 3000 4800 -0.0426 3.7595 -0.2948 35.920018 1.9669 3.2176 3.7713 3000 5000 -0.0426 3.7595 -0.2948 36.197949 1.9824 3.2080 3.7713 3000 5200 -0.0426 3.7595 -0.2948 36.471551 1.9977 3.1985 3.7713 3000 5400 -0.0426 3.7595 -0.2948 36.740944 2.0127 3.1890 3.7713 3000 5600 -0.0426 3.7595 -0.2948 37.006243 2.0275 3.1797 3.7713 3000 5800 -0.0426 3.7595 -0.2948 37.267558 2.0420 3.1704 3.7713 3000 6000 -0.0426 3.7595 -0.2948 37.524995 2.0562 3.1612 3.7713 3000 6200 -0.0426 3.7595 -0.2948 37.778654 2.0702 3.1521 3.7713 3000 6400 -0.0426 3.7595 -0.2948 38.028634 2.0839 3.1430 3.7713 3000 6600 -0.0426 3.7595 -0.2948 38.275026 2.0974 3.1340 3.7713 3000 6800 -0.0426 3.7595 -0.2948 38.517921 2.1107 3.1251 3.7713 3000 7000 -0.0426 3.7595 -0.2948 38.757404 2.1237 3.1162 3.7713 3000 7200 -0.0426 3.7595 -0.2948 38.993558 2.1365 3.1075 3.7713 3000 7400 -0.0426 3.7595 -0.2948 39.226462 2.1492 3.0987 3.7713 3000 7600 -0.0426 3.7595 -0.2948 39.456192 2.1616 3.0901 3.7713 3000 7800 -0.0426 3.7595 -0.2948 39.682823 2.1738 3.0815 3.7713 3000 8000 -0.0426 3.7595 -0.2948 39.906425 2.1858 3.0730 3.7713 3000 8200 -0.0426 3.7595 -0.2948 40.127067 2.1976 3.0646 3.7713 3000 8400 -0.0426 3.7595 -0.2948 40.344815 2.2092 3.0562 3.7713 3500 0 -0.0426 3.8667 -0.1454 27.489747 1.6558 3.4973 3.8696 3500 200 -0.0426 3.8667 -0.1454 27.932104 1.6828 3.4844 3.8696 3500 400 -0.0426 3.8667 -0.1454 28.363820 1.7090 3.4716 3.8696 3500 600 -0.0426 3.8667 -0.1454 28.785420 1.7345 3.4590 3.8696 3500 800 -0.0426 3.8667 -0.1454 29.197383 1.7593 3.4464 3.8696 3500 1000 -0.0426 3.8667 -0.1454 29.600153 1.7835 3.4339 3.8696 3500 1200 -0.0426 3.8667 -0.1454 29.994142 1.8071 3.4216 3.8696 3500 1400 -0.0426 3.8667 -0.1454 30.379728 1.8301 3.4094 3.8696 3500 1600 -0.0426 3.8667 -0.1454 30.757267 1.8525 3.3972 3.8696 3500 1800 -0.0426 3.8667 -0.1454 31.127088 1.8744 3.3852 3.8696 3500 2000 -0.0426 3.8667 -0.1454 31.489499 1.8957 3.3733 3.8696 3500 2200 -0.0426 3.8667 -0.1454 31.844788 1.9166 3.3614 3.8696 3500 2400 -0.0426 3.8667 -0.1454 32.193224 1.9370 3.3497 3.8696 3500 2600 -0.0426 3.8667 -0.1454 32.535061 1.9570 3.3381 3.8696 3500 2800 -0.0426 3.8667 -0.1454 32.870537 1.9765 3.3266 3.8696 3500 3000 -0.0426 3.8667 -0.1454 33.199877 1.9956 3.3152 3.8696 3500 3200 -0.0426 3.8667 -0.1454 33.523292 2.0143 3.3039 3.8696 3500 3400 -0.0426 3.8667 -0.1454 33.840982 2.0326 3.2926 3.8696 3500 3600 -0.0426 3.8667 -0.1454 34.153136 2.0505 3.2815 3.8696 3500 3800 -0.0426 3.8667 -0.1454 34.459933 2.0680 3.2705 3.8696 3500 4000 -0.0426 3.8667 -0.1454 34.761543 2.0852 3.2596 3.8696 3500 4200 -0.0426 3.8667 -0.1454 35.058127 2.1020 3.2487 3.8696 3500 4400 -0.0426 3.8667 -0.1454 35.349838 2.1185 3.2380 3.8696 3500 4600 -0.0426 3.8667 -0.1454 35.636821 2.1347 3.2273 3.8696 3500 4800 -0.0426 3.8667 -0.1454 35.919217 2.1506 3.2168 3.8696 3500 5000 -0.0426 3.8667 -0.1454 36.197156 2.1662 3.2063 3.8696 3500 5200 -0.0426 3.8667 -0.1454 36.470765 2.1815 3.1959 3.8696 3500 5400 -0.0426 3.8667 -0.1454 36.740165 2.1965 3.1856 3.8696 3500 5600 -0.0426 3.8667 -0.1454 37.005471 2.2112 3.1754 3.8696 3500 5800 -0.0426 3.8667 -0.1454 37.266792 2.2257 3.1653 3.8696 3500 6000 -0.0426 3.8667 -0.1454 37.524236 2.2399 3.1553 3.8696 3500 6200 -0.0426 3.8667 -0.1454 37.777902 2.2538 3.1453 3.8696 3500 6400 -0.0426 3.8667 -0.1454 38.027888 2.2675 3.1354 3.8696 3500 6600 -0.0426 3.8667 -0.1454 38.274286 2.2810 3.1257 3.8696 3500 6800 -0.0426 3.8667 -0.1454 38.517187 2.2942 3.1160 3.8696 3500 7000 -0.0426 3.8667 -0.1454 38.756676 2.3072 3.1064 3.8696 3500 7200 -0.0426 3.8667 -0.1454 38.992835 2.3200 3.0968 3.8696 3500 7400 -0.0426 3.8667 -0.1454 39.225745 2.3326 3.0874 3.8696 3500 7600 -0.0426 3.8667 -0.1454 39.455480 2.3449 3.0780 3.8696 3500 7800 -0.0426 3.8667 -0.1454 39.682117 2.3571 3.0687 3.8696 3500 8000 -0.0426 3.8667 -0.1454 39.905724 2.3690 3.0595 3.8696 3500 8200 -0.0426 3.8667 -0.1454 40.126371 2.3808 3.0503 3.8696 3500 8400 -0.0426 3.8667 -0.1454 40.344124 2.3924 3.0412 3.8696 4000 0 -0.0426 3.9738 0.0039 27.488641 1.8377 3.5235 3.9741 4000 200 -0.0426 3.9738 0.0039 27.931016 1.8649 3.5092 3.9741 4000 400 -0.0426 3.9738 0.0039 28.362751 1.8913 3.4950 3.9741 4000 600 -0.0426 3.9738 0.0039 28.784368 1.9169 3.4810 3.9741 4000 800 -0.0426 3.9738 0.0039 29.196347 1.9419 3.4671 3.9741 4000 1000 -0.0426 3.9738 0.0039 29.599133 1.9662 3.4534 3.9741 4000 1200 -0.0426 3.9738 0.0039 29.993137 1.9899 3.4398 3.9741 4000 1400 -0.0426 3.9738 0.0039 30.378737 2.0130 3.4263 3.9741 4000 1600 -0.0426 3.9738 0.0039 30.756290 2.0356 3.4130 3.9741 4000 1800 -0.0426 3.9738 0.0039 31.126124 2.0576 3.3998 3.9741 4000 2000 -0.0426 3.9738 0.0039 31.488547 2.0790 3.3867 3.9741 4000 2200 -0.0426 3.9738 0.0039 31.843848 2.1000 3.3737 3.9741 4000 2400 -0.0426 3.9738 0.0039 32.192296 2.1205 3.3609 3.9741 4000 2600 -0.0426 3.9738 0.0039 32.534144 2.1405 3.3482 3.9741 4000 2800 -0.0426 3.9738 0.0039 32.869631 2.1600 3.3356 3.9741 4000 3000 -0.0426 3.9738 0.0039 33.198982 2.1792 3.3231 3.9741 4000 3200 -0.0426 3.9738 0.0039 33.522407 2.1979 3.3108 3.9741 4000 3400 -0.0426 3.9738 0.0039 33.840106 2.2162 3.2985 3.9741 4000 3600 -0.0426 3.9738 0.0039 34.152270 2.2342 3.2864 3.9741 4000 3800 -0.0426 3.9738 0.0039 34.459076 2.2517 3.2744 3.9741 4000 4000 -0.0426 3.9738 0.0039 34.760695 2.2689 3.2625 3.9741 4000 4200 -0.0426 3.9738 0.0039 35.057287 2.2858 3.2507 3.9741 4000 4400 -0.0426 3.9738 0.0039 35.349006 2.3023 3.2390 3.9741 4000 4600 -0.0426 3.9738 0.0039 35.635998 2.3185 3.2274 3.9741 4000 4800 -0.0426 3.9738 0.0039 35.918401 2.3344 3.2160 3.9741 4000 5000 -0.0426 3.9738 0.0039 36.196348 2.3500 3.2046 3.9741 4000 5200 -0.0426 3.9738 0.0039 36.469965 2.3652 3.1934 3.9741 4000 5400 -0.0426 3.9738 0.0039 36.739372 2.3802 3.1822 3.9741 4000 5600 -0.0426 3.9738 0.0039 37.004685 2.3949 3.1711 3.9741 4000 5800 -0.0426 3.9738 0.0039 37.266013 2.4094 3.1602 3.9741 4000 6000 -0.0426 3.9738 0.0039 37.523463 2.4235 3.1493 3.9741 4000 6200 -0.0426 3.9738 0.0039 37.777136 2.4375 3.1386 3.9741 4000 6400 -0.0426 3.9738 0.0039 38.027128 2.4511 3.1279 3.9741 4000 6600 -0.0426 3.9738 0.0039 38.273533 2.4646 3.1173 3.9741 4000 6800 -0.0426 3.9738 0.0039 38.516440 2.4778 3.1069 3.9741 4000 7000 -0.0426 3.9738 0.0039 38.755934 2.4907 3.0965 3.9741 4000 7200 -0.0426 3.9738 0.0039 38.992100 2.5035 3.0862 3.9741 4000 7400 -0.0426 3.9738 0.0039 39.225015 2.5160 3.0760 3.9741 4000 7600 -0.0426 3.9738 0.0039 39.454756 2.5283 3.0659 3.9741 4000 7800 -0.0426 3.9738 0.0039 39.681398 2.5404 3.0558 3.9741 4000 8000 -0.0426 3.9738 0.0039 39.905010 2.5523 3.0459 3.9741 4000 8200 -0.0426 3.9738 0.0039 40.125663 2.5640 3.0360 3.9741 4000 8400 -0.0426 3.9738 0.0039 40.343421 2.5755 3.0263 3.9741 4500 0 -0.0426 4.0810 0.1532 27.487515 2.0196 3.5496 4.0841 4500 200 -0.0426 4.0810 0.1532 27.929909 2.0469 3.5339 4.0841 4500 400 -0.0426 4.0810 0.1532 28.361662 2.0735 3.5184 4.0841 4500 600 -0.0426 4.0810 0.1532 28.783297 2.0993 3.5031 4.0841 4500 800 -0.0426 4.0810 0.1532 29.195293 2.1245 3.4879 4.0841 4500 1000 -0.0426 4.0810 0.1532 29.598095 2.1489 3.4729 4.0841 4500 1200 -0.0426 4.0810 0.1532 29.992113 2.1728 3.4580 4.0841 4500 1400 -0.0426 4.0810 0.1532 30.377729 2.1960 3.4433 4.0841 4500 1600 -0.0426 4.0810 0.1532 30.755295 2.2186 3.4287 4.0841 4500 1800 -0.0426 4.0810 0.1532 31.125143 2.2407 3.4143 4.0841 4500 2000 -0.0426 4.0810 0.1532 31.487579 2.2623 3.4001 4.0841 4500 2200 -0.0426 4.0810 0.1532 31.842892 2.2833 3.3860 4.0841 4500 2400 -0.0426 4.0810 0.1532 32.191352 2.3039 3.3721 4.0841 4500 2600 -0.0426 4.0810 0.1532 32.533211 2.3240 3.3583 4.0841 4500 2800 -0.0426 4.0810 0.1532 32.868710 2.3436 3.3446 4.0841 4500 3000 -0.0426 4.0810 0.1532 33.198070 2.3628 3.3311 4.0841 4500 3200 -0.0426 4.0810 0.1532 33.521506 2.3815 3.3177 4.0841 4500 3400 -0.0426 4.0810 0.1532 33.839215 2.3999 3.3044 4.0841 4500 3600 -0.0426 4.0810 0.1532 34.151388 2.4179 3.2913 4.0841 4500 3800 -0.0426 4.0810 0.1532 34.458204 2.4354 3.2783 4.0841 4500 4000 -0.0426 4.0810 0.1532 34.759832 2.4527 3.2654 4.0841 4500 4200 -0.0426 4.0810 0.1532 35.056433 2.4695 3.2527 4.0841 4500 4400 -0.0426 4.0810 0.1532 35.348161 2.4861 3.2401 4.0841 4500 4600 -0.0426 4.0810 0.1532 35.635161 2.5023 3.2276 4.0841 4500 4800 -0.0426 4.0810 0.1532 35.917572 2.5181 3.2152 4.0841 4500 5000 -0.0426 4.0810 0.1532 36.195527 2.5337 3.2029 4.0841 4500 5200 -0.0426 4.0810 0.1532 36.469151 2.5490 3.1908 4.0841 4500 5400 -0.0426 4.0810 0.1532 36.738565 2.5640 3.1788 4.0841 4500 5600 -0.0426 4.0810 0.1532 37.003885 2.5786 3.1669 4.0841 4500 5800 -0.0426 4.0810 0.1532 37.265221 2.5931 3.1551 4.0841 4500 6000 -0.0426 4.0810 0.1532 37.522678 2.6072 3.1434 4.0841 4500 6200 -0.0426 4.0810 0.1532 37.776357 2.6211 3.1318 4.0841 4500 6400 -0.0426 4.0810 0.1532 38.026356 2.6347 3.1204 4.0841 4500 6600 -0.0426 4.0810 0.1532 38.272767 2.6481 3.1090 4.0841 4500 6800 -0.0426 4.0810 0.1532 38.515680 2.6613 3.0977 4.0841 4500 7000 -0.0426 4.0810 0.1532 38.755181 2.6742 3.0866 4.0841 4500 7200 -0.0426 4.0810 0.1532 38.991352 2.6869 3.0755 4.0841 4500 7400 -0.0426 4.0810 0.1532 39.224272 2.6994 3.0646 4.0841 4500 7600 -0.0426 4.0810 0.1532 39.454020 2.7117 3.0537 4.0841 4500 7800 -0.0426 4.0810 0.1532 39.680667 2.7237 3.0430 4.0841 4500 8000 -0.0426 4.0810 0.1532 39.904285 2.7356 3.0323 4.0841 4500 8200 -0.0426 4.0810 0.1532 40.124942 2.7472 3.0218 4.0841 4500 8400 -0.0426 4.0810 0.1532 40.342705 2.7587 3.0113 4.0841 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.000 ================================================ FILE: tests/test_data/cropA/geometry/20180106-20180412_VV_8rlks_base.par ================================================ initial_baseline(TCN): -3.3824897 -65.5423278 36.9516791 m m m initial_baseline_rate: 0.0000000 -0.0300356 0.1047678 m/s m/s m/s precision_baseline(TCN): 0.0000000 -65.6149034 36.9707023 m m m precision_baseline_rate: 0.0000000 -0.0194198 0.0946307 m/s m/s m/s unwrap_phase_constant: -0.00012 radians ================================================ FILE: tests/test_data/cropA/geometry/20180106-20180412_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): -3.382 -65.542 36.952 orbit baseline rate (TCN) (m/s): 0.000e+00 -3.004e-02 1.048e-01 baseline vector (TCN) (m): 0.000 -65.615 36.971 baseline rate (TCN) (m/s): 0.000e+00 -1.942e-02 9.463e-02 SLC-1 center baseline length (m): 75.3137 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 0.0000 -65.4336 36.0874 27.496918 1.8001 -74.7034 74.7252 0 200 0.0000 -65.4336 36.0874 27.939152 1.2235 -74.7151 74.7252 0 400 0.0000 -65.4336 36.0874 28.370753 0.6606 -74.7222 74.7252 0 600 0.0000 -65.4336 36.0874 28.792243 0.1109 -74.7250 74.7252 0 800 0.0000 -65.4336 36.0874 29.204102 -0.4262 -74.7239 74.7252 0 1000 0.0000 -65.4336 36.0874 29.606772 -0.9514 -74.7191 74.7252 0 1200 0.0000 -65.4336 36.0874 30.000665 -1.4650 -74.7107 74.7252 0 1400 0.0000 -65.4336 36.0874 30.386160 -1.9676 -74.6992 74.7252 0 1600 0.0000 -65.4336 36.0874 30.763612 -2.4597 -74.6846 74.7252 0 1800 0.0000 -65.4336 36.0874 31.133349 -2.9416 -74.6672 74.7252 0 2000 0.0000 -65.4336 36.0874 31.495679 -3.4137 -74.6471 74.7252 0 2200 0.0000 -65.4336 36.0874 31.850890 -3.8764 -74.6245 74.7252 0 2400 0.0000 -65.4336 36.0874 32.199251 -4.3301 -74.5996 74.7252 0 2600 0.0000 -65.4336 36.0874 32.541017 -4.7750 -74.5724 74.7252 0 2800 0.0000 -65.4336 36.0874 32.876424 -5.2115 -74.5432 74.7252 0 3000 0.0000 -65.4336 36.0874 33.205696 -5.6398 -74.5120 74.7252 0 3200 0.0000 -65.4336 36.0874 33.529047 -6.0602 -74.4790 74.7252 0 3400 0.0000 -65.4336 36.0874 33.846674 -6.4730 -74.4443 74.7252 0 3600 0.0000 -65.4336 36.0874 34.158768 -6.8784 -74.4079 74.7252 0 3800 0.0000 -65.4336 36.0874 34.465506 -7.2766 -74.3700 74.7252 0 4000 0.0000 -65.4336 36.0874 34.767059 -7.6679 -74.3307 74.7252 0 4200 0.0000 -65.4336 36.0874 35.063588 -8.0525 -74.2900 74.7252 0 4400 0.0000 -65.4336 36.0874 35.355245 -8.4306 -74.2480 74.7252 0 4600 0.0000 -65.4336 36.0874 35.642177 -8.8023 -74.2049 74.7252 0 4800 0.0000 -65.4336 36.0874 35.924522 -9.1679 -74.1606 74.7252 0 5000 0.0000 -65.4336 36.0874 36.202412 -9.5274 -74.1153 74.7252 0 5200 0.0000 -65.4336 36.0874 36.475974 -9.8812 -74.0690 74.7252 0 5400 0.0000 -65.4336 36.0874 36.745327 -10.2293 -74.0217 74.7252 0 5600 0.0000 -65.4336 36.0874 37.010588 -10.5719 -73.9735 74.7252 0 5800 0.0000 -65.4336 36.0874 37.271866 -10.9091 -73.9246 74.7252 0 6000 0.0000 -65.4336 36.0874 37.529266 -11.2411 -73.8748 74.7252 0 6200 0.0000 -65.4336 36.0874 37.782890 -11.5680 -73.8243 74.7252 0 6400 0.0000 -65.4336 36.0874 38.032835 -11.8899 -73.7732 74.7252 0 6600 0.0000 -65.4336 36.0874 38.279194 -12.2070 -73.7214 74.7252 0 6800 0.0000 -65.4336 36.0874 38.522056 -12.5194 -73.6689 74.7252 0 7000 0.0000 -65.4336 36.0874 38.761507 -12.8272 -73.6160 74.7252 0 7200 0.0000 -65.4336 36.0874 38.997629 -13.1305 -73.5625 74.7252 0 7400 0.0000 -65.4336 36.0874 39.230502 -13.4293 -73.5085 74.7252 0 7600 0.0000 -65.4336 36.0874 39.460203 -13.7239 -73.4541 74.7252 0 7800 0.0000 -65.4336 36.0874 39.686804 -14.0143 -73.3992 74.7252 0 8000 0.0000 -65.4336 36.0874 39.910378 -14.3006 -73.3440 74.7252 0 8200 0.0000 -65.4336 36.0874 40.130991 -14.5829 -73.2884 74.7252 0 8400 0.0000 -65.4336 36.0874 40.348712 -14.8613 -73.2325 74.7252 500 0 0.0000 -65.4736 36.2819 27.495955 1.9555 -74.8286 74.8543 500 200 0.0000 -65.4736 36.2819 27.938206 1.3778 -74.8415 74.8543 500 400 0.0000 -65.4736 36.2819 28.369822 0.8140 -74.8497 74.8543 500 600 0.0000 -65.4736 36.2819 28.791327 0.2634 -74.8537 74.8543 500 800 0.0000 -65.4736 36.2819 29.203199 -0.2747 -74.8536 74.8543 500 1000 0.0000 -65.4736 36.2819 29.605883 -0.8008 -74.8499 74.8543 500 1200 0.0000 -65.4736 36.2819 29.999788 -1.3154 -74.8426 74.8543 500 1400 0.0000 -65.4736 36.2819 30.385296 -1.8189 -74.8321 74.8543 500 1600 0.0000 -65.4736 36.2819 30.762759 -2.3119 -74.8185 74.8543 500 1800 0.0000 -65.4736 36.2819 31.132507 -2.7946 -74.8020 74.8543 500 2000 0.0000 -65.4736 36.2819 31.494848 -3.2676 -74.7828 74.8543 500 2200 0.0000 -65.4736 36.2819 31.850069 -3.7312 -74.7611 74.8543 500 2400 0.0000 -65.4736 36.2819 32.198441 -4.1857 -74.7371 74.8543 500 2600 0.0000 -65.4736 36.2819 32.540215 -4.6314 -74.7108 74.8543 500 2800 0.0000 -65.4736 36.2819 32.875632 -5.0687 -74.6824 74.8543 500 3000 0.0000 -65.4736 36.2819 33.204913 -5.4978 -74.6520 74.8543 500 3200 0.0000 -65.4736 36.2819 33.528272 -5.9191 -74.6198 74.8543 500 3400 0.0000 -65.4736 36.2819 33.845908 -6.3326 -74.5858 74.8543 500 3600 0.0000 -65.4736 36.2819 34.158010 -6.7388 -74.5502 74.8543 500 3800 0.0000 -65.4736 36.2819 34.464756 -7.1379 -74.5131 74.8543 500 4000 0.0000 -65.4736 36.2819 34.766316 -7.5299 -74.4745 74.8543 500 4200 0.0000 -65.4736 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37.4490 31.127088 -1.9123 -75.6106 75.6349 3500 2000 0.0000 -65.7131 37.4490 31.489499 -2.3905 -75.5970 75.6349 3500 2200 0.0000 -65.7131 37.4490 31.844788 -2.8592 -75.5807 75.6349 3500 2400 0.0000 -65.7131 37.4490 32.193224 -3.3188 -75.5619 75.6349 3500 2600 0.0000 -65.7131 37.4490 32.535061 -3.7696 -75.5408 75.6349 3500 2800 0.0000 -65.7131 37.4490 32.870537 -4.2118 -75.5174 75.6349 3500 3000 0.0000 -65.7131 37.4490 33.199877 -4.6458 -75.4920 75.6349 3500 3200 0.0000 -65.7131 37.4490 33.523292 -5.0719 -75.4645 75.6349 3500 3400 0.0000 -65.7131 37.4490 33.840982 -5.4902 -75.4353 75.6349 3500 3600 0.0000 -65.7131 37.4490 34.153136 -5.9011 -75.4042 75.6349 3500 3800 0.0000 -65.7131 37.4490 34.459933 -6.3048 -75.3716 75.6349 3500 4000 0.0000 -65.7131 37.4490 34.761543 -6.7015 -75.3373 75.6349 3500 4200 0.0000 -65.7131 37.4490 35.058127 -7.0914 -75.3016 75.6349 3500 4400 0.0000 -65.7131 37.4490 35.349838 -7.4747 -75.2646 75.6349 3500 4600 0.0000 -65.7131 37.4490 35.636821 -7.8515 -75.2262 75.6349 3500 4800 0.0000 -65.7131 37.4490 35.919217 -8.2222 -75.1866 75.6349 3500 5000 0.0000 -65.7131 37.4490 36.197156 -8.5868 -75.1458 75.6349 3500 5200 0.0000 -65.7131 37.4490 36.470765 -8.9456 -75.1039 75.6349 3500 5400 0.0000 -65.7131 37.4490 36.740165 -9.2986 -75.0610 75.6349 3500 5600 0.0000 -65.7131 37.4490 37.005471 -9.6461 -75.0172 75.6349 3500 5800 0.0000 -65.7131 37.4490 37.266792 -9.9881 -74.9724 75.6349 3500 6000 0.0000 -65.7131 37.4490 37.524236 -10.3249 -74.9268 75.6349 3500 6200 0.0000 -65.7131 37.4490 37.777902 -10.6565 -74.8803 75.6349 3500 6400 0.0000 -65.7131 37.4490 38.027888 -10.9831 -74.8331 75.6349 3500 6600 0.0000 -65.7131 37.4490 38.274286 -11.3049 -74.7852 75.6349 3500 6800 0.0000 -65.7131 37.4490 38.517187 -11.6218 -74.7366 75.6349 3500 7000 0.0000 -65.7131 37.4490 38.756676 -11.9341 -74.6874 75.6349 3500 7200 0.0000 -65.7131 37.4490 38.992835 -12.2418 -74.6375 75.6349 3500 7400 0.0000 -65.7131 37.4490 39.225745 -12.5451 -74.5872 75.6349 3500 7600 0.0000 -65.7131 37.4490 39.455480 -12.8441 -74.5363 75.6349 3500 7800 0.0000 -65.7131 37.4490 39.682117 -13.1388 -74.4849 75.6349 3500 8000 0.0000 -65.7131 37.4490 39.905724 -13.4294 -74.4330 75.6349 3500 8200 0.0000 -65.7131 37.4490 40.126371 -13.7160 -74.3808 75.6349 3500 8400 0.0000 -65.7131 37.4490 40.344124 -13.9986 -74.3281 75.6349 4000 0 0.0000 -65.7530 37.6435 27.488641 3.0440 -75.7047 75.7660 4000 200 0.0000 -65.7530 37.6435 27.931016 2.4594 -75.7260 75.7660 4000 400 0.0000 -65.7530 37.6435 28.362751 1.8887 -75.7423 75.7660 4000 600 0.0000 -65.7530 37.6435 28.784368 1.3313 -75.7542 75.7660 4000 800 0.0000 -65.7530 37.6435 29.196347 0.7866 -75.7618 75.7660 4000 1000 0.0000 -65.7530 37.6435 29.599133 0.2539 -75.7655 75.7660 4000 1200 0.0000 -65.7530 37.6435 29.993137 -0.2671 -75.7654 75.7660 4000 1400 0.0000 -65.7530 37.6435 30.378737 -0.7770 -75.7619 75.7660 4000 1600 0.0000 -65.7530 37.6435 30.756290 -1.2762 -75.7552 75.7660 4000 1800 0.0000 -65.7530 37.6435 31.126124 -1.7651 -75.7454 75.7660 4000 2000 0.0000 -65.7530 37.6435 31.488547 -2.2442 -75.7327 75.7660 4000 2200 0.0000 -65.7530 37.6435 31.843848 -2.7138 -75.7173 75.7660 4000 2400 0.0000 -65.7530 37.6435 32.192296 -3.1743 -75.6994 75.7660 4000 2600 0.0000 -65.7530 37.6435 32.534144 -3.6258 -75.6791 75.7660 4000 2800 0.0000 -65.7530 37.6435 32.869631 -4.0689 -75.6566 75.7660 4000 3000 0.0000 -65.7530 37.6435 33.198982 -4.5037 -75.6320 75.7660 4000 3200 0.0000 -65.7530 37.6435 33.522407 -4.9306 -75.6053 75.7660 4000 3400 0.0000 -65.7530 37.6435 33.840106 -5.3497 -75.5768 75.7660 4000 3600 0.0000 -65.7530 37.6435 34.152270 -5.7614 -75.5466 75.7660 4000 3800 0.0000 -65.7530 37.6435 34.459076 -6.1659 -75.5146 75.7660 4000 4000 0.0000 -65.7530 37.6435 34.760695 -6.5633 -75.4811 75.7660 4000 4200 0.0000 -65.7530 37.6435 35.057287 -6.9540 -75.4461 75.7660 4000 4400 0.0000 -65.7530 37.6435 35.349006 -7.3380 -75.4098 75.7660 4000 4600 0.0000 -65.7530 37.6435 35.635998 -7.7156 -75.3721 75.7660 4000 4800 0.0000 -65.7530 37.6435 35.918401 -8.0870 -75.3331 75.7660 4000 5000 0.0000 -65.7530 37.6435 36.196348 -8.4524 -75.2930 75.7660 4000 5200 0.0000 -65.7530 37.6435 36.469965 -8.8118 -75.2518 75.7660 4000 5400 0.0000 -65.7530 37.6435 36.739372 -9.1656 -75.2095 75.7660 4000 5600 0.0000 -65.7530 37.6435 37.004685 -9.5138 -75.1663 75.7660 4000 5800 0.0000 -65.7530 37.6435 37.266013 -9.8565 -75.1221 75.7660 4000 6000 0.0000 -65.7530 37.6435 37.523463 -10.1939 -75.0770 75.7660 4000 6200 0.0000 -65.7530 37.6435 37.777136 -10.5262 -75.0312 75.7660 4000 6400 0.0000 -65.7530 37.6435 38.027128 -10.8535 -74.9845 75.7660 4000 6600 0.0000 -65.7530 37.6435 38.273533 -11.1759 -74.9372 75.7660 4000 6800 0.0000 -65.7530 37.6435 38.516440 -11.4935 -74.8891 75.7660 4000 7000 0.0000 -65.7530 37.6435 38.755934 -11.8064 -74.8404 75.7660 4000 7200 0.0000 -65.7530 37.6435 38.992100 -12.1148 -74.7911 75.7660 4000 7400 0.0000 -65.7530 37.6435 39.225015 -12.4187 -74.7413 75.7660 4000 7600 0.0000 -65.7530 37.6435 39.454756 -12.7183 -74.6909 75.7660 4000 7800 0.0000 -65.7530 37.6435 39.681398 -13.0137 -74.6400 75.7660 4000 8000 0.0000 -65.7530 37.6435 39.905010 -13.3049 -74.5886 75.7660 4000 8200 0.0000 -65.7530 37.6435 40.125663 -13.5920 -74.5368 75.7660 4000 8400 0.0000 -65.7530 37.6435 40.343421 -13.8752 -74.4846 75.7660 4500 0 0.0000 -65.7929 37.8381 27.487515 3.1996 -75.8298 75.8975 4500 200 0.0000 -65.7929 37.8381 27.929909 2.6140 -75.8523 75.8975 4500 400 0.0000 -65.7929 37.8381 28.361662 2.0423 -75.8698 75.8975 4500 600 0.0000 -65.7929 37.8381 28.783297 1.4840 -75.8828 75.8975 4500 800 0.0000 -65.7929 37.8381 29.195293 0.9383 -75.8915 75.8975 4500 1000 0.0000 -65.7929 37.8381 29.598095 0.4047 -75.8963 75.8975 4500 1200 0.0000 -65.7929 37.8381 29.992113 -0.1172 -75.8972 75.8975 4500 1400 0.0000 -65.7929 37.8381 30.377729 -0.6280 -75.8947 75.8975 4500 1600 0.0000 -65.7929 37.8381 30.755295 -1.1281 -75.8890 75.8975 4500 1800 0.0000 -65.7929 37.8381 31.125143 -1.6180 -75.8801 75.8975 4500 2000 0.0000 -65.7929 37.8381 31.487579 -2.0979 -75.8684 75.8975 4500 2200 0.0000 -65.7929 37.8381 31.842892 -2.5684 -75.8539 75.8975 4500 2400 0.0000 -65.7929 37.8381 32.191352 -3.0297 -75.8369 75.8975 4500 2600 0.0000 -65.7929 37.8381 32.533211 -3.4821 -75.8174 75.8975 4500 2800 0.0000 -65.7929 37.8381 32.868710 -3.9260 -75.7958 75.8975 4500 3000 0.0000 -65.7929 37.8381 33.198070 -4.3616 -75.7719 75.8975 4500 3200 0.0000 -65.7929 37.8381 33.521506 -4.7893 -75.7461 75.8975 4500 3400 0.0000 -65.7929 37.8381 33.839215 -5.2092 -75.7184 75.8975 4500 3600 0.0000 -65.7929 37.8381 34.151388 -5.6217 -75.6889 75.8975 4500 3800 0.0000 -65.7929 37.8381 34.458204 -6.0269 -75.6577 75.8975 4500 4000 0.0000 -65.7929 37.8381 34.759832 -6.4251 -75.6249 75.8975 4500 4200 0.0000 -65.7929 37.8381 35.056433 -6.8165 -75.5906 75.8975 4500 4400 0.0000 -65.7929 37.8381 35.348161 -7.2013 -75.5550 75.8975 4500 4600 0.0000 -65.7929 37.8381 35.635161 -7.5797 -75.5179 75.8975 4500 4800 0.0000 -65.7929 37.8381 35.917572 -7.9518 -75.4797 75.8975 4500 5000 0.0000 -65.7929 37.8381 36.195527 -8.3179 -75.4402 75.8975 4500 5200 0.0000 -65.7929 37.8381 36.469151 -8.6781 -75.3996 75.8975 4500 5400 0.0000 -65.7929 37.8381 36.738565 -9.0325 -75.3580 75.8975 4500 5600 0.0000 -65.7929 37.8381 37.003885 -9.3814 -75.3154 75.8975 4500 5800 0.0000 -65.7929 37.8381 37.265221 -9.7248 -75.2718 75.8975 4500 6000 0.0000 -65.7929 37.8381 37.522678 -10.0629 -75.2273 75.8975 4500 6200 0.0000 -65.7929 37.8381 37.776357 -10.3959 -75.1820 75.8975 4500 6400 0.0000 -65.7929 37.8381 38.026356 -10.7239 -75.1360 75.8975 4500 6600 0.0000 -65.7929 37.8381 38.272767 -11.0469 -75.0891 75.8975 4500 6800 0.0000 -65.7929 37.8381 38.515680 -11.3652 -75.0416 75.8975 4500 7000 0.0000 -65.7929 37.8381 38.755181 -11.6787 -74.9935 75.8975 4500 7200 0.0000 -65.7929 37.8381 38.991352 -11.9878 -74.9447 75.8975 4500 7400 0.0000 -65.7929 37.8381 39.224272 -12.2923 -74.8953 75.8975 4500 7600 0.0000 -65.7929 37.8381 39.454020 -12.5925 -74.8455 75.8975 4500 7800 0.0000 -65.7929 37.8381 39.680667 -12.8885 -74.7951 75.8975 4500 8000 0.0000 -65.7929 37.8381 39.904285 -13.1803 -74.7442 75.8975 4500 8200 0.0000 -65.7929 37.8381 40.124942 -13.4681 -74.6929 75.8975 4500 8400 0.0000 -65.7929 37.8381 40.342705 -13.7519 -74.6411 75.8975 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.010 ================================================ FILE: tests/test_data/cropA/geometry/20180106-20180518_VV_8rlks_base.par ================================================ initial_baseline(TCN): 0.8185852 -15.9799853 28.0671376 m m m initial_baseline_rate: 0.0000000 0.0487790 0.1002578 m/s m/s m/s precision_baseline(TCN): 0.0000000 -15.6884185 27.9327136 m m m precision_baseline_rate: 0.0000000 0.0490924 0.1006091 m/s m/s m/s unwrap_phase_constant: 0.00028 radians ================================================ FILE: tests/test_data/cropA/geometry/20180106-20180518_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): 0.819 -15.980 28.067 orbit baseline rate (TCN) (m/s): 0.000e+00 4.878e-02 1.003e-01 baseline vector (TCN) (m): 0.000 -15.688 27.933 baseline rate (TCN) (m/s): 0.000e+00 4.909e-02 1.006e-01 SLC-1 center baseline length (m): 32.0369 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 0.0000 -16.1467 26.9936 27.496918 16.4894 -26.7856 31.4542 0 200 0.0000 -16.1467 26.9936 27.939152 16.2821 -26.9121 31.4542 0 400 0.0000 -16.1467 26.9936 28.370753 16.0790 -27.0340 31.4542 0 600 0.0000 -16.1467 26.9936 28.792243 15.8797 -27.1515 31.4542 0 800 0.0000 -16.1467 26.9936 29.204102 15.6841 -27.2650 31.4542 0 1000 0.0000 -16.1467 26.9936 29.606772 15.4921 -27.3745 31.4542 0 1200 0.0000 -16.1467 26.9936 30.000665 15.3035 -27.4804 31.4542 0 1400 0.0000 -16.1467 26.9936 30.386160 15.1183 -27.5827 31.4542 0 1600 0.0000 -16.1467 26.9936 30.763612 14.9362 -27.6817 31.4542 0 1800 0.0000 -16.1467 26.9936 31.133349 14.7573 -27.7775 31.4542 0 2000 0.0000 -16.1467 26.9936 31.495679 14.5813 -27.8703 31.4542 0 2200 0.0000 -16.1467 26.9936 31.850890 14.4083 -27.9601 31.4542 0 2400 0.0000 -16.1467 26.9936 32.199251 14.2380 -28.0472 31.4542 0 2600 0.0000 -16.1467 26.9936 32.541017 14.0705 -28.1317 31.4542 0 2800 0.0000 -16.1467 26.9936 32.876424 13.9055 -28.2135 31.4542 0 3000 0.0000 -16.1467 26.9936 33.205696 13.7432 -28.2930 31.4542 0 3200 0.0000 -16.1467 26.9936 33.529047 13.5833 -28.3701 31.4542 0 3400 0.0000 -16.1467 26.9936 33.846674 13.4258 -28.4450 31.4542 0 3600 0.0000 -16.1467 26.9936 34.158768 13.2707 -28.5177 31.4542 0 3800 0.0000 -16.1467 26.9936 34.465506 13.1178 -28.5883 31.4542 0 4000 0.0000 -16.1467 26.9936 34.767059 12.9671 -28.6570 31.4542 0 4200 0.0000 -16.1467 26.9936 35.063588 12.8187 -28.7237 31.4542 0 4400 0.0000 -16.1467 26.9936 35.355245 12.6723 -28.7886 31.4542 0 4600 0.0000 -16.1467 26.9936 35.642177 12.5280 -28.8517 31.4542 0 4800 0.0000 -16.1467 26.9936 35.924522 12.3856 -28.9131 31.4542 0 5000 0.0000 -16.1467 26.9936 36.202412 12.2452 -28.9728 31.4542 0 5200 0.0000 -16.1467 26.9936 36.475974 12.1068 -29.0309 31.4542 0 5400 0.0000 -16.1467 26.9936 36.745327 11.9702 -29.0875 31.4542 0 5600 0.0000 -16.1467 26.9936 37.010588 11.8354 -29.1426 31.4542 0 5800 0.0000 -16.1467 26.9936 37.271866 11.7024 -29.1963 31.4542 0 6000 0.0000 -16.1467 26.9936 37.529266 11.5711 -29.2486 31.4542 0 6200 0.0000 -16.1467 26.9936 37.782890 11.4415 -29.2995 31.4542 0 6400 0.0000 -16.1467 26.9936 38.032835 11.3136 -29.3491 31.4542 0 6600 0.0000 -16.1467 26.9936 38.279194 11.1873 -29.3975 31.4542 0 6800 0.0000 -16.1467 26.9936 38.522056 11.0626 -29.4447 31.4542 0 7000 0.0000 -16.1467 26.9936 38.761507 10.9394 -29.4906 31.4542 0 7200 0.0000 -16.1467 26.9936 38.997629 10.8178 -29.5355 31.4542 0 7400 0.0000 -16.1467 26.9936 39.230502 10.6976 -29.5792 31.4542 0 7600 0.0000 -16.1467 26.9936 39.460203 10.5790 -29.6218 31.4542 0 7800 0.0000 -16.1467 26.9936 39.686804 10.4617 -29.6635 31.4542 0 8000 0.0000 -16.1467 26.9936 39.910378 10.3459 -29.7041 31.4542 0 8200 0.0000 -16.1467 26.9936 40.130991 10.2315 -29.7437 31.4542 0 8400 0.0000 -16.1467 26.9936 40.348712 10.1184 -29.7823 31.4542 500 0 0.0000 -16.0457 27.2004 27.495955 16.7199 -26.7913 31.5805 500 200 0.0000 -16.0457 27.2004 27.938206 16.5126 -26.9195 31.5805 500 400 0.0000 -16.0457 27.2004 28.369822 16.3093 -27.0432 31.5805 500 600 0.0000 -16.0457 27.2004 28.791327 16.1099 -27.1624 31.5805 500 800 0.0000 -16.0457 27.2004 29.203199 15.9143 -27.2775 31.5805 500 1000 0.0000 -16.0457 27.2004 29.605883 15.7222 -27.3887 31.5805 500 1200 0.0000 -16.0457 27.2004 29.999788 15.5335 -27.4961 31.5805 500 1400 0.0000 -16.0457 27.2004 30.385296 15.3481 -27.6000 31.5805 500 1600 0.0000 -16.0457 27.2004 30.762759 15.1660 -27.7005 31.5805 500 1800 0.0000 -16.0457 27.2004 31.132507 14.9869 -27.7978 31.5805 500 2000 0.0000 -16.0457 27.2004 31.494848 14.8108 -27.8921 31.5805 500 2200 0.0000 -16.0457 27.2004 31.850069 14.6376 -27.9834 31.5805 500 2400 0.0000 -16.0457 27.2004 32.198441 14.4672 -28.0718 31.5805 500 2600 0.0000 -16.0457 27.2004 32.540215 14.2995 -28.1576 31.5805 500 2800 0.0000 -16.0457 27.2004 32.875632 14.1344 -28.2409 31.5805 500 3000 0.0000 -16.0457 27.2004 33.204913 13.9719 -28.3216 31.5805 500 3200 0.0000 -16.0457 27.2004 33.528272 13.8118 -28.4000 31.5805 500 3400 0.0000 -16.0457 27.2004 33.845908 13.6541 -28.4762 31.5805 500 3600 0.0000 -16.0457 27.2004 34.158010 13.4988 -28.5501 31.5805 500 3800 0.0000 -16.0457 27.2004 34.464756 13.3458 -28.6220 31.5805 500 4000 0.0000 -16.0457 27.2004 34.766316 13.1950 -28.6918 31.5805 500 4200 0.0000 -16.0457 27.2004 35.062852 13.0463 -28.7597 31.5805 500 4400 0.0000 -16.0457 27.2004 35.354517 12.8997 -28.8258 31.5805 500 4600 0.0000 -16.0457 27.2004 35.641456 12.7552 -28.8900 31.5805 500 4800 0.0000 -16.0457 27.2004 35.923807 12.6127 -28.9525 31.5805 500 5000 0.0000 -16.0457 27.2004 36.201704 12.4721 -29.0133 31.5805 500 5200 0.0000 -16.0457 27.2004 36.475272 12.3334 -29.0726 31.5805 500 5400 0.0000 -16.0457 27.2004 36.744632 12.1966 -29.1302 31.5805 500 5600 0.0000 -16.0457 27.2004 37.009898 12.0616 -29.1864 31.5805 500 5800 0.0000 -16.0457 27.2004 37.271182 11.9284 -29.2411 31.5805 500 6000 0.0000 -16.0457 27.2004 37.528588 11.7969 -29.2944 31.5805 500 6200 0.0000 -16.0457 27.2004 37.782218 11.6671 -29.3463 31.5805 500 6400 0.0000 -16.0457 27.2004 38.032168 11.5390 -29.3969 31.5805 500 6600 0.0000 -16.0457 27.2004 38.278532 11.4125 -29.4463 31.5805 500 6800 0.0000 -16.0457 27.2004 38.521399 11.2875 -29.4944 31.5805 500 7000 0.0000 -16.0457 27.2004 38.760855 11.1642 -29.5413 31.5805 500 7200 0.0000 -16.0457 27.2004 38.996982 11.0423 -29.5871 31.5805 500 7400 0.0000 -16.0457 27.2004 39.229860 10.9220 -29.6317 31.5805 500 7600 0.0000 -16.0457 27.2004 39.459566 10.8031 -29.6753 31.5805 500 7800 0.0000 -16.0457 27.2004 39.686172 10.6857 -29.7177 31.5805 500 8000 0.0000 -16.0457 27.2004 39.909750 10.5696 -29.7592 31.5805 500 8200 0.0000 -16.0457 27.2004 40.130368 10.4549 -29.7997 31.5805 500 8400 0.0000 -16.0457 27.2004 40.348092 10.3416 -29.8392 31.5805 1000 0 0.0000 -15.9448 27.4072 27.494972 16.9504 -26.7970 31.7079 1000 200 0.0000 -15.9448 27.4072 27.937239 16.7430 -26.9270 31.7079 1000 400 0.0000 -15.9448 27.4072 28.368871 16.5397 -27.0524 31.7079 1000 600 0.0000 -15.9448 27.4072 28.790391 16.3402 -27.1733 31.7079 1000 800 0.0000 -15.9448 27.4072 29.202277 16.1445 -27.2901 31.7079 1000 1000 0.0000 -15.9448 27.4072 29.604975 15.9523 -27.4029 31.7079 1000 1200 0.0000 -15.9448 27.4072 29.998893 15.7635 -27.5119 31.7079 1000 1400 0.0000 -15.9448 27.4072 30.384413 15.5780 -27.6174 31.7079 1000 1600 0.0000 -15.9448 27.4072 30.761888 15.3957 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-29.9891 32.2291 3000 8000 0.0000 -15.5412 28.2344 39.906425 11.6882 -30.0349 32.2291 3000 8200 0.0000 -15.5412 28.2344 40.127067 11.5725 -30.0797 32.2291 3000 8400 0.0000 -15.5412 28.2344 40.344815 11.4581 -30.1235 32.2291 3500 0 0.0000 -15.4403 28.4413 27.489747 18.1030 -26.8251 32.3621 3500 200 0.0000 -15.4403 28.4413 27.932104 17.8953 -26.9641 32.3621 3500 400 0.0000 -15.4403 28.4413 28.363820 17.6917 -27.0982 32.3621 3500 600 0.0000 -15.4403 28.4413 28.785420 17.4918 -27.2276 32.3621 3500 800 0.0000 -15.4403 28.4413 29.197383 17.2956 -27.3527 32.3621 3500 1000 0.0000 -15.4403 28.4413 29.600153 17.1029 -27.4736 32.3621 3500 1200 0.0000 -15.4403 28.4413 29.994142 16.9135 -27.5905 32.3621 3500 1400 0.0000 -15.4403 28.4413 30.379728 16.7275 -27.7037 32.3621 3500 1600 0.0000 -15.4403 28.4413 30.757267 16.5446 -27.8134 32.3621 3500 1800 0.0000 -15.4403 28.4413 31.127088 16.3647 -27.9196 32.3621 3500 2000 0.0000 -15.4403 28.4413 31.489499 16.1878 -28.0225 32.3621 3500 2200 0.0000 -15.4403 28.4413 31.844788 16.0137 -28.1224 32.3621 3500 2400 0.0000 -15.4403 28.4413 32.193224 15.8424 -28.2192 32.3621 3500 2600 0.0000 -15.4403 28.4413 32.535061 15.6737 -28.3132 32.3621 3500 2800 0.0000 -15.4403 28.4413 32.870537 15.5077 -28.4045 32.3621 3500 3000 0.0000 -15.4403 28.4413 33.199877 15.3442 -28.4932 32.3621 3500 3200 0.0000 -15.4403 28.4413 33.523292 15.1831 -28.5794 32.3621 3500 3400 0.0000 -15.4403 28.4413 33.840982 15.0244 -28.6631 32.3621 3500 3600 0.0000 -15.4403 28.4413 34.153136 14.8680 -28.7445 32.3621 3500 3800 0.0000 -15.4403 28.4413 34.459933 14.7139 -28.8237 32.3621 3500 4000 0.0000 -15.4403 28.4413 34.761543 14.5620 -28.9008 32.3621 3500 4200 0.0000 -15.4403 28.4413 35.058127 14.4122 -28.9758 32.3621 3500 4400 0.0000 -15.4403 28.4413 35.349838 14.2644 -29.0488 32.3621 3500 4600 0.0000 -15.4403 28.4413 35.636821 14.1188 -29.1199 32.3621 3500 4800 0.0000 -15.4403 28.4413 35.919217 13.9751 -29.1891 32.3621 3500 5000 0.0000 -15.4403 28.4413 36.197156 13.8333 -29.2566 32.3621 3500 5200 0.0000 -15.4403 28.4413 36.470765 13.6934 -29.3223 32.3621 3500 5400 0.0000 -15.4403 28.4413 36.740165 13.5554 -29.3863 32.3621 3500 5600 0.0000 -15.4403 28.4413 37.005471 13.4192 -29.4488 32.3621 3500 5800 0.0000 -15.4403 28.4413 37.266792 13.2848 -29.5097 32.3621 3500 6000 0.0000 -15.4403 28.4413 37.524236 13.1520 -29.5691 32.3621 3500 6200 0.0000 -15.4403 28.4413 37.777902 13.0210 -29.6270 32.3621 3500 6400 0.0000 -15.4403 28.4413 38.027888 12.8916 -29.6835 32.3621 3500 6600 0.0000 -15.4403 28.4413 38.274286 12.7638 -29.7387 32.3621 3500 6800 0.0000 -15.4403 28.4413 38.517187 12.6376 -29.7926 32.3621 3500 7000 0.0000 -15.4403 28.4413 38.756676 12.5130 -29.8451 32.3621 3500 7200 0.0000 -15.4403 28.4413 38.992835 12.3899 -29.8964 32.3621 3500 7400 0.0000 -15.4403 28.4413 39.225745 12.2682 -29.9466 32.3621 3500 7600 0.0000 -15.4403 28.4413 39.455480 12.1481 -29.9955 32.3621 3500 7800 0.0000 -15.4403 28.4413 39.682117 12.0293 -30.0433 32.3621 3500 8000 0.0000 -15.4403 28.4413 39.905724 11.9120 -30.0900 32.3621 3500 8200 0.0000 -15.4403 28.4413 40.126371 11.7960 -30.1357 32.3621 3500 8400 0.0000 -15.4403 28.4413 40.344124 11.6814 -30.1803 32.3621 4000 0 0.0000 -15.3394 28.6481 27.488641 18.3335 -26.8307 32.4963 4000 200 0.0000 -15.3394 28.6481 27.931016 18.1258 -26.9715 32.4963 4000 400 0.0000 -15.3394 28.6481 28.362751 17.9221 -27.1073 32.4963 4000 600 0.0000 -15.3394 28.6481 28.784368 17.7221 -27.2384 32.4963 4000 800 0.0000 -15.3394 28.6481 29.196347 17.5258 -27.3652 32.4963 4000 1000 0.0000 -15.3394 28.6481 29.599133 17.3330 -27.4877 32.4963 4000 1200 0.0000 -15.3394 28.6481 29.993137 17.1436 -27.6062 32.4963 4000 1400 0.0000 -15.3394 28.6481 30.378737 16.9574 -27.7210 32.4963 4000 1600 0.0000 -15.3394 28.6481 30.756290 16.7744 -27.8321 32.4963 4000 1800 0.0000 -15.3394 28.6481 31.126124 16.5944 -27.9398 32.4963 4000 2000 0.0000 -15.3394 28.6481 31.488547 16.4173 -28.0442 32.4963 4000 2200 0.0000 -15.3394 28.6481 31.843848 16.2431 -28.1455 32.4963 4000 2400 0.0000 -15.3394 28.6481 32.192296 16.0716 -28.2438 32.4963 4000 2600 0.0000 -15.3394 28.6481 32.534144 15.9028 -28.3391 32.4963 4000 2800 0.0000 -15.3394 28.6481 32.869631 15.7366 -28.4318 32.4963 4000 3000 0.0000 -15.3394 28.6481 33.198982 15.5729 -28.5218 32.4963 4000 3200 0.0000 -15.3394 28.6481 33.522407 15.4117 -28.6092 32.4963 4000 3400 0.0000 -15.3394 28.6481 33.840106 15.2528 -28.6942 32.4963 4000 3600 0.0000 -15.3394 28.6481 34.152270 15.0962 -28.7769 32.4963 4000 3800 0.0000 -15.3394 28.6481 34.459076 14.9419 -28.8573 32.4963 4000 4000 0.0000 -15.3394 28.6481 34.760695 14.7898 -28.9356 32.4963 4000 4200 0.0000 -15.3394 28.6481 35.057287 14.6398 -29.0118 32.4963 4000 4400 0.0000 -15.3394 28.6481 35.349006 14.4919 -29.0859 32.4963 4000 4600 0.0000 -15.3394 28.6481 35.635998 14.3461 -29.1581 32.4963 4000 4800 0.0000 -15.3394 28.6481 35.918401 14.2022 -29.2285 32.4963 4000 5000 0.0000 -15.3394 28.6481 36.196348 14.0602 -29.2971 32.4963 4000 5200 0.0000 -15.3394 28.6481 36.469965 13.9201 -29.3639 32.4963 4000 5400 0.0000 -15.3394 28.6481 36.739372 13.7819 -29.4290 32.4963 4000 5600 0.0000 -15.3394 28.6481 37.004685 13.6455 -29.4925 32.4963 4000 5800 0.0000 -15.3394 28.6481 37.266013 13.5108 -29.5544 32.4963 4000 6000 0.0000 -15.3394 28.6481 37.523463 13.3779 -29.6148 32.4963 4000 6200 0.0000 -15.3394 28.6481 37.777136 13.2467 -29.6738 32.4963 4000 6400 0.0000 -15.3394 28.6481 38.027128 13.1171 -29.7313 32.4963 4000 6600 0.0000 -15.3394 28.6481 38.273533 12.9891 -29.7874 32.4963 4000 6800 0.0000 -15.3394 28.6481 38.516440 12.8627 -29.8422 32.4963 4000 7000 0.0000 -15.3394 28.6481 38.755934 12.7378 -29.8957 32.4963 4000 7200 0.0000 -15.3394 28.6481 38.992100 12.6145 -29.9480 32.4963 4000 7400 0.0000 -15.3394 28.6481 39.225015 12.4926 -29.9990 32.4963 4000 7600 0.0000 -15.3394 28.6481 39.454756 12.3723 -30.0489 32.4963 4000 7800 0.0000 -15.3394 28.6481 39.681398 12.2533 -30.0976 32.4963 4000 8000 0.0000 -15.3394 28.6481 39.905010 12.1357 -30.1452 32.4963 4000 8200 0.0000 -15.3394 28.6481 40.125663 12.0196 -30.1917 32.4963 4000 8400 0.0000 -15.3394 28.6481 40.343421 11.9047 -30.2371 32.4963 4500 0 0.0000 -15.2385 28.8549 27.487515 18.5641 -26.8363 32.6315 4500 200 0.0000 -15.2385 28.8549 27.929909 18.3564 -26.9788 32.6315 4500 400 0.0000 -15.2385 28.8549 28.361662 18.1525 -27.1164 32.6315 4500 600 0.0000 -15.2385 28.8549 28.783297 17.9525 -27.2492 32.6315 4500 800 0.0000 -15.2385 28.8549 29.195293 17.7561 -27.3776 32.6315 4500 1000 0.0000 -15.2385 28.8549 29.598095 17.5632 -27.5018 32.6315 4500 1200 0.0000 -15.2385 28.8549 29.992113 17.3736 -27.6219 32.6315 4500 1400 0.0000 -15.2385 28.8549 30.377729 17.1873 -27.7382 32.6315 4500 1600 0.0000 -15.2385 28.8549 30.755295 17.0042 -27.8509 32.6315 4500 1800 0.0000 -15.2385 28.8549 31.125143 16.8241 -27.9600 32.6315 4500 2000 0.0000 -15.2385 28.8549 31.487579 16.6469 -28.0659 32.6315 4500 2200 0.0000 -15.2385 28.8549 31.842892 16.4725 -28.1686 32.6315 4500 2400 0.0000 -15.2385 28.8549 32.191352 16.3009 -28.2683 32.6315 4500 2600 0.0000 -15.2385 28.8549 32.533211 16.1319 -28.3650 32.6315 4500 2800 0.0000 -15.2385 28.8549 32.868710 15.9655 -28.4590 32.6315 4500 3000 0.0000 -15.2385 28.8549 33.198070 15.8017 -28.5503 32.6315 4500 3200 0.0000 -15.2385 28.8549 33.521506 15.6403 -28.6390 32.6315 4500 3400 0.0000 -15.2385 28.8549 33.839215 15.4812 -28.7253 32.6315 4500 3600 0.0000 -15.2385 28.8549 34.151388 15.3245 -28.8093 32.6315 4500 3800 0.0000 -15.2385 28.8549 34.458204 15.1700 -28.8909 32.6315 4500 4000 0.0000 -15.2385 28.8549 34.759832 15.0177 -28.9704 32.6315 4500 4200 0.0000 -15.2385 28.8549 35.056433 14.8675 -29.0477 32.6315 4500 4400 0.0000 -15.2385 28.8549 35.348161 14.7194 -29.1230 32.6315 4500 4600 0.0000 -15.2385 28.8549 35.635161 14.5734 -29.1964 32.6315 4500 4800 0.0000 -15.2385 28.8549 35.917572 14.4293 -29.2679 32.6315 4500 5000 0.0000 -15.2385 28.8549 36.195527 14.2871 -29.3375 32.6315 4500 5200 0.0000 -15.2385 28.8549 36.469151 14.1469 -29.4054 32.6315 4500 5400 0.0000 -15.2385 28.8549 36.738565 14.0084 -29.4716 32.6315 4500 5600 0.0000 -15.2385 28.8549 37.003885 13.8718 -29.5362 32.6315 4500 5800 0.0000 -15.2385 28.8549 37.265221 13.7369 -29.5992 32.6315 4500 6000 0.0000 -15.2385 28.8549 37.522678 13.6038 -29.6606 32.6315 4500 6200 0.0000 -15.2385 28.8549 37.776357 13.4723 -29.7205 32.6315 4500 6400 0.0000 -15.2385 28.8549 38.026356 13.3425 -29.7790 32.6315 4500 6600 0.0000 -15.2385 28.8549 38.272767 13.2143 -29.8361 32.6315 4500 6800 0.0000 -15.2385 28.8549 38.515680 13.0877 -29.8919 32.6315 4500 7000 0.0000 -15.2385 28.8549 38.755181 12.9627 -29.9463 32.6315 4500 7200 0.0000 -15.2385 28.8549 38.991352 12.8391 -29.9995 32.6315 4500 7400 0.0000 -15.2385 28.8549 39.224272 12.7171 -30.0515 32.6315 4500 7600 0.0000 -15.2385 28.8549 39.454020 12.5965 -30.1022 32.6315 4500 7800 0.0000 -15.2385 28.8549 39.680667 12.4773 -30.1518 32.6315 4500 8000 0.0000 -15.2385 28.8549 39.904285 12.3595 -30.2003 32.6315 4500 8200 0.0000 -15.2385 28.8549 40.124942 12.2431 -30.2476 32.6315 4500 8400 0.0000 -15.2385 28.8549 40.342705 12.1281 -30.2940 32.6315 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.000 ================================================ FILE: tests/test_data/cropA/geometry/20180130-20180307_VV_8rlks_base.par ================================================ initial_baseline(TCN): 0.6939756 -40.1971021 -5.6555867 m m m initial_baseline_rate: 0.0000000 -0.1938983 0.0418034 m/s m/s m/s precision_baseline(TCN): 0.0000000 -40.2128077 -5.6388078 m m m precision_baseline_rate: 0.0000000 -0.1881275 0.0374671 m/s m/s m/s unwrap_phase_constant: 0.00002 radians ================================================ FILE: tests/test_data/cropA/geometry/20180130-20180307_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): 0.694 -40.197 -5.656 orbit baseline rate (TCN) (m/s): 0.000e+00 -1.939e-01 4.180e-02 baseline vector (TCN) (m): 0.000 -40.213 -5.639 baseline rate (TCN) (m/s): 0.000e+00 -1.881e-01 3.747e-02 SLC-1 center baseline length (m): 40.6062 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 0.0000 -38.4568 -5.9885 27.494719 -23.0663 -31.3484 38.9203 0 200 0.0000 -38.4568 -5.9885 27.936991 -23.3076 -31.1695 38.9203 0 400 0.0000 -38.4568 -5.9885 28.368628 -23.5418 -30.9930 38.9203 0 600 0.0000 -38.4568 -5.9885 28.790152 -23.7692 -30.8190 38.9203 0 800 0.0000 -38.4568 -5.9885 29.202043 -23.9901 -30.6473 38.9203 0 1000 0.0000 -38.4568 -5.9885 29.604744 -24.2049 -30.4779 38.9203 0 1200 0.0000 -38.4568 -5.9885 29.998667 -24.4139 -30.3108 38.9203 0 1400 0.0000 -38.4568 -5.9885 30.384190 -24.6173 -30.1458 38.9203 0 1600 0.0000 -38.4568 -5.9885 30.761669 -24.8153 -29.9830 38.9203 0 1800 0.0000 -38.4568 -5.9885 31.131432 -25.0083 -29.8222 38.9203 0 2000 0.0000 -38.4568 -5.9885 31.493787 -25.1964 -29.6635 38.9203 0 2200 0.0000 -38.4568 -5.9885 31.849022 -25.3799 -29.5067 38.9203 0 2400 0.0000 -38.4568 -5.9885 32.197407 -25.5588 -29.3518 38.9203 0 2600 0.0000 -38.4568 -5.9885 32.539195 -25.7334 -29.1988 38.9203 0 2800 0.0000 -38.4568 -5.9885 32.874623 -25.9039 -29.0477 38.9203 0 3000 0.0000 -38.4568 -5.9885 33.203917 -26.0705 -28.8983 38.9203 0 3200 0.0000 -38.4568 -5.9885 33.527288 -26.2331 -28.7507 38.9203 0 3400 0.0000 -38.4568 -5.9885 33.844935 -26.3921 -28.6049 38.9203 0 3600 0.0000 -38.4568 -5.9885 34.157047 -26.5476 -28.4607 38.9203 0 3800 0.0000 -38.4568 -5.9885 34.463804 -26.6996 -28.3181 38.9203 0 4000 0.0000 -38.4568 -5.9885 34.765375 -26.8482 -28.1772 38.9203 0 4200 0.0000 -38.4568 -5.9885 35.061921 -26.9937 -28.0379 38.9203 0 4400 0.0000 -38.4568 -5.9885 35.353595 -27.1361 -27.9001 38.9203 0 4600 0.0000 -38.4568 -5.9885 35.640543 -27.2755 -27.7638 38.9203 0 4800 0.0000 -38.4568 -5.9885 35.922904 -27.4120 -27.6291 38.9203 0 5000 0.0000 -38.4568 -5.9885 36.200810 -27.5457 -27.4958 38.9203 0 5200 0.0000 -38.4568 -5.9885 36.474387 -27.6766 -27.3640 38.9203 0 5400 0.0000 -38.4568 -5.9885 36.743755 -27.8050 -27.2335 38.9203 0 5600 0.0000 -38.4568 -5.9885 37.009030 -27.9308 -27.1045 38.9203 0 5800 0.0000 -38.4568 -5.9885 37.270321 -28.0541 -26.9769 38.9203 0 6000 0.0000 -38.4568 -5.9885 37.527736 -28.1750 -26.8506 38.9203 0 6200 0.0000 -38.4568 -5.9885 37.781373 -28.2936 -26.7256 38.9203 0 6400 0.0000 -38.4568 -5.9885 38.031331 -28.4099 -26.6019 38.9203 0 6600 0.0000 -38.4568 -5.9885 38.277703 -28.5240 -26.4795 38.9203 0 6800 0.0000 -38.4568 -5.9885 38.520577 -28.6360 -26.3583 38.9203 0 7000 0.0000 -38.4568 -5.9885 38.760041 -28.7459 -26.2384 38.9203 0 7200 0.0000 -38.4568 -5.9885 38.996175 -28.8538 -26.1197 38.9203 0 7400 0.0000 -38.4568 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-32.2155 41.1498 3000 1800 0.0000 -40.7770 -5.5264 31.126136 -25.8094 -32.0496 41.1498 3000 2000 0.0000 -40.7770 -5.5264 31.488559 -26.0116 -31.8857 41.1498 3000 2200 0.0000 -40.7770 -5.5264 31.843860 -26.2088 -31.7238 41.1498 3000 2400 0.0000 -40.7770 -5.5264 32.192308 -26.4013 -31.5638 41.1498 3000 2600 0.0000 -40.7770 -5.5264 32.534156 -26.5891 -31.4057 41.1498 3000 2800 0.0000 -40.7770 -5.5264 32.869643 -26.7726 -31.2495 41.1498 3000 3000 0.0000 -40.7770 -5.5264 33.198993 -26.9517 -31.0951 41.1498 3000 3200 0.0000 -40.7770 -5.5264 33.522418 -27.1268 -30.9425 41.1498 3000 3400 0.0000 -40.7770 -5.5264 33.840118 -27.2980 -30.7916 41.1498 3000 3600 0.0000 -40.7770 -5.5264 34.152282 -27.4654 -30.6424 41.1498 3000 3800 0.0000 -40.7770 -5.5264 34.459088 -27.6290 -30.4949 41.1498 3000 4000 0.0000 -40.7770 -5.5264 34.760707 -27.7892 -30.3490 41.1498 3000 4200 0.0000 -40.7770 -5.5264 35.057299 -27.9459 -30.2048 41.1498 3000 4400 0.0000 -40.7770 -5.5264 35.349019 -28.0993 -30.0621 41.1498 3000 4600 0.0000 -40.7770 -5.5264 35.636011 -28.2496 -29.9210 41.1498 3000 4800 0.0000 -40.7770 -5.5264 35.918414 -28.3967 -29.7814 41.1498 3000 5000 0.0000 -40.7770 -5.5264 36.196361 -28.5408 -29.6433 41.1498 3000 5200 0.0000 -40.7770 -5.5264 36.469978 -28.6821 -29.5066 41.1498 3000 5400 0.0000 -40.7770 -5.5264 36.739386 -28.8205 -29.3714 41.1498 3000 5600 0.0000 -40.7770 -5.5264 37.004699 -28.9562 -29.2377 41.1498 3000 5800 0.0000 -40.7770 -5.5264 37.266027 -29.0893 -29.1053 41.1498 3000 6000 0.0000 -40.7770 -5.5264 37.523478 -29.2197 -28.9743 41.1498 3000 6200 0.0000 -40.7770 -5.5264 37.777151 -29.3477 -28.8446 41.1498 3000 6400 0.0000 -40.7770 -5.5264 38.027143 -29.4733 -28.7163 41.1498 3000 6600 0.0000 -40.7770 -5.5264 38.273549 -29.5965 -28.5893 41.1498 3000 6800 0.0000 -40.7770 -5.5264 38.516456 -29.7175 -28.4636 41.1498 3000 7000 0.0000 -40.7770 -5.5264 38.755951 -29.8362 -28.3391 41.1498 3000 7200 0.0000 -40.7770 -5.5264 38.992117 -29.9528 -28.2159 41.1498 3000 7400 0.0000 -40.7770 -5.5264 39.225032 -30.0672 -28.0939 41.1498 3000 7600 0.0000 -40.7770 -5.5264 39.454774 -30.1796 -27.9731 41.1498 3000 7800 0.0000 -40.7770 -5.5264 39.681416 -30.2900 -27.8535 41.1498 3000 8000 0.0000 -40.7770 -5.5264 39.905029 -30.3985 -27.7351 41.1498 3000 8200 0.0000 -40.7770 -5.5264 40.125682 -30.5051 -27.6178 41.1498 3000 8400 0.0000 -40.7770 -5.5264 40.343441 -30.6098 -27.5017 41.1498 3500 0 0.0000 -41.1637 -5.4494 27.487573 -23.8336 -34.0015 41.5229 3500 200 0.0000 -41.1637 -5.4494 27.929966 -24.0954 -33.8165 41.5229 3500 400 0.0000 -41.1637 -5.4494 28.361718 -24.3495 -33.6339 41.5229 3500 600 0.0000 -41.1637 -5.4494 28.783351 -24.5964 -33.4538 41.5229 3500 800 0.0000 -41.1637 -5.4494 29.195346 -24.8363 -33.2761 41.5229 3500 1000 0.0000 -41.1637 -5.4494 29.598148 -25.0696 -33.1007 41.5229 3500 1200 0.0000 -41.1637 -5.4494 29.992165 -25.2967 -32.9275 41.5229 3500 1400 0.0000 -41.1637 -5.4494 30.377780 -25.5177 -32.7565 41.5229 3500 1600 0.0000 -41.1637 -5.4494 30.755345 -25.7330 -32.5877 41.5229 3500 1800 0.0000 -41.1637 -5.4494 31.125192 -25.9428 -32.4209 41.5229 3500 2000 0.0000 -41.1637 -5.4494 31.487628 -26.1474 -32.2561 41.5229 3500 2200 0.0000 -41.1637 -5.4494 31.842940 -26.3469 -32.0934 41.5229 3500 2400 0.0000 -41.1637 -5.4494 32.191399 -26.5416 -31.9325 41.5229 3500 2600 0.0000 -41.1637 -5.4494 32.533258 -26.7317 -31.7736 41.5229 3500 2800 0.0000 -41.1637 -5.4494 32.868756 -26.9173 -31.6165 41.5229 3500 3000 0.0000 -41.1637 -5.4494 33.198117 -27.0986 -31.4613 41.5229 3500 3200 0.0000 -41.1637 -5.4494 33.521551 -27.2757 -31.3078 41.5229 3500 3400 0.0000 -41.1637 -5.4494 33.839261 -27.4489 -31.1561 41.5229 3500 3600 0.0000 -41.1637 -5.4494 34.151433 -27.6183 -31.0061 41.5229 3500 3800 0.0000 -41.1637 -5.4494 34.458249 -27.7839 -30.8577 41.5229 3500 4000 0.0000 -41.1637 -5.4494 34.759876 -27.9460 -30.7110 41.5229 3500 4200 0.0000 -41.1637 -5.4494 35.056477 -28.1046 -30.5660 41.5229 3500 4400 0.0000 -41.1637 -5.4494 35.348205 -28.2598 -30.4225 41.5229 3500 4600 0.0000 -41.1637 -5.4494 35.635205 -28.4119 -30.2805 41.5229 3500 4800 0.0000 -41.1637 -5.4494 35.917616 -28.5608 -30.1401 41.5229 3500 5000 0.0000 -41.1637 -5.4494 36.195570 -28.7067 -30.0012 41.5229 3500 5200 0.0000 -41.1637 -5.4494 36.469194 -28.8496 -29.8638 41.5229 3500 5400 0.0000 -41.1637 -5.4494 36.738609 -28.9897 -29.7278 41.5229 3500 5600 0.0000 -41.1637 -5.4494 37.003929 -29.1271 -29.5932 41.5229 3500 5800 0.0000 -41.1637 -5.4494 37.265264 -29.2617 -29.4601 41.5229 3500 6000 0.0000 -41.1637 -5.4494 37.522721 -29.3938 -29.3283 41.5229 3500 6200 0.0000 -41.1637 -5.4494 37.776401 -29.5234 -29.1979 41.5229 3500 6400 0.0000 -41.1637 -5.4494 38.026399 -29.6505 -29.0688 41.5229 3500 6600 0.0000 -41.1637 -5.4494 38.272811 -29.7752 -28.9410 41.5229 3500 6800 0.0000 -41.1637 -5.4494 38.515724 -29.8977 -28.8145 41.5229 3500 7000 0.0000 -41.1637 -5.4494 38.755225 -30.0179 -28.6893 41.5229 3500 7200 0.0000 -41.1637 -5.4494 38.991396 -30.1359 -28.5653 41.5229 3500 7400 0.0000 -41.1637 -5.4494 39.224317 -30.2517 -28.4425 41.5229 3500 7600 0.0000 -41.1637 -5.4494 39.454064 -30.3655 -28.3210 41.5229 3500 7800 0.0000 -41.1637 -5.4494 39.680712 -30.4773 -28.2007 41.5229 3500 8000 0.0000 -41.1637 -5.4494 39.904330 -30.5872 -28.0815 41.5229 3500 8200 0.0000 -41.1637 -5.4494 40.124988 -30.6951 -27.9635 41.5229 3500 8400 0.0000 -41.1637 -5.4494 40.342751 -30.8011 -27.8466 41.5229 4000 0 0.0000 -41.5504 -5.3724 27.486470 -23.9431 -34.3806 41.8963 4000 200 0.0000 -41.5504 -5.3724 27.928882 -24.2078 -34.1947 41.8963 4000 400 0.0000 -41.5504 -5.3724 28.360652 -24.4648 -34.0113 41.8963 4000 600 0.0000 -41.5504 -5.3724 28.782302 -24.7145 -33.8303 41.8963 4000 800 0.0000 -41.5504 -5.3724 29.194314 -24.9571 -33.6517 41.8963 4000 1000 0.0000 -41.5504 -5.3724 29.597131 -25.1931 -33.4754 41.8963 4000 1200 0.0000 -41.5504 -5.3724 29.991163 -25.4227 -33.3014 41.8963 4000 1400 0.0000 -41.5504 -5.3724 30.376792 -25.6462 -33.1295 41.8963 4000 1600 0.0000 -41.5504 -5.3724 30.754371 -25.8640 -32.9598 41.8963 4000 1800 0.0000 -41.5504 -5.3724 31.124231 -26.0762 -32.7922 41.8963 4000 2000 0.0000 -41.5504 -5.3724 31.486679 -26.2832 -32.6265 41.8963 4000 2200 0.0000 -41.5504 -5.3724 31.842003 -26.4850 -32.4629 41.8963 4000 2400 0.0000 -41.5504 -5.3724 32.190474 -26.6819 -32.3012 41.8963 4000 2600 0.0000 -41.5504 -5.3724 32.532345 -26.8742 -32.1415 41.8963 4000 2800 0.0000 -41.5504 -5.3724 32.867853 -27.0619 -31.9836 41.8963 4000 3000 0.0000 -41.5504 -5.3724 33.197224 -27.2454 -31.8275 41.8963 4000 3200 0.0000 -41.5504 -5.3724 33.520669 -27.4246 -31.6731 41.8963 4000 3400 0.0000 -41.5504 -5.3724 33.838388 -27.5998 -31.5206 41.8963 4000 3600 0.0000 -41.5504 -5.3724 34.150570 -27.7711 -31.3697 41.8963 4000 3800 0.0000 -41.5504 -5.3724 34.457394 -27.9387 -31.2206 41.8963 4000 4000 0.0000 -41.5504 -5.3724 34.759031 -28.1027 -31.0731 41.8963 4000 4200 0.0000 -41.5504 -5.3724 35.055640 -28.2632 -30.9272 41.8963 4000 4400 0.0000 -41.5504 -5.3724 35.347376 -28.4203 -30.7828 41.8963 4000 4600 0.0000 -41.5504 -5.3724 35.634384 -28.5741 -30.6401 41.8963 4000 4800 0.0000 -41.5504 -5.3724 35.916803 -28.7248 -30.4989 41.8963 4000 5000 0.0000 -41.5504 -5.3724 36.194765 -28.8724 -30.3592 41.8963 4000 5200 0.0000 -41.5504 -5.3724 36.468397 -29.0171 -30.2209 41.8963 4000 5400 0.0000 -41.5504 -5.3724 36.737818 -29.1589 -30.0842 41.8963 4000 5600 0.0000 -41.5504 -5.3724 37.003145 -29.2979 -29.9488 41.8963 4000 5800 0.0000 -41.5504 -5.3724 37.264488 -29.4342 -29.8149 41.8963 4000 6000 0.0000 -41.5504 -5.3724 37.521951 -29.5679 -29.6823 41.8963 4000 6200 0.0000 -41.5504 -5.3724 37.775637 -29.6990 -29.5511 41.8963 4000 6400 0.0000 -41.5504 -5.3724 38.025642 -29.8277 -29.4212 41.8963 4000 6600 0.0000 -41.5504 -5.3724 38.272060 -29.9539 -29.2927 41.8963 4000 6800 0.0000 -41.5504 -5.3724 38.514979 -30.0778 -29.1654 41.8963 4000 7000 0.0000 -41.5504 -5.3724 38.754486 -30.1995 -29.0394 41.8963 4000 7200 0.0000 -41.5504 -5.3724 38.990663 -30.3189 -28.9147 41.8963 4000 7400 0.0000 -41.5504 -5.3724 39.223589 -30.4362 -28.7912 41.8963 4000 7600 0.0000 -41.5504 -5.3724 39.453342 -30.5514 -28.6689 41.8963 4000 7800 0.0000 -41.5504 -5.3724 39.679995 -30.6646 -28.5478 41.8963 4000 8000 0.0000 -41.5504 -5.3724 39.903618 -30.7758 -28.4279 41.8963 4000 8200 0.0000 -41.5504 -5.3724 40.124281 -30.8851 -28.3092 41.8963 4000 8400 0.0000 -41.5504 -5.3724 40.342050 -30.9924 -28.1916 41.8963 4500 0 0.0000 -41.9371 -5.2954 27.485347 -24.0526 -34.7596 42.2701 4500 200 0.0000 -41.9371 -5.2954 27.927779 -24.3203 -34.5729 42.2701 4500 400 0.0000 -41.9371 -5.2954 28.359567 -24.5801 -34.3886 42.2701 4500 600 0.0000 -41.9371 -5.2954 28.781235 -24.8325 -34.2068 42.2701 4500 800 0.0000 -41.9371 -5.2954 29.193263 -25.0779 -34.0273 42.2701 4500 1000 0.0000 -41.9371 -5.2954 29.596095 -25.3165 -33.8502 42.2701 4500 1200 0.0000 -41.9371 -5.2954 29.990143 -25.5487 -33.6753 42.2701 4500 1400 0.0000 -41.9371 -5.2954 30.375786 -25.7748 -33.5025 42.2701 4500 1600 0.0000 -41.9371 -5.2954 30.753379 -25.9950 -33.3320 42.2701 4500 1800 0.0000 -41.9371 -5.2954 31.123252 -26.2096 -33.1635 42.2701 4500 2000 0.0000 -41.9371 -5.2954 31.485713 -26.4189 -32.9970 42.2701 4500 2200 0.0000 -41.9371 -5.2954 31.841050 -26.6230 -32.8325 42.2701 4500 2400 0.0000 -41.9371 -5.2954 32.189533 -26.8222 -32.6700 42.2701 4500 2600 0.0000 -41.9371 -5.2954 32.531414 -27.0167 -32.5094 42.2701 4500 2800 0.0000 -41.9371 -5.2954 32.866934 -27.2066 -32.3506 42.2701 4500 3000 0.0000 -41.9371 -5.2954 33.196315 -27.3921 -32.1936 42.2701 4500 3200 0.0000 -41.9371 -5.2954 33.519771 -27.5734 -32.0385 42.2701 4500 3400 0.0000 -41.9371 -5.2954 33.837500 -27.7507 -31.8851 42.2701 4500 3600 0.0000 -41.9371 -5.2954 34.149691 -27.9240 -31.7334 42.2701 4500 3800 0.0000 -41.9371 -5.2954 34.456525 -28.0935 -31.5834 42.2701 4500 4000 0.0000 -41.9371 -5.2954 34.758170 -28.2594 -31.4351 42.2701 4500 4200 0.0000 -41.9371 -5.2954 35.054788 -28.4218 -31.2884 42.2701 4500 4400 0.0000 -41.9371 -5.2954 35.346533 -28.5807 -31.1432 42.2701 4500 4600 0.0000 -41.9371 -5.2954 35.633549 -28.7364 -30.9997 42.2701 4500 4800 0.0000 -41.9371 -5.2954 35.915976 -28.8888 -30.8577 42.2701 4500 5000 0.0000 -41.9371 -5.2954 36.193946 -29.0382 -30.7171 42.2701 4500 5200 0.0000 -41.9371 -5.2954 36.467585 -29.1846 -30.5781 42.2701 4500 5400 0.0000 -41.9371 -5.2954 36.737014 -29.3280 -30.4405 42.2701 4500 5600 0.0000 -41.9371 -5.2954 37.002348 -29.4687 -30.3044 42.2701 4500 5800 0.0000 -41.9371 -5.2954 37.263697 -29.6066 -30.1697 42.2701 4500 6000 0.0000 -41.9371 -5.2954 37.521168 -29.7419 -30.0363 42.2701 4500 6200 0.0000 -41.9371 -5.2954 37.774860 -29.8746 -29.9043 42.2701 4500 6400 0.0000 -41.9371 -5.2954 38.024872 -30.0048 -29.7737 42.2701 4500 6600 0.0000 -41.9371 -5.2954 38.271296 -30.1326 -29.6444 42.2701 4500 6800 0.0000 -41.9371 -5.2954 38.514221 -30.2580 -29.5163 42.2701 4500 7000 0.0000 -41.9371 -5.2954 38.753734 -30.3811 -29.3896 42.2701 4500 7200 0.0000 -41.9371 -5.2954 38.989917 -30.5020 -29.2641 42.2701 4500 7400 0.0000 -41.9371 -5.2954 39.222849 -30.6207 -29.1399 42.2701 4500 7600 0.0000 -41.9371 -5.2954 39.452608 -30.7373 -29.0168 42.2701 4500 7800 0.0000 -41.9371 -5.2954 39.679266 -30.8519 -28.8950 42.2701 4500 8000 0.0000 -41.9371 -5.2954 39.902895 -30.9644 -28.7744 42.2701 4500 8200 0.0000 -41.9371 -5.2954 40.123563 -31.0750 -28.6549 42.2701 4500 8400 0.0000 -41.9371 -5.2954 40.341337 -31.1837 -28.5366 42.2701 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.000 ================================================ FILE: tests/test_data/cropA/geometry/20180130-20180412_VV_8rlks_base.par ================================================ initial_baseline(TCN): -0.7080870 -105.5946431 32.4031046 m m m initial_baseline_rate: 0.0000000 -0.0986267 0.0940323 m/s m/s m/s precision_baseline(TCN): 0.0000000 -105.7019368 32.4448897 m m m precision_baseline_rate: 0.0000000 -0.0898244 0.0863512 m/s m/s m/s unwrap_phase_constant: -0.00013 radians ================================================ FILE: tests/test_data/cropA/geometry/20180130-20180412_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): -0.708 -105.595 32.403 orbit baseline rate (TCN) (m/s): 0.000e+00 -9.863e-02 9.403e-02 baseline vector (TCN) (m): 0.000 -105.702 32.445 baseline rate (TCN) (m/s): 0.000e+00 -8.982e-02 8.635e-02 SLC-1 center baseline length (m): 110.5693 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 0.0000 -104.8635 31.6389 27.494719 -20.3465 -107.6260 109.5325 0 200 0.0000 -104.8635 31.6389 27.936991 -21.1767 -107.4657 109.5325 0 400 0.0000 -104.8635 31.6389 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-105.7867 32.5264 35.349818 -34.6750 -105.1019 110.6742 2500 4600 0.0000 -105.7867 32.5264 35.636803 -35.2010 -104.9269 110.6742 2500 4800 0.0000 -105.7867 32.5264 35.919199 -35.7177 -104.7521 110.6742 2500 5000 0.0000 -105.7867 32.5264 36.197138 -36.2254 -104.5776 110.6742 2500 5200 0.0000 -105.7867 32.5264 36.470748 -36.7244 -104.4035 110.6742 2500 5400 0.0000 -105.7867 32.5264 36.740149 -37.2149 -104.2296 110.6742 2500 5600 0.0000 -105.7867 32.5264 37.005455 -37.6972 -104.0562 110.6742 2500 5800 0.0000 -105.7867 32.5264 37.266777 -38.1714 -103.8832 110.6742 2500 6000 0.0000 -105.7867 32.5264 37.524221 -38.6377 -103.7106 110.6742 2500 6200 0.0000 -105.7867 32.5264 37.777888 -39.0965 -103.5385 110.6742 2500 6400 0.0000 -105.7867 32.5264 38.027874 -39.5479 -103.3670 110.6742 2500 6600 0.0000 -105.7867 32.5264 38.274274 -39.9921 -103.1959 110.6742 2500 6800 0.0000 -105.7867 32.5264 38.517175 -40.4292 -103.0255 110.6742 2500 7000 0.0000 -105.7867 32.5264 38.756665 -40.8595 -102.8556 110.6742 2500 7200 0.0000 -105.7867 32.5264 38.992825 -41.2831 -102.6863 110.6742 2500 7400 0.0000 -105.7867 32.5264 39.225735 -41.7002 -102.5176 110.6742 2500 7600 0.0000 -105.7867 32.5264 39.455472 -42.1109 -102.3496 110.6742 2500 7800 0.0000 -105.7867 32.5264 39.682108 -42.5154 -102.1822 110.6742 2500 8000 0.0000 -105.7867 32.5264 39.905717 -42.9139 -102.0155 110.6742 2500 8200 0.0000 -105.7867 32.5264 40.126364 -43.3064 -101.8495 110.6742 2500 8400 0.0000 -105.7867 32.5264 40.344118 -43.6932 -101.6842 110.6742 3000 0 0.0000 -105.9713 32.7039 27.488655 -19.9017 -109.1024 110.9030 3000 200 0.0000 -105.9713 32.7039 27.931030 -20.7435 -108.9455 110.9030 3000 400 0.0000 -105.9713 32.7039 28.362764 -21.5638 -108.7861 110.9030 3000 600 0.0000 -105.9713 32.7039 28.784381 -22.3638 -108.6245 110.9030 3000 800 0.0000 -105.9713 32.7039 29.196360 -23.1442 -108.4609 110.9030 3000 1000 0.0000 -105.9713 32.7039 29.599146 -23.9061 -108.2955 110.9030 3000 1200 0.0000 -105.9713 32.7039 29.993149 -24.6503 -108.1286 110.9030 3000 1400 0.0000 -105.9713 32.7039 30.378750 -25.3774 -107.9603 110.9030 3000 1600 0.0000 -105.9713 32.7039 30.756302 -26.0883 -107.7907 110.9030 3000 1800 0.0000 -105.9713 32.7039 31.126136 -26.7835 -107.6201 110.9030 3000 2000 0.0000 -105.9713 32.7039 31.488559 -27.4637 -107.4485 110.9030 3000 2200 0.0000 -105.9713 32.7039 31.843860 -28.1295 -107.2761 110.9030 3000 2400 0.0000 -105.9713 32.7039 32.192308 -28.7814 -107.1031 110.9030 3000 2600 0.0000 -105.9713 32.7039 32.534156 -29.4199 -106.9294 110.9030 3000 2800 0.0000 -105.9713 32.7039 32.869643 -30.0455 -106.7554 110.9030 3000 3000 0.0000 -105.9713 32.7039 33.198993 -30.6586 -106.5809 110.9030 3000 3200 0.0000 -105.9713 32.7039 33.522418 -31.2598 -106.4061 110.9030 3000 3400 0.0000 -105.9713 32.7039 33.840118 -31.8493 -106.2312 110.9030 3000 3600 0.0000 -105.9713 32.7039 34.152282 -32.4276 -106.0561 110.9030 3000 3800 0.0000 -105.9713 32.7039 34.459088 -32.9950 -105.8809 110.9030 3000 4000 0.0000 -105.9713 32.7039 34.760707 -33.5520 -105.7057 110.9030 3000 4200 0.0000 -105.9713 32.7039 35.057299 -34.0987 -105.5306 110.9030 3000 4400 0.0000 -105.9713 32.7039 35.349019 -34.6356 -105.3557 110.9030 3000 4600 0.0000 -105.9713 32.7039 35.636011 -35.1629 -105.1809 110.9030 3000 4800 0.0000 -105.9713 32.7039 35.918414 -35.6809 -105.0063 110.9030 3000 5000 0.0000 -105.9713 32.7039 36.196361 -36.1898 -104.8320 110.9030 3000 5200 0.0000 -105.9713 32.7039 36.469978 -36.6900 -104.6579 110.9030 3000 5400 0.0000 -105.9713 32.7039 36.739386 -37.1817 -104.4843 110.9030 3000 5600 0.0000 -105.9713 32.7039 37.004699 -37.6652 -104.3110 110.9030 3000 5800 0.0000 -105.9713 32.7039 37.266027 -38.1405 -104.1381 110.9030 3000 6000 0.0000 -105.9713 32.7039 37.523478 -38.6081 -103.9657 110.9030 3000 6200 0.0000 -105.9713 32.7039 37.777151 -39.0680 -103.7937 110.9030 3000 6400 0.0000 -105.9713 32.7039 38.027143 -39.5205 -103.6223 110.9030 3000 6600 0.0000 -105.9713 32.7039 38.273549 -39.9658 -103.4513 110.9030 3000 6800 0.0000 -105.9713 32.7039 38.516456 -40.4040 -103.2810 110.9030 3000 7000 0.0000 -105.9713 32.7039 38.755951 -40.8354 -103.1112 110.9030 3000 7200 0.0000 -105.9713 32.7039 38.992117 -41.2600 -102.9420 110.9030 3000 7400 0.0000 -105.9713 32.7039 39.225032 -41.6782 -102.7734 110.9030 3000 7600 0.0000 -105.9713 32.7039 39.454774 -42.0899 -102.6055 110.9030 3000 7800 0.0000 -105.9713 32.7039 39.681416 -42.4955 -102.4382 110.9030 3000 8000 0.0000 -105.9713 32.7039 39.905029 -42.8949 -102.2716 110.9030 3000 8200 0.0000 -105.9713 32.7039 40.125682 -43.2885 -102.1056 110.9030 3000 8400 0.0000 -105.9713 32.7039 40.343441 -43.6762 -101.9403 110.9030 3500 0 0.0000 -106.1560 32.8814 27.487573 -19.8274 -109.3485 111.1318 3500 200 0.0000 -106.1560 32.8814 27.929966 -20.6711 -109.1922 111.1318 3500 400 0.0000 -106.1560 32.8814 28.361718 -21.4934 -109.0333 111.1318 3500 600 0.0000 -106.1560 32.8814 28.783351 -22.2951 -108.8722 111.1318 3500 800 0.0000 -106.1560 32.8814 29.195346 -23.0774 -108.7091 111.1318 3500 1000 0.0000 -106.1560 32.8814 29.598148 -23.8411 -108.5442 111.1318 3500 1200 0.0000 -106.1560 32.8814 29.992165 -24.5870 -108.3777 111.1318 3500 1400 0.0000 -106.1560 32.8814 30.377780 -25.3158 -108.2097 111.1318 3500 1600 0.0000 -106.1560 32.8814 30.755345 -26.0284 -108.0406 111.1318 3500 1800 0.0000 -106.1560 32.8814 31.125192 -26.7252 -107.8703 111.1318 3500 2000 0.0000 -106.1560 32.8814 31.487628 -27.4070 -107.6991 111.1318 3500 2200 0.0000 -106.1560 32.8814 31.842940 -28.0744 -107.5271 111.1318 3500 2400 0.0000 -106.1560 32.8814 32.191399 -28.7278 -107.3543 111.1318 3500 2600 0.0000 -106.1560 32.8814 32.533258 -29.3678 -107.1810 111.1318 3500 2800 0.0000 -106.1560 32.8814 32.868756 -29.9949 -107.0072 111.1318 3500 3000 0.0000 -106.1560 32.8814 33.198117 -30.6096 -106.8330 111.1318 3500 3200 0.0000 -106.1560 32.8814 33.521551 -31.2122 -106.6586 111.1318 3500 3400 0.0000 -106.1560 32.8814 33.839261 -31.8031 -106.4838 111.1318 3500 3600 0.0000 -106.1560 32.8814 34.151433 -32.3828 -106.3090 111.1318 3500 3800 0.0000 -106.1560 32.8814 34.458249 -32.9516 -106.1341 111.1318 3500 4000 0.0000 -106.1560 32.8814 34.759876 -33.5099 -105.9591 111.1318 3500 4200 0.0000 -106.1560 32.8814 35.056477 -34.0579 -105.7842 111.1318 3500 4400 0.0000 -106.1560 32.8814 35.348205 -34.5961 -105.6095 111.1318 3500 4600 0.0000 -106.1560 32.8814 35.635205 -35.1247 -105.4348 111.1318 3500 4800 0.0000 -106.1560 32.8814 35.917616 -35.6439 -105.2604 111.1318 3500 5000 0.0000 -106.1560 32.8814 36.195570 -36.1542 -105.0863 111.1318 3500 5200 0.0000 -106.1560 32.8814 36.469194 -36.6556 -104.9124 111.1318 3500 5400 0.0000 -106.1560 32.8814 36.738609 -37.1485 -104.7389 111.1318 3500 5600 0.0000 -106.1560 32.8814 37.003929 -37.6331 -104.5658 111.1318 3500 5800 0.0000 -106.1560 32.8814 37.265264 -38.1097 -104.3930 111.1318 3500 6000 0.0000 -106.1560 32.8814 37.522721 -38.5784 -104.2207 111.1318 3500 6200 0.0000 -106.1560 32.8814 37.776401 -39.0395 -104.0489 111.1318 3500 6400 0.0000 -106.1560 32.8814 38.026399 -39.4931 -103.8776 111.1318 3500 6600 0.0000 -106.1560 32.8814 38.272811 -39.9395 -103.7068 111.1318 3500 6800 0.0000 -106.1560 32.8814 38.515724 -40.3788 -103.5365 111.1318 3500 7000 0.0000 -106.1560 32.8814 38.755225 -40.8112 -103.3668 111.1318 3500 7200 0.0000 -106.1560 32.8814 38.991396 -41.2369 -103.1977 111.1318 3500 7400 0.0000 -106.1560 32.8814 39.224317 -41.6561 -103.0292 111.1318 3500 7600 0.0000 -106.1560 32.8814 39.454064 -42.0689 -102.8614 111.1318 3500 7800 0.0000 -106.1560 32.8814 39.680712 -42.4755 -102.6941 111.1318 3500 8000 0.0000 -106.1560 32.8814 39.904330 -42.8760 -102.5276 111.1318 3500 8200 0.0000 -106.1560 32.8814 40.124988 -43.2705 -102.3617 111.1318 3500 8400 0.0000 -106.1560 32.8814 40.342751 -43.6592 -102.1965 111.1318 4000 0 0.0000 -106.3406 33.0589 27.486470 -19.7531 -109.5946 111.3607 4000 200 0.0000 -106.3406 33.0589 27.928882 -20.5987 -109.4389 111.3607 4000 400 0.0000 -106.3406 33.0589 28.360652 -21.4229 -109.2805 111.3607 4000 600 0.0000 -106.3406 33.0589 28.782302 -22.2265 -109.1199 111.3607 4000 800 0.0000 -106.3406 33.0589 29.194314 -23.0106 -108.9573 111.3607 4000 1000 0.0000 -106.3406 33.0589 29.597131 -23.7760 -108.7928 111.3607 4000 1200 0.0000 -106.3406 33.0589 29.991163 -24.5236 -108.6267 111.3607 4000 1400 0.0000 -106.3406 33.0589 30.376792 -25.2542 -108.4592 111.3607 4000 1600 0.0000 -106.3406 33.0589 30.754371 -25.9684 -108.2904 111.3607 4000 1800 0.0000 -106.3406 33.0589 31.124231 -26.6669 -108.1206 111.3607 4000 2000 0.0000 -106.3406 33.0589 31.486679 -27.3503 -107.9497 111.3607 4000 2200 0.0000 -106.3406 33.0589 31.842003 -28.0193 -107.7780 111.3607 4000 2400 0.0000 -106.3406 33.0589 32.190474 -28.6742 -107.6056 111.3607 4000 2600 0.0000 -106.3406 33.0589 32.532345 -29.3158 -107.4326 111.3607 4000 2800 0.0000 -106.3406 33.0589 32.867853 -29.9444 -107.2591 111.3607 4000 3000 0.0000 -106.3406 33.0589 33.197224 -30.5605 -107.0852 111.3607 4000 3200 0.0000 -106.3406 33.0589 33.520669 -31.1645 -106.9110 111.3607 4000 3400 0.0000 -106.3406 33.0589 33.838388 -31.7569 -106.7365 111.3607 4000 3600 0.0000 -106.3406 33.0589 34.150570 -32.3380 -106.5619 111.3607 4000 3800 0.0000 -106.3406 33.0589 34.457394 -32.9081 -106.3872 111.3607 4000 4000 0.0000 -106.3406 33.0589 34.759031 -33.4678 -106.2125 111.3607 4000 4200 0.0000 -106.3406 33.0589 35.055640 -34.0172 -106.0378 111.3607 4000 4400 0.0000 -106.3406 33.0589 35.347376 -34.5566 -105.8632 111.3607 4000 4600 0.0000 -106.3406 33.0589 35.634384 -35.0865 -105.6888 111.3607 4000 4800 0.0000 -106.3406 33.0589 35.916803 -35.6070 -105.5146 111.3607 4000 5000 0.0000 -106.3406 33.0589 36.194765 -36.1185 -105.3406 111.3607 4000 5200 0.0000 -106.3406 33.0589 36.468397 -36.6212 -105.1669 111.3607 4000 5400 0.0000 -106.3406 33.0589 36.737818 -37.1153 -104.9936 111.3607 4000 5600 0.0000 -106.3406 33.0589 37.003145 -37.6011 -104.8206 111.3607 4000 5800 0.0000 -106.3406 33.0589 37.264488 -38.0788 -104.6480 111.3607 4000 6000 0.0000 -106.3406 33.0589 37.521951 -38.5487 -104.4758 111.3607 4000 6200 0.0000 -106.3406 33.0589 37.775637 -39.0109 -104.3041 111.3607 4000 6400 0.0000 -106.3406 33.0589 38.025642 -39.4656 -104.1329 111.3607 4000 6600 0.0000 -106.3406 33.0589 38.272060 -39.9131 -103.9622 111.3607 4000 6800 0.0000 -106.3406 33.0589 38.514979 -40.3535 -103.7920 111.3607 4000 7000 0.0000 -106.3406 33.0589 38.754486 -40.7870 -103.6224 111.3607 4000 7200 0.0000 -106.3406 33.0589 38.990663 -41.2138 -103.4534 111.3607 4000 7400 0.0000 -106.3406 33.0589 39.223589 -41.6341 -103.2850 111.3607 4000 7600 0.0000 -106.3406 33.0589 39.453342 -42.0479 -103.1173 111.3607 4000 7800 0.0000 -106.3406 33.0589 39.679995 -42.4555 -102.9501 111.3607 4000 8000 0.0000 -106.3406 33.0589 39.903618 -42.8570 -102.7836 111.3607 4000 8200 0.0000 -106.3406 33.0589 40.124281 -43.2525 -102.6178 111.3607 4000 8400 0.0000 -106.3406 33.0589 40.342050 -43.6422 -102.4527 111.3607 4500 0 0.0000 -106.5252 33.2364 27.485347 -19.6787 -109.8407 111.5898 4500 200 0.0000 -106.5252 33.2364 27.927779 -20.5263 -109.6855 111.5898 4500 400 0.0000 -106.5252 33.2364 28.359567 -21.3523 -109.5277 111.5898 4500 600 0.0000 -106.5252 33.2364 28.781235 -22.1578 -109.3676 111.5898 4500 800 0.0000 -106.5252 33.2364 29.193263 -22.9437 -109.2055 111.5898 4500 1000 0.0000 -106.5252 33.2364 29.596095 -23.7109 -109.0415 111.5898 4500 1200 0.0000 -106.5252 33.2364 29.990143 -24.4603 -108.8758 111.5898 4500 1400 0.0000 -106.5252 33.2364 30.375786 -25.1925 -108.7087 111.5898 4500 1600 0.0000 -106.5252 33.2364 30.753379 -25.9084 -108.5403 111.5898 4500 1800 0.0000 -106.5252 33.2364 31.123252 -26.6085 -108.3708 111.5898 4500 2000 0.0000 -106.5252 33.2364 31.485713 -27.2936 -108.2003 111.5898 4500 2200 0.0000 -106.5252 33.2364 31.841050 -27.9641 -108.0290 111.5898 4500 2400 0.0000 -106.5252 33.2364 32.189533 -28.6206 -107.8569 111.5898 4500 2600 0.0000 -106.5252 33.2364 32.531414 -29.2637 -107.6842 111.5898 4500 2800 0.0000 -106.5252 33.2364 32.866934 -29.8938 -107.5110 111.5898 4500 3000 0.0000 -106.5252 33.2364 33.196315 -30.5113 -107.3374 111.5898 4500 3200 0.0000 -106.5252 33.2364 33.519771 -31.1168 -107.1634 111.5898 4500 3400 0.0000 -106.5252 33.2364 33.837500 -31.7106 -106.9892 111.5898 4500 3600 0.0000 -106.5252 33.2364 34.149691 -32.2931 -106.8149 111.5898 4500 3800 0.0000 -106.5252 33.2364 34.456525 -32.8646 -106.6404 111.5898 4500 4000 0.0000 -106.5252 33.2364 34.758170 -33.4256 -106.4659 111.5898 4500 4200 0.0000 -106.5252 33.2364 35.054788 -33.9763 -106.2914 111.5898 4500 4400 0.0000 -106.5252 33.2364 35.346533 -34.5171 -106.1170 111.5898 4500 4600 0.0000 -106.5252 33.2364 35.633549 -35.0483 -105.9428 111.5898 4500 4800 0.0000 -106.5252 33.2364 35.915976 -35.5700 -105.7688 111.5898 4500 5000 0.0000 -106.5252 33.2364 36.193946 -36.0828 -105.5950 111.5898 4500 5200 0.0000 -106.5252 33.2364 36.467585 -36.5867 -105.4214 111.5898 4500 5400 0.0000 -106.5252 33.2364 36.737014 -37.0820 -105.2482 111.5898 4500 5600 0.0000 -106.5252 33.2364 37.002348 -37.5690 -105.0754 111.5898 4500 5800 0.0000 -106.5252 33.2364 37.263697 -38.0479 -104.9029 111.5898 4500 6000 0.0000 -106.5252 33.2364 37.521168 -38.5189 -104.7309 111.5898 4500 6200 0.0000 -106.5252 33.2364 37.774860 -38.9823 -104.5593 111.5898 4500 6400 0.0000 -106.5252 33.2364 38.024872 -39.4381 -104.3882 111.5898 4500 6600 0.0000 -106.5252 33.2364 38.271296 -39.8867 -104.2176 111.5898 4500 6800 0.0000 -106.5252 33.2364 38.514221 -40.3282 -104.0476 111.5898 4500 7000 0.0000 -106.5252 33.2364 38.753734 -40.7628 -103.8781 111.5898 4500 7200 0.0000 -106.5252 33.2364 38.989917 -41.1907 -103.7092 111.5898 4500 7400 0.0000 -106.5252 33.2364 39.222849 -41.6120 -103.5408 111.5898 4500 7600 0.0000 -106.5252 33.2364 39.452608 -42.0268 -103.3732 111.5898 4500 7800 0.0000 -106.5252 33.2364 39.679266 -42.4355 -103.2061 111.5898 4500 8000 0.0000 -106.5252 33.2364 39.902895 -42.8380 -103.0397 111.5898 4500 8200 0.0000 -106.5252 33.2364 40.123563 -43.2345 -102.8739 111.5898 4500 8400 0.0000 -106.5252 33.2364 40.341337 -43.6252 -102.7089 111.5898 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.000 ================================================ FILE: tests/test_data/cropA/geometry/20180307-20180319_VV_8rlks_base.par ================================================ initial_baseline(TCN): -0.0000004 4.1283381 0.3230800 m m m initial_baseline_rate: 0.0000000 0.1863333 0.0175918 m/s m/s m/s precision_baseline(TCN): -0.0000004 4.1283381 0.3230800 m m m precision_baseline_rate: 0.0000000 0.1863333 0.0175918 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/cropA/geometry/20180307-20180319_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): -0.000 4.128 0.323 orbit baseline rate (TCN) (m/s): 0.000e+00 1.863e-01 1.759e-02 baseline vector (TCN) (m): -0.000 4.128 0.323 baseline rate (TCN) (m/s): 0.000e+00 1.863e-01 1.759e-02 SLC-1 center baseline length (m): 4.1410 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 -0.0000 2.3891 0.1589 27.496815 1.2439 2.0458 2.3943 0 200 -0.0000 2.3891 0.1589 27.939050 1.2597 2.0362 2.3943 0 400 -0.0000 2.3891 0.1589 28.370652 1.2750 2.0266 2.3943 0 600 -0.0000 2.3891 0.1589 28.792142 1.2899 2.0172 2.3943 0 800 -0.0000 2.3891 0.1589 29.204001 1.3043 2.0079 2.3943 0 1000 -0.0000 2.3891 0.1589 29.606672 1.3184 1.9986 2.3943 0 1200 -0.0000 2.3891 0.1589 30.000566 1.3321 1.9895 2.3943 0 1400 -0.0000 2.3891 0.1589 30.386062 1.3455 1.9805 2.3943 0 1600 -0.0000 2.3891 0.1589 30.763514 1.3585 1.9716 2.3943 0 1800 -0.0000 2.3891 0.1589 31.133251 1.3712 1.9628 2.3943 0 2000 -0.0000 2.3891 0.1589 31.495582 1.3836 1.9541 2.3943 0 2200 -0.0000 2.3891 0.1589 31.850793 1.3957 1.9455 2.3943 0 2400 -0.0000 2.3891 0.1589 32.199155 1.4075 1.9370 2.3943 0 2600 -0.0000 2.3891 0.1589 32.540921 1.4190 1.9285 2.3943 0 2800 -0.0000 2.3891 0.1589 32.876328 1.4303 1.9202 2.3943 0 3000 -0.0000 2.3891 0.1589 33.205602 1.4413 1.9119 2.3943 0 3200 -0.0000 2.3891 0.1589 33.528952 1.4520 1.9038 2.3943 0 3400 -0.0000 2.3891 0.1589 33.846580 1.4626 1.8957 2.3943 0 3600 -0.0000 2.3891 0.1589 34.158674 1.4729 1.8877 2.3943 0 3800 -0.0000 2.3891 0.1589 34.465413 1.4830 1.8798 2.3943 0 4000 -0.0000 2.3891 0.1589 34.766966 1.4928 1.8720 2.3943 0 4200 -0.0000 2.3891 0.1589 35.063495 1.5025 1.8642 2.3943 0 4400 -0.0000 2.3891 0.1589 35.355153 1.5120 1.8565 2.3943 0 4600 -0.0000 2.3891 0.1589 35.642085 1.5213 1.8489 2.3943 0 4800 -0.0000 2.3891 0.1589 35.924431 1.5304 1.8414 2.3943 0 5000 -0.0000 2.3891 0.1589 36.202321 1.5393 1.8340 2.3943 0 5200 -0.0000 2.3891 0.1589 36.475883 1.5480 1.8266 2.3943 0 5400 -0.0000 2.3891 0.1589 36.745237 1.5566 1.8193 2.3943 0 5600 -0.0000 2.3891 0.1589 37.010498 1.5650 1.8121 2.3943 0 5800 -0.0000 2.3891 0.1589 37.271776 1.5732 1.8049 2.3943 0 6000 -0.0000 2.3891 0.1589 37.529176 1.5813 1.7978 2.3943 0 6200 -0.0000 2.3891 0.1589 37.782801 1.5893 1.7908 2.3943 0 6400 -0.0000 2.3891 0.1589 38.032746 1.5971 1.7839 2.3943 0 6600 -0.0000 2.3891 0.1589 38.279105 1.6047 1.7770 2.3943 0 6800 -0.0000 2.3891 0.1589 38.521968 1.6122 1.7702 2.3943 0 7000 -0.0000 2.3891 0.1589 38.761419 1.6196 1.7634 2.3943 0 7200 -0.0000 2.3891 0.1589 38.997541 1.6269 1.7567 2.3943 0 7400 -0.0000 2.3891 0.1589 39.230415 1.6340 1.7501 2.3943 0 7600 -0.0000 2.3891 0.1589 39.460116 1.6410 1.7435 2.3943 0 7800 -0.0000 2.3891 0.1589 39.686717 1.6479 1.7370 2.3943 0 8000 -0.0000 2.3891 0.1589 39.910291 1.6546 1.7306 2.3943 0 8200 -0.0000 2.3891 0.1589 40.130905 1.6613 1.7242 2.3943 0 8400 -0.0000 2.3891 0.1589 40.348626 1.6678 1.7179 2.3943 500 0 -0.0000 2.7721 0.1950 27.495868 1.4528 2.3689 2.7789 500 200 -0.0000 2.7721 0.1950 27.938119 1.4711 2.3576 2.7789 500 400 -0.0000 2.7721 0.1950 28.369736 1.4888 2.3465 2.7789 500 600 -0.0000 2.7721 0.1950 28.791241 1.5060 2.3354 2.7789 500 800 -0.0000 2.7721 0.1950 29.203113 1.5228 2.3246 2.7789 500 1000 -0.0000 2.7721 0.1950 29.605798 1.5391 2.3138 2.7789 500 1200 -0.0000 2.7721 0.1950 29.999704 1.5549 2.3032 2.7789 500 1400 -0.0000 2.7721 0.1950 30.385211 1.5704 2.2927 2.7789 500 1600 -0.0000 2.7721 0.1950 30.762675 1.5855 2.2823 2.7789 500 1800 -0.0000 2.7721 0.1950 31.132423 1.6002 2.2720 2.7789 500 2000 -0.0000 2.7721 0.1950 31.494764 1.6145 2.2618 2.7789 500 2200 -0.0000 2.7721 0.1950 31.849986 1.6285 2.2518 2.7789 500 2400 -0.0000 2.7721 0.1950 32.198358 1.6421 2.2418 2.7789 500 2600 -0.0000 2.7721 0.1950 32.540133 1.6555 2.2320 2.7789 500 2800 -0.0000 2.7721 0.1950 32.875549 1.6685 2.2223 2.7789 500 3000 -0.0000 2.7721 0.1950 33.204831 1.6813 2.2126 2.7789 500 3200 -0.0000 2.7721 0.1950 33.528190 1.6937 2.2031 2.7789 500 3400 -0.0000 2.7721 0.1950 33.845826 1.7059 2.1937 2.7789 500 3600 -0.0000 2.7721 0.1950 34.157928 1.7178 2.1844 2.7789 500 3800 -0.0000 2.7721 0.1950 34.464675 1.7295 2.1751 2.7789 500 4000 -0.0000 2.7721 0.1950 34.766235 1.7409 2.1660 2.7789 500 4200 -0.0000 2.7721 0.1950 35.062772 1.7521 2.1570 2.7789 500 4400 -0.0000 2.7721 0.1950 35.354436 1.7631 2.1480 2.7789 500 4600 -0.0000 2.7721 0.1950 35.641375 1.7738 2.1392 2.7789 500 4800 -0.0000 2.7721 0.1950 35.923727 1.7843 2.1304 2.7789 500 5000 -0.0000 2.7721 0.1950 36.201624 1.7946 2.1217 2.7789 500 5200 -0.0000 2.7721 0.1950 36.475192 1.8048 2.1131 2.7789 500 5400 -0.0000 2.7721 0.1950 36.744552 1.8147 2.1046 2.7789 500 5600 -0.0000 2.7721 0.1950 37.009819 1.8244 2.0962 2.7789 500 5800 -0.0000 2.7721 0.1950 37.271103 1.8339 2.0878 2.7789 500 6000 -0.0000 2.7721 0.1950 37.528509 1.8433 2.0796 2.7789 500 6200 -0.0000 2.7721 0.1950 37.782139 1.8525 2.0714 2.7789 500 6400 -0.0000 2.7721 0.1950 38.032090 1.8615 2.0633 2.7789 500 6600 -0.0000 2.7721 0.1950 38.278454 1.8704 2.0553 2.7789 500 6800 -0.0000 2.7721 0.1950 38.521321 1.8790 2.0473 2.7789 500 7000 -0.0000 2.7721 0.1950 38.760777 1.8876 2.0395 2.7789 500 7200 -0.0000 2.7721 0.1950 38.996905 1.8960 2.0317 2.7789 500 7400 -0.0000 2.7721 0.1950 39.229783 1.9042 2.0239 2.7789 500 7600 -0.0000 2.7721 0.1950 39.459488 1.9123 2.0163 2.7789 500 7800 -0.0000 2.7721 0.1950 39.686095 1.9203 2.0087 2.7789 500 8000 -0.0000 2.7721 0.1950 39.909673 1.9281 2.0012 2.7789 500 8200 -0.0000 2.7721 0.1950 40.130291 1.9358 1.9938 2.7789 500 8400 -0.0000 2.7721 0.1950 40.348016 1.9434 1.9864 2.7789 1000 0 -0.0000 3.1551 0.2312 27.494901 1.6617 2.6920 3.1635 1000 200 -0.0000 3.1551 0.2312 27.937168 1.6824 2.6791 3.1635 1000 400 -0.0000 3.1551 0.2312 28.368800 1.7026 2.6663 3.1635 1000 600 -0.0000 3.1551 0.2312 28.790320 1.7221 2.6537 3.1635 1000 800 -0.0000 3.1551 0.2312 29.202206 1.7412 2.6413 3.1635 1000 1000 -0.0000 3.1551 0.2312 29.604904 1.7597 2.6290 3.1635 1000 1200 -0.0000 3.1551 0.2312 29.998823 1.7777 2.6168 3.1635 1000 1400 -0.0000 3.1551 0.2312 30.384343 1.7953 2.6048 3.1635 1000 1600 -0.0000 3.1551 0.2312 30.761818 1.8124 2.5929 3.1635 1000 1800 -0.0000 3.1551 0.2312 31.131578 1.8291 2.5812 3.1635 1000 2000 -0.0000 3.1551 0.2312 31.493929 1.8454 2.5695 3.1635 1000 2200 -0.0000 3.1551 0.2312 31.849161 1.8613 2.5581 3.1635 1000 2400 -0.0000 3.1551 0.2312 32.197543 1.8768 2.5467 3.1635 1000 2600 -0.0000 3.1551 0.2312 32.539328 1.8920 2.5354 3.1635 1000 2800 -0.0000 3.1551 0.2312 32.874754 1.9068 2.5243 3.1635 1000 3000 -0.0000 3.1551 0.2312 33.204045 1.9212 2.5133 3.1635 1000 3200 -0.0000 3.1551 0.2312 33.527412 1.9354 2.5024 3.1635 1000 3400 -0.0000 3.1551 0.2312 33.845057 1.9492 2.4917 3.1635 1000 3600 -0.0000 3.1551 0.2312 34.157166 1.9628 2.4810 3.1635 1000 3800 -0.0000 3.1551 0.2312 34.463921 1.9760 2.4705 3.1635 1000 4000 -0.0000 3.1551 0.2312 34.765489 1.9890 2.4600 3.1635 1000 4200 -0.0000 3.1551 0.2312 35.062033 2.0017 2.4497 3.1635 1000 4400 -0.0000 3.1551 0.2312 35.353705 2.0142 2.4395 3.1635 1000 4600 -0.0000 3.1551 0.2312 35.640651 2.0264 2.4294 3.1635 1000 4800 -0.0000 3.1551 0.2312 35.923009 2.0383 2.4194 3.1635 1000 5000 -0.0000 3.1551 0.2312 36.200913 2.0500 2.4094 3.1635 1000 5200 -0.0000 3.1551 0.2312 36.474487 2.0615 2.3996 3.1635 1000 5400 -0.0000 3.1551 0.2312 36.743853 2.0728 2.3899 3.1635 1000 5600 -0.0000 3.1551 0.2312 37.009126 2.0838 2.3803 3.1635 1000 5800 -0.0000 3.1551 0.2312 37.270416 2.0946 2.3708 3.1635 1000 6000 -0.0000 3.1551 0.2312 37.527828 2.1053 2.3613 3.1635 1000 6200 -0.0000 3.1551 0.2312 37.781464 2.1157 2.3520 3.1635 1000 6400 -0.0000 3.1551 0.2312 38.031420 2.1259 2.3427 3.1635 1000 6600 -0.0000 3.1551 0.2312 38.277789 2.1360 2.3336 3.1635 1000 6800 -0.0000 3.1551 0.2312 38.520662 2.1459 2.3245 3.1635 1000 7000 -0.0000 3.1551 0.2312 38.760123 2.1556 2.3155 3.1635 1000 7200 -0.0000 3.1551 0.2312 38.996255 2.1651 2.3066 3.1635 1000 7400 -0.0000 3.1551 0.2312 39.229138 2.1744 2.2978 3.1635 1000 7600 -0.0000 3.1551 0.2312 39.458848 2.1836 2.2891 3.1635 1000 7800 -0.0000 3.1551 0.2312 39.685459 2.1927 2.2804 3.1635 1000 8000 -0.0000 3.1551 0.2312 39.909042 2.2016 2.2718 3.1635 1000 8200 -0.0000 3.1551 0.2312 40.129665 2.2103 2.2633 3.1635 1000 8400 -0.0000 3.1551 0.2312 40.347394 2.2189 2.2549 3.1635 1500 0 -0.0000 3.5381 0.2674 27.493913 1.8705 3.0151 3.5482 1500 200 -0.0000 3.5381 0.2674 27.936197 1.8938 3.0005 3.5482 1500 400 -0.0000 3.5381 0.2674 28.367845 1.9163 2.9862 3.5482 1500 600 -0.0000 3.5381 0.2674 28.789379 1.9382 2.9720 3.5482 1500 800 -0.0000 3.5381 0.2674 29.201280 1.9595 2.9580 3.5482 1500 1000 -0.0000 3.5381 0.2674 29.603992 1.9803 2.9442 3.5482 1500 1200 -0.0000 3.5381 0.2674 29.997923 2.0005 2.9305 3.5482 1500 1400 -0.0000 3.5381 0.2674 30.383456 2.0202 2.9170 3.5482 1500 1600 -0.0000 3.5381 0.2674 30.760943 2.0393 2.9036 3.5482 1500 1800 -0.0000 3.5381 0.2674 31.130714 2.0580 2.8904 3.5482 1500 2000 -0.0000 3.5381 0.2674 31.493077 2.0763 2.8773 3.5482 1500 2200 -0.0000 3.5381 0.2674 31.848320 2.0941 2.8644 3.5482 1500 2400 -0.0000 3.5381 0.2674 32.196712 2.1114 2.8516 3.5482 1500 2600 -0.0000 3.5381 0.2674 32.538506 2.1284 2.8389 3.5482 1500 2800 -0.0000 3.5381 0.2674 32.873942 2.1450 2.8264 3.5482 1500 3000 -0.0000 3.5381 0.2674 33.203242 2.1612 2.8140 3.5482 1500 3200 -0.0000 3.5381 0.2674 33.526618 2.1771 2.8018 3.5482 1500 3400 -0.0000 3.5381 0.2674 33.844271 2.1926 2.7897 3.5482 1500 3600 -0.0000 3.5381 0.2674 34.156389 2.2077 2.7777 3.5482 1500 3800 -0.0000 3.5381 0.2674 34.463152 2.2226 2.7658 3.5482 1500 4000 -0.0000 3.5381 0.2674 34.764728 2.2371 2.7541 3.5482 1500 4200 -0.0000 3.5381 0.2674 35.061279 2.2513 2.7425 3.5482 1500 4400 -0.0000 3.5381 0.2674 35.352959 2.2652 2.7310 3.5482 1500 4600 -0.0000 3.5381 0.2674 35.639912 2.2789 2.7196 3.5482 1500 4800 -0.0000 3.5381 0.2674 35.922277 2.2923 2.7083 3.5482 1500 5000 -0.0000 3.5381 0.2674 36.200187 2.3054 2.6972 3.5482 1500 5200 -0.0000 3.5381 0.2674 36.473768 2.3182 2.6862 3.5482 1500 5400 -0.0000 3.5381 0.2674 36.743141 2.3308 2.6752 3.5482 1500 5600 -0.0000 3.5381 0.2674 37.008420 2.3432 2.6644 3.5482 1500 5800 -0.0000 3.5381 0.2674 37.269715 2.3553 2.6537 3.5482 1500 6000 -0.0000 3.5381 0.2674 37.527133 2.3672 2.6431 3.5482 1500 6200 -0.0000 3.5381 0.2674 37.780775 2.3789 2.6326 3.5482 1500 6400 -0.0000 3.5381 0.2674 38.030736 2.3904 2.6222 3.5482 1500 6600 -0.0000 3.5381 0.2674 38.277111 2.4016 2.6119 3.5482 1500 6800 -0.0000 3.5381 0.2674 38.519989 2.4127 2.6017 3.5482 1500 7000 -0.0000 3.5381 0.2674 38.759456 2.4235 2.5916 3.5482 1500 7200 -0.0000 3.5381 0.2674 38.995593 2.4342 2.5816 3.5482 1500 7400 -0.0000 3.5381 0.2674 39.228481 2.4446 2.5716 3.5482 1500 7600 -0.0000 3.5381 0.2674 39.458196 2.4549 2.5618 3.5482 1500 7800 -0.0000 3.5381 0.2674 39.684812 2.4651 2.5521 3.5482 1500 8000 -0.0000 3.5381 0.2674 39.908399 2.4750 2.5425 3.5482 1500 8200 -0.0000 3.5381 0.2674 40.129027 2.4848 2.5329 3.5482 1500 8400 -0.0000 3.5381 0.2674 40.346760 2.4944 2.5234 3.5482 2000 0 -0.0000 3.9211 0.3035 27.492904 2.0794 3.3382 3.9329 2000 200 -0.0000 3.9211 0.3035 27.935205 2.1051 3.3220 3.9329 2000 400 -0.0000 3.9211 0.3035 28.366870 2.1301 3.3061 3.9329 2000 600 -0.0000 3.9211 0.3035 28.788420 2.1543 3.2903 3.9329 2000 800 -0.0000 3.9211 0.3035 29.200335 2.1779 3.2747 3.9329 2000 1000 -0.0000 3.9211 0.3035 29.603061 2.2009 3.2594 3.9329 2000 1200 -0.0000 3.9211 0.3035 29.997006 2.2232 3.2441 3.9329 2000 1400 -0.0000 3.9211 0.3035 30.382551 2.2450 3.2291 3.9329 2000 1600 -0.0000 3.9211 0.3035 30.760051 2.2662 3.2142 3.9329 2000 1800 -0.0000 3.9211 0.3035 31.129834 2.2869 3.1996 3.9329 2000 2000 -0.0000 3.9211 0.3035 31.492208 2.3071 3.1850 3.9329 2000 2200 -0.0000 3.9211 0.3035 31.847461 2.3268 3.1707 3.9329 2000 2400 -0.0000 3.9211 0.3035 32.195864 2.3461 3.1565 3.9329 2000 2600 -0.0000 3.9211 0.3035 32.537668 2.3649 3.1424 3.9329 2000 2800 -0.0000 3.9211 0.3035 32.873113 2.3832 3.1285 3.9329 2000 3000 -0.0000 3.9211 0.3035 33.202423 2.4012 3.1148 3.9329 2000 3200 -0.0000 3.9211 0.3035 33.525809 2.4187 3.1012 3.9329 2000 3400 -0.0000 3.9211 0.3035 33.843470 2.4359 3.0877 3.9329 2000 3600 -0.0000 3.9211 0.3035 34.155597 2.4526 3.0744 3.9329 2000 3800 -0.0000 3.9211 0.3035 34.462368 2.4691 3.0612 3.9329 2000 4000 -0.0000 3.9211 0.3035 34.763952 2.4852 3.0482 3.9329 2000 4200 -0.0000 3.9211 0.3035 35.060511 2.5009 3.0353 3.9329 2000 4400 -0.0000 3.9211 0.3035 35.352197 2.5163 3.0225 3.9329 2000 4600 -0.0000 3.9211 0.3035 35.639158 2.5314 3.0099 3.9329 2000 4800 -0.0000 3.9211 0.3035 35.921530 2.5462 2.9973 3.9329 2000 5000 -0.0000 3.9211 0.3035 36.199447 2.5607 2.9850 3.9329 2000 5200 -0.0000 3.9211 0.3035 36.473035 2.5750 2.9727 3.9329 2000 5400 -0.0000 3.9211 0.3035 36.742414 2.5889 2.9606 3.9329 2000 5600 -0.0000 3.9211 0.3035 37.007699 2.6026 2.9485 3.9329 2000 5800 -0.0000 3.9211 0.3035 37.269001 2.6160 2.9366 3.9329 2000 6000 -0.0000 3.9211 0.3035 37.526425 2.6292 2.9249 3.9329 2000 6200 -0.0000 3.9211 0.3035 37.780072 2.6421 2.9132 3.9329 2000 6400 -0.0000 3.9211 0.3035 38.030040 2.6548 2.9016 3.9329 2000 6600 -0.0000 3.9211 0.3035 38.276421 2.6672 2.8902 3.9329 2000 6800 -0.0000 3.9211 0.3035 38.519304 2.6795 2.8789 3.9329 2000 7000 -0.0000 3.9211 0.3035 38.758775 2.6915 2.8676 3.9329 2000 7200 -0.0000 3.9211 0.3035 38.994918 2.7033 2.8565 3.9329 2000 7400 -0.0000 3.9211 0.3035 39.227811 2.7149 2.8455 3.9329 2000 7600 -0.0000 3.9211 0.3035 39.457531 2.7262 2.8346 3.9329 2000 7800 -0.0000 3.9211 0.3035 39.684152 2.7374 2.8238 3.9329 2000 8000 -0.0000 3.9211 0.3035 39.907744 2.7484 2.8131 3.9329 2000 8200 -0.0000 3.9211 0.3035 40.128376 2.7592 2.8025 3.9329 2000 8400 -0.0000 3.9211 0.3035 40.346114 2.7699 2.7920 3.9329 2500 0 -0.0000 4.3041 0.3397 27.491875 2.2882 3.6613 4.3175 2500 200 -0.0000 4.3041 0.3397 27.934194 2.3164 3.6435 4.3175 2500 400 -0.0000 4.3041 0.3397 28.365875 2.3438 3.6260 4.3175 2500 600 -0.0000 4.3041 0.3397 28.787440 2.3704 3.6086 4.3175 2500 800 -0.0000 4.3041 0.3397 29.199371 2.3963 3.5915 4.3175 2500 1000 -0.0000 4.3041 0.3397 29.602111 2.4215 3.5746 4.3175 2500 1200 -0.0000 4.3041 0.3397 29.996070 2.4460 3.5578 4.3175 2500 1400 -0.0000 4.3041 0.3397 30.381628 2.4699 3.5413 4.3175 2500 1600 -0.0000 4.3041 0.3397 30.759140 2.4932 3.5249 4.3175 2500 1800 -0.0000 4.3041 0.3397 31.128935 2.5159 3.5088 4.3175 2500 2000 -0.0000 4.3041 0.3397 31.491321 2.5380 3.4928 4.3175 2500 2200 -0.0000 4.3041 0.3397 31.846586 2.5596 3.4770 4.3175 2500 2400 -0.0000 4.3041 0.3397 32.194999 2.5807 3.4613 4.3175 2500 2600 -0.0000 4.3041 0.3397 32.536814 2.6013 3.4459 4.3175 2500 2800 -0.0000 4.3041 0.3397 32.872269 2.6214 3.4306 4.3175 2500 3000 -0.0000 4.3041 0.3397 33.201588 2.6411 3.4155 4.3175 2500 3200 -0.0000 4.3041 0.3397 33.524983 2.6603 3.4005 4.3175 2500 3400 -0.0000 4.3041 0.3397 33.842654 2.6792 3.3857 4.3175 2500 3600 -0.0000 4.3041 0.3397 34.154789 2.6976 3.3711 4.3175 2500 3800 -0.0000 4.3041 0.3397 34.461568 2.7156 3.3566 4.3175 2500 4000 -0.0000 4.3041 0.3397 34.763160 2.7332 3.3422 4.3175 2500 4200 -0.0000 4.3041 0.3397 35.059727 2.7505 3.3280 4.3175 2500 4400 -0.0000 4.3041 0.3397 35.351422 2.7674 3.3140 4.3175 2500 4600 -0.0000 4.3041 0.3397 35.638389 2.7839 3.3001 4.3175 2500 4800 -0.0000 4.3041 0.3397 35.920769 2.8002 3.2863 4.3175 2500 5000 -0.0000 4.3041 0.3397 36.198693 2.8161 3.2727 4.3175 2500 5200 -0.0000 4.3041 0.3397 36.472288 2.8317 3.2592 4.3175 2500 5400 -0.0000 4.3041 0.3397 36.741674 2.8470 3.2459 4.3175 2500 5600 -0.0000 4.3041 0.3397 37.006965 2.8620 3.2327 4.3175 2500 5800 -0.0000 4.3041 0.3397 37.268273 2.8767 3.2196 4.3175 2500 6000 -0.0000 4.3041 0.3397 37.525704 2.8911 3.2066 4.3175 2500 6200 -0.0000 4.3041 0.3397 37.779357 2.9053 3.1938 4.3175 2500 6400 -0.0000 4.3041 0.3397 38.029330 2.9192 3.1811 4.3175 2500 6600 -0.0000 4.3041 0.3397 38.275717 2.9328 3.1685 4.3175 2500 6800 -0.0000 4.3041 0.3397 38.518605 2.9462 3.1560 4.3175 2500 7000 -0.0000 4.3041 0.3397 38.758083 2.9594 3.1437 4.3175 2500 7200 -0.0000 4.3041 0.3397 38.994231 2.9723 3.1315 4.3175 2500 7400 -0.0000 4.3041 0.3397 39.227129 2.9850 3.1194 4.3175 2500 7600 -0.0000 4.3041 0.3397 39.456854 2.9975 3.1074 4.3175 2500 7800 -0.0000 4.3041 0.3397 39.683480 3.0098 3.0955 4.3175 2500 8000 -0.0000 4.3041 0.3397 39.907076 3.0219 3.0837 4.3175 2500 8200 -0.0000 4.3041 0.3397 40.127713 3.0337 3.0721 4.3175 2500 8400 -0.0000 4.3041 0.3397 40.345456 3.0454 3.0605 4.3175 3000 0 -0.0000 4.6872 0.3758 27.490826 2.4970 3.9844 4.7022 3000 200 -0.0000 4.6872 0.3758 27.933162 2.5277 3.9650 4.7022 3000 400 -0.0000 4.6872 0.3758 28.364860 2.5575 3.9459 4.7022 3000 600 -0.0000 4.6872 0.3758 28.786442 2.5865 3.9269 4.7022 3000 800 -0.0000 4.6872 0.3758 29.198388 2.6146 3.9082 4.7022 3000 1000 -0.0000 4.6872 0.3758 29.601143 2.6420 3.8898 4.7022 3000 1200 -0.0000 4.6872 0.3758 29.995115 2.6687 3.8715 4.7022 3000 1400 -0.0000 4.6872 0.3758 30.380687 2.6947 3.8535 4.7022 3000 1600 -0.0000 4.6872 0.3758 30.758212 2.7201 3.8356 4.7022 3000 1800 -0.0000 4.6872 0.3758 31.128020 2.7448 3.8180 4.7022 3000 2000 -0.0000 4.6872 0.3758 31.490417 2.7688 3.8005 4.7022 3000 2200 -0.0000 4.6872 0.3758 31.845693 2.7924 3.7833 4.7022 3000 2400 -0.0000 4.6872 0.3758 32.194117 2.8153 3.7662 4.7022 3000 2600 -0.0000 4.6872 0.3758 32.535943 2.8377 3.7494 4.7022 3000 2800 -0.0000 4.6872 0.3758 32.871408 2.8596 3.7327 4.7022 3000 3000 -0.0000 4.6872 0.3758 33.200737 2.8810 3.7162 4.7022 3000 3200 -0.0000 4.6872 0.3758 33.524142 2.9020 3.6999 4.7022 3000 3400 -0.0000 4.6872 0.3758 33.841821 2.9224 3.6837 4.7022 3000 3600 -0.0000 4.6872 0.3758 34.153966 2.9425 3.6678 4.7022 3000 3800 -0.0000 4.6872 0.3758 34.460753 2.9621 3.6520 4.7022 3000 4000 -0.0000 4.6872 0.3758 34.762354 2.9813 3.6363 4.7022 3000 4200 -0.0000 4.6872 0.3758 35.058929 3.0000 3.6208 4.7022 3000 4400 -0.0000 4.6872 0.3758 35.350631 3.0184 3.6055 4.7022 3000 4600 -0.0000 4.6872 0.3758 35.637607 3.0365 3.5904 4.7022 3000 4800 -0.0000 4.6872 0.3758 35.919994 3.0541 3.5753 4.7022 3000 5000 -0.0000 4.6872 0.3758 36.197925 3.0714 3.5605 4.7022 3000 5200 -0.0000 4.6872 0.3758 36.471527 3.0884 3.5458 4.7022 3000 5400 -0.0000 4.6872 0.3758 36.740919 3.1050 3.5312 4.7022 3000 5600 -0.0000 4.6872 0.3758 37.006218 3.1213 3.5168 4.7022 3000 5800 -0.0000 4.6872 0.3758 37.267532 3.1373 3.5025 4.7022 3000 6000 -0.0000 4.6872 0.3758 37.524969 3.1531 3.4884 4.7022 3000 6200 -0.0000 4.6872 0.3758 37.778628 3.1685 3.4744 4.7022 3000 6400 -0.0000 4.6872 0.3758 38.028607 3.1836 3.4606 4.7022 3000 6600 -0.0000 4.6872 0.3758 38.275000 3.1984 3.4468 4.7022 3000 6800 -0.0000 4.6872 0.3758 38.517894 3.2130 3.4332 4.7022 3000 7000 -0.0000 4.6872 0.3758 38.757377 3.2274 3.4198 4.7022 3000 7200 -0.0000 4.6872 0.3758 38.993530 3.2414 3.4064 4.7022 3000 7400 -0.0000 4.6872 0.3758 39.226434 3.2552 3.3932 4.7022 3000 7600 -0.0000 4.6872 0.3758 39.456164 3.2688 3.3802 4.7022 3000 7800 -0.0000 4.6872 0.3758 39.682795 3.2822 3.3672 4.7022 3000 8000 -0.0000 4.6872 0.3758 39.906397 3.2953 3.3544 4.7022 3000 8200 -0.0000 4.6872 0.3758 40.127039 3.3082 3.3417 4.7022 3000 8400 -0.0000 4.6872 0.3758 40.344786 3.3208 3.3291 4.7022 3500 0 -0.0000 5.0702 0.4120 27.489756 2.7058 4.3075 5.0869 3500 200 -0.0000 5.0702 0.4120 27.932111 2.7390 4.2865 5.0869 3500 400 -0.0000 5.0702 0.4120 28.363826 2.7712 4.2658 5.0869 3500 600 -0.0000 5.0702 0.4120 28.785424 2.8025 4.2453 5.0869 3500 800 -0.0000 5.0702 0.4120 29.197386 2.8330 4.2250 5.0869 3500 1000 -0.0000 5.0702 0.4120 29.600156 2.8626 4.2050 5.0869 3500 1200 -0.0000 5.0702 0.4120 29.994143 2.8915 4.1852 5.0869 3500 1400 -0.0000 5.0702 0.4120 30.379728 2.9196 4.1656 5.0869 3500 1600 -0.0000 5.0702 0.4120 30.757266 2.9469 4.1463 5.0869 3500 1800 -0.0000 5.0702 0.4120 31.127086 2.9736 4.1272 5.0869 3500 2000 -0.0000 5.0702 0.4120 31.489496 2.9997 4.1083 5.0869 3500 2200 -0.0000 5.0702 0.4120 31.844784 3.0251 4.0896 5.0869 3500 2400 -0.0000 5.0702 0.4120 32.193219 3.0499 4.0712 5.0869 3500 2600 -0.0000 5.0702 0.4120 32.535056 3.0742 4.0529 5.0869 3500 2800 -0.0000 5.0702 0.4120 32.870531 3.0978 4.0348 5.0869 3500 3000 -0.0000 5.0702 0.4120 33.199870 3.1210 4.0169 5.0869 3500 3200 -0.0000 5.0702 0.4120 33.523284 3.1436 3.9993 5.0869 3500 3400 -0.0000 5.0702 0.4120 33.840974 3.1657 3.9818 5.0869 3500 3600 -0.0000 5.0702 0.4120 34.153127 3.1874 3.9645 5.0869 3500 3800 -0.0000 5.0702 0.4120 34.459923 3.2086 3.9473 5.0869 3500 4000 -0.0000 5.0702 0.4120 34.761533 3.2293 3.9304 5.0869 3500 4200 -0.0000 5.0702 0.4120 35.058116 3.2496 3.9136 5.0869 3500 4400 -0.0000 5.0702 0.4120 35.349826 3.2695 3.8970 5.0869 3500 4600 -0.0000 5.0702 0.4120 35.636809 3.2890 3.8806 5.0869 3500 4800 -0.0000 5.0702 0.4120 35.919204 3.3080 3.8644 5.0869 3500 5000 -0.0000 5.0702 0.4120 36.197143 3.3267 3.8483 5.0869 3500 5200 -0.0000 5.0702 0.4120 36.470752 3.3451 3.8323 5.0869 3500 5400 -0.0000 5.0702 0.4120 36.740151 3.3631 3.8166 5.0869 3500 5600 -0.0000 5.0702 0.4120 37.005456 3.3807 3.8010 5.0869 3500 5800 -0.0000 5.0702 0.4120 37.266777 3.3980 3.7855 5.0869 3500 6000 -0.0000 5.0702 0.4120 37.524220 3.4150 3.7702 5.0869 3500 6200 -0.0000 5.0702 0.4120 37.777886 3.4316 3.7550 5.0869 3500 6400 -0.0000 5.0702 0.4120 38.027871 3.4480 3.7400 5.0869 3500 6600 -0.0000 5.0702 0.4120 38.274270 3.4640 3.7252 5.0869 3500 6800 -0.0000 5.0702 0.4120 38.517170 3.4798 3.7104 5.0869 3500 7000 -0.0000 5.0702 0.4120 38.756658 3.4953 3.6959 5.0869 3500 7200 -0.0000 5.0702 0.4120 38.992817 3.5105 3.6814 5.0869 3500 7400 -0.0000 5.0702 0.4120 39.225727 3.5254 3.6671 5.0869 3500 7600 -0.0000 5.0702 0.4120 39.455462 3.5401 3.6530 5.0869 3500 7800 -0.0000 5.0702 0.4120 39.682098 3.5545 3.6389 5.0869 3500 8000 -0.0000 5.0702 0.4120 39.905705 3.5687 3.6250 5.0869 3500 8200 -0.0000 5.0702 0.4120 40.126352 3.5826 3.6113 5.0869 3500 8400 -0.0000 5.0702 0.4120 40.344104 3.5963 3.5976 5.0869 4000 0 -0.0000 5.4532 0.4482 27.488666 2.9146 4.6307 5.4716 4000 200 -0.0000 5.4532 0.4482 27.931039 2.9503 4.6080 5.4716 4000 400 -0.0000 5.4532 0.4482 28.362772 2.9849 4.5857 5.4716 4000 600 -0.0000 5.4532 0.4482 28.784387 3.0186 4.5636 5.4716 4000 800 -0.0000 5.4532 0.4482 29.196366 3.0513 4.5418 5.4716 4000 1000 -0.0000 5.4532 0.4482 29.599150 3.0832 4.5202 5.4716 4000 1200 -0.0000 5.4532 0.4482 29.993152 3.1142 4.4989 5.4716 4000 1400 -0.0000 5.4532 0.4482 30.378752 3.1444 4.4778 5.4716 4000 1600 -0.0000 5.4532 0.4482 30.756303 3.1738 4.4570 5.4716 4000 1800 -0.0000 5.4532 0.4482 31.126136 3.2025 4.4364 5.4716 4000 2000 -0.0000 5.4532 0.4482 31.488558 3.2305 4.4161 5.4716 4000 2200 -0.0000 5.4532 0.4482 31.843858 3.2578 4.3960 5.4716 4000 2400 -0.0000 5.4532 0.4482 32.192305 3.2845 4.3761 5.4716 4000 2600 -0.0000 5.4532 0.4482 32.534152 3.3106 4.3564 5.4716 4000 2800 -0.0000 5.4532 0.4482 32.869638 3.3360 4.3369 5.4716 4000 3000 -0.0000 5.4532 0.4482 33.198987 3.3609 4.3177 5.4716 4000 3200 -0.0000 5.4532 0.4482 33.522412 3.3852 4.2987 5.4716 4000 3400 -0.0000 5.4532 0.4482 33.840110 3.4090 4.2798 5.4716 4000 3600 -0.0000 5.4532 0.4482 34.152273 3.4323 4.2612 5.4716 4000 3800 -0.0000 5.4532 0.4482 34.459078 3.4550 4.2427 5.4716 4000 4000 -0.0000 5.4532 0.4482 34.760696 3.4773 4.2245 5.4716 4000 4200 -0.0000 5.4532 0.4482 35.057288 3.4991 4.2064 5.4716 4000 4400 -0.0000 5.4532 0.4482 35.349007 3.5205 4.1886 5.4716 4000 4600 -0.0000 5.4532 0.4482 35.635998 3.5414 4.1709 5.4716 4000 4800 -0.0000 5.4532 0.4482 35.918400 3.5620 4.1534 5.4716 4000 5000 -0.0000 5.4532 0.4482 36.196346 3.5821 4.1361 5.4716 4000 5200 -0.0000 5.4532 0.4482 36.469963 3.6018 4.1189 5.4716 4000 5400 -0.0000 5.4532 0.4482 36.739369 3.6211 4.1019 5.4716 4000 5600 -0.0000 5.4532 0.4482 37.004681 3.6401 4.0851 5.4716 4000 5800 -0.0000 5.4532 0.4482 37.266009 3.6587 4.0685 5.4716 4000 6000 -0.0000 5.4532 0.4482 37.523459 3.6769 4.0520 5.4716 4000 6200 -0.0000 5.4532 0.4482 37.777131 3.6948 4.0357 5.4716 4000 6400 -0.0000 5.4532 0.4482 38.027122 3.7124 4.0195 5.4716 4000 6600 -0.0000 5.4532 0.4482 38.273527 3.7296 4.0035 5.4716 4000 6800 -0.0000 5.4532 0.4482 38.516433 3.7466 3.9877 5.4716 4000 7000 -0.0000 5.4532 0.4482 38.755927 3.7632 3.9720 5.4716 4000 7200 -0.0000 5.4532 0.4482 38.992092 3.7795 3.9564 5.4716 4000 7400 -0.0000 5.4532 0.4482 39.225007 3.7956 3.9410 5.4716 4000 7600 -0.0000 5.4532 0.4482 39.454748 3.8114 3.9258 5.4716 4000 7800 -0.0000 5.4532 0.4482 39.681389 3.8269 3.9107 5.4716 4000 8000 -0.0000 5.4532 0.4482 39.905001 3.8421 3.8957 5.4716 4000 8200 -0.0000 5.4532 0.4482 40.125653 3.8571 3.8809 5.4716 4000 8400 -0.0000 5.4532 0.4482 40.343411 3.8718 3.8662 5.4716 4500 0 -0.0000 5.8362 0.4843 27.487555 3.1234 4.9538 5.8563 4500 200 -0.0000 5.8362 0.4843 27.929948 3.1615 4.9296 5.8563 4500 400 -0.0000 5.8362 0.4843 28.361699 3.1986 4.9056 5.8563 4500 600 -0.0000 5.8362 0.4843 28.783331 3.2346 4.8819 5.8563 4500 800 -0.0000 5.8362 0.4843 29.195326 3.2696 4.8585 5.8563 4500 1000 -0.0000 5.8362 0.4843 29.598126 3.3037 4.8354 5.8563 4500 1200 -0.0000 5.8362 0.4843 29.992143 3.3369 4.8126 5.8563 4500 1400 -0.0000 5.8362 0.4843 30.377757 3.3692 4.7900 5.8563 4500 1600 -0.0000 5.8362 0.4843 30.755322 3.4007 4.7677 5.8563 4500 1800 -0.0000 5.8362 0.4843 31.125168 3.4314 4.7457 5.8563 4500 2000 -0.0000 5.8362 0.4843 31.487603 3.4613 4.7239 5.8563 4500 2200 -0.0000 5.8362 0.4843 31.842915 3.4906 4.7023 5.8563 4500 2400 -0.0000 5.8362 0.4843 32.191373 3.5191 4.6810 5.8563 4500 2600 -0.0000 5.8362 0.4843 32.533232 3.5470 4.6599 5.8563 4500 2800 -0.0000 5.8362 0.4843 32.868729 3.5742 4.6391 5.8563 4500 3000 -0.0000 5.8362 0.4843 33.198089 3.6008 4.6185 5.8563 4500 3200 -0.0000 5.8362 0.4843 33.521523 3.6268 4.5981 5.8563 4500 3400 -0.0000 5.8362 0.4843 33.839232 3.6523 4.5779 5.8563 4500 3600 -0.0000 5.8362 0.4843 34.151404 3.6771 4.5579 5.8563 4500 3800 -0.0000 5.8362 0.4843 34.458218 3.7015 4.5381 5.8563 4500 4000 -0.0000 5.8362 0.4843 34.759845 3.7253 4.5186 5.8563 4500 4200 -0.0000 5.8362 0.4843 35.056446 3.7487 4.4993 5.8563 4500 4400 -0.0000 5.8362 0.4843 35.348172 3.7715 4.4801 5.8563 4500 4600 -0.0000 5.8362 0.4843 35.635172 3.7939 4.4612 5.8563 4500 4800 -0.0000 5.8362 0.4843 35.917582 3.8159 4.4424 5.8563 4500 5000 -0.0000 5.8362 0.4843 36.195536 3.8374 4.4238 5.8563 4500 5200 -0.0000 5.8362 0.4843 36.469160 3.8585 4.4055 5.8563 4500 5400 -0.0000 5.8362 0.4843 36.738573 3.8791 4.3873 5.8563 4500 5600 -0.0000 5.8362 0.4843 37.003892 3.8994 4.3693 5.8563 4500 5800 -0.0000 5.8362 0.4843 37.265227 3.9193 4.3514 5.8563 4500 6000 -0.0000 5.8362 0.4843 37.522684 3.9388 4.3338 5.8563 4500 6200 -0.0000 5.8362 0.4843 37.776362 3.9580 4.3163 5.8563 4500 6400 -0.0000 5.8362 0.4843 38.026360 3.9768 4.2990 5.8563 4500 6600 -0.0000 5.8362 0.4843 38.272771 3.9952 4.2818 5.8563 4500 6800 -0.0000 5.8362 0.4843 38.515683 4.0133 4.2649 5.8563 4500 7000 -0.0000 5.8362 0.4843 38.755184 4.0311 4.2481 5.8563 4500 7200 -0.0000 5.8362 0.4843 38.991354 4.0486 4.2314 5.8563 4500 7400 -0.0000 5.8362 0.4843 39.224274 4.0658 4.2149 5.8563 4500 7600 -0.0000 5.8362 0.4843 39.454021 4.0826 4.1986 5.8563 4500 7800 -0.0000 5.8362 0.4843 39.680668 4.0992 4.1824 5.8563 4500 8000 -0.0000 5.8362 0.4843 39.904285 4.1155 4.1664 5.8563 4500 8200 -0.0000 5.8362 0.4843 40.124942 4.1315 4.1505 5.8563 4500 8400 -0.0000 5.8362 0.4843 40.342705 4.1473 4.1347 5.8563 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.000 ================================================ FILE: tests/test_data/cropA/geometry/20180307-20180331_VV_8rlks_base.par ================================================ initial_baseline(TCN): -0.1112816 -1.2495340 5.0924280 m m m initial_baseline_rate: 0.0000000 0.2291424 0.0401035 m/s m/s m/s precision_baseline(TCN): -0.1112816 -1.2495340 5.0924280 m m m precision_baseline_rate: 0.0000000 0.2291424 0.0401035 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/cropA/geometry/20180307-20180331_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): -0.111 -1.250 5.092 orbit baseline rate (TCN) (m/s): 0.000e+00 2.291e-01 4.010e-02 baseline vector (TCN) (m): -0.111 -1.250 5.092 baseline rate (TCN) (m/s): 0.000e+00 2.291e-01 4.010e-02 SLC-1 center baseline length (m): 5.2447 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 -0.1113 -3.3884 4.7181 27.496815 2.6208 -5.1837 5.8098 0 200 -0.1113 -3.3884 4.7181 27.939050 2.5807 -5.2038 5.8098 0 400 -0.1113 -3.3884 4.7181 28.370652 2.5415 -5.2231 5.8098 0 600 -0.1113 -3.3884 4.7181 28.792142 2.5030 -5.2417 5.8098 0 800 -0.1113 -3.3884 4.7181 29.204001 2.4652 -5.2595 5.8098 0 1000 -0.1113 -3.3884 4.7181 29.606672 2.4282 -5.2767 5.8098 0 1200 -0.1113 -3.3884 4.7181 30.000566 2.3919 -5.2933 5.8098 0 1400 -0.1113 -3.3884 4.7181 30.386062 2.3562 -5.3093 5.8098 0 1600 -0.1113 -3.3884 4.7181 30.763514 2.3212 -5.3247 5.8098 0 1800 -0.1113 -3.3884 4.7181 31.133251 2.2867 -5.3396 5.8098 0 2000 -0.1113 -3.3884 4.7181 31.495582 2.2529 -5.3539 5.8098 0 2200 -0.1113 -3.3884 4.7181 31.850793 2.2197 -5.3678 5.8098 0 2400 -0.1113 -3.3884 4.7181 32.199155 2.1870 -5.3812 5.8098 0 2600 -0.1113 -3.3884 4.7181 32.540921 2.1549 -5.3941 5.8098 0 2800 -0.1113 -3.3884 4.7181 32.876328 2.1233 -5.4067 5.8098 0 3000 -0.1113 -3.3884 4.7181 33.205602 2.0922 -5.4188 5.8098 0 3200 -0.1113 -3.3884 4.7181 33.528952 2.0615 -5.4305 5.8098 0 3400 -0.1113 -3.3884 4.7181 33.846580 2.0314 -5.4418 5.8098 0 3600 -0.1113 -3.3884 4.7181 34.158674 2.0017 -5.4528 5.8098 0 3800 -0.1113 -3.3884 4.7181 34.465413 1.9725 -5.4635 5.8098 0 4000 -0.1113 -3.3884 4.7181 34.766966 1.9437 -5.4738 5.8098 0 4200 -0.1113 -3.3884 4.7181 35.063495 1.9154 -5.4838 5.8098 0 4400 -0.1113 -3.3884 4.7181 35.355153 1.8874 -5.4934 5.8098 0 4600 -0.1113 -3.3884 4.7181 35.642085 1.8599 -5.5028 5.8098 0 4800 -0.1113 -3.3884 4.7181 35.924431 1.8328 -5.5119 5.8098 0 5000 -0.1113 -3.3884 4.7181 36.202321 1.8060 -5.5208 5.8098 0 5200 -0.1113 -3.3884 4.7181 36.475883 1.7796 -5.5293 5.8098 0 5400 -0.1113 -3.3884 4.7181 36.745237 1.7536 -5.5376 5.8098 0 5600 -0.1113 -3.3884 4.7181 37.010498 1.7279 -5.5457 5.8098 0 5800 -0.1113 -3.3884 4.7181 37.271776 1.7026 -5.5535 5.8098 0 6000 -0.1113 -3.3884 4.7181 37.529176 1.6777 -5.5611 5.8098 0 6200 -0.1113 -3.3884 4.7181 37.782801 1.6530 -5.5685 5.8098 0 6400 -0.1113 -3.3884 4.7181 38.032746 1.6287 -5.5756 5.8098 0 6600 -0.1113 -3.3884 4.7181 38.279105 1.6047 -5.5826 5.8098 0 6800 -0.1113 -3.3884 4.7181 38.521968 1.5811 -5.5893 5.8098 0 7000 -0.1113 -3.3884 4.7181 38.761419 1.5577 -5.5959 5.8098 0 7200 -0.1113 -3.3884 4.7181 38.997541 1.5346 -5.6023 5.8098 0 7400 -0.1113 -3.3884 4.7181 39.230415 1.5118 -5.6085 5.8098 0 7600 -0.1113 -3.3884 4.7181 39.460116 1.4893 -5.6145 5.8098 0 7800 -0.1113 -3.3884 4.7181 39.686717 1.4671 -5.6203 5.8098 0 8000 -0.1113 -3.3884 4.7181 39.910291 1.4452 -5.6260 5.8098 0 8200 -0.1113 -3.3884 4.7181 40.130905 1.4235 -5.6315 5.8098 0 8400 -0.1113 -3.3884 4.7181 40.348626 1.4021 -5.6369 5.8098 500 0 -0.1113 -2.9174 4.8005 27.495868 2.9115 -4.8039 5.6186 500 200 -0.1113 -2.9174 4.8005 27.938119 2.8743 -4.8263 5.6186 500 400 -0.1113 -2.9174 4.8005 28.369736 2.8379 -4.8478 5.6186 500 600 -0.1113 -2.9174 4.8005 28.791241 2.8021 -4.8685 5.6186 500 800 -0.1113 -2.9174 4.8005 29.203113 2.7671 -4.8886 5.6186 500 1000 -0.1113 -2.9174 4.8005 29.605798 2.7326 -4.9079 5.6186 500 1200 -0.1113 -2.9174 4.8005 29.999704 2.6988 -4.9266 5.6186 500 1400 -0.1113 -2.9174 4.8005 30.385211 2.6656 -4.9446 5.6186 500 1600 -0.1113 -2.9174 4.8005 30.762675 2.6330 -4.9621 5.6186 500 1800 -0.1113 -2.9174 4.8005 31.132423 2.6009 -4.9790 5.6186 500 2000 -0.1113 -2.9174 4.8005 31.494764 2.5694 -4.9953 5.6186 500 2200 -0.1113 -2.9174 4.8005 31.849986 2.5383 -5.0112 5.6186 500 2400 -0.1113 -2.9174 4.8005 32.198358 2.5078 -5.0265 5.6186 500 2600 -0.1113 -2.9174 4.8005 32.540133 2.4778 -5.0414 5.6186 500 2800 -0.1113 -2.9174 4.8005 32.875549 2.4482 -5.0558 5.6186 500 3000 -0.1113 -2.9174 4.8005 33.204831 2.4191 -5.0698 5.6186 500 3200 -0.1113 -2.9174 4.8005 33.528190 2.3905 -5.0834 5.6186 500 3400 -0.1113 -2.9174 4.8005 33.845826 2.3623 -5.0965 5.6186 500 3600 -0.1113 -2.9174 4.8005 34.157928 2.3345 -5.1093 5.6186 500 3800 -0.1113 -2.9174 4.8005 34.464675 2.3071 -5.1217 5.6186 500 4000 -0.1113 -2.9174 4.8005 34.766235 2.2801 -5.1338 5.6186 500 4200 -0.1113 -2.9174 4.8005 35.062772 2.2535 -5.1456 5.6186 500 4400 -0.1113 -2.9174 4.8005 35.354436 2.2273 -5.1570 5.6186 500 4600 -0.1113 -2.9174 4.8005 35.641375 2.2014 -5.1681 5.6186 500 4800 -0.1113 -2.9174 4.8005 35.923727 2.1759 -5.1788 5.6186 500 5000 -0.1113 -2.9174 4.8005 36.201624 2.1508 -5.1893 5.6186 500 5200 -0.1113 -2.9174 4.8005 36.475192 2.1260 -5.1995 5.6186 500 5400 -0.1113 -2.9174 4.8005 36.744552 2.1015 -5.2095 5.6186 500 5600 -0.1113 -2.9174 4.8005 37.009819 2.0774 -5.2192 5.6186 500 5800 -0.1113 -2.9174 4.8005 37.271103 2.0535 -5.2286 5.6186 500 6000 -0.1113 -2.9174 4.8005 37.528509 2.0300 -5.2378 5.6186 500 6200 -0.1113 -2.9174 4.8005 37.782139 2.0068 -5.2467 5.6186 500 6400 -0.1113 -2.9174 4.8005 38.032090 1.9839 -5.2554 5.6186 500 6600 -0.1113 -2.9174 4.8005 38.278454 1.9613 -5.2639 5.6186 500 6800 -0.1113 -2.9174 4.8005 38.521321 1.9390 -5.2721 5.6186 500 7000 -0.1113 -2.9174 4.8005 38.760777 1.9169 -5.2802 5.6186 500 7200 -0.1113 -2.9174 4.8005 38.996905 1.8951 -5.2881 5.6186 500 7400 -0.1113 -2.9174 4.8005 39.229783 1.8736 -5.2957 5.6186 500 7600 -0.1113 -2.9174 4.8005 39.459488 1.8524 -5.3032 5.6186 500 7800 -0.1113 -2.9174 4.8005 39.686095 1.8314 -5.3105 5.6186 500 8000 -0.1113 -2.9174 4.8005 39.909673 1.8107 -5.3176 5.6186 500 8200 -0.1113 -2.9174 4.8005 40.130291 1.7902 -5.3245 5.6186 500 8400 -0.1113 -2.9174 4.8005 40.348016 1.7699 -5.3313 5.6186 1000 0 -0.1113 -2.4464 4.8830 27.494901 3.2021 -4.4241 5.4626 1000 200 -0.1113 -2.4464 4.8830 27.937168 3.1679 -4.4487 5.4626 1000 400 -0.1113 -2.4464 4.8830 28.368800 3.1343 -4.4725 5.4626 1000 600 -0.1113 -2.4464 4.8830 28.790320 3.1013 -4.4954 5.4626 1000 800 -0.1113 -2.4464 4.8830 29.202206 3.0689 -4.5176 5.4626 1000 1000 -0.1113 -2.4464 4.8830 29.604904 3.0371 -4.5391 5.4626 1000 1200 -0.1113 -2.4464 4.8830 29.998823 3.0058 -4.5598 5.4626 1000 1400 -0.1113 -2.4464 4.8830 30.384343 2.9750 -4.5800 5.4626 1000 1600 -0.1113 -2.4464 4.8830 30.761818 2.9448 -4.5995 5.4626 1000 1800 -0.1113 -2.4464 4.8830 31.131578 2.9151 -4.6184 5.4626 1000 2000 -0.1113 -2.4464 4.8830 31.493929 2.8858 -4.6367 5.4626 1000 2200 -0.1113 -2.4464 4.8830 31.849161 2.8570 -4.6545 5.4626 1000 2400 -0.1113 -2.4464 4.8830 32.197543 2.8286 -4.6718 5.4626 1000 2600 -0.1113 -2.4464 4.8830 32.539328 2.8007 -4.6886 5.4626 1000 2800 -0.1113 -2.4464 4.8830 32.874754 2.7732 -4.7049 5.4626 1000 3000 -0.1113 -2.4464 4.8830 33.204045 2.7461 -4.7208 5.4626 1000 3200 -0.1113 -2.4464 4.8830 33.527412 2.7194 -4.7362 5.4626 1000 3400 -0.1113 -2.4464 4.8830 33.845057 2.6931 -4.7512 5.4626 1000 3600 -0.1113 -2.4464 4.8830 34.157166 2.6672 -4.7658 5.4626 1000 3800 -0.1113 -2.4464 4.8830 34.463921 2.6417 -4.7800 5.4626 1000 4000 -0.1113 -2.4464 4.8830 34.765489 2.6165 -4.7939 5.4626 1000 4200 -0.1113 -2.4464 4.8830 35.062033 2.5916 -4.8073 5.4626 1000 4400 -0.1113 -2.4464 4.8830 35.353705 2.5671 -4.8205 5.4626 1000 4600 -0.1113 -2.4464 4.8830 35.640651 2.5429 -4.8333 5.4626 1000 4800 -0.1113 -2.4464 4.8830 35.923009 2.5191 -4.8457 5.4626 1000 5000 -0.1113 -2.4464 4.8830 36.200913 2.4956 -4.8579 5.4626 1000 5200 -0.1113 -2.4464 4.8830 36.474487 2.4723 -4.8698 5.4626 1000 5400 -0.1113 -2.4464 4.8830 36.743853 2.4494 -4.8813 5.4626 1000 5600 -0.1113 -2.4464 4.8830 37.009126 2.4268 -4.8926 5.4626 1000 5800 -0.1113 -2.4464 4.8830 37.270416 2.4044 -4.9036 5.4626 1000 6000 -0.1113 -2.4464 4.8830 37.527828 2.3824 -4.9144 5.4626 1000 6200 -0.1113 -2.4464 4.8830 37.781464 2.3606 -4.9249 5.4626 1000 6400 -0.1113 -2.4464 4.8830 38.031420 2.3391 -4.9352 5.4626 1000 6600 -0.1113 -2.4464 4.8830 38.277789 2.3179 -4.9452 5.4626 1000 6800 -0.1113 -2.4464 4.8830 38.520662 2.2969 -4.9550 5.4626 1000 7000 -0.1113 -2.4464 4.8830 38.760123 2.2761 -4.9645 5.4626 1000 7200 -0.1113 -2.4464 4.8830 38.996255 2.2557 -4.9738 5.4626 1000 7400 -0.1113 -2.4464 4.8830 39.229138 2.2354 -4.9830 5.4626 1000 7600 -0.1113 -2.4464 4.8830 39.458848 2.2154 -4.9919 5.4626 1000 7800 -0.1113 -2.4464 4.8830 39.685459 2.1957 -5.0006 5.4626 1000 8000 -0.1113 -2.4464 4.8830 39.909042 2.1761 -5.0092 5.4626 1000 8200 -0.1113 -2.4464 4.8830 40.129665 2.1568 -5.0175 5.4626 1000 8400 -0.1113 -2.4464 4.8830 40.347394 2.1377 -5.0257 5.4626 1500 0 -0.1113 -1.9754 4.9654 27.493913 3.4928 -4.0443 5.3451 1500 200 -0.1113 -1.9754 4.9654 27.936197 3.4615 -4.0712 5.3451 1500 400 -0.1113 -1.9754 4.9654 28.367845 3.4307 -4.0971 5.3451 1500 600 -0.1113 -1.9754 4.9654 28.789379 3.4005 -4.1223 5.3451 1500 800 -0.1113 -1.9754 4.9654 29.201280 3.3707 -4.1466 5.3451 1500 1000 -0.1113 -1.9754 4.9654 29.603992 3.3415 -4.1702 5.3451 1500 1200 -0.1113 -1.9754 4.9654 29.997923 3.3128 -4.1931 5.3451 1500 1400 -0.1113 -1.9754 4.9654 30.383456 3.2845 -4.2153 5.3451 1500 1600 -0.1113 -1.9754 4.9654 30.760943 3.2566 -4.2368 5.3451 1500 1800 -0.1113 -1.9754 4.9654 31.130714 3.2292 -4.2578 5.3451 1500 2000 -0.1113 -1.9754 4.9654 31.493077 3.2022 -4.2781 5.3451 1500 2200 -0.1113 -1.9754 4.9654 31.848320 3.1756 -4.2979 5.3451 1500 2400 -0.1113 -1.9754 4.9654 32.196712 3.1494 -4.3171 5.3451 1500 2600 -0.1113 -1.9754 4.9654 32.538506 3.1236 -4.3358 5.3451 1500 2800 -0.1113 -1.9754 4.9654 32.873942 3.0982 -4.3540 5.3451 1500 3000 -0.1113 -1.9754 4.9654 33.203242 3.0731 -4.3718 5.3451 1500 3200 -0.1113 -1.9754 4.9654 33.526618 3.0484 -4.3890 5.3451 1500 3400 -0.1113 -1.9754 4.9654 33.844271 3.0240 -4.4059 5.3451 1500 3600 -0.1113 -1.9754 4.9654 34.156389 3.0000 -4.4223 5.3451 1500 3800 -0.1113 -1.9754 4.9654 34.463152 2.9762 -4.4383 5.3451 1500 4000 -0.1113 -1.9754 4.9654 34.764728 2.9528 -4.4539 5.3451 1500 4200 -0.1113 -1.9754 4.9654 35.061279 2.9297 -4.4691 5.3451 1500 4400 -0.1113 -1.9754 4.9654 35.352959 2.9069 -4.4840 5.3451 1500 4600 -0.1113 -1.9754 4.9654 35.639912 2.8844 -4.4985 5.3451 1500 4800 -0.1113 -1.9754 4.9654 35.922277 2.8622 -4.5126 5.3451 1500 5000 -0.1113 -1.9754 4.9654 36.200187 2.8403 -4.5265 5.3451 1500 5200 -0.1113 -1.9754 4.9654 36.473768 2.8187 -4.5400 5.3451 1500 5400 -0.1113 -1.9754 4.9654 36.743141 2.7973 -4.5532 5.3451 1500 5600 -0.1113 -1.9754 4.9654 37.008420 2.7762 -4.5661 5.3451 1500 5800 -0.1113 -1.9754 4.9654 37.269715 2.7553 -4.5787 5.3451 1500 6000 -0.1113 -1.9754 4.9654 37.527133 2.7347 -4.5910 5.3451 1500 6200 -0.1113 -1.9754 4.9654 37.780775 2.7144 -4.6031 5.3451 1500 6400 -0.1113 -1.9754 4.9654 38.030736 2.6943 -4.6149 5.3451 1500 6600 -0.1113 -1.9754 4.9654 38.277111 2.6744 -4.6264 5.3451 1500 6800 -0.1113 -1.9754 4.9654 38.519989 2.6548 -4.6377 5.3451 1500 7000 -0.1113 -1.9754 4.9654 38.759456 2.6354 -4.6488 5.3451 1500 7200 -0.1113 -1.9754 4.9654 38.995593 2.6162 -4.6596 5.3451 1500 7400 -0.1113 -1.9754 4.9654 39.228481 2.5972 -4.6702 5.3451 1500 7600 -0.1113 -1.9754 4.9654 39.458196 2.5785 -4.6806 5.3451 1500 7800 -0.1113 -1.9754 4.9654 39.684812 2.5599 -4.6908 5.3451 1500 8000 -0.1113 -1.9754 4.9654 39.908399 2.5416 -4.7007 5.3451 1500 8200 -0.1113 -1.9754 4.9654 40.129027 2.5235 -4.7105 5.3451 1500 8400 -0.1113 -1.9754 4.9654 40.346760 2.5056 -4.7200 5.3451 2000 0 -0.1113 -1.5044 5.0478 27.492904 3.7834 -3.6645 5.2684 2000 200 -0.1113 -1.5044 5.0478 27.935205 3.7550 -3.6936 5.2684 2000 400 -0.1113 -1.5044 5.0478 28.366870 3.7271 -3.7218 5.2684 2000 600 -0.1113 -1.5044 5.0478 28.788420 3.6996 -3.7491 5.2684 2000 800 -0.1113 -1.5044 5.0478 29.200335 3.6726 -3.7756 5.2684 2000 1000 -0.1113 -1.5044 5.0478 29.603061 3.6459 -3.8013 5.2684 2000 1200 -0.1113 -1.5044 5.0478 29.997006 3.6197 -3.8263 5.2684 2000 1400 -0.1113 -1.5044 5.0478 30.382551 3.5939 -3.8506 5.2684 2000 1600 -0.1113 -1.5044 5.0478 30.760051 3.5684 -3.8742 5.2684 2000 1800 -0.1113 -1.5044 5.0478 31.129834 3.5433 -3.8971 5.2684 2000 2000 -0.1113 -1.5044 5.0478 31.492208 3.5186 -3.9195 5.2684 2000 2200 -0.1113 -1.5044 5.0478 31.847461 3.4942 -3.9412 5.2684 2000 2400 -0.1113 -1.5044 5.0478 32.195864 3.4702 -3.9624 5.2684 2000 2600 -0.1113 -1.5044 5.0478 32.537668 3.4465 -3.9830 5.2684 2000 2800 -0.1113 -1.5044 5.0478 32.873113 3.4231 -4.0031 5.2684 2000 3000 -0.1113 -1.5044 5.0478 33.202423 3.4001 -4.0227 5.2684 2000 3200 -0.1113 -1.5044 5.0478 33.525809 3.3773 -4.0419 5.2684 2000 3400 -0.1113 -1.5044 5.0478 33.843470 3.3548 -4.0605 5.2684 2000 3600 -0.1113 -1.5044 5.0478 34.155597 3.3327 -4.0788 5.2684 2000 3800 -0.1113 -1.5044 5.0478 34.462368 3.3108 -4.0965 5.2684 2000 4000 -0.1113 -1.5044 5.0478 34.763952 3.2892 -4.1139 5.2684 2000 4200 -0.1113 -1.5044 5.0478 35.060511 3.2678 -4.1309 5.2684 2000 4400 -0.1113 -1.5044 5.0478 35.352197 3.2468 -4.1475 5.2684 2000 4600 -0.1113 -1.5044 5.0478 35.639158 3.2260 -4.1637 5.2684 2000 4800 -0.1113 -1.5044 5.0478 35.921530 3.2054 -4.1795 5.2684 2000 5000 -0.1113 -1.5044 5.0478 36.199447 3.1851 -4.1950 5.2684 2000 5200 -0.1113 -1.5044 5.0478 36.473035 3.1650 -4.2102 5.2684 2000 5400 -0.1113 -1.5044 5.0478 36.742414 3.1452 -4.2250 5.2684 2000 5600 -0.1113 -1.5044 5.0478 37.007699 3.1256 -4.2395 5.2684 2000 5800 -0.1113 -1.5044 5.0478 37.269001 3.1062 -4.2538 5.2684 2000 6000 -0.1113 -1.5044 5.0478 37.526425 3.0871 -4.2677 5.2684 2000 6200 -0.1113 -1.5044 5.0478 37.780072 3.0682 -4.2813 5.2684 2000 6400 -0.1113 -1.5044 5.0478 38.030040 3.0494 -4.2946 5.2684 2000 6600 -0.1113 -1.5044 5.0478 38.276421 3.0309 -4.3077 5.2684 2000 6800 -0.1113 -1.5044 5.0478 38.519304 3.0127 -4.3205 5.2684 2000 7000 -0.1113 -1.5044 5.0478 38.758775 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-3.0327 5.3921 4000 6800 -0.1113 0.3797 5.3776 38.516433 4.4441 -3.0516 5.3921 4000 7000 -0.1113 0.3797 5.3776 38.755927 4.4314 -3.0701 5.3921 4000 7200 -0.1113 0.3797 5.3776 38.992092 4.4187 -3.0884 5.3921 4000 7400 -0.1113 0.3797 5.3776 39.225007 4.4061 -3.1063 5.3921 4000 7600 -0.1113 0.3797 5.3776 39.454748 4.3936 -3.1240 5.3921 4000 7800 -0.1113 0.3797 5.3776 39.681389 4.3812 -3.1413 5.3921 4000 8000 -0.1113 0.3797 5.3776 39.905001 4.3689 -3.1584 5.3921 4000 8200 -0.1113 0.3797 5.3776 40.125653 4.3567 -3.1752 5.3921 4000 8400 -0.1113 0.3797 5.3776 40.343411 4.3446 -3.1917 5.3921 4500 0 -0.1113 0.8507 5.4600 27.487555 5.2364 -1.7652 5.5270 4500 200 -0.1113 0.8507 5.4600 27.929948 5.2226 -1.8056 5.5270 4500 400 -0.1113 0.8507 5.4600 28.361699 5.2089 -1.8449 5.5270 4500 600 -0.1113 0.8507 5.4600 28.783331 5.1951 -1.8831 5.5270 4500 800 -0.1113 0.8507 5.4600 29.195326 5.1815 -1.9205 5.5270 4500 1000 -0.1113 0.8507 5.4600 29.598126 5.1678 -1.9568 5.5270 4500 1200 -0.1113 0.8507 5.4600 29.992143 5.1543 -1.9923 5.5270 4500 1400 -0.1113 0.8507 5.4600 30.377757 5.1407 -2.0270 5.5270 4500 1600 -0.1113 0.8507 5.4600 30.755322 5.1273 -2.0608 5.5270 4500 1800 -0.1113 0.8507 5.4600 31.125168 5.1139 -2.0939 5.5270 4500 2000 -0.1113 0.8507 5.4600 31.487603 5.1005 -2.1262 5.5270 4500 2200 -0.1113 0.8507 5.4600 31.842915 5.0872 -2.1578 5.5270 4500 2400 -0.1113 0.8507 5.4600 32.191373 5.0740 -2.1887 5.5270 4500 2600 -0.1113 0.8507 5.4600 32.533232 5.0609 -2.2189 5.5270 4500 2800 -0.1113 0.8507 5.4600 32.868729 5.0478 -2.2485 5.5270 4500 3000 -0.1113 0.8507 5.4600 33.198089 5.0348 -2.2775 5.5270 4500 3200 -0.1113 0.8507 5.4600 33.521523 5.0218 -2.3059 5.5270 4500 3400 -0.1113 0.8507 5.4600 33.839232 5.0090 -2.3337 5.5270 4500 3600 -0.1113 0.8507 5.4600 34.151404 4.9962 -2.3609 5.5270 4500 3800 -0.1113 0.8507 5.4600 34.458218 4.9835 -2.3877 5.5270 4500 4000 -0.1113 0.8507 5.4600 34.759845 4.9708 -2.4139 5.5270 4500 4200 -0.1113 0.8507 5.4600 35.056446 4.9582 -2.4396 5.5270 4500 4400 -0.1113 0.8507 5.4600 35.348172 4.9458 -2.4648 5.5270 4500 4600 -0.1113 0.8507 5.4600 35.635172 4.9334 -2.4895 5.5270 4500 4800 -0.1113 0.8507 5.4600 35.917582 4.9210 -2.5138 5.5270 4500 5000 -0.1113 0.8507 5.4600 36.195536 4.9088 -2.5377 5.5270 4500 5200 -0.1113 0.8507 5.4600 36.469160 4.8966 -2.5611 5.5270 4500 5400 -0.1113 0.8507 5.4600 36.738573 4.8845 -2.5841 5.5270 4500 5600 -0.1113 0.8507 5.4600 37.003892 4.8725 -2.6067 5.5270 4500 5800 -0.1113 0.8507 5.4600 37.265227 4.8605 -2.6289 5.5270 4500 6000 -0.1113 0.8507 5.4600 37.522684 4.8487 -2.6507 5.5270 4500 6200 -0.1113 0.8507 5.4600 37.776362 4.8369 -2.6721 5.5270 4500 6400 -0.1113 0.8507 5.4600 38.026360 4.8252 -2.6932 5.5270 4500 6600 -0.1113 0.8507 5.4600 38.272771 4.8136 -2.7139 5.5270 4500 6800 -0.1113 0.8507 5.4600 38.515683 4.8020 -2.7343 5.5270 4500 7000 -0.1113 0.8507 5.4600 38.755184 4.7905 -2.7544 5.5270 4500 7200 -0.1113 0.8507 5.4600 38.991354 4.7791 -2.7741 5.5270 4500 7400 -0.1113 0.8507 5.4600 39.224274 4.7678 -2.7935 5.5270 4500 7600 -0.1113 0.8507 5.4600 39.454021 4.7566 -2.8126 5.5270 4500 7800 -0.1113 0.8507 5.4600 39.680668 4.7454 -2.8314 5.5270 4500 8000 -0.1113 0.8507 5.4600 39.904285 4.7343 -2.8499 5.5270 4500 8200 -0.1113 0.8507 5.4600 40.124942 4.7233 -2.8681 5.5270 4500 8400 -0.1113 0.8507 5.4600 40.342705 4.7124 -2.8860 5.5270 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.000 ================================================ FILE: tests/test_data/cropA/geometry/20180307-20180506_VV_8rlks_base.par ================================================ initial_baseline(TCN): -0.0000004 -2.5497431 28.2483011 m m m initial_baseline_rate: 0.0000000 0.1254563 0.0490788 m/s m/s m/s precision_baseline(TCN): 0.0000000 -1.9353785 27.9022509 m m m precision_baseline_rate: 0.0000000 0.1116583 0.0582004 m/s m/s m/s unwrap_phase_constant: 0.00038 radians ================================================ FILE: tests/test_data/cropA/geometry/20180307-20180506_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): -0.000 -2.550 28.248 orbit baseline rate (TCN) (m/s): 0.000e+00 1.255e-01 4.908e-02 baseline vector (TCN) (m): 0.000 -1.935 27.902 baseline rate (TCN) (m/s): 0.000e+00 1.117e-01 5.820e-02 SLC-1 center baseline length (m): 27.9693 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 0.0000 -2.9776 27.3590 27.496815 22.8937 -15.2729 27.5206 0 200 0.0000 -2.9776 27.3590 27.939050 22.7751 -15.4491 27.5206 0 400 0.0000 -2.9776 27.3590 28.370652 22.6581 -15.6203 27.5206 0 600 0.0000 -2.9776 27.3590 28.792142 22.5426 -15.7865 27.5206 0 800 0.0000 -2.9776 27.3590 29.204001 22.4285 -15.9481 27.5206 0 1000 0.0000 -2.9776 27.3590 29.606672 22.3159 -16.1054 27.5206 0 1200 0.0000 -2.9776 27.3590 30.000566 22.2046 -16.2584 27.5206 0 1400 0.0000 -2.9776 27.3590 30.386062 22.0947 -16.4074 27.5206 0 1600 0.0000 -2.9776 27.3590 30.763514 21.9862 -16.5526 27.5206 0 1800 0.0000 -2.9776 27.3590 31.133251 21.8789 -16.6942 27.5206 0 2000 0.0000 -2.9776 27.3590 31.495582 21.7729 -16.8322 27.5206 0 2200 0.0000 -2.9776 27.3590 31.850793 21.6681 -16.9669 27.5206 0 2400 0.0000 -2.9776 27.3590 32.199155 21.5645 -17.0983 27.5206 0 2600 0.0000 -2.9776 27.3590 32.540921 21.4622 -17.2266 27.5206 0 2800 0.0000 -2.9776 27.3590 32.876328 21.3610 -17.3520 27.5206 0 3000 0.0000 -2.9776 27.3590 33.205602 21.2609 -17.4744 27.5206 0 3200 0.0000 -2.9776 27.3590 33.528952 21.1619 -17.5941 27.5206 0 3400 0.0000 -2.9776 27.3590 33.846580 21.0641 -17.7112 27.5206 0 3600 0.0000 -2.9776 27.3590 34.158674 20.9673 -17.8257 27.5206 0 3800 0.0000 -2.9776 27.3590 34.465413 20.8715 -17.9376 27.5206 0 4000 0.0000 -2.9776 27.3590 34.766966 20.7769 -18.0472 27.5206 0 4200 0.0000 -2.9776 27.3590 35.063495 20.6832 -18.1545 27.5206 0 4400 0.0000 -2.9776 27.3590 35.355153 20.5905 -18.2596 27.5206 0 4600 0.0000 -2.9776 27.3590 35.642085 20.4988 -18.3625 27.5206 0 4800 0.0000 -2.9776 27.3590 35.924431 20.4081 -18.4633 27.5206 0 5000 0.0000 -2.9776 27.3590 36.202321 20.3183 -18.5620 27.5206 0 5200 0.0000 -2.9776 27.3590 36.475883 20.2294 -18.6588 27.5206 0 5400 0.0000 -2.9776 27.3590 36.745237 20.1415 -18.7537 27.5206 0 5600 0.0000 -2.9776 27.3590 37.010498 20.0544 -18.8468 27.5206 0 5800 0.0000 -2.9776 27.3590 37.271776 19.9683 -18.9380 27.5206 0 6000 0.0000 -2.9776 27.3590 37.529176 19.8830 -19.0275 27.5206 0 6200 0.0000 -2.9776 27.3590 37.782801 19.7986 -19.1154 27.5206 0 6400 0.0000 -2.9776 27.3590 38.032746 19.7150 -19.2015 27.5206 0 6600 0.0000 -2.9776 27.3590 38.279105 19.6323 -19.2861 27.5206 0 6800 0.0000 -2.9776 27.3590 38.521968 19.5503 -19.3692 27.5206 0 7000 0.0000 -2.9776 27.3590 38.761419 19.4692 -19.4507 27.5206 0 7200 0.0000 -2.9776 27.3590 38.997541 19.3889 -19.5308 27.5206 0 7400 0.0000 -2.9776 27.3590 39.230415 19.3093 -19.6094 27.5206 0 7600 0.0000 -2.9776 27.3590 39.460116 19.2306 -19.6867 27.5206 0 7800 0.0000 -2.9776 27.3590 39.686717 19.1526 -19.7626 27.5206 0 8000 0.0000 -2.9776 27.3590 39.910291 19.0753 -19.8372 27.5206 0 8200 0.0000 -2.9776 27.3590 40.130905 18.9988 -19.9105 27.5206 0 8400 0.0000 -2.9776 27.3590 40.348626 18.9230 -19.9825 27.5206 500 0 0.0000 -2.7481 27.4786 27.495868 23.1060 -15.1241 27.6157 500 200 0.0000 -2.7481 27.4786 27.938119 22.9886 -15.3020 27.6157 500 400 0.0000 -2.7481 27.4786 28.369736 22.8726 -15.4748 27.6157 500 600 0.0000 -2.7481 27.4786 28.791241 22.7582 -15.6426 27.6157 500 800 0.0000 -2.7481 27.4786 29.203113 22.6452 -15.8058 27.6157 500 1000 0.0000 -2.7481 27.4786 29.605798 22.5335 -15.9646 27.6157 500 1200 0.0000 -2.7481 27.4786 29.999704 22.4232 -16.1191 27.6157 500 1400 0.0000 -2.7481 27.4786 30.385211 22.3143 -16.2696 27.6157 500 1600 0.0000 -2.7481 27.4786 30.762675 22.2066 -16.4163 27.6157 500 1800 0.0000 -2.7481 27.4786 31.132423 22.1002 -16.5592 27.6157 500 2000 0.0000 -2.7481 27.4786 31.494764 21.9950 -16.6987 27.6157 500 2200 0.0000 -2.7481 27.4786 31.849986 21.8911 -16.8347 27.6157 500 2400 0.0000 -2.7481 27.4786 32.198358 21.7883 -16.9675 27.6157 500 2600 0.0000 -2.7481 27.4786 32.540133 21.6867 -17.0972 27.6157 500 2800 0.0000 -2.7481 27.4786 32.875549 21.5863 -17.2238 27.6157 500 3000 0.0000 -2.7481 27.4786 33.204831 21.4869 -17.3476 27.6157 500 3200 0.0000 -2.7481 27.4786 33.528190 21.3887 -17.4686 27.6157 500 3400 0.0000 -2.7481 27.4786 33.845826 21.2915 -17.5869 27.6157 500 3600 0.0000 -2.7481 27.4786 34.157928 21.1954 -17.7026 27.6157 500 3800 0.0000 -2.7481 27.4786 34.464675 21.1003 -17.8158 27.6157 500 4000 0.0000 -2.7481 27.4786 34.766235 21.0062 -17.9267 27.6157 500 4200 0.0000 -2.7481 27.4786 35.062772 20.9132 -18.0351 27.6157 500 4400 0.0000 -2.7481 27.4786 35.354436 20.8211 -18.1414 27.6157 500 4600 0.0000 -2.7481 27.4786 35.641375 20.7300 -18.2454 27.6157 500 4800 0.0000 -2.7481 27.4786 35.923727 20.6398 -18.3473 27.6157 500 5000 0.0000 -2.7481 27.4786 36.201624 20.5506 -18.4472 27.6157 500 5200 0.0000 -2.7481 27.4786 36.475192 20.4623 -18.5451 27.6157 500 5400 0.0000 -2.7481 27.4786 36.744552 20.3749 -18.6411 27.6157 500 5600 0.0000 -2.7481 27.4786 37.009819 20.2883 -18.7353 27.6157 500 5800 0.0000 -2.7481 27.4786 37.271103 20.2027 -18.8276 27.6157 500 6000 0.0000 -2.7481 27.4786 37.528509 20.1179 -18.9182 27.6157 500 6200 0.0000 -2.7481 27.4786 37.782139 20.0340 -19.0070 27.6157 500 6400 0.0000 -2.7481 27.4786 38.032090 19.9509 -19.0942 27.6157 500 6600 0.0000 -2.7481 27.4786 38.278454 19.8686 -19.1799 27.6157 500 6800 0.0000 -2.7481 27.4786 38.521321 19.7871 -19.2639 27.6157 500 7000 0.0000 -2.7481 27.4786 38.760777 19.7064 -19.3464 27.6157 500 7200 0.0000 -2.7481 27.4786 38.996905 19.6265 -19.4275 27.6157 500 7400 0.0000 -2.7481 27.4786 39.229783 19.5474 -19.5071 27.6157 500 7600 0.0000 -2.7481 27.4786 39.459488 19.4690 -19.5853 27.6157 500 7800 0.0000 -2.7481 27.4786 39.686095 19.3914 -19.6621 27.6157 500 8000 0.0000 -2.7481 27.4786 39.909673 19.3145 -19.7377 27.6157 500 8200 0.0000 -2.7481 27.4786 40.130291 19.2384 -19.8119 27.6157 500 8400 0.0000 -2.7481 27.4786 40.348016 19.1630 -19.8849 27.6157 1000 0 0.0000 -2.5186 27.5983 27.494901 23.3183 -14.9754 27.7129 1000 200 0.0000 -2.5186 27.5983 27.937168 23.2020 -15.1549 27.7129 1000 400 0.0000 -2.5186 27.5983 28.368800 23.0872 -15.3293 27.7129 1000 600 0.0000 -2.5186 27.5983 28.790320 22.9738 -15.4987 27.7129 1000 800 0.0000 -2.5186 27.5983 29.202206 22.8618 -15.6635 27.7129 1000 1000 0.0000 -2.5186 27.5983 29.604904 22.7512 -15.8238 27.7129 1000 1200 0.0000 -2.5186 27.5983 29.998823 22.6418 -15.9798 27.7129 1000 1400 0.0000 -2.5186 27.5983 30.384343 22.5338 -16.1318 27.7129 1000 1600 0.0000 -2.5186 27.5983 30.761818 22.4270 -16.2799 27.7129 1000 1800 0.0000 -2.5186 27.5983 31.131578 22.3215 -16.4243 27.7129 1000 2000 0.0000 -2.5186 27.5983 31.493929 22.2172 -16.5651 27.7129 1000 2200 0.0000 -2.5186 27.5983 31.849161 22.1141 -16.7026 27.7129 1000 2400 0.0000 -2.5186 27.5983 32.197543 22.0121 -16.8367 27.7129 1000 2600 0.0000 -2.5186 27.5983 32.539328 21.9113 -16.9677 27.7129 1000 2800 0.0000 -2.5186 27.5983 32.874754 21.8116 -17.0957 27.7129 1000 3000 0.0000 -2.5186 27.5983 33.204045 21.7129 -17.2208 27.7129 1000 3200 0.0000 -2.5186 27.5983 33.527412 21.6154 -17.3431 27.7129 1000 3400 0.0000 -2.5186 27.5983 33.845057 21.5189 -17.4626 27.7129 1000 3600 0.0000 -2.5186 27.5983 34.157166 21.4235 -17.5796 27.7129 1000 3800 0.0000 -2.5186 27.5983 34.463921 21.3291 -17.6940 27.7129 1000 4000 0.0000 -2.5186 27.5983 34.765489 21.2356 -17.8060 27.7129 1000 4200 0.0000 -2.5186 27.5983 35.062033 21.1432 -17.9157 27.7129 1000 4400 0.0000 -2.5186 27.5983 35.353705 21.0517 -18.0231 27.7129 1000 4600 0.0000 -2.5186 27.5983 35.640651 20.9612 -18.1283 27.7129 1000 4800 0.0000 -2.5186 27.5983 35.923009 20.8716 -18.2314 27.7129 1000 5000 0.0000 -2.5186 27.5983 36.200913 20.7829 -18.3324 27.7129 1000 5200 0.0000 -2.5186 27.5983 36.474487 20.6951 -18.4314 27.7129 1000 5400 0.0000 -2.5186 27.5983 36.743853 20.6083 -18.5285 27.7129 1000 5600 0.0000 -2.5186 27.5983 37.009126 20.5223 -18.6237 27.7129 1000 5800 0.0000 -2.5186 27.5983 37.270416 20.4371 -18.7171 27.7129 1000 6000 0.0000 -2.5186 27.5983 37.527828 20.3528 -18.8088 27.7129 1000 6200 0.0000 -2.5186 27.5983 37.781464 20.2694 -18.8987 27.7129 1000 6400 0.0000 -2.5186 27.5983 38.031420 20.1867 -18.9869 27.7129 1000 6600 0.0000 -2.5186 27.5983 38.277789 20.1049 -19.0736 27.7129 1000 6800 0.0000 -2.5186 27.5983 38.520662 20.0239 -19.1586 27.7129 1000 7000 0.0000 -2.5186 27.5983 38.760123 19.9436 -19.2421 27.7129 1000 7200 0.0000 -2.5186 27.5983 38.996255 19.8641 -19.3242 27.7129 1000 7400 0.0000 -2.5186 27.5983 39.229138 19.7854 -19.4047 27.7129 1000 7600 0.0000 -2.5186 27.5983 39.458848 19.7075 -19.4839 27.7129 1000 7800 0.0000 -2.5186 27.5983 39.685459 19.6303 -19.5617 27.7129 1000 8000 0.0000 -2.5186 27.5983 39.909042 19.5538 -19.6382 27.7129 1000 8200 0.0000 -2.5186 27.5983 40.129665 19.4780 -19.7133 27.7129 1000 8400 0.0000 -2.5186 27.5983 40.347394 19.4030 -19.7872 27.7129 1500 0 0.0000 -2.2891 27.7179 27.493913 23.5307 -14.8266 27.8123 1500 200 0.0000 -2.2891 27.7179 27.936197 23.4155 -15.0078 27.8123 1500 400 0.0000 -2.2891 27.7179 28.367845 23.3018 -15.1838 27.8123 1500 600 0.0000 -2.2891 27.7179 28.789379 23.1895 -15.3548 27.8123 1500 800 0.0000 -2.2891 27.7179 29.201280 23.0785 -15.5211 27.8123 1500 1000 0.0000 -2.2891 27.7179 29.603992 22.9688 -15.6830 27.8123 1500 1200 0.0000 -2.2891 27.7179 29.997923 22.8604 -15.8405 27.8123 1500 1400 0.0000 -2.2891 27.7179 30.383456 22.7533 -15.9940 27.8123 1500 1600 0.0000 -2.2891 27.7179 30.760943 22.6475 -16.1435 27.8123 1500 1800 0.0000 -2.2891 27.7179 31.130714 22.5428 -16.2894 27.8123 1500 2000 0.0000 -2.2891 27.7179 31.493077 22.4393 -16.4316 27.8123 1500 2200 0.0000 -2.2891 27.7179 31.848320 22.3370 -16.5704 27.8123 1500 2400 0.0000 -2.2891 27.7179 32.196712 22.2359 -16.7059 27.8123 1500 2600 0.0000 -2.2891 27.7179 32.538506 22.1358 -16.8383 27.8123 1500 2800 0.0000 -2.2891 27.7179 32.873942 22.0369 -16.9676 27.8123 1500 3000 0.0000 -2.2891 27.7179 33.203242 21.9390 -17.0939 27.8123 1500 3200 0.0000 -2.2891 27.7179 33.526618 21.8421 -17.2175 27.8123 1500 3400 0.0000 -2.2891 27.7179 33.844271 21.7464 -17.3383 27.8123 1500 3600 0.0000 -2.2891 27.7179 34.156389 21.6516 -17.4565 27.8123 1500 3800 0.0000 -2.2891 27.7179 34.463152 21.5578 -17.5722 27.8123 1500 4000 0.0000 -2.2891 27.7179 34.764728 21.4650 -17.6854 27.8123 1500 4200 0.0000 -2.2891 27.7179 35.061279 21.3732 -17.7963 27.8123 1500 4400 0.0000 -2.2891 27.7179 35.352959 21.2823 -17.9049 27.8123 1500 4600 0.0000 -2.2891 27.7179 35.639912 21.1924 -18.0112 27.8123 1500 4800 0.0000 -2.2891 27.7179 35.922277 21.1034 -18.1155 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33.521523 23.2026 -16.4639 28.4503 4500 3400 0.0000 -0.9119 28.4357 33.839232 23.1110 -16.5923 28.4503 4500 3600 0.0000 -0.9119 28.4357 34.151404 23.0202 -16.7180 28.4503 4500 3800 0.0000 -0.9119 28.4357 34.458218 22.9304 -16.8410 28.4503 4500 4000 0.0000 -0.9119 28.4357 34.759845 22.8414 -16.9615 28.4503 4500 4200 0.0000 -0.9119 28.4357 35.056446 22.7533 -17.0795 28.4503 4500 4400 0.0000 -0.9119 28.4357 35.348172 22.6660 -17.1951 28.4503 4500 4600 0.0000 -0.9119 28.4357 35.635172 22.5796 -17.3084 28.4503 4500 4800 0.0000 -0.9119 28.4357 35.917582 22.4940 -17.4195 28.4503 4500 5000 0.0000 -0.9119 28.4357 36.195536 22.4092 -17.5284 28.4503 4500 5200 0.0000 -0.9119 28.4357 36.469160 22.3253 -17.6353 28.4503 4500 5400 0.0000 -0.9119 28.4357 36.738573 22.2421 -17.7400 28.4503 4500 5600 0.0000 -0.9119 28.4357 37.003892 22.1597 -17.8429 28.4503 4500 5800 0.0000 -0.9119 28.4357 37.265227 22.0781 -17.9437 28.4503 4500 6000 0.0000 -0.9119 28.4357 37.522684 21.9973 -18.0428 28.4503 4500 6200 0.0000 -0.9119 28.4357 37.776362 21.9172 -18.1400 28.4503 4500 6400 0.0000 -0.9119 28.4357 38.026360 21.8378 -18.2354 28.4503 4500 6600 0.0000 -0.9119 28.4357 38.272771 21.7592 -18.3292 28.4503 4500 6800 0.0000 -0.9119 28.4357 38.515683 21.6813 -18.4213 28.4503 4500 7000 0.0000 -0.9119 28.4357 38.755184 21.6041 -18.5117 28.4503 4500 7200 0.0000 -0.9119 28.4357 38.991354 21.5276 -18.6006 28.4503 4500 7400 0.0000 -0.9119 28.4357 39.224274 21.4518 -18.6880 28.4503 4500 7600 0.0000 -0.9119 28.4357 39.454021 21.3767 -18.7739 28.4503 4500 7800 0.0000 -0.9119 28.4357 39.680668 21.3023 -18.8583 28.4503 4500 8000 0.0000 -0.9119 28.4357 39.904285 21.2285 -18.9413 28.4503 4500 8200 0.0000 -0.9119 28.4357 40.124942 21.1554 -19.0229 28.4503 4500 8400 0.0000 -0.9119 28.4357 40.342705 21.0829 -19.1032 28.4503 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.010 ================================================ FILE: tests/test_data/cropA/geometry/20180307-20180530_VV_8rlks_base.par ================================================ initial_baseline(TCN): -0.6454202 33.7680109 43.0365483 m m m initial_baseline_rate: 0.0000000 0.1681843 0.0413736 m/s m/s m/s precision_baseline(TCN): -0.6454202 33.7680109 43.0365483 m m m precision_baseline_rate: 0.0000000 0.1681843 0.0413736 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/cropA/geometry/20180307-20180530_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): -0.645 33.768 43.037 orbit baseline rate (TCN) (m/s): 0.000e+00 1.682e-01 4.137e-02 baseline vector (TCN) (m): -0.645 33.768 43.037 baseline rate (TCN) (m/s): 0.000e+00 1.682e-01 4.137e-02 SLC-1 center baseline length (m): 54.7069 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 -0.6454 32.1981 42.6504 27.496815 52.6990 8.8706 53.4433 0 200 -0.6454 32.1981 42.6504 27.939050 52.7658 8.4635 53.4433 0 400 -0.6454 32.1981 42.6504 28.370652 52.8281 8.0658 53.4433 0 600 -0.6454 32.1981 42.6504 28.792142 52.8860 7.6770 53.4433 0 800 -0.6454 32.1981 42.6504 29.204001 52.9398 7.2966 53.4433 0 1000 -0.6454 32.1981 42.6504 29.606672 52.9898 6.9243 53.4433 0 1200 -0.6454 32.1981 42.6504 30.000566 53.0361 6.5599 53.4433 0 1400 -0.6454 32.1981 42.6504 30.386062 53.0790 6.2029 53.4433 0 1600 -0.6454 32.1981 42.6504 30.763514 53.1187 5.8531 53.4433 0 1800 -0.6454 32.1981 42.6504 31.133251 53.1554 5.5101 53.4433 0 2000 -0.6454 32.1981 42.6504 31.495582 53.1892 5.1739 53.4433 0 2200 -0.6454 32.1981 42.6504 31.850793 53.2202 4.8440 53.4433 0 2400 -0.6454 32.1981 42.6504 32.199155 53.2487 4.5203 53.4433 0 2600 -0.6454 32.1981 42.6504 32.540921 53.2747 4.2026 53.4433 0 2800 -0.6454 32.1981 42.6504 32.876328 53.2984 3.8907 53.4433 0 3000 -0.6454 32.1981 42.6504 33.205602 53.3198 3.5843 53.4433 0 3200 -0.6454 32.1981 42.6504 33.528952 53.3392 3.2833 53.4433 0 3400 -0.6454 32.1981 42.6504 33.846580 53.3566 2.9876 53.4433 0 3600 -0.6454 32.1981 42.6504 34.158674 53.3721 2.6969 53.4433 0 3800 -0.6454 32.1981 42.6504 34.465413 53.3857 2.4111 53.4433 0 4000 -0.6454 32.1981 42.6504 34.766966 53.3977 2.1301 53.4433 0 4200 -0.6454 32.1981 42.6504 35.063495 53.4080 1.8537 53.4433 0 4400 -0.6454 32.1981 42.6504 35.355153 53.4167 1.5818 53.4433 0 4600 -0.6454 32.1981 42.6504 35.642085 53.4240 1.3143 53.4433 0 4800 -0.6454 32.1981 42.6504 35.924431 53.4298 1.0510 53.4433 0 5000 -0.6454 32.1981 42.6504 36.202321 53.4342 0.7918 53.4433 0 5200 -0.6454 32.1981 42.6504 36.475883 53.4374 0.5367 53.4433 0 5400 -0.6454 32.1981 42.6504 36.745237 53.4393 0.2855 53.4433 0 5600 -0.6454 32.1981 42.6504 37.010498 53.4401 0.0380 53.4433 0 5800 -0.6454 32.1981 42.6504 37.271776 53.4397 -0.2057 53.4433 0 6000 -0.6454 32.1981 42.6504 37.529176 53.4382 -0.4457 53.4433 0 6200 -0.6454 32.1981 42.6504 37.782801 53.4357 -0.6823 53.4433 0 6400 -0.6454 32.1981 42.6504 38.032746 53.4322 -0.9154 53.4433 0 6600 -0.6454 32.1981 42.6504 38.279105 53.4278 -1.1451 53.4433 0 6800 -0.6454 32.1981 42.6504 38.521968 53.4225 -1.3716 53.4433 0 7000 -0.6454 32.1981 42.6504 38.761419 53.4163 -1.5949 53.4433 0 7200 -0.6454 32.1981 42.6504 38.997541 53.4092 -1.8150 53.4433 0 7400 -0.6454 32.1981 42.6504 39.230415 53.4014 -2.0321 53.4433 0 7600 -0.6454 32.1981 42.6504 39.460116 53.3928 -2.2461 53.4433 0 7800 -0.6454 32.1981 42.6504 39.686717 53.3835 -2.4573 53.4433 0 8000 -0.6454 32.1981 42.6504 39.910291 53.3735 -2.6656 53.4433 0 8200 -0.6454 32.1981 42.6504 40.130905 53.3629 -2.8711 53.4433 0 8400 -0.6454 32.1981 42.6504 40.348626 53.3516 -3.0738 53.4433 500 0 -0.6454 32.5438 42.7354 27.495868 52.9339 9.1388 53.7199 500 200 -0.6454 32.5438 42.7354 27.938119 53.0028 8.7300 53.7199 500 400 -0.6454 32.5438 42.7354 28.369736 53.0671 8.3304 53.7199 500 600 -0.6454 32.5438 42.7354 28.791241 53.1269 7.9398 53.7199 500 800 -0.6454 32.5438 42.7354 29.203113 53.1826 7.5577 53.7199 500 1000 -0.6454 32.5438 42.7354 29.605798 53.2344 7.1837 53.7199 500 1200 -0.6454 32.5438 42.7354 29.999704 53.2825 6.8175 53.7199 500 1400 -0.6454 32.5438 42.7354 30.385211 53.3272 6.4589 53.7199 500 1600 -0.6454 32.5438 42.7354 30.762675 53.3685 6.1074 53.7199 500 1800 -0.6454 32.5438 42.7354 31.132423 53.4068 5.7629 53.7199 500 2000 -0.6454 32.5438 42.7354 31.494764 53.4422 5.4250 53.7199 500 2200 -0.6454 32.5438 42.7354 31.849986 53.4748 5.0935 53.7199 500 2400 -0.6454 32.5438 42.7354 32.198358 53.5048 4.7683 53.7199 500 2600 -0.6454 32.5438 42.7354 32.540133 53.5323 4.4490 53.7199 500 2800 -0.6454 32.5438 42.7354 32.875549 53.5574 4.1356 53.7199 500 3000 -0.6454 32.5438 42.7354 33.204831 53.5803 3.8277 53.7199 500 3200 -0.6454 32.5438 42.7354 33.528190 53.6010 3.5252 53.7199 500 3400 -0.6454 32.5438 42.7354 33.845826 53.6197 3.2280 53.7199 500 3600 -0.6454 32.5438 42.7354 34.157928 53.6365 2.9359 53.7199 500 3800 -0.6454 32.5438 42.7354 34.464675 53.6514 2.6487 53.7199 500 4000 -0.6454 32.5438 42.7354 34.766235 53.6646 2.3663 53.7199 500 4200 -0.6454 32.5438 42.7354 35.062772 53.6762 2.0885 53.7199 500 4400 -0.6454 32.5438 42.7354 35.354436 53.6861 1.8152 53.7199 500 4600 -0.6454 32.5438 42.7354 35.641375 53.6945 1.5463 53.7199 500 4800 -0.6454 32.5438 42.7354 35.923727 53.7015 1.2817 53.7199 500 5000 -0.6454 32.5438 42.7354 36.201624 53.7070 1.0212 53.7199 500 5200 -0.6454 32.5438 42.7354 36.475192 53.7113 0.7648 53.7199 500 5400 -0.6454 32.5438 42.7354 36.744552 53.7143 0.5122 53.7199 500 5600 -0.6454 32.5438 42.7354 37.009819 53.7161 0.2635 53.7199 500 5800 -0.6454 32.5438 42.7354 37.271103 53.7167 0.0186 53.7199 500 6000 -0.6454 32.5438 42.7354 37.528509 53.7163 -0.2228 53.7199 500 6200 -0.6454 32.5438 42.7354 37.782139 53.7148 -0.4606 53.7199 500 6400 -0.6454 32.5438 42.7354 38.032090 53.7122 -0.6949 53.7199 500 6600 -0.6454 32.5438 42.7354 38.278454 53.7087 -0.9258 53.7199 500 6800 -0.6454 32.5438 42.7354 38.521321 53.7043 -1.1535 53.7199 500 7000 -0.6454 32.5438 42.7354 38.760777 53.6990 -1.3779 53.7199 500 7200 -0.6454 32.5438 42.7354 38.996905 53.6929 -1.5992 53.7199 500 7400 -0.6454 32.5438 42.7354 39.229783 53.6859 -1.8175 53.7199 500 7600 -0.6454 32.5438 42.7354 39.459488 53.6782 -2.0327 53.7199 500 7800 -0.6454 32.5438 42.7354 39.686095 53.6698 -2.2450 53.7199 500 8000 -0.6454 32.5438 42.7354 39.909673 53.6606 -2.4544 53.7199 500 8200 -0.6454 32.5438 42.7354 40.130291 53.6507 -2.6610 53.7199 500 8400 -0.6454 32.5438 42.7354 40.348016 53.6402 -2.8649 53.7199 1000 0 -0.6454 32.8896 42.8204 27.494901 53.1687 9.4071 53.9975 1000 200 -0.6454 32.8896 42.8204 27.937168 53.2398 8.9964 53.9975 1000 400 -0.6454 32.8896 42.8204 28.368800 53.3060 8.5951 53.9975 1000 600 -0.6454 32.8896 42.8204 28.790320 53.3678 8.2027 53.9975 1000 800 -0.6454 32.8896 42.8204 29.202206 53.4254 7.8188 53.9975 1000 1000 -0.6454 32.8896 42.8204 29.604904 53.4790 7.4431 53.9975 1000 1200 -0.6454 32.8896 42.8204 29.998823 53.5289 7.0752 53.9975 1000 1400 -0.6454 32.8896 42.8204 30.384343 53.5753 6.7149 53.9975 1000 1600 -0.6454 32.8896 42.8204 30.761818 53.6184 6.3618 53.9975 1000 1800 -0.6454 32.8896 42.8204 31.131578 53.6583 6.0156 53.9975 1000 2000 -0.6454 32.8896 42.8204 31.493929 53.6953 5.6761 53.9975 1000 2200 -0.6454 32.8896 42.8204 31.849161 53.7294 5.3431 53.9975 1000 2400 -0.6454 32.8896 42.8204 32.197543 53.7609 5.0163 53.9975 1000 2600 -0.6454 32.8896 42.8204 32.539328 53.7899 4.6955 53.9975 1000 2800 -0.6454 32.8896 42.8204 32.874754 53.8164 4.3805 53.9975 1000 3000 -0.6454 32.8896 42.8204 33.204045 53.8407 4.0711 53.9975 1000 3200 -0.6454 32.8896 42.8204 33.527412 53.8628 3.7672 53.9975 1000 3400 -0.6454 32.8896 42.8204 33.845057 53.8829 3.4685 53.9975 1000 3600 -0.6454 32.8896 42.8204 34.157166 53.9009 3.1749 53.9975 1000 3800 -0.6454 32.8896 42.8204 34.463921 53.9172 2.8863 53.9975 1000 4000 -0.6454 32.8896 42.8204 34.765489 53.9316 2.6025 53.9975 1000 4200 -0.6454 32.8896 42.8204 35.062033 53.9443 2.3233 53.9975 1000 4400 -0.6454 32.8896 42.8204 35.353705 53.9555 2.0487 53.9975 1000 4600 -0.6454 32.8896 42.8204 35.640651 53.9650 1.7784 53.9975 1000 4800 -0.6454 32.8896 42.8204 35.923009 53.9732 1.5124 53.9975 1000 5000 -0.6454 32.8896 42.8204 36.200913 53.9798 1.2506 53.9975 1000 5200 -0.6454 32.8896 42.8204 36.474487 53.9852 0.9929 53.9975 1000 5400 -0.6454 32.8896 42.8204 36.743853 53.9893 0.7390 53.9975 1000 5600 -0.6454 32.8896 42.8204 37.009126 53.9921 0.4891 53.9975 1000 5800 -0.6454 32.8896 42.8204 37.270416 53.9938 0.2428 53.9975 1000 6000 -0.6454 32.8896 42.8204 37.527828 53.9943 0.0002 53.9975 1000 6200 -0.6454 32.8896 42.8204 37.781464 53.9938 -0.2388 53.9975 1000 6400 -0.6454 32.8896 42.8204 38.031420 53.9922 -0.4743 53.9975 1000 6600 -0.6454 32.8896 42.8204 38.277789 53.9897 -0.7065 53.9975 1000 6800 -0.6454 32.8896 42.8204 38.520662 53.9862 -0.9354 53.9975 1000 7000 -0.6454 32.8896 42.8204 38.760123 53.9818 -1.1610 53.9975 1000 7200 -0.6454 32.8896 42.8204 38.996255 53.9766 -1.3835 53.9975 1000 7400 -0.6454 32.8896 42.8204 39.229138 53.9705 -1.6028 53.9975 1000 7600 -0.6454 32.8896 42.8204 39.458848 53.9636 -1.8192 53.9975 1000 7800 -0.6454 32.8896 42.8204 39.685459 53.9560 -2.0326 53.9975 1000 8000 -0.6454 32.8896 42.8204 39.909042 53.9476 -2.2432 53.9975 1000 8200 -0.6454 32.8896 42.8204 40.129665 53.9386 -2.4509 53.9975 1000 8400 -0.6454 32.8896 42.8204 40.347394 53.9289 -2.6559 53.9975 1500 0 -0.6454 33.2353 42.9055 27.493913 53.4036 9.6755 54.2760 1500 200 -0.6454 33.2353 42.9055 27.936197 53.4767 9.2629 54.2760 1500 400 -0.6454 33.2353 42.9055 28.367845 53.5450 8.8598 54.2760 1500 600 -0.6454 33.2353 42.9055 28.789379 53.6087 8.4656 54.2760 1500 800 -0.6454 33.2353 42.9055 29.201280 53.6682 8.0799 54.2760 1500 1000 -0.6454 33.2353 42.9055 29.603992 53.7236 7.7025 54.2760 1500 1200 -0.6454 33.2353 42.9055 29.997923 53.7753 7.3330 54.2760 1500 1400 -0.6454 33.2353 42.9055 30.383456 53.8234 6.9709 54.2760 1500 1600 -0.6454 33.2353 42.9055 30.760943 53.8682 6.6162 54.2760 1500 1800 -0.6454 33.2353 42.9055 31.130714 53.9097 6.2684 54.2760 1500 2000 -0.6454 33.2353 42.9055 31.493077 53.9483 5.9273 54.2760 1500 2200 -0.6454 33.2353 42.9055 31.848320 53.9840 5.5927 54.2760 1500 2400 -0.6454 33.2353 42.9055 32.196712 54.0170 5.2643 54.2760 1500 2600 -0.6454 33.2353 42.9055 32.538506 54.0474 4.9420 54.2760 1500 2800 -0.6454 33.2353 42.9055 32.873942 54.0754 4.6255 54.2760 1500 3000 -0.6454 33.2353 42.9055 33.203242 54.1011 4.3146 54.2760 1500 3200 -0.6454 33.2353 42.9055 33.526618 54.1246 4.0092 54.2760 1500 3400 -0.6454 33.2353 42.9055 33.844271 54.1460 3.7090 54.2760 1500 3600 -0.6454 33.2353 42.9055 34.156389 54.1654 3.4140 54.2760 1500 3800 -0.6454 33.2353 42.9055 34.463152 54.1829 3.1239 54.2760 1500 4000 -0.6454 33.2353 42.9055 34.764728 54.1986 2.8387 54.2760 1500 4200 -0.6454 33.2353 42.9055 35.061279 54.2125 2.5581 54.2760 1500 4400 -0.6454 33.2353 42.9055 35.352959 54.2248 2.2821 54.2760 1500 4600 -0.6454 33.2353 42.9055 35.639912 54.2356 2.0105 54.2760 1500 4800 -0.6454 33.2353 42.9055 35.922277 54.2448 1.7432 54.2760 1500 5000 -0.6454 33.2353 42.9055 36.200187 54.2526 1.4800 54.2760 1500 5200 -0.6454 33.2353 42.9055 36.473768 54.2591 1.2210 54.2760 1500 5400 -0.6454 33.2353 42.9055 36.743141 54.2642 0.9659 54.2760 1500 5600 -0.6454 33.2353 42.9055 37.008420 54.2681 0.7146 54.2760 1500 5800 -0.6454 33.2353 42.9055 37.269715 54.2708 0.4671 54.2760 1500 6000 -0.6454 33.2353 42.9055 37.527133 54.2723 0.2233 54.2760 1500 6200 -0.6454 33.2353 42.9055 37.780775 54.2728 -0.0170 54.2760 1500 6400 -0.6454 33.2353 42.9055 38.030736 54.2722 -0.2538 54.2760 1500 6600 -0.6454 33.2353 42.9055 38.277111 54.2706 -0.4872 54.2760 1500 6800 -0.6454 33.2353 42.9055 38.519989 54.2680 -0.7172 54.2760 1500 7000 -0.6454 33.2353 42.9055 38.759456 54.2646 -0.9440 54.2760 1500 7200 -0.6454 33.2353 42.9055 38.995593 54.2602 -1.1677 54.2760 1500 7400 -0.6454 33.2353 42.9055 39.228481 54.2550 -1.3882 54.2760 1500 7600 -0.6454 33.2353 42.9055 39.458196 54.2490 -1.6057 54.2760 1500 7800 -0.6454 33.2353 42.9055 39.684812 54.2422 -1.8203 54.2760 1500 8000 -0.6454 33.2353 42.9055 39.908399 54.2347 -2.0319 54.2760 1500 8200 -0.6454 33.2353 42.9055 40.129027 54.2265 -2.2408 54.2760 1500 8400 -0.6454 33.2353 42.9055 40.346760 54.2176 -2.4468 54.2760 2000 0 -0.6454 33.5810 42.9905 27.492904 53.6385 9.9438 54.5553 2000 200 -0.6454 33.5810 42.9905 27.935205 53.7136 9.5294 54.5553 2000 400 -0.6454 33.5810 42.9905 28.366870 53.7839 9.1245 54.5553 2000 600 -0.6454 33.5810 42.9905 28.788420 53.8496 8.7285 54.5553 2000 800 -0.6454 33.5810 42.9905 29.200335 53.9109 8.3411 54.5553 2000 1000 -0.6454 33.5810 42.9905 29.603061 53.9682 7.9620 54.5553 2000 1200 -0.6454 33.5810 42.9905 29.997006 54.0217 7.5907 54.5553 2000 1400 -0.6454 33.5810 42.9905 30.382551 54.0715 7.2270 54.5553 2000 1600 -0.6454 33.5810 42.9905 30.760051 54.1179 6.8706 54.5553 2000 1800 -0.6454 33.5810 42.9905 31.129834 54.1612 6.5212 54.5553 2000 2000 -0.6454 33.5810 42.9905 31.492208 54.2013 6.1785 54.5553 2000 2200 -0.6454 33.5810 42.9905 31.847461 54.2386 5.8423 54.5553 2000 2400 -0.6454 33.5810 42.9905 32.195864 54.2731 5.5123 54.5553 2000 2600 -0.6454 33.5810 42.9905 32.537668 54.3050 5.1885 54.5553 2000 2800 -0.6454 33.5810 42.9905 32.873113 54.3344 4.8704 54.5553 2000 3000 -0.6454 33.5810 42.9905 33.202423 54.3615 4.5581 54.5553 2000 3200 -0.6454 33.5810 42.9905 33.525809 54.3864 4.2512 54.5553 2000 3400 -0.6454 33.5810 42.9905 33.843470 54.4091 3.9495 54.5553 2000 3600 -0.6454 33.5810 42.9905 34.155597 54.4298 3.6531 54.5553 2000 3800 -0.6454 33.5810 42.9905 34.462368 54.4486 3.3616 54.5553 2000 4000 -0.6454 33.5810 42.9905 34.763952 54.4655 3.0749 54.5553 2000 4200 -0.6454 33.5810 42.9905 35.060511 54.4807 2.7930 54.5553 2000 4400 -0.6454 33.5810 42.9905 35.352197 54.4942 2.5156 54.5553 2000 4600 -0.6454 33.5810 42.9905 35.639158 54.5061 2.2426 54.5553 2000 4800 -0.6454 33.5810 42.9905 35.921530 54.5165 1.9740 54.5553 2000 5000 -0.6454 33.5810 42.9905 36.199447 54.5254 1.7095 54.5553 2000 5200 -0.6454 33.5810 42.9905 36.473035 54.5330 1.4491 54.5553 2000 5400 -0.6454 33.5810 42.9905 36.742414 54.5392 1.1927 54.5553 2000 5600 -0.6454 33.5810 42.9905 37.007699 54.5441 0.9402 54.5553 2000 5800 -0.6454 33.5810 42.9905 37.269001 54.5478 0.6914 54.5553 2000 6000 -0.6454 33.5810 42.9905 37.526425 54.5504 0.4463 54.5553 2000 6200 -0.6454 33.5810 42.9905 37.780072 54.5518 0.2048 54.5553 2000 6400 -0.6454 33.5810 42.9905 38.030040 54.5522 -0.0332 54.5553 2000 6600 -0.6454 33.5810 42.9905 38.276421 54.5515 -0.2678 54.5553 2000 6800 -0.6454 33.5810 42.9905 38.519304 54.5499 -0.4990 54.5553 2000 7000 -0.6454 33.5810 42.9905 38.758775 54.5473 -0.7270 54.5553 2000 7200 -0.6454 33.5810 42.9905 38.994918 54.5439 -0.9519 54.5553 2000 7400 -0.6454 33.5810 42.9905 39.227811 54.5395 -1.1736 54.5553 2000 7600 -0.6454 33.5810 42.9905 39.457531 54.5344 -1.3922 54.5553 2000 7800 -0.6454 33.5810 42.9905 39.684152 54.5284 -1.6079 54.5553 2000 8000 -0.6454 33.5810 42.9905 39.907744 54.5218 -1.8207 54.5553 2000 8200 -0.6454 33.5810 42.9905 40.128376 54.5143 -2.0306 54.5553 2000 8400 -0.6454 33.5810 42.9905 40.346114 54.5062 -2.2378 54.5553 2500 0 -0.6454 33.9267 43.0756 27.491875 53.8733 10.2122 54.8356 2500 200 -0.6454 33.9267 43.0756 27.934194 53.9506 9.7960 54.8356 2500 400 -0.6454 33.9267 43.0756 28.365875 54.0228 9.3892 54.8356 2500 600 -0.6454 33.9267 43.0756 28.787440 54.0904 8.9915 54.8356 2500 800 -0.6454 33.9267 43.0756 29.199371 54.1537 8.6023 54.8356 2500 1000 -0.6454 33.9267 43.0756 29.602111 54.2128 8.2214 54.8356 2500 1200 -0.6454 33.9267 43.0756 29.996070 54.2680 7.8485 54.8356 2500 1400 -0.6454 33.9267 43.0756 30.381628 54.3196 7.4831 54.8356 2500 1600 -0.6454 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-0.6454 33.9267 43.0756 35.638389 54.7766 2.4748 54.8356 2500 4800 -0.6454 33.9267 43.0756 35.920769 54.7882 2.2048 54.8356 2500 5000 -0.6454 33.9267 43.0756 36.198693 54.7982 1.9390 54.8356 2500 5200 -0.6454 33.9267 43.0756 36.472288 54.8068 1.6773 54.8356 2500 5400 -0.6454 33.9267 43.0756 36.741674 54.8141 1.4196 54.8356 2500 5600 -0.6454 33.9267 43.0756 37.006965 54.8201 1.1657 54.8356 2500 5800 -0.6454 33.9267 43.0756 37.268273 54.8248 0.9157 54.8356 2500 6000 -0.6454 33.9267 43.0756 37.525704 54.8284 0.6694 54.8356 2500 6200 -0.6454 33.9267 43.0756 37.779357 54.8308 0.4266 54.8356 2500 6400 -0.6454 33.9267 43.0756 38.029330 54.8321 0.1874 54.8356 2500 6600 -0.6454 33.9267 43.0756 38.275717 54.8324 -0.0484 54.8356 2500 6800 -0.6454 33.9267 43.0756 38.518605 54.8317 -0.2809 54.8356 2500 7000 -0.6454 33.9267 43.0756 38.758083 54.8301 -0.5100 54.8356 2500 7200 -0.6454 33.9267 43.0756 38.994231 54.8275 -0.7360 54.8356 2500 7400 -0.6454 33.9267 43.0756 39.227129 54.8241 -0.9589 54.8356 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43.2457 31.844784 55.0022 6.5912 55.3987 3500 2400 -0.6454 34.6181 43.2457 32.193219 55.0413 6.2566 55.3987 3500 2600 -0.6454 34.6181 43.2457 32.535056 55.0776 5.9281 55.3987 3500 2800 -0.6454 34.6181 43.2457 32.870531 55.1114 5.6055 55.3987 3500 3000 -0.6454 34.6181 43.2457 33.199870 55.1427 5.2886 55.3987 3500 3200 -0.6454 34.6181 43.2457 33.523284 55.1717 4.9773 55.3987 3500 3400 -0.6454 34.6181 43.2457 33.840974 55.1984 4.6713 55.3987 3500 3600 -0.6454 34.6181 43.2457 34.153127 55.2230 4.3705 55.3987 3500 3800 -0.6454 34.6181 43.2457 34.459923 55.2456 4.0747 55.3987 3500 4000 -0.6454 34.6181 43.2457 34.761533 55.2663 3.7838 55.3987 3500 4200 -0.6454 34.6181 43.2457 35.058116 55.2852 3.4977 55.3987 3500 4400 -0.6454 34.6181 43.2457 35.349826 55.3023 3.2162 55.3987 3500 4600 -0.6454 34.6181 43.2457 35.636809 55.3177 2.9391 55.3987 3500 4800 -0.6454 34.6181 43.2457 35.919204 55.3315 2.6664 55.3987 3500 5000 -0.6454 34.6181 43.2457 36.197143 55.3437 2.3980 55.3987 3500 5200 -0.6454 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4000 2600 -0.6454 34.9638 43.3307 32.534152 55.3352 6.1747 55.6816 4000 2800 -0.6454 34.9638 43.3307 32.869638 55.3704 5.8506 55.6816 4000 3000 -0.6454 34.9638 43.3307 33.198987 55.4031 5.5322 55.6816 4000 3200 -0.6454 34.9638 43.3307 33.522412 55.4334 5.2193 55.6816 4000 3400 -0.6454 34.9638 43.3307 33.840110 55.4615 4.9119 55.6816 4000 3600 -0.6454 34.9638 43.3307 34.152273 55.4874 4.6096 55.6816 4000 3800 -0.6454 34.9638 43.3307 34.459078 55.5113 4.3124 55.6816 4000 4000 -0.6454 34.9638 43.3307 34.760696 55.5332 4.0202 55.6816 4000 4200 -0.6454 34.9638 43.3307 35.057288 55.5533 3.7326 55.6816 4000 4400 -0.6454 34.9638 43.3307 35.349007 55.5716 3.4497 55.6816 4000 4600 -0.6454 34.9638 43.3307 35.635998 55.5882 3.1713 55.6816 4000 4800 -0.6454 34.9638 43.3307 35.918400 55.6031 2.8973 55.6816 4000 5000 -0.6454 34.9638 43.3307 36.196346 55.6165 2.6275 55.6816 4000 5200 -0.6454 34.9638 43.3307 36.469963 55.6284 2.3619 55.6816 4000 5400 -0.6454 34.9638 43.3307 36.739369 55.6389 2.1003 55.6816 4000 5600 -0.6454 34.9638 43.3307 37.004681 55.6480 1.8426 55.6816 4000 5800 -0.6454 34.9638 43.3307 37.266009 55.6558 1.5888 55.6816 4000 6000 -0.6454 34.9638 43.3307 37.523459 55.6624 1.3387 55.6816 4000 6200 -0.6454 34.9638 43.3307 37.777131 55.6678 1.0922 55.6816 4000 6400 -0.6454 34.9638 43.3307 38.027122 55.6720 0.8493 55.6816 4000 6600 -0.6454 34.9638 43.3307 38.273527 55.6752 0.6099 55.6816 4000 6800 -0.6454 34.9638 43.3307 38.516433 55.6772 0.3738 55.6816 4000 7000 -0.6454 34.9638 43.3307 38.755927 55.6783 0.1411 55.6816 4000 7200 -0.6454 34.9638 43.3307 38.992092 55.6784 -0.0884 55.6816 4000 7400 -0.6454 34.9638 43.3307 39.225007 55.6776 -0.3148 55.6816 4000 7600 -0.6454 34.9638 43.3307 39.454748 55.6759 -0.5380 55.6816 4000 7800 -0.6454 34.9638 43.3307 39.681389 55.6733 -0.7582 55.6816 4000 8000 -0.6454 34.9638 43.3307 39.905001 55.6699 -0.9755 55.6816 4000 8200 -0.6454 34.9638 43.3307 40.125653 55.6657 -1.1899 55.6816 4000 8400 -0.6454 34.9638 43.3307 40.343411 55.6608 -1.4015 55.6816 4500 0 -0.6454 35.3095 43.4158 27.487555 54.8126 11.2860 55.9652 4500 200 -0.6454 35.3095 43.4158 27.929948 54.8981 10.8624 55.9652 4500 400 -0.6454 35.3095 43.4158 28.361699 54.9784 10.4484 55.9652 4500 600 -0.6454 35.3095 43.4158 28.783331 55.0538 10.0435 55.9652 4500 800 -0.6454 35.3095 43.4158 29.195326 55.1246 9.6474 55.9652 4500 1000 -0.6454 35.3095 43.4158 29.598126 55.1910 9.2596 55.9652 4500 1200 -0.6454 35.3095 43.4158 29.992143 55.2534 8.8798 55.9652 4500 1400 -0.6454 35.3095 43.4158 30.377757 55.3119 8.5077 55.9652 4500 1600 -0.6454 35.3095 43.4158 30.755322 55.3667 8.1431 55.9652 4500 1800 -0.6454 35.3095 43.4158 31.125168 55.4181 7.7855 55.9652 4500 2000 -0.6454 35.3095 43.4158 31.487603 55.4663 7.4348 55.9652 4500 2200 -0.6454 35.3095 43.4158 31.842915 55.5113 7.0906 55.9652 4500 2400 -0.6454 35.3095 43.4158 32.191373 55.5534 6.7529 55.9652 4500 2600 -0.6454 35.3095 43.4158 32.533232 55.5927 6.4213 55.9652 4500 2800 -0.6454 35.3095 43.4158 32.868729 55.6293 6.0957 55.9652 4500 3000 -0.6454 35.3095 43.4158 33.198089 55.6635 5.7758 55.9652 4500 3200 -0.6454 35.3095 43.4158 33.521523 55.6952 5.4615 55.9652 4500 3400 -0.6454 35.3095 43.4158 33.839232 55.7246 5.1525 55.9652 4500 3600 -0.6454 35.3095 43.4158 34.151404 55.7518 4.8488 55.9652 4500 3800 -0.6454 35.3095 43.4158 34.458218 55.7770 4.5502 55.9652 4500 4000 -0.6454 35.3095 43.4158 34.759845 55.8002 4.2565 55.9652 4500 4200 -0.6454 35.3095 43.4158 35.056446 55.8214 3.9676 55.9652 4500 4400 -0.6454 35.3095 43.4158 35.348172 55.8409 3.6833 55.9652 4500 4600 -0.6454 35.3095 43.4158 35.635172 55.8587 3.4035 55.9652 4500 4800 -0.6454 35.3095 43.4158 35.917582 55.8747 3.1282 55.9652 4500 5000 -0.6454 35.3095 43.4158 36.195536 55.8893 2.8571 55.9652 4500 5200 -0.6454 35.3095 43.4158 36.469160 55.9023 2.5901 55.9652 4500 5400 -0.6454 35.3095 43.4158 36.738573 55.9138 2.3272 55.9652 4500 5600 -0.6454 35.3095 43.4158 37.003892 55.9240 2.0683 55.9652 4500 5800 -0.6454 35.3095 43.4158 37.265227 55.9328 1.8132 55.9652 4500 6000 -0.6454 35.3095 43.4158 37.522684 55.9404 1.5618 55.9652 4500 6200 -0.6454 35.3095 43.4158 37.776362 55.9468 1.3141 55.9652 4500 6400 -0.6454 35.3095 43.4158 38.026360 55.9520 1.0700 55.9652 4500 6600 -0.6454 35.3095 43.4158 38.272771 55.9561 0.8293 55.9652 4500 6800 -0.6454 35.3095 43.4158 38.515683 55.9591 0.5921 55.9652 4500 7000 -0.6454 35.3095 43.4158 38.755184 55.9610 0.3582 55.9652 4500 7200 -0.6454 35.3095 43.4158 38.991354 55.9620 0.1275 55.9652 4500 7400 -0.6454 35.3095 43.4158 39.224274 55.9621 -0.1000 55.9652 4500 7600 -0.6454 35.3095 43.4158 39.454021 55.9612 -0.3244 55.9652 4500 7800 -0.6454 35.3095 43.4158 39.680668 55.9595 -0.5458 55.9652 4500 8000 -0.6454 35.3095 43.4158 39.904285 55.9569 -0.7642 55.9652 4500 8200 -0.6454 35.3095 43.4158 40.124942 55.9536 -0.9797 55.9652 4500 8400 -0.6454 35.3095 43.4158 40.342705 55.9495 -1.1923 55.9652 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.010 ================================================ FILE: tests/test_data/cropA/geometry/20180307-20180611_VV_8rlks_base.par ================================================ initial_baseline(TCN): -1.0202033 -42.5608037 28.9838632 m m m initial_baseline_rate: 0.0000000 0.1856629 0.0418846 m/s m/s m/s precision_baseline(TCN): 0.0000000 -42.7289394 29.0749561 m m m precision_baseline_rate: 0.0000000 0.1888319 0.0419065 m/s m/s m/s unwrap_phase_constant: -0.00012 radians ================================================ FILE: tests/test_data/cropA/geometry/20180307-20180611_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): -1.020 -42.561 28.984 orbit baseline rate (TCN) (m/s): 0.000e+00 1.857e-01 4.188e-02 baseline vector (TCN) (m): 0.000 -42.729 29.075 baseline rate (TCN) (m/s): 0.000e+00 1.888e-01 4.191e-02 SLC-1 center baseline length (m): 51.6828 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 0.0000 -44.4915 28.6838 27.496815 4.9019 -52.7088 52.9364 0 200 0.0000 -44.4915 28.6838 27.939050 4.4949 -52.7451 52.9364 0 400 0.0000 -44.4915 28.6838 28.370652 4.0975 -52.7774 52.9364 0 600 0.0000 -44.4915 28.6838 28.792142 3.7091 -52.8062 52.9364 0 800 0.0000 -44.4915 28.6838 29.204001 3.3294 -52.8315 52.9364 0 1000 0.0000 -44.4915 28.6838 29.606672 2.9581 -52.8535 52.9364 0 1200 0.0000 -44.4915 28.6838 30.000566 2.5946 -52.8726 52.9364 0 1400 0.0000 -44.4915 28.6838 30.386062 2.2389 -52.8889 52.9364 0 1600 0.0000 -44.4915 28.6838 30.763514 1.8904 -52.9025 52.9364 0 1800 0.0000 -44.4915 28.6838 31.133251 1.5490 -52.9136 52.9364 0 2000 0.0000 -44.4915 28.6838 31.495582 1.2143 -52.9223 52.9364 0 2200 0.0000 -44.4915 28.6838 31.850793 0.8862 -52.9289 52.9364 0 2400 0.0000 -44.4915 28.6838 32.199155 0.5644 -52.9333 52.9364 0 2600 0.0000 -44.4915 28.6838 32.540921 0.2486 -52.9357 52.9364 0 2800 0.0000 -44.4915 28.6838 32.876328 -0.0613 -52.9362 52.9364 0 3000 0.0000 -44.4915 28.6838 33.205602 -0.3655 -52.9350 52.9364 0 3200 0.0000 -44.4915 28.6838 33.528952 -0.6642 -52.9321 52.9364 0 3400 0.0000 -44.4915 28.6838 33.846580 -0.9577 -52.9276 52.9364 0 3600 0.0000 -44.4915 28.6838 34.158674 -1.2459 -52.9216 52.9364 0 3800 0.0000 -44.4915 28.6838 34.465413 -1.5292 -52.9142 52.9364 0 4000 0.0000 -44.4915 28.6838 34.766966 -1.8077 -52.9054 52.9364 0 4200 0.0000 -44.4915 28.6838 35.063495 -2.0815 -52.8953 52.9364 0 4400 0.0000 -44.4915 28.6838 35.355153 -2.3507 -52.8841 52.9364 0 4600 0.0000 -44.4915 28.6838 35.642085 -2.6155 -52.8716 52.9364 0 4800 0.0000 -44.4915 28.6838 35.924431 -2.8760 -52.8581 52.9364 0 5000 0.0000 -44.4915 28.6838 36.202321 -3.1324 -52.8435 52.9364 0 5200 0.0000 -44.4915 28.6838 36.475883 -3.3846 -52.8280 52.9364 0 5400 0.0000 -44.4915 28.6838 36.745237 -3.6330 -52.8115 52.9364 0 5600 0.0000 -44.4915 28.6838 37.010498 -3.8774 -52.7941 52.9364 0 5800 0.0000 -44.4915 28.6838 37.271776 -4.1181 -52.7759 52.9364 0 6000 0.0000 -44.4915 28.6838 37.529176 -4.3552 -52.7568 52.9364 0 6200 0.0000 -44.4915 28.6838 37.782801 -4.5887 -52.7370 52.9364 0 6400 0.0000 -44.4915 28.6838 38.032746 -4.8187 -52.7165 52.9364 0 6600 0.0000 -44.4915 28.6838 38.279105 -5.0453 -52.6953 52.9364 0 6800 0.0000 -44.4915 28.6838 38.521968 -5.2686 -52.6735 52.9364 0 7000 0.0000 -44.4915 28.6838 38.761419 -5.4887 -52.6510 52.9364 0 7200 0.0000 -44.4915 28.6838 38.997541 -5.7057 -52.6279 52.9364 0 7400 0.0000 -44.4915 28.6838 39.230415 -5.9195 -52.6043 52.9364 0 7600 0.0000 -44.4915 28.6838 39.460116 -6.1303 -52.5801 52.9364 0 7800 0.0000 -44.4915 28.6838 39.686717 -6.3383 -52.5555 52.9364 0 8000 0.0000 -44.4915 28.6838 39.910291 -6.5433 -52.5304 52.9364 0 8200 0.0000 -44.4915 28.6838 40.130905 -6.7455 -52.5048 52.9364 0 8400 0.0000 -44.4915 28.6838 40.348626 -6.9450 -52.4788 52.9364 500 0 0.0000 -44.1034 28.7699 27.495868 5.1584 -52.4042 52.6576 500 200 0.0000 -44.1034 28.7699 27.938119 4.7538 -52.4424 52.6576 500 400 0.0000 -44.1034 28.7699 28.369736 4.3586 -52.4768 52.6576 500 600 0.0000 -44.1034 28.7699 28.791241 3.9724 -52.5074 52.6576 500 800 0.0000 -44.1034 28.7699 29.203113 3.5948 -52.5346 52.6576 500 1000 0.0000 -44.1034 28.7699 29.605798 3.2255 -52.5586 52.6576 500 1200 0.0000 -44.1034 28.7699 29.999704 2.8641 -52.5795 52.6576 500 1400 0.0000 -44.1034 28.7699 30.385211 2.5103 -52.5976 52.6576 500 1600 0.0000 -44.1034 28.7699 30.762675 2.1637 -52.6130 52.6576 500 1800 0.0000 -44.1034 28.7699 31.132423 1.8241 -52.6259 52.6576 500 2000 0.0000 -44.1034 28.7699 31.494764 1.4913 -52.6364 52.6576 500 2200 0.0000 -44.1034 28.7699 31.849986 1.1649 -52.6446 52.6576 500 2400 0.0000 -44.1034 28.7699 32.198358 0.8448 -52.6507 52.6576 500 2600 0.0000 -44.1034 28.7699 32.540133 0.5307 -52.6548 52.6576 500 2800 0.0000 -44.1034 28.7699 32.875549 0.2225 -52.6570 52.6576 500 3000 0.0000 -44.1034 28.7699 33.204831 -0.0801 -52.6574 52.6576 500 3200 0.0000 -44.1034 28.7699 33.528190 -0.3773 -52.6561 52.6576 500 3400 0.0000 -44.1034 28.7699 33.845826 -0.6692 -52.6532 52.6576 500 3600 0.0000 -44.1034 28.7699 34.157928 -0.9560 -52.6488 52.6576 500 3800 0.0000 -44.1034 28.7699 34.464675 -1.2379 -52.6429 52.6576 500 4000 0.0000 -44.1034 28.7699 34.766235 -1.5149 -52.6357 52.6576 500 4200 0.0000 -44.1034 28.7699 35.062772 -1.7873 -52.6271 52.6576 500 4400 0.0000 -44.1034 28.7699 35.354436 -2.0552 -52.6174 52.6576 500 4600 0.0000 -44.1034 28.7699 35.641375 -2.3187 -52.6064 52.6576 500 4800 0.0000 -44.1034 28.7699 35.923727 -2.5779 -52.5944 52.6576 500 5000 0.0000 -44.1034 28.7699 36.201624 -2.8330 -52.5812 52.6576 500 5200 0.0000 -44.1034 28.7699 36.475192 -3.0840 -52.5671 52.6576 500 5400 0.0000 -44.1034 28.7699 36.744552 -3.3311 -52.5520 52.6576 500 5600 0.0000 -44.1034 28.7699 37.009819 -3.5744 -52.5360 52.6576 500 5800 0.0000 -44.1034 28.7699 37.271103 -3.8139 -52.5192 52.6576 500 6000 0.0000 -44.1034 28.7699 37.528509 -4.0498 -52.5015 52.6576 500 6200 0.0000 -44.1034 28.7699 37.782139 -4.2822 -52.4831 52.6576 500 6400 0.0000 -44.1034 28.7699 38.032090 -4.5111 -52.4639 52.6576 500 6600 0.0000 -44.1034 28.7699 38.278454 -4.7366 -52.4440 52.6576 500 6800 0.0000 -44.1034 28.7699 38.521321 -4.9589 -52.4235 52.6576 500 7000 0.0000 -44.1034 28.7699 38.760777 -5.1779 -52.4023 52.6576 500 7200 0.0000 -44.1034 28.7699 38.996905 -5.3939 -52.3805 52.6576 500 7400 0.0000 -44.1034 28.7699 39.229783 -5.6067 -52.3582 52.6576 500 7600 0.0000 -44.1034 28.7699 39.459488 -5.8166 -52.3353 52.6576 500 7800 0.0000 -44.1034 28.7699 39.686095 -6.0235 -52.3119 52.6576 500 8000 0.0000 -44.1034 28.7699 39.909673 -6.2276 -52.2880 52.6576 500 8200 0.0000 -44.1034 28.7699 40.130291 -6.4289 -52.2636 52.6576 500 8400 0.0000 -44.1034 28.7699 40.348016 -6.6275 -52.2388 52.6576 1000 0 0.0000 -43.7152 28.8561 27.494901 5.4149 -52.0995 52.3803 1000 200 0.0000 -43.7152 28.8561 27.937168 5.0126 -52.1398 52.3803 1000 400 0.0000 -43.7152 28.8561 28.368800 4.6196 -52.1761 52.3803 1000 600 0.0000 -43.7152 28.8561 28.790320 4.2357 -52.2087 52.3803 1000 800 0.0000 -43.7152 28.8561 29.202206 3.8602 -52.2378 52.3803 1000 1000 0.0000 -43.7152 28.8561 29.604904 3.4930 -52.2636 52.3803 1000 1200 0.0000 -43.7152 28.8561 29.998823 3.1336 -52.2864 52.3803 1000 1400 0.0000 -43.7152 28.8561 30.384343 2.7817 -52.3063 52.3803 1000 1600 0.0000 -43.7152 28.8561 30.761818 2.4371 -52.3235 52.3803 1000 1800 0.0000 -43.7152 28.8561 31.131578 2.0993 -52.3381 52.3803 1000 2000 0.0000 -43.7152 28.8561 31.493929 1.7683 -52.3504 52.3803 1000 2200 0.0000 -43.7152 28.8561 31.849161 1.4437 -52.3603 52.3803 1000 2400 0.0000 -43.7152 28.8561 32.197543 1.1253 -52.3681 52.3803 1000 2600 0.0000 -43.7152 28.8561 32.539328 0.8129 -52.3739 52.3803 1000 2800 0.0000 -43.7152 28.8561 32.874754 0.5063 -52.3778 52.3803 1000 3000 0.0000 -43.7152 28.8561 33.204045 0.2052 -52.3798 52.3803 1000 3200 0.0000 -43.7152 28.8561 33.527412 -0.0904 -52.3801 52.3803 1000 3400 0.0000 -43.7152 28.8561 33.845057 -0.3808 -52.3788 52.3803 1000 3600 0.0000 -43.7152 28.8561 34.157166 -0.6661 -52.3760 52.3803 1000 3800 0.0000 -43.7152 28.8561 34.463921 -0.9465 -52.3717 52.3803 1000 4000 0.0000 -43.7152 28.8561 34.765489 -1.2222 -52.3660 52.3803 1000 4200 0.0000 -43.7152 28.8561 35.062033 -1.4932 -52.3589 52.3803 1000 4400 0.0000 -43.7152 28.8561 35.353705 -1.7597 -52.3507 52.3803 1000 4600 0.0000 -43.7152 28.8561 35.640651 -2.0218 -52.3412 52.3803 1000 4800 0.0000 -43.7152 28.8561 35.923009 -2.2798 -52.3306 52.3803 1000 5000 0.0000 -43.7152 28.8561 36.200913 -2.5336 -52.3189 52.3803 1000 5200 0.0000 -43.7152 28.8561 36.474487 -2.7833 -52.3062 52.3803 1000 5400 0.0000 -43.7152 28.8561 36.743853 -3.0292 -52.2926 52.3803 1000 5600 0.0000 -43.7152 28.8561 37.009126 -3.2713 -52.2780 52.3803 1000 5800 0.0000 -43.7152 28.8561 37.270416 -3.5097 -52.2625 52.3803 1000 6000 0.0000 -43.7152 28.8561 37.527828 -3.7444 -52.2462 52.3803 1000 6200 0.0000 -43.7152 28.8561 37.781464 -3.9757 -52.2291 52.3803 1000 6400 0.0000 -43.7152 28.8561 38.031420 -4.2035 -52.2113 52.3803 1000 6600 0.0000 -43.7152 28.8561 38.277789 -4.4280 -52.1927 52.3803 1000 6800 0.0000 -43.7152 28.8561 38.520662 -4.6492 -52.1735 52.3803 1000 7000 0.0000 -43.7152 28.8561 38.760123 -4.8672 -52.1536 52.3803 1000 7200 0.0000 -43.7152 28.8561 38.996255 -5.0821 -52.1331 52.3803 1000 7400 0.0000 -43.7152 28.8561 39.229138 -5.2939 -52.1120 52.3803 1000 7600 0.0000 -43.7152 28.8561 39.458848 -5.5028 -52.0904 52.3803 1000 7800 0.0000 -43.7152 28.8561 39.685459 -5.7088 -52.0682 52.3803 1000 8000 0.0000 -43.7152 28.8561 39.909042 -5.9119 -52.0455 52.3803 1000 8200 0.0000 -43.7152 28.8561 40.129665 -6.1123 -52.0224 52.3803 1000 8400 0.0000 -43.7152 28.8561 40.347394 -6.3099 -51.9988 52.3803 1500 0 0.0000 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-41.3863 29.3729 38.273527 -2.5757 -50.6849 50.7503 4000 6800 0.0000 -41.3863 29.3729 38.516433 -2.7906 -50.6735 50.7503 4000 7000 0.0000 -41.3863 29.3729 38.755927 -3.0024 -50.6614 50.7503 4000 7200 0.0000 -41.3863 29.3729 38.992092 -3.2112 -50.6486 50.7503 4000 7400 0.0000 -41.3863 29.3729 39.225007 -3.4170 -50.6351 50.7503 4000 7600 0.0000 -41.3863 29.3729 39.454748 -3.6201 -50.6210 50.7503 4000 7800 0.0000 -41.3863 29.3729 39.681389 -3.8203 -50.6063 50.7503 4000 8000 0.0000 -41.3863 29.3729 39.905001 -4.0177 -50.5910 50.7503 4000 8200 0.0000 -41.3863 29.3729 40.125653 -4.2125 -50.5751 50.7503 4000 8400 0.0000 -41.3863 29.3729 40.343411 -4.4047 -50.5588 50.7503 4500 0 0.0000 -40.9982 29.4591 27.487555 7.2106 -49.9668 50.4845 4500 200 0.0000 -40.9982 29.4591 27.929948 6.8246 -50.0210 50.4845 4500 400 0.0000 -40.9982 29.4591 28.361699 6.4474 -50.0710 50.4845 4500 600 0.0000 -40.9982 29.4591 28.783331 6.0788 -50.1171 50.4845 4500 800 0.0000 -40.9982 29.4591 29.195326 5.7183 -50.1595 50.4845 4500 1000 0.0000 -40.9982 29.4591 29.598126 5.3655 -50.1985 50.4845 4500 1200 0.0000 -40.9982 29.4591 29.992143 5.0202 -50.2342 50.4845 4500 1400 0.0000 -40.9982 29.4591 30.377757 4.6820 -50.2669 50.4845 4500 1600 0.0000 -40.9982 29.4591 30.755322 4.3506 -50.2966 50.4845 4500 1800 0.0000 -40.9982 29.4591 31.125168 4.0259 -50.3237 50.4845 4500 2000 0.0000 -40.9982 29.4591 31.487603 3.7074 -50.3481 50.4845 4500 2200 0.0000 -40.9982 29.4591 31.842915 3.3951 -50.3701 50.4845 4500 2400 0.0000 -40.9982 29.4591 32.191373 3.0887 -50.3899 50.4845 4500 2600 0.0000 -40.9982 29.4591 32.533232 2.7880 -50.4074 50.4845 4500 2800 0.0000 -40.9982 29.4591 32.868729 2.4928 -50.4229 50.4845 4500 3000 0.0000 -40.9982 29.4591 33.198089 2.2029 -50.4364 50.4845 4500 3200 0.0000 -40.9982 29.4591 33.521523 1.9182 -50.4480 50.4845 4500 3400 0.0000 -40.9982 29.4591 33.839232 1.6384 -50.4578 50.4845 4500 3600 0.0000 -40.9982 29.4591 34.151404 1.3635 -50.4660 50.4845 4500 3800 0.0000 -40.9982 29.4591 34.458218 1.0932 -50.4726 50.4845 4500 4000 0.0000 -40.9982 29.4591 34.759845 0.8275 -50.4777 50.4845 4500 4200 0.0000 -40.9982 29.4591 35.056446 0.5662 -50.4813 50.4845 4500 4400 0.0000 -40.9982 29.4591 35.348172 0.3091 -50.4835 50.4845 4500 4600 0.0000 -40.9982 29.4591 35.635172 0.0563 -50.4844 50.4845 4500 4800 0.0000 -40.9982 29.4591 35.917582 -0.1926 -50.4841 50.4845 4500 5000 0.0000 -40.9982 29.4591 36.195536 -0.4375 -50.4826 50.4845 4500 5200 0.0000 -40.9982 29.4591 36.469160 -0.6786 -50.4799 50.4845 4500 5400 0.0000 -40.9982 29.4591 36.738573 -0.9159 -50.4761 50.4845 4500 5600 0.0000 -40.9982 29.4591 37.003892 -1.1496 -50.4714 50.4845 4500 5800 0.0000 -40.9982 29.4591 37.265227 -1.3798 -50.4656 50.4845 4500 6000 0.0000 -40.9982 29.4591 37.522684 -1.6066 -50.4589 50.4845 4500 6200 0.0000 -40.9982 29.4591 37.776362 -1.8300 -50.4513 50.4845 4500 6400 0.0000 -40.9982 29.4591 38.026360 -2.0501 -50.4428 50.4845 4500 6600 0.0000 -40.9982 29.4591 38.272771 -2.2670 -50.4335 50.4845 4500 6800 0.0000 -40.9982 29.4591 38.515683 -2.4808 -50.4235 50.4845 4500 7000 0.0000 -40.9982 29.4591 38.755184 -2.6916 -50.4127 50.4845 4500 7200 0.0000 -40.9982 29.4591 38.991354 -2.8993 -50.4011 50.4845 4500 7400 0.0000 -40.9982 29.4591 39.224274 -3.1042 -50.3889 50.4845 4500 7600 0.0000 -40.9982 29.4591 39.454021 -3.3062 -50.3761 50.4845 4500 7800 0.0000 -40.9982 29.4591 39.680668 -3.5055 -50.3626 50.4845 4500 8000 0.0000 -40.9982 29.4591 39.904285 -3.7020 -50.3485 50.4845 4500 8200 0.0000 -40.9982 29.4591 40.124942 -3.8959 -50.3339 50.4845 4500 8400 0.0000 -40.9982 29.4591 40.342705 -4.0872 -50.3187 50.4845 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.000 ================================================ FILE: tests/test_data/cropA/geometry/20180319-20180331_VV_8rlks_base.par ================================================ initial_baseline(TCN): 0.0940080 -4.4095585 4.0878617 m m m initial_baseline_rate: 0.0000000 0.0564518 0.0226244 m/s m/s m/s precision_baseline(TCN): 0.0940080 -4.4095585 4.0878617 m m m precision_baseline_rate: 0.0000000 0.0564518 0.0226244 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/cropA/geometry/20180319-20180331_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): 0.094 -4.410 4.088 orbit baseline rate (TCN) (m/s): 0.000e+00 5.645e-02 2.262e-02 baseline vector (TCN) (m): 0.094 -4.410 4.088 baseline rate (TCN) (m/s): 0.000e+00 5.645e-02 2.262e-02 SLC-1 center baseline length (m): 6.0136 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 0.0940 -4.9365 3.8767 27.496626 1.1595 -6.1689 6.2775 0 200 0.0940 -4.9365 3.8767 27.938864 1.1119 -6.1777 6.2775 0 400 0.0940 -4.9365 3.8767 28.370470 1.0653 -6.1859 6.2775 0 600 0.0940 -4.9365 3.8767 28.791963 1.0198 -6.1935 6.2775 0 800 0.0940 -4.9365 3.8767 29.203825 0.9752 -6.2007 6.2775 0 1000 0.0940 -4.9365 3.8767 29.606500 0.9316 -6.2074 6.2775 0 1200 0.0940 -4.9365 3.8767 30.000396 0.8889 -6.2136 6.2775 0 1400 0.0940 -4.9365 3.8767 30.385895 0.8471 -6.2195 6.2775 0 1600 0.0940 -4.9365 3.8767 30.763349 0.8061 -6.2249 6.2775 0 1800 0.0940 -4.9365 3.8767 31.133089 0.7659 -6.2300 6.2775 0 2000 0.0940 -4.9365 3.8767 31.495422 0.7265 -6.2347 6.2775 0 2200 0.0940 -4.9365 3.8767 31.850636 0.6878 -6.2391 6.2775 0 2400 0.0940 -4.9365 3.8767 32.199000 0.6499 -6.2432 6.2775 0 2600 0.0940 -4.9365 3.8767 32.540768 0.6126 -6.2469 6.2775 0 2800 0.0940 -4.9365 3.8767 32.876178 0.5761 -6.2504 6.2775 0 3000 0.0940 -4.9365 3.8767 33.205453 0.5401 -6.2536 6.2775 0 3200 0.0940 -4.9365 3.8767 33.528805 0.5048 -6.2566 6.2775 0 3400 0.0940 -4.9365 3.8767 33.846435 0.4701 -6.2593 6.2775 0 3600 0.0940 -4.9365 3.8767 34.158531 0.4360 -6.2617 6.2775 0 3800 0.0940 -4.9365 3.8767 34.465271 0.4025 -6.2640 6.2775 0 4000 0.0940 -4.9365 3.8767 34.766827 0.3695 -6.2660 6.2775 0 4200 0.0940 -4.9365 3.8767 35.063357 0.3371 -6.2678 6.2775 0 4400 0.0940 -4.9365 3.8767 35.355017 0.3052 -6.2695 6.2775 0 4600 0.0940 -4.9365 3.8767 35.641950 0.2738 -6.2709 6.2775 0 4800 0.0940 -4.9365 3.8767 35.924297 0.2429 -6.2722 6.2775 0 5000 0.0940 -4.9365 3.8767 36.202189 0.2125 -6.2733 6.2775 0 5200 0.0940 -4.9365 3.8767 36.475752 0.1825 -6.2742 6.2775 0 5400 0.0940 -4.9365 3.8767 36.745108 0.1530 -6.2750 6.2775 0 5600 0.0940 -4.9365 3.8767 37.010370 0.1240 -6.2757 6.2775 0 5800 0.0940 -4.9365 3.8767 37.271649 0.0953 -6.2762 6.2775 0 6000 0.0940 -4.9365 3.8767 37.529051 0.0672 -6.2765 6.2775 0 6200 0.0940 -4.9365 3.8767 37.782677 0.0394 -6.2768 6.2775 0 6400 0.0940 -4.9365 3.8767 38.032623 0.0120 -6.2769 6.2775 0 6600 0.0940 -4.9365 3.8767 38.278984 -0.0150 -6.2769 6.2775 0 6800 0.0940 -4.9365 3.8767 38.521847 -0.0416 -6.2767 6.2775 0 7000 0.0940 -4.9365 3.8767 38.761299 -0.0678 -6.2765 6.2775 0 7200 0.0940 -4.9365 3.8767 38.997423 -0.0937 -6.2762 6.2775 0 7400 0.0940 -4.9365 3.8767 39.230297 -0.1192 -6.2757 6.2775 0 7600 0.0940 -4.9365 3.8767 39.459999 -0.1444 -6.2752 6.2775 0 7800 0.0940 -4.9365 3.8767 39.686602 -0.1692 -6.2746 6.2775 0 8000 0.0940 -4.9365 3.8767 39.910176 -0.1937 -6.2739 6.2775 0 8200 0.0940 -4.9365 3.8767 40.130792 -0.2178 -6.2731 6.2775 0 8400 0.0940 -4.9365 3.8767 40.348513 -0.2417 -6.2722 6.2775 500 0 0.0940 -4.8205 3.9232 27.495684 1.2544 -6.0874 6.2159 500 200 0.0940 -4.8205 3.9232 27.937938 1.2074 -6.0969 6.2159 500 400 0.0940 -4.8205 3.9232 28.369559 1.1614 -6.1058 6.2159 500 600 0.0940 -4.8205 3.9232 28.791066 1.1165 -6.1142 6.2159 500 800 0.0940 -4.8205 3.9232 29.202942 1.0725 -6.1221 6.2159 500 1000 0.0940 -4.8205 3.9232 29.605629 1.0295 -6.1295 6.2159 500 1200 0.0940 -4.8205 3.9232 29.999538 0.9873 -6.1364 6.2159 500 1400 0.0940 -4.8205 3.9232 30.385049 0.9460 -6.1429 6.2159 500 1600 0.0940 -4.8205 3.9232 30.762515 0.9055 -6.1490 6.2159 500 1800 0.0940 -4.8205 3.9232 31.132265 0.8658 -6.1547 6.2159 500 2000 0.0940 -4.8205 3.9232 31.494609 0.8269 -6.1601 6.2159 500 2200 0.0940 -4.8205 3.9232 31.849833 0.7887 -6.1651 6.2159 500 2400 0.0940 -4.8205 3.9232 32.198207 0.7512 -6.1697 6.2159 500 2600 0.0940 -4.8205 3.9232 32.539984 0.7143 -6.1741 6.2159 500 2800 0.0940 -4.8205 3.9232 32.875402 0.6782 -6.1782 6.2159 500 3000 0.0940 -4.8205 3.9232 33.204686 0.6427 -6.1820 6.2159 500 3200 0.0940 -4.8205 3.9232 33.528047 0.6078 -6.1855 6.2159 500 3400 0.0940 -4.8205 3.9232 33.845685 0.5735 -6.1888 6.2159 500 3600 0.0940 -4.8205 3.9232 34.157789 0.5398 -6.1918 6.2159 500 3800 0.0940 -4.8205 3.9232 34.464537 0.5066 -6.1946 6.2159 500 4000 0.0940 -4.8205 3.9232 34.766099 0.4740 -6.1972 6.2159 500 4200 0.0940 -4.8205 3.9232 35.062637 0.4419 -6.1996 6.2159 500 4400 0.0940 -4.8205 3.9232 35.354304 0.4103 -6.2017 6.2159 500 4600 0.0940 -4.8205 3.9232 35.641244 0.3793 -6.2037 6.2159 500 4800 0.0940 -4.8205 3.9232 35.923597 0.3487 -6.2055 6.2159 500 5000 0.0940 -4.8205 3.9232 36.201496 0.3186 -6.2071 6.2159 500 5200 0.0940 -4.8205 3.9232 36.475065 0.2890 -6.2086 6.2159 500 5400 0.0940 -4.8205 3.9232 36.744426 0.2598 -6.2098 6.2159 500 5600 0.0940 -4.8205 3.9232 37.009694 0.2310 -6.2110 6.2159 500 5800 0.0940 -4.8205 3.9232 37.270979 0.2027 -6.2120 6.2159 500 6000 0.0940 -4.8205 3.9232 37.528387 0.1748 -6.2128 6.2159 500 6200 0.0940 -4.8205 3.9232 37.782018 0.1473 -6.2135 6.2159 500 6400 0.0940 -4.8205 3.9232 38.031970 0.1202 -6.2141 6.2159 500 6600 0.0940 -4.8205 3.9232 38.278335 0.0935 -6.2146 6.2159 500 6800 0.0940 -4.8205 3.9232 38.521204 0.0671 -6.2149 6.2159 500 7000 0.0940 -4.8205 3.9232 38.760661 0.0411 -6.2151 6.2159 500 7200 0.0940 -4.8205 3.9232 38.996789 0.0155 -6.2153 6.2159 500 7400 0.0940 -4.8205 3.9232 39.229668 -0.0097 -6.2153 6.2159 500 7600 0.0940 -4.8205 3.9232 39.459375 -0.0346 -6.2152 6.2159 500 7800 0.0940 -4.8205 3.9232 39.685982 -0.0592 -6.2150 6.2159 500 8000 0.0940 -4.8205 3.9232 39.909561 -0.0835 -6.2147 6.2159 500 8200 0.0940 -4.8205 3.9232 40.130181 -0.1074 -6.2143 6.2159 500 8400 0.0940 -4.8205 3.9232 40.347906 -0.1310 -6.2139 6.2159 1000 0 0.0940 -4.7044 3.9697 27.494721 1.3494 -6.0059 6.1562 1000 200 0.0940 -4.7044 3.9697 27.936992 1.3030 -6.0162 6.1562 1000 400 0.0940 -4.7044 3.9697 28.368628 1.2576 -6.0258 6.1562 1000 600 0.0940 -4.7044 3.9697 28.790150 1.2132 -6.0349 6.1562 1000 800 0.0940 -4.7044 3.9697 29.202040 1.1698 -6.0435 6.1562 1000 1000 0.0940 -4.7044 3.9697 29.604740 1.1273 -6.0515 6.1562 1000 1200 0.0940 -4.7044 3.9697 29.998662 1.0857 -6.0591 6.1562 1000 1400 0.0940 -4.7044 3.9697 30.384184 1.0449 -6.0663 6.1562 1000 1600 0.0940 -4.7044 3.9697 30.761662 1.0049 -6.0730 6.1562 1000 1800 0.0940 -4.7044 3.9697 31.131424 0.9657 -6.0794 6.1562 1000 2000 0.0940 -4.7044 3.9697 31.493778 0.9272 -6.0854 6.1562 1000 2200 0.0940 -4.7044 3.9697 31.849012 0.8895 -6.0910 6.1562 1000 2400 0.0940 -4.7044 3.9697 32.197396 0.8524 -6.0963 6.1562 1000 2600 0.0940 -4.7044 3.9697 32.539183 0.8161 -6.1013 6.1562 1000 2800 0.0940 -4.7044 3.9697 32.874611 0.7803 -6.1060 6.1562 1000 3000 0.0940 -4.7044 3.9697 33.203904 0.7452 -6.1103 6.1562 1000 3200 0.0940 -4.7044 3.9697 33.527273 0.7107 -6.1144 6.1562 1000 3400 0.0940 -4.7044 3.9697 33.844919 0.6768 -6.1183 6.1562 1000 3600 0.0940 -4.7044 3.9697 34.157031 0.6435 -6.1219 6.1562 1000 3800 0.0940 -4.7044 3.9697 34.463787 0.6107 -6.1252 6.1562 1000 4000 0.0940 -4.7044 3.9697 34.765357 0.5784 -6.1284 6.1562 1000 4200 0.0940 -4.7044 3.9697 35.061902 0.5467 -6.1313 6.1562 1000 4400 0.0940 -4.7044 3.9697 35.353576 0.5155 -6.1340 6.1562 1000 4600 0.0940 -4.7044 3.9697 35.640523 0.4848 -6.1365 6.1562 1000 4800 0.0940 -4.7044 3.9697 35.922883 0.4545 -6.1388 6.1562 1000 5000 0.0940 -4.7044 3.9697 36.200788 0.4247 -6.1409 6.1562 1000 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29.995922 1.3808 -5.8273 5.9893 2500 1400 0.0940 -4.3563 4.1092 30.381483 1.3416 -5.8365 5.9893 2500 1600 0.0940 -4.3563 4.1092 30.758997 1.3031 -5.8452 5.9893 2500 1800 0.0940 -4.3563 4.1092 31.128795 1.2654 -5.8535 5.9893 2500 2000 0.0940 -4.3563 4.1092 31.491183 1.2283 -5.8614 5.9893 2500 2200 0.0940 -4.3563 4.1092 31.846449 1.1920 -5.8689 5.9893 2500 2400 0.0940 -4.3563 4.1092 32.194864 1.1562 -5.8760 5.9893 2500 2600 0.0940 -4.3563 4.1092 32.536681 1.1212 -5.8828 5.9893 2500 2800 0.0940 -4.3563 4.1092 32.872138 1.0867 -5.8893 5.9893 2500 3000 0.0940 -4.3563 4.1092 33.201459 1.0528 -5.8954 5.9893 2500 3200 0.0940 -4.3563 4.1092 33.524856 1.0196 -5.9013 5.9893 2500 3400 0.0940 -4.3563 4.1092 33.842528 0.9868 -5.9068 5.9893 2500 3600 0.0940 -4.3563 4.1092 34.154665 0.9546 -5.9121 5.9893 2500 3800 0.0940 -4.3563 4.1092 34.461445 0.9230 -5.9171 5.9893 2500 4000 0.0940 -4.3563 4.1092 34.763039 0.8918 -5.9219 5.9893 2500 4200 0.0940 -4.3563 4.1092 35.059607 0.8611 -5.9265 5.9893 2500 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0.0940 -4.1242 4.2022 32.870408 1.2910 -5.7448 5.8887 3500 3000 0.0940 -4.1242 4.2022 33.199749 1.2579 -5.7521 5.8887 3500 3200 0.0940 -4.1242 4.2022 33.523165 1.2254 -5.7591 5.8887 3500 3400 0.0940 -4.1242 4.2022 33.840856 1.1935 -5.7658 5.8887 3500 3600 0.0940 -4.1242 4.2022 34.153010 1.1621 -5.7722 5.8887 3500 3800 0.0940 -4.1242 4.2022 34.459808 1.1311 -5.7784 5.8887 3500 4000 0.0940 -4.1242 4.2022 34.761419 1.1007 -5.7843 5.8887 3500 4200 0.0940 -4.1242 4.2022 35.058003 1.0707 -5.7899 5.8887 3500 4400 0.0940 -4.1242 4.2022 35.349715 1.0413 -5.7953 5.8887 3500 4600 0.0940 -4.1242 4.2022 35.636699 1.0122 -5.8004 5.8887 3500 4800 0.0940 -4.1242 4.2022 35.919095 0.9836 -5.8053 5.8887 3500 5000 0.0940 -4.1242 4.2022 36.197035 0.9554 -5.8100 5.8887 3500 5200 0.0940 -4.1242 4.2022 36.470645 0.9277 -5.8145 5.8887 3500 5400 0.0940 -4.1242 4.2022 36.740046 0.9003 -5.8188 5.8887 3500 5600 0.0940 -4.1242 4.2022 37.005352 0.8734 -5.8229 5.8887 3500 5800 0.0940 -4.1242 4.2022 37.266674 0.8468 -5.8268 5.8887 3500 6000 0.0940 -4.1242 4.2022 37.524118 0.8206 -5.8306 5.8887 3500 6200 0.0940 -4.1242 4.2022 37.777785 0.7948 -5.8342 5.8887 3500 6400 0.0940 -4.1242 4.2022 38.027771 0.7693 -5.8376 5.8887 3500 6600 0.0940 -4.1242 4.2022 38.274170 0.7442 -5.8408 5.8887 3500 6800 0.0940 -4.1242 4.2022 38.517072 0.7195 -5.8439 5.8887 3500 7000 0.0940 -4.1242 4.2022 38.756561 0.6950 -5.8469 5.8887 3500 7200 0.0940 -4.1242 4.2022 38.992721 0.6709 -5.8497 5.8887 3500 7400 0.0940 -4.1242 4.2022 39.225631 0.6471 -5.8524 5.8887 3500 7600 0.0940 -4.1242 4.2022 39.455367 0.6237 -5.8549 5.8887 3500 7800 0.0940 -4.1242 4.2022 39.682004 0.6005 -5.8573 5.8887 3500 8000 0.0940 -4.1242 4.2022 39.905612 0.5777 -5.8596 5.8887 3500 8200 0.0940 -4.1242 4.2022 40.126259 0.5551 -5.8618 5.8887 3500 8400 0.0940 -4.1242 4.2022 40.344013 0.5328 -5.8639 5.8887 4000 0 0.0940 -4.0082 4.2487 27.488516 1.9189 -5.5169 5.8417 4000 200 0.0940 -4.0082 4.2487 27.930893 1.8763 -5.5316 5.8417 4000 400 0.0940 -4.0082 4.2487 28.362629 1.8345 -5.5456 5.8417 4000 600 0.0940 -4.0082 4.2487 28.784246 1.7937 -5.5589 5.8417 4000 800 0.0940 -4.0082 4.2487 29.196227 1.7536 -5.5717 5.8417 4000 1000 0.0940 -4.0082 4.2487 29.599014 1.7144 -5.5839 5.8417 4000 1200 0.0940 -4.0082 4.2487 29.993018 1.6760 -5.5955 5.8417 4000 1400 0.0940 -4.0082 4.2487 30.378620 1.6383 -5.6067 5.8417 4000 1600 0.0940 -4.0082 4.2487 30.756173 1.6013 -5.6173 5.8417 4000 1800 0.0940 -4.0082 4.2487 31.126008 1.5650 -5.6275 5.8417 4000 2000 0.0940 -4.0082 4.2487 31.488432 1.5294 -5.6373 5.8417 4000 2200 0.0940 -4.0082 4.2487 31.843734 1.4944 -5.6467 5.8417 4000 2400 0.0940 -4.0082 4.2487 32.192182 1.4601 -5.6557 5.8417 4000 2600 0.0940 -4.0082 4.2487 32.534031 1.4263 -5.6643 5.8417 4000 2800 0.0940 -4.0082 4.2487 32.869519 1.3931 -5.6726 5.8417 4000 3000 0.0940 -4.0082 4.2487 33.198870 1.3605 -5.6805 5.8417 4000 3200 0.0940 -4.0082 4.2487 33.522296 1.3284 -5.6881 5.8417 4000 3400 0.0940 -4.0082 4.2487 33.839996 1.2968 -5.6953 5.8417 4000 3600 0.0940 -4.0082 4.2487 34.152160 1.2658 -5.7023 5.8417 4000 3800 0.0940 -4.0082 4.2487 34.458967 1.2352 -5.7090 5.8417 4000 4000 0.0940 -4.0082 4.2487 34.760586 1.2052 -5.7154 5.8417 4000 4200 0.0940 -4.0082 4.2487 35.057179 1.1756 -5.7216 5.8417 4000 4400 0.0940 -4.0082 4.2487 35.348899 1.1464 -5.7275 5.8417 4000 4600 0.0940 -4.0082 4.2487 35.635891 1.1177 -5.7332 5.8417 4000 4800 0.0940 -4.0082 4.2487 35.918295 1.0894 -5.7386 5.8417 4000 5000 0.0940 -4.0082 4.2487 36.196242 1.0616 -5.7438 5.8417 4000 5200 0.0940 -4.0082 4.2487 36.469860 1.0341 -5.7488 5.8417 4000 5400 0.0940 -4.0082 4.2487 36.739267 1.0071 -5.7536 5.8417 4000 5600 0.0940 -4.0082 4.2487 37.004580 0.9804 -5.7582 5.8417 4000 5800 0.0940 -4.0082 4.2487 37.265909 0.9542 -5.7626 5.8417 4000 6000 0.0940 -4.0082 4.2487 37.523360 0.9283 -5.7669 5.8417 4000 6200 0.0940 -4.0082 4.2487 37.777033 0.9027 -5.7709 5.8417 4000 6400 0.0940 -4.0082 4.2487 38.027025 0.8775 -5.7748 5.8417 4000 6600 0.0940 -4.0082 4.2487 38.273431 0.8527 -5.7785 5.8417 4000 6800 0.0940 -4.0082 4.2487 38.516338 0.8282 -5.7821 5.8417 4000 7000 0.0940 -4.0082 4.2487 38.755833 0.8040 -5.7855 5.8417 4000 7200 0.0940 -4.0082 4.2487 38.991999 0.7802 -5.7888 5.8417 4000 7400 0.0940 -4.0082 4.2487 39.224914 0.7566 -5.7919 5.8417 4000 7600 0.0940 -4.0082 4.2487 39.454656 0.7334 -5.7949 5.8417 4000 7800 0.0940 -4.0082 4.2487 39.681298 0.7105 -5.7977 5.8417 4000 8000 0.0940 -4.0082 4.2487 39.904911 0.6878 -5.8004 5.8417 4000 8200 0.0940 -4.0082 4.2487 40.125563 0.6655 -5.8031 5.8417 4000 8400 0.0940 -4.0082 4.2487 40.343322 0.6434 -5.8055 5.8417 4500 0 0.0940 -3.8921 4.2952 27.487411 2.0138 -5.4354 5.7971 4500 200 0.0940 -3.8921 4.2952 27.929806 1.9718 -5.4508 5.7971 4500 400 0.0940 -3.8921 4.2952 28.361560 1.9307 -5.4655 5.7971 4500 600 0.0940 -3.8921 4.2952 28.783195 1.8904 -5.4796 5.7971 4500 800 0.0940 -3.8921 4.2952 29.195192 1.8509 -5.4930 5.7971 4500 1000 0.0940 -3.8921 4.2952 29.597995 1.8123 -5.5059 5.7971 4500 1200 0.0940 -3.8921 4.2952 29.992014 1.7744 -5.5182 5.7971 4500 1400 0.0940 -3.8921 4.2952 30.377630 1.7372 -5.5300 5.7971 4500 1600 0.0940 -3.8921 4.2952 30.755197 1.7007 -5.5414 5.7971 4500 1800 0.0940 -3.8921 4.2952 31.125045 1.6649 -5.5522 5.7971 4500 2000 0.0940 -3.8921 4.2952 31.487481 1.6298 -5.5626 5.7971 4500 2200 0.0940 -3.8921 4.2952 31.842795 1.5952 -5.5726 5.7971 4500 2400 0.0940 -3.8921 4.2952 32.191255 1.5613 -5.5822 5.7971 4500 2600 0.0940 -3.8921 4.2952 32.533115 1.5280 -5.5915 5.7971 4500 2800 0.0940 -3.8921 4.2952 32.868614 1.4952 -5.6003 5.7971 4500 3000 0.0940 -3.8921 4.2952 33.197975 1.4630 -5.6088 5.7971 4500 3200 0.0940 -3.8921 4.2952 33.521411 1.4313 -5.6170 5.7971 4500 3400 0.0940 -3.8921 4.2952 33.839121 1.4002 -5.6248 5.7971 4500 3600 0.0940 -3.8921 4.2952 34.151295 1.3695 -5.6324 5.7971 4500 3800 0.0940 -3.8921 4.2952 34.458111 1.3393 -5.6396 5.7971 4500 4000 0.0940 -3.8921 4.2952 34.759739 1.3096 -5.6466 5.7971 4500 4200 0.0940 -3.8921 4.2952 35.056340 1.2804 -5.6533 5.7971 4500 4400 0.0940 -3.8921 4.2952 35.348068 1.2516 -5.6597 5.7971 4500 4600 0.0940 -3.8921 4.2952 35.635069 1.2232 -5.6659 5.7971 4500 4800 0.0940 -3.8921 4.2952 35.917480 1.1952 -5.6719 5.7971 4500 5000 0.0940 -3.8921 4.2952 36.195435 1.1677 -5.6776 5.7971 4500 5200 0.0940 -3.8921 4.2952 36.469060 1.1406 -5.6831 5.7971 4500 5400 0.0940 -3.8921 4.2952 36.738475 1.1139 -5.6884 5.7971 4500 5600 0.0940 -3.8921 4.2952 37.003795 1.0875 -5.6935 5.7971 4500 5800 0.0940 -3.8921 4.2952 37.265131 1.0615 -5.6984 5.7971 4500 6000 0.0940 -3.8921 4.2952 37.522588 1.0359 -5.7031 5.7971 4500 6200 0.0940 -3.8921 4.2952 37.776268 1.0106 -5.7077 5.7971 4500 6400 0.0940 -3.8921 4.2952 38.026267 0.9857 -5.7120 5.7971 4500 6600 0.0940 -3.8921 4.2952 38.272678 0.9612 -5.7162 5.7971 4500 6800 0.0940 -3.8921 4.2952 38.515591 0.9369 -5.7202 5.7971 4500 7000 0.0940 -3.8921 4.2952 38.755092 0.9130 -5.7241 5.7971 4500 7200 0.0940 -3.8921 4.2952 38.991264 0.8894 -5.7278 5.7971 4500 7400 0.0940 -3.8921 4.2952 39.224185 0.8661 -5.7314 5.7971 4500 7600 0.0940 -3.8921 4.2952 39.453932 0.8431 -5.7348 5.7971 4500 7800 0.0940 -3.8921 4.2952 39.680580 0.8204 -5.7381 5.7971 4500 8000 0.0940 -3.8921 4.2952 39.904198 0.7980 -5.7413 5.7971 4500 8200 0.0940 -3.8921 4.2952 40.124856 0.7759 -5.7443 5.7971 4500 8400 0.0940 -3.8921 4.2952 40.342619 0.7541 -5.7472 5.7971 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.000 ================================================ FILE: tests/test_data/cropA/geometry/20180319-20180506_VV_8rlks_base.par ================================================ initial_baseline(TCN): 0.9353410 -5.3971995 27.0244745 m m m initial_baseline_rate: 0.0000000 -0.0603629 0.0321722 m/s m/s m/s precision_baseline(TCN): 0.0000000 -5.3217217 27.0078715 m m m precision_baseline_rate: 0.0000000 -0.0556305 0.0294090 m/s m/s m/s unwrap_phase_constant: 0.00014 radians ================================================ FILE: tests/test_data/cropA/geometry/20180319-20180506_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): 0.935 -5.397 27.024 orbit baseline rate (TCN) (m/s): 0.000e+00 -6.036e-02 3.217e-02 baseline vector (TCN) (m): 0.000 -5.322 27.008 baseline rate (TCN) (m/s): 0.000e+00 -5.563e-02 2.941e-02 SLC-1 center baseline length (m): 27.5272 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 0.0000 -4.8025 26.7334 27.496626 21.4962 -16.6026 27.1613 0 200 0.0000 -4.8025 26.7334 27.938864 21.3675 -16.7681 27.1613 0 400 0.0000 -4.8025 26.7334 28.370470 21.2405 -16.9285 27.1613 0 600 0.0000 -4.8025 26.7334 28.791963 21.1154 -17.0843 27.1613 0 800 0.0000 -4.8025 26.7334 29.203825 20.9921 -17.2357 27.1613 0 1000 0.0000 -4.8025 26.7334 29.606500 20.8704 -17.3828 27.1613 0 1200 0.0000 -4.8025 26.7334 30.000396 20.7504 -17.5259 27.1613 0 1400 0.0000 -4.8025 26.7334 30.385895 20.6320 -17.6651 27.1613 0 1600 0.0000 -4.8025 26.7334 30.763349 20.5152 -17.8006 27.1613 0 1800 0.0000 -4.8025 26.7334 31.133089 20.3999 -17.9326 27.1613 0 2000 0.0000 -4.8025 26.7334 31.495422 20.2861 -18.0613 27.1613 0 2200 0.0000 -4.8025 26.7334 31.850636 20.1737 -18.1867 27.1613 0 2400 0.0000 -4.8025 26.7334 32.199000 20.0628 -18.3090 27.1613 0 2600 0.0000 -4.8025 26.7334 32.540768 19.9532 -18.4284 27.1613 0 2800 0.0000 -4.8025 26.7334 32.876178 19.8450 -18.5449 27.1613 0 3000 0.0000 -4.8025 26.7334 33.205453 19.7381 -18.6586 27.1613 0 3200 0.0000 -4.8025 26.7334 33.528805 19.6325 -18.7697 27.1613 0 3400 0.0000 -4.8025 26.7334 33.846435 19.5281 -18.8782 27.1613 0 3600 0.0000 -4.8025 26.7334 34.158531 19.4250 -18.9843 27.1613 0 3800 0.0000 -4.8025 26.7334 34.465271 19.3231 -19.0881 27.1613 0 4000 0.0000 -4.8025 26.7334 34.766827 19.2224 -19.1895 27.1613 0 4200 0.0000 -4.8025 26.7334 35.063357 19.1228 -19.2887 27.1613 0 4400 0.0000 -4.8025 26.7334 35.355017 19.0244 -19.3858 27.1613 0 4600 0.0000 -4.8025 26.7334 35.641950 18.9270 -19.4808 27.1613 0 4800 0.0000 -4.8025 26.7334 35.924297 18.8308 -19.5739 27.1613 0 5000 0.0000 -4.8025 26.7334 36.202189 18.7357 -19.6650 27.1613 0 5200 0.0000 -4.8025 26.7334 36.475752 18.6416 -19.7542 27.1613 0 5400 0.0000 -4.8025 26.7334 36.745108 18.5485 -19.8416 27.1613 0 5600 0.0000 -4.8025 26.7334 37.010370 18.4564 -19.9273 27.1613 0 5800 0.0000 -4.8025 26.7334 37.271649 18.3654 -20.0112 27.1613 0 6000 0.0000 -4.8025 26.7334 37.529051 18.2753 -20.0935 27.1613 0 6200 0.0000 -4.8025 26.7334 37.782677 18.1861 -20.1742 27.1613 0 6400 0.0000 -4.8025 26.7334 38.032623 18.0980 -20.2534 27.1613 0 6600 0.0000 -4.8025 26.7334 38.278984 18.0107 -20.3310 27.1613 0 6800 0.0000 -4.8025 26.7334 38.521847 17.9244 -20.4072 27.1613 0 7000 0.0000 -4.8025 26.7334 38.761299 17.8389 -20.4819 27.1613 0 7200 0.0000 -4.8025 26.7334 38.997423 17.7544 -20.5553 27.1613 0 7400 0.0000 -4.8025 26.7334 39.230297 17.6707 -20.6272 27.1613 0 7600 0.0000 -4.8025 26.7334 39.459999 17.5878 -20.6979 27.1613 0 7800 0.0000 -4.8025 26.7334 39.686602 17.5058 -20.7673 27.1613 0 8000 0.0000 -4.8025 26.7334 39.910176 17.4247 -20.8355 27.1613 0 8200 0.0000 -4.8025 26.7334 40.130792 17.3443 -20.9024 27.1613 0 8400 0.0000 -4.8025 26.7334 40.348513 17.2648 -20.9682 27.1613 500 0 0.0000 -4.9168 26.7938 27.495684 21.4973 -16.7316 27.2412 500 200 0.0000 -4.9168 26.7938 27.937938 21.3676 -16.8971 27.2412 500 400 0.0000 -4.9168 26.7938 28.369559 21.2397 -17.0576 27.2412 500 600 0.0000 -4.9168 26.7938 28.791066 21.1136 -17.2133 27.2412 500 800 0.0000 -4.9168 26.7938 29.202942 20.9893 -17.3647 27.2412 500 1000 0.0000 -4.9168 26.7938 29.605629 20.8668 -17.5118 27.2412 500 1200 0.0000 -4.9168 26.7938 29.999538 20.7459 -17.6548 27.2412 500 1400 0.0000 -4.9168 26.7938 30.385049 20.6266 -17.7940 27.2412 500 1600 0.0000 -4.9168 26.7938 30.762515 20.5089 -17.9295 27.2412 500 1800 0.0000 -4.9168 26.7938 31.132265 20.3928 -18.0615 27.2412 500 2000 0.0000 -4.9168 26.7938 31.494609 20.2782 -18.1901 27.2412 500 2200 0.0000 -4.9168 26.7938 31.849833 20.1650 -18.3154 27.2412 500 2400 0.0000 -4.9168 26.7938 32.198207 20.0533 -18.4377 27.2412 500 2600 0.0000 -4.9168 26.7938 32.539984 19.9429 -18.5570 27.2412 500 2800 0.0000 -4.9168 26.7938 32.875402 19.8340 -18.6734 27.2412 500 3000 0.0000 -4.9168 26.7938 33.204686 19.7263 -18.7871 27.2412 500 3200 0.0000 -4.9168 26.7938 33.528047 19.6200 -18.8981 27.2412 500 3400 0.0000 -4.9168 26.7938 33.845685 19.5149 -19.0066 27.2412 500 3600 0.0000 -4.9168 26.7938 34.157789 19.4111 -19.1126 27.2412 500 3800 0.0000 -4.9168 26.7938 34.464537 19.3085 -19.2163 27.2412 500 4000 0.0000 -4.9168 26.7938 34.766099 19.2071 -19.3177 27.2412 500 4200 0.0000 -4.9168 26.7938 35.062637 19.1068 -19.4168 27.2412 500 4400 0.0000 -4.9168 26.7938 35.354304 19.0077 -19.5138 27.2412 500 4600 0.0000 -4.9168 26.7938 35.641244 18.9098 -19.6088 27.2412 500 4800 0.0000 -4.9168 26.7938 35.923597 18.8129 -19.7017 27.2412 500 5000 0.0000 -4.9168 26.7938 36.201496 18.7171 -19.7927 27.2412 500 5200 0.0000 -4.9168 26.7938 36.475065 18.6224 -19.8819 27.2412 500 5400 0.0000 -4.9168 26.7938 36.744426 18.5287 -19.9692 27.2412 500 5600 0.0000 -4.9168 26.7938 37.009694 18.4361 -20.0548 27.2412 500 5800 0.0000 -4.9168 26.7938 37.270979 18.3444 -20.1386 27.2412 500 6000 0.0000 -4.9168 26.7938 37.528387 18.2538 -20.2208 27.2412 500 6200 0.0000 -4.9168 26.7938 37.782018 18.1641 -20.3014 27.2412 500 6400 0.0000 -4.9168 26.7938 38.031970 18.0754 -20.3805 27.2412 500 6600 0.0000 -4.9168 26.7938 38.278335 17.9876 -20.4580 27.2412 500 6800 0.0000 -4.9168 26.7938 38.521204 17.9007 -20.5341 27.2412 500 7000 0.0000 -4.9168 26.7938 38.760661 17.8147 -20.6087 27.2412 500 7200 0.0000 -4.9168 26.7938 38.996789 17.7296 -20.6820 27.2412 500 7400 0.0000 -4.9168 26.7938 39.229668 17.6454 -20.7539 27.2412 500 7600 0.0000 -4.9168 26.7938 39.459375 17.5621 -20.8244 27.2412 500 7800 0.0000 -4.9168 26.7938 39.685982 17.4796 -20.8937 27.2412 500 8000 0.0000 -4.9168 26.7938 39.909561 17.3979 -20.9618 27.2412 500 8200 0.0000 -4.9168 26.7938 40.130181 17.3171 -21.0286 27.2412 500 8400 0.0000 -4.9168 26.7938 40.347906 17.2370 -21.0943 27.2412 1000 0 0.0000 -5.0312 26.8543 27.494721 21.4985 -16.8606 27.3215 1000 200 0.0000 -5.0312 26.8543 27.936992 21.3677 -17.0261 27.3215 1000 400 0.0000 -5.0312 26.8543 28.368628 21.2388 -17.1865 27.3215 1000 600 0.0000 -5.0312 26.8543 28.790150 21.1118 -17.3423 27.3215 1000 800 0.0000 -5.0312 26.8543 29.202040 20.9866 -17.4937 27.3215 1000 1000 0.0000 -5.0312 26.8543 29.604740 20.8631 -17.6407 27.3215 1000 1200 0.0000 -5.0312 26.8543 29.998662 20.7413 -17.7837 27.3215 1000 1400 0.0000 -5.0312 26.8543 30.384184 20.6212 -17.9229 27.3215 1000 1600 0.0000 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27.7285 3500 5600 0.0000 -5.6029 27.1565 37.005352 18.3143 -20.8196 27.7285 3500 5800 0.0000 -5.6029 27.1565 37.266674 18.2192 -20.9029 27.7285 3500 6000 0.0000 -5.6029 27.1565 37.524118 18.1251 -20.9846 27.7285 3500 6200 0.0000 -5.6029 27.1565 37.777785 18.0320 -21.0646 27.7285 3500 6400 0.0000 -5.6029 27.1565 38.027771 17.9399 -21.1431 27.7285 3500 6600 0.0000 -5.6029 27.1565 38.274170 17.8488 -21.2200 27.7285 3500 6800 0.0000 -5.6029 27.1565 38.517072 17.7587 -21.2955 27.7285 3500 7000 0.0000 -5.6029 27.1565 38.756561 17.6695 -21.3695 27.7285 3500 7200 0.0000 -5.6029 27.1565 38.992721 17.5813 -21.4422 27.7285 3500 7400 0.0000 -5.6029 27.1565 39.225631 17.4940 -21.5135 27.7285 3500 7600 0.0000 -5.6029 27.1565 39.455367 17.4076 -21.5835 27.7285 3500 7800 0.0000 -5.6029 27.1565 39.682004 17.3221 -21.6521 27.7285 3500 8000 0.0000 -5.6029 27.1565 39.905612 17.2374 -21.7196 27.7285 3500 8200 0.0000 -5.6029 27.1565 40.126259 17.1537 -21.7858 27.7285 3500 8400 0.0000 -5.6029 27.1565 40.344013 17.0707 -21.8508 27.7285 4000 0 0.0000 -5.7173 27.2170 27.488516 21.5054 -17.6344 27.8110 4000 200 0.0000 -5.7173 27.2170 27.930893 21.3686 -17.7999 27.8110 4000 400 0.0000 -5.7173 27.2170 28.362629 21.2338 -17.9604 27.8110 4000 600 0.0000 -5.7173 27.2170 28.784246 21.1011 -18.1161 27.8110 4000 800 0.0000 -5.7173 27.2170 29.196227 20.9703 -18.2674 27.8110 4000 1000 0.0000 -5.7173 27.2170 29.599014 20.8413 -18.4144 27.8110 4000 1200 0.0000 -5.7173 27.2170 29.993018 20.7142 -18.5572 27.8110 4000 1400 0.0000 -5.7173 27.2170 30.378620 20.5889 -18.6962 27.8110 4000 1600 0.0000 -5.7173 27.2170 30.756173 20.4652 -18.8315 27.8110 4000 1800 0.0000 -5.7173 27.2170 31.126008 20.3432 -18.9632 27.8110 4000 2000 0.0000 -5.7173 27.2170 31.488432 20.2229 -19.0915 27.8110 4000 2200 0.0000 -5.7173 27.2170 31.843734 20.1041 -19.2165 27.8110 4000 2400 0.0000 -5.7173 27.2170 32.192182 19.9869 -19.3385 27.8110 4000 2600 0.0000 -5.7173 27.2170 32.534031 19.8711 -19.4574 27.8110 4000 2800 0.0000 -5.7173 27.2170 32.869519 19.7569 -19.5734 27.8110 4000 3000 0.0000 -5.7173 27.2170 33.198870 19.6440 -19.6866 27.8110 4000 3200 0.0000 -5.7173 27.2170 33.522296 19.5326 -19.7972 27.8110 4000 3400 0.0000 -5.7173 27.2170 33.839996 19.4225 -19.9052 27.8110 4000 3600 0.0000 -5.7173 27.2170 34.152160 19.3138 -20.0107 27.8110 4000 3800 0.0000 -5.7173 27.2170 34.458967 19.2063 -20.1139 27.8110 4000 4000 0.0000 -5.7173 27.2170 34.760586 19.1002 -20.2147 27.8110 4000 4200 0.0000 -5.7173 27.2170 35.057179 18.9953 -20.3133 27.8110 4000 4400 0.0000 -5.7173 27.2170 35.348899 18.8916 -20.4097 27.8110 4000 4600 0.0000 -5.7173 27.2170 35.635891 18.7892 -20.5041 27.8110 4000 4800 0.0000 -5.7173 27.2170 35.918295 18.6879 -20.5965 27.8110 4000 5000 0.0000 -5.7173 27.2170 36.196242 18.5877 -20.6869 27.8110 4000 5200 0.0000 -5.7173 27.2170 36.469860 18.4887 -20.7754 27.8110 4000 5400 0.0000 -5.7173 27.2170 36.739267 18.3908 -20.8621 27.8110 4000 5600 0.0000 -5.7173 27.2170 37.004580 18.2940 -20.9470 27.8110 4000 5800 0.0000 -5.7173 27.2170 37.265909 18.1983 -21.0303 27.8110 4000 6000 0.0000 -5.7173 27.2170 37.523360 18.1036 -21.1118 27.8110 4000 6200 0.0000 -5.7173 27.2170 37.777033 18.0100 -21.1918 27.8110 4000 6400 0.0000 -5.7173 27.2170 38.027025 17.9173 -21.2702 27.8110 4000 6600 0.0000 -5.7173 27.2170 38.273431 17.8257 -21.3470 27.8110 4000 6800 0.0000 -5.7173 27.2170 38.516338 17.7350 -21.4224 27.8110 4000 7000 0.0000 -5.7173 27.2170 38.755833 17.6453 -21.4963 27.8110 4000 7200 0.0000 -5.7173 27.2170 38.991999 17.5566 -21.5689 27.8110 4000 7400 0.0000 -5.7173 27.2170 39.224914 17.4688 -21.6401 27.8110 4000 7600 0.0000 -5.7173 27.2170 39.454656 17.3819 -21.7100 27.8110 4000 7800 0.0000 -5.7173 27.2170 39.681298 17.2958 -21.7785 27.8110 4000 8000 0.0000 -5.7173 27.2170 39.904911 17.2107 -21.8459 27.8110 4000 8200 0.0000 -5.7173 27.2170 40.125563 17.1265 -21.9120 27.8110 4000 8400 0.0000 -5.7173 27.2170 40.343322 17.0430 -21.9769 27.8110 4500 0 0.0000 -5.8316 27.2774 27.487411 21.5065 -17.7633 27.8938 4500 200 0.0000 -5.8316 27.2774 27.929806 21.3687 -17.9288 27.8938 4500 400 0.0000 -5.8316 27.2774 28.361560 21.2330 -18.0893 27.8938 4500 600 0.0000 -5.8316 27.2774 28.783195 21.0993 -18.2451 27.8938 4500 800 0.0000 -5.8316 27.2774 29.195192 20.9676 -18.3963 27.8938 4500 1000 0.0000 -5.8316 27.2774 29.597995 20.8378 -18.5433 27.8938 4500 1200 0.0000 -5.8316 27.2774 29.992014 20.7097 -18.6861 27.8938 4500 1400 0.0000 -5.8316 27.2774 30.377630 20.5835 -18.8251 27.8938 4500 1600 0.0000 -5.8316 27.2774 30.755197 20.4590 -18.9603 27.8938 4500 1800 0.0000 -5.8316 27.2774 31.125045 20.3362 -19.0920 27.8938 4500 2000 0.0000 -5.8316 27.2774 31.487481 20.2150 -19.2203 27.8938 4500 2200 0.0000 -5.8316 27.2774 31.842795 20.0954 -19.3452 27.8938 4500 2400 0.0000 -5.8316 27.2774 32.191255 19.9774 -19.4671 27.8938 4500 2600 0.0000 -5.8316 27.2774 32.533115 19.8609 -19.5860 27.8938 4500 2800 0.0000 -5.8316 27.2774 32.868614 19.7459 -19.7019 27.8938 4500 3000 0.0000 -5.8316 27.2774 33.197975 19.6323 -19.8151 27.8938 4500 3200 0.0000 -5.8316 27.2774 33.521411 19.5201 -19.9256 27.8938 4500 3400 0.0000 -5.8316 27.2774 33.839121 19.4093 -20.0335 27.8938 4500 3600 0.0000 -5.8316 27.2774 34.151295 19.2999 -20.1390 27.8938 4500 3800 0.0000 -5.8316 27.2774 34.458111 19.1918 -20.2421 27.8938 4500 4000 0.0000 -5.8316 27.2774 34.759739 19.0850 -20.3428 27.8938 4500 4200 0.0000 -5.8316 27.2774 35.056340 18.9794 -20.4413 27.8938 4500 4400 0.0000 -5.8316 27.2774 35.348068 18.8751 -20.5377 27.8938 4500 4600 0.0000 -5.8316 27.2774 35.635069 18.7720 -20.6320 27.8938 4500 4800 0.0000 -5.8316 27.2774 35.917480 18.6700 -20.7243 27.8938 4500 5000 0.0000 -5.8316 27.2774 36.195435 18.5693 -20.8146 27.8938 4500 5200 0.0000 -5.8316 27.2774 36.469060 18.4697 -20.9030 27.8938 4500 5400 0.0000 -5.8316 27.2774 36.738475 18.3712 -20.9897 27.8938 4500 5600 0.0000 -5.8316 27.2774 37.003795 18.2738 -21.0745 27.8938 4500 5800 0.0000 -5.8316 27.2774 37.265131 18.1775 -21.1576 27.8938 4500 6000 0.0000 -5.8316 27.2774 37.522588 18.0822 -21.2391 27.8938 4500 6200 0.0000 -5.8316 27.2774 37.776268 17.9880 -21.3190 27.8938 4500 6400 0.0000 -5.8316 27.2774 38.026267 17.8948 -21.3972 27.8938 4500 6600 0.0000 -5.8316 27.2774 38.272678 17.8026 -21.4740 27.8938 4500 6800 0.0000 -5.8316 27.2774 38.515591 17.7114 -21.5493 27.8938 4500 7000 0.0000 -5.8316 27.2774 38.755092 17.6212 -21.6231 27.8938 4500 7200 0.0000 -5.8316 27.2774 38.991264 17.5319 -21.6956 27.8938 4500 7400 0.0000 -5.8316 27.2774 39.224185 17.4436 -21.7667 27.8938 4500 7600 0.0000 -5.8316 27.2774 39.453932 17.3561 -21.8364 27.8938 4500 7800 0.0000 -5.8316 27.2774 39.680580 17.2696 -21.9049 27.8938 4500 8000 0.0000 -5.8316 27.2774 39.904198 17.1840 -21.9722 27.8938 4500 8200 0.0000 -5.8316 27.2774 40.124856 17.0993 -22.0382 27.8938 4500 8400 0.0000 -5.8316 27.2774 40.342619 17.0154 -22.1030 27.8938 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.000 ================================================ FILE: tests/test_data/cropA/geometry/20180319-20180518_VV_8rlks_base.par ================================================ initial_baseline(TCN): -1.0633968 -19.5288097 28.5389506 m m m initial_baseline_rate: 0.0000000 -0.0082175 0.0277582 m/s m/s m/s precision_baseline(TCN): 0.0000000 -19.4409579 28.4937618 m m m precision_baseline_rate: 0.0000000 0.0020898 0.0233207 m/s m/s m/s unwrap_phase_constant: 0.00007 radians ================================================ FILE: tests/test_data/cropA/geometry/20180319-20180518_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): -1.063 -19.529 28.539 orbit baseline rate (TCN) (m/s): 0.000e+00 -8.218e-03 2.776e-02 baseline vector (TCN) (m): 0.000 -19.441 28.494 baseline rate (TCN) (m/s): 0.000e+00 2.090e-03 2.332e-02 SLC-1 center baseline length (m): 34.4941 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 0.0000 -19.4605 28.2761 27.496626 16.0972 -30.3171 34.3256 0 200 0.0000 -19.4605 28.2761 27.938864 15.8627 -30.4404 34.3256 0 400 0.0000 -19.4605 28.2761 28.370470 15.6329 -30.5591 34.3256 0 600 0.0000 -19.4605 28.2761 28.791963 15.4077 -30.6732 34.3256 0 800 0.0000 -19.4605 28.2761 29.203825 15.1868 -30.7832 34.3256 0 1000 0.0000 -19.4605 28.2761 29.606500 14.9701 -30.8892 34.3256 0 1200 0.0000 -19.4605 28.2761 30.000396 14.7574 -30.9914 34.3256 0 1400 0.0000 -19.4605 28.2761 30.385895 14.5485 -31.0899 34.3256 0 1600 0.0000 -19.4605 28.2761 30.763349 14.3434 -31.1851 34.3256 0 1800 0.0000 -19.4605 28.2761 31.133089 14.1419 -31.2770 34.3256 0 2000 0.0000 -19.4605 28.2761 31.495422 13.9438 -31.3658 34.3256 0 2200 0.0000 -19.4605 28.2761 31.850636 13.7491 -31.4517 34.3256 0 2400 0.0000 -19.4605 28.2761 32.199000 13.5576 -31.5347 34.3256 0 2600 0.0000 -19.4605 28.2761 32.540768 13.3692 -31.6150 34.3256 0 2800 0.0000 -19.4605 28.2761 32.876178 13.1839 -31.6927 34.3256 0 3000 0.0000 -19.4605 28.2761 33.205453 13.0016 -31.7680 34.3256 0 3200 0.0000 -19.4605 28.2761 33.528805 12.8221 -31.8408 34.3256 0 3400 0.0000 -19.4605 28.2761 33.846435 12.6454 -31.9114 34.3256 0 3600 0.0000 -19.4605 28.2761 34.158531 12.4714 -31.9798 34.3256 0 3800 0.0000 -19.4605 28.2761 34.465271 12.3000 -32.0461 34.3256 0 4000 0.0000 -19.4605 28.2761 34.766827 12.1311 -32.1104 34.3256 0 4200 0.0000 -19.4605 28.2761 35.063357 11.9648 -32.1728 34.3256 0 4400 0.0000 -19.4605 28.2761 35.355017 11.8009 -32.2333 34.3256 0 4600 0.0000 -19.4605 28.2761 35.641950 11.6393 -32.2920 34.3256 0 4800 0.0000 -19.4605 28.2761 35.924297 11.4800 -32.3489 34.3256 0 5000 0.0000 -19.4605 28.2761 36.202189 11.3230 -32.4042 34.3256 0 5200 0.0000 -19.4605 28.2761 36.475752 11.1681 -32.4579 34.3256 0 5400 0.0000 -19.4605 28.2761 36.745108 11.0154 -32.5101 34.3256 0 5600 0.0000 -19.4605 28.2761 37.010370 10.8648 -32.5607 34.3256 0 5800 0.0000 -19.4605 28.2761 37.271649 10.7162 -32.6099 34.3256 0 6000 0.0000 -19.4605 28.2761 37.529051 10.5696 -32.6578 34.3256 0 6200 0.0000 -19.4605 28.2761 37.782677 10.4249 -32.7042 34.3256 0 6400 0.0000 -19.4605 28.2761 38.032623 10.2822 -32.7494 34.3256 0 6600 0.0000 -19.4605 28.2761 38.278984 10.1413 -32.7933 34.3256 0 6800 0.0000 -19.4605 28.2761 38.521847 10.0022 -32.8360 34.3256 0 7000 0.0000 -19.4605 28.2761 38.761299 9.8648 -32.8775 34.3256 0 7200 0.0000 -19.4605 28.2761 38.997423 9.7293 -32.9179 34.3256 0 7400 0.0000 -19.4605 28.2761 39.230297 9.5954 -32.9571 34.3256 0 7600 0.0000 -19.4605 28.2761 39.459999 9.4632 -32.9954 34.3256 0 7800 0.0000 -19.4605 28.2761 39.686602 9.3326 -33.0325 34.3256 0 8000 0.0000 -19.4605 28.2761 39.910176 9.2037 -33.0687 34.3256 0 8200 0.0000 -19.4605 28.2761 40.130792 9.0763 -33.1039 34.3256 0 8400 0.0000 -19.4605 28.2761 40.348513 8.9504 -33.1381 34.3256 500 0 0.0000 -19.4562 28.3240 27.495684 16.1422 -30.3351 34.3627 500 200 0.0000 -19.4562 28.3240 27.937938 15.9075 -30.4588 34.3627 500 400 0.0000 -19.4562 28.3240 28.369559 15.6776 -30.5778 34.3627 500 600 0.0000 -19.4562 28.3240 28.791066 15.4523 -30.6923 34.3627 500 800 0.0000 -19.4562 28.3240 29.202942 15.2312 -30.8026 34.3627 500 1000 0.0000 -19.4562 28.3240 29.605629 15.0144 -30.9089 34.3627 500 1200 0.0000 -19.4562 28.3240 29.999538 14.8015 -31.0114 34.3627 500 1400 0.0000 -19.4562 28.3240 30.385049 14.5925 -31.1103 34.3627 500 1600 0.0000 -19.4562 28.3240 30.762515 14.3872 -31.2057 34.3627 500 1800 0.0000 -19.4562 28.3240 31.132265 14.1856 -31.2979 34.3627 500 2000 0.0000 -19.4562 28.3240 31.494609 13.9873 -31.3870 34.3627 500 2200 0.0000 -19.4562 28.3240 31.849833 13.7925 -31.4731 34.3627 500 2400 0.0000 -19.4562 28.3240 32.198207 13.6009 -31.5564 34.3627 500 2600 0.0000 -19.4562 28.3240 32.539984 13.4124 -31.6370 34.3627 500 2800 0.0000 -19.4562 28.3240 32.875402 13.2270 -31.7150 34.3627 500 3000 0.0000 -19.4562 28.3240 33.204686 13.0445 -31.7905 34.3627 500 3200 0.0000 -19.4562 28.3240 33.528047 12.8648 -31.8636 34.3627 500 3400 0.0000 -19.4562 28.3240 33.845685 12.6880 -31.9344 34.3627 500 3600 0.0000 -19.4562 28.3240 34.157789 12.5139 -32.0030 34.3627 500 3800 0.0000 -19.4562 28.3240 34.464537 12.3423 -32.0696 34.3627 500 4000 0.0000 -19.4562 28.3240 34.766099 12.1734 -32.1341 34.3627 500 4200 0.0000 -19.4562 28.3240 35.062637 12.0069 -32.1967 34.3627 500 4400 0.0000 -19.4562 28.3240 35.354304 11.8429 -32.2574 34.3627 500 4600 0.0000 -19.4562 28.3240 35.641244 11.6812 -32.3163 34.3627 500 4800 0.0000 -19.4562 28.3240 35.923597 11.5218 -32.3734 34.3627 500 5000 0.0000 -19.4562 28.3240 36.201496 11.3646 -32.4290 34.3627 500 5200 0.0000 -19.4562 28.3240 36.475065 11.2096 -32.4828 34.3627 500 5400 0.0000 -19.4562 28.3240 36.744426 11.0568 -32.5352 34.3627 500 5600 0.0000 -19.4562 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34.5856 3500 3600 0.0000 -19.4304 28.6116 34.153010 12.7690 -32.1421 34.5856 3500 3800 0.0000 -19.4304 28.6116 34.459808 12.5967 -32.2101 34.5856 3500 4000 0.0000 -19.4304 28.6116 34.761419 12.4270 -32.2759 34.5856 3500 4200 0.0000 -19.4304 28.6116 35.058003 12.2598 -32.3398 34.5856 3500 4400 0.0000 -19.4304 28.6116 35.349715 12.0949 -32.4018 34.5856 3500 4600 0.0000 -19.4304 28.6116 35.636699 11.9325 -32.4620 34.5856 3500 4800 0.0000 -19.4304 28.6116 35.919095 11.7724 -32.5204 34.5856 3500 5000 0.0000 -19.4304 28.6116 36.197035 11.6145 -32.5771 34.5856 3500 5200 0.0000 -19.4304 28.6116 36.470645 11.4588 -32.6322 34.5856 3500 5400 0.0000 -19.4304 28.6116 36.740046 11.3052 -32.6857 34.5856 3500 5600 0.0000 -19.4304 28.6116 37.005352 11.1537 -32.7377 34.5856 3500 5800 0.0000 -19.4304 28.6116 37.266674 11.0043 -32.7883 34.5856 3500 6000 0.0000 -19.4304 28.6116 37.524118 10.8569 -32.8374 34.5856 3500 6200 0.0000 -19.4304 28.6116 37.777785 10.7114 -32.8851 34.5856 3500 6400 0.0000 -19.4304 28.6116 38.027771 10.5678 -32.9315 34.5856 3500 6600 0.0000 -19.4304 28.6116 38.274170 10.4261 -32.9767 34.5856 3500 6800 0.0000 -19.4304 28.6116 38.517072 10.2862 -33.0206 34.5856 3500 7000 0.0000 -19.4304 28.6116 38.756561 10.1481 -33.0633 34.5856 3500 7200 0.0000 -19.4304 28.6116 38.992721 10.0117 -33.1048 34.5856 3500 7400 0.0000 -19.4304 28.6116 39.225631 9.8770 -33.1453 34.5856 3500 7600 0.0000 -19.4304 28.6116 39.455367 9.7441 -33.1846 34.5856 3500 7800 0.0000 -19.4304 28.6116 39.682004 9.6127 -33.2229 34.5856 3500 8000 0.0000 -19.4304 28.6116 39.905612 9.4830 -33.2602 34.5856 3500 8200 0.0000 -19.4304 28.6116 40.126259 9.3548 -33.2964 34.5856 3500 8400 0.0000 -19.4304 28.6116 40.344013 9.2282 -33.3317 34.5856 4000 0 0.0000 -19.4261 28.6596 27.488516 16.4575 -30.4613 34.6229 4000 200 0.0000 -19.4261 28.6596 27.930893 16.2218 -30.5875 34.6229 4000 400 0.0000 -19.4261 28.6596 28.362629 15.9909 -30.7089 34.6229 4000 600 0.0000 -19.4261 28.6596 28.784246 15.7645 -30.8257 34.6229 4000 800 0.0000 -19.4261 28.6596 29.196227 15.5424 -30.9383 34.6229 4000 1000 0.0000 -19.4261 28.6596 29.599014 15.3245 -31.0468 34.6229 4000 1200 0.0000 -19.4261 28.6596 29.993018 15.1107 -31.1514 34.6229 4000 1400 0.0000 -19.4261 28.6596 30.378620 14.9007 -31.2524 34.6229 4000 1600 0.0000 -19.4261 28.6596 30.756173 14.6944 -31.3499 34.6229 4000 1800 0.0000 -19.4261 28.6596 31.126008 14.4918 -31.4441 34.6229 4000 2000 0.0000 -19.4261 28.6596 31.488432 14.2926 -31.5351 34.6229 4000 2200 0.0000 -19.4261 28.6596 31.843734 14.0968 -31.6232 34.6229 4000 2400 0.0000 -19.4261 28.6596 32.192182 13.9042 -31.7083 34.6229 4000 2600 0.0000 -19.4261 28.6596 32.534031 13.7147 -31.7907 34.6229 4000 2800 0.0000 -19.4261 28.6596 32.869519 13.5284 -31.8705 34.6229 4000 3000 0.0000 -19.4261 28.6596 33.198870 13.3449 -31.9477 34.6229 4000 3200 0.0000 -19.4261 28.6596 33.522296 13.1644 -32.0225 34.6229 4000 3400 0.0000 -19.4261 28.6596 33.839996 12.9866 -32.0950 34.6229 4000 3600 0.0000 -19.4261 28.6596 34.152160 12.8116 -32.1653 34.6229 4000 3800 0.0000 -19.4261 28.6596 34.458967 12.6392 -32.2334 34.6229 4000 4000 0.0000 -19.4261 28.6596 34.760586 12.4693 -32.2995 34.6229 4000 4200 0.0000 -19.4261 28.6596 35.057179 12.3019 -32.3637 34.6229 4000 4400 0.0000 -19.4261 28.6596 35.348899 12.1370 -32.4259 34.6229 4000 4600 0.0000 -19.4261 28.6596 35.635891 11.9744 -32.4863 34.6229 4000 4800 0.0000 -19.4261 28.6596 35.918295 11.8141 -32.5449 34.6229 4000 5000 0.0000 -19.4261 28.6596 36.196242 11.6561 -32.6018 34.6229 4000 5200 0.0000 -19.4261 28.6596 36.469860 11.5003 -32.6571 34.6229 4000 5400 0.0000 -19.4261 28.6596 36.739267 11.3466 -32.7108 34.6229 4000 5600 0.0000 -19.4261 28.6596 37.004580 11.1950 -32.7630 34.6229 4000 5800 0.0000 -19.4261 28.6596 37.265909 11.0455 -32.8137 34.6229 4000 6000 0.0000 -19.4261 28.6596 37.523360 10.8979 -32.8630 34.6229 4000 6200 0.0000 -19.4261 28.6596 37.777033 10.7523 -32.9110 34.6229 4000 6400 0.0000 -19.4261 28.6596 38.027025 10.6086 -32.9576 34.6229 4000 6600 0.0000 -19.4261 28.6596 38.273431 10.4668 -33.0029 34.6229 4000 6800 0.0000 -19.4261 28.6596 38.516338 10.3268 -33.0470 34.6229 4000 7000 0.0000 -19.4261 28.6596 38.755833 10.1886 -33.0898 34.6229 4000 7200 0.0000 -19.4261 28.6596 38.991999 10.0521 -33.1315 34.6229 4000 7400 0.0000 -19.4261 28.6596 39.224914 9.9173 -33.1721 34.6229 4000 7600 0.0000 -19.4261 28.6596 39.454656 9.7842 -33.2116 34.6229 4000 7800 0.0000 -19.4261 28.6596 39.681298 9.6528 -33.2501 34.6229 4000 8000 0.0000 -19.4261 28.6596 39.904911 9.5229 -33.2875 34.6229 4000 8200 0.0000 -19.4261 28.6596 40.125563 9.3947 -33.3239 34.6229 4000 8400 0.0000 -19.4261 28.6596 40.343322 9.2679 -33.3594 34.6229 4500 0 0.0000 -19.4218 28.7075 27.487411 16.5026 -30.4793 34.6602 4500 200 0.0000 -19.4218 28.7075 27.929806 16.2668 -30.6058 34.6602 4500 400 0.0000 -19.4218 28.7075 28.361560 16.0357 -30.7276 34.6602 4500 600 0.0000 -19.4218 28.7075 28.783195 15.8091 -30.8447 34.6602 4500 800 0.0000 -19.4218 28.7075 29.195192 15.5869 -30.9576 34.6602 4500 1000 0.0000 -19.4218 28.7075 29.597995 15.3689 -31.0664 34.6602 4500 1200 0.0000 -19.4218 28.7075 29.992014 15.1549 -31.1714 34.6602 4500 1400 0.0000 -19.4218 28.7075 30.377630 14.9448 -31.2727 34.6602 4500 1600 0.0000 -19.4218 28.7075 30.755197 14.7384 -31.3705 34.6602 4500 1800 0.0000 -19.4218 28.7075 31.125045 14.5356 -31.4650 34.6602 4500 2000 0.0000 -19.4218 28.7075 31.487481 14.3362 -31.5563 34.6602 4500 2200 0.0000 -19.4218 28.7075 31.842795 14.1403 -31.6446 34.6602 4500 2400 0.0000 -19.4218 28.7075 32.191255 13.9475 -31.7300 34.6602 4500 2600 0.0000 -19.4218 28.7075 32.533115 13.7580 -31.8126 34.6602 4500 2800 0.0000 -19.4218 28.7075 32.868614 13.5715 -31.8927 34.6602 4500 3000 0.0000 -19.4218 28.7075 33.197975 13.3879 -31.9701 34.6602 4500 3200 0.0000 -19.4218 28.7075 33.521411 13.2072 -32.0452 34.6602 4500 3400 0.0000 -19.4218 28.7075 33.839121 13.0293 -32.1180 34.6602 4500 3600 0.0000 -19.4218 28.7075 34.151295 12.8541 -32.1885 34.6602 4500 3800 0.0000 -19.4218 28.7075 34.458111 12.6816 -32.2568 34.6602 4500 4000 0.0000 -19.4218 28.7075 34.759739 12.5116 -32.3232 34.6602 4500 4200 0.0000 -19.4218 28.7075 35.056340 12.3441 -32.3875 34.6602 4500 4400 0.0000 -19.4218 28.7075 35.348068 12.1790 -32.4499 34.6602 4500 4600 0.0000 -19.4218 28.7075 35.635069 12.0163 -32.5105 34.6602 4500 4800 0.0000 -19.4218 28.7075 35.917480 11.8560 -32.5694 34.6602 4500 5000 0.0000 -19.4218 28.7075 36.195435 11.6978 -32.6265 34.6602 4500 5200 0.0000 -19.4218 28.7075 36.469060 11.5419 -32.6820 34.6602 4500 5400 0.0000 -19.4218 28.7075 36.738475 11.3881 -32.7359 34.6602 4500 5600 0.0000 -19.4218 28.7075 37.003795 11.2364 -32.7883 34.6602 4500 5800 0.0000 -19.4218 28.7075 37.265131 11.0867 -32.8392 34.6602 4500 6000 0.0000 -19.4218 28.7075 37.522588 10.9390 -32.8887 34.6602 4500 6200 0.0000 -19.4218 28.7075 37.776268 10.7933 -32.9368 34.6602 4500 6400 0.0000 -19.4218 28.7075 38.026267 10.6495 -32.9836 34.6602 4500 6600 0.0000 -19.4218 28.7075 38.272678 10.5075 -33.0291 34.6602 4500 6800 0.0000 -19.4218 28.7075 38.515591 10.3674 -33.0733 34.6602 4500 7000 0.0000 -19.4218 28.7075 38.755092 10.2291 -33.1164 34.6602 4500 7200 0.0000 -19.4218 28.7075 38.991264 10.0925 -33.1582 34.6602 4500 7400 0.0000 -19.4218 28.7075 39.224185 9.9576 -33.1990 34.6602 4500 7600 0.0000 -19.4218 28.7075 39.453932 9.8244 -33.2387 34.6602 4500 7800 0.0000 -19.4218 28.7075 39.680580 9.6928 -33.2773 34.6602 4500 8000 0.0000 -19.4218 28.7075 39.904198 9.5629 -33.3148 34.6602 4500 8200 0.0000 -19.4218 28.7075 40.124856 9.4345 -33.3514 34.6602 4500 8400 0.0000 -19.4218 28.7075 40.342619 9.3077 -33.3870 34.6602 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.000 ================================================ FILE: tests/test_data/cropA/geometry/20180319-20180530_VV_8rlks_base.par ================================================ initial_baseline(TCN): -0.6579066 30.3023419 42.2468260 m m m initial_baseline_rate: 0.0000000 -0.0223397 0.0234496 m/s m/s m/s precision_baseline(TCN): -0.6579066 30.3023419 42.2468260 m m m precision_baseline_rate: 0.0000000 -0.0223397 0.0234496 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/cropA/geometry/20180319-20180530_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): -0.658 30.302 42.247 orbit baseline rate (TCN) (m/s): 0.000e+00 -2.234e-02 2.345e-02 baseline vector (TCN) (m): -0.658 30.302 42.247 baseline rate (TCN) (m/s): 0.000e+00 -2.234e-02 2.345e-02 SLC-1 center baseline length (m): 51.9948 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 -0.6579 30.5109 42.0279 27.496626 51.3678 7.6614 51.9393 0 200 -0.6579 30.5109 42.0279 27.938864 51.4254 7.2647 51.9393 0 400 -0.6579 30.5109 42.0279 28.370470 51.4787 6.8771 51.9393 0 600 -0.6579 30.5109 42.0279 28.791963 51.5278 6.4982 51.9393 0 800 -0.6579 30.5109 42.0279 29.203825 51.5732 6.1276 51.9393 0 1000 -0.6579 30.5109 42.0279 29.606500 51.6150 5.7650 51.9393 0 1200 -0.6579 30.5109 42.0279 30.000396 51.6534 5.4100 51.9393 0 1400 -0.6579 30.5109 42.0279 30.385895 51.6886 5.0624 51.9393 0 1600 -0.6579 30.5109 42.0279 30.763349 51.7209 4.7217 51.9393 0 1800 -0.6579 30.5109 42.0279 31.133089 51.7502 4.3878 51.9393 0 2000 -0.6579 30.5109 42.0279 31.495422 51.7769 4.0605 51.9393 0 2200 -0.6579 30.5109 42.0279 31.850636 51.8011 3.7394 51.9393 0 2400 -0.6579 30.5109 42.0279 32.199000 51.8229 3.4244 51.9393 0 2600 -0.6579 30.5109 42.0279 32.540768 51.8424 3.1152 51.9393 0 2800 -0.6579 30.5109 42.0279 32.876178 51.8597 2.8116 51.9393 0 3000 -0.6579 30.5109 42.0279 33.205453 51.8750 2.5135 51.9393 0 3200 -0.6579 30.5109 42.0279 33.528805 51.8884 2.2207 51.9393 0 3400 -0.6579 30.5109 42.0279 33.846435 51.8999 1.9330 51.9393 0 3600 -0.6579 30.5109 42.0279 34.158531 51.9096 1.6503 51.9393 0 3800 -0.6579 30.5109 42.0279 34.465271 51.9177 1.3724 51.9393 0 4000 -0.6579 30.5109 42.0279 34.766827 51.9242 1.0991 51.9393 0 4200 -0.6579 30.5109 42.0279 35.063357 51.9292 0.8303 51.9393 0 4400 -0.6579 30.5109 42.0279 35.355017 51.9328 0.5660 51.9393 0 4600 -0.6579 30.5109 42.0279 35.641950 51.9349 0.3059 51.9393 0 4800 -0.6579 30.5109 42.0279 35.924297 51.9358 0.0500 51.9393 0 5000 -0.6579 30.5109 42.0279 36.202189 51.9354 -0.2020 51.9393 0 5200 -0.6579 30.5109 42.0279 36.475752 51.9339 -0.4499 51.9393 0 5400 -0.6579 30.5109 42.0279 36.745108 51.9312 -0.6941 51.9393 0 5600 -0.6579 30.5109 42.0279 37.010370 51.9274 -0.9345 51.9393 0 5800 -0.6579 30.5109 42.0279 37.271649 51.9226 -1.1713 51.9393 0 6000 -0.6579 30.5109 42.0279 37.529051 51.9168 -1.4045 51.9393 0 6200 -0.6579 30.5109 42.0279 37.782677 51.9101 -1.6344 51.9393 0 6400 -0.6579 30.5109 42.0279 38.032623 51.9024 -1.8608 51.9393 0 6600 -0.6579 30.5109 42.0279 38.278984 51.8940 -2.0840 51.9393 0 6800 -0.6579 30.5109 42.0279 38.521847 51.8847 -2.3039 51.9393 0 7000 -0.6579 30.5109 42.0279 38.761299 51.8746 -2.5207 51.9393 0 7200 -0.6579 30.5109 42.0279 38.997423 51.8637 -2.7345 51.9393 0 7400 -0.6579 30.5109 42.0279 39.230297 51.8522 -2.9453 51.9393 0 7600 -0.6579 30.5109 42.0279 39.459999 51.8400 -3.1531 51.9393 0 7800 -0.6579 30.5109 42.0279 39.686602 51.8271 -3.3581 51.9393 0 8000 -0.6579 30.5109 42.0279 39.910176 51.8136 -3.5604 51.9393 0 8200 -0.6579 30.5109 42.0279 40.130792 51.7995 -3.7598 51.9393 0 8400 -0.6579 30.5109 42.0279 40.348513 51.7848 -3.9567 51.9393 500 0 -0.6579 30.4649 42.0761 27.495684 51.3892 7.5993 51.9514 500 200 -0.6579 30.4649 42.0761 27.937938 51.4464 7.2024 51.9514 500 400 -0.6579 30.4649 42.0761 28.369559 51.4991 6.8146 51.9514 500 600 -0.6579 30.4649 42.0761 28.791066 51.5479 6.4356 51.9514 500 800 -0.6579 30.4649 42.0761 29.202942 51.5928 6.0648 51.9514 500 1000 -0.6579 30.4649 42.0761 29.605629 51.6341 5.7021 51.9514 500 1200 -0.6579 30.4649 42.0761 29.999538 51.6721 5.3469 51.9514 500 1400 -0.6579 30.4649 42.0761 30.385049 51.7069 4.9991 51.9514 500 1600 -0.6579 30.4649 42.0761 30.762515 51.7387 4.6584 51.9514 500 1800 -0.6579 30.4649 42.0761 31.132265 51.7677 4.3244 51.9514 500 2000 -0.6579 30.4649 42.0761 31.494609 51.7940 3.9969 51.9514 500 2200 -0.6579 30.4649 42.0761 31.849833 51.8178 3.6757 51.9514 500 2400 -0.6579 30.4649 42.0761 32.198207 51.8392 3.3605 51.9514 500 2600 -0.6579 30.4649 42.0761 32.539984 51.8583 3.0512 51.9514 500 2800 -0.6579 30.4649 42.0761 32.875402 51.8752 2.7476 51.9514 500 3000 -0.6579 30.4649 42.0761 33.204686 51.8902 2.4494 51.9514 500 3200 -0.6579 30.4649 42.0761 33.528047 51.9032 2.1565 51.9514 500 3400 -0.6579 30.4649 42.0761 33.845685 51.9143 1.8687 51.9514 500 3600 -0.6579 30.4649 42.0761 34.157789 51.9237 1.5859 51.9514 500 3800 -0.6579 30.4649 42.0761 34.464537 51.9315 1.3079 51.9514 500 4000 -0.6579 30.4649 42.0761 34.766099 51.9376 1.0345 51.9514 500 4200 -0.6579 30.4649 42.0761 35.062637 51.9423 0.7657 51.9514 500 4400 -0.6579 30.4649 42.0761 35.354304 51.9455 0.5013 51.9514 500 4600 -0.6579 30.4649 42.0761 35.641244 51.9473 0.2411 51.9514 500 4800 -0.6579 30.4649 42.0761 35.923597 51.9479 -0.0149 51.9514 500 5000 -0.6579 30.4649 42.0761 36.201496 51.9472 -0.2668 51.9514 500 5200 -0.6579 30.4649 42.0761 36.475065 51.9453 -0.5149 51.9514 500 5400 -0.6579 30.4649 42.0761 36.744426 51.9423 -0.7591 51.9514 500 5600 -0.6579 30.4649 42.0761 37.009694 51.9383 -0.9996 51.9514 500 5800 -0.6579 30.4649 42.0761 37.270979 51.9332 -1.2364 51.9514 500 6000 -0.6579 30.4649 42.0761 37.528387 51.9271 -1.4697 51.9514 500 6200 -0.6579 30.4649 42.0761 37.782018 51.9201 -1.6996 51.9514 500 6400 -0.6579 30.4649 42.0761 38.031970 51.9121 -1.9261 51.9514 500 6600 -0.6579 30.4649 42.0761 38.278335 51.9034 -2.1493 51.9514 500 6800 -0.6579 30.4649 42.0761 38.521204 51.8938 -2.3693 51.9514 500 7000 -0.6579 30.4649 42.0761 38.760661 51.8834 -2.5861 51.9514 500 7200 -0.6579 30.4649 42.0761 38.996789 51.8723 -2.7999 51.9514 500 7400 -0.6579 30.4649 42.0761 39.229668 51.8605 -3.0108 51.9514 500 7600 -0.6579 30.4649 42.0761 39.459375 51.8480 -3.2187 51.9514 500 7800 -0.6579 30.4649 42.0761 39.685982 51.8349 -3.4237 51.9514 500 8000 -0.6579 30.4649 42.0761 39.909561 51.8211 -3.6259 51.9514 500 8200 -0.6579 30.4649 42.0761 40.130181 51.8068 -3.8255 51.9514 500 8400 -0.6579 30.4649 42.0761 40.347906 51.7919 -4.0223 51.9514 1000 0 -0.6579 30.4190 42.1243 27.494721 51.4107 7.5372 51.9635 1000 200 -0.6579 30.4190 42.1243 27.936992 51.4673 7.1401 51.9635 1000 400 -0.6579 30.4190 42.1243 28.368628 51.5196 6.7521 51.9635 1000 600 -0.6579 30.4190 42.1243 28.790150 51.5679 6.3729 51.9635 1000 800 -0.6579 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51.8740 -3.7513 52.0130 3000 8000 -0.6579 30.2353 42.3172 39.906301 51.8589 -3.9537 52.0130 3000 8200 -0.6579 30.2353 42.3172 40.126943 51.8433 -4.1534 52.0130 3000 8400 -0.6579 30.2353 42.3172 40.344692 51.8271 -4.3504 52.0130 3500 0 -0.6579 30.1894 42.3654 27.489602 51.5178 7.2268 52.0255 3500 200 -0.6579 30.1894 42.3654 27.931959 51.5721 6.8289 52.0255 3500 400 -0.6579 30.1894 42.3654 28.363677 51.6220 6.4401 52.0255 3500 600 -0.6579 30.1894 42.3654 28.785278 51.6680 6.0600 52.0255 3500 800 -0.6579 30.1894 42.3654 29.197243 51.7103 5.6884 52.0255 3500 1000 -0.6579 30.1894 42.3654 29.600015 51.7490 5.3247 52.0255 3500 1200 -0.6579 30.1894 42.3654 29.994004 51.7843 4.9687 52.0255 3500 1400 -0.6579 30.1894 42.3654 30.379592 51.8166 4.6201 52.0255 3500 1600 -0.6579 30.1894 42.3654 30.757132 51.8459 4.2786 52.0255 3500 1800 -0.6579 30.1894 42.3654 31.126954 51.8724 3.9438 52.0255 3500 2000 -0.6579 30.1894 42.3654 31.489366 51.8963 3.6156 52.0255 3500 2200 -0.6579 30.1894 42.3654 31.844656 51.9178 3.2937 52.0255 3500 2400 -0.6579 30.1894 42.3654 32.193093 51.9368 2.9779 52.0255 3500 2600 -0.6579 30.1894 42.3654 32.534931 51.9537 2.6680 52.0255 3500 2800 -0.6579 30.1894 42.3654 32.870408 51.9684 2.3637 52.0255 3500 3000 -0.6579 30.1894 42.3654 33.199749 51.9811 2.0650 52.0255 3500 3200 -0.6579 30.1894 42.3654 33.523165 51.9919 1.7715 52.0255 3500 3400 -0.6579 30.1894 42.3654 33.840856 52.0009 1.4832 52.0255 3500 3600 -0.6579 30.1894 42.3654 34.153010 52.0082 1.1999 52.0255 3500 3800 -0.6579 30.1894 42.3654 34.459808 52.0139 0.9214 52.0255 3500 4000 -0.6579 30.1894 42.3654 34.761419 52.0180 0.6475 52.0255 3500 4200 -0.6579 30.1894 42.3654 35.058003 52.0207 0.3783 52.0255 3500 4400 -0.6579 30.1894 42.3654 35.349715 52.0219 0.1134 52.0255 3500 4600 -0.6579 30.1894 42.3654 35.636699 52.0218 -0.1472 52.0255 3500 4800 -0.6579 30.1894 42.3654 35.919095 52.0205 -0.4036 52.0255 3500 5000 -0.6579 30.1894 42.3654 36.197035 52.0179 -0.6559 52.0255 3500 5200 -0.6579 30.1894 42.3654 36.470645 52.0142 -0.9043 52.0255 3500 5400 -0.6579 30.1894 42.3654 36.740046 52.0093 -1.1489 52.0255 3500 5600 -0.6579 30.1894 42.3654 37.005352 52.0035 -1.3897 52.0255 3500 5800 -0.6579 30.1894 42.3654 37.266674 51.9966 -1.6269 52.0255 3500 6000 -0.6579 30.1894 42.3654 37.524118 51.9887 -1.8605 52.0255 3500 6200 -0.6579 30.1894 42.3654 37.777785 51.9800 -2.0907 52.0255 3500 6400 -0.6579 30.1894 42.3654 38.027771 51.9704 -2.3175 52.0255 3500 6600 -0.6579 30.1894 42.3654 38.274170 51.9599 -2.5409 52.0255 3500 6800 -0.6579 30.1894 42.3654 38.517072 51.9487 -2.7612 52.0255 3500 7000 -0.6579 30.1894 42.3654 38.756561 51.9367 -2.9783 52.0255 3500 7200 -0.6579 30.1894 42.3654 38.992721 51.9240 -3.1924 52.0255 3500 7400 -0.6579 30.1894 42.3654 39.225631 51.9105 -3.4034 52.0255 3500 7600 -0.6579 30.1894 42.3654 39.455367 51.8965 -3.6116 52.0255 3500 7800 -0.6579 30.1894 42.3654 39.682004 51.8818 -3.8168 52.0255 3500 8000 -0.6579 30.1894 42.3654 39.905612 51.8665 -4.0193 52.0255 3500 8200 -0.6579 30.1894 42.3654 40.126259 51.8506 -4.2190 52.0255 3500 8400 -0.6579 30.1894 42.3654 40.344013 51.8342 -4.4160 52.0255 4000 0 -0.6579 30.1435 42.4136 27.488516 51.5392 7.1648 52.0382 4000 200 -0.6579 30.1435 42.4136 27.930893 51.5930 6.7667 52.0382 4000 400 -0.6579 30.1435 42.4136 28.362629 51.6425 6.3777 52.0382 4000 600 -0.6579 30.1435 42.4136 28.784246 51.6881 5.9975 52.0382 4000 800 -0.6579 30.1435 42.4136 29.196227 51.7298 5.6257 52.0382 4000 1000 -0.6579 30.1435 42.4136 29.599014 51.7681 5.2619 52.0382 4000 1200 -0.6579 30.1435 42.4136 29.993018 51.8030 4.9057 52.0382 4000 1400 -0.6579 30.1435 42.4136 30.378620 51.8349 4.5570 52.0382 4000 1600 -0.6579 30.1435 42.4136 30.756173 51.8638 4.2153 52.0382 4000 1800 -0.6579 30.1435 42.4136 31.126008 51.8899 3.8804 52.0382 4000 2000 -0.6579 30.1435 42.4136 31.488432 51.9134 3.5521 52.0382 4000 2200 -0.6579 30.1435 42.4136 31.843734 51.9344 3.2301 52.0382 4000 2400 -0.6579 30.1435 42.4136 32.192182 51.9531 2.9142 52.0382 4000 2600 -0.6579 30.1435 42.4136 32.534031 51.9696 2.6042 52.0382 4000 2800 -0.6579 30.1435 42.4136 32.869519 51.9839 2.2998 52.0382 4000 3000 -0.6579 30.1435 42.4136 33.198870 51.9963 2.0010 52.0382 4000 3200 -0.6579 30.1435 42.4136 33.522296 52.0067 1.7074 52.0382 4000 3400 -0.6579 30.1435 42.4136 33.839996 52.0154 1.4190 52.0382 4000 3600 -0.6579 30.1435 42.4136 34.152160 52.0223 1.1356 52.0382 4000 3800 -0.6579 30.1435 42.4136 34.458967 52.0277 0.8570 52.0382 4000 4000 -0.6579 30.1435 42.4136 34.760586 52.0315 0.5831 52.0382 4000 4200 -0.6579 30.1435 42.4136 35.057179 52.0338 0.3137 52.0382 4000 4400 -0.6579 30.1435 42.4136 35.348899 52.0347 0.0488 52.0382 4000 4600 -0.6579 30.1435 42.4136 35.635891 52.0343 -0.2119 52.0382 4000 4800 -0.6579 30.1435 42.4136 35.918295 52.0326 -0.4683 52.0382 4000 5000 -0.6579 30.1435 42.4136 36.196242 52.0297 -0.7207 52.0382 4000 5200 -0.6579 30.1435 42.4136 36.469860 52.0257 -0.9692 52.0382 4000 5400 -0.6579 30.1435 42.4136 36.739267 52.0205 -1.2138 52.0382 4000 5600 -0.6579 30.1435 42.4136 37.004580 52.0143 -1.4547 52.0382 4000 5800 -0.6579 30.1435 42.4136 37.265909 52.0072 -1.6919 52.0382 4000 6000 -0.6579 30.1435 42.4136 37.523360 51.9990 -1.9256 52.0382 4000 6200 -0.6579 30.1435 42.4136 37.777033 51.9900 -2.1558 52.0382 4000 6400 -0.6579 30.1435 42.4136 38.027025 51.9801 -2.3827 52.0382 4000 6600 -0.6579 30.1435 42.4136 38.273431 51.9693 -2.6062 52.0382 4000 6800 -0.6579 30.1435 42.4136 38.516338 51.9578 -2.8265 52.0382 4000 7000 -0.6579 30.1435 42.4136 38.755833 51.9456 -3.0437 52.0382 4000 7200 -0.6579 30.1435 42.4136 38.991999 51.9326 -3.2577 52.0382 4000 7400 -0.6579 30.1435 42.4136 39.224914 51.9189 -3.4688 52.0382 4000 7600 -0.6579 30.1435 42.4136 39.454656 51.9046 -3.6770 52.0382 4000 7800 -0.6579 30.1435 42.4136 39.681298 51.8896 -3.8823 52.0382 4000 8000 -0.6579 30.1435 42.4136 39.904911 51.8740 -4.0848 52.0382 4000 8200 -0.6579 30.1435 42.4136 40.125563 51.8579 -4.2845 52.0382 4000 8400 -0.6579 30.1435 42.4136 40.343322 51.8413 -4.4816 52.0382 4500 0 -0.6579 30.0976 42.4618 27.487411 51.5607 7.1028 52.0509 4500 200 -0.6579 30.0976 42.4618 27.929806 51.6140 6.7045 52.0509 4500 400 -0.6579 30.0976 42.4618 28.361560 51.6630 6.3154 52.0509 4500 600 -0.6579 30.0976 42.4618 28.783195 51.7081 5.9350 52.0509 4500 800 -0.6579 30.0976 42.4618 29.195192 51.7494 5.5630 52.0509 4500 1000 -0.6579 30.0976 42.4618 29.597995 51.7872 5.1991 52.0509 4500 1200 -0.6579 30.0976 42.4618 29.992014 51.8218 4.8428 52.0509 4500 1400 -0.6579 30.0976 42.4618 30.377630 51.8532 4.4939 52.0509 4500 1600 -0.6579 30.0976 42.4618 30.755197 51.8816 4.1521 52.0509 4500 1800 -0.6579 30.0976 42.4618 31.125045 51.9074 3.8171 52.0509 4500 2000 -0.6579 30.0976 42.4618 31.487481 51.9305 3.4887 52.0509 4500 2200 -0.6579 30.0976 42.4618 31.842795 51.9511 3.1665 52.0509 4500 2400 -0.6579 30.0976 42.4618 32.191255 51.9694 2.8505 52.0509 4500 2600 -0.6579 30.0976 42.4618 32.533115 51.9855 2.5404 52.0509 4500 2800 -0.6579 30.0976 42.4618 32.868614 51.9994 2.2359 52.0509 4500 3000 -0.6579 30.0976 42.4618 33.197975 52.0114 1.9370 52.0509 4500 3200 -0.6579 30.0976 42.4618 33.521411 52.0215 1.6433 52.0509 4500 3400 -0.6579 30.0976 42.4618 33.839121 52.0298 1.3548 52.0509 4500 3600 -0.6579 30.0976 42.4618 34.151295 52.0364 1.0713 52.0509 4500 3800 -0.6579 30.0976 42.4618 34.458111 52.0414 0.7926 52.0509 4500 4000 -0.6579 30.0976 42.4618 34.759739 52.0449 0.5186 52.0509 4500 4200 -0.6579 30.0976 42.4618 35.056340 52.0468 0.2492 52.0509 4500 4400 -0.6579 30.0976 42.4618 35.348068 52.0474 -0.0158 52.0509 4500 4600 -0.6579 30.0976 42.4618 35.635069 52.0467 -0.2765 52.0509 4500 4800 -0.6579 30.0976 42.4618 35.917480 52.0447 -0.5331 52.0509 4500 5000 -0.6579 30.0976 42.4618 36.195435 52.0415 -0.7855 52.0509 4500 5200 -0.6579 30.0976 42.4618 36.469060 52.0371 -1.0341 52.0509 4500 5400 -0.6579 30.0976 42.4618 36.738475 52.0317 -1.2787 52.0509 4500 5600 -0.6579 30.0976 42.4618 37.003795 52.0252 -1.5197 52.0509 4500 5800 -0.6579 30.0976 42.4618 37.265131 52.0177 -1.7570 52.0509 4500 6000 -0.6579 30.0976 42.4618 37.522588 52.0093 -1.9907 52.0509 4500 6200 -0.6579 30.0976 42.4618 37.776268 52.0000 -2.2210 52.0509 4500 6400 -0.6579 30.0976 42.4618 38.026267 51.9898 -2.4478 52.0509 4500 6600 -0.6579 30.0976 42.4618 38.272678 51.9788 -2.6714 52.0509 4500 6800 -0.6579 30.0976 42.4618 38.515591 51.9670 -2.8918 52.0509 4500 7000 -0.6579 30.0976 42.4618 38.755092 51.9544 -3.1090 52.0509 4500 7200 -0.6579 30.0976 42.4618 38.991264 51.9412 -3.3231 52.0509 4500 7400 -0.6579 30.0976 42.4618 39.224185 51.9272 -3.5342 52.0509 4500 7600 -0.6579 30.0976 42.4618 39.453932 51.9126 -3.7424 52.0509 4500 7800 -0.6579 30.0976 42.4618 39.680580 51.8974 -3.9478 52.0509 4500 8000 -0.6579 30.0976 42.4618 39.904198 51.8816 -4.1503 52.0509 4500 8200 -0.6579 30.0976 42.4618 40.124856 51.8652 -4.3501 52.0509 4500 8400 -0.6579 30.0976 42.4618 40.342619 51.8483 -4.5472 52.0509 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.010 ================================================ FILE: tests/test_data/cropA/geometry/20180319-20180623_VV_8rlks_base.par ================================================ initial_baseline(TCN): 0.2737791 -19.7407601 42.8055396 m m m initial_baseline_rate: 0.0000000 -0.0412166 0.0250700 m/s m/s m/s precision_baseline(TCN): 0.0000000 -19.9138798 42.8891912 m m m precision_baseline_rate: 0.0000000 -0.0633762 0.0375154 m/s m/s m/s unwrap_phase_constant: -0.00015 radians ================================================ FILE: tests/test_data/cropA/geometry/20180319-20180623_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): 0.274 -19.741 42.806 orbit baseline rate (TCN) (m/s): 0.000e+00 -4.122e-02 2.507e-02 baseline vector (TCN) (m): 0.000 -19.914 42.889 baseline rate (TCN) (m/s): 0.000e+00 -6.338e-02 3.752e-02 SLC-1 center baseline length (m): 47.2868 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 0.0000 -19.3223 42.5390 27.496626 28.8127 -36.7797 46.7217 0 200 0.0000 -19.3223 42.5390 27.938864 28.5280 -37.0010 46.7217 0 400 0.0000 -19.3223 42.5390 28.370470 28.2484 -37.2148 46.7217 0 600 0.0000 -19.3223 42.5390 28.791963 27.9739 -37.4216 46.7217 0 800 0.0000 -19.3223 42.5390 29.203825 27.7042 -37.6217 46.7217 0 1000 0.0000 -19.3223 42.5390 29.606500 27.4391 -37.8155 46.7217 0 1200 0.0000 -19.3223 42.5390 30.000396 27.1785 -38.0033 46.7217 0 1400 0.0000 -19.3223 42.5390 30.385895 26.9222 -38.1853 46.7217 0 1600 0.0000 -19.3223 42.5390 30.763349 26.6700 -38.3618 46.7217 0 1800 0.0000 -19.3223 42.5390 31.133089 26.4219 -38.5331 46.7217 0 2000 0.0000 -19.3223 42.5390 31.495422 26.1777 -38.6994 46.7217 0 2200 0.0000 -19.3223 42.5390 31.850636 25.9373 -38.8610 46.7217 0 2400 0.0000 -19.3223 42.5390 32.199000 25.7005 -39.0180 46.7217 0 2600 0.0000 -19.3223 42.5390 32.540768 25.4673 -39.1706 46.7217 0 2800 0.0000 -19.3223 42.5390 32.876178 25.2376 -39.3190 46.7217 0 3000 0.0000 -19.3223 42.5390 33.205453 25.0112 -39.4634 46.7217 0 3200 0.0000 -19.3223 42.5390 33.528805 24.7881 -39.6039 46.7217 0 3400 0.0000 -19.3223 42.5390 33.846435 24.5682 -39.7407 46.7217 0 3600 0.0000 -19.3223 42.5390 34.158531 24.3513 -39.8739 46.7217 0 3800 0.0000 -19.3223 42.5390 34.465271 24.1375 -40.0037 46.7217 0 4000 0.0000 -19.3223 42.5390 34.766827 23.9266 -40.1302 46.7217 0 4200 0.0000 -19.3223 42.5390 35.063357 23.7186 -40.2535 46.7217 0 4400 0.0000 -19.3223 42.5390 35.355017 23.5134 -40.3737 46.7217 0 4600 0.0000 -19.3223 42.5390 35.641950 23.3109 -40.4910 46.7217 0 4800 0.0000 -19.3223 42.5390 35.924297 23.1111 -40.6054 46.7217 0 5000 0.0000 -19.3223 42.5390 36.202189 22.9139 -40.7170 46.7217 0 5200 0.0000 -19.3223 42.5390 36.475752 22.7192 -40.8259 46.7217 0 5400 0.0000 -19.3223 42.5390 36.745108 22.5270 -40.9323 46.7217 0 5600 0.0000 -19.3223 42.5390 37.010370 22.3373 -41.0361 46.7217 0 5800 0.0000 -19.3223 42.5390 37.271649 22.1499 -41.1376 46.7217 0 6000 0.0000 -19.3223 42.5390 37.529051 21.9649 -41.2366 46.7217 0 6200 0.0000 -19.3223 42.5390 37.782677 21.7821 -41.3335 46.7217 0 6400 0.0000 -19.3223 42.5390 38.032623 21.6016 -41.4281 46.7217 0 6600 0.0000 -19.3223 42.5390 38.278984 21.4233 -41.5206 46.7217 0 6800 0.0000 -19.3223 42.5390 38.521847 21.2471 -41.6110 46.7217 0 7000 0.0000 -19.3223 42.5390 38.761299 21.0730 -41.6995 46.7217 0 7200 0.0000 -19.3223 42.5390 38.997423 20.9010 -41.7860 46.7217 0 7400 0.0000 -19.3223 42.5390 39.230297 20.7310 -41.8706 46.7217 0 7600 0.0000 -19.3223 42.5390 39.459999 20.5630 -41.9533 46.7217 0 7800 0.0000 -19.3223 42.5390 39.686602 20.3969 -42.0343 46.7217 0 8000 0.0000 -19.3223 42.5390 39.910176 20.2327 -42.1136 46.7217 0 8200 0.0000 -19.3223 42.5390 40.130792 20.0704 -42.1912 46.7217 0 8400 0.0000 -19.3223 42.5390 40.348513 19.9099 -42.2672 46.7217 500 0 0.0000 -19.4526 42.6161 27.495684 28.8216 -36.9304 46.8459 500 200 0.0000 -19.4526 42.6161 27.937938 28.5357 -37.1517 46.8459 500 400 0.0000 -19.4526 42.6161 28.369559 28.2550 -37.3656 46.8459 500 600 0.0000 -19.4526 42.6161 28.791066 27.9793 -37.5725 46.8459 500 800 0.0000 -19.4526 42.6161 29.202942 27.7085 -37.7727 46.8459 500 1000 0.0000 -19.4526 42.6161 29.605629 27.4424 -37.9665 46.8459 500 1200 0.0000 -19.4526 42.6161 29.999538 27.1807 -38.1542 46.8459 500 1400 0.0000 -19.4526 42.6161 30.385049 26.9234 -38.3363 46.8459 500 1600 0.0000 -19.4526 42.6161 30.762515 26.6702 -38.5128 46.8459 500 1800 0.0000 -19.4526 42.6161 31.132265 26.4211 -38.6841 46.8459 500 2000 0.0000 -19.4526 42.6161 31.494609 26.1760 -38.8504 46.8459 500 2200 0.0000 -19.4526 42.6161 31.849833 25.9346 -39.0120 46.8459 500 2400 0.0000 -19.4526 42.6161 32.198207 25.6969 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-20.1039 43.0017 37.524863 21.8588 -42.1368 47.4691 3000 6200 0.0000 -20.1039 43.0017 37.778524 21.6720 -42.2331 47.4691 3000 6400 0.0000 -20.1039 43.0017 38.028504 21.4875 -42.3273 47.4691 3000 6600 0.0000 -20.1039 43.0017 38.274897 21.3053 -42.4193 47.4691 3000 6800 0.0000 -20.1039 43.0017 38.517793 21.1253 -42.5092 47.4691 3000 7000 0.0000 -20.1039 43.0017 38.757276 20.9474 -42.5972 47.4691 3000 7200 0.0000 -20.1039 43.0017 38.993431 20.7717 -42.6831 47.4691 3000 7400 0.0000 -20.1039 43.0017 39.226335 20.5980 -42.7672 47.4691 3000 7600 0.0000 -20.1039 43.0017 39.456066 20.4264 -42.8495 47.4691 3000 7800 0.0000 -20.1039 43.0017 39.682698 20.2567 -42.9299 47.4691 3000 8000 0.0000 -20.1039 43.0017 39.906301 20.0890 -43.0087 47.4691 3000 8200 0.0000 -20.1039 43.0017 40.126943 19.9233 -43.0857 47.4691 3000 8400 0.0000 -20.1039 43.0017 40.344692 19.7594 -43.1611 47.4691 3500 0 0.0000 -20.2342 43.0788 27.489602 28.8751 -37.8343 47.5942 3500 200 0.0000 -20.2342 43.0788 27.931959 28.5822 -38.0561 47.5942 3500 400 0.0000 -20.2342 43.0788 28.363677 28.2946 -38.2704 47.5942 3500 600 0.0000 -20.2342 43.0788 28.785278 28.0123 -38.4775 47.5942 3500 800 0.0000 -20.2342 43.0788 29.197243 27.7349 -38.6779 47.5942 3500 1000 0.0000 -20.2342 43.0788 29.600015 27.4623 -38.8719 47.5942 3500 1200 0.0000 -20.2342 43.0788 29.994004 27.1944 -39.0599 47.5942 3500 1400 0.0000 -20.2342 43.0788 30.379592 26.9309 -39.2420 47.5942 3500 1600 0.0000 -20.2342 43.0788 30.757132 26.6717 -39.4186 47.5942 3500 1800 0.0000 -20.2342 43.0788 31.126954 26.4167 -39.5899 47.5942 3500 2000 0.0000 -20.2342 43.0788 31.489366 26.1658 -39.7562 47.5942 3500 2200 0.0000 -20.2342 43.0788 31.844656 25.9187 -39.9177 47.5942 3500 2400 0.0000 -20.2342 43.0788 32.193093 25.6755 -40.0746 47.5942 3500 2600 0.0000 -20.2342 43.0788 32.534931 25.4360 -40.2271 47.5942 3500 2800 0.0000 -20.2342 43.0788 32.870408 25.2000 -40.3753 47.5942 3500 3000 0.0000 -20.2342 43.0788 33.199749 24.9675 -40.5195 47.5942 3500 3200 0.0000 -20.2342 43.0788 33.523165 24.7384 -40.6598 47.5942 3500 3400 0.0000 -20.2342 43.0788 33.840856 24.5126 -40.7963 47.5942 3500 3600 0.0000 -20.2342 43.0788 34.153010 24.2899 -40.9293 47.5942 3500 3800 0.0000 -20.2342 43.0788 34.459808 24.0704 -41.0588 47.5942 3500 4000 0.0000 -20.2342 43.0788 34.761419 23.8540 -41.1849 47.5942 3500 4200 0.0000 -20.2342 43.0788 35.058003 23.6404 -41.3078 47.5942 3500 4400 0.0000 -20.2342 43.0788 35.349715 23.4298 -41.4276 47.5942 3500 4600 0.0000 -20.2342 43.0788 35.636699 23.2220 -41.5445 47.5942 3500 4800 0.0000 -20.2342 43.0788 35.919095 23.0170 -41.6584 47.5942 3500 5000 0.0000 -20.2342 43.0788 36.197035 22.8146 -41.7696 47.5942 3500 5200 0.0000 -20.2342 43.0788 36.470645 22.6149 -41.8781 47.5942 3500 5400 0.0000 -20.2342 43.0788 36.740046 22.4177 -41.9839 47.5942 3500 5600 0.0000 -20.2342 43.0788 37.005352 22.2231 -42.0873 47.5942 3500 5800 0.0000 -20.2342 43.0788 37.266674 22.0309 -42.1882 47.5942 3500 6000 0.0000 -20.2342 43.0788 37.524118 21.8411 -42.2868 47.5942 3500 6200 0.0000 -20.2342 43.0788 37.777785 21.6537 -42.3831 47.5942 3500 6400 0.0000 -20.2342 43.0788 38.027771 21.4686 -42.4771 47.5942 3500 6600 0.0000 -20.2342 43.0788 38.274170 21.2857 -42.5691 47.5942 3500 6800 0.0000 -20.2342 43.0788 38.517072 21.1050 -42.6589 47.5942 3500 7000 0.0000 -20.2342 43.0788 38.756561 20.9265 -42.7468 47.5942 3500 7200 0.0000 -20.2342 43.0788 38.992721 20.7502 -42.8327 47.5942 3500 7400 0.0000 -20.2342 43.0788 39.225631 20.5759 -42.9167 47.5942 3500 7600 0.0000 -20.2342 43.0788 39.455367 20.4036 -42.9988 47.5942 3500 7800 0.0000 -20.2342 43.0788 39.682004 20.2334 -43.0792 47.5942 3500 8000 0.0000 -20.2342 43.0788 39.905612 20.0651 -43.1578 47.5942 3500 8200 0.0000 -20.2342 43.0788 40.126259 19.8988 -43.2348 47.5942 3500 8400 0.0000 -20.2342 43.0788 40.344013 19.7343 -43.3101 47.5942 4000 0 0.0000 -20.3645 43.1559 27.488516 28.8841 -37.9849 47.7195 4000 200 0.0000 -20.3645 43.1559 27.930893 28.5900 -38.2068 47.7195 4000 400 0.0000 -20.3645 43.1559 28.362629 28.3013 -38.4211 47.7195 4000 600 0.0000 -20.3645 43.1559 28.784246 28.0178 -38.6283 47.7195 4000 800 0.0000 -20.3645 43.1559 29.196227 27.7393 -38.8288 47.7195 4000 1000 0.0000 -20.3645 43.1559 29.599014 27.4657 -39.0228 47.7195 4000 1200 0.0000 -20.3645 43.1559 29.993018 27.1967 -39.2108 47.7195 4000 1400 0.0000 -20.3645 43.1559 30.378620 26.9322 -39.3929 47.7195 4000 1600 0.0000 -20.3645 43.1559 30.756173 26.6720 -39.5695 47.7195 4000 1800 0.0000 -20.3645 43.1559 31.126008 26.4160 -39.7409 47.7195 4000 2000 0.0000 -20.3645 43.1559 31.488432 26.1641 -39.9072 47.7195 4000 2200 0.0000 -20.3645 43.1559 31.843734 25.9162 -40.0687 47.7195 4000 2400 0.0000 -20.3645 43.1559 32.192182 25.6720 -40.2255 47.7195 4000 2600 0.0000 -20.3645 43.1559 32.534031 25.4316 -40.3780 47.7195 4000 2800 0.0000 -20.3645 43.1559 32.869519 25.1947 -40.5262 47.7195 4000 3000 0.0000 -20.3645 43.1559 33.198870 24.9613 -40.6704 47.7195 4000 3200 0.0000 -20.3645 43.1559 33.522296 24.7313 -40.8106 47.7195 4000 3400 0.0000 -20.3645 43.1559 33.839996 24.5047 -40.9471 47.7195 4000 3600 0.0000 -20.3645 43.1559 34.152160 24.2812 -41.0800 47.7195 4000 3800 0.0000 -20.3645 43.1559 34.458967 24.0609 -41.2095 47.7195 4000 4000 0.0000 -20.3645 43.1559 34.760586 23.8436 -41.3355 47.7195 4000 4200 0.0000 -20.3645 43.1559 35.057179 23.6293 -41.4584 47.7195 4000 4400 0.0000 -20.3645 43.1559 35.348899 23.4179 -41.5782 47.7195 4000 4600 0.0000 -20.3645 43.1559 35.635891 23.2094 -41.6950 47.7195 4000 4800 0.0000 -20.3645 43.1559 35.918295 23.0036 -41.8089 47.7195 4000 5000 0.0000 -20.3645 43.1559 36.196242 22.8005 -41.9200 47.7195 4000 5200 0.0000 -20.3645 43.1559 36.469860 22.6001 -42.0284 47.7195 4000 5400 0.0000 -20.3645 43.1559 36.739267 22.4022 -42.1342 47.7195 4000 5600 0.0000 -20.3645 43.1559 37.004580 22.2068 -42.2374 47.7195 4000 5800 0.0000 -20.3645 43.1559 37.265909 22.0140 -42.3383 47.7195 4000 6000 0.0000 -20.3645 43.1559 37.523360 21.8235 -42.4368 47.7195 4000 6200 0.0000 -20.3645 43.1559 37.777033 21.6354 -42.5330 47.7195 4000 6400 0.0000 -20.3645 43.1559 38.027025 21.4496 -42.6270 47.7195 4000 6600 0.0000 -20.3645 43.1559 38.273431 21.2661 -42.7188 47.7195 4000 6800 0.0000 -20.3645 43.1559 38.516338 21.0848 -42.8086 47.7195 4000 7000 0.0000 -20.3645 43.1559 38.755833 20.9057 -42.8964 47.7195 4000 7200 0.0000 -20.3645 43.1559 38.991999 20.7287 -42.9822 47.7195 4000 7400 0.0000 -20.3645 43.1559 39.224914 20.5538 -43.0661 47.7195 4000 7600 0.0000 -20.3645 43.1559 39.454656 20.3809 -43.1482 47.7195 4000 7800 0.0000 -20.3645 43.1559 39.681298 20.2101 -43.2284 47.7195 4000 8000 0.0000 -20.3645 43.1559 39.904911 20.0412 -43.3070 47.7195 4000 8200 0.0000 -20.3645 43.1559 40.125563 19.8743 -43.3838 47.7195 4000 8400 0.0000 -20.3645 43.1559 40.343322 19.7093 -43.4591 47.7195 4500 0 0.0000 -20.4948 43.2330 27.487411 28.8932 -38.1355 47.8449 4500 200 0.0000 -20.4948 43.2330 27.929806 28.5978 -38.3574 47.8449 4500 400 0.0000 -20.4948 43.2330 28.361560 28.3080 -38.5718 47.8449 4500 600 0.0000 -20.4948 43.2330 28.783195 28.0234 -38.7791 47.8449 4500 800 0.0000 -20.4948 43.2330 29.195192 27.7438 -38.9796 47.8449 4500 1000 0.0000 -20.4948 43.2330 29.597995 27.4691 -39.1737 47.8449 4500 1200 0.0000 -20.4948 43.2330 29.992014 27.1990 -39.3617 47.8449 4500 1400 0.0000 -20.4948 43.2330 30.377630 26.9335 -39.5438 47.8449 4500 1600 0.0000 -20.4948 43.2330 30.755197 26.6723 -39.7205 47.8449 4500 1800 0.0000 -20.4948 43.2330 31.125045 26.4154 -39.8918 47.8449 4500 2000 0.0000 -20.4948 43.2330 31.487481 26.1625 -40.0581 47.8449 4500 2200 0.0000 -20.4948 43.2330 31.842795 25.9136 -40.2196 47.8449 4500 2400 0.0000 -20.4948 43.2330 32.191255 25.6685 -40.3764 47.8449 4500 2600 0.0000 -20.4948 43.2330 32.533115 25.4272 -40.5289 47.8449 4500 2800 0.0000 -20.4948 43.2330 32.868614 25.1894 -40.6771 47.8449 4500 3000 0.0000 -20.4948 43.2330 33.197975 24.9552 -40.8212 47.8449 4500 3200 0.0000 -20.4948 43.2330 33.521411 24.7243 -40.9614 47.8449 4500 3400 0.0000 -20.4948 43.2330 33.839121 24.4968 -41.0979 47.8449 4500 3600 0.0000 -20.4948 43.2330 34.151295 24.2725 -41.2308 47.8449 4500 3800 0.0000 -20.4948 43.2330 34.458111 24.0514 -41.3601 47.8449 4500 4000 0.0000 -20.4948 43.2330 34.759739 23.8333 -41.4862 47.8449 4500 4200 0.0000 -20.4948 43.2330 35.056340 23.6182 -41.6090 47.8449 4500 4400 0.0000 -20.4948 43.2330 35.348068 23.4061 -41.7287 47.8449 4500 4600 0.0000 -20.4948 43.2330 35.635069 23.1968 -41.8454 47.8449 4500 4800 0.0000 -20.4948 43.2330 35.917480 22.9902 -41.9593 47.8449 4500 5000 0.0000 -20.4948 43.2330 36.195435 22.7864 -42.0703 47.8449 4500 5200 0.0000 -20.4948 43.2330 36.469060 22.5852 -42.1786 47.8449 4500 5400 0.0000 -20.4948 43.2330 36.738475 22.3866 -42.2844 47.8449 4500 5600 0.0000 -20.4948 43.2330 37.003795 22.1906 -42.3876 47.8449 4500 5800 0.0000 -20.4948 43.2330 37.265131 21.9970 -42.4884 47.8449 4500 6000 0.0000 -20.4948 43.2330 37.522588 21.8059 -42.5868 47.8449 4500 6200 0.0000 -20.4948 43.2330 37.776268 21.6171 -42.6829 47.8449 4500 6400 0.0000 -20.4948 43.2330 38.026267 21.4307 -42.7768 47.8449 4500 6600 0.0000 -20.4948 43.2330 38.272678 21.2465 -42.8686 47.8449 4500 6800 0.0000 -20.4948 43.2330 38.515591 21.0646 -42.9583 47.8449 4500 7000 0.0000 -20.4948 43.2330 38.755092 20.8848 -43.0460 47.8449 4500 7200 0.0000 -20.4948 43.2330 38.991264 20.7072 -43.1317 47.8449 4500 7400 0.0000 -20.4948 43.2330 39.224185 20.5317 -43.2155 47.8449 4500 7600 0.0000 -20.4948 43.2330 39.453932 20.3582 -43.2975 47.8449 4500 7800 0.0000 -20.4948 43.2330 39.680580 20.1868 -43.3777 47.8449 4500 8000 0.0000 -20.4948 43.2330 39.904198 20.0174 -43.4561 47.8449 4500 8200 0.0000 -20.4948 43.2330 40.124856 19.8499 -43.5329 47.8449 4500 8400 0.0000 -20.4948 43.2330 40.342619 19.6843 -43.6080 47.8449 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.010 ================================================ FILE: tests/test_data/cropA/geometry/20180331-20180412_VV_8rlks_base.par ================================================ initial_baseline(TCN): -0.5207146 -64.6524710 33.3215888 m m m initial_baseline_rate: 0.0000000 -0.1463346 0.0086984 m/s m/s m/s precision_baseline(TCN): 0.0000000 -64.8239215 33.4257189 m m m precision_baseline_rate: 0.0000000 -0.1516492 0.0110900 m/s m/s m/s unwrap_phase_constant: -0.00008 radians ================================================ FILE: tests/test_data/cropA/geometry/20180331-20180412_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): -0.521 -64.652 33.322 orbit baseline rate (TCN) (m/s): 0.000e+00 -1.463e-01 8.698e-03 baseline vector (TCN) (m): 0.000 -64.824 33.426 baseline rate (TCN) (m/s): 0.000e+00 -1.516e-01 1.109e-02 SLC-1 center baseline length (m): 72.9344 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 0.0000 -63.4084 33.3222 27.497013 0.2822 -71.6302 71.6309 0 200 0.0000 -63.4084 33.3222 27.939249 -0.2706 -71.6303 71.6309 0 400 0.0000 -63.4084 33.3222 28.370851 -0.8102 -71.6262 71.6309 0 600 0.0000 -63.4084 33.3222 28.792342 -1.3371 -71.6183 71.6309 0 800 0.0000 -63.4084 33.3222 29.204202 -1.8519 -71.6069 71.6309 0 1000 0.0000 -63.4084 33.3222 29.606873 -2.3551 -71.5921 71.6309 0 1200 0.0000 -63.4084 33.3222 30.000767 -2.8472 -71.5742 71.6309 0 1400 0.0000 -63.4084 33.3222 30.386264 -3.3287 -71.5534 71.6309 0 1600 0.0000 -63.4084 33.3222 30.763716 -3.8000 -71.5300 71.6309 0 1800 0.0000 -63.4084 33.3222 31.133454 -4.2615 -71.5039 71.6309 0 2000 0.0000 -63.4084 33.3222 31.495785 -4.7136 -71.4756 71.6309 0 2200 0.0000 -63.4084 33.3222 31.850997 -5.1566 -71.4450 71.6309 0 2400 0.0000 -63.4084 33.3222 32.199360 -5.5909 -71.4123 71.6309 0 2600 0.0000 -63.4084 33.3222 32.541126 -6.0168 -71.3777 71.6309 0 2800 0.0000 -63.4084 33.3222 32.876534 -6.4345 -71.3412 71.6309 0 3000 0.0000 -63.4084 33.3222 33.205808 -6.8444 -71.3031 71.6309 0 3200 0.0000 -63.4084 33.3222 33.529159 -7.2467 -71.2633 71.6309 0 3400 0.0000 -63.4084 33.3222 33.846787 -7.6417 -71.2221 71.6309 0 3600 0.0000 -63.4084 33.3222 34.158881 -8.0295 -71.1794 71.6309 0 3800 0.0000 -63.4084 33.3222 34.465620 -8.4105 -71.1354 71.6309 0 4000 0.0000 -63.4084 33.3222 34.767174 -8.7847 -71.0901 71.6309 0 4200 0.0000 -63.4084 33.3222 35.063703 -9.1525 -71.0437 71.6309 0 4400 0.0000 -63.4084 33.3222 35.355362 -9.5141 -70.9962 71.6309 0 4600 0.0000 -63.4084 33.3222 35.642294 -9.8695 -70.9477 71.6309 0 4800 0.0000 -63.4084 33.3222 35.924640 -10.2190 -70.8982 71.6309 0 5000 0.0000 -63.4084 33.3222 36.202531 -10.5627 -70.8478 71.6309 0 5200 0.0000 -63.4084 33.3222 36.476093 -10.9009 -70.7965 71.6309 0 5400 0.0000 -63.4084 33.3222 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-66.2139 33.5274 37.522963 -13.7386 -72.9356 74.2184 4500 6200 0.0000 -66.2139 33.5274 37.776641 -14.0614 -72.8741 74.2184 4500 6400 0.0000 -66.2139 33.5274 38.026639 -14.3792 -72.8120 74.2184 4500 6600 0.0000 -66.2139 33.5274 38.273049 -14.6922 -72.7495 74.2184 4500 6800 0.0000 -66.2139 33.5274 38.515961 -15.0005 -72.6866 74.2184 4500 7000 0.0000 -66.2139 33.5274 38.755460 -15.3042 -72.6232 74.2184 4500 7200 0.0000 -66.2139 33.5274 38.991631 -15.6034 -72.5595 74.2184 4500 7400 0.0000 -66.2139 33.5274 39.224551 -15.8983 -72.4955 74.2184 4500 7600 0.0000 -66.2139 33.5274 39.454297 -16.1888 -72.4312 74.2184 4500 7800 0.0000 -66.2139 33.5274 39.680943 -16.4752 -72.3666 74.2184 4500 8000 0.0000 -66.2139 33.5274 39.904560 -16.7576 -72.3017 74.2184 4500 8200 0.0000 -66.2139 33.5274 40.125217 -17.0359 -72.2366 74.2184 4500 8400 0.0000 -66.2139 33.5274 40.342979 -17.3103 -72.1714 74.2184 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.010 ================================================ FILE: tests/test_data/cropA/geometry/20180331-20180506_VV_8rlks_base.par ================================================ initial_baseline(TCN): -0.4776116 -0.7573620 22.7725658 m m m initial_baseline_rate: 0.0000000 -0.1235698 0.0091351 m/s m/s m/s precision_baseline(TCN): 0.0000000 -0.7275811 22.8105130 m m m precision_baseline_rate: 0.0000000 -0.1397472 0.0220372 m/s m/s m/s unwrap_phase_constant: 0.00021 radians ================================================ FILE: tests/test_data/cropA/geometry/20180331-20180506_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): -0.478 -0.757 22.773 orbit baseline rate (TCN) (m/s): 0.000e+00 -1.236e-01 9.135e-03 baseline vector (TCN) (m): 0.000 -0.728 22.811 baseline rate (TCN) (m/s): 0.000e+00 -1.397e-01 2.204e-02 SLC-1 center baseline length (m): 22.8221 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 0.0000 0.5769 22.6048 27.497013 20.3176 -9.9250 22.6122 0 200 0.0000 0.5769 22.6048 27.939249 20.2404 -10.0815 22.6122 0 400 0.0000 0.5769 22.6048 28.370851 20.1639 -10.2337 22.6122 0 600 0.0000 0.5769 22.6048 28.792342 20.0880 -10.3818 22.6122 0 800 0.0000 0.5769 22.6048 29.204202 20.0129 -10.5259 22.6122 0 1000 0.0000 0.5769 22.6048 29.606873 19.9384 -10.6663 22.6122 0 1200 0.0000 0.5769 22.6048 30.000767 19.8646 -10.8031 22.6122 0 1400 0.0000 0.5769 22.6048 30.386264 19.7915 -10.9365 22.6122 0 1600 0.0000 0.5769 22.6048 30.763716 19.7190 -11.0666 22.6122 0 1800 0.0000 0.5769 22.6048 31.133454 19.6472 -11.1937 22.6122 0 2000 0.0000 0.5769 22.6048 31.495785 19.5760 -11.3177 22.6122 0 2200 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0.0000 -0.8594 22.8313 34.763411 18.2662 -13.7242 22.8475 2500 4200 0.0000 -0.8594 22.8313 35.059977 18.1949 -13.8186 22.8475 2500 4400 0.0000 -0.8594 22.8313 35.351672 18.1243 -13.9110 22.8475 2500 4600 0.0000 -0.8594 22.8313 35.638639 18.0544 -14.0016 22.8475 2500 4800 0.0000 -0.8594 22.8313 35.921019 17.9852 -14.0904 22.8475 2500 5000 0.0000 -0.8594 22.8313 36.198943 17.9166 -14.1775 22.8475 2500 5200 0.0000 -0.8594 22.8313 36.472537 17.8487 -14.2629 22.8475 2500 5400 0.0000 -0.8594 22.8313 36.741923 17.7815 -14.3466 22.8475 2500 5600 0.0000 -0.8594 22.8313 37.007214 17.7149 -14.4288 22.8475 2500 5800 0.0000 -0.8594 22.8313 37.268522 17.6489 -14.5095 22.8475 2500 6000 0.0000 -0.8594 22.8313 37.525952 17.5835 -14.5886 22.8475 2500 6200 0.0000 -0.8594 22.8313 37.779605 17.5187 -14.6663 22.8475 2500 6400 0.0000 -0.8594 22.8313 38.029579 17.4546 -14.7426 22.8475 2500 6600 0.0000 -0.8594 22.8313 38.275965 17.3910 -14.8175 22.8475 2500 6800 0.0000 -0.8594 22.8313 38.518854 17.3281 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29.995379 19.2394 -12.4298 22.9053 3000 1400 0.0000 -1.1467 22.8766 30.380950 19.1553 -12.5590 22.9053 3000 1600 0.0000 -1.1467 22.8766 30.758475 19.0721 -12.6849 22.9053 3000 1800 0.0000 -1.1467 22.8766 31.128282 18.9899 -12.8078 22.9053 3000 2000 0.0000 -1.1467 22.8766 31.490679 18.9085 -12.9276 22.9053 3000 2200 0.0000 -1.1467 22.8766 31.845955 18.8279 -13.0446 22.9053 3000 2400 0.0000 -1.1467 22.8766 32.194378 18.7483 -13.1589 22.9053 3000 2600 0.0000 -1.1467 22.8766 32.536204 18.6694 -13.2705 22.9053 3000 2800 0.0000 -1.1467 22.8766 32.871668 18.5914 -13.3796 22.9053 3000 3000 0.0000 -1.1467 22.8766 33.200997 18.5142 -13.4862 22.9053 3000 3200 0.0000 -1.1467 22.8766 33.524401 18.4378 -13.5905 22.9053 3000 3400 0.0000 -1.1467 22.8766 33.842081 18.3622 -13.6925 22.9053 3000 3600 0.0000 -1.1467 22.8766 34.154225 18.2873 -13.7924 22.9053 3000 3800 0.0000 -1.1467 22.8766 34.461012 18.2132 -13.8901 22.9053 3000 4000 0.0000 -1.1467 22.8766 34.762613 18.1398 -13.9858 22.9053 3000 4200 0.0000 -1.1467 22.8766 35.059187 18.0672 -14.0795 22.9053 3000 4400 0.0000 -1.1467 22.8766 35.350889 17.9953 -14.1713 22.9053 3000 4600 0.0000 -1.1467 22.8766 35.637865 17.9240 -14.2612 22.9053 3000 4800 0.0000 -1.1467 22.8766 35.920252 17.8535 -14.3494 22.9053 3000 5000 0.0000 -1.1467 22.8766 36.198183 17.7837 -14.4358 22.9053 3000 5200 0.0000 -1.1467 22.8766 36.471784 17.7146 -14.5206 22.9053 3000 5400 0.0000 -1.1467 22.8766 36.741176 17.6461 -14.6037 22.9053 3000 5600 0.0000 -1.1467 22.8766 37.006474 17.5783 -14.6853 22.9053 3000 5800 0.0000 -1.1467 22.8766 37.267789 17.5112 -14.7653 22.9053 3000 6000 0.0000 -1.1467 22.8766 37.525225 17.4446 -14.8438 22.9053 3000 6200 0.0000 -1.1467 22.8766 37.778884 17.3787 -14.9209 22.9053 3000 6400 0.0000 -1.1467 22.8766 38.028863 17.3135 -14.9966 22.9053 3000 6600 0.0000 -1.1467 22.8766 38.275255 17.2488 -15.0709 22.9053 3000 6800 0.0000 -1.1467 22.8766 38.518150 17.1848 -15.1439 22.9053 3000 7000 0.0000 -1.1467 22.8766 38.757632 17.1213 -15.2156 22.9053 3000 7200 0.0000 -1.1467 22.8766 38.993785 17.0585 -15.2860 22.9053 3000 7400 0.0000 -1.1467 22.8766 39.226689 16.9962 -15.3552 22.9053 3000 7600 0.0000 -1.1467 22.8766 39.456419 16.9345 -15.4232 22.9053 3000 7800 0.0000 -1.1467 22.8766 39.683049 16.8734 -15.4901 22.9053 3000 8000 0.0000 -1.1467 22.8766 39.906651 16.8128 -15.5558 22.9053 3000 8200 0.0000 -1.1467 22.8766 40.127293 16.7528 -15.6205 22.9053 3000 8400 0.0000 -1.1467 22.8766 40.345040 16.6933 -15.6840 22.9053 3500 0 0.0000 -1.4339 22.9219 27.490034 19.6719 -11.8527 22.9667 3500 200 0.0000 -1.4339 22.9219 27.932388 19.5798 -12.0042 22.9667 3500 400 0.0000 -1.4339 22.9219 28.364103 19.4888 -12.1514 22.9667 3500 600 0.0000 -1.4339 22.9219 28.785700 19.3989 -12.2944 22.9667 3500 800 0.0000 -1.4339 22.9219 29.197661 19.3100 -12.4336 22.9667 3500 1000 0.0000 -1.4339 22.9219 29.600430 19.2221 -12.5690 22.9667 3500 1200 0.0000 -1.4339 22.9219 29.994416 19.1352 -12.7009 22.9667 3500 1400 0.0000 -1.4339 22.9219 30.380001 19.0493 -12.8294 22.9667 3500 1600 0.0000 -1.4339 22.9219 30.757539 18.9644 -12.9546 22.9667 3500 1800 0.0000 -1.4339 22.9219 31.127358 18.8803 -13.0768 22.9667 3500 2000 0.0000 -1.4339 22.9219 31.489768 18.7973 -13.1959 22.9667 3500 2200 0.0000 -1.4339 22.9219 31.845055 18.7151 -13.3123 22.9667 3500 2400 0.0000 -1.4339 22.9219 32.193490 18.6338 -13.4258 22.9667 3500 2600 0.0000 -1.4339 22.9219 32.535326 18.5533 -13.5367 22.9667 3500 2800 0.0000 -1.4339 22.9219 32.870801 18.4738 -13.6451 22.9667 3500 3000 0.0000 -1.4339 22.9219 33.200139 18.3950 -13.7511 22.9667 3500 3200 0.0000 -1.4339 22.9219 33.523553 18.3171 -13.8547 22.9667 3500 3400 0.0000 -1.4339 22.9219 33.841242 18.2400 -13.9561 22.9667 3500 3600 0.0000 -1.4339 22.9219 34.153395 18.1637 -14.0552 22.9667 3500 3800 0.0000 -1.4339 22.9219 34.460191 18.0882 -14.1523 22.9667 3500 4000 0.0000 -1.4339 22.9219 34.761800 18.0134 -14.2473 22.9667 3500 4200 0.0000 -1.4339 22.9219 35.058383 17.9394 -14.3404 22.9667 3500 4400 0.0000 -1.4339 22.9219 35.350093 17.8662 -14.4315 22.9667 3500 4600 0.0000 -1.4339 22.9219 35.637076 17.7937 -14.5208 22.9667 3500 4800 0.0000 -1.4339 22.9219 35.919470 17.7219 -14.6083 22.9667 3500 5000 0.0000 -1.4339 22.9219 36.197408 17.6508 -14.6941 22.9667 3500 5200 0.0000 -1.4339 22.9219 36.471017 17.5805 -14.7783 22.9667 3500 5400 0.0000 -1.4339 22.9219 36.740416 17.5108 -14.8608 22.9667 3500 5600 0.0000 -1.4339 22.9219 37.005721 17.4418 -14.9417 22.9667 3500 5800 0.0000 -1.4339 22.9219 37.267042 17.3735 -15.0211 22.9667 3500 6000 0.0000 -1.4339 22.9219 37.524484 17.3058 -15.0990 22.9667 3500 6200 0.0000 -1.4339 22.9219 37.778150 17.2388 -15.1755 22.9667 3500 6400 0.0000 -1.4339 22.9219 38.028135 17.1724 -15.2505 22.9667 3500 6600 0.0000 -1.4339 22.9219 38.274533 17.1067 -15.3242 22.9667 3500 6800 0.0000 -1.4339 22.9219 38.517433 17.0415 -15.3966 22.9667 3500 7000 0.0000 -1.4339 22.9219 38.756921 16.9770 -15.4677 22.9667 3500 7200 0.0000 -1.4339 22.9219 38.993080 16.9131 -15.5376 22.9667 3500 7400 0.0000 -1.4339 22.9219 39.225988 16.8498 -15.6062 22.9667 3500 7600 0.0000 -1.4339 22.9219 39.455724 16.7871 -15.6736 22.9667 3500 7800 0.0000 -1.4339 22.9219 39.682359 16.7250 -15.7399 22.9667 3500 8000 0.0000 -1.4339 22.9219 39.905966 16.6634 -15.8051 22.9667 3500 8200 0.0000 -1.4339 22.9219 40.126613 16.6024 -15.8691 22.9667 3500 8400 0.0000 -1.4339 22.9219 40.344365 16.5420 -15.9321 22.9667 4000 0 0.0000 -1.7212 22.9672 27.488955 19.5797 -12.1280 23.0316 4000 200 0.0000 -1.7212 22.9672 27.931328 19.4855 -12.2788 23.0316 4000 400 0.0000 -1.7212 22.9672 28.363060 19.3924 -12.4253 23.0316 4000 600 0.0000 -1.7212 22.9672 28.784674 19.3005 -12.5677 23.0316 4000 800 0.0000 -1.7212 22.9672 29.196651 19.2096 -12.7061 23.0316 4000 1000 0.0000 -1.7212 22.9672 29.599435 19.1198 -12.8409 23.0316 4000 1200 0.0000 -1.7212 22.9672 29.993436 19.0311 -12.9720 23.0316 4000 1400 0.0000 -1.7212 22.9672 30.379035 18.9433 -13.0998 23.0316 4000 1600 0.0000 -1.7212 22.9672 30.756585 18.8566 -13.2244 23.0316 4000 1800 0.0000 -1.7212 22.9672 31.126418 18.7708 -13.3458 23.0316 4000 2000 0.0000 -1.7212 22.9672 31.488839 18.6861 -13.4643 23.0316 4000 2200 0.0000 -1.7212 22.9672 31.844138 18.6022 -13.5799 23.0316 4000 2400 0.0000 -1.7212 22.9672 32.192584 18.5193 -13.6928 23.0316 4000 2600 0.0000 -1.7212 22.9672 32.534431 18.4372 -13.8030 23.0316 4000 2800 0.0000 -1.7212 22.9672 32.869917 18.3561 -13.9107 23.0316 4000 3000 0.0000 -1.7212 22.9672 33.199265 18.2758 -14.0160 23.0316 4000 3200 0.0000 -1.7212 22.9672 33.522689 18.1964 -14.1189 23.0316 4000 3400 0.0000 -1.7212 22.9672 33.840387 18.1179 -14.2196 23.0316 4000 3600 0.0000 -1.7212 22.9672 34.152550 18.0401 -14.3181 23.0316 4000 3800 0.0000 -1.7212 22.9672 34.459354 17.9632 -14.4145 23.0316 4000 4000 0.0000 -1.7212 22.9672 34.760972 17.8871 -14.5089 23.0316 4000 4200 0.0000 -1.7212 22.9672 35.057563 17.8117 -14.6013 23.0316 4000 4400 0.0000 -1.7212 22.9672 35.349281 17.7372 -14.6918 23.0316 4000 4600 0.0000 -1.7212 22.9672 35.636272 17.6633 -14.7804 23.0316 4000 4800 0.0000 -1.7212 22.9672 35.918674 17.5903 -14.8673 23.0316 4000 5000 0.0000 -1.7212 22.9672 36.196620 17.5179 -14.9525 23.0316 4000 5200 0.0000 -1.7212 22.9672 36.470236 17.4463 -15.0360 23.0316 4000 5400 0.0000 -1.7212 22.9672 36.739642 17.3755 -15.1178 23.0316 4000 5600 0.0000 -1.7212 22.9672 37.004953 17.3053 -15.1981 23.0316 4000 5800 0.0000 -1.7212 22.9672 37.266281 17.2358 -15.2769 23.0316 4000 6000 0.0000 -1.7212 22.9672 37.523730 17.1669 -15.3542 23.0316 4000 6200 0.0000 -1.7212 22.9672 37.777402 17.0988 -15.4300 23.0316 4000 6400 0.0000 -1.7212 22.9672 38.027393 17.0313 -15.5045 23.0316 4000 6600 0.0000 -1.7212 22.9672 38.273797 16.9645 -15.5776 23.0316 4000 6800 0.0000 -1.7212 22.9672 38.516703 16.8983 -15.6494 23.0316 4000 7000 0.0000 -1.7212 22.9672 38.756197 16.8327 -15.7199 23.0316 4000 7200 0.0000 -1.7212 22.9672 38.992361 16.7678 -15.7891 23.0316 4000 7400 0.0000 -1.7212 22.9672 39.225276 16.7035 -15.8572 23.0316 4000 7600 0.0000 -1.7212 22.9672 39.455016 16.6397 -15.9240 23.0316 4000 7800 0.0000 -1.7212 22.9672 39.681657 16.5766 -15.9897 23.0316 4000 8000 0.0000 -1.7212 22.9672 39.905269 16.5141 -16.0543 23.0316 4000 8200 0.0000 -1.7212 22.9672 40.125921 16.4521 -16.1177 23.0316 4000 8400 0.0000 -1.7212 22.9672 40.343678 16.3908 -16.1802 23.0316 4500 0 0.0000 -2.0085 23.0125 27.487857 19.4876 -12.4034 23.1000 4500 200 0.0000 -2.0085 23.0125 27.930248 19.3912 -12.5535 23.1000 4500 400 0.0000 -2.0085 23.0125 28.361998 19.2961 -12.6992 23.1000 4500 600 0.0000 -2.0085 23.0125 28.783629 19.2021 -12.8409 23.1000 4500 800 0.0000 -2.0085 23.0125 29.195622 19.1093 -12.9786 23.1000 4500 1000 0.0000 -2.0085 23.0125 29.598421 19.0176 -13.1127 23.1000 4500 1200 0.0000 -2.0085 23.0125 29.992437 18.9269 -13.2431 23.1000 4500 1400 0.0000 -2.0085 23.0125 30.378050 18.8374 -13.3702 23.1000 4500 1600 0.0000 -2.0085 23.0125 30.755614 18.7489 -13.4941 23.1000 4500 1800 0.0000 -2.0085 23.0125 31.125460 18.6614 -13.6148 23.1000 4500 2000 0.0000 -2.0085 23.0125 31.487894 18.5749 -13.7326 23.1000 4500 2200 0.0000 -2.0085 23.0125 31.843205 18.4893 -13.8475 23.1000 4500 2400 0.0000 -2.0085 23.0125 32.191662 18.4048 -13.9597 23.1000 4500 2600 0.0000 -2.0085 23.0125 32.533520 18.3212 -14.0692 23.1000 4500 2800 0.0000 -2.0085 23.0125 32.869017 18.2385 -14.1763 23.1000 4500 3000 0.0000 -2.0085 23.0125 33.198376 18.1567 -14.2809 23.1000 4500 3200 0.0000 -2.0085 23.0125 33.521810 18.0758 -14.3832 23.1000 4500 3400 0.0000 -2.0085 23.0125 33.839518 17.9957 -14.4832 23.1000 4500 3600 0.0000 -2.0085 23.0125 34.151689 17.9166 -14.5810 23.1000 4500 3800 0.0000 -2.0085 23.0125 34.458503 17.8382 -14.6767 23.1000 4500 4000 0.0000 -2.0085 23.0125 34.760129 17.7607 -14.7704 23.1000 4500 4200 0.0000 -2.0085 23.0125 35.056729 17.6840 -14.8622 23.1000 4500 4400 0.0000 -2.0085 23.0125 35.348455 17.6081 -14.9520 23.1000 4500 4600 0.0000 -2.0085 23.0125 35.635454 17.5330 -15.0400 23.1000 4500 4800 0.0000 -2.0085 23.0125 35.917864 17.4587 -15.1263 23.1000 4500 5000 0.0000 -2.0085 23.0125 36.195818 17.3851 -15.2108 23.1000 4500 5200 0.0000 -2.0085 23.0125 36.469441 17.3122 -15.2936 23.1000 4500 5400 0.0000 -2.0085 23.0125 36.738854 17.2401 -15.3749 23.1000 4500 5600 0.0000 -2.0085 23.0125 37.004173 17.1688 -15.4545 23.1000 4500 5800 0.0000 -2.0085 23.0125 37.265507 17.0981 -15.5327 23.1000 4500 6000 0.0000 -2.0085 23.0125 37.522963 17.0281 -15.6094 23.1000 4500 6200 0.0000 -2.0085 23.0125 37.776641 16.9588 -15.6846 23.1000 4500 6400 0.0000 -2.0085 23.0125 38.026639 16.8902 -15.7585 23.1000 4500 6600 0.0000 -2.0085 23.0125 38.273049 16.8223 -15.8309 23.1000 4500 6800 0.0000 -2.0085 23.0125 38.515961 16.7550 -15.9021 23.1000 4500 7000 0.0000 -2.0085 23.0125 38.755460 16.6884 -15.9720 23.1000 4500 7200 0.0000 -2.0085 23.0125 38.991631 16.6224 -16.0407 23.1000 4500 7400 0.0000 -2.0085 23.0125 39.224551 16.5571 -16.1081 23.1000 4500 7600 0.0000 -2.0085 23.0125 39.454297 16.4924 -16.1744 23.1000 4500 7800 0.0000 -2.0085 23.0125 39.680943 16.4283 -16.2395 23.1000 4500 8000 0.0000 -2.0085 23.0125 39.904560 16.3648 -16.3035 23.1000 4500 8200 0.0000 -2.0085 23.0125 40.125217 16.3019 -16.3664 23.1000 4500 8400 0.0000 -2.0085 23.0125 40.342979 16.2395 -16.4282 23.1000 user time (s): 0.010 system time (s): 0.010 elapsed time (s): 0.010 ================================================ FILE: tests/test_data/cropA/geometry/20180331-20180518_VV_8rlks_base.par ================================================ initial_baseline(TCN): 0.2959757 -15.0330123 24.3922942 m m m initial_baseline_rate: 0.0000000 -0.0699261 0.0056151 m/s m/s m/s precision_baseline(TCN): 0.0000000 -14.8908566 24.3852158 m m m precision_baseline_rate: 0.0000000 -0.0836758 0.0170065 m/s m/s m/s unwrap_phase_constant: 0.00035 radians ================================================ FILE: tests/test_data/cropA/geometry/20180331-20180518_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): 0.296 -15.033 24.392 orbit baseline rate (TCN) (m/s): 0.000e+00 -6.993e-02 5.615e-03 baseline vector (TCN) (m): 0.000 -14.891 24.385 baseline rate (TCN) (m/s): 0.000e+00 -8.368e-02 1.701e-02 SLC-1 center baseline length (m): 28.5723 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 0.0000 -14.1098 24.2265 27.497013 14.9752 -23.7013 28.0358 0 200 0.0000 -14.1098 24.2265 27.939249 14.7918 -23.8162 28.0358 0 400 0.0000 -14.1098 24.2265 28.370851 14.6120 -23.9269 28.0358 0 600 0.0000 -14.1098 24.2265 28.792342 14.4356 -24.0337 28.0358 0 800 0.0000 -14.1098 24.2265 29.204202 14.2625 -24.1369 28.0358 0 1000 0.0000 -14.1098 24.2265 29.606873 14.0925 -24.2365 28.0358 0 1200 0.0000 -14.1098 24.2265 30.000767 13.9255 -24.3328 28.0358 0 1400 0.0000 -14.1098 24.2265 30.386264 13.7615 -24.4260 28.0358 0 1600 0.0000 -14.1098 24.2265 30.763716 13.6003 -24.5161 28.0358 0 1800 0.0000 -14.1098 24.2265 31.133454 13.4418 -24.6034 28.0358 0 2000 0.0000 -14.1098 24.2265 31.495785 13.2859 -24.6879 28.0358 0 2200 0.0000 -14.1098 24.2265 31.850997 13.1326 -24.7698 28.0358 0 2400 0.0000 -14.1098 24.2265 32.199360 12.9818 -24.8492 28.0358 0 2600 0.0000 -14.1098 24.2265 32.541126 12.8333 -24.9262 28.0358 0 2800 0.0000 -14.1098 24.2265 32.876534 12.6872 -25.0009 28.0358 0 3000 0.0000 -14.1098 24.2265 33.205808 12.5433 -25.0734 28.0358 0 3200 0.0000 -14.1098 24.2265 33.529159 12.4016 -25.1437 28.0358 0 3400 0.0000 -14.1098 24.2265 33.846787 12.2620 -25.2121 28.0358 0 3600 0.0000 -14.1098 24.2265 34.158881 12.1245 -25.2785 28.0358 0 3800 0.0000 -14.1098 24.2265 34.465620 11.9890 -25.3431 28.0358 0 4000 0.0000 -14.1098 24.2265 34.767174 11.8555 -25.4058 28.0358 0 4200 0.0000 -14.1098 24.2265 35.063703 11.7238 -25.4668 28.0358 0 4400 0.0000 -14.1098 24.2265 35.355362 11.5940 -25.5262 28.0358 0 4600 0.0000 -14.1098 24.2265 35.642294 11.4661 -25.5839 28.0358 0 4800 0.0000 -14.1098 24.2265 35.924640 11.3398 -25.6401 28.0358 0 5000 0.0000 -14.1098 24.2265 36.202531 11.2154 -25.6948 28.0358 0 5200 0.0000 -14.1098 24.2265 36.476093 11.0925 -25.7481 28.0358 0 5400 0.0000 -14.1098 24.2265 36.745447 10.9714 -25.7999 28.0358 0 5600 0.0000 -14.1098 24.2265 37.010708 10.8518 -25.8505 28.0358 0 5800 0.0000 -14.1098 24.2265 37.271986 10.7338 -25.8997 28.0358 0 6000 0.0000 -14.1098 24.2265 37.529387 10.6174 -25.9476 28.0358 0 6200 0.0000 -14.1098 24.2265 37.783012 10.5024 -25.9944 28.0358 0 6400 0.0000 -14.1098 24.2265 38.032957 10.3889 -26.0399 28.0358 0 6600 0.0000 -14.1098 24.2265 38.279317 10.2768 -26.0844 28.0358 0 6800 0.0000 -14.1098 24.2265 38.522179 10.1662 -26.1277 28.0358 0 7000 0.0000 -14.1098 24.2265 38.761630 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-27.1019 28.6272 2500 8000 0.0000 -14.9698 24.4013 39.907324 9.1140 -27.1376 28.6272 2500 8200 0.0000 -14.9698 24.4013 40.127960 9.0094 -27.1725 28.6272 2500 8400 0.0000 -14.9698 24.4013 40.345703 8.9061 -27.2066 28.6272 3000 0 0.0000 -15.1418 24.4362 27.491093 14.6873 -24.7120 28.7472 3000 200 0.0000 -15.1418 24.4362 27.933428 14.4961 -24.8247 28.7472 3000 400 0.0000 -15.1418 24.4362 28.365126 14.3087 -24.9332 28.7472 3000 600 0.0000 -15.1418 24.4362 28.786707 14.1248 -25.0378 28.7472 3000 800 0.0000 -15.1418 24.4362 29.198653 13.9444 -25.1387 28.7472 3000 1000 0.0000 -15.1418 24.4362 29.601406 13.7674 -25.2361 28.7472 3000 1200 0.0000 -15.1418 24.4362 29.995379 13.5935 -25.3302 28.7472 3000 1400 0.0000 -15.1418 24.4362 30.380950 13.4228 -25.4211 28.7472 3000 1600 0.0000 -15.1418 24.4362 30.758475 13.2550 -25.5090 28.7472 3000 1800 0.0000 -15.1418 24.4362 31.128282 13.0901 -25.5940 28.7472 3000 2000 0.0000 -15.1418 24.4362 31.490679 12.9279 -25.6763 28.7472 3000 2200 0.0000 -15.1418 24.4362 31.845955 12.7685 -25.7559 28.7472 3000 2400 0.0000 -15.1418 24.4362 32.194378 12.6116 -25.8331 28.7472 3000 2600 0.0000 -15.1418 24.4362 32.536204 12.4573 -25.9079 28.7472 3000 2800 0.0000 -15.1418 24.4362 32.871668 12.3054 -25.9804 28.7472 3000 3000 0.0000 -15.1418 24.4362 33.200997 12.1558 -26.0507 28.7472 3000 3200 0.0000 -15.1418 24.4362 33.524401 12.0086 -26.1189 28.7472 3000 3400 0.0000 -15.1418 24.4362 33.842081 11.8636 -26.1851 28.7472 3000 3600 0.0000 -15.1418 24.4362 34.154225 11.7208 -26.2493 28.7472 3000 3800 0.0000 -15.1418 24.4362 34.461012 11.5800 -26.3117 28.7472 3000 4000 0.0000 -15.1418 24.4362 34.762613 11.4414 -26.3723 28.7472 3000 4200 0.0000 -15.1418 24.4362 35.059187 11.3047 -26.4312 28.7472 3000 4400 0.0000 -15.1418 24.4362 35.350889 11.1700 -26.4884 28.7472 3000 4600 0.0000 -15.1418 24.4362 35.637865 11.0372 -26.5440 28.7472 3000 4800 0.0000 -15.1418 24.4362 35.920252 10.9062 -26.5981 28.7472 3000 5000 0.0000 -15.1418 24.4362 36.198183 10.7771 -26.6506 28.7472 3000 5200 0.0000 -15.1418 24.4362 36.471784 10.6497 -26.7018 28.7472 3000 5400 0.0000 -15.1418 24.4362 36.741176 10.5240 -26.7516 28.7472 3000 5600 0.0000 -15.1418 24.4362 37.006474 10.4001 -26.8000 28.7472 3000 5800 0.0000 -15.1418 24.4362 37.267789 10.2777 -26.8472 28.7472 3000 6000 0.0000 -15.1418 24.4362 37.525225 10.1570 -26.8931 28.7472 3000 6200 0.0000 -15.1418 24.4362 37.778884 10.0378 -26.9378 28.7472 3000 6400 0.0000 -15.1418 24.4362 38.028863 9.9202 -26.9813 28.7472 3000 6600 0.0000 -15.1418 24.4362 38.275255 9.8041 -27.0237 28.7472 3000 6800 0.0000 -15.1418 24.4362 38.518150 9.6894 -27.0651 28.7472 3000 7000 0.0000 -15.1418 24.4362 38.757632 9.5762 -27.1053 28.7472 3000 7200 0.0000 -15.1418 24.4362 38.993785 9.4644 -27.1446 28.7472 3000 7400 0.0000 -15.1418 24.4362 39.226689 9.3540 -27.1828 28.7472 3000 7600 0.0000 -15.1418 24.4362 39.456419 9.2449 -27.2201 28.7472 3000 7800 0.0000 -15.1418 24.4362 39.683049 9.1372 -27.2565 28.7472 3000 8000 0.0000 -15.1418 24.4362 39.906651 9.0308 -27.2919 28.7472 3000 8200 0.0000 -15.1418 24.4362 40.127293 8.9256 -27.3265 28.7472 3000 8400 0.0000 -15.1418 24.4362 40.345040 8.8217 -27.3602 28.7472 3500 0 0.0000 -15.3138 24.4712 27.490034 14.6394 -24.8805 28.8678 3500 200 0.0000 -15.3138 24.4712 27.932388 14.4469 -24.9927 28.8678 3500 400 0.0000 -15.3138 24.4712 28.364103 14.2582 -25.1009 28.8678 3500 600 0.0000 -15.3138 24.4712 28.785700 14.0731 -25.2051 28.8678 3500 800 0.0000 -15.3138 24.4712 29.197661 13.8915 -25.3057 28.8678 3500 1000 0.0000 -15.3138 24.4712 29.600430 13.7133 -25.4027 28.8678 3500 1200 0.0000 -15.3138 24.4712 29.994416 13.5383 -25.4964 28.8678 3500 1400 0.0000 -15.3138 24.4712 30.380001 13.3664 -25.5869 28.8678 3500 1600 0.0000 -15.3138 24.4712 30.757539 13.1975 -25.6744 28.8678 3500 1800 0.0000 -15.3138 24.4712 31.127358 13.0315 -25.7591 28.8678 3500 2000 0.0000 -15.3138 24.4712 31.489768 12.8683 -25.8410 28.8678 3500 2200 0.0000 -15.3138 24.4712 31.845055 12.7078 -25.9203 28.8678 3500 2400 0.0000 -15.3138 24.4712 32.193490 12.5500 -25.9971 28.8678 3500 2600 0.0000 -15.3138 24.4712 32.535326 12.3946 -26.0715 28.8678 3500 2800 0.0000 -15.3138 24.4712 32.870801 12.2418 -26.1436 28.8678 3500 3000 0.0000 -15.3138 24.4712 33.200139 12.0913 -26.2136 28.8678 3500 3200 0.0000 -15.3138 24.4712 33.523553 11.9431 -26.2814 28.8678 3500 3400 0.0000 -15.3138 24.4712 33.841242 11.7972 -26.3472 28.8678 3500 3600 0.0000 -15.3138 24.4712 34.153395 11.6535 -26.4111 28.8678 3500 3800 0.0000 -15.3138 24.4712 34.460191 11.5119 -26.4731 28.8678 3500 4000 0.0000 -15.3138 24.4712 34.761800 11.3724 -26.5334 28.8678 3500 4200 0.0000 -15.3138 24.4712 35.058383 11.2349 -26.5919 28.8678 3500 4400 0.0000 -15.3138 24.4712 35.350093 11.0994 -26.6487 28.8678 3500 4600 0.0000 -15.3138 24.4712 35.637076 10.9658 -26.7040 28.8678 3500 4800 0.0000 -15.3138 24.4712 35.919470 10.8340 -26.7577 28.8678 3500 5000 0.0000 -15.3138 24.4712 36.197408 10.7041 -26.8099 28.8678 3500 5200 0.0000 -15.3138 24.4712 36.471017 10.5759 -26.8608 28.8678 3500 5400 0.0000 -15.3138 24.4712 36.740416 10.4495 -26.9102 28.8678 3500 5600 0.0000 -15.3138 24.4712 37.005721 10.3248 -26.9583 28.8678 3500 5800 0.0000 -15.3138 24.4712 37.267042 10.2017 -27.0051 28.8678 3500 6000 0.0000 -15.3138 24.4712 37.524484 10.0803 -27.0507 28.8678 3500 6200 0.0000 -15.3138 24.4712 37.778150 9.9604 -27.0950 28.8678 3500 6400 0.0000 -15.3138 24.4712 38.028135 9.8421 -27.1382 28.8678 3500 6600 0.0000 -15.3138 24.4712 38.274533 9.7253 -27.1803 28.8678 3500 6800 0.0000 -15.3138 24.4712 38.517433 9.6100 -27.2213 28.8678 3500 7000 0.0000 -15.3138 24.4712 38.756921 9.4961 -27.2612 28.8678 3500 7200 0.0000 -15.3138 24.4712 38.993080 9.3837 -27.3001 28.8678 3500 7400 0.0000 -15.3138 24.4712 39.225988 9.2726 -27.3380 28.8678 3500 7600 0.0000 -15.3138 24.4712 39.455724 9.1630 -27.3750 28.8678 3500 7800 0.0000 -15.3138 24.4712 39.682359 9.0546 -27.4110 28.8678 3500 8000 0.0000 -15.3138 24.4712 39.905966 8.9476 -27.4462 28.8678 3500 8200 0.0000 -15.3138 24.4712 40.126613 8.8418 -27.4804 28.8678 3500 8400 0.0000 -15.3138 24.4712 40.344365 8.7373 -27.5138 28.8678 4000 0 0.0000 -15.4858 24.5061 27.488955 14.5915 -25.0489 28.9890 4000 200 0.0000 -15.4858 24.5061 27.931328 14.3977 -25.1608 28.9890 4000 400 0.0000 -15.4858 24.5061 28.363060 14.2077 -25.2686 28.9890 4000 600 0.0000 -15.4858 24.5061 28.784674 14.0214 -25.3724 28.9890 4000 800 0.0000 -15.4858 24.5061 29.196651 13.8386 -25.4726 28.9890 4000 1000 0.0000 -15.4858 24.5061 29.599435 13.6591 -25.5693 28.9890 4000 1200 0.0000 -15.4858 24.5061 29.993436 13.4830 -25.6626 28.9890 4000 1400 0.0000 -15.4858 24.5061 30.379035 13.3100 -25.7528 28.9890 4000 1600 0.0000 -15.4858 24.5061 30.756585 13.1400 -25.8399 28.9890 4000 1800 0.0000 -15.4858 24.5061 31.126418 12.9729 -25.9242 28.9890 4000 2000 0.0000 -15.4858 24.5061 31.488839 12.8087 -26.0057 28.9890 4000 2200 0.0000 -15.4858 24.5061 31.844138 12.6472 -26.0846 28.9890 4000 2400 0.0000 -15.4858 24.5061 32.192584 12.4883 -26.1611 28.9890 4000 2600 0.0000 -15.4858 24.5061 32.534431 12.3320 -26.2351 28.9890 4000 2800 0.0000 -15.4858 24.5061 32.869917 12.1782 -26.3069 28.9890 4000 3000 0.0000 -15.4858 24.5061 33.199265 12.0268 -26.3764 28.9890 4000 3200 0.0000 -15.4858 24.5061 33.522689 11.8777 -26.4439 28.9890 4000 3400 0.0000 -15.4858 24.5061 33.840387 11.7309 -26.5094 28.9890 4000 3600 0.0000 -15.4858 24.5061 34.152550 11.5863 -26.5729 28.9890 4000 3800 0.0000 -15.4858 24.5061 34.459354 11.4438 -26.6346 28.9890 4000 4000 0.0000 -15.4858 24.5061 34.760972 11.3034 -26.6944 28.9890 4000 4200 0.0000 -15.4858 24.5061 35.057563 11.1651 -26.7526 28.9890 4000 4400 0.0000 -15.4858 24.5061 35.349281 11.0287 -26.8091 28.9890 4000 4600 0.0000 -15.4858 24.5061 35.636272 10.8943 -26.8640 28.9890 4000 4800 0.0000 -15.4858 24.5061 35.918674 10.7618 -26.9174 28.9890 4000 5000 0.0000 -15.4858 24.5061 36.196620 10.6311 -26.9692 28.9890 4000 5200 0.0000 -15.4858 24.5061 36.470236 10.5022 -27.0197 28.9890 4000 5400 0.0000 -15.4858 24.5061 36.739642 10.3750 -27.0688 28.9890 4000 5600 0.0000 -15.4858 24.5061 37.004953 10.2495 -27.1165 28.9890 4000 5800 0.0000 -15.4858 24.5061 37.266281 10.1258 -27.1630 28.9890 4000 6000 0.0000 -15.4858 24.5061 37.523730 10.0036 -27.2082 28.9890 4000 6200 0.0000 -15.4858 24.5061 37.777402 9.8830 -27.2523 28.9890 4000 6400 0.0000 -15.4858 24.5061 38.027393 9.7640 -27.2951 28.9890 4000 6600 0.0000 -15.4858 24.5061 38.273797 9.6466 -27.3369 28.9890 4000 6800 0.0000 -15.4858 24.5061 38.516703 9.5306 -27.3775 28.9890 4000 7000 0.0000 -15.4858 24.5061 38.756197 9.4161 -27.4171 28.9890 4000 7200 0.0000 -15.4858 24.5061 38.992361 9.3030 -27.4557 28.9890 4000 7400 0.0000 -15.4858 24.5061 39.225276 9.1913 -27.4933 28.9890 4000 7600 0.0000 -15.4858 24.5061 39.455016 9.0810 -27.5299 28.9890 4000 7800 0.0000 -15.4858 24.5061 39.681657 8.9720 -27.5656 28.9890 4000 8000 0.0000 -15.4858 24.5061 39.905269 8.8644 -27.6004 28.9890 4000 8200 0.0000 -15.4858 24.5061 40.125921 8.7580 -27.6344 28.9890 4000 8400 0.0000 -15.4858 24.5061 40.343678 8.6529 -27.6674 28.9890 4500 0 0.0000 -15.6578 24.5411 27.487857 14.5436 -25.2173 29.1107 4500 200 0.0000 -15.6578 24.5411 27.930248 14.3485 -25.3289 29.1107 4500 400 0.0000 -15.6578 24.5411 28.361998 14.1572 -25.4363 29.1107 4500 600 0.0000 -15.6578 24.5411 28.783629 13.9696 -25.5398 29.1107 4500 800 0.0000 -15.6578 24.5411 29.195622 13.7856 -25.6396 29.1107 4500 1000 0.0000 -15.6578 24.5411 29.598421 13.6050 -25.7358 29.1107 4500 1200 0.0000 -15.6578 24.5411 29.992437 13.4277 -25.8288 29.1107 4500 1400 0.0000 -15.6578 24.5411 30.378050 13.2536 -25.9186 29.1107 4500 1600 0.0000 -15.6578 24.5411 30.755614 13.0825 -26.0054 29.1107 4500 1800 0.0000 -15.6578 24.5411 31.125460 12.9144 -26.0893 29.1107 4500 2000 0.0000 -15.6578 24.5411 31.487894 12.7491 -26.1704 29.1107 4500 2200 0.0000 -15.6578 24.5411 31.843205 12.5866 -26.2490 29.1107 4500 2400 0.0000 -15.6578 24.5411 32.191662 12.4267 -26.3251 29.1107 4500 2600 0.0000 -15.6578 24.5411 32.533520 12.2694 -26.3987 29.1107 4500 2800 0.0000 -15.6578 24.5411 32.869017 12.1146 -26.4701 29.1107 4500 3000 0.0000 -15.6578 24.5411 33.198376 11.9622 -26.5393 29.1107 4500 3200 0.0000 -15.6578 24.5411 33.521810 11.8122 -26.6064 29.1107 4500 3400 0.0000 -15.6578 24.5411 33.839518 11.6645 -26.6715 29.1107 4500 3600 0.0000 -15.6578 24.5411 34.151689 11.5190 -26.7347 29.1107 4500 3800 0.0000 -15.6578 24.5411 34.458503 11.3757 -26.7960 29.1107 4500 4000 0.0000 -15.6578 24.5411 34.760129 11.2345 -26.8555 29.1107 4500 4200 0.0000 -15.6578 24.5411 35.056729 11.0953 -26.9133 29.1107 4500 4400 0.0000 -15.6578 24.5411 35.348455 10.9581 -26.9694 29.1107 4500 4600 0.0000 -15.6578 24.5411 35.635454 10.8229 -27.0240 29.1107 4500 4800 0.0000 -15.6578 24.5411 35.917864 10.6896 -27.0770 29.1107 4500 5000 0.0000 -15.6578 24.5411 36.195818 10.5581 -27.1285 29.1107 4500 5200 0.0000 -15.6578 24.5411 36.469441 10.4284 -27.1787 29.1107 4500 5400 0.0000 -15.6578 24.5411 36.738854 10.3005 -27.2274 29.1107 4500 5600 0.0000 -15.6578 24.5411 37.004173 10.1743 -27.2748 29.1107 4500 5800 0.0000 -15.6578 24.5411 37.265507 10.0498 -27.3209 29.1107 4500 6000 0.0000 -15.6578 24.5411 37.522963 9.9269 -27.3658 29.1107 4500 6200 0.0000 -15.6578 24.5411 37.776641 9.8057 -27.4095 29.1107 4500 6400 0.0000 -15.6578 24.5411 38.026639 9.6860 -27.4520 29.1107 4500 6600 0.0000 -15.6578 24.5411 38.273049 9.5678 -27.4934 29.1107 4500 6800 0.0000 -15.6578 24.5411 38.515961 9.4512 -27.5337 29.1107 4500 7000 0.0000 -15.6578 24.5411 38.755460 9.3360 -27.5730 29.1107 4500 7200 0.0000 -15.6578 24.5411 38.991631 9.2223 -27.6112 29.1107 4500 7400 0.0000 -15.6578 24.5411 39.224551 9.1100 -27.6485 29.1107 4500 7600 0.0000 -15.6578 24.5411 39.454297 8.9990 -27.6848 29.1107 4500 7800 0.0000 -15.6578 24.5411 39.680943 8.8894 -27.7202 29.1107 4500 8000 0.0000 -15.6578 24.5411 39.904560 8.7812 -27.7547 29.1107 4500 8200 0.0000 -15.6578 24.5411 40.125217 8.6742 -27.7883 29.1107 4500 8400 0.0000 -15.6578 24.5411 40.342979 8.5686 -27.8211 29.1107 user time (s): 0.010 system time (s): 0.000 elapsed time (s): 0.000 ================================================ FILE: tests/test_data/cropA/geometry/20180331-20180530_VV_8rlks_base.par ================================================ initial_baseline(TCN): -0.6160465 34.5715540 38.2579471 m m m initial_baseline_rate: 0.0000000 -0.0803903 0.0012571 m/s m/s m/s precision_baseline(TCN): -0.6160465 34.5715540 38.2579471 m m m precision_baseline_rate: 0.0000000 -0.0803903 0.0012571 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/cropA/geometry/20180331-20180530_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): -0.616 34.572 38.258 orbit baseline rate (TCN) (m/s): 0.000e+00 -8.039e-02 1.257e-03 baseline vector (TCN) (m): -0.616 34.572 38.258 baseline rate (TCN) (m/s): 0.000e+00 -8.039e-02 1.257e-03 SLC-1 center baseline length (m): 51.5678 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 -0.6160 35.3219 38.2462 27.497013 50.2346 13.6746 52.0653 0 200 -0.6160 35.3219 38.2462 27.939249 50.3386 13.2865 52.0653 0 400 -0.6160 35.3219 38.2462 28.370851 50.4373 12.9069 52.0653 0 600 -0.6160 35.3219 38.2462 28.792342 50.5308 12.5355 52.0653 0 800 -0.6160 35.3219 38.2462 29.204202 50.6196 12.1719 52.0653 0 1000 -0.6160 35.3219 38.2462 29.606873 50.7039 11.8159 52.0653 0 1200 -0.6160 35.3219 38.2462 30.000767 50.7840 11.4670 52.0653 0 1400 -0.6160 35.3219 38.2462 30.386264 50.8599 11.1251 52.0653 0 1600 -0.6160 35.3219 38.2462 30.763716 50.9321 10.7898 52.0653 0 1800 -0.6160 35.3219 38.2462 31.133454 51.0007 10.4608 52.0653 0 2000 -0.6160 35.3219 38.2462 31.495785 51.0658 10.1381 52.0653 0 2200 -0.6160 35.3219 38.2462 31.850997 51.1277 9.8213 52.0653 0 2400 -0.6160 35.3219 38.2462 32.199360 51.1864 9.5103 52.0653 0 2600 -0.6160 35.3219 38.2462 32.541126 51.2422 9.2048 52.0653 0 2800 -0.6160 35.3219 38.2462 32.876534 51.2952 8.9046 52.0653 0 3000 -0.6160 35.3219 38.2462 33.205808 51.3456 8.6097 52.0653 0 3200 -0.6160 35.3219 38.2462 33.529159 51.3933 8.3198 52.0653 0 3400 -0.6160 35.3219 38.2462 33.846787 51.4387 8.0347 52.0653 0 3600 -0.6160 35.3219 38.2462 34.158881 51.4817 7.7544 52.0653 0 3800 -0.6160 35.3219 38.2462 34.465620 51.5224 7.4787 52.0653 0 4000 -0.6160 35.3219 38.2462 34.767174 51.5611 7.2074 52.0653 0 4200 -0.6160 35.3219 38.2462 35.063703 51.5977 6.9404 52.0653 0 4400 -0.6160 35.3219 38.2462 35.355362 51.6323 6.6777 52.0653 0 4600 -0.6160 35.3219 38.2462 35.642294 51.6651 6.4190 52.0653 0 4800 -0.6160 35.3219 38.2462 35.924640 51.6961 6.1644 52.0653 0 5000 -0.6160 35.3219 38.2462 36.202531 51.7254 5.9135 52.0653 0 5200 -0.6160 35.3219 38.2462 36.476093 51.7530 5.6665 52.0653 0 5400 -0.6160 35.3219 38.2462 36.745447 51.7791 5.4231 52.0653 0 5600 -0.6160 35.3219 38.2462 37.010708 51.8036 5.1834 52.0653 0 5800 -0.6160 35.3219 38.2462 37.271986 51.8267 4.9471 52.0653 0 6000 -0.6160 35.3219 38.2462 37.529387 51.8484 4.7142 52.0653 0 6200 -0.6160 35.3219 38.2462 37.783012 51.8688 4.4846 52.0653 0 6400 -0.6160 35.3219 38.2462 38.032957 51.8879 4.2583 52.0653 0 6600 -0.6160 35.3219 38.2462 38.279317 51.9057 4.0351 52.0653 0 6800 -0.6160 35.3219 38.2462 38.522179 51.9223 3.8151 52.0653 0 7000 -0.6160 35.3219 38.2462 38.761630 51.9378 3.5981 52.0653 0 7200 -0.6160 35.3219 38.2462 38.997753 51.9522 3.3840 52.0653 0 7400 -0.6160 35.3219 38.2462 39.230627 51.9655 3.1728 52.0653 0 7600 -0.6160 35.3219 38.2462 39.460328 51.9778 2.9644 52.0653 0 7800 -0.6160 35.3219 38.2462 39.686930 51.9891 2.7588 52.0653 0 8000 -0.6160 35.3219 38.2462 39.910504 51.9995 2.5559 52.0653 0 8200 -0.6160 35.3219 38.2462 40.131118 52.0089 2.3557 52.0653 0 8400 -0.6160 35.3219 38.2462 40.348838 52.0175 2.1580 52.0653 500 0 -0.6160 35.1567 38.2488 27.496077 50.1604 13.5277 51.9552 500 200 -0.6160 35.1567 38.2488 27.938329 50.2633 13.1401 51.9552 500 400 -0.6160 35.1567 38.2488 28.369946 50.3608 12.7611 51.9552 500 600 -0.6160 35.1567 38.2488 28.791451 50.4533 12.3902 51.9552 500 800 -0.6160 35.1567 38.2488 29.203324 50.5411 12.0272 51.9552 500 1000 -0.6160 35.1567 38.2488 29.606009 50.6244 11.6717 51.9552 500 1200 -0.6160 35.1567 38.2488 29.999915 50.7034 11.3234 51.9552 500 1400 -0.6160 35.1567 38.2488 30.385423 50.7784 10.9819 51.9552 500 1600 -0.6160 35.1567 38.2488 30.762887 50.8497 10.6472 51.9552 500 1800 -0.6160 35.1567 38.2488 31.132636 50.9173 10.3188 51.9552 500 2000 -0.6160 35.1567 38.2488 31.494978 50.9815 9.9966 51.9552 500 2200 -0.6160 35.1567 38.2488 31.850200 51.0425 9.6803 51.9552 500 2400 -0.6160 35.1567 38.2488 32.198572 51.1004 9.3698 51.9552 500 2600 -0.6160 35.1567 38.2488 32.540347 51.1554 9.0648 51.9552 500 2800 -0.6160 35.1567 38.2488 32.875764 51.2076 8.7651 51.9552 500 3000 -0.6160 35.1567 38.2488 33.205046 51.2571 8.4707 51.9552 500 3200 -0.6160 35.1567 38.2488 33.528405 51.3041 8.1813 51.9552 500 3400 -0.6160 35.1567 38.2488 33.846042 51.3487 7.8967 51.9552 500 3600 -0.6160 35.1567 38.2488 34.158144 51.3909 7.6169 51.9552 500 3800 -0.6160 35.1567 38.2488 34.464890 51.4309 7.3416 51.9552 500 4000 -0.6160 35.1567 38.2488 34.766452 51.4689 7.0708 51.9552 500 4200 -0.6160 35.1567 38.2488 35.062988 51.5048 6.8043 51.9552 500 4400 -0.6160 35.1567 38.2488 35.354653 51.5387 6.5421 51.9552 500 4600 -0.6160 35.1567 38.2488 35.641592 51.5708 6.2839 51.9552 500 4800 -0.6160 35.1567 38.2488 35.923944 51.6012 6.0296 51.9552 500 5000 -0.6160 35.1567 38.2488 36.201842 51.6298 5.7793 51.9552 500 5200 -0.6160 35.1567 38.2488 36.475410 51.6568 5.5327 51.9552 500 5400 -0.6160 35.1567 38.2488 36.744770 51.6822 5.2898 51.9552 500 5600 -0.6160 35.1567 38.2488 37.010037 51.7062 5.0504 51.9552 500 5800 -0.6160 35.1567 38.2488 37.271321 51.7287 4.8146 51.9552 500 6000 -0.6160 35.1567 38.2488 37.528727 51.7498 4.5821 51.9552 500 6200 -0.6160 35.1567 38.2488 37.782357 51.7695 4.3530 51.9552 500 6400 -0.6160 35.1567 38.2488 38.032308 51.7880 4.1271 51.9552 500 6600 -0.6160 35.1567 38.2488 38.278673 51.8053 3.9044 51.9552 500 6800 -0.6160 35.1567 38.2488 38.521540 51.8214 3.6848 51.9552 500 7000 -0.6160 35.1567 38.2488 38.760996 51.8363 3.4682 51.9552 500 7200 -0.6160 35.1567 38.2488 38.997124 51.8502 3.2545 51.9552 500 7400 -0.6160 35.1567 38.2488 39.230002 51.8630 3.0437 51.9552 500 7600 -0.6160 35.1567 38.2488 39.459708 51.8747 2.8358 51.9552 500 7800 -0.6160 35.1567 38.2488 39.686314 51.8856 2.6306 51.9552 500 8000 -0.6160 35.1567 38.2488 39.909892 51.8954 2.4281 51.9552 500 8200 -0.6160 35.1567 38.2488 40.130511 51.9044 2.2282 51.9552 500 8400 -0.6160 35.1567 38.2488 40.348236 51.9125 2.0310 51.9552 1000 0 -0.6160 34.9914 38.2514 27.495122 50.0861 13.3808 51.8454 1000 200 -0.6160 34.9914 38.2514 27.937389 50.1879 12.9937 51.8454 1000 400 -0.6160 34.9914 38.2514 28.369022 50.2844 12.6153 51.8454 1000 600 -0.6160 34.9914 38.2514 28.790541 50.3758 12.2450 51.8454 1000 800 -0.6160 34.9914 38.2514 29.202428 50.4625 11.8825 51.8454 1000 1000 -0.6160 34.9914 38.2514 29.605126 50.5448 11.5275 51.8454 1000 1200 -0.6160 34.9914 38.2514 29.999045 50.6228 11.1797 51.8454 1000 1400 -0.6160 34.9914 38.2514 30.384565 50.6969 10.8389 51.8454 1000 1600 -0.6160 34.9914 38.2514 30.762040 50.7672 10.5046 51.8454 1000 1800 -0.6160 34.9914 38.2514 31.131800 50.8339 10.1767 51.8454 1000 2000 -0.6160 34.9914 38.2514 31.494152 50.8973 9.8551 51.8454 1000 2200 -0.6160 34.9914 38.2514 31.849385 50.9574 9.5393 51.8454 1000 2400 -0.6160 34.9914 38.2514 32.197766 51.0144 9.2293 51.8454 1000 2600 -0.6160 34.9914 38.2514 32.539551 51.0686 8.9248 51.8454 1000 2800 -0.6160 34.9914 38.2514 32.874977 51.1199 8.6256 51.8454 1000 3000 -0.6160 34.9914 38.2514 33.204268 51.1687 8.3317 51.8454 1000 3200 -0.6160 34.9914 38.2514 33.527636 51.2149 8.0428 51.8454 1000 3400 -0.6160 34.9914 38.2514 33.845281 51.2587 7.7587 51.8454 1000 3600 -0.6160 34.9914 38.2514 34.157391 51.3002 7.4794 51.8454 1000 3800 -0.6160 34.9914 38.2514 34.464145 51.3395 7.2046 51.8454 1000 4000 -0.6160 34.9914 38.2514 34.765714 51.3767 6.9343 51.8454 1000 4200 -0.6160 34.9914 38.2514 35.062258 51.4119 6.6683 51.8454 1000 4400 -0.6160 34.9914 38.2514 35.353930 51.4451 6.4064 51.8454 1000 4600 -0.6160 34.9914 38.2514 35.640876 51.4766 6.1487 51.8454 1000 4800 -0.6160 34.9914 38.2514 35.923235 51.5062 5.8950 51.8454 1000 5000 -0.6160 34.9914 38.2514 36.201138 51.5342 5.6451 51.8454 1000 5200 -0.6160 34.9914 38.2514 36.474713 51.5606 5.3989 51.8454 1000 5400 -0.6160 34.9914 38.2514 36.744079 51.5854 5.1565 51.8454 1000 5600 -0.6160 34.9914 38.2514 37.009352 51.6087 4.9176 51.8454 1000 5800 -0.6160 34.9914 38.2514 37.270642 51.6306 4.6821 51.8454 1000 6000 -0.6160 34.9914 38.2514 37.528054 51.6511 4.4501 51.8454 1000 6200 -0.6160 34.9914 38.2514 37.781689 51.6703 4.2214 51.8454 1000 6400 -0.6160 34.9914 38.2514 38.031646 51.6882 3.9960 51.8454 1000 6600 -0.6160 34.9914 38.2514 38.278015 51.7049 3.7737 51.8454 1000 6800 -0.6160 34.9914 38.2514 38.520888 51.7204 3.5545 51.8454 1000 7000 -0.6160 34.9914 38.2514 38.760349 51.7348 3.3383 51.8454 1000 7200 -0.6160 34.9914 38.2514 38.996482 51.7482 3.1250 51.8454 1000 7400 -0.6160 34.9914 38.2514 39.229365 51.7604 2.9147 51.8454 1000 7600 -0.6160 34.9914 38.2514 39.459075 51.7717 2.7071 51.8454 1000 7800 -0.6160 34.9914 38.2514 39.685686 51.7820 2.5023 51.8454 1000 8000 -0.6160 34.9914 38.2514 39.909268 51.7914 2.3002 51.8454 1000 8200 -0.6160 34.9914 38.2514 40.129891 51.7998 2.1008 51.8454 1000 8400 -0.6160 34.9914 38.2514 40.347621 51.8074 1.9039 51.8454 1500 0 -0.6160 34.8262 38.2540 27.494145 50.0119 13.2338 51.7360 1500 200 -0.6160 34.8262 38.2540 27.936429 50.1126 12.8474 51.7360 1500 400 -0.6160 34.8262 38.2540 28.368077 50.2079 12.4695 51.7360 1500 600 -0.6160 34.8262 38.2540 28.789612 50.2983 12.0997 51.7360 1500 800 -0.6160 34.8262 38.2540 29.201513 50.3840 11.7378 51.7360 1500 1000 -0.6160 34.8262 38.2540 29.604224 50.4652 11.3834 51.7360 1500 1200 -0.6160 34.8262 38.2540 29.998156 50.5423 11.0361 51.7360 1500 1400 -0.6160 34.8262 38.2540 30.383688 50.6154 10.6958 51.7360 1500 1600 -0.6160 34.8262 38.2540 30.761176 50.6848 10.3621 51.7360 1500 1800 -0.6160 34.8262 38.2540 31.130947 50.7506 10.0347 51.7360 1500 2000 -0.6160 34.8262 38.2540 31.493310 50.8130 9.7135 51.7360 1500 2200 -0.6160 34.8262 38.2540 31.848553 50.8722 9.3983 51.7360 1500 2400 -0.6160 34.8262 38.2540 32.196945 50.9284 9.0888 51.7360 1500 2600 -0.6160 34.8262 38.2540 32.538739 50.9818 8.7848 51.7360 1500 2800 -0.6160 34.8262 38.2540 32.874174 51.0323 8.4862 51.7360 1500 3000 -0.6160 34.8262 38.2540 33.203475 51.0802 8.1927 51.7360 1500 3200 -0.6160 34.8262 38.2540 33.526851 51.1256 7.9043 51.7360 1500 3400 -0.6160 34.8262 38.2540 33.844504 51.1687 7.6207 51.7360 1500 3600 -0.6160 34.8262 38.2540 34.156623 51.2094 7.3419 51.7360 1500 3800 -0.6160 34.8262 38.2540 34.463385 51.2480 7.0676 51.7360 1500 4000 -0.6160 34.8262 38.2540 34.764961 51.2845 6.7977 51.7360 1500 4200 -0.6160 34.8262 38.2540 35.061513 51.3190 6.5322 51.7360 1500 4400 -0.6160 34.8262 38.2540 35.353192 51.3516 6.2708 51.7360 1500 4600 -0.6160 34.8262 38.2540 35.640145 51.3823 6.0136 51.7360 1500 4800 -0.6160 34.8262 38.2540 35.922510 51.4113 5.7603 51.7360 1500 5000 -0.6160 34.8262 38.2540 36.200421 51.4386 5.5108 51.7360 1500 5200 -0.6160 34.8262 38.2540 36.474002 51.4644 5.2651 51.7360 1500 5400 -0.6160 34.8262 38.2540 36.743374 51.4886 5.0231 51.7360 1500 5600 -0.6160 34.8262 38.2540 37.008653 51.5113 4.7847 51.7360 1500 5800 -0.6160 34.8262 38.2540 37.269949 51.5325 4.5497 51.7360 1500 6000 -0.6160 34.8262 38.2540 37.527367 51.5524 4.3181 51.7360 1500 6200 -0.6160 34.8262 38.2540 37.781008 51.5711 4.0899 51.7360 1500 6400 -0.6160 34.8262 38.2540 38.030970 51.5884 3.8648 51.7360 1500 6600 -0.6160 34.8262 38.2540 38.277345 51.6045 3.6430 51.7360 1500 6800 -0.6160 34.8262 38.2540 38.520223 51.6195 3.4242 51.7360 1500 7000 -0.6160 34.8262 38.2540 38.759689 51.6334 3.2084 51.7360 1500 7200 -0.6160 34.8262 38.2540 38.995827 51.6461 2.9956 51.7360 1500 7400 -0.6160 34.8262 38.2540 39.228715 51.6579 2.7856 51.7360 1500 7600 -0.6160 34.8262 38.2540 39.458429 51.6686 2.5785 51.7360 1500 7800 -0.6160 34.8262 38.2540 39.685045 51.6784 2.3741 51.7360 1500 8000 -0.6160 34.8262 38.2540 39.908632 51.6873 2.1724 51.7360 1500 8200 -0.6160 34.8262 38.2540 40.129260 51.6953 1.9733 51.7360 1500 8400 -0.6160 34.8262 38.2540 40.346993 51.7024 1.7769 51.7360 2000 0 -0.6160 34.6610 38.2565 27.493148 49.9377 13.0869 51.6268 2000 200 -0.6160 34.6610 38.2565 27.935449 50.0372 12.7010 51.6268 2000 400 -0.6160 34.6610 38.2565 28.367113 50.1315 12.3237 51.6268 2000 600 -0.6160 34.6610 38.2565 28.788663 50.2208 11.9545 51.6268 2000 800 -0.6160 34.6610 38.2565 29.200578 50.3054 11.5931 51.6268 2000 1000 -0.6160 34.6610 38.2565 29.603304 50.3857 11.2392 51.6268 2000 1200 -0.6160 34.6610 38.2565 29.997248 50.4617 10.8925 51.6268 2000 1400 -0.6160 34.6610 38.2565 30.382794 50.5339 10.5527 51.6268 2000 1600 -0.6160 34.6610 38.2565 30.760293 50.6023 10.2195 51.6268 2000 1800 -0.6160 34.6610 38.2565 31.130076 50.6672 9.8927 51.6268 2000 2000 -0.6160 34.6610 38.2565 31.492450 50.7287 9.5721 51.6268 2000 2200 -0.6160 34.6610 38.2565 31.847704 50.7871 9.2573 51.6268 2000 2400 -0.6160 34.6610 38.2565 32.196106 50.8425 8.9483 51.6268 2000 2600 -0.6160 34.6610 38.2565 32.537910 50.8949 8.6448 51.6268 2000 2800 -0.6160 34.6610 38.2565 32.873355 50.9447 8.3467 51.6268 2000 3000 -0.6160 34.6610 38.2565 33.202665 50.9918 8.0538 51.6268 2000 3200 -0.6160 34.6610 38.2565 33.526051 51.0364 7.7658 51.6268 2000 3400 -0.6160 34.6610 38.2565 33.843712 51.0787 7.4827 51.6268 2000 3600 -0.6160 34.6610 38.2565 34.155839 51.1187 7.2044 51.6268 2000 3800 -0.6160 34.6610 38.2565 34.462609 51.1565 6.9306 51.6268 2000 4000 -0.6160 34.6610 38.2565 34.764193 51.1923 6.6612 51.6268 2000 4200 -0.6160 34.6610 38.2565 35.060752 51.2261 6.3961 51.6268 2000 4400 -0.6160 34.6610 38.2565 35.352439 51.2580 6.1352 51.6268 2000 4600 -0.6160 34.6610 38.2565 35.639399 51.2880 5.8784 51.6268 2000 4800 -0.6160 34.6610 38.2565 35.921772 51.3164 5.6256 51.6268 2000 5000 -0.6160 34.6610 38.2565 36.199689 51.3431 5.3766 51.6268 2000 5200 -0.6160 34.6610 38.2565 36.473276 51.3682 5.1314 51.6268 2000 5400 -0.6160 34.6610 38.2565 36.742655 51.3917 4.8898 51.6268 2000 5600 -0.6160 34.6610 38.2565 37.007941 51.4138 4.6518 51.6268 2000 5800 -0.6160 34.6610 38.2565 37.269242 51.4345 4.4173 51.6268 2000 6000 -0.6160 34.6610 38.2565 37.526666 51.4538 4.1861 51.6268 2000 6200 -0.6160 34.6610 38.2565 37.780313 51.4718 3.9583 51.6268 2000 6400 -0.6160 34.6610 38.2565 38.030281 51.4886 3.7337 51.6268 2000 6600 -0.6160 34.6610 38.2565 38.276661 51.5042 3.5123 51.6268 2000 6800 -0.6160 34.6610 38.2565 38.519545 51.5186 3.2939 51.6268 2000 7000 -0.6160 34.6610 38.2565 38.759016 51.5319 3.0785 51.6268 2000 7200 -0.6160 34.6610 38.2565 38.995159 51.5441 2.8661 51.6268 2000 7400 -0.6160 34.6610 38.2565 39.228052 51.5554 2.6566 51.6268 2000 7600 -0.6160 34.6610 38.2565 39.457772 51.5656 2.4498 51.6268 2000 7800 -0.6160 34.6610 38.2565 39.684392 51.5749 2.2459 51.6268 2000 8000 -0.6160 34.6610 38.2565 39.907984 51.5832 2.0446 51.6268 2000 8200 -0.6160 34.6610 38.2565 40.128616 51.5907 1.8459 51.6268 2000 8400 -0.6160 34.6610 38.2565 40.346354 51.5974 1.6498 51.6268 2500 0 -0.6160 34.4957 38.2591 27.492131 49.8635 12.9400 51.5179 2500 200 -0.6160 34.4957 38.2591 27.934449 49.9619 12.5547 51.5179 2500 400 -0.6160 34.4957 38.2591 28.366129 50.0550 12.1779 51.5179 2500 600 -0.6160 34.4957 38.2591 28.787694 50.1433 11.8093 51.5179 2500 800 -0.6160 34.4957 38.2591 29.199625 50.2269 11.4484 51.5179 2500 1000 -0.6160 34.4957 38.2591 29.602364 50.3061 11.0951 51.5179 2500 1200 -0.6160 34.4957 38.2591 29.996323 50.3812 10.7489 51.5179 2500 1400 -0.6160 34.4957 38.2591 30.381881 50.4524 10.4096 51.5179 2500 1600 -0.6160 34.4957 38.2591 30.759393 50.5198 10.0770 51.5179 2500 1800 -0.6160 34.4957 38.2591 31.129187 50.5838 9.7507 51.5179 2500 2000 -0.6160 34.4957 38.2591 31.491573 50.6445 9.4306 51.5179 2500 2200 -0.6160 34.4957 38.2591 31.846838 50.7020 9.1164 51.5179 2500 2400 -0.6160 34.4957 38.2591 32.195250 50.7565 8.8079 51.5179 2500 2600 -0.6160 34.4957 38.2591 32.537065 50.8081 8.5049 51.5179 2500 2800 -0.6160 34.4957 38.2591 32.872520 50.8570 8.2073 51.5179 2500 3000 -0.6160 34.4957 38.2591 33.201839 50.9033 7.9148 51.5179 2500 3200 -0.6160 34.4957 38.2591 33.525234 50.9472 7.6274 51.5179 2500 3400 -0.6160 34.4957 38.2591 33.842904 50.9887 7.3448 51.5179 2500 3600 -0.6160 34.4957 38.2591 34.155039 51.0280 7.0669 51.5179 2500 3800 -0.6160 34.4957 38.2591 34.461818 51.0651 6.7936 51.5179 2500 4000 -0.6160 34.4957 38.2591 34.763411 51.1001 6.5247 51.5179 2500 4200 -0.6160 34.4957 38.2591 35.059977 51.1332 6.2601 51.5179 2500 4400 -0.6160 34.4957 38.2591 35.351672 51.1644 5.9997 51.5179 2500 4600 -0.6160 34.4957 38.2591 35.638639 51.1938 5.7433 51.5179 2500 4800 -0.6160 34.4957 38.2591 35.921019 51.2215 5.4909 51.5179 2500 5000 -0.6160 34.4957 38.2591 36.198943 51.2475 5.2424 51.5179 2500 5200 -0.6160 34.4957 38.2591 36.472537 51.2719 4.9976 51.5179 2500 5400 -0.6160 34.4957 38.2591 36.741923 51.2949 4.7565 51.5179 2500 5600 -0.6160 34.4957 38.2591 37.007214 51.3163 4.5189 51.5179 2500 5800 -0.6160 34.4957 38.2591 37.268522 51.3364 4.2849 51.5179 2500 6000 -0.6160 34.4957 38.2591 37.525952 51.3551 4.0541 51.5179 2500 6200 -0.6160 34.4957 38.2591 37.779605 51.3726 3.8267 51.5179 2500 6400 -0.6160 34.4957 38.2591 38.029579 51.3888 3.6026 51.5179 2500 6600 -0.6160 34.4957 38.2591 38.275965 51.4038 3.3816 51.5179 2500 6800 -0.6160 34.4957 38.2591 38.518854 51.4177 3.1636 51.5179 2500 7000 -0.6160 34.4957 38.2591 38.758331 51.4304 2.9487 51.5179 2500 7200 -0.6160 34.4957 38.2591 38.994478 51.4421 2.7367 51.5179 2500 7400 -0.6160 34.4957 38.2591 39.227377 51.4528 2.5275 51.5179 2500 7600 -0.6160 34.4957 38.2591 39.457102 51.4625 2.3212 51.5179 2500 7800 -0.6160 34.4957 38.2591 39.683727 51.4713 2.1176 51.5179 2500 8000 -0.6160 34.4957 38.2591 39.907324 51.4792 1.9167 51.5179 2500 8200 -0.6160 34.4957 38.2591 40.127960 51.4862 1.7185 51.5179 2500 8400 -0.6160 34.4957 38.2591 40.345703 51.4923 1.5228 51.5179 3000 0 -0.6160 34.3305 38.2617 27.491093 49.7892 12.7932 51.4093 3000 200 -0.6160 34.3305 38.2617 27.933428 49.8865 12.4084 51.4093 3000 400 -0.6160 34.3305 38.2617 28.365126 49.9786 12.0321 51.4093 3000 600 -0.6160 34.3305 38.2617 28.786707 50.0658 11.6641 51.4093 3000 800 -0.6160 34.3305 38.2617 29.198653 50.1483 11.3038 51.4093 3000 1000 -0.6160 34.3305 38.2617 29.601406 50.2265 10.9510 51.4093 3000 1200 -0.6160 34.3305 38.2617 29.995379 50.3006 10.6054 51.4093 3000 1400 -0.6160 34.3305 38.2617 30.380950 50.3709 10.2666 51.4093 3000 1600 -0.6160 34.3305 38.2617 30.758475 50.4374 9.9345 51.4093 3000 1800 -0.6160 34.3305 38.2617 31.128282 50.5005 9.6087 51.4093 3000 2000 -0.6160 34.3305 38.2617 31.490679 50.5602 9.2891 51.4093 3000 2200 -0.6160 34.3305 38.2617 31.845955 50.6168 8.9754 51.4093 3000 2400 -0.6160 34.3305 38.2617 32.194378 50.6705 8.6674 51.4093 3000 2600 -0.6160 34.3305 38.2617 32.536204 50.7213 8.3650 51.4093 3000 2800 -0.6160 34.3305 38.2617 32.871668 50.7694 8.0678 51.4093 3000 3000 -0.6160 34.3305 38.2617 33.200997 50.8149 7.7759 51.4093 3000 3200 -0.6160 34.3305 38.2617 33.524401 50.8580 7.4889 51.4093 3000 3400 -0.6160 34.3305 38.2617 33.842081 50.8987 7.2068 51.4093 3000 3600 -0.6160 34.3305 38.2617 34.154225 50.9372 6.9294 51.4093 3000 3800 -0.6160 34.3305 38.2617 34.461012 50.9736 6.6566 51.4093 3000 4000 -0.6160 34.3305 38.2617 34.762613 51.0079 6.3881 51.4093 3000 4200 -0.6160 34.3305 38.2617 35.059187 51.0403 6.1240 51.4093 3000 4400 -0.6160 34.3305 38.2617 35.350889 51.0708 5.8641 51.4093 3000 4600 -0.6160 34.3305 38.2617 35.637865 51.0995 5.6082 51.4093 3000 4800 -0.6160 34.3305 38.2617 35.920252 51.1265 5.3563 51.4093 3000 5000 -0.6160 34.3305 38.2617 36.198183 51.1519 5.1082 51.4093 3000 5200 -0.6160 34.3305 38.2617 36.471784 51.1757 4.8639 51.4093 3000 5400 -0.6160 34.3305 38.2617 36.741176 51.1980 4.6232 51.4093 3000 5600 -0.6160 34.3305 38.2617 37.006474 51.2189 4.3861 51.4093 3000 5800 -0.6160 34.3305 38.2617 37.267789 51.2383 4.1524 51.4093 3000 6000 -0.6160 34.3305 38.2617 37.525225 51.2565 3.9222 51.4093 3000 6200 -0.6160 34.3305 38.2617 37.778884 51.2733 3.6952 51.4093 3000 6400 -0.6160 34.3305 38.2617 38.028863 51.2890 3.4715 51.4093 3000 6600 -0.6160 34.3305 38.2617 38.275255 51.3034 3.2509 51.4093 3000 6800 -0.6160 34.3305 38.2617 38.518150 51.3167 3.0333 51.4093 3000 7000 -0.6160 34.3305 38.2617 38.757632 51.3290 2.8188 51.4093 3000 7200 -0.6160 34.3305 38.2617 38.993785 51.3401 2.6072 51.4093 3000 7400 -0.6160 34.3305 38.2617 39.226689 51.3503 2.3985 51.4093 3000 7600 -0.6160 34.3305 38.2617 39.456419 51.3595 2.1926 51.4093 3000 7800 -0.6160 34.3305 38.2617 39.683049 51.3678 1.9894 51.4093 3000 8000 -0.6160 34.3305 38.2617 39.906651 51.3751 1.7889 51.4093 3000 8200 -0.6160 34.3305 38.2617 40.127293 51.3816 1.5911 51.4093 3000 8400 -0.6160 34.3305 38.2617 40.345040 51.3873 1.3958 51.4093 3500 0 -0.6160 34.1652 38.2643 27.490034 49.7150 12.6463 51.3011 3500 200 -0.6160 34.1652 38.2643 27.932388 49.8112 12.2621 51.3011 3500 400 -0.6160 34.1652 38.2643 28.364103 49.9021 11.8864 51.3011 3500 600 -0.6160 34.1652 38.2643 28.785700 49.9882 11.5189 51.3011 3500 800 -0.6160 34.1652 38.2643 29.197661 50.0698 11.1591 51.3011 3500 1000 -0.6160 34.1652 38.2643 29.600430 50.1470 10.8069 51.3011 3500 1200 -0.6160 34.1652 38.2643 29.994416 50.2201 10.4618 51.3011 3500 1400 -0.6160 34.1652 38.2643 30.380001 50.2893 10.1236 51.3011 3500 1600 -0.6160 34.1652 38.2643 30.757539 50.3549 9.7920 51.3011 3500 1800 -0.6160 34.1652 38.2643 31.127358 50.4171 9.4667 51.3011 3500 2000 -0.6160 34.1652 38.2643 31.489768 50.4760 9.1476 51.3011 3500 2200 -0.6160 34.1652 38.2643 31.845055 50.5317 8.8345 51.3011 3500 2400 -0.6160 34.1652 38.2643 32.193490 50.5845 8.5270 51.3011 3500 2600 -0.6160 34.1652 38.2643 32.535326 50.6345 8.2250 51.3011 3500 2800 -0.6160 34.1652 38.2643 32.870801 50.6817 7.9284 51.3011 3500 3000 -0.6160 34.1652 38.2643 33.200139 50.7265 7.6370 51.3011 3500 3200 -0.6160 34.1652 38.2643 33.523553 50.7688 7.3505 51.3011 3500 3400 -0.6160 34.1652 38.2643 33.841242 50.8087 7.0689 51.3011 3500 3600 -0.6160 34.1652 38.2643 34.153395 50.8465 6.7919 51.3011 3500 3800 -0.6160 34.1652 38.2643 34.460191 50.8821 6.5196 51.3011 3500 4000 -0.6160 34.1652 38.2643 34.761800 50.9157 6.2516 51.3011 3500 4200 -0.6160 34.1652 38.2643 35.058383 50.9474 5.9880 51.3011 3500 4400 -0.6160 34.1652 38.2643 35.350093 50.9772 5.7285 51.3011 3500 4600 -0.6160 34.1652 38.2643 35.637076 51.0053 5.4731 51.3011 3500 4800 -0.6160 34.1652 38.2643 35.919470 51.0316 5.2216 51.3011 3500 5000 -0.6160 34.1652 38.2643 36.197408 51.0563 4.9740 51.3011 3500 5200 -0.6160 34.1652 38.2643 36.471017 51.0795 4.7301 51.3011 3500 5400 -0.6160 34.1652 38.2643 36.740416 51.1012 4.4899 51.3011 3500 5600 -0.6160 34.1652 38.2643 37.005721 51.1214 4.2532 51.3011 3500 5800 -0.6160 34.1652 38.2643 37.267042 51.1403 4.0200 51.3011 3500 6000 -0.6160 34.1652 38.2643 37.524484 51.1578 3.7902 51.3011 3500 6200 -0.6160 34.1652 38.2643 37.778150 51.1741 3.5637 51.3011 3500 6400 -0.6160 34.1652 38.2643 38.028135 51.1892 3.3404 51.3011 3500 6600 -0.6160 34.1652 38.2643 38.274533 51.2030 3.1202 51.3011 3500 6800 -0.6160 34.1652 38.2643 38.517433 51.2158 2.9031 51.3011 3500 7000 -0.6160 34.1652 38.2643 38.756921 51.2275 2.6890 51.3011 3500 7200 -0.6160 34.1652 38.2643 38.993080 51.2381 2.4778 51.3011 3500 7400 -0.6160 34.1652 38.2643 39.225988 51.2478 2.2695 51.3011 3500 7600 -0.6160 34.1652 38.2643 39.455724 51.2565 2.0640 51.3011 3500 7800 -0.6160 34.1652 38.2643 39.682359 51.2642 1.8612 51.3011 3500 8000 -0.6160 34.1652 38.2643 39.905966 51.2711 1.6611 51.3011 3500 8200 -0.6160 34.1652 38.2643 40.126613 51.2771 1.4637 51.3011 3500 8400 -0.6160 34.1652 38.2643 40.344365 51.2823 1.2688 51.3011 4000 0 -0.6160 34.0000 38.2669 27.488955 49.6408 12.4995 51.1931 4000 200 -0.6160 34.0000 38.2669 27.931328 49.7358 12.1158 51.1931 4000 400 -0.6160 34.0000 38.2669 28.363060 49.8257 11.7407 51.1931 4000 600 -0.6160 34.0000 38.2669 28.784674 49.9107 11.3737 51.1931 4000 800 -0.6160 34.0000 38.2669 29.196651 49.9912 11.0145 51.1931 4000 1000 -0.6160 34.0000 38.2669 29.599435 50.0674 10.6628 51.1931 4000 1200 -0.6160 34.0000 38.2669 29.993436 50.1395 10.3182 51.1931 4000 1400 -0.6160 34.0000 38.2669 30.379035 50.2078 9.9806 51.1931 4000 1600 -0.6160 34.0000 38.2669 30.756585 50.2725 9.6495 51.1931 4000 1800 -0.6160 34.0000 38.2669 31.126418 50.3337 9.3248 51.1931 4000 2000 -0.6160 34.0000 38.2669 31.488839 50.3917 9.0062 51.1931 4000 2200 -0.6160 34.0000 38.2669 31.844138 50.4466 8.6935 51.1931 4000 2400 -0.6160 34.0000 38.2669 32.192584 50.4985 8.3866 51.1931 4000 2600 -0.6160 34.0000 38.2669 32.534431 50.5476 8.0851 51.1931 4000 2800 -0.6160 34.0000 38.2669 32.869917 50.5941 7.7890 51.1931 4000 3000 -0.6160 34.0000 38.2669 33.199265 50.6380 7.4980 51.1931 4000 3200 -0.6160 34.0000 38.2669 33.522689 50.6795 7.2121 51.1931 4000 3400 -0.6160 34.0000 38.2669 33.840387 50.7188 6.9309 51.1931 4000 3600 -0.6160 34.0000 38.2669 34.152550 50.7558 6.6545 51.1931 4000 3800 -0.6160 34.0000 38.2669 34.459354 50.7907 6.3826 51.1931 4000 4000 -0.6160 34.0000 38.2669 34.760972 50.8235 6.1151 51.1931 4000 4200 -0.6160 34.0000 38.2669 35.057563 50.8545 5.8520 51.1931 4000 4400 -0.6160 34.0000 38.2669 35.349281 50.8836 5.5930 51.1931 4000 4600 -0.6160 34.0000 38.2669 35.636272 50.9110 5.3380 51.1931 4000 4800 -0.6160 34.0000 38.2669 35.918674 50.9367 5.0870 51.1931 4000 5000 -0.6160 34.0000 38.2669 36.196620 50.9608 4.8398 51.1931 4000 5200 -0.6160 34.0000 38.2669 36.470236 50.9833 4.5964 51.1931 4000 5400 -0.6160 34.0000 38.2669 36.739642 51.0043 4.3566 51.1931 4000 5600 -0.6160 34.0000 38.2669 37.004953 51.0240 4.1204 51.1931 4000 5800 -0.6160 34.0000 38.2669 37.266281 51.0422 3.8876 51.1931 4000 6000 -0.6160 34.0000 38.2669 37.523730 51.0592 3.6582 51.1931 4000 6200 -0.6160 34.0000 38.2669 37.777402 51.0749 3.4321 51.1931 4000 6400 -0.6160 34.0000 38.2669 38.027393 51.0893 3.2093 51.1931 4000 6600 -0.6160 34.0000 38.2669 38.273797 51.1027 2.9895 51.1931 4000 6800 -0.6160 34.0000 38.2669 38.516703 51.1149 2.7728 51.1931 4000 7000 -0.6160 34.0000 38.2669 38.756197 51.1260 2.5591 51.1931 4000 7200 -0.6160 34.0000 38.2669 38.992361 51.1361 2.3484 51.1931 4000 7400 -0.6160 34.0000 38.2669 39.225276 51.1452 2.1405 51.1931 4000 7600 -0.6160 34.0000 38.2669 39.455016 51.1534 1.9354 51.1931 4000 7800 -0.6160 34.0000 38.2669 39.681657 51.1607 1.7330 51.1931 4000 8000 -0.6160 34.0000 38.2669 39.905269 51.1670 1.5333 51.1931 4000 8200 -0.6160 34.0000 38.2669 40.125921 51.1726 1.3363 51.1931 4000 8400 -0.6160 34.0000 38.2669 40.343678 51.1773 1.1418 51.1931 4500 0 -0.6160 33.8347 38.2695 27.487857 49.5666 12.3526 51.0854 4500 200 -0.6160 33.8347 38.2695 27.930248 49.6605 11.9695 51.0854 4500 400 -0.6160 33.8347 38.2695 28.361998 49.7493 11.5950 51.0854 4500 600 -0.6160 33.8347 38.2695 28.783629 49.8332 11.2285 51.0854 4500 800 -0.6160 33.8347 38.2695 29.195622 49.9127 10.8699 51.0854 4500 1000 -0.6160 33.8347 38.2695 29.598421 49.9878 10.5187 51.0854 4500 1200 -0.6160 33.8347 38.2695 29.992437 50.0590 10.1747 51.0854 4500 1400 -0.6160 33.8347 38.2695 30.378050 50.1263 9.8376 51.0854 4500 1600 -0.6160 33.8347 38.2695 30.755614 50.1901 9.5070 51.0854 4500 1800 -0.6160 33.8347 38.2695 31.125460 50.2504 9.1828 51.0854 4500 2000 -0.6160 33.8347 38.2695 31.487894 50.3074 8.8648 51.0854 4500 2200 -0.6160 33.8347 38.2695 31.843205 50.3614 8.5526 51.0854 4500 2400 -0.6160 33.8347 38.2695 32.191662 50.4125 8.2462 51.0854 4500 2600 -0.6160 33.8347 38.2695 32.533520 50.4608 7.9452 51.0854 4500 2800 -0.6160 33.8347 38.2695 32.869017 50.5065 7.6496 51.0854 4500 3000 -0.6160 33.8347 38.2695 33.198376 50.5496 7.3591 51.0854 4500 3200 -0.6160 33.8347 38.2695 33.521810 50.5903 7.0737 51.0854 4500 3400 -0.6160 33.8347 38.2695 33.839518 50.6288 6.7930 51.0854 4500 3600 -0.6160 33.8347 38.2695 34.151689 50.6650 6.5171 51.0854 4500 3800 -0.6160 33.8347 38.2695 34.458503 50.6992 6.2457 51.0854 4500 4000 -0.6160 33.8347 38.2695 34.760129 50.7314 5.9787 51.0854 4500 4200 -0.6160 33.8347 38.2695 35.056729 50.7616 5.7160 51.0854 4500 4400 -0.6160 33.8347 38.2695 35.348455 50.7901 5.4574 51.0854 4500 4600 -0.6160 33.8347 38.2695 35.635454 50.8168 5.2029 51.0854 4500 4800 -0.6160 33.8347 38.2695 35.917864 50.8418 4.9524 51.0854 4500 5000 -0.6160 33.8347 38.2695 36.195818 50.8652 4.7057 51.0854 4500 5200 -0.6160 33.8347 38.2695 36.469441 50.8871 4.4627 51.0854 4500 5400 -0.6160 33.8347 38.2695 36.738854 50.9075 4.2234 51.0854 4500 5600 -0.6160 33.8347 38.2695 37.004173 50.9265 3.9876 51.0854 4500 5800 -0.6160 33.8347 38.2695 37.265507 50.9442 3.7553 51.0854 4500 6000 -0.6160 33.8347 38.2695 37.522963 50.9605 3.5263 51.0854 4500 6200 -0.6160 33.8347 38.2695 37.776641 50.9756 3.3006 51.0854 4500 6400 -0.6160 33.8347 38.2695 38.026639 50.9895 3.0782 51.0854 4500 6600 -0.6160 33.8347 38.2695 38.273049 51.0023 2.8588 51.0854 4500 6800 -0.6160 33.8347 38.2695 38.515961 51.0140 2.6426 51.0854 4500 7000 -0.6160 33.8347 38.2695 38.755460 51.0246 2.4293 51.0854 4500 7200 -0.6160 33.8347 38.2695 38.991631 51.0341 2.2190 51.0854 4500 7400 -0.6160 33.8347 38.2695 39.224551 51.0427 2.0115 51.0854 4500 7600 -0.6160 33.8347 38.2695 39.454297 51.0504 1.8068 51.0854 4500 7800 -0.6160 33.8347 38.2695 39.680943 51.0571 1.6048 51.0854 4500 8000 -0.6160 33.8347 38.2695 39.904560 51.0630 1.4055 51.0854 4500 8200 -0.6160 33.8347 38.2695 40.125217 51.0680 1.2089 51.0854 4500 8400 -0.6160 33.8347 38.2695 40.342979 51.0722 1.0148 51.0854 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.000 ================================================ FILE: tests/test_data/cropA/geometry/20180331-20180623_VV_8rlks_base.par ================================================ initial_baseline(TCN): 0.2764284 -15.6218708 38.9224121 m m m initial_baseline_rate: 0.0000000 -0.1012466 0.0028457 m/s m/s m/s precision_baseline(TCN): 0.0000000 -15.6997972 38.9643539 m m m precision_baseline_rate: 0.0000000 -0.1035150 0.0023676 m/s m/s m/s unwrap_phase_constant: -0.00005 radians ================================================ FILE: tests/test_data/cropA/geometry/20180331-20180623_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): 0.276 -15.622 38.922 orbit baseline rate (TCN) (m/s): 0.000e+00 -1.012e-01 2.846e-03 baseline vector (TCN) (m): 0.000 -15.700 38.964 baseline rate (TCN) (m/s): 0.000e+00 -1.035e-01 2.368e-03 SLC-1 center baseline length (m): 42.0084 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 0.0000 -14.7336 38.9423 27.497013 27.7406 -31.0489 41.6362 0 200 0.0000 -14.7336 38.9423 27.939249 27.5002 -31.2621 41.6362 0 400 0.0000 -14.7336 38.9423 28.370851 27.2639 -31.4683 41.6362 0 600 0.0000 -14.7336 38.9423 28.792342 27.0317 -31.6680 41.6362 0 800 0.0000 -14.7336 38.9423 29.204202 26.8033 -31.8615 41.6362 0 1000 0.0000 -14.7336 38.9423 29.606873 26.5787 -32.0491 41.6362 0 1200 0.0000 -14.7336 38.9423 30.000767 26.3578 -32.2311 41.6362 0 1400 0.0000 -14.7336 38.9423 30.386264 26.1403 -32.4077 41.6362 0 1600 0.0000 -14.7336 38.9423 30.763716 25.9263 -32.5792 41.6362 0 1800 0.0000 -14.7336 38.9423 31.133454 25.7155 -32.7458 41.6362 0 2000 0.0000 -14.7336 38.9423 31.495785 25.5079 -32.9078 41.6362 0 2200 0.0000 -14.7336 38.9423 31.850997 25.3034 -33.0653 41.6362 0 2400 0.0000 -14.7336 38.9423 32.199360 25.1019 -33.2185 41.6362 0 2600 0.0000 -14.7336 38.9423 32.541126 24.9033 -33.3677 41.6362 0 2800 0.0000 -14.7336 38.9423 32.876534 24.7075 -33.5129 41.6362 0 3000 0.0000 -14.7336 38.9423 33.205808 24.5145 -33.6543 41.6362 0 3200 0.0000 -14.7336 38.9423 33.529159 24.3242 -33.7921 41.6362 0 3400 0.0000 -14.7336 38.9423 33.846787 24.1365 -33.9265 41.6362 0 3600 0.0000 -14.7336 38.9423 34.158881 23.9514 -34.0574 41.6362 0 3800 0.0000 -14.7336 38.9423 34.465620 23.7687 -34.1852 41.6362 0 4000 0.0000 -14.7336 38.9423 34.767174 23.5884 -34.3098 41.6362 0 4200 0.0000 -14.7336 38.9423 35.063703 23.4106 -34.4314 41.6362 0 4400 0.0000 -14.7336 38.9423 35.355362 23.2350 -34.5501 41.6362 0 4600 0.0000 -14.7336 38.9423 35.642294 23.0617 -34.6661 41.6362 0 4800 0.0000 -14.7336 38.9423 35.924640 22.8906 -34.7793 41.6362 0 5000 0.0000 -14.7336 38.9423 36.202531 22.7216 -34.8899 41.6362 0 5200 0.0000 -14.7336 38.9423 36.476093 22.5548 -34.9980 41.6362 0 5400 0.0000 -14.7336 38.9423 36.745447 22.3900 -35.1036 41.6362 0 5600 0.0000 -14.7336 38.9423 37.010708 22.2272 -35.2069 41.6362 0 5800 0.0000 -14.7336 38.9423 37.271986 22.0664 -35.3079 41.6362 0 6000 0.0000 -14.7336 38.9423 37.529387 21.9076 -35.4067 41.6362 0 6200 0.0000 -14.7336 38.9423 37.783012 21.7507 -35.5033 41.6362 0 6400 0.0000 -14.7336 38.9423 38.032957 21.5956 -35.5979 41.6362 0 6600 0.0000 -14.7336 38.9423 38.279317 21.4423 -35.6904 41.6362 0 6800 0.0000 -14.7336 38.9423 38.522179 21.2908 -35.7810 41.6362 0 7000 0.0000 -14.7336 38.9423 38.761630 21.1411 -35.8696 41.6362 0 7200 0.0000 -14.7336 38.9423 38.997753 20.9931 -35.9564 41.6362 0 7400 0.0000 -14.7336 38.9423 39.230627 20.8468 -36.0415 41.6362 0 7600 0.0000 -14.7336 38.9423 39.460328 20.7021 -36.1248 41.6362 0 7800 0.0000 -14.7336 38.9423 39.686930 20.5591 -36.2063 41.6362 0 8000 0.0000 -14.7336 38.9423 39.910504 20.4177 -36.2863 41.6362 0 8200 0.0000 -14.7336 38.9423 40.131118 20.2778 -36.3646 41.6362 0 8400 0.0000 -14.7336 38.9423 40.348838 20.1395 -36.4414 41.6362 500 0 0.0000 -14.9463 38.9471 27.496077 27.6472 -31.2394 41.7166 500 200 0.0000 -14.9463 38.9471 27.938329 27.4053 -31.4519 41.7166 500 400 0.0000 -14.9463 38.9471 28.369946 27.1676 -31.6574 41.7166 500 600 0.0000 -14.9463 38.9471 28.791451 26.9339 -31.8564 41.7166 500 800 0.0000 -14.9463 38.9471 29.203324 26.7042 -32.0492 41.7166 500 1000 0.0000 -14.9463 38.9471 29.606009 26.4783 -32.2361 41.7166 500 1200 0.0000 -14.9463 38.9471 29.999915 26.2561 -32.4174 41.7166 500 1400 0.0000 -14.9463 38.9471 30.385423 26.0374 -32.5933 41.7166 500 1600 0.0000 -14.9463 38.9471 30.762887 25.8221 -32.7642 41.7166 500 1800 0.0000 -14.9463 38.9471 31.132636 25.6101 -32.9301 41.7166 500 2000 0.0000 -14.9463 38.9471 31.494978 25.4014 -33.0914 41.7166 500 2200 0.0000 -14.9463 38.9471 31.850200 25.1957 -33.2483 41.7166 500 2400 0.0000 -14.9463 38.9471 32.198572 24.9931 -33.4008 41.7166 500 2600 0.0000 -14.9463 38.9471 32.540347 24.7934 -33.5493 41.7166 500 2800 0.0000 -14.9463 38.9471 32.875764 24.5966 -33.6939 41.7166 500 3000 0.0000 -14.9463 38.9471 33.205046 24.4025 -33.8347 41.7166 500 3200 0.0000 -14.9463 38.9471 33.528405 24.2112 -33.9719 41.7166 500 3400 0.0000 -14.9463 38.9471 33.846042 24.0225 -34.1056 41.7166 500 3600 0.0000 -14.9463 38.9471 34.158144 23.8364 -34.2359 41.7166 500 3800 0.0000 -14.9463 38.9471 34.464890 23.6527 -34.3631 41.7166 500 4000 0.0000 -14.9463 38.9471 34.766452 23.4715 -34.4871 41.7166 500 4200 0.0000 -14.9463 38.9471 35.062988 23.2927 -34.6081 41.7166 500 4400 0.0000 -14.9463 38.9471 35.354653 23.1163 -34.7262 41.7166 500 4600 0.0000 -14.9463 38.9471 35.641592 22.9421 -34.8415 41.7166 500 4800 0.0000 -14.9463 38.9471 35.923944 22.7701 -34.9542 41.7166 500 5000 0.0000 -14.9463 38.9471 36.201842 22.6003 -35.0642 41.7166 500 5200 0.0000 -14.9463 38.9471 36.475410 22.4326 -35.1717 41.7166 500 5400 0.0000 -14.9463 38.9471 36.744770 22.2670 -35.2768 41.7166 500 5600 0.0000 -14.9463 38.9471 37.010037 22.1034 -35.3795 41.7166 500 5800 0.0000 -14.9463 38.9471 37.271321 21.9419 -35.4799 41.7166 500 6000 0.0000 -14.9463 38.9471 37.528727 21.7823 -35.5781 41.7166 500 6200 0.0000 -14.9463 38.9471 37.782357 21.6245 -35.6742 41.7166 500 6400 0.0000 -14.9463 38.9471 38.032308 21.4687 -35.7682 41.7166 500 6600 0.0000 -14.9463 38.9471 38.278673 21.3147 -35.8602 41.7166 500 6800 0.0000 -14.9463 38.9471 38.521540 21.1625 -35.9502 41.7166 500 7000 0.0000 -14.9463 38.9471 38.760996 21.0121 -36.0384 41.7166 500 7200 0.0000 -14.9463 38.9471 38.997124 20.8634 -36.1246 41.7166 500 7400 0.0000 -14.9463 38.9471 39.230002 20.7164 -36.2091 41.7166 500 7600 0.0000 -14.9463 38.9471 39.459708 20.5711 -36.2919 41.7166 500 7800 0.0000 -14.9463 38.9471 39.686314 20.4274 -36.3730 41.7166 500 8000 0.0000 -14.9463 38.9471 39.909892 20.2853 -36.4524 41.7166 500 8200 0.0000 -14.9463 38.9471 40.130511 20.1448 -36.5303 41.7166 500 8400 0.0000 -14.9463 38.9471 40.348236 20.0058 -36.6065 41.7166 1000 0 0.0000 -15.1591 38.9520 27.495122 27.5538 -31.4299 41.7978 1000 200 0.0000 -15.1591 38.9520 27.937389 27.3104 -31.6417 41.7978 1000 400 0.0000 -15.1591 38.9520 28.369022 27.0713 -31.8465 41.7978 1000 600 0.0000 -15.1591 38.9520 28.790541 26.8362 -32.0448 41.7978 1000 800 0.0000 -15.1591 38.9520 29.202428 26.6052 -32.2369 41.7978 1000 1000 0.0000 -15.1591 38.9520 29.605126 26.3779 -32.4231 41.7978 1000 1200 0.0000 -15.1591 38.9520 29.999045 26.1544 -32.6037 41.7978 1000 1400 0.0000 -15.1591 38.9520 30.384565 25.9344 -32.7790 41.7978 1000 1600 0.0000 -15.1591 38.9520 30.762040 25.7179 -32.9491 41.7978 1000 1800 0.0000 -15.1591 38.9520 31.131800 25.5048 -33.1144 41.7978 1000 2000 0.0000 -15.1591 38.9520 31.494152 25.2948 -33.2750 41.7978 1000 2200 0.0000 -15.1591 38.9520 31.849385 25.0880 -33.4312 41.7978 1000 2400 0.0000 -15.1591 38.9520 32.197766 24.8843 -33.5831 41.7978 1000 2600 0.0000 -15.1591 38.9520 32.539551 24.6835 -33.7310 41.7978 1000 2800 0.0000 -15.1591 38.9520 32.874977 24.4856 -33.8749 41.7978 1000 3000 0.0000 -15.1591 38.9520 33.204268 24.2905 -34.0151 41.7978 1000 3200 0.0000 -15.1591 38.9520 33.527636 24.0982 -34.1516 41.7978 1000 3400 0.0000 -15.1591 38.9520 33.845281 23.9085 -34.2847 41.7978 1000 3600 0.0000 -15.1591 38.9520 34.157391 23.7214 -34.4144 41.7978 1000 3800 0.0000 -15.1591 38.9520 34.464145 23.5368 -34.5409 41.7978 1000 4000 0.0000 -15.1591 38.9520 34.765714 23.3546 -34.6643 41.7978 1000 4200 0.0000 -15.1591 38.9520 35.062258 23.1749 -34.7847 41.7978 1000 4400 0.0000 -15.1591 38.9520 35.353930 22.9975 -34.9023 41.7978 1000 4600 0.0000 -15.1591 38.9520 35.640876 22.8225 -35.0170 41.7978 1000 4800 0.0000 -15.1591 38.9520 35.923235 22.6496 -35.1291 41.7978 1000 5000 0.0000 -15.1591 38.9520 36.201138 22.4790 -35.2385 41.7978 1000 5200 0.0000 -15.1591 38.9520 36.474713 22.3104 -35.3454 41.7978 1000 5400 0.0000 -15.1591 38.9520 36.744079 22.1440 -35.4499 41.7978 1000 5600 0.0000 -15.1591 38.9520 37.009352 21.9797 -35.5521 41.7978 1000 5800 0.0000 -15.1591 38.9520 37.270642 21.8173 -35.6519 41.7978 1000 6000 0.0000 -15.1591 38.9520 37.528054 21.6569 -35.7496 41.7978 1000 6200 0.0000 -15.1591 38.9520 37.781689 21.4984 -35.8451 41.7978 1000 6400 0.0000 -15.1591 38.9520 38.031646 21.3419 -35.9386 41.7978 1000 6600 0.0000 -15.1591 38.9520 38.278015 21.1871 -36.0300 41.7978 1000 6800 0.0000 -15.1591 38.9520 38.520888 21.0342 -36.1195 41.7978 1000 7000 0.0000 -15.1591 38.9520 38.760349 20.8831 -36.2071 41.7978 1000 7200 0.0000 -15.1591 38.9520 38.996482 20.7337 -36.2928 41.7978 1000 7400 0.0000 -15.1591 38.9520 39.229365 20.5860 -36.3768 41.7978 1000 7600 0.0000 -15.1591 38.9520 39.459075 20.4400 -36.4591 41.7978 1000 7800 0.0000 -15.1591 38.9520 39.685686 20.2956 -36.5396 41.7978 1000 8000 0.0000 -15.1591 38.9520 39.909268 20.1529 -36.6185 41.7978 1000 8200 0.0000 -15.1591 38.9520 40.129891 20.0117 -36.6959 41.7978 1000 8400 0.0000 -15.1591 38.9520 40.347621 19.8721 -36.7716 41.7978 1500 0 0.0000 -15.3719 38.9569 27.494145 27.4604 -31.6205 41.8800 1500 200 0.0000 -15.3719 38.9569 27.936429 27.2155 -31.8315 41.8800 1500 400 0.0000 -15.3719 38.9569 28.368077 26.9750 -32.0356 41.8800 1500 600 0.0000 -15.3719 38.9569 28.789612 26.7385 -32.2332 41.8800 1500 800 0.0000 -15.3719 38.9569 29.201513 26.5061 -32.4246 41.8800 1500 1000 0.0000 -15.3719 38.9569 29.604224 26.2776 -32.6101 41.8800 1500 1200 0.0000 -15.3719 38.9569 29.998156 26.0527 -32.7900 41.8800 1500 1400 0.0000 -15.3719 38.9569 30.383688 25.8315 -32.9646 41.8800 1500 1600 0.0000 -15.3719 38.9569 30.761176 25.6138 -33.1340 41.8800 1500 1800 0.0000 -15.3719 38.9569 31.130947 25.3994 -33.2987 41.8800 1500 2000 0.0000 -15.3719 38.9569 31.493310 25.1883 -33.4586 41.8800 1500 2200 0.0000 -15.3719 38.9569 31.848553 24.9804 -33.6142 41.8800 1500 2400 0.0000 -15.3719 38.9569 32.196945 24.7755 -33.7654 41.8800 1500 2600 0.0000 -15.3719 38.9569 32.538739 24.5737 -33.9126 41.8800 1500 2800 0.0000 -15.3719 38.9569 32.874174 24.3747 -34.0559 41.8800 1500 3000 0.0000 -15.3719 38.9569 33.203475 24.1786 -34.1954 41.8800 1500 3200 0.0000 -15.3719 38.9569 33.526851 23.9852 -34.3314 41.8800 1500 3400 0.0000 -15.3719 38.9569 33.844504 23.7945 -34.4638 41.8800 1500 3600 0.0000 -15.3719 38.9569 34.156623 23.6064 -34.5929 41.8800 1500 3800 0.0000 -15.3719 38.9569 34.463385 23.4208 -34.7188 41.8800 1500 4000 0.0000 -15.3719 38.9569 34.764961 23.2378 -34.8416 41.8800 1500 4200 0.0000 -15.3719 38.9569 35.061513 23.0571 -34.9614 41.8800 1500 4400 0.0000 -15.3719 38.9569 35.353192 22.8788 -35.0783 41.8800 1500 4600 0.0000 -15.3719 38.9569 35.640145 22.7029 -35.1925 41.8800 1500 4800 0.0000 -15.3719 38.9569 35.922510 22.5292 -35.3039 41.8800 1500 5000 0.0000 -15.3719 38.9569 36.200421 22.3577 -35.4128 41.8800 1500 5200 0.0000 -15.3719 38.9569 36.474002 22.1883 -35.5191 41.8800 1500 5400 0.0000 -15.3719 38.9569 36.743374 22.0211 -35.6231 41.8800 1500 5600 0.0000 -15.3719 38.9569 37.008653 21.8559 -35.7246 41.8800 1500 5800 0.0000 -15.3719 38.9569 37.269949 21.6928 -35.8240 41.8800 1500 6000 0.0000 -15.3719 38.9569 37.527367 21.5316 -35.9210 41.8800 1500 6200 0.0000 -15.3719 38.9569 37.781008 21.3724 -36.0160 41.8800 1500 6400 0.0000 -15.3719 38.9569 38.030970 21.2150 -36.1089 41.8800 1500 6600 0.0000 -15.3719 38.9569 38.277345 21.0596 -36.1998 41.8800 1500 6800 0.0000 -15.3719 38.9569 38.520223 20.9059 -36.2888 41.8800 1500 7000 0.0000 -15.3719 38.9569 38.759689 20.7541 -36.3758 41.8800 1500 7200 0.0000 -15.3719 38.9569 38.995827 20.6040 -36.4610 41.8800 1500 7400 0.0000 -15.3719 38.9569 39.228715 20.4556 -36.5445 41.8800 1500 7600 0.0000 -15.3719 38.9569 39.458429 20.3089 -36.6262 41.8800 1500 7800 0.0000 -15.3719 38.9569 39.685045 20.1639 -36.7062 41.8800 1500 8000 0.0000 -15.3719 38.9569 39.908632 20.0205 -36.7846 41.8800 1500 8200 0.0000 -15.3719 38.9569 40.129260 19.8787 -36.8615 41.8800 1500 8400 0.0000 -15.3719 38.9569 40.346993 19.7385 -36.9367 41.8800 2000 0 0.0000 -15.5847 38.9617 27.493148 27.3671 -31.8110 41.9631 2000 200 0.0000 -15.5847 38.9617 27.935449 27.1207 -32.0213 41.9631 2000 400 0.0000 -15.5847 38.9617 28.367113 26.8787 -32.2247 41.9631 2000 600 0.0000 -15.5847 38.9617 28.788663 26.6409 -32.4216 41.9631 2000 800 0.0000 -15.5847 38.9617 29.200578 26.4071 -32.6123 41.9631 2000 1000 0.0000 -15.5847 38.9617 29.603304 26.1772 -32.7971 41.9631 2000 1200 0.0000 -15.5847 38.9617 29.997248 25.9511 -32.9763 41.9631 2000 1400 0.0000 -15.5847 38.9617 30.382794 25.7286 -33.1502 41.9631 2000 1600 0.0000 -15.5847 38.9617 30.760293 25.5096 -33.3190 41.9631 2000 1800 0.0000 -15.5847 38.9617 31.130076 25.2941 -33.4829 41.9631 2000 2000 0.0000 -15.5847 38.9617 31.492450 25.0818 -33.6422 41.9631 2000 2200 0.0000 -15.5847 38.9617 31.847704 24.8727 -33.7971 41.9631 2000 2400 0.0000 -15.5847 38.9617 32.196106 24.6668 -33.9477 41.9631 2000 2600 0.0000 -15.5847 38.9617 32.537910 24.4638 -34.0943 41.9631 2000 2800 0.0000 -15.5847 38.9617 32.873355 24.2638 -34.2369 41.9631 2000 3000 0.0000 -15.5847 38.9617 33.202665 24.0666 -34.3758 41.9631 2000 3200 0.0000 -15.5847 38.9617 33.526051 23.8722 -34.5111 41.9631 2000 3400 0.0000 -15.5847 38.9617 33.843712 23.6805 -34.6429 41.9631 2000 3600 0.0000 -15.5847 38.9617 34.155839 23.4914 -34.7714 41.9631 2000 3800 0.0000 -15.5847 38.9617 34.462609 23.3049 -34.8967 41.9631 2000 4000 0.0000 -15.5847 38.9617 34.764193 23.1209 -35.0189 41.9631 2000 4200 0.0000 -15.5847 38.9617 35.060752 22.9393 -35.1381 41.9631 2000 4400 0.0000 -15.5847 38.9617 35.352439 22.7602 -35.2544 41.9631 2000 4600 0.0000 -15.5847 38.9617 35.639399 22.5833 -35.3679 41.9631 2000 4800 0.0000 -15.5847 38.9617 35.921772 22.4087 -35.4788 41.9631 2000 5000 0.0000 -15.5847 38.9617 36.199689 22.2364 -35.5871 41.9631 2000 5200 0.0000 -15.5847 38.9617 36.473276 22.0662 -35.6929 41.9631 2000 5400 0.0000 -15.5847 38.9617 36.742655 21.8981 -35.7962 41.9631 2000 5600 0.0000 -15.5847 38.9617 37.007941 21.7322 -35.8972 41.9631 2000 5800 0.0000 -15.5847 38.9617 37.269242 21.5682 -35.9960 41.9631 2000 6000 0.0000 -15.5847 38.9617 37.526666 21.4063 -36.0925 41.9631 2000 6200 0.0000 -15.5847 38.9617 37.780313 21.2463 -36.1869 41.9631 2000 6400 0.0000 -15.5847 38.9617 38.030281 21.0882 -36.2793 41.9631 2000 6600 0.0000 -15.5847 38.9617 38.276661 20.9320 -36.3696 41.9631 2000 6800 0.0000 -15.5847 38.9617 38.519545 20.7776 -36.4580 41.9631 2000 7000 0.0000 -15.5847 38.9617 38.759016 20.6251 -36.5445 41.9631 2000 7200 0.0000 -15.5847 38.9617 38.995159 20.4743 -36.6292 41.9631 2000 7400 0.0000 -15.5847 38.9617 39.228052 20.3252 -36.7122 41.9631 2000 7600 0.0000 -15.5847 38.9617 39.457772 20.1779 -36.7933 41.9631 2000 7800 0.0000 -15.5847 38.9617 39.684392 20.0322 -36.8729 41.9631 2000 8000 0.0000 -15.5847 38.9617 39.907984 19.8881 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-36.7601 42.3921 4500 5800 0.0000 -16.6486 38.9861 37.265507 20.9457 -36.8560 42.3921 4500 6000 0.0000 -16.6486 38.9861 37.522963 20.7799 -36.9497 42.3921 4500 6200 0.0000 -16.6486 38.9861 37.776641 20.6161 -37.0414 42.3921 4500 6400 0.0000 -16.6486 38.9861 38.026639 20.4543 -37.1310 42.3921 4500 6600 0.0000 -16.6486 38.9861 38.273049 20.2944 -37.2186 42.3921 4500 6800 0.0000 -16.6486 38.9861 38.515961 20.1364 -37.3043 42.3921 4500 7000 0.0000 -16.6486 38.9861 38.755460 19.9803 -37.3881 42.3921 4500 7200 0.0000 -16.6486 38.9861 38.991631 19.8260 -37.4702 42.3921 4500 7400 0.0000 -16.6486 38.9861 39.224551 19.6736 -37.5505 42.3921 4500 7600 0.0000 -16.6486 38.9861 39.454297 19.5228 -37.6291 42.3921 4500 7800 0.0000 -16.6486 38.9861 39.680943 19.3738 -37.7060 42.3921 4500 8000 0.0000 -16.6486 38.9861 39.904560 19.2265 -37.7813 42.3921 4500 8200 0.0000 -16.6486 38.9861 40.125217 19.0809 -37.8551 42.3921 4500 8400 0.0000 -16.6486 38.9861 40.342979 18.9369 -37.9273 42.3921 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.000 ================================================ FILE: tests/test_data/cropA/geometry/20180331-20180717_VV_8rlks_base.par ================================================ initial_baseline(TCN): 0.6757929 -6.1269114 33.0701755 m m m initial_baseline_rate: 0.0000000 -0.1067184 0.0085317 m/s m/s m/s precision_baseline(TCN): 0.0000000 -6.0583173 33.0501396 m m m precision_baseline_rate: 0.0000000 -0.0985459 -0.0011029 m/s m/s m/s unwrap_phase_constant: 0.00011 radians ================================================ FILE: tests/test_data/cropA/geometry/20180331-20180717_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): 0.676 -6.127 33.070 orbit baseline rate (TCN) (m/s): 0.000e+00 -1.067e-01 8.532e-03 baseline vector (TCN) (m): 0.000 -6.058 33.050 baseline rate (TCN) (m/s): 0.000e+00 -9.855e-02 -1.103e-03 SLC-1 center baseline length (m): 33.6008 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 0.0000 -5.1385 33.0604 27.497013 26.9533 -19.8221 33.4574 0 200 0.0000 -5.1385 33.0604 27.939249 26.7995 -20.0295 33.4574 0 400 0.0000 -5.1385 33.0604 28.370851 26.6479 -20.2308 33.4574 0 600 0.0000 -5.1385 33.0604 28.792342 26.4983 -20.4263 33.4574 0 800 0.0000 -5.1385 33.0604 29.204202 26.3508 -20.6163 33.4574 0 1000 0.0000 -5.1385 33.0604 29.606873 26.2053 -20.8009 33.4574 0 1200 0.0000 -5.1385 33.0604 30.000767 26.0617 -20.9806 33.4574 0 1400 0.0000 -5.1385 33.0604 30.386264 25.9199 -21.1555 33.4574 0 1600 0.0000 -5.1385 33.0604 30.763716 25.7800 -21.3258 33.4574 0 1800 0.0000 -5.1385 33.0604 31.133454 25.6418 -21.4917 33.4574 0 2000 0.0000 -5.1385 33.0604 31.495785 25.5054 -21.6534 33.4574 0 2200 0.0000 -5.1385 33.0604 31.850997 25.3707 -21.8111 33.4574 0 2400 0.0000 -5.1385 33.0604 32.199360 25.2376 -21.9650 33.4574 0 2600 0.0000 -5.1385 33.0604 32.541126 25.1061 -22.1151 33.4574 0 2800 0.0000 -5.1385 33.0604 32.876534 24.9762 -22.2617 33.4574 0 3000 0.0000 -5.1385 33.0604 33.205808 24.8479 -22.4049 33.4574 0 3200 0.0000 -5.1385 33.0604 33.529159 24.7211 -22.5447 33.4574 0 3400 0.0000 -5.1385 33.0604 33.846787 24.5957 -22.6814 33.4574 0 3600 0.0000 -5.1385 33.0604 34.158881 24.4718 -22.8151 33.4574 0 3800 0.0000 -5.1385 33.0604 34.465620 24.3493 -22.9458 33.4574 0 4000 0.0000 -5.1385 33.0604 34.767174 24.2282 -23.0736 33.4574 0 4200 0.0000 -5.1385 33.0604 35.063703 24.1084 -23.1987 33.4574 0 4400 0.0000 -5.1385 33.0604 35.355362 23.9900 -23.3211 33.4574 0 4600 0.0000 -5.1385 33.0604 35.642294 23.8730 -23.4409 33.4574 0 4800 0.0000 -5.1385 33.0604 35.924640 23.7572 -23.5583 33.4574 0 5000 0.0000 -5.1385 33.0604 36.202531 23.6426 -23.6733 33.4574 0 5200 0.0000 -5.1385 33.0604 36.476093 23.5293 -23.7859 33.4574 0 5400 0.0000 -5.1385 33.0604 36.745447 23.4172 -23.8962 33.4574 0 5600 0.0000 -5.1385 33.0604 37.010708 23.3064 -24.0044 33.4574 0 5800 0.0000 -5.1385 33.0604 37.271986 23.1966 -24.1104 33.4574 0 6000 0.0000 -5.1385 33.0604 37.529387 23.0881 -24.2144 33.4574 0 6200 0.0000 -5.1385 33.0604 37.783012 22.9807 -24.3163 33.4574 0 6400 0.0000 -5.1385 33.0604 38.032957 22.8744 -24.4164 33.4574 0 6600 0.0000 -5.1385 33.0604 38.279317 22.7692 -24.5145 33.4574 0 6800 0.0000 -5.1385 33.0604 38.522179 22.6651 -24.6108 33.4574 0 7000 0.0000 -5.1385 33.0604 38.761630 22.5620 -24.7053 33.4574 0 7200 0.0000 -5.1385 33.0604 38.997753 22.4600 -24.7981 33.4574 0 7400 0.0000 -5.1385 33.0604 39.230627 22.3590 -24.8891 33.4574 0 7600 0.0000 -5.1385 33.0604 39.460328 22.2591 -24.9786 33.4574 0 7800 0.0000 -5.1385 33.0604 39.686930 22.1601 -25.0664 33.4574 0 8000 0.0000 -5.1385 33.0604 39.910504 22.0621 -25.1527 33.4574 0 8200 0.0000 -5.1385 33.0604 40.131118 21.9651 -25.2375 33.4574 0 8400 0.0000 -5.1385 33.0604 40.348838 21.8691 -25.3207 33.4574 500 0 0.0000 -5.3410 33.0582 27.496077 26.8581 -20.0003 33.4868 500 200 0.0000 -5.3410 33.0582 27.938329 26.7029 -20.2070 33.4868 500 400 0.0000 -5.3410 33.0582 28.369946 26.5500 -20.4076 33.4868 500 600 0.0000 -5.3410 33.0582 28.791451 26.3991 -20.6023 33.4868 500 800 0.0000 -5.3410 33.0582 29.203324 26.2503 -20.7916 33.4868 500 1000 0.0000 -5.3410 33.0582 29.606009 26.1036 -20.9755 33.4868 500 1200 0.0000 -5.3410 33.0582 29.999915 25.9587 -21.1545 33.4868 500 1400 0.0000 -5.3410 33.0582 30.385423 25.8158 -21.3287 33.4868 500 1600 0.0000 -5.3410 33.0582 30.762887 25.6747 -21.4983 33.4868 500 1800 0.0000 -5.3410 33.0582 31.132636 25.5355 -21.6635 33.4868 500 2000 0.0000 -5.3410 33.0582 31.494978 25.3980 -21.8246 33.4868 500 2200 0.0000 -5.3410 33.0582 31.850200 25.2622 -21.9816 33.4868 500 2400 0.0000 -5.3410 33.0582 32.198572 25.1280 -22.1348 33.4868 500 2600 0.0000 -5.3410 33.0582 32.540347 24.9956 -22.2843 33.4868 500 2800 0.0000 -5.3410 33.0582 32.875764 24.8647 -22.4303 33.4868 500 3000 0.0000 -5.3410 33.0582 33.205046 24.7354 -22.5728 33.4868 500 3200 0.0000 -5.3410 33.0582 33.528405 24.6076 -22.7120 33.4868 500 3400 0.0000 -5.3410 33.0582 33.846042 24.4813 -22.8481 33.4868 500 3600 0.0000 -5.3410 33.0582 34.158144 24.3565 -22.9811 33.4868 500 3800 0.0000 -5.3410 33.0582 34.464890 24.2331 -23.1112 33.4868 500 4000 0.0000 -5.3410 33.0582 34.766452 24.1111 -23.2384 33.4868 500 4200 0.0000 -5.3410 33.0582 35.062988 23.9905 -23.3629 33.4868 500 4400 0.0000 -5.3410 33.0582 35.354653 23.8713 -23.4847 33.4868 500 4600 0.0000 -5.3410 33.0582 35.641592 23.7534 -23.6040 33.4868 500 4800 0.0000 -5.3410 33.0582 35.923944 23.6368 -23.7207 33.4868 500 5000 0.0000 -5.3410 33.0582 36.201842 23.5214 -23.8351 33.4868 500 5200 0.0000 -5.3410 33.0582 36.475410 23.4074 -23.9471 33.4868 500 5400 0.0000 -5.3410 33.0582 36.744770 23.2945 -24.0569 33.4868 500 5600 0.0000 -5.3410 33.0582 37.010037 23.1829 -24.1645 33.4868 500 5800 0.0000 -5.3410 33.0582 37.271321 23.0725 -24.2700 33.4868 500 6000 0.0000 -5.3410 33.0582 37.528727 22.9632 -24.3734 33.4868 500 6200 0.0000 -5.3410 33.0582 37.782357 22.8551 -24.4748 33.4868 500 6400 0.0000 -5.3410 33.0582 38.032308 22.7481 -24.5743 33.4868 500 6600 0.0000 -5.3410 33.0582 38.278673 22.6422 -24.6718 33.4868 500 6800 0.0000 -5.3410 33.0582 38.521540 22.5374 -24.7676 33.4868 500 7000 0.0000 -5.3410 33.0582 38.760996 22.4337 -24.8616 33.4868 500 7200 0.0000 -5.3410 33.0582 38.997124 22.3311 -24.9538 33.4868 500 7400 0.0000 -5.3410 33.0582 39.230002 22.2295 -25.0444 33.4868 500 7600 0.0000 -5.3410 33.0582 39.459708 22.1289 -25.1333 33.4868 500 7800 0.0000 -5.3410 33.0582 39.686314 22.0293 -25.2206 33.4868 500 8000 0.0000 -5.3410 33.0582 39.909892 21.9307 -25.3064 33.4868 500 8200 0.0000 -5.3410 33.0582 40.130511 21.8331 -25.3906 33.4868 500 8400 0.0000 -5.3410 33.0582 40.348236 21.7365 -25.4734 33.4868 1000 0 0.0000 -5.5436 33.0559 27.495122 26.7629 -20.1785 33.5175 1000 200 0.0000 -5.5436 33.0559 27.937389 26.6064 -20.3844 33.5175 1000 400 0.0000 -5.5436 33.0559 28.369022 26.4520 -20.5843 33.5175 1000 600 0.0000 -5.5436 33.0559 28.790541 26.2999 -20.7783 33.5175 1000 800 0.0000 -5.5436 33.0559 29.202428 26.1498 -20.9669 33.5175 1000 1000 0.0000 -5.5436 33.0559 29.605126 26.0018 -21.1501 33.5175 1000 1200 0.0000 -5.5436 33.0559 29.999045 25.8558 -21.3284 33.5175 1000 1400 0.0000 -5.5436 33.0559 30.384565 25.7117 -21.5019 33.5175 1000 1600 0.0000 -5.5436 33.0559 30.762040 25.5695 -21.6708 33.5175 1000 1800 0.0000 -5.5436 33.0559 31.131800 25.4291 -21.8354 33.5175 1000 2000 0.0000 -5.5436 33.0559 31.494152 25.2905 -21.9958 33.5175 1000 2200 0.0000 -5.5436 33.0559 31.849385 25.1537 -22.1521 33.5175 1000 2400 0.0000 -5.5436 33.0559 32.197766 25.0185 -22.3047 33.5175 1000 2600 0.0000 -5.5436 33.0559 32.539551 24.8850 -22.4535 33.5175 1000 2800 0.0000 -5.5436 33.0559 32.874977 24.7531 -22.5988 33.5175 1000 3000 0.0000 -5.5436 33.0559 33.204268 24.6228 -22.7407 33.5175 1000 3200 0.0000 -5.5436 33.0559 33.527636 24.4941 -22.8793 33.5175 1000 3400 0.0000 -5.5436 33.0559 33.845281 24.3669 -23.0147 33.5175 1000 3600 0.0000 -5.5436 33.0559 34.157391 24.2412 -23.1471 33.5175 1000 3800 0.0000 -5.5436 33.0559 34.464145 24.1169 -23.2766 33.5175 1000 4000 0.0000 -5.5436 33.0559 34.765714 23.9940 -23.4032 33.5175 1000 4200 0.0000 -5.5436 33.0559 35.062258 23.8726 -23.5271 33.5175 1000 4400 0.0000 -5.5436 33.0559 35.353930 23.7525 -23.6483 33.5175 1000 4600 0.0000 -5.5436 33.0559 35.640876 23.6338 -23.7670 33.5175 1000 4800 0.0000 -5.5436 33.0559 35.923235 23.5164 -23.8831 33.5175 1000 5000 0.0000 -5.5436 33.0559 36.201138 23.4002 -23.9969 33.5175 1000 5200 0.0000 -5.5436 33.0559 36.474713 23.2854 -24.1084 33.5175 1000 5400 0.0000 -5.5436 33.0559 36.744079 23.1718 -24.2176 33.5175 1000 5600 0.0000 -5.5436 33.0559 37.009352 23.0594 -24.3246 33.5175 1000 5800 0.0000 -5.5436 33.0559 37.270642 22.9483 -24.4295 33.5175 1000 6000 0.0000 -5.5436 33.0559 37.528054 22.8383 -24.5324 33.5175 1000 6200 0.0000 -5.5436 33.0559 37.781689 22.7295 -24.6332 33.5175 1000 6400 0.0000 -5.5436 33.0559 38.031646 22.6218 -24.7321 33.5175 1000 6600 0.0000 -5.5436 33.0559 38.278015 22.5152 -24.8292 33.5175 1000 6800 0.0000 -5.5436 33.0559 38.520888 22.4098 -24.9244 33.5175 1000 7000 0.0000 -5.5436 33.0559 38.760349 22.3054 -25.0179 33.5175 1000 7200 0.0000 -5.5436 33.0559 38.996482 22.2021 -25.1096 33.5175 1000 7400 0.0000 -5.5436 33.0559 39.229365 22.0999 -25.1996 33.5175 1000 7600 0.0000 -5.5436 33.0559 39.459075 21.9987 -25.2880 33.5175 1000 7800 0.0000 -5.5436 33.0559 39.685686 21.8985 -25.3748 33.5175 1000 8000 0.0000 -5.5436 33.0559 39.909268 21.7993 -25.4601 33.5175 1000 8200 0.0000 -5.5436 33.0559 40.129891 21.7011 -25.5438 33.5175 1000 8400 0.0000 -5.5436 33.0559 40.347621 21.6039 -25.6261 33.5175 1500 0 0.0000 -5.7462 33.0536 27.494145 26.6677 -20.3566 33.5494 1500 200 0.0000 -5.7462 33.0536 27.936429 26.5098 -20.5619 33.5494 1500 400 0.0000 -5.7462 33.0536 28.368077 26.3541 -20.7610 33.5494 1500 600 0.0000 -5.7462 33.0536 28.789612 26.2007 -20.9543 33.5494 1500 800 0.0000 -5.7462 33.0536 29.201513 26.0494 -21.1422 33.5494 1500 1000 0.0000 -5.7462 33.0536 29.604224 25.9001 -21.3247 33.5494 1500 1200 0.0000 -5.7462 33.0536 29.998156 25.7529 -21.5023 33.5494 1500 1400 0.0000 -5.7462 33.0536 30.383688 25.6076 -21.6751 33.5494 1500 1600 0.0000 -5.7462 33.0536 30.761176 25.4643 -21.8433 33.5494 1500 1800 0.0000 -5.7462 33.0536 31.130947 25.3228 -22.0072 33.5494 1500 2000 0.0000 -5.7462 33.0536 31.493310 25.1831 -22.1669 33.5494 1500 2200 0.0000 -5.7462 33.0536 31.848553 25.0452 -22.3226 33.5494 1500 2400 0.0000 -5.7462 33.0536 32.196945 24.9090 -22.4745 33.5494 1500 2600 0.0000 -5.7462 33.0536 32.538739 24.7745 -22.6227 33.5494 1500 2800 0.0000 -5.7462 33.0536 32.874174 24.6416 -22.7674 33.5494 1500 3000 0.0000 -5.7462 33.0536 33.203475 24.5103 -22.9086 33.5494 1500 3200 0.0000 -5.7462 33.0536 33.526851 24.3806 -23.0466 33.5494 1500 3400 0.0000 -5.7462 33.0536 33.844504 24.2525 -23.1814 33.5494 1500 3600 0.0000 -5.7462 33.0536 34.156623 24.1259 -23.3132 33.5494 1500 3800 0.0000 -5.7462 33.0536 34.463385 24.0007 -23.4420 33.5494 1500 4000 0.0000 -5.7462 33.0536 34.764961 23.8770 -23.5680 33.5494 1500 4200 0.0000 -5.7462 33.0536 35.061513 23.7547 -23.6913 33.5494 1500 4400 0.0000 -5.7462 33.0536 35.353192 23.6338 -23.8119 33.5494 1500 4600 0.0000 -5.7462 33.0536 35.640145 23.5142 -23.9300 33.5494 1500 4800 0.0000 -5.7462 33.0536 35.922510 23.3960 -24.0456 33.5494 1500 5000 0.0000 -5.7462 33.0536 36.200421 23.2791 -24.1588 33.5494 1500 5200 0.0000 -5.7462 33.0536 36.474002 23.1635 -24.2696 33.5494 1500 5400 0.0000 -5.7462 33.0536 36.743374 23.0491 -24.3783 33.5494 1500 5600 0.0000 -5.7462 33.0536 37.008653 22.9360 -24.4847 33.5494 1500 5800 0.0000 -5.7462 33.0536 37.269949 22.8241 -24.5891 33.5494 1500 6000 0.0000 -5.7462 33.0536 37.527367 22.7134 -24.6914 33.5494 1500 6200 0.0000 -5.7462 33.0536 37.781008 22.6039 -24.7917 33.5494 1500 6400 0.0000 -5.7462 33.0536 38.030970 22.4955 -24.8900 33.5494 1500 6600 0.0000 -5.7462 33.0536 38.277345 22.3882 -24.9865 33.5494 1500 6800 0.0000 -5.7462 33.0536 38.520223 22.2821 -25.0812 33.5494 1500 7000 0.0000 -5.7462 33.0536 38.759689 22.1771 -25.1741 33.5494 1500 7200 0.0000 -5.7462 33.0536 38.995827 22.0732 -25.2653 33.5494 1500 7400 0.0000 -5.7462 33.0536 39.228715 21.9703 -25.3548 33.5494 1500 7600 0.0000 -5.7462 33.0536 39.458429 21.8685 -25.4427 33.5494 1500 7800 0.0000 -5.7462 33.0536 39.685045 21.7677 -25.5290 33.5494 1500 8000 0.0000 -5.7462 33.0536 39.908632 21.6679 -25.6138 33.5494 1500 8200 0.0000 -5.7462 33.0536 40.129260 21.5691 -25.6970 33.5494 1500 8400 0.0000 -5.7462 33.0536 40.346993 21.4713 -25.7788 33.5494 2000 0 0.0000 -5.9487 33.0514 27.493148 26.5726 -20.5348 33.5824 2000 200 0.0000 -5.9487 33.0514 27.935449 26.4133 -20.7393 33.5824 2000 400 0.0000 -5.9487 33.0514 28.367113 26.2563 -20.9377 33.5824 2000 600 0.0000 -5.9487 33.0514 28.788663 26.1015 -21.1303 33.5824 2000 800 0.0000 -5.9487 33.0514 29.200578 25.9489 -21.3175 33.5824 2000 1000 0.0000 -5.9487 33.0514 29.603304 25.7984 -21.4993 33.5824 2000 1200 0.0000 -5.9487 33.0514 29.997248 25.6500 -21.6762 33.5824 2000 1400 0.0000 -5.9487 33.0514 30.382794 25.5036 -21.8483 33.5824 2000 1600 0.0000 -5.9487 33.0514 30.760293 25.3591 -22.0159 33.5824 2000 1800 0.0000 -5.9487 33.0514 31.130076 25.2164 -22.1791 33.5824 2000 2000 0.0000 -5.9487 33.0514 31.492450 25.0757 -22.3381 33.5824 2000 2200 0.0000 -5.9487 33.0514 31.847704 24.9367 -22.4931 33.5824 2000 2400 0.0000 -5.9487 33.0514 32.196106 24.7994 -22.6444 33.5824 2000 2600 0.0000 -5.9487 33.0514 32.537910 24.6639 -22.7919 33.5824 2000 2800 0.0000 -5.9487 33.0514 32.873355 24.5301 -22.9359 33.5824 2000 3000 0.0000 -5.9487 33.0514 33.202665 24.3978 -23.0765 33.5824 2000 3200 0.0000 -5.9487 33.0514 33.526051 24.2672 -23.2139 33.5824 2000 3400 0.0000 -5.9487 33.0514 33.843712 24.1381 -23.3480 33.5824 2000 3600 0.0000 -5.9487 33.0514 34.155839 24.0106 -23.4792 33.5824 2000 3800 0.0000 -5.9487 33.0514 34.462609 23.8845 -23.6074 33.5824 2000 4000 0.0000 -5.9487 33.0514 34.764193 23.7599 -23.7328 33.5824 2000 4200 0.0000 -5.9487 33.0514 35.060752 23.6368 -23.8555 33.5824 2000 4400 0.0000 -5.9487 33.0514 35.352439 23.5150 -23.9755 33.5824 2000 4600 0.0000 -5.9487 33.0514 35.639399 23.3946 -24.0930 33.5824 2000 4800 0.0000 -5.9487 33.0514 35.921772 23.2756 -24.2080 33.5824 2000 5000 0.0000 -5.9487 33.0514 36.199689 23.1579 -24.3206 33.5824 2000 5200 0.0000 -5.9487 33.0514 36.473276 23.0415 -24.4309 33.5824 2000 5400 0.0000 -5.9487 33.0514 36.742655 22.9264 -24.5389 33.5824 2000 5600 0.0000 -5.9487 33.0514 37.007941 22.8126 -24.6448 33.5824 2000 5800 0.0000 -5.9487 33.0514 37.269242 22.6999 -24.7486 33.5824 2000 6000 0.0000 -5.9487 33.0514 37.526666 22.5885 -24.8503 33.5824 2000 6200 0.0000 -5.9487 33.0514 37.780313 22.4783 -24.9501 33.5824 2000 6400 0.0000 -5.9487 33.0514 38.030281 22.3692 -25.0479 33.5824 2000 6600 0.0000 -5.9487 33.0514 38.276661 22.2613 -25.1439 33.5824 2000 6800 0.0000 -5.9487 33.0514 38.519545 22.1545 -25.2380 33.5824 2000 7000 0.0000 -5.9487 33.0514 38.759016 22.0488 -25.3304 33.5824 2000 7200 0.0000 -5.9487 33.0514 38.995159 21.9442 -25.4211 33.5824 2000 7400 0.0000 -5.9487 33.0514 39.228052 21.8407 -25.5101 33.5824 2000 7600 0.0000 -5.9487 33.0514 39.457772 21.7383 -25.5974 33.5824 2000 7800 0.0000 -5.9487 33.0514 39.684392 21.6369 -25.6832 33.5824 2000 8000 0.0000 -5.9487 33.0514 39.907984 21.5365 -25.7674 33.5824 2000 8200 0.0000 -5.9487 33.0514 40.128616 21.4371 -25.8502 33.5824 2000 8400 0.0000 -5.9487 33.0514 40.346354 21.3387 -25.9315 33.5824 2500 0 0.0000 -6.1513 33.0491 27.492131 26.4774 -20.7130 33.6167 2500 200 0.0000 -6.1513 33.0491 27.934449 26.3167 -20.9168 33.6167 2500 400 0.0000 -6.1513 33.0491 28.366129 26.1584 -21.1145 33.6167 2500 600 0.0000 -6.1513 33.0491 28.787694 26.0023 -21.3063 33.6167 2500 800 0.0000 -6.1513 33.0491 29.199625 25.8485 -21.4927 33.6167 2500 1000 0.0000 -6.1513 33.0491 29.602364 25.6968 -21.6739 33.6167 2500 1200 0.0000 -6.1513 33.0491 29.996323 25.5471 -21.8501 33.6167 2500 1400 0.0000 -6.1513 33.0491 30.381881 25.3995 -22.0215 33.6167 2500 1600 0.0000 -6.1513 33.0491 30.759393 25.2539 -22.1884 33.6167 2500 1800 0.0000 -6.1513 33.0491 31.129187 25.1101 -22.3509 33.6167 2500 2000 0.0000 -6.1513 33.0491 31.491573 24.9683 -22.5093 33.6167 2500 2200 0.0000 -6.1513 33.0491 31.846838 24.8282 -22.6636 33.6167 2500 2400 0.0000 -6.1513 33.0491 32.195250 24.6899 -22.8142 33.6167 2500 2600 0.0000 -6.1513 33.0491 32.537065 24.5534 -22.9611 33.6167 2500 2800 0.0000 -6.1513 33.0491 32.872520 24.4185 -23.1045 33.6167 2500 3000 0.0000 -6.1513 33.0491 33.201839 24.2853 -23.2444 33.6167 2500 3200 0.0000 -6.1513 33.0491 33.525234 24.1538 -23.3811 33.6167 2500 3400 0.0000 -6.1513 33.0491 33.842904 24.0238 -23.5147 33.6167 2500 3600 0.0000 -6.1513 33.0491 34.155039 23.8953 -23.6452 33.6167 2500 3800 0.0000 -6.1513 33.0491 34.461818 23.7683 -23.7728 33.6167 2500 4000 0.0000 -6.1513 33.0491 34.763411 23.6429 -23.8976 33.6167 2500 4200 0.0000 -6.1513 33.0491 35.059977 23.5189 -24.0197 33.6167 2500 4400 0.0000 -6.1513 33.0491 35.351672 23.3963 -24.1391 33.6167 2500 4600 0.0000 -6.1513 33.0491 35.638639 23.2751 -24.2560 33.6167 2500 4800 0.0000 -6.1513 33.0491 35.921019 23.1553 -24.3704 33.6167 2500 5000 0.0000 -6.1513 33.0491 36.198943 23.0368 -24.4824 33.6167 2500 5200 0.0000 -6.1513 33.0491 36.472537 22.9196 -24.5921 33.6167 2500 5400 0.0000 -6.1513 33.0491 36.741923 22.8037 -24.6996 33.6167 2500 5600 0.0000 -6.1513 33.0491 37.007214 22.6891 -24.8049 33.6167 2500 5800 0.0000 -6.1513 33.0491 37.268522 22.5758 -24.9082 33.6167 2500 6000 0.0000 -6.1513 33.0491 37.525952 22.4636 -25.0093 33.6167 2500 6200 0.0000 -6.1513 33.0491 37.779605 22.3527 -25.1085 33.6167 2500 6400 0.0000 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3000 800 0.0000 -6.3539 33.0468 29.198653 25.7480 -21.6680 33.6521 3000 1000 0.0000 -6.3539 33.0468 29.601406 25.5951 -21.8485 33.6521 3000 1200 0.0000 -6.3539 33.0468 29.995379 25.4442 -22.0240 33.6521 3000 1400 0.0000 -6.3539 33.0468 30.380950 25.2955 -22.1947 33.6521 3000 1600 0.0000 -6.3539 33.0468 30.758475 25.1487 -22.3609 33.6521 3000 1800 0.0000 -6.3539 33.0468 31.128282 25.0038 -22.5227 33.6521 3000 2000 0.0000 -6.3539 33.0468 31.490679 24.8609 -22.6804 33.6521 3000 2200 0.0000 -6.3539 33.0468 31.845955 24.7198 -22.8341 33.6521 3000 2400 0.0000 -6.3539 33.0468 32.194378 24.5804 -22.9840 33.6521 3000 2600 0.0000 -6.3539 33.0468 32.536204 24.4429 -23.1303 33.6521 3000 2800 0.0000 -6.3539 33.0468 32.871668 24.3070 -23.2730 33.6521 3000 3000 0.0000 -6.3539 33.0468 33.200997 24.1729 -23.4123 33.6521 3000 3200 0.0000 -6.3539 33.0468 33.524401 24.0403 -23.5484 33.6521 3000 3400 0.0000 -6.3539 33.0468 33.842081 23.9094 -23.6813 33.6521 3000 3600 0.0000 -6.3539 33.0468 34.154225 23.7800 -23.8112 33.6521 3000 3800 0.0000 -6.3539 33.0468 34.461012 23.6522 -23.9382 33.6521 3000 4000 0.0000 -6.3539 33.0468 34.762613 23.5259 -24.0624 33.6521 3000 4200 0.0000 -6.3539 33.0468 35.059187 23.4010 -24.1838 33.6521 3000 4400 0.0000 -6.3539 33.0468 35.350889 23.2776 -24.3027 33.6521 3000 4600 0.0000 -6.3539 33.0468 35.637865 23.1555 -24.4189 33.6521 3000 4800 0.0000 -6.3539 33.0468 35.920252 23.0349 -24.5328 33.6521 3000 5000 0.0000 -6.3539 33.0468 36.198183 22.9156 -24.6442 33.6521 3000 5200 0.0000 -6.3539 33.0468 36.471784 22.7977 -24.7534 33.6521 3000 5400 0.0000 -6.3539 33.0468 36.741176 22.6811 -24.8603 33.6521 3000 5600 0.0000 -6.3539 33.0468 37.006474 22.5657 -24.9650 33.6521 3000 5800 0.0000 -6.3539 33.0468 37.267789 22.4516 -25.0677 33.6521 3000 6000 0.0000 -6.3539 33.0468 37.525225 22.3388 -25.1683 33.6521 3000 6200 0.0000 -6.3539 33.0468 37.778884 22.2271 -25.2670 33.6521 3000 6400 0.0000 -6.3539 33.0468 38.028863 22.1167 -25.3637 33.6521 3000 6600 0.0000 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1000 0.0000 -6.5564 33.0446 29.600430 25.4934 -22.0231 33.6887 3500 1200 0.0000 -6.5564 33.0446 29.994416 25.3414 -22.1978 33.6887 3500 1400 0.0000 -6.5564 33.0446 30.380001 25.1914 -22.3679 33.6887 3500 1600 0.0000 -6.5564 33.0446 30.757539 25.0435 -22.5334 33.6887 3500 1800 0.0000 -6.5564 33.0446 31.127358 24.8975 -22.6946 33.6887 3500 2000 0.0000 -6.5564 33.0446 31.489768 24.7535 -22.8516 33.6887 3500 2200 0.0000 -6.5564 33.0446 31.845055 24.6113 -23.0046 33.6887 3500 2400 0.0000 -6.5564 33.0446 32.193490 24.4710 -23.1539 33.6887 3500 2600 0.0000 -6.5564 33.0446 32.535326 24.3324 -23.2995 33.6887 3500 2800 0.0000 -6.5564 33.0446 32.870801 24.1955 -23.4415 33.6887 3500 3000 0.0000 -6.5564 33.0446 33.200139 24.0604 -23.5802 33.6887 3500 3200 0.0000 -6.5564 33.0446 33.523553 23.9269 -23.7157 33.6887 3500 3400 0.0000 -6.5564 33.0446 33.841242 23.7951 -23.8480 33.6887 3500 3600 0.0000 -6.5564 33.0446 34.153395 23.6648 -23.9772 33.6887 3500 3800 0.0000 -6.5564 33.0446 34.460191 23.5360 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33.0446 38.517433 21.7717 -25.7085 33.6887 3500 7000 0.0000 -6.5564 33.0446 38.756921 21.6640 -25.7992 33.6887 3500 7200 0.0000 -6.5564 33.0446 38.993080 21.5575 -25.8883 33.6887 3500 7400 0.0000 -6.5564 33.0446 39.225988 21.4521 -25.9757 33.6887 3500 7600 0.0000 -6.5564 33.0446 39.455724 21.3477 -26.0615 33.6887 3500 7800 0.0000 -6.5564 33.0446 39.682359 21.2445 -26.1458 33.6887 3500 8000 0.0000 -6.5564 33.0446 39.905966 21.1423 -26.2285 33.6887 3500 8200 0.0000 -6.5564 33.0446 40.126613 21.0411 -26.3097 33.6887 3500 8400 0.0000 -6.5564 33.0446 40.344365 20.9410 -26.3895 33.6887 4000 0 0.0000 -6.7590 33.0423 27.488955 26.1920 -21.2475 33.7265 4000 200 0.0000 -6.7590 33.0423 27.931328 26.0272 -21.4491 33.7265 4000 400 0.0000 -6.7590 33.0423 28.363060 25.8648 -21.6446 33.7265 4000 600 0.0000 -6.7590 33.0423 28.784674 25.7049 -21.8343 33.7265 4000 800 0.0000 -6.7590 33.0423 29.196651 25.5472 -22.0186 33.7265 4000 1000 0.0000 -6.7590 33.0423 29.599435 25.3918 -22.1976 33.7265 4000 1200 0.0000 -6.7590 33.0423 29.993436 25.2385 -22.3717 33.7265 4000 1400 0.0000 -6.7590 33.0423 30.379035 25.0874 -22.5411 33.7265 4000 1600 0.0000 -6.7590 33.0423 30.756585 24.9383 -22.7059 33.7265 4000 1800 0.0000 -6.7590 33.0423 31.126418 24.7913 -22.8664 33.7265 4000 2000 0.0000 -6.7590 33.0423 31.488839 24.6461 -23.0227 33.7265 4000 2200 0.0000 -6.7590 33.0423 31.844138 24.5029 -23.1751 33.7265 4000 2400 0.0000 -6.7590 33.0423 32.192584 24.3615 -23.3237 33.7265 4000 2600 0.0000 -6.7590 33.0423 32.534431 24.2219 -23.4686 33.7265 4000 2800 0.0000 -6.7590 33.0423 32.869917 24.0841 -23.6101 33.7265 4000 3000 0.0000 -6.7590 33.0423 33.199265 23.9480 -23.7481 33.7265 4000 3200 0.0000 -6.7590 33.0423 33.522689 23.8135 -23.8829 33.7265 4000 3400 0.0000 -6.7590 33.0423 33.840387 23.6807 -24.0146 33.7265 4000 3600 0.0000 -6.7590 33.0423 34.152550 23.5495 -24.1433 33.7265 4000 3800 0.0000 -6.7590 33.0423 34.459354 23.4199 -24.2690 33.7265 4000 4000 0.0000 -6.7590 33.0423 34.760972 23.2918 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33.0423 38.756197 21.5358 -25.9555 33.7265 4000 7200 0.0000 -6.7590 33.0423 38.992361 21.4286 -26.0441 33.7265 4000 7400 0.0000 -6.7590 33.0423 39.225276 21.3225 -26.1309 33.7265 4000 7600 0.0000 -6.7590 33.0423 39.455016 21.2176 -26.2162 33.7265 4000 7800 0.0000 -6.7590 33.0423 39.681657 21.1137 -26.3000 33.7265 4000 8000 0.0000 -6.7590 33.0423 39.905269 21.0109 -26.3822 33.7265 4000 8200 0.0000 -6.7590 33.0423 40.125921 20.9092 -26.4629 33.7265 4000 8400 0.0000 -6.7590 33.0423 40.343678 20.8084 -26.5422 33.7265 4500 0 0.0000 -6.9616 33.0400 27.487857 26.0969 -21.4256 33.7655 4500 200 0.0000 -6.9616 33.0400 27.930248 25.9307 -21.6265 33.7655 4500 400 0.0000 -6.9616 33.0400 28.361998 25.7670 -21.8213 33.7655 4500 600 0.0000 -6.9616 33.0400 28.783629 25.6057 -22.0103 33.7655 4500 800 0.0000 -6.9616 33.0400 29.195622 25.4468 -22.1938 33.7655 4500 1000 0.0000 -6.9616 33.0400 29.598421 25.2902 -22.3722 33.7655 4500 1200 0.0000 -6.9616 33.0400 29.992437 25.1357 -22.5456 33.7655 4500 1400 0.0000 -6.9616 33.0400 30.378050 24.9834 -22.7142 33.7655 4500 1600 0.0000 -6.9616 33.0400 30.755614 24.8332 -22.8784 33.7655 4500 1800 0.0000 -6.9616 33.0400 31.125460 24.6850 -23.0382 33.7655 4500 2000 0.0000 -6.9616 33.0400 31.487894 24.5388 -23.1939 33.7655 4500 2200 0.0000 -6.9616 33.0400 31.843205 24.3945 -23.3456 33.7655 4500 2400 0.0000 -6.9616 33.0400 32.191662 24.2520 -23.4935 33.7655 4500 2600 0.0000 -6.9616 33.0400 32.533520 24.1114 -23.6378 33.7655 4500 2800 0.0000 -6.9616 33.0400 32.869017 23.9726 -23.7786 33.7655 4500 3000 0.0000 -6.9616 33.0400 33.198376 23.8355 -23.9160 33.7655 4500 3200 0.0000 -6.9616 33.0400 33.521810 23.7001 -24.0502 33.7655 4500 3400 0.0000 -6.9616 33.0400 33.839518 23.5664 -24.1812 33.7655 4500 3600 0.0000 -6.9616 33.0400 34.151689 23.4343 -24.3093 33.7655 4500 3800 0.0000 -6.9616 33.0400 34.458503 23.3038 -24.4344 33.7655 4500 4000 0.0000 -6.9616 33.0400 34.760129 23.1748 -24.5567 33.7655 4500 4200 0.0000 -6.9616 33.0400 35.056729 23.0474 -24.6764 33.7655 4500 4400 0.0000 -6.9616 33.0400 35.348455 22.9215 -24.7934 33.7655 4500 4600 0.0000 -6.9616 33.0400 35.635454 22.7970 -24.9079 33.7655 4500 4800 0.0000 -6.9616 33.0400 35.917864 22.6739 -25.0200 33.7655 4500 5000 0.0000 -6.9616 33.0400 36.195818 22.5523 -25.1297 33.7655 4500 5200 0.0000 -6.9616 33.0400 36.469441 22.4320 -25.2371 33.7655 4500 5400 0.0000 -6.9616 33.0400 36.738854 22.3131 -25.3423 33.7655 4500 5600 0.0000 -6.9616 33.0400 37.004173 22.1955 -25.4453 33.7655 4500 5800 0.0000 -6.9616 33.0400 37.265507 22.0792 -25.5463 33.7655 4500 6000 0.0000 -6.9616 33.0400 37.522963 21.9642 -25.6453 33.7655 4500 6200 0.0000 -6.9616 33.0400 37.776641 21.8505 -25.7423 33.7655 4500 6400 0.0000 -6.9616 33.0400 38.026639 21.7379 -25.8374 33.7655 4500 6600 0.0000 -6.9616 33.0400 38.273049 21.6266 -25.9306 33.7655 4500 6800 0.0000 -6.9616 33.0400 38.515961 21.5165 -26.0221 33.7655 4500 7000 0.0000 -6.9616 33.0400 38.755460 21.4075 -26.1118 33.7655 4500 7200 0.0000 -6.9616 33.0400 38.991631 21.2997 -26.1998 33.7655 4500 7400 0.0000 -6.9616 33.0400 39.224551 21.1930 -26.2862 33.7655 4500 7600 0.0000 -6.9616 33.0400 39.454297 21.0875 -26.3709 33.7655 4500 7800 0.0000 -6.9616 33.0400 39.680943 20.9830 -26.4541 33.7655 4500 8000 0.0000 -6.9616 33.0400 39.904560 20.8796 -26.5358 33.7655 4500 8200 0.0000 -6.9616 33.0400 40.125217 20.7772 -26.6161 33.7655 4500 8400 0.0000 -6.9616 33.0400 40.342979 20.6759 -26.6948 33.7655 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.010 ================================================ FILE: tests/test_data/cropA/geometry/20180412-20180506_VV_8rlks_base.par ================================================ initial_baseline(TCN): -0.8598882 63.9985273 -10.6224613 m m m initial_baseline_rate: 0.0000000 0.0255046 0.0007563 m/s m/s m/s precision_baseline(TCN): 0.0000000 63.9442366 -10.5557685 m m m precision_baseline_rate: 0.0000000 0.0078066 0.0132544 m/s m/s m/s unwrap_phase_constant: 0.00010 radians ================================================ FILE: tests/test_data/cropA/geometry/20180412-20180506_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): -0.860 63.999 -10.622 orbit baseline rate (TCN) (m/s): 0.000e+00 2.550e-02 7.563e-04 baseline vector (TCN) (m): 0.000 63.944 -10.556 baseline rate (TCN) (m/s): 0.000e+00 7.807e-03 1.325e-02 SLC-1 center baseline length (m): 64.8096 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 0.0000 63.8714 -10.6795 27.501881 20.0216 61.5850 64.7580 0 200 0.0000 63.8714 -10.6795 27.944066 20.4963 61.4287 64.7580 0 400 0.0000 63.8714 -10.6795 28.375620 20.9584 61.2726 64.7580 0 600 0.0000 63.8714 -10.6795 28.797066 21.4086 61.1168 64.7580 0 800 0.0000 63.8714 -10.6795 29.208882 21.8473 60.9613 64.7580 0 1000 0.0000 63.8714 -10.6795 29.611513 22.2751 60.8063 64.7580 0 1200 0.0000 63.8714 -10.6795 30.005367 22.6926 60.6517 64.7580 0 1400 0.0000 63.8714 -10.6795 30.390826 23.1001 60.4977 64.7580 0 1600 0.0000 63.8714 -10.6795 30.768243 23.4981 60.3442 64.7580 0 1800 0.0000 63.8714 -10.6795 31.137946 23.8870 60.1914 64.7580 0 2000 0.0000 63.8714 -10.6795 31.500244 24.2671 60.0391 64.7580 0 2200 0.0000 63.8714 -10.6795 31.855425 24.6389 59.8875 64.7580 0 2400 0.0000 63.8714 -10.6795 32.203756 25.0025 59.7366 64.7580 0 2600 0.0000 63.8714 -10.6795 32.545493 25.3583 59.5864 64.7580 0 2800 0.0000 63.8714 -10.6795 32.880872 25.7067 59.4370 64.7580 0 3000 0.0000 63.8714 -10.6795 33.210118 26.0478 59.2883 64.7580 0 3200 0.0000 63.8714 -10.6795 33.533443 26.3820 59.1404 64.7580 0 3400 0.0000 63.8714 -10.6795 33.851045 26.7094 58.9932 64.7580 0 3600 0.0000 63.8714 -10.6795 34.163114 27.0303 58.8469 64.7580 0 3800 0.0000 63.8714 -10.6795 34.469829 27.3449 58.7013 64.7580 0 4000 0.0000 63.8714 -10.6795 34.771360 27.6535 58.5566 64.7580 0 4200 0.0000 63.8714 -10.6795 35.067866 27.9561 58.4127 64.7580 0 4400 0.0000 63.8714 -10.6795 35.359502 28.2531 58.2697 64.7580 0 4600 0.0000 63.8714 -10.6795 35.646413 28.5445 58.1275 64.7580 0 4800 0.0000 63.8714 -10.6795 35.928737 28.8306 57.9861 64.7580 0 5000 0.0000 63.8714 -10.6795 36.206607 29.1115 57.8456 64.7580 0 5200 0.0000 63.8714 -10.6795 36.480149 29.3873 57.7060 64.7580 0 5400 0.0000 63.8714 -10.6795 36.749483 29.6583 57.5672 64.7580 0 5600 0.0000 63.8714 -10.6795 37.014725 29.9244 57.4293 64.7580 0 5800 0.0000 63.8714 -10.6795 37.275984 30.1860 57.2922 64.7580 0 6000 0.0000 63.8714 -10.6795 37.533367 30.4431 57.1560 64.7580 0 6200 0.0000 63.8714 -10.6795 37.786973 30.6957 57.0207 64.7580 0 6400 0.0000 63.8714 -10.6795 38.036901 30.9442 56.8863 64.7580 0 6600 0.0000 63.8714 -10.6795 38.283243 31.1885 56.7527 64.7580 0 6800 0.0000 63.8714 -10.6795 38.526088 31.4287 56.6200 64.7580 0 7000 0.0000 63.8714 -10.6795 38.765522 31.6651 56.4882 64.7580 0 7200 0.0000 63.8714 -10.6795 39.001629 31.8976 56.3572 64.7580 0 7400 0.0000 63.8714 -10.6795 39.234486 32.1264 56.2271 64.7580 0 7600 0.0000 63.8714 -10.6795 39.464171 32.3515 56.0979 64.7580 0 7800 0.0000 63.8714 -10.6795 39.690757 32.5731 55.9695 64.7580 0 8000 0.0000 63.8714 -10.6795 39.914315 32.7912 55.8420 64.7580 0 8200 0.0000 63.8714 -10.6795 40.134914 33.0060 55.7153 64.7580 0 8400 0.0000 63.8714 -10.6795 40.352620 33.2175 55.5895 64.7580 500 0 0.0000 63.8874 -10.6522 27.500948 20.0522 61.5870 64.7694 500 200 0.0000 63.8874 -10.6522 27.943149 20.5269 61.4304 64.7694 500 400 0.0000 63.8874 -10.6522 28.374718 20.9891 61.2741 64.7694 500 600 0.0000 63.8874 -10.6522 28.796177 21.4392 61.1180 64.7694 500 800 0.0000 63.8874 -10.6522 29.208007 21.8780 60.9624 64.7694 500 1000 0.0000 63.8874 -10.6522 29.610651 22.3058 60.8071 64.7694 500 1200 0.0000 63.8874 -10.6522 30.004518 22.7233 60.6523 64.7694 500 1400 0.0000 63.8874 -10.6522 30.389988 23.1308 60.4981 64.7694 500 1600 0.0000 63.8874 -10.6522 30.767416 23.5289 60.3444 64.7694 500 1800 0.0000 63.8874 -10.6522 31.137130 23.9178 60.1913 64.7694 500 2000 0.0000 63.8874 -10.6522 31.499439 24.2979 60.0389 64.7694 500 2200 0.0000 63.8874 -10.6522 31.854629 24.6696 59.8871 64.7694 500 2400 0.0000 63.8874 -10.6522 32.202970 25.0333 59.7360 64.7694 500 2600 0.0000 63.8874 -10.6522 32.544716 25.3891 59.5857 64.7694 500 2800 0.0000 63.8874 -10.6522 32.880104 25.7375 59.4360 64.7694 500 3000 0.0000 63.8874 -10.6522 33.209359 26.0786 59.2871 64.7694 500 3200 0.0000 63.8874 -10.6522 33.532692 26.4128 59.1390 64.7694 500 3400 0.0000 63.8874 -10.6522 33.850302 26.7402 58.9917 64.7694 500 3600 0.0000 63.8874 -10.6522 34.162379 27.0611 58.8452 64.7694 500 3800 0.0000 63.8874 -10.6522 34.469102 27.3757 58.6995 64.7694 500 4000 0.0000 63.8874 -10.6522 34.770639 27.6843 58.5546 64.7694 500 4200 0.0000 63.8874 -10.6522 35.067153 27.9869 58.4105 64.7694 500 4400 0.0000 63.8874 -10.6522 35.358795 28.2839 58.2673 64.7694 500 4600 0.0000 63.8874 -10.6522 35.645713 28.5753 58.1250 64.7694 500 4800 0.0000 63.8874 -10.6522 35.928043 28.8614 57.9835 64.7694 500 5000 0.0000 63.8874 -10.6522 36.205920 29.1423 57.8428 64.7694 500 5200 0.0000 63.8874 -10.6522 36.479468 29.4181 57.7030 64.7694 500 5400 0.0000 63.8874 -10.6522 36.748808 29.6890 57.5641 64.7694 500 5600 0.0000 63.8874 -10.6522 37.014055 29.9552 57.4260 64.7694 500 5800 0.0000 63.8874 -10.6522 37.275320 30.2167 57.2888 64.7694 500 6000 0.0000 63.8874 -10.6522 37.532708 30.4738 57.1525 64.7694 500 6200 0.0000 63.8874 -10.6522 37.786320 30.7265 57.0171 64.7694 500 6400 0.0000 63.8874 -10.6522 38.036253 30.9749 56.8825 64.7694 500 6600 0.0000 63.8874 -10.6522 38.282600 31.2192 56.7488 64.7694 500 6800 0.0000 63.8874 -10.6522 38.525450 31.4594 56.6160 64.7694 500 7000 0.0000 63.8874 -10.6522 38.764890 31.6957 56.4840 64.7694 500 7200 0.0000 63.8874 -10.6522 39.001001 31.9282 56.3529 64.7694 500 7400 0.0000 63.8874 -10.6522 39.233863 32.1570 56.2227 64.7694 500 7600 0.0000 63.8874 -10.6522 39.463552 32.3821 56.0933 64.7694 500 7800 0.0000 63.8874 -10.6522 39.690143 32.6037 55.9648 64.7694 500 8000 0.0000 63.8874 -10.6522 39.913705 32.8218 55.8372 64.7694 500 8200 0.0000 63.8874 -10.6522 40.134309 33.0366 55.7104 64.7694 500 8400 0.0000 63.8874 -10.6522 40.352019 33.2480 55.5845 64.7694 1000 0 0.0000 63.9035 -10.6250 27.499995 20.0828 61.5890 64.7807 1000 200 0.0000 63.9035 -10.6250 27.942212 20.5575 61.4322 64.7807 1000 400 0.0000 63.9035 -10.6250 28.373796 21.0197 61.2756 64.7807 1000 600 0.0000 63.9035 -10.6250 28.795270 21.4699 61.1193 64.7807 1000 800 0.0000 63.9035 -10.6250 29.207113 21.9086 60.9634 64.7807 1000 1000 0.0000 63.9035 -10.6250 29.609770 22.3365 60.8079 64.7807 1000 1200 0.0000 63.9035 -10.6250 30.003649 22.7540 60.6530 64.7807 1000 1400 0.0000 63.9035 -10.6250 30.389132 23.1616 60.4985 64.7807 1000 1600 0.0000 63.9035 -10.6250 30.766571 23.5596 60.3446 64.7807 1000 1800 0.0000 63.9035 -10.6250 31.136297 23.9485 60.1913 64.7807 1000 2000 0.0000 63.9035 -10.6250 31.498616 24.3287 60.0387 64.7807 1000 2200 0.0000 63.9035 -10.6250 31.853816 24.7004 59.8867 64.7807 1000 2400 0.0000 63.9035 -10.6250 32.202167 25.0640 59.7354 64.7807 1000 2600 0.0000 63.9035 -10.6250 32.543923 25.4199 59.5849 64.7807 1000 2800 0.0000 63.9035 -10.6250 32.879320 25.7683 59.4351 64.7807 1000 3000 0.0000 63.9035 -10.6250 33.208583 26.1094 59.2860 64.7807 1000 3200 0.0000 63.9035 -10.6250 33.531925 26.4435 59.1377 64.7807 1000 3400 0.0000 63.9035 -10.6250 33.849543 26.7710 58.9902 64.7807 1000 3600 0.0000 63.9035 -10.6250 34.161628 27.0919 58.8435 64.7807 1000 3800 0.0000 63.9035 -10.6250 34.468358 27.4065 58.6976 64.7807 1000 4000 0.0000 63.9035 -10.6250 34.769903 27.7151 58.5526 64.7807 1000 4200 0.0000 63.9035 -10.6250 35.066424 28.0177 58.4084 64.7807 1000 4400 0.0000 63.9035 -10.6250 35.358074 28.3147 58.2650 64.7807 1000 4600 0.0000 63.9035 -10.6250 35.644998 28.6061 58.1225 64.7807 1000 4800 0.0000 63.9035 -10.6250 35.927335 28.8921 57.9808 64.7807 1000 5000 0.0000 63.9035 -10.6250 36.205218 29.1730 57.8400 64.7807 1000 5200 0.0000 63.9035 -10.6250 36.478772 29.4488 57.7001 64.7807 1000 5400 0.0000 63.9035 -10.6250 36.748119 29.7198 57.5610 64.7807 1000 5600 0.0000 63.9035 -10.6250 37.013372 29.9859 57.4228 64.7807 1000 5800 0.0000 63.9035 -10.6250 37.274643 30.2475 57.2855 64.7807 1000 6000 0.0000 63.9035 -10.6250 37.532037 30.5045 57.1490 64.7807 1000 6200 0.0000 63.9035 -10.6250 37.785654 30.7572 57.0134 64.7807 1000 6400 0.0000 63.9035 -10.6250 38.035592 31.0056 56.8787 64.7807 1000 6600 0.0000 63.9035 -10.6250 38.281945 31.2498 56.7449 64.7807 1000 6800 0.0000 63.9035 -10.6250 38.524800 31.4901 56.6119 64.7807 1000 7000 0.0000 63.9035 -10.6250 38.764244 31.7264 56.4798 64.7807 1000 7200 0.0000 63.9035 -10.6250 39.000360 31.9589 56.3486 64.7807 1000 7400 0.0000 63.9035 -10.6250 39.233227 32.1876 56.2182 64.7807 1000 7600 0.0000 63.9035 -10.6250 39.462921 32.4128 56.0887 64.7807 1000 7800 0.0000 63.9035 -10.6250 39.689516 32.6343 55.9601 64.7807 1000 8000 0.0000 63.9035 -10.6250 39.913083 32.8524 55.8324 64.7807 1000 8200 0.0000 63.9035 -10.6250 40.133691 33.0672 55.7054 64.7807 1000 8400 0.0000 63.9035 -10.6250 40.351405 33.2786 55.5794 64.7807 1500 0 0.0000 63.9195 -10.5978 27.499021 20.1133 61.5910 64.7921 1500 200 0.0000 63.9195 -10.5978 27.941254 20.5881 61.4339 64.7921 1500 400 0.0000 63.9195 -10.5978 28.372854 21.0503 61.2771 64.7921 1500 600 0.0000 63.9195 -10.5978 28.794343 21.5005 61.1206 64.7921 1500 800 0.0000 63.9195 -10.5978 29.206200 21.9393 60.9645 64.7921 1500 1000 0.0000 63.9195 -10.5978 29.608871 22.3672 60.8088 64.7921 1500 1200 0.0000 63.9195 -10.5978 30.002763 22.7847 60.6536 64.7921 1500 1400 0.0000 63.9195 -10.5978 30.388258 23.1923 60.4989 64.7921 1500 1600 0.0000 63.9195 -10.5978 30.765709 23.5903 60.3448 64.7921 1500 1800 0.0000 63.9195 -10.5978 31.135446 23.9792 60.1913 64.7921 1500 2000 0.0000 63.9195 -10.5978 31.497775 24.3594 60.0385 64.7921 1500 2200 0.0000 63.9195 -10.5978 31.852986 24.7311 59.8863 64.7921 1500 2400 0.0000 63.9195 -10.5978 32.201348 25.0948 59.7349 64.7921 1500 2600 0.0000 63.9195 -10.5978 32.543112 25.4507 59.5841 64.7921 1500 2800 0.0000 63.9195 -10.5978 32.878519 25.7990 59.4341 64.7921 1500 3000 0.0000 63.9195 -10.5978 33.207792 26.1402 59.2849 64.7921 1500 3200 0.0000 63.9195 -10.5978 33.531142 26.4743 59.1364 64.7921 1500 3400 0.0000 63.9195 -10.5978 33.848769 26.8017 58.9887 64.7921 1500 3600 0.0000 63.9195 -10.5978 34.160862 27.1227 58.8419 64.7921 1500 3800 0.0000 63.9195 -10.5978 34.467600 27.4373 58.6958 64.7921 1500 4000 0.0000 63.9195 -10.5978 34.769152 27.7458 58.5506 64.7921 1500 4200 0.0000 63.9195 -10.5978 35.065681 28.0485 58.4062 64.7921 1500 4400 0.0000 63.9195 -10.5978 35.357338 28.3454 58.2627 64.7921 1500 4600 0.0000 63.9195 -10.5978 35.644269 28.6368 58.1200 64.7921 1500 4800 0.0000 63.9195 -10.5978 35.926613 28.9229 57.9782 64.7921 1500 5000 0.0000 63.9195 -10.5978 36.204502 29.2037 57.8372 64.7921 1500 5200 0.0000 63.9195 -10.5978 36.478063 29.4796 57.6971 64.7921 1500 5400 0.0000 63.9195 -10.5978 36.747416 29.7505 57.5579 64.7921 1500 5600 0.0000 63.9195 -10.5978 37.012675 30.0166 57.4196 64.7921 1500 5800 0.0000 63.9195 -10.5978 37.273952 30.2782 57.2821 64.7921 1500 6000 0.0000 63.9195 -10.5978 37.531351 30.5352 57.1455 64.7921 1500 6200 0.0000 63.9195 -10.5978 37.784974 30.7879 57.0098 64.7921 1500 6400 0.0000 63.9195 -10.5978 38.034918 31.0363 56.8749 64.7921 1500 6600 0.0000 63.9195 -10.5978 38.281276 31.2805 56.7409 64.7921 1500 6800 0.0000 63.9195 -10.5978 38.524136 31.5207 56.6079 64.7921 1500 7000 0.0000 63.9195 -10.5978 38.763586 31.7570 56.4756 64.7921 1500 7200 0.0000 63.9195 -10.5978 38.999707 31.9895 56.3443 64.7921 1500 7400 0.0000 63.9195 -10.5978 39.232578 32.2183 56.2138 64.7921 1500 7600 0.0000 63.9195 -10.5978 39.462277 32.4434 56.0842 64.7921 1500 7800 0.0000 63.9195 -10.5978 39.688877 32.6649 55.9554 64.7921 1500 8000 0.0000 63.9195 -10.5978 39.912449 32.8830 55.8275 64.7921 1500 8200 0.0000 63.9195 -10.5978 40.133061 33.0977 55.7005 64.7921 1500 8400 0.0000 63.9195 -10.5978 40.350779 33.3091 55.5744 64.7921 2000 0 0.0000 63.9356 -10.5705 27.498027 20.1438 61.5930 64.8035 2000 200 0.0000 63.9356 -10.5705 27.940276 20.6186 61.4357 64.8035 2000 400 0.0000 63.9356 -10.5705 28.371892 21.0808 61.2786 64.8035 2000 600 0.0000 63.9356 -10.5705 28.793396 21.5311 61.1219 64.8035 2000 800 0.0000 63.9356 -10.5705 29.205268 21.9699 60.9655 64.8035 2000 1000 0.0000 63.9356 -10.5705 29.607952 22.3978 60.8096 64.8035 2000 1200 0.0000 63.9356 -10.5705 30.001858 22.8154 60.6542 64.8035 2000 1400 0.0000 63.9356 -10.5705 30.387365 23.2229 60.4993 64.8035 2000 1600 0.0000 63.9356 -10.5705 30.764828 23.6210 60.3450 64.8035 2000 1800 0.0000 63.9356 -10.5705 31.134577 24.0099 60.1913 64.8035 2000 2000 0.0000 63.9356 -10.5705 31.496918 24.3901 60.0383 64.8035 2000 2200 0.0000 63.9356 -10.5705 31.852139 24.7619 59.8859 64.8035 2000 2400 0.0000 63.9356 -10.5705 32.200511 25.1255 59.7343 64.8035 2000 2600 0.0000 63.9356 -10.5705 32.542286 25.4814 59.5833 64.8035 2000 2800 0.0000 63.9356 -10.5705 32.877702 25.8298 59.4332 64.8035 2000 3000 0.0000 63.9356 -10.5705 33.206984 26.1709 59.2837 64.8035 2000 3200 0.0000 63.9356 -10.5705 33.530343 26.5051 59.1351 64.8035 2000 3400 0.0000 63.9356 -10.5705 33.847979 26.8325 58.9872 64.8035 2000 3600 0.0000 63.9356 -10.5705 34.160080 27.1534 58.8402 64.8035 2000 3800 0.0000 63.9356 -10.5705 34.466826 27.4680 58.6940 64.8035 2000 4000 0.0000 63.9356 -10.5705 34.768386 27.7766 58.5486 64.8035 2000 4200 0.0000 63.9356 -10.5705 35.064922 28.0792 58.4041 64.8035 2000 4400 0.0000 63.9356 -10.5705 35.356587 28.3762 58.2604 64.8035 2000 4600 0.0000 63.9356 -10.5705 35.643525 28.6676 58.1175 64.8035 2000 4800 0.0000 63.9356 -10.5705 35.925876 28.9536 57.9756 64.8035 2000 5000 0.0000 63.9356 -10.5705 36.203772 29.2345 57.8345 64.8035 2000 5200 0.0000 63.9356 -10.5705 36.477340 29.5103 57.6942 64.8035 2000 5400 0.0000 63.9356 -10.5705 36.746699 29.7812 57.5548 64.8035 2000 5600 0.0000 63.9356 -10.5705 37.011964 30.0473 57.4164 64.8035 2000 5800 0.0000 63.9356 -10.5705 37.273247 30.3089 57.2787 64.8035 2000 6000 0.0000 63.9356 -10.5705 37.530653 30.5659 57.1420 64.8035 2000 6200 0.0000 63.9356 -10.5705 37.784281 30.8185 57.0061 64.8035 2000 6400 0.0000 63.9356 -10.5705 38.034231 31.0669 56.8711 64.8035 2000 6600 0.0000 63.9356 -10.5705 38.280594 31.3112 56.7370 64.8035 2000 6800 0.0000 63.9356 -10.5705 38.523460 31.5514 56.6038 64.8035 2000 7000 0.0000 63.9356 -10.5705 38.762915 31.7877 56.4715 64.8035 2000 7200 0.0000 63.9356 -10.5705 38.999041 32.0201 56.3400 64.8035 2000 7400 0.0000 63.9356 -10.5705 39.231917 32.2489 56.2094 64.8035 2000 7600 0.0000 63.9356 -10.5705 39.461621 32.4739 56.0796 64.8035 2000 7800 0.0000 63.9356 -10.5705 39.688226 32.6955 55.9508 64.8035 2000 8000 0.0000 63.9356 -10.5705 39.911802 32.9136 55.8227 64.8035 2000 8200 0.0000 63.9356 -10.5705 40.132419 33.1283 55.6956 64.8035 2000 8400 0.0000 63.9356 -10.5705 40.350142 33.3397 55.5693 64.8035 2500 0 0.0000 63.9516 -10.5433 27.497012 20.1743 61.5950 64.8149 2500 200 0.0000 63.9516 -10.5433 27.939279 20.6491 61.4375 64.8149 2500 400 0.0000 63.9516 -10.5433 28.370911 21.1114 61.2802 64.8149 2500 600 0.0000 63.9516 -10.5433 28.792430 21.5616 61.1232 64.8149 2500 800 0.0000 63.9516 -10.5433 29.204317 22.0005 60.9666 64.8149 2500 1000 0.0000 63.9516 -10.5433 29.607015 22.4284 60.8105 64.8149 2500 1200 0.0000 63.9516 -10.5433 30.000934 22.8460 60.6549 64.8149 2500 1400 0.0000 63.9516 -10.5433 30.386455 23.2536 60.4998 64.8149 2500 1600 0.0000 63.9516 -10.5433 30.763930 23.6517 60.3453 64.8149 2500 1800 0.0000 63.9516 -10.5433 31.133691 24.0406 60.1914 64.8149 2500 2000 0.0000 63.9516 -10.5433 31.496043 24.4208 60.0381 64.8149 2500 2200 0.0000 63.9516 -10.5433 31.851275 24.7926 59.8856 64.8149 2500 2400 0.0000 63.9516 -10.5433 32.199658 25.1562 59.7337 64.8149 2500 2600 0.0000 63.9516 -10.5433 32.541443 25.5121 59.5826 64.8149 2500 2800 0.0000 63.9516 -10.5433 32.876869 25.8605 59.4322 64.8149 2500 3000 0.0000 63.9516 -10.5433 33.206160 26.2016 59.2826 64.8149 2500 3200 0.0000 63.9516 -10.5433 33.529528 26.5358 59.1338 64.8149 2500 3400 0.0000 63.9516 -10.5433 33.847173 26.8632 58.9858 64.8149 2500 3600 0.0000 63.9516 -10.5433 34.159283 27.1841 58.8386 64.8149 2500 3800 0.0000 63.9516 -10.5433 34.466037 27.4988 58.6922 64.8149 2500 4000 0.0000 63.9516 -10.5433 34.767605 27.8073 58.5466 64.8149 2500 4200 0.0000 63.9516 -10.5433 35.064149 28.1099 58.4019 64.8149 2500 4400 0.0000 63.9516 -10.5433 35.355821 28.4069 58.2581 64.8149 2500 4600 0.0000 63.9516 -10.5433 35.642767 28.6983 58.1151 64.8149 2500 4800 0.0000 63.9516 -10.5433 35.925125 28.9843 57.9730 64.8149 2500 5000 0.0000 63.9516 -10.5433 36.203028 29.2652 57.8317 64.8149 2500 5200 0.0000 63.9516 -10.5433 36.476602 29.5410 57.6913 64.8149 2500 5400 0.0000 63.9516 -10.5433 36.745968 29.8119 57.5518 64.8149 2500 5600 0.0000 63.9516 -10.5433 37.011240 30.0780 57.4131 64.8149 2500 5800 0.0000 63.9516 -10.5433 37.272529 30.3395 57.2754 64.8149 2500 6000 0.0000 63.9516 -10.5433 37.529940 30.5965 57.1385 64.8149 2500 6200 0.0000 63.9516 -10.5433 37.783575 30.8492 57.0025 64.8149 2500 6400 0.0000 63.9516 -10.5433 38.033530 31.0976 56.8674 64.8149 2500 6600 0.0000 63.9516 -10.5433 38.279899 31.3418 56.7331 64.8149 2500 6800 0.0000 63.9516 -10.5433 38.522770 31.5820 56.5998 64.8149 2500 7000 0.0000 63.9516 -10.5433 38.762231 31.8183 56.4673 64.8149 2500 7200 0.0000 63.9516 -10.5433 38.998362 32.0507 56.3357 64.8149 2500 7400 0.0000 63.9516 -10.5433 39.231244 32.2794 56.2049 64.8149 2500 7600 0.0000 63.9516 -10.5433 39.460952 32.5045 56.0751 64.8149 2500 7800 0.0000 63.9516 -10.5433 39.687562 32.7261 55.9461 64.8149 2500 8000 0.0000 63.9516 -10.5433 39.911143 32.9441 55.8180 64.8149 2500 8200 0.0000 63.9516 -10.5433 40.131765 33.1588 55.6907 64.8149 2500 8400 0.0000 63.9516 -10.5433 40.349492 33.3702 55.5643 64.8149 3000 0 0.0000 63.9676 -10.5160 27.495976 20.2048 61.5970 64.8263 3000 200 0.0000 63.9676 -10.5160 27.938261 20.6796 61.4392 64.8263 3000 400 0.0000 63.9676 -10.5160 28.369910 21.1419 61.2817 64.8263 3000 600 0.0000 63.9676 -10.5160 28.791445 21.5922 61.1245 64.8263 3000 800 0.0000 63.9676 -10.5160 29.203347 22.0311 60.9677 64.8263 3000 1000 0.0000 63.9676 -10.5160 29.606060 22.4590 60.8114 64.8263 3000 1200 0.0000 63.9676 -10.5160 29.999993 22.8766 60.6555 64.8263 3000 1400 0.0000 63.9676 -10.5160 30.385526 23.2842 60.5002 64.8263 3000 1600 0.0000 63.9676 -10.5160 30.763014 23.6823 60.3455 64.8263 3000 1800 0.0000 63.9676 -10.5160 31.132787 24.0713 60.1914 64.8263 3000 2000 0.0000 63.9676 -10.5160 31.495151 24.4515 60.0380 64.8263 3000 2200 0.0000 63.9676 -10.5160 31.850395 24.8233 59.8852 64.8263 3000 2400 0.0000 63.9676 -10.5160 32.198788 25.1869 59.7332 64.8263 3000 2600 0.0000 63.9676 -10.5160 32.540583 25.5428 59.5818 64.8263 3000 2800 0.0000 63.9676 -10.5160 32.876019 25.8912 59.4313 64.8263 3000 3000 0.0000 63.9676 -10.5160 33.205320 26.2323 59.2815 64.8263 3000 3200 0.0000 63.9676 -10.5160 33.528698 26.5665 59.1325 64.8263 3000 3400 0.0000 63.9676 -10.5160 33.846351 26.8939 58.9843 64.8263 3000 3600 0.0000 63.9676 -10.5160 34.158470 27.2149 58.8369 64.8263 3000 3800 0.0000 63.9676 -10.5160 34.465233 27.5295 58.6904 64.8263 3000 4000 0.0000 63.9676 -10.5160 34.766809 27.8380 58.5447 64.8263 3000 4200 0.0000 63.9676 -10.5160 35.063361 28.1407 58.3998 64.8263 3000 4400 0.0000 63.9676 -10.5160 35.355041 28.4376 58.2558 64.8263 3000 4600 0.0000 63.9676 -10.5160 35.641994 28.7290 58.1126 64.8263 3000 4800 0.0000 63.9676 -10.5160 35.924359 29.0150 57.9704 64.8263 3000 5000 0.0000 63.9676 -10.5160 36.202270 29.2959 57.8289 64.8263 3000 5200 0.0000 63.9676 -10.5160 36.475851 29.5717 57.6884 64.8263 3000 5400 0.0000 63.9676 -10.5160 36.745223 29.8426 57.5487 64.8263 3000 5600 0.0000 63.9676 -10.5160 37.010502 30.1087 57.4099 64.8263 3000 5800 0.0000 63.9676 -10.5160 37.271797 30.3702 57.2720 64.8263 3000 6000 0.0000 63.9676 -10.5160 37.529215 30.6272 57.1350 64.8263 3000 6200 0.0000 63.9676 -10.5160 37.782855 30.8798 56.9989 64.8263 3000 6400 0.0000 63.9676 -10.5160 38.032817 31.1282 56.8636 64.8263 3000 6600 0.0000 63.9676 -10.5160 38.279191 31.3724 56.7292 64.8263 3000 6800 0.0000 63.9676 -10.5160 38.522068 31.6126 56.5957 64.8263 3000 7000 0.0000 63.9676 -10.5160 38.761534 31.8489 56.4631 64.8263 3000 7200 0.0000 63.9676 -10.5160 38.997670 32.0813 56.3314 64.8263 3000 7400 0.0000 63.9676 -10.5160 39.230557 32.3100 56.2005 64.8263 3000 7600 0.0000 63.9676 -10.5160 39.460271 32.5351 56.0705 64.8263 3000 7800 0.0000 63.9676 -10.5160 39.686886 32.7566 55.9414 64.8263 3000 8000 0.0000 63.9676 -10.5160 39.910472 32.9747 55.8132 64.8263 3000 8200 0.0000 63.9676 -10.5160 40.131098 33.1893 55.6858 64.8263 3000 8400 0.0000 63.9676 -10.5160 40.348830 33.4007 55.5593 64.8263 3500 0 0.0000 63.9837 -10.4888 27.494920 20.2352 61.5991 64.8377 3500 200 0.0000 63.9837 -10.4888 27.937223 20.7101 61.4410 64.8377 3500 400 0.0000 63.9837 -10.4888 28.368889 21.1724 61.2833 64.8377 3500 600 0.0000 63.9837 -10.4888 28.790441 21.6227 61.1258 64.8377 3500 800 0.0000 63.9837 -10.4888 29.202358 22.0616 60.9688 64.8377 3500 1000 0.0000 63.9837 -10.4888 29.605086 22.4896 60.8122 64.8377 3500 1200 0.0000 63.9837 -10.4888 29.999033 22.9072 60.6562 64.8377 3500 1400 0.0000 63.9837 -10.4888 30.384580 23.3149 60.5007 64.8377 3500 1600 0.0000 63.9837 -10.4888 30.762081 23.7130 60.3457 64.8377 3500 1800 0.0000 63.9837 -10.4888 31.131866 24.1019 60.1914 64.8377 3500 2000 0.0000 63.9837 -10.4888 31.494242 24.4821 60.0378 64.8377 3500 2200 0.0000 63.9837 -10.4888 31.849497 24.8539 59.8848 64.8377 3500 2400 0.0000 63.9837 -10.4888 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0.0000 63.9837 -10.4888 36.744464 29.8732 57.5457 64.8377 3500 5600 0.0000 63.9837 -10.4888 37.009750 30.1394 57.4068 64.8377 3500 5800 0.0000 63.9837 -10.4888 37.271052 30.4009 57.2687 64.8377 3500 6000 0.0000 63.9837 -10.4888 37.528475 30.6579 57.1315 64.8377 3500 6200 0.0000 63.9837 -10.4888 37.782123 30.9105 56.9953 64.8377 3500 6400 0.0000 63.9837 -10.4888 38.032090 31.1588 56.8599 64.8377 3500 6600 0.0000 63.9837 -10.4888 38.278470 31.4030 56.7254 64.8377 3500 6800 0.0000 63.9837 -10.4888 38.521353 31.6432 56.5917 64.8377 3500 7000 0.0000 63.9837 -10.4888 38.760824 31.8795 56.4590 64.8377 3500 7200 0.0000 63.9837 -10.4888 38.996966 32.1119 56.3271 64.8377 3500 7400 0.0000 63.9837 -10.4888 39.229859 32.3406 56.1961 64.8377 3500 7600 0.0000 63.9837 -10.4888 39.459578 32.5656 56.0660 64.8377 3500 7800 0.0000 63.9837 -10.4888 39.686197 32.7871 55.9368 64.8377 3500 8000 0.0000 63.9837 -10.4888 39.909789 33.0052 55.8084 64.8377 3500 8200 0.0000 63.9837 -10.4888 40.130420 33.2198 55.6809 64.8377 3500 8400 0.0000 63.9837 -10.4888 40.348157 33.4312 55.5542 64.8377 4000 0 0.0000 63.9997 -10.4615 27.493844 20.2656 61.6011 64.8491 4000 200 0.0000 63.9997 -10.4615 27.936165 20.7406 61.4428 64.8491 4000 400 0.0000 63.9997 -10.4615 28.367849 21.2029 61.2848 64.8491 4000 600 0.0000 63.9997 -10.4615 28.789417 21.6532 61.1272 64.8491 4000 800 0.0000 63.9997 -10.4615 29.201351 22.0922 60.9699 64.8491 4000 1000 0.0000 63.9997 -10.4615 29.604093 22.5202 60.8131 64.8491 4000 1200 0.0000 63.9997 -10.4615 29.998054 22.9378 60.6568 64.8491 4000 1400 0.0000 63.9997 -10.4615 30.383615 23.3455 60.5011 64.8491 4000 1600 0.0000 63.9997 -10.4615 30.761130 23.7436 60.3460 64.8491 4000 1800 0.0000 63.9997 -10.4615 31.130927 24.1326 60.1915 64.8491 4000 2000 0.0000 63.9997 -10.4615 31.493315 24.5128 60.0376 64.8491 4000 2200 0.0000 63.9997 -10.4615 31.848582 24.8846 59.8845 64.8491 4000 2400 0.0000 63.9997 -10.4615 32.196998 25.2483 59.7321 64.8491 4000 2600 0.0000 63.9997 -10.4615 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0.0000 63.9997 -10.4615 37.008984 30.1700 57.4036 64.8491 4000 5800 0.0000 63.9997 -10.4615 37.270293 30.4315 57.2654 64.8491 4000 6000 0.0000 63.9997 -10.4615 37.527723 30.6885 57.1281 64.8491 4000 6200 0.0000 63.9997 -10.4615 37.781376 30.9411 56.9917 64.8491 4000 6400 0.0000 63.9997 -10.4615 38.031350 31.1894 56.8561 64.8491 4000 6600 0.0000 63.9997 -10.4615 38.277736 31.4337 56.7215 64.8491 4000 6800 0.0000 63.9997 -10.4615 38.520625 31.6738 56.5877 64.8491 4000 7000 0.0000 63.9997 -10.4615 38.760102 31.9101 56.4548 64.8491 4000 7200 0.0000 63.9997 -10.4615 38.996249 32.1425 56.3228 64.8491 4000 7400 0.0000 63.9997 -10.4615 39.229147 32.3712 56.1917 64.8491 4000 7600 0.0000 63.9997 -10.4615 39.458872 32.5962 56.0615 64.8491 4000 7800 0.0000 63.9997 -10.4615 39.685497 32.8177 55.9321 64.8491 4000 8000 0.0000 63.9997 -10.4615 39.909093 33.0357 55.8036 64.8491 4000 8200 0.0000 63.9997 -10.4615 40.129729 33.2503 55.6760 64.8491 4000 8400 0.0000 63.9997 -10.4615 40.347471 33.4617 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32.873373 25.9832 59.4286 64.8606 4500 3000 0.0000 64.0158 -10.4343 33.202705 26.3244 59.2782 64.8606 4500 3200 0.0000 64.0158 -10.4343 33.526111 26.6586 59.1287 64.8606 4500 3400 0.0000 64.0158 -10.4343 33.843793 26.9860 58.9800 64.8606 4500 3600 0.0000 64.0158 -10.4343 34.155940 27.3069 58.8321 64.8606 4500 3800 0.0000 64.0158 -10.4343 34.462729 27.6216 58.6850 64.8606 4500 4000 0.0000 64.0158 -10.4343 34.764332 27.9301 58.5388 64.8606 4500 4200 0.0000 64.0158 -10.4343 35.060908 28.2327 58.3935 64.8606 4500 4400 0.0000 64.0158 -10.4343 35.352612 28.5296 58.2490 64.8606 4500 4600 0.0000 64.0158 -10.4343 35.639589 28.8210 58.1053 64.8606 4500 4800 0.0000 64.0158 -10.4343 35.921977 29.1071 57.9626 64.8606 4500 5000 0.0000 64.0158 -10.4343 36.199910 29.3879 57.8207 64.8606 4500 5200 0.0000 64.0158 -10.4343 36.473512 29.6637 57.6797 64.8606 4500 5400 0.0000 64.0158 -10.4343 36.742906 29.9345 57.5396 64.8606 4500 5600 0.0000 64.0158 -10.4343 37.008205 30.2006 57.4004 64.8606 4500 5800 0.0000 64.0158 -10.4343 37.269520 30.4621 57.2621 64.8606 4500 6000 0.0000 64.0158 -10.4343 37.526957 30.7191 57.1246 64.8606 4500 6200 0.0000 64.0158 -10.4343 37.780617 30.9717 56.9881 64.8606 4500 6400 0.0000 64.0158 -10.4343 38.030597 31.2200 56.8524 64.8606 4500 6600 0.0000 64.0158 -10.4343 38.276989 31.4642 56.7176 64.8606 4500 6800 0.0000 64.0158 -10.4343 38.519884 31.7044 56.5837 64.8606 4500 7000 0.0000 64.0158 -10.4343 38.759367 31.9406 56.4507 64.8606 4500 7200 0.0000 64.0158 -10.4343 38.995520 32.1730 56.3186 64.8606 4500 7400 0.0000 64.0158 -10.4343 39.228424 32.4017 56.1873 64.8606 4500 7600 0.0000 64.0158 -10.4343 39.458154 32.6267 56.0570 64.8606 4500 7800 0.0000 64.0158 -10.4343 39.684784 32.8482 55.9275 64.8606 4500 8000 0.0000 64.0158 -10.4343 39.908385 33.0662 55.7989 64.8606 4500 8200 0.0000 64.0158 -10.4343 40.129027 33.2808 55.6711 64.8606 4500 8400 0.0000 64.0158 -10.4343 40.346774 33.4922 55.5442 64.8606 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.000 ================================================ FILE: tests/test_data/cropA/geometry/20180412-20180518_VV_8rlks_base.par ================================================ initial_baseline(TCN): 0.2932894 49.7232694 -9.0026781 m m m initial_baseline_rate: 0.0000000 0.0835121 -0.0035232 m/s m/s m/s precision_baseline(TCN): 0.0000000 49.7313924 -8.9840098 m m m precision_baseline_rate: 0.0000000 0.0801545 -0.0014154 m/s m/s m/s unwrap_phase_constant: 0.00009 radians ================================================ FILE: tests/test_data/cropA/geometry/20180412-20180518_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): 0.293 49.723 -9.003 orbit baseline rate (TCN) (m/s): 0.000e+00 8.351e-02 -3.523e-03 baseline vector (TCN) (m): 0.000 49.731 -8.984 baseline rate (TCN) (m/s): 0.000e+00 8.015e-02 -1.415e-03 SLC-1 center baseline length (m): 50.5364 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 0.0000 48.9832 -8.9708 27.501881 14.6622 47.5903 49.7979 0 200 0.0000 48.9832 -8.9708 27.944066 15.0291 47.4757 49.7979 0 400 0.0000 48.9832 -8.9708 28.375620 15.3862 47.3612 49.7979 0 600 0.0000 48.9832 -8.9708 28.797066 15.7342 47.2467 49.7979 0 800 0.0000 48.9832 -8.9708 29.208882 16.0734 47.1324 49.7979 0 1000 0.0000 48.9832 -8.9708 29.611513 16.4042 47.0183 49.7979 0 1200 0.0000 48.9832 -8.9708 30.005367 16.7270 46.9044 49.7979 0 1400 0.0000 48.9832 -8.9708 30.390826 17.0422 46.7909 49.7979 0 1600 0.0000 48.9832 -8.9708 30.768243 17.3500 46.6776 49.7979 0 1800 0.0000 48.9832 -8.9708 31.137946 17.6509 46.5647 49.7979 0 2000 0.0000 48.9832 -8.9708 31.500244 17.9449 46.4521 49.7979 0 2200 0.0000 48.9832 -8.9708 31.855425 18.2326 46.3400 49.7979 0 2400 0.0000 48.9832 -8.9708 32.203756 18.5139 46.2283 49.7979 0 2600 0.0000 48.9832 -8.9708 32.545493 18.7893 46.1170 49.7979 0 2800 0.0000 48.9832 -8.9708 32.880872 19.0590 46.0063 49.7979 0 3000 0.0000 48.9832 -8.9708 33.210118 19.3230 45.8960 49.7979 0 3200 0.0000 48.9832 -8.9708 33.533443 19.5817 45.7862 49.7979 0 3400 0.0000 48.9832 -8.9708 33.851045 19.8352 45.6770 49.7979 0 3600 0.0000 48.9832 -8.9708 34.163114 20.0837 45.5683 49.7979 0 3800 0.0000 48.9832 -8.9708 34.469829 20.3273 45.4601 49.7979 0 4000 0.0000 48.9832 -8.9708 34.771360 20.5663 45.3525 49.7979 0 4200 0.0000 48.9832 -8.9708 35.067866 20.8007 45.2455 49.7979 0 4400 0.0000 48.9832 -8.9708 35.359502 21.0308 45.1390 49.7979 0 4600 0.0000 48.9832 -8.9708 35.646413 21.2565 45.0331 49.7979 0 4800 0.0000 48.9832 -8.9708 35.928737 21.4782 44.9278 49.7979 0 5000 0.0000 48.9832 -8.9708 36.206607 21.6958 44.8232 49.7979 0 5200 0.0000 48.9832 -8.9708 36.480149 21.9096 44.7191 49.7979 0 5400 0.0000 48.9832 -8.9708 36.749483 22.1195 44.6156 49.7979 0 5600 0.0000 48.9832 -8.9708 37.014725 22.3258 44.5127 49.7979 0 5800 0.0000 48.9832 -8.9708 37.275984 22.5286 44.4104 49.7979 0 6000 0.0000 48.9832 -8.9708 37.533367 22.7278 44.3088 49.7979 0 6200 0.0000 48.9832 -8.9708 37.786973 22.9237 44.2078 49.7979 0 6400 0.0000 48.9832 -8.9708 38.036901 23.1164 44.1073 49.7979 0 6600 0.0000 48.9832 -8.9708 38.283243 23.3058 44.0075 49.7979 0 6800 0.0000 48.9832 -8.9708 38.526088 23.4921 43.9084 49.7979 0 7000 0.0000 48.9832 -8.9708 38.765522 23.6754 43.8098 49.7979 0 7200 0.0000 48.9832 -8.9708 39.001629 23.8557 43.7119 49.7979 0 7400 0.0000 48.9832 -8.9708 39.234486 24.0332 43.6146 49.7979 0 7600 0.0000 48.9832 -8.9708 39.464171 24.2078 43.5179 49.7979 0 7800 0.0000 48.9832 -8.9708 39.690757 24.3797 43.4218 49.7979 0 8000 0.0000 48.9832 -8.9708 39.914315 24.5490 43.3264 49.7979 0 8200 0.0000 48.9832 -8.9708 40.134914 24.7156 43.2315 49.7979 0 8400 0.0000 48.9832 -8.9708 40.352620 24.8797 43.1373 49.7979 500 0 0.0000 49.1480 -8.9737 27.500948 14.7350 47.7380 49.9605 500 200 0.0000 49.1480 -8.9737 27.943149 15.1030 47.6229 49.9605 500 400 0.0000 49.1480 -8.9737 28.374718 15.4612 47.5078 49.9605 500 600 0.0000 49.1480 -8.9737 28.796177 15.8103 47.3928 49.9605 500 800 0.0000 49.1480 -8.9737 29.208007 16.1505 47.2779 49.9605 500 1000 0.0000 49.1480 -8.9737 29.610651 16.4824 47.1632 49.9605 500 1200 0.0000 49.1480 -8.9737 30.004518 16.8062 47.0488 49.9605 500 1400 0.0000 49.1480 -8.9737 30.389988 17.1223 46.9347 49.9605 500 1600 0.0000 49.1480 -8.9737 30.767416 17.4311 46.8209 49.9605 500 1800 0.0000 49.1480 -8.9737 31.137130 17.7329 46.7074 49.9605 500 2000 0.0000 49.1480 -8.9737 31.499439 18.0279 46.5944 49.9605 500 2200 0.0000 49.1480 -8.9737 31.854629 18.3164 46.4817 49.9605 500 2400 0.0000 49.1480 -8.9737 32.202970 18.5987 46.3695 49.9605 500 2600 0.0000 49.1480 -8.9737 32.544716 18.8749 46.2578 49.9605 500 2800 0.0000 49.1480 -8.9737 32.880104 19.1453 46.1465 49.9605 500 3000 0.0000 49.1480 -8.9737 33.209359 19.4102 46.0357 49.9605 500 3200 0.0000 49.1480 -8.9737 33.532692 19.6697 45.9254 49.9605 500 3400 0.0000 49.1480 -8.9737 33.850302 19.9240 45.8157 49.9605 500 3600 0.0000 49.1480 -8.9737 34.162379 20.1732 45.7065 49.9605 500 3800 0.0000 49.1480 -8.9737 34.469102 20.4176 45.5978 49.9605 500 4000 0.0000 49.1480 -8.9737 34.770639 20.6573 45.4898 49.9605 500 4200 0.0000 49.1480 -8.9737 35.067153 20.8924 45.3823 49.9605 500 4400 0.0000 49.1480 -8.9737 35.358795 21.1232 45.2753 49.9605 500 4600 0.0000 49.1480 -8.9737 35.645713 21.3496 45.1690 49.9605 500 4800 0.0000 49.1480 -8.9737 35.928043 21.5720 45.0632 49.9605 500 5000 0.0000 49.1480 -8.9737 36.205920 21.7902 44.9581 49.9605 500 5200 0.0000 49.1480 -8.9737 36.479468 22.0046 44.8535 49.9605 500 5400 0.0000 49.1480 -8.9737 36.748808 22.2153 44.7496 49.9605 500 5600 0.0000 49.1480 -8.9737 37.014055 22.4222 44.6463 49.9605 500 5800 0.0000 49.1480 -8.9737 37.275320 22.6255 44.5436 49.9605 500 6000 0.0000 49.1480 -8.9737 37.532708 22.8254 44.4415 49.9605 500 6200 0.0000 49.1480 -8.9737 37.786320 23.0219 44.3400 49.9605 500 6400 0.0000 49.1480 -8.9737 38.036253 23.2151 44.2392 49.9605 500 6600 0.0000 49.1480 -8.9737 38.282600 23.4051 44.1389 49.9605 500 6800 0.0000 49.1480 -8.9737 38.525450 23.5920 44.0393 49.9605 500 7000 0.0000 49.1480 -8.9737 38.764890 23.7758 43.9404 49.9605 500 7200 0.0000 49.1480 -8.9737 39.001001 23.9567 43.8420 49.9605 500 7400 0.0000 49.1480 -8.9737 39.233863 24.1347 43.7443 49.9605 500 7600 0.0000 49.1480 -8.9737 39.463552 24.3098 43.6472 49.9605 500 7800 0.0000 49.1480 -8.9737 39.690143 24.4822 43.5507 49.9605 500 8000 0.0000 49.1480 -8.9737 39.913705 24.6520 43.4549 49.9605 500 8200 0.0000 49.1480 -8.9737 40.134309 24.8191 43.3596 49.9605 500 8400 0.0000 49.1480 -8.9737 40.352019 24.9837 43.2650 49.9605 1000 0 0.0000 49.3127 -8.9766 27.499995 14.8077 47.8858 50.1231 1000 200 0.0000 49.3127 -8.9766 27.942212 15.1768 47.7701 50.1231 1000 400 0.0000 49.3127 -8.9766 28.373796 15.5362 47.6544 50.1231 1000 600 0.0000 49.3127 -8.9766 28.795270 15.8863 47.5388 50.1231 1000 800 0.0000 49.3127 -8.9766 29.207113 16.2276 47.4234 50.1231 1000 1000 0.0000 49.3127 -8.9766 29.609770 16.5605 47.3082 50.1231 1000 1200 0.0000 49.3127 -8.9766 30.003649 16.8853 47.1932 50.1231 1000 1400 0.0000 49.3127 -8.9766 30.389132 17.2025 47.0786 50.1231 1000 1600 0.0000 49.3127 -8.9766 30.766571 17.5122 46.9642 50.1231 1000 1800 0.0000 49.3127 -8.9766 31.136297 17.8149 46.8502 50.1231 1000 2000 0.0000 49.3127 -8.9766 31.498616 18.1108 46.7366 50.1231 1000 2200 0.0000 49.3127 -8.9766 31.853816 18.4002 46.6235 50.1231 1000 2400 0.0000 49.3127 -8.9766 32.202167 18.6833 46.5107 50.1231 1000 2600 0.0000 49.3127 -8.9766 32.543923 18.9604 46.3985 50.1231 1000 2800 0.0000 49.3127 -8.9766 32.879320 19.2317 46.2867 50.1231 1000 3000 0.0000 49.3127 -8.9766 33.208583 19.4974 46.1754 50.1231 1000 3200 0.0000 49.3127 -8.9766 33.531925 19.7577 46.0646 50.1231 1000 3400 0.0000 49.3127 -8.9766 33.849543 20.0127 45.9544 50.1231 1000 3600 0.0000 49.3127 -8.9766 34.161628 20.2627 45.8447 50.1231 1000 3800 0.0000 49.3127 -8.9766 34.468358 20.5079 45.7356 50.1231 1000 4000 0.0000 49.3127 -8.9766 34.769903 20.7483 45.6270 50.1231 1000 4200 0.0000 49.3127 -8.9766 35.066424 20.9841 45.5190 50.1231 1000 4400 0.0000 49.3127 -8.9766 35.358074 21.2156 45.4116 50.1231 1000 4600 0.0000 49.3127 -8.9766 35.644998 21.4427 45.3048 50.1231 1000 4800 0.0000 49.3127 -8.9766 35.927335 21.6657 45.1986 50.1231 1000 5000 0.0000 49.3127 -8.9766 36.205218 21.8847 45.0930 50.1231 1000 5200 0.0000 49.3127 -8.9766 36.478772 22.0997 44.9880 50.1231 1000 5400 0.0000 49.3127 -8.9766 36.748119 22.3110 44.8836 50.1231 1000 5600 0.0000 49.3127 -8.9766 37.013372 22.5185 44.7799 50.1231 1000 5800 0.0000 49.3127 -8.9766 37.274643 22.7225 44.6767 50.1231 1000 6000 0.0000 49.3127 -8.9766 37.532037 22.9229 44.5742 50.1231 1000 6200 0.0000 49.3127 -8.9766 37.785654 23.1200 44.4723 50.1231 1000 6400 0.0000 49.3127 -8.9766 38.035592 23.3138 44.3710 50.1231 1000 6600 0.0000 49.3127 -8.9766 38.281945 23.5044 44.2703 50.1231 1000 6800 0.0000 49.3127 -8.9766 38.524800 23.6918 44.1703 50.1231 1000 7000 0.0000 49.3127 -8.9766 38.764244 23.8762 44.0709 50.1231 1000 7200 0.0000 49.3127 -8.9766 39.000360 24.0576 43.9722 50.1231 1000 7400 0.0000 49.3127 -8.9766 39.233227 24.2361 43.8740 50.1231 1000 7600 0.0000 49.3127 -8.9766 39.462921 24.4118 43.7765 50.1231 1000 7800 0.0000 49.3127 -8.9766 39.689516 24.5848 43.6796 50.1231 1000 8000 0.0000 49.3127 -8.9766 39.913083 24.7550 43.5834 50.1231 1000 8200 0.0000 49.3127 -8.9766 40.133691 24.9226 43.4877 50.1231 1000 8400 0.0000 49.3127 -8.9766 40.351405 25.0877 43.3927 50.1231 1500 0 0.0000 49.4775 -8.9795 27.499021 14.8804 48.0335 50.2857 1500 200 0.0000 49.4775 -8.9795 27.941254 15.2506 47.9172 50.2857 1500 400 0.0000 49.4775 -8.9795 28.372854 15.6112 47.8010 50.2857 1500 600 0.0000 49.4775 -8.9795 28.794343 15.9624 47.6849 50.2857 1500 800 0.0000 49.4775 -8.9795 29.206200 16.3047 47.5689 50.2857 1500 1000 0.0000 49.4775 -8.9795 29.608871 16.6386 47.4531 50.2857 1500 1200 0.0000 49.4775 -8.9795 30.002763 16.9645 47.3376 50.2857 1500 1400 0.0000 49.4775 -8.9795 30.388258 17.2826 47.2224 50.2857 1500 1600 0.0000 49.4775 -8.9795 30.765709 17.5933 47.1075 50.2857 1500 1800 0.0000 49.4775 -8.9795 31.135446 17.8969 46.9930 50.2857 1500 2000 0.0000 49.4775 -8.9795 31.497775 18.1937 46.8789 50.2857 1500 2200 0.0000 49.4775 -8.9795 31.852986 18.4840 46.7652 50.2857 1500 2400 0.0000 49.4775 -8.9795 32.201348 18.7680 46.6520 50.2857 1500 2600 0.0000 49.4775 -8.9795 32.543112 19.0460 46.5392 50.2857 1500 2800 0.0000 49.4775 -8.9795 32.878519 19.3181 46.4269 50.2857 1500 3000 0.0000 49.4775 -8.9795 33.207792 19.5846 46.3151 50.2857 1500 3200 0.0000 49.4775 -8.9795 33.531142 19.8456 46.2039 50.2857 1500 3400 0.0000 49.4775 -8.9795 33.848769 20.1015 46.0931 50.2857 1500 3600 0.0000 49.4775 -8.9795 34.160862 20.3522 45.9830 50.2857 1500 3800 0.0000 49.4775 -8.9795 34.467600 20.5981 45.8734 50.2857 1500 4000 0.0000 49.4775 -8.9795 34.769152 20.8393 45.7643 50.2857 1500 4200 0.0000 49.4775 -8.9795 35.065681 21.0758 45.6558 50.2857 1500 4400 0.0000 49.4775 -8.9795 35.357338 21.3080 45.5480 50.2857 1500 4600 0.0000 49.4775 -8.9795 35.644269 21.5358 45.4407 50.2857 1500 4800 0.0000 49.4775 -8.9795 35.926613 21.7595 45.3340 50.2857 1500 5000 0.0000 49.4775 -8.9795 36.204502 21.9791 45.2280 50.2857 1500 5200 0.0000 49.4775 -8.9795 36.478063 22.1948 45.1225 50.2857 1500 5400 0.0000 49.4775 -8.9795 36.747416 22.4067 45.0177 50.2857 1500 5600 0.0000 49.4775 -8.9795 37.012675 22.6148 44.9134 50.2857 1500 5800 0.0000 49.4775 -8.9795 37.273952 22.8194 44.8099 50.2857 1500 6000 0.0000 49.4775 -8.9795 37.531351 23.0205 44.7069 50.2857 1500 6200 0.0000 49.4775 -8.9795 37.784974 23.2182 44.6045 50.2857 1500 6400 0.0000 49.4775 -8.9795 38.034918 23.4125 44.5028 50.2857 1500 6600 0.0000 49.4775 -8.9795 38.281276 23.6036 44.4018 50.2857 1500 6800 0.0000 49.4775 -8.9795 38.524136 23.7916 44.3013 50.2857 1500 7000 0.0000 49.4775 -8.9795 38.763586 23.9766 44.2015 50.2857 1500 7200 0.0000 49.4775 -8.9795 38.999707 24.1585 44.1023 50.2857 1500 7400 0.0000 49.4775 -8.9795 39.232578 24.3376 44.0038 50.2857 1500 7600 0.0000 49.4775 -8.9795 39.462277 24.5138 43.9058 50.2857 1500 7800 0.0000 49.4775 -8.9795 39.688877 24.6872 43.8085 50.2857 1500 8000 0.0000 49.4775 -8.9795 39.912449 24.8580 43.7119 50.2857 1500 8200 0.0000 49.4775 -8.9795 40.133061 25.0261 43.6158 50.2857 1500 8400 0.0000 49.4775 -8.9795 40.350779 25.1917 43.5204 50.2857 2000 0 0.0000 49.6423 -8.9824 27.498027 14.9530 48.1813 50.4484 2000 200 0.0000 49.6423 -8.9824 27.940276 15.3245 48.0644 50.4484 2000 400 0.0000 49.6423 -8.9824 28.371892 15.6861 47.9476 50.4484 2000 600 0.0000 49.6423 -8.9824 28.793396 16.0384 47.8309 50.4484 2000 800 0.0000 49.6423 -8.9824 29.205268 16.3818 47.7144 50.4484 2000 1000 0.0000 49.6423 -8.9824 29.607952 16.7168 47.5981 50.4484 2000 1200 0.0000 49.6423 -8.9824 30.001858 17.0436 47.4820 50.4484 2000 1400 0.0000 49.6423 -8.9824 30.387365 17.3627 47.3663 50.4484 2000 1600 0.0000 49.6423 -8.9824 30.764828 17.6744 47.2509 50.4484 2000 1800 0.0000 49.6423 -8.9824 31.134577 17.9789 47.1358 50.4484 2000 2000 0.0000 49.6423 -8.9824 31.496918 18.2766 47.0212 50.4484 2000 2200 0.0000 49.6423 -8.9824 31.852139 18.5678 46.9070 50.4484 2000 2400 0.0000 49.6423 -8.9824 32.200511 18.8527 46.7932 50.4484 2000 2600 0.0000 49.6423 -8.9824 32.542286 19.1315 46.6799 50.4484 2000 2800 0.0000 49.6423 -8.9824 32.877702 19.4044 46.5671 50.4484 2000 3000 0.0000 49.6423 -8.9824 33.206984 19.6717 46.4549 50.4484 2000 3200 0.0000 49.6423 -8.9824 33.530343 19.9336 46.3431 50.4484 2000 3400 0.0000 49.6423 -8.9824 33.847979 20.1902 46.2319 50.4484 2000 3600 0.0000 49.6423 -8.9824 34.160080 20.4417 46.1212 50.4484 2000 3800 0.0000 49.6423 -8.9824 34.466826 20.6883 46.0111 50.4484 2000 4000 0.0000 49.6423 -8.9824 34.768386 20.9302 45.9016 50.4484 2000 4200 0.0000 49.6423 -8.9824 35.064922 21.1675 45.7927 50.4484 2000 4400 0.0000 49.6423 -8.9824 35.356587 21.4003 45.6843 50.4484 2000 4600 0.0000 49.6423 -8.9824 35.643525 21.6289 45.5766 50.4484 2000 4800 0.0000 49.6423 -8.9824 35.925876 21.8532 45.4694 50.4484 2000 5000 0.0000 49.6423 -8.9824 36.203772 22.0735 45.3629 50.4484 2000 5200 0.0000 49.6423 -8.9824 36.477340 22.2898 45.2570 50.4484 2000 5400 0.0000 49.6423 -8.9824 36.746699 22.5023 45.1517 50.4484 2000 5600 0.0000 49.6423 -8.9824 37.011964 22.7111 45.0470 50.4484 2000 5800 0.0000 49.6423 -8.9824 37.273247 22.9163 44.9430 50.4484 2000 6000 0.0000 49.6423 -8.9824 37.530653 23.1180 44.8396 50.4484 2000 6200 0.0000 49.6423 -8.9824 37.784281 23.3163 44.7368 50.4484 2000 6400 0.0000 49.6423 -8.9824 38.034231 23.5112 44.6347 50.4484 2000 6600 0.0000 49.6423 -8.9824 38.280594 23.7029 44.5332 50.4484 2000 6800 0.0000 49.6423 -8.9824 38.523460 23.8915 44.4323 50.4484 2000 7000 0.0000 49.6423 -8.9824 38.762915 24.0769 44.3321 50.4484 2000 7200 0.0000 49.6423 -8.9824 38.999041 24.2594 44.2325 50.4484 2000 7400 0.0000 49.6423 -8.9824 39.231917 24.4390 44.1335 50.4484 2000 7600 0.0000 49.6423 -8.9824 39.461621 24.6158 44.0352 50.4484 2000 7800 0.0000 49.6423 -8.9824 39.688226 24.7897 43.9375 50.4484 2000 8000 0.0000 49.6423 -8.9824 39.911802 24.9610 43.8404 50.4484 2000 8200 0.0000 49.6423 -8.9824 40.132419 25.1296 43.7440 50.4484 2000 8400 0.0000 49.6423 -8.9824 40.350142 25.2957 43.6482 50.4484 2500 0 0.0000 49.8070 -8.9853 27.497012 15.0256 48.3290 50.6110 2500 200 0.0000 49.8070 -8.9853 27.939279 15.3982 48.2116 50.6110 2500 400 0.0000 49.8070 -8.9853 28.370911 15.7610 48.0942 50.6110 2500 600 0.0000 49.8070 -8.9853 28.792430 16.1144 47.9770 50.6110 2500 800 0.0000 49.8070 -8.9853 29.204317 16.4589 47.8599 50.6110 2500 1000 0.0000 49.8070 -8.9853 29.607015 16.7949 47.7430 50.6110 2500 1200 0.0000 49.8070 -8.9853 30.000934 17.1227 47.6264 50.6110 2500 1400 0.0000 49.8070 -8.9853 30.386455 17.4428 47.5102 50.6110 2500 1600 0.0000 49.8070 -8.9853 30.763930 17.7554 47.3942 50.6110 2500 1800 0.0000 49.8070 -8.9853 31.133691 18.0609 47.2786 50.6110 2500 2000 0.0000 49.8070 -8.9853 31.496043 18.3595 47.1635 50.6110 2500 2200 0.0000 49.8070 -8.9853 31.851275 18.6516 47.0488 50.6110 2500 2400 0.0000 49.8070 -8.9853 32.199658 18.9373 46.9345 50.6110 2500 2600 0.0000 49.8070 -8.9853 32.541443 19.2170 46.8207 50.6110 2500 2800 0.0000 49.8070 -8.9853 32.876869 19.4907 46.7074 50.6110 2500 3000 0.0000 49.8070 -8.9853 33.206160 19.7588 46.5946 50.6110 2500 3200 0.0000 49.8070 -8.9853 33.529528 20.0215 46.4823 50.6110 2500 3400 0.0000 49.8070 -8.9853 33.847173 20.2789 46.3706 50.6110 2500 3600 0.0000 49.8070 -8.9853 34.159283 20.5312 46.2595 50.6110 2500 3800 0.0000 49.8070 -8.9853 34.466037 20.7786 46.1489 50.6110 2500 4000 0.0000 49.8070 -8.9853 34.767605 21.0212 46.0389 50.6110 2500 4200 0.0000 49.8070 -8.9853 35.064149 21.2592 45.9295 50.6110 2500 4400 0.0000 49.8070 -8.9853 35.355821 21.4927 45.8207 50.6110 2500 4600 0.0000 49.8070 -8.9853 35.642767 21.7219 45.7124 50.6110 2500 4800 0.0000 49.8070 -8.9853 35.925125 21.9469 45.6048 50.6110 2500 5000 0.0000 49.8070 -8.9853 36.203028 22.1679 45.4979 50.6110 2500 5200 0.0000 49.8070 -8.9853 36.476602 22.3848 45.3915 50.6110 2500 5400 0.0000 49.8070 -8.9853 36.745968 22.5980 45.2858 50.6110 2500 5600 0.0000 49.8070 -8.9853 37.011240 22.8074 45.1806 50.6110 2500 5800 0.0000 49.8070 -8.9853 37.272529 23.0132 45.0762 50.6110 2500 6000 0.0000 49.8070 -8.9853 37.529940 23.2155 44.9723 50.6110 2500 6200 0.0000 49.8070 -8.9853 37.783575 23.4144 44.8691 50.6110 2500 6400 0.0000 49.8070 -8.9853 38.033530 23.6099 44.7665 50.6110 2500 6600 0.0000 49.8070 -8.9853 38.279899 23.8022 44.6646 50.6110 2500 6800 0.0000 49.8070 -8.9853 38.522770 23.9913 44.5633 50.6110 2500 7000 0.0000 49.8070 -8.9853 38.762231 24.1773 44.4627 50.6110 2500 7200 0.0000 49.8070 -8.9853 38.998362 24.3603 44.3626 50.6110 2500 7400 0.0000 49.8070 -8.9853 39.231244 24.5405 44.2633 50.6110 2500 7600 0.0000 49.8070 -8.9853 39.460952 24.7177 44.1645 50.6110 2500 7800 0.0000 49.8070 -8.9853 39.687562 24.8922 44.0664 50.6110 2500 8000 0.0000 49.8070 -8.9853 39.911143 25.0640 43.9689 50.6110 2500 8200 0.0000 49.8070 -8.9853 40.131765 25.2331 43.8721 50.6110 2500 8400 0.0000 49.8070 -8.9853 40.349492 25.3996 43.7759 50.6110 3000 0 0.0000 49.9718 -8.9883 27.495976 15.0983 48.4768 50.7737 3000 200 0.0000 49.9718 -8.9883 27.938261 15.4720 48.3588 50.7737 3000 400 0.0000 49.9718 -8.9883 28.369910 15.8359 48.2409 50.7737 3000 600 0.0000 49.9718 -8.9883 28.791445 16.1904 48.1231 50.7737 3000 800 0.0000 49.9718 -8.9883 29.203347 16.5359 48.0054 50.7737 3000 1000 0.0000 49.9718 -8.9883 29.606060 16.8729 47.8880 50.7737 3000 1200 0.0000 49.9718 -8.9883 29.999993 17.2018 47.7709 50.7737 3000 1400 0.0000 49.9718 -8.9883 30.385526 17.5228 47.6540 50.7737 3000 1600 0.0000 49.9718 -8.9883 30.763014 17.8364 47.5376 50.7737 3000 1800 0.0000 49.9718 -8.9883 31.132787 18.1428 47.4215 50.7737 3000 2000 0.0000 49.9718 -8.9883 31.495151 18.4424 47.3058 50.7737 3000 2200 0.0000 49.9718 -8.9883 31.850395 18.7353 47.1905 50.7737 3000 2400 0.0000 49.9718 -8.9883 32.198788 19.0219 47.0757 50.7737 3000 2600 0.0000 49.9718 -8.9883 32.540583 19.3024 46.9614 50.7737 3000 2800 0.0000 49.9718 -8.9883 32.876019 19.5770 46.8476 50.7737 3000 3000 0.0000 49.9718 -8.9883 33.205320 19.8460 46.7343 50.7737 3000 3200 0.0000 49.9718 -8.9883 33.528698 20.1094 46.6216 50.7737 3000 3400 0.0000 49.9718 -8.9883 33.846351 20.3676 46.5094 50.7737 3000 3600 0.0000 49.9718 -8.9883 34.158470 20.6206 46.3977 50.7737 3000 3800 0.0000 49.9718 -8.9883 34.465233 20.8687 46.2867 50.7737 3000 4000 0.0000 49.9718 -8.9883 34.766809 21.1121 46.1762 50.7737 3000 4200 0.0000 49.9718 -8.9883 35.063361 21.3508 46.0663 50.7737 3000 4400 0.0000 49.9718 -8.9883 35.355041 21.5850 45.9570 50.7737 3000 4600 0.0000 49.9718 -8.9883 35.641994 21.8149 45.8483 50.7737 3000 4800 0.0000 49.9718 -8.9883 35.924359 22.0406 45.7403 50.7737 3000 5000 0.0000 49.9718 -8.9883 36.202270 22.2622 45.6328 50.7737 3000 5200 0.0000 49.9718 -8.9883 36.475851 22.4799 45.5260 50.7737 3000 5400 0.0000 49.9718 -8.9883 36.745223 22.6936 45.4198 50.7737 3000 5600 0.0000 49.9718 -8.9883 37.010502 22.9037 45.3143 50.7737 3000 5800 0.0000 49.9718 -8.9883 37.271797 23.1101 45.2093 50.7737 3000 6000 0.0000 49.9718 -8.9883 37.529215 23.3130 45.1050 50.7737 3000 6200 0.0000 49.9718 -8.9883 37.782855 23.5124 45.0014 50.7737 3000 6400 0.0000 49.9718 -8.9883 38.032817 23.7085 44.8984 50.7737 3000 6600 0.0000 49.9718 -8.9883 38.279191 23.9014 44.7960 50.7737 3000 6800 0.0000 49.9718 -8.9883 38.522068 24.0911 44.6943 50.7737 3000 7000 0.0000 49.9718 -8.9883 38.761534 24.2776 44.5932 50.7737 3000 7200 0.0000 49.9718 -8.9883 38.997670 24.4612 44.4928 50.7737 3000 7400 0.0000 49.9718 -8.9883 39.230557 24.6419 44.3930 50.7737 3000 7600 0.0000 49.9718 -8.9883 39.460271 24.8197 44.2939 50.7737 3000 7800 0.0000 49.9718 -8.9883 39.686886 24.9947 44.1954 50.7737 3000 8000 0.0000 49.9718 -8.9883 39.910472 25.1669 44.0975 50.7737 3000 8200 0.0000 49.9718 -8.9883 40.131098 25.3365 44.0002 50.7737 3000 8400 0.0000 49.9718 -8.9883 40.348830 25.5036 43.9036 50.7737 3500 0 0.0000 50.1365 -8.9912 27.494920 15.1709 48.6246 50.9364 3500 200 0.0000 50.1365 -8.9912 27.937223 15.5458 48.5060 50.9364 3500 400 0.0000 50.1365 -8.9912 28.368889 15.9108 48.3875 50.9364 3500 600 0.0000 50.1365 -8.9912 28.790441 16.2663 48.2691 50.9364 3500 800 0.0000 50.1365 -8.9912 29.202358 16.6129 48.1509 50.9364 3500 1000 0.0000 50.1365 -8.9912 29.605086 16.9510 48.0330 50.9364 3500 1200 0.0000 50.1365 -8.9912 29.999033 17.2808 47.9153 50.9364 3500 1400 0.0000 50.1365 -8.9912 30.384580 17.6029 47.7979 50.9364 3500 1600 0.0000 50.1365 -8.9912 30.762081 17.9174 47.6809 50.9364 3500 1800 0.0000 50.1365 -8.9912 31.131866 18.2248 47.5643 50.9364 3500 2000 0.0000 50.1365 -8.9912 31.494242 18.5252 47.4481 50.9364 3500 2200 0.0000 50.1365 -8.9912 31.849497 18.8191 47.3323 50.9364 3500 2400 0.0000 50.1365 -8.9912 32.197901 19.1065 47.2170 50.9364 3500 2600 0.0000 50.1365 -8.9912 32.539707 19.3879 47.1022 50.9364 3500 2800 0.0000 50.1365 -8.9912 32.875153 19.6633 46.9879 50.9364 3500 3000 0.0000 50.1365 -8.9912 33.204464 19.9330 46.8741 50.9364 3500 3200 0.0000 50.1365 -8.9912 33.527851 20.1973 46.7608 50.9364 3500 3400 0.0000 50.1365 -8.9912 33.845514 20.4562 46.6481 50.9364 3500 3600 0.0000 50.1365 -8.9912 34.157642 20.7101 46.5360 50.9364 3500 3800 0.0000 50.1365 -8.9912 34.464413 20.9589 46.4245 50.9364 3500 4000 0.0000 50.1365 -8.9912 34.765998 21.2030 46.3135 50.9364 3500 4200 0.0000 50.1365 -8.9912 35.062558 21.4424 46.2031 50.9364 3500 4400 0.0000 50.1365 -8.9912 35.354246 21.6774 46.0934 50.9364 3500 4600 0.0000 50.1365 -8.9912 35.641207 21.9079 45.9842 50.9364 3500 4800 0.0000 50.1365 -8.9912 35.923580 22.1343 45.8757 50.9364 3500 5000 0.0000 50.1365 -8.9912 36.201497 22.3566 45.7678 50.9364 3500 5200 0.0000 50.1365 -8.9912 36.475085 22.5749 45.6605 50.9364 3500 5400 0.0000 50.1365 -8.9912 36.744464 22.7893 45.5539 50.9364 3500 5600 0.0000 50.1365 -8.9912 37.009750 23.0000 45.4479 50.9364 3500 5800 0.0000 50.1365 -8.9912 37.271052 23.2070 45.3425 50.9364 3500 6000 0.0000 50.1365 -8.9912 37.528475 23.4105 45.2378 50.9364 3500 6200 0.0000 50.1365 -8.9912 37.782123 23.6105 45.1337 50.9364 3500 6400 0.0000 50.1365 -8.9912 38.032090 23.8072 45.0303 50.9364 3500 6600 0.0000 50.1365 -8.9912 38.278470 24.0006 44.9275 50.9364 3500 6800 0.0000 50.1365 -8.9912 38.521353 24.1908 44.8253 50.9364 3500 7000 0.0000 50.1365 -8.9912 38.760824 24.3780 44.7238 50.9364 3500 7200 0.0000 50.1365 -8.9912 38.996966 24.5621 44.6230 50.9364 3500 7400 0.0000 50.1365 -8.9912 39.229859 24.7433 44.5228 50.9364 3500 7600 0.0000 50.1365 -8.9912 39.459578 24.9216 44.4232 50.9364 3500 7800 0.0000 50.1365 -8.9912 39.686197 25.0971 44.3243 50.9364 3500 8000 0.0000 50.1365 -8.9912 39.909789 25.2699 44.2260 50.9364 3500 8200 0.0000 50.1365 -8.9912 40.130420 25.4400 44.1284 50.9364 3500 8400 0.0000 50.1365 -8.9912 40.348157 25.6075 44.0314 50.9364 4000 0 0.0000 50.3013 -8.9941 27.493844 15.2434 48.7723 51.0991 4000 200 0.0000 50.3013 -8.9941 27.936165 15.6195 48.6532 51.0991 4000 400 0.0000 50.3013 -8.9941 28.367849 15.9856 48.5341 51.0991 4000 600 0.0000 50.3013 -8.9941 28.789417 16.3423 48.4152 51.0991 4000 800 0.0000 50.3013 -8.9941 29.201351 16.6899 48.2965 51.0991 4000 1000 0.0000 50.3013 -8.9941 29.604093 17.0290 48.1780 51.0991 4000 1200 0.0000 50.3013 -8.9941 29.998054 17.3599 48.0597 51.0991 4000 1400 0.0000 50.3013 -8.9941 30.383615 17.6829 47.9418 51.0991 4000 1600 0.0000 50.3013 -8.9941 30.761130 17.9984 47.8243 51.0991 4000 1800 0.0000 50.3013 -8.9941 31.130927 18.3067 47.7071 51.0991 4000 2000 0.0000 50.3013 -8.9941 31.493315 18.6080 47.5904 51.0991 4000 2200 0.0000 50.3013 -8.9941 31.848582 18.9028 47.4741 51.0991 4000 2400 0.0000 50.3013 -8.9941 32.196998 19.1911 47.3583 51.0991 4000 2600 0.0000 50.3013 -8.9941 32.538815 19.4733 47.2429 51.0991 4000 2800 0.0000 50.3013 -8.9941 32.874271 19.7496 47.1281 51.0991 4000 3000 0.0000 50.3013 -8.9941 33.203592 20.0201 47.0138 51.0991 4000 3200 0.0000 50.3013 -8.9941 33.526989 20.2852 46.9001 51.0991 4000 3400 0.0000 50.3013 -8.9941 33.844662 20.5449 46.7869 51.0991 4000 3600 0.0000 50.3013 -8.9941 34.156798 20.7995 46.6743 51.0991 4000 3800 0.0000 50.3013 -8.9941 34.463579 21.0491 46.5622 51.0991 4000 4000 0.0000 50.3013 -8.9941 34.765172 21.2939 46.4508 51.0991 4000 4200 0.0000 50.3013 -8.9941 35.061741 21.5340 46.3400 51.0991 4000 4400 0.0000 50.3013 -8.9941 35.353436 21.7697 46.2297 51.0991 4000 4600 0.0000 50.3013 -8.9941 35.640405 22.0009 46.1201 51.0991 4000 4800 0.0000 50.3013 -8.9941 35.922786 22.2280 46.0111 51.0991 4000 5000 0.0000 50.3013 -8.9941 36.200710 22.4509 45.9028 51.0991 4000 5200 0.0000 50.3013 -8.9941 36.474306 22.6698 45.7950 51.0991 4000 5400 0.0000 50.3013 -8.9941 36.743692 22.8849 45.6880 51.0991 4000 5600 0.0000 50.3013 -8.9941 37.008984 23.0962 45.5815 51.0991 4000 5800 0.0000 50.3013 -8.9941 37.270293 23.3038 45.4757 51.0991 4000 6000 0.0000 50.3013 -8.9941 37.527723 23.5079 45.3705 51.0991 4000 6200 0.0000 50.3013 -8.9941 37.781376 23.7086 45.2660 51.0991 4000 6400 0.0000 50.3013 -8.9941 38.031350 23.9058 45.1622 51.0991 4000 6600 0.0000 50.3013 -8.9941 38.277736 24.0998 45.0589 51.0991 4000 6800 0.0000 50.3013 -8.9941 38.520625 24.2906 44.9564 51.0991 4000 7000 0.0000 50.3013 -8.9941 38.760102 24.4783 44.8544 51.0991 4000 7200 0.0000 50.3013 -8.9941 38.996249 24.6630 44.7532 51.0991 4000 7400 0.0000 50.3013 -8.9941 39.229147 24.8447 44.6526 51.0991 4000 7600 0.0000 50.3013 -8.9941 39.458872 25.0235 44.5526 51.0991 4000 7800 0.0000 50.3013 -8.9941 39.685497 25.1995 44.4533 51.0991 4000 8000 0.0000 50.3013 -8.9941 39.909093 25.3728 44.3546 51.0991 4000 8200 0.0000 50.3013 -8.9941 40.129729 25.5434 44.2565 51.0991 4000 8400 0.0000 50.3013 -8.9941 40.347471 25.7114 44.1592 51.0991 4500 0 0.0000 50.4661 -8.9970 27.492748 15.3160 48.9201 51.2618 4500 200 0.0000 50.4661 -8.9970 27.935088 15.6932 48.8004 51.2618 4500 400 0.0000 50.4661 -8.9970 28.366789 16.0604 48.6808 51.2618 4500 600 0.0000 50.4661 -8.9970 28.788374 16.4182 48.5613 51.2618 4500 800 0.0000 50.4661 -8.9970 29.200324 16.7669 48.4420 51.2618 4500 1000 0.0000 50.4661 -8.9970 29.603082 17.1070 48.3230 51.2618 4500 1200 0.0000 50.4661 -8.9970 29.997058 17.4389 48.2042 51.2618 4500 1400 0.0000 50.4661 -8.9970 30.382633 17.7629 48.0857 51.2618 4500 1600 0.0000 50.4661 -8.9970 30.760161 18.0793 47.9677 51.2618 4500 1800 0.0000 50.4661 -8.9970 31.129971 18.3886 47.8500 51.2618 4500 2000 0.0000 50.4661 -8.9970 31.492372 18.6909 47.7327 51.2618 4500 2200 0.0000 50.4661 -8.9970 31.847651 18.9865 47.6159 51.2618 4500 2400 0.0000 50.4661 -8.9970 32.196078 19.2757 47.4996 51.2618 4500 2600 0.0000 50.4661 -8.9970 32.537906 19.5587 47.3837 51.2618 4500 2800 0.0000 50.4661 -8.9970 32.873373 19.8358 47.2684 51.2618 4500 3000 0.0000 50.4661 -8.9970 33.202705 20.1072 47.1536 51.2618 4500 3200 0.0000 50.4661 -8.9970 33.526111 20.3730 47.0394 51.2618 4500 3400 0.0000 50.4661 -8.9970 33.843793 20.6335 46.9257 51.2618 4500 3600 0.0000 50.4661 -8.9970 34.155940 20.8889 46.8126 51.2618 4500 3800 0.0000 50.4661 -8.9970 34.462729 21.1392 46.7001 51.2618 4500 4000 0.0000 50.4661 -8.9970 34.764332 21.3848 46.5881 51.2618 4500 4200 0.0000 50.4661 -8.9970 35.060908 21.6256 46.4768 51.2618 4500 4400 0.0000 50.4661 -8.9970 35.352612 21.8620 46.3661 51.2618 4500 4600 0.0000 50.4661 -8.9970 35.639589 22.0939 46.2560 51.2618 4500 4800 0.0000 50.4661 -8.9970 35.921977 22.3216 46.1466 51.2618 4500 5000 0.0000 50.4661 -8.9970 36.199910 22.5452 46.0378 51.2618 4500 5200 0.0000 50.4661 -8.9970 36.473512 22.7648 45.9296 51.2618 4500 5400 0.0000 50.4661 -8.9970 36.742906 22.9805 45.8220 51.2618 4500 5600 0.0000 50.4661 -8.9970 37.008205 23.1924 45.7151 51.2618 4500 5800 0.0000 50.4661 -8.9970 37.269520 23.4007 45.6089 51.2618 4500 6000 0.0000 50.4661 -8.9970 37.526957 23.6054 45.5033 51.2618 4500 6200 0.0000 50.4661 -8.9970 37.780617 23.8066 45.3983 51.2618 4500 6400 0.0000 50.4661 -8.9970 38.030597 24.0044 45.2940 51.2618 4500 6600 0.0000 50.4661 -8.9970 38.276989 24.1990 45.1904 51.2618 4500 6800 0.0000 50.4661 -8.9970 38.519884 24.3904 45.0874 51.2618 4500 7000 0.0000 50.4661 -8.9970 38.759367 24.5786 44.9851 51.2618 4500 7200 0.0000 50.4661 -8.9970 38.995520 24.7638 44.8834 51.2618 4500 7400 0.0000 50.4661 -8.9970 39.228424 24.9461 44.7823 51.2618 4500 7600 0.0000 50.4661 -8.9970 39.458154 25.1254 44.6820 51.2618 4500 7800 0.0000 50.4661 -8.9970 39.684784 25.3019 44.5822 51.2618 4500 8000 0.0000 50.4661 -8.9970 39.908385 25.4757 44.4831 51.2618 4500 8200 0.0000 50.4661 -8.9970 40.129027 25.6469 44.3847 51.2618 4500 8400 0.0000 50.4661 -8.9970 40.346774 25.8153 44.2869 51.2618 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.010 ================================================ FILE: tests/test_data/cropA/geometry/20180506-20180518_VV_8rlks_base.par ================================================ initial_baseline(TCN): -0.4958283 -14.4402710 1.7338821 m m m initial_baseline_rate: 0.0000000 0.0527872 -0.0035656 m/s m/s m/s precision_baseline(TCN): 0.0000000 -14.1367727 1.5899685 m m m precision_baseline_rate: 0.0000000 0.0555532 -0.0044993 m/s m/s m/s unwrap_phase_constant: 0.00028 radians ================================================ FILE: tests/test_data/cropA/geometry/20180506-20180518_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): -0.496 -14.440 1.734 orbit baseline rate (TCN) (m/s): 0.000e+00 5.279e-02 -3.566e-03 baseline vector (TCN) (m): 0.000 -14.137 1.590 baseline rate (TCN) (m/s): 0.000e+00 5.555e-02 -4.499e-03 SLC-1 center baseline length (m): 14.2259 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 0.0000 -14.6553 1.6320 27.497816 -5.3190 -13.7532 14.7459 0 200 0.0000 -14.6553 1.6320 27.940057 -5.4250 -13.7117 14.7459 0 400 0.0000 -14.6553 1.6320 28.371664 -5.5281 -13.6704 14.7459 0 600 0.0000 -14.6553 1.6320 28.793160 -5.6285 -13.6294 14.7459 0 800 0.0000 -14.6553 1.6320 29.205025 -5.7263 -13.5886 14.7459 0 1000 0.0000 -14.6553 1.6320 29.607701 -5.8217 -13.5480 14.7459 0 1200 0.0000 -14.6553 1.6320 30.001600 -5.9147 -13.5077 14.7459 0 1400 0.0000 -14.6553 1.6320 30.387101 -6.0054 -13.4676 14.7459 0 1600 0.0000 -14.6553 1.6320 30.764557 -6.0940 -13.4277 14.7459 0 1800 0.0000 -14.6553 1.6320 31.134300 -6.1806 -13.3881 14.7459 0 2000 0.0000 -14.6553 1.6320 31.496635 -6.2651 -13.3488 14.7459 0 2200 0.0000 -14.6553 1.6320 31.851850 -6.3477 -13.3097 14.7459 0 2400 0.0000 -14.6553 1.6320 32.200217 -6.4285 -13.2708 14.7459 0 2600 0.0000 -14.6553 1.6320 32.541986 -6.5076 -13.2322 14.7459 0 2800 0.0000 -14.6553 1.6320 32.877397 -6.5849 -13.1939 14.7459 0 3000 0.0000 -14.6553 1.6320 33.206674 -6.6607 -13.1559 14.7459 0 3200 0.0000 -14.6553 1.6320 33.530029 -6.7348 -13.1181 14.7459 0 3400 0.0000 -14.6553 1.6320 33.847660 -6.8074 -13.0805 14.7459 0 3600 0.0000 -14.6553 1.6320 34.159757 -6.8786 -13.0432 14.7459 0 3800 0.0000 -14.6553 1.6320 34.466499 -6.9483 -13.0062 14.7459 0 4000 0.0000 -14.6553 1.6320 34.768056 -7.0167 -12.9695 14.7459 0 4200 0.0000 -14.6553 1.6320 35.064588 -7.0837 -12.9330 14.7459 0 4400 0.0000 -14.6553 1.6320 35.356248 -7.1494 -12.8968 14.7459 0 4600 0.0000 -14.6553 1.6320 35.643183 -7.2139 -12.8608 14.7459 0 4800 0.0000 -14.6553 1.6320 35.925531 -7.2772 -12.8251 14.7459 0 5000 0.0000 -14.6553 1.6320 36.203424 -7.3393 -12.7897 14.7459 0 5200 0.0000 -14.6553 1.6320 36.476989 -7.4003 -12.7545 14.7459 0 5400 0.0000 -14.6553 1.6320 36.746345 -7.4602 -12.7195 14.7459 0 5600 0.0000 -14.6553 1.6320 37.011608 -7.5190 -12.6849 14.7459 0 5800 0.0000 -14.6553 1.6320 37.272888 -7.5768 -12.6504 14.7459 0 6000 0.0000 -14.6553 1.6320 37.530291 -7.6335 -12.6163 14.7459 0 6200 0.0000 -14.6553 1.6320 37.783917 -7.6893 -12.5824 14.7459 0 6400 0.0000 -14.6553 1.6320 38.033864 -7.7441 -12.5487 14.7459 0 6600 0.0000 -14.6553 1.6320 38.280225 -7.7980 -12.5153 14.7459 0 6800 0.0000 -14.6553 1.6320 38.523089 -7.8510 -12.4821 14.7459 0 7000 0.0000 -14.6553 1.6320 38.762542 -7.9031 -12.4492 14.7459 0 7200 0.0000 -14.6553 1.6320 38.998666 -7.9543 -12.4165 14.7459 0 7400 0.0000 -14.6553 1.6320 39.231541 -8.0047 -12.3841 14.7459 0 7600 0.0000 -14.6553 1.6320 39.461243 -8.0543 -12.3519 14.7459 0 7800 0.0000 -14.6553 1.6320 39.687846 -8.1031 -12.3199 14.7459 0 8000 0.0000 -14.6553 1.6320 39.911421 -8.1511 -12.2882 14.7459 0 8200 0.0000 -14.6553 1.6320 40.132036 -8.1984 -12.2568 14.7459 0 8400 0.0000 -14.6553 1.6320 40.349758 -8.2449 -12.2255 14.7459 500 0 0.0000 -14.5411 1.6227 27.496883 -5.2742 -13.6477 14.6314 500 200 0.0000 -14.5411 1.6227 27.939140 -5.3794 -13.6066 14.6314 500 400 0.0000 -14.5411 1.6227 28.370762 -5.4818 -13.5657 14.6314 500 600 0.0000 -14.5411 1.6227 28.792272 -5.5814 -13.5250 14.6314 500 800 0.0000 -14.5411 1.6227 29.204150 -5.6785 -13.4845 14.6314 500 1000 0.0000 -14.5411 1.6227 29.606840 -5.7731 -13.4442 14.6314 500 1200 0.0000 -14.5411 1.6227 30.000750 -5.8654 -13.4042 14.6314 500 1400 0.0000 -14.5411 1.6227 30.386263 -5.9555 -13.3645 14.6314 500 1600 0.0000 -14.5411 1.6227 30.763731 -6.0434 -13.3249 14.6314 500 1800 0.0000 -14.5411 1.6227 31.133484 -6.1292 -13.2857 14.6314 500 2000 0.0000 -14.5411 1.6227 31.495829 -6.2131 -13.2466 14.6314 500 2200 0.0000 -14.5411 1.6227 31.851055 -6.2952 -13.2079 14.6314 500 2400 0.0000 -14.5411 1.6227 32.199431 -6.3753 -13.1693 14.6314 500 2600 0.0000 -14.5411 1.6227 32.541209 -6.4538 -13.1311 14.6314 500 2800 0.0000 -14.5411 1.6227 32.876630 -6.5305 -13.0931 14.6314 500 3000 0.0000 -14.5411 1.6227 33.205915 -6.6057 -13.0553 14.6314 500 3200 0.0000 -14.5411 1.6227 33.529278 -6.6793 -13.0178 14.6314 500 3400 0.0000 -14.5411 1.6227 33.846917 -6.7513 -12.9806 14.6314 500 3600 0.0000 -14.5411 1.6227 34.159022 -6.8219 -12.9436 14.6314 500 3800 0.0000 -14.5411 1.6227 34.465771 -6.8911 -12.9069 14.6314 500 4000 0.0000 -14.5411 1.6227 34.767335 -6.9590 -12.8705 14.6314 500 4200 0.0000 -14.5411 1.6227 35.063874 -7.0255 -12.8343 14.6314 500 4400 0.0000 -14.5411 1.6227 35.355542 -7.0907 -12.7984 14.6314 500 4600 0.0000 -14.5411 1.6227 35.642483 -7.1547 -12.7627 14.6314 500 4800 0.0000 -14.5411 1.6227 35.924838 -7.2176 -12.7273 14.6314 500 5000 0.0000 -14.5411 1.6227 36.202737 -7.2792 -12.6921 14.6314 500 5200 0.0000 -14.5411 1.6227 36.476308 -7.3397 -12.6572 14.6314 500 5400 0.0000 -14.5411 1.6227 36.745670 -7.3991 -12.6226 14.6314 500 5600 0.0000 -14.5411 1.6227 37.010938 -7.4575 -12.5882 14.6314 500 5800 0.0000 -14.5411 1.6227 37.272224 -7.5148 -12.5541 14.6314 500 6000 0.0000 -14.5411 1.6227 37.529632 -7.5712 -12.5202 14.6314 500 6200 0.0000 -14.5411 1.6227 37.783264 -7.6265 -12.4865 14.6314 500 6400 0.0000 -14.5411 1.6227 38.033217 -7.6809 -12.4531 14.6314 500 6600 0.0000 -14.5411 1.6227 38.279583 -7.7344 -12.4200 14.6314 500 6800 0.0000 -14.5411 1.6227 38.522452 -7.7870 -12.3871 14.6314 500 7000 0.0000 -14.5411 1.6227 38.761909 -7.8387 -12.3545 14.6314 500 7200 0.0000 -14.5411 1.6227 38.998038 -7.8895 -12.3220 14.6314 500 7400 0.0000 -14.5411 1.6227 39.230918 -7.9395 -12.2899 14.6314 500 7600 0.0000 -14.5411 1.6227 39.460624 -7.9887 -12.2579 14.6314 500 7800 0.0000 -14.5411 1.6227 39.687232 -8.0372 -12.2263 14.6314 500 8000 0.0000 -14.5411 1.6227 39.910811 -8.0848 -12.1948 14.6314 500 8200 0.0000 -14.5411 1.6227 40.131431 -8.1317 -12.1636 14.6314 500 8400 0.0000 -14.5411 1.6227 40.349156 -8.1779 -12.1326 14.6314 1000 0 0.0000 -14.4269 1.6135 27.495930 -5.2295 -13.5422 14.5169 1000 200 0.0000 -14.4269 1.6135 27.938203 -5.3339 -13.5014 14.5169 1000 400 0.0000 -14.4269 1.6135 28.369840 -5.4354 -13.4609 14.5169 1000 600 0.0000 -14.4269 1.6135 28.791365 -5.5343 -13.4205 14.5169 1000 800 0.0000 -14.4269 1.6135 29.203256 -5.6306 -13.3804 14.5169 1000 1000 0.0000 -14.4269 1.6135 29.605959 -5.7245 -13.3405 14.5169 1000 1200 0.0000 -14.4269 1.6135 29.999882 -5.8161 -13.3008 14.5169 1000 1400 0.0000 -14.4269 1.6135 30.385407 -5.9055 -13.2614 14.5169 1000 1600 0.0000 -14.4269 1.6135 30.762886 -5.9927 -13.2222 14.5169 1000 1800 0.0000 -14.4269 1.6135 31.132650 -6.0779 -13.1832 14.5169 1000 2000 0.0000 -14.4269 1.6135 31.495006 -6.1612 -13.1445 14.5169 1000 2200 0.0000 -14.4269 1.6135 31.850242 -6.2426 -13.1061 14.5169 1000 2400 0.0000 -14.4269 1.6135 32.198628 -6.3221 -13.0679 14.5169 1000 2600 0.0000 -14.4269 1.6135 32.540416 -6.4000 -13.0299 14.5169 1000 2800 0.0000 -14.4269 1.6135 32.875845 -6.4762 -12.9922 14.5169 1000 3000 0.0000 -14.4269 1.6135 33.205140 -6.5507 -12.9548 14.5169 1000 3200 0.0000 -14.4269 1.6135 33.528511 -6.6237 -12.9176 14.5169 1000 3400 0.0000 -14.4269 1.6135 33.846158 -6.6952 -12.8807 14.5169 1000 3600 0.0000 -14.4269 1.6135 34.158271 -6.7653 -12.8441 14.5169 1000 3800 0.0000 -14.4269 1.6135 34.465028 -6.8340 -12.8076 14.5169 1000 4000 0.0000 -14.4269 1.6135 34.766600 -6.9013 -12.7715 14.5169 1000 4200 0.0000 -14.4269 1.6135 35.063146 -6.9673 -12.7356 14.5169 1000 4400 0.0000 -14.4269 1.6135 35.354821 -7.0320 -12.7000 14.5169 1000 4600 0.0000 -14.4269 1.6135 35.641769 -7.0956 -12.6646 14.5169 1000 4800 0.0000 -14.4269 1.6135 35.924130 -7.1579 -12.6295 14.5169 1000 5000 0.0000 -14.4269 1.6135 36.202036 -7.2191 -12.5946 14.5169 1000 5200 0.0000 -14.4269 1.6135 36.475612 -7.2791 -12.5600 14.5169 1000 5400 0.0000 -14.4269 1.6135 36.744981 -7.3381 -12.5256 14.5169 1000 5600 0.0000 -14.4269 1.6135 37.010255 -7.3960 -12.4915 14.5169 1000 5800 0.0000 -14.4269 1.6135 37.271547 -7.4529 -12.4577 14.5169 1000 6000 0.0000 -14.4269 1.6135 37.528961 -7.5088 -12.4241 14.5169 1000 6200 0.0000 -14.4269 1.6135 37.782598 -7.5637 -12.3907 14.5169 1000 6400 0.0000 -14.4269 1.6135 38.032556 -7.6177 -12.3576 14.5169 1000 6600 0.0000 -14.4269 1.6135 38.278927 -7.6708 -12.3247 14.5169 1000 6800 0.0000 -14.4269 1.6135 38.521801 -7.7229 -12.2921 14.5169 1000 7000 0.0000 -14.4269 1.6135 38.761264 -7.7742 -12.2597 14.5169 1000 7200 0.0000 -14.4269 1.6135 38.997398 -7.8247 -12.2276 14.5169 1000 7400 0.0000 -14.4269 1.6135 39.230282 -7.8743 -12.1957 14.5169 1000 7600 0.0000 -14.4269 1.6135 39.459993 -7.9232 -12.1640 14.5169 1000 7800 0.0000 -14.4269 1.6135 39.686605 -7.9712 -12.1326 14.5169 1000 8000 0.0000 -14.4269 1.6135 39.910189 -8.0185 -12.1014 14.5169 1000 8200 0.0000 -14.4269 1.6135 40.130813 -8.0650 -12.0704 14.5169 1000 8400 0.0000 -14.4269 1.6135 40.348543 -8.1108 -12.0397 14.5169 1500 0 0.0000 -14.3127 1.6042 27.494956 -5.1847 -13.4367 14.4024 1500 200 0.0000 -14.3127 1.6042 27.937245 -5.2883 -13.3963 14.4024 1500 400 0.0000 -14.3127 1.6042 28.368898 -5.3891 -13.3561 14.4024 1500 600 0.0000 -14.3127 1.6042 28.790438 -5.4872 -13.3161 14.4024 1500 800 0.0000 -14.3127 1.6042 29.202344 -5.5828 -13.2763 14.4024 1500 1000 0.0000 -14.3127 1.6042 29.605059 -5.6760 -13.2367 14.4024 1500 1200 0.0000 -14.3127 1.6042 29.998996 -5.7668 -13.1974 14.4024 1500 1400 0.0000 -14.3127 1.6042 30.384532 -5.8555 -13.1583 14.4024 1500 1600 0.0000 -14.3127 1.6042 30.762024 -5.9421 -13.1194 14.4024 1500 1800 0.0000 -14.3127 1.6042 31.131799 -6.0266 -13.0808 14.4024 1500 2000 0.0000 -14.3127 1.6042 31.494166 -6.1092 -13.0424 14.4024 1500 2200 0.0000 -14.3127 1.6042 31.849412 -6.1900 -13.0043 14.4024 1500 2400 0.0000 -14.3127 1.6042 32.197808 -6.2689 -12.9664 14.4024 1500 2600 0.0000 -14.3127 1.6042 32.539606 -6.3462 -12.9288 14.4024 1500 2800 0.0000 -14.3127 1.6042 32.875045 -6.4218 -12.8914 14.4024 1500 3000 0.0000 -14.3127 1.6042 33.204348 -6.4957 -12.8543 14.4024 1500 3200 0.0000 -14.3127 1.6042 33.527728 -6.5682 -12.8174 14.4024 1500 3400 0.0000 -14.3127 1.6042 33.845384 -6.6391 -12.7808 14.4024 1500 3600 0.0000 -14.3127 1.6042 34.157505 -6.7087 -12.7445 14.4024 1500 3800 0.0000 -14.3127 1.6042 34.464270 -6.7768 -12.7084 14.4024 1500 4000 0.0000 -14.3127 1.6042 34.765849 -6.8436 -12.6725 14.4024 1500 4200 0.0000 -14.3127 1.6042 35.062403 -6.9091 -12.6369 14.4024 1500 4400 0.0000 -14.3127 1.6042 35.354084 -6.9733 -12.6016 14.4024 1500 4600 0.0000 -14.3127 1.6042 35.641040 -7.0364 -12.5665 14.4024 1500 4800 0.0000 -14.3127 1.6042 35.923408 -7.0982 -12.5317 14.4024 1500 5000 0.0000 -14.3127 1.6042 36.201320 -7.1589 -12.4971 14.4024 1500 5200 0.0000 -14.3127 1.6042 36.474903 -7.2185 -12.4628 14.4024 1500 5400 0.0000 -14.3127 1.6042 36.744278 -7.2770 -12.4287 14.4024 1500 5600 0.0000 -14.3127 1.6042 37.009558 -7.3345 -12.3949 14.4024 1500 5800 0.0000 -14.3127 1.6042 37.270856 -7.3909 -12.3613 14.4024 1500 6000 0.0000 -14.3127 1.6042 37.528276 -7.4464 -12.3280 14.4024 1500 6200 0.0000 -14.3127 1.6042 37.781919 -7.5009 -12.2949 14.4024 1500 6400 0.0000 -14.3127 1.6042 38.031882 -7.5545 -12.2620 14.4024 1500 6600 0.0000 -14.3127 1.6042 38.278259 -7.6071 -12.2294 14.4024 1500 6800 0.0000 -14.3127 1.6042 38.521138 -7.6589 -12.1971 14.4024 1500 7000 0.0000 -14.3127 1.6042 38.760605 -7.7098 -12.1650 14.4024 1500 7200 0.0000 -14.3127 1.6042 38.996744 -7.7599 -12.1331 14.4024 1500 7400 0.0000 -14.3127 1.6042 39.229633 -7.8091 -12.1014 14.4024 1500 7600 0.0000 -14.3127 1.6042 39.459350 -7.8576 -12.0700 14.4024 1500 7800 0.0000 -14.3127 1.6042 39.685966 -7.9053 -12.0389 14.4024 1500 8000 0.0000 -14.3127 1.6042 39.909555 -7.9522 -12.0079 14.4024 1500 8200 0.0000 -14.3127 1.6042 40.130183 -7.9984 -11.9772 14.4024 1500 8400 0.0000 -14.3127 1.6042 40.347918 -8.0438 -11.9467 14.4024 2000 0 0.0000 -14.1986 1.5950 27.493962 -5.1400 -13.3313 14.2879 2000 200 0.0000 -14.1986 1.5950 27.936268 -5.2427 -13.2912 14.2879 2000 400 0.0000 -14.1986 1.5950 28.367937 -5.3427 -13.2513 14.2879 2000 600 0.0000 -14.1986 1.5950 28.789491 -5.4401 -13.2116 14.2879 2000 800 0.0000 -14.1986 1.5950 29.201412 -5.5349 -13.1722 14.2879 2000 1000 0.0000 -14.1986 1.5950 29.604141 -5.6274 -13.1330 14.2879 2000 1200 0.0000 -14.1986 1.5950 29.998091 -5.7175 -13.0940 14.2879 2000 1400 0.0000 -14.1986 1.5950 30.383640 -5.8055 -13.0552 14.2879 2000 1600 0.0000 -14.1986 1.5950 30.761143 -5.8914 -13.0167 14.2879 2000 1800 0.0000 -14.1986 1.5950 31.130930 -5.9753 -12.9784 14.2879 2000 2000 0.0000 -14.1986 1.5950 31.493308 -6.0573 -12.9403 14.2879 2000 2200 0.0000 -14.1986 1.5950 31.848565 -6.1374 -12.9025 14.2879 2000 2400 0.0000 -14.1986 1.5950 32.196971 -6.2157 -12.8649 14.2879 2000 2600 0.0000 -14.1986 1.5950 32.538779 -6.2924 -12.8276 14.2879 2000 2800 0.0000 -14.1986 1.5950 32.874228 -6.3674 -12.7906 14.2879 2000 3000 0.0000 -14.1986 1.5950 33.203540 -6.4408 -12.7538 14.2879 2000 3200 0.0000 -14.1986 1.5950 33.526929 -6.5126 -12.7172 14.2879 2000 3400 0.0000 -14.1986 1.5950 33.844594 -6.5831 -12.6809 14.2879 2000 3600 0.0000 -14.1986 1.5950 34.156723 -6.6520 -12.6449 14.2879 2000 3800 0.0000 -14.1986 1.5950 34.463496 -6.7196 -12.6091 14.2879 2000 4000 0.0000 -14.1986 1.5950 34.765083 -6.7859 -12.5735 14.2879 2000 4200 0.0000 -14.1986 1.5950 35.061644 -6.8509 -12.5382 14.2879 2000 4400 0.0000 -14.1986 1.5950 35.353334 -6.9147 -12.5032 14.2879 2000 4600 0.0000 -14.1986 1.5950 35.640296 -6.9772 -12.4684 14.2879 2000 4800 0.0000 -14.1986 1.5950 35.922671 -7.0386 -12.4339 14.2879 2000 5000 0.0000 -14.1986 1.5950 36.200590 -7.0988 -12.3996 14.2879 2000 5200 0.0000 -14.1986 1.5950 36.474180 -7.1579 -12.3655 14.2879 2000 5400 0.0000 -14.1986 1.5950 36.743561 -7.2160 -12.3317 14.2879 2000 5600 0.0000 -14.1986 1.5950 37.008848 -7.2730 -12.2982 14.2879 2000 5800 0.0000 -14.1986 1.5950 37.270151 -7.3290 -12.2649 14.2879 2000 6000 0.0000 -14.1986 1.5950 37.527577 -7.3840 -12.2319 14.2879 2000 6200 0.0000 -14.1986 1.5950 37.781226 -7.4381 -12.1990 14.2879 2000 6400 0.0000 -14.1986 1.5950 38.031195 -7.4913 -12.1665 14.2879 2000 6600 0.0000 -14.1986 1.5950 38.277577 -7.5435 -12.1342 14.2879 2000 6800 0.0000 -14.1986 1.5950 38.520461 -7.5949 -12.1021 14.2879 2000 7000 0.0000 -14.1986 1.5950 38.759934 -7.6454 -12.0702 14.2879 2000 7200 0.0000 -14.1986 1.5950 38.996078 -7.6951 -12.0386 14.2879 2000 7400 0.0000 -14.1986 1.5950 39.228972 -7.7439 -12.0072 14.2879 2000 7600 0.0000 -14.1986 1.5950 39.458693 -7.7920 -11.9761 14.2879 2000 7800 0.0000 -14.1986 1.5950 39.685315 -7.8393 -11.9452 14.2879 2000 8000 0.0000 -14.1986 1.5950 39.908908 -7.8859 -11.9145 14.2879 2000 8200 0.0000 -14.1986 1.5950 40.129541 -7.9317 -11.8840 14.2879 2000 8400 0.0000 -14.1986 1.5950 40.347280 -7.9768 -11.8538 14.2879 2500 0 0.0000 -14.0844 1.5857 27.492947 -5.0952 -13.2258 14.1733 2500 200 0.0000 -14.0844 1.5857 27.935270 -5.1972 -13.1860 14.1733 2500 400 0.0000 -14.0844 1.5857 28.366955 -5.2964 -13.1465 14.1733 2500 600 0.0000 -14.0844 1.5857 28.788525 -5.3930 -13.1072 14.1733 2500 800 0.0000 -14.0844 1.5857 29.200461 -5.4871 -13.0681 14.1733 2500 1000 0.0000 -14.0844 1.5857 29.603204 -5.5788 -13.0292 14.1733 2500 1200 0.0000 -14.0844 1.5857 29.997167 -5.6682 -12.9905 14.1733 2500 1400 0.0000 -14.0844 1.5857 30.382730 -5.7555 -12.9521 14.1733 2500 1600 0.0000 -14.0844 1.5857 30.760245 -5.8407 -12.9139 14.1733 2500 1800 0.0000 -14.0844 1.5857 31.130044 -5.9240 -12.8759 14.1733 2500 2000 0.0000 -14.0844 1.5857 31.492434 -6.0053 -12.8382 14.1733 2500 2200 0.0000 -14.0844 1.5857 31.847702 -6.0848 -12.8007 14.1733 2500 2400 0.0000 -14.0844 1.5857 32.196118 -6.1625 -12.7635 14.1733 2500 2600 0.0000 -14.0844 1.5857 32.537936 -6.2385 -12.7265 14.1733 2500 2800 0.0000 -14.0844 1.5857 32.873394 -6.3130 -12.6897 14.1733 2500 3000 0.0000 -14.0844 1.5857 33.202716 -6.3858 -12.6532 14.1733 2500 3200 0.0000 -14.0844 1.5857 33.526114 -6.4571 -12.6170 14.1733 2500 3400 0.0000 -14.0844 1.5857 33.843788 -6.5270 -12.5810 14.1733 2500 3600 0.0000 -14.0844 1.5857 34.155926 -6.5954 -12.5453 14.1733 2500 3800 0.0000 -14.0844 1.5857 34.462707 -6.6625 -12.5098 14.1733 2500 4000 0.0000 -14.0844 1.5857 34.764302 -6.7282 -12.4745 14.1733 2500 4200 0.0000 -14.0844 1.5857 35.060871 -6.7927 -12.4395 14.1733 2500 4400 0.0000 -14.0844 1.5857 35.352568 -6.8560 -12.4048 14.1733 2500 4600 0.0000 -14.0844 1.5857 35.639538 -6.9180 -12.3703 14.1733 2500 4800 0.0000 -14.0844 1.5857 35.921920 -6.9789 -12.3361 14.1733 2500 5000 0.0000 -14.0844 1.5857 36.199846 -7.0386 -12.3021 14.1733 2500 5200 0.0000 -14.0844 1.5857 36.473442 -7.0973 -12.2683 14.1733 2500 5400 0.0000 -14.0844 1.5857 36.742830 -7.1549 -12.2348 14.1733 2500 5600 0.0000 -14.0844 1.5857 37.008123 -7.2115 -12.2015 14.1733 2500 5800 0.0000 -14.0844 1.5857 37.269433 -7.2671 -12.1685 14.1733 2500 6000 0.0000 -14.0844 1.5857 37.526865 -7.3217 -12.1358 14.1733 2500 6200 0.0000 -14.0844 1.5857 37.780519 -7.3753 -12.1032 14.1733 2500 6400 0.0000 -14.0844 1.5857 38.030494 -7.4280 -12.0709 14.1733 2500 6600 0.0000 -14.0844 1.5857 38.276882 -7.4799 -12.0389 14.1733 2500 6800 0.0000 -14.0844 1.5857 38.519772 -7.5309 -12.0071 14.1733 2500 7000 0.0000 -14.0844 1.5857 38.759250 -7.5810 -11.9755 14.1733 2500 7200 0.0000 -14.0844 1.5857 38.995399 -7.6303 -11.9441 14.1733 2500 7400 0.0000 -14.0844 1.5857 39.228299 -7.6788 -11.9130 14.1733 2500 7600 0.0000 -14.0844 1.5857 39.458025 -7.7265 -11.8821 14.1733 2500 7800 0.0000 -14.0844 1.5857 39.684651 -7.7734 -11.8515 14.1733 2500 8000 0.0000 -14.0844 1.5857 39.908249 -7.8196 -11.8211 14.1733 2500 8200 0.0000 -14.0844 1.5857 40.128887 -7.8650 -11.7909 14.1733 2500 8400 0.0000 -14.0844 1.5857 40.346630 -7.9098 -11.7609 14.1733 3000 0 0.0000 -13.9702 1.5765 27.491911 -5.0505 -13.1203 14.0588 3000 200 0.0000 -13.9702 1.5765 27.934252 -5.1516 -13.0809 14.0588 3000 400 0.0000 -13.9702 1.5765 28.365954 -5.2500 -13.0417 14.0588 3000 600 0.0000 -13.9702 1.5765 28.787540 -5.3459 -13.0028 14.0588 3000 800 0.0000 -13.9702 1.5765 29.199491 -5.4392 -12.9640 14.0588 3000 1000 0.0000 -13.9702 1.5765 29.602249 -5.5302 -12.9254 14.0588 3000 1200 0.0000 -13.9702 1.5765 29.996226 -5.6190 -12.8871 14.0588 3000 1400 0.0000 -13.9702 1.5765 30.381801 -5.7055 -12.8490 14.0588 3000 1600 0.0000 -13.9702 1.5765 30.759330 -5.7901 -12.8111 14.0588 3000 1800 0.0000 -13.9702 1.5765 31.129141 -5.8727 -12.7735 14.0588 3000 2000 0.0000 -13.9702 1.5765 31.491542 -5.9533 -12.7361 14.0588 3000 2200 0.0000 -13.9702 1.5765 31.846821 -6.0322 -12.6989 14.0588 3000 2400 0.0000 -13.9702 1.5765 32.195248 -6.1093 -12.6620 14.0588 3000 2600 0.0000 -13.9702 1.5765 32.537077 -6.1847 -12.6253 14.0588 3000 2800 0.0000 -13.9702 1.5765 32.872545 -6.2586 -12.5889 14.0588 3000 3000 0.0000 -13.9702 1.5765 33.201877 -6.3308 -12.5527 14.0588 3000 3200 0.0000 -13.9702 1.5765 33.525284 -6.4016 -12.5168 14.0588 3000 3400 0.0000 -13.9702 1.5765 33.842966 -6.4709 -12.4811 14.0588 3000 3600 0.0000 -13.9702 1.5765 34.155113 -6.5388 -12.4457 14.0588 3000 3800 0.0000 -13.9702 1.5765 34.461903 -6.6053 -12.4105 14.0588 3000 4000 0.0000 -13.9702 1.5765 34.763506 -6.6705 -12.3755 14.0588 3000 4200 0.0000 -13.9702 1.5765 35.060083 -6.7345 -12.3408 14.0588 3000 4400 0.0000 -13.9702 1.5765 35.351788 -6.7973 -12.3064 14.0588 3000 4600 0.0000 -13.9702 1.5765 35.638765 -6.8588 -12.2722 14.0588 3000 4800 0.0000 -13.9702 1.5765 35.921154 -6.9192 -12.2382 14.0588 3000 5000 0.0000 -13.9702 1.5765 36.199088 -6.9785 -12.2045 14.0588 3000 5200 0.0000 -13.9702 1.5765 36.472691 -7.0367 -12.1711 14.0588 3000 5400 0.0000 -13.9702 1.5765 36.742085 -7.0938 -12.1379 14.0588 3000 5600 0.0000 -13.9702 1.5765 37.007385 -7.1500 -12.1049 14.0588 3000 5800 0.0000 -13.9702 1.5765 37.268701 -7.2051 -12.0721 14.0588 3000 6000 0.0000 -13.9702 1.5765 37.526139 -7.2593 -12.0396 14.0588 3000 6200 0.0000 -13.9702 1.5765 37.779800 -7.3125 -12.0074 14.0588 3000 6400 0.0000 -13.9702 1.5765 38.029781 -7.3648 -11.9754 14.0588 3000 6600 0.0000 -13.9702 1.5765 38.276174 -7.4163 -11.9436 14.0588 3000 6800 0.0000 -13.9702 1.5765 38.519070 -7.4668 -11.9120 14.0588 3000 7000 0.0000 -13.9702 1.5765 38.758554 -7.5165 -11.8807 14.0588 3000 7200 0.0000 -13.9702 1.5765 38.994708 -7.5655 -11.8496 14.0588 3000 7400 0.0000 -13.9702 1.5765 39.227613 -7.6136 -11.8188 14.0588 3000 7600 0.0000 -13.9702 1.5765 39.457344 -7.6609 -11.7882 14.0588 3000 7800 0.0000 -13.9702 1.5765 39.683975 -7.7075 -11.7578 14.0588 3000 8000 0.0000 -13.9702 1.5765 39.907578 -7.7533 -11.7276 14.0588 3000 8200 0.0000 -13.9702 1.5765 40.128221 -7.7984 -11.6977 14.0588 3000 8400 0.0000 -13.9702 1.5765 40.345969 -7.8428 -11.6679 14.0588 3500 0 0.0000 -13.8560 1.5672 27.490855 -5.0057 -13.0148 13.9443 3500 200 0.0000 -13.8560 1.5672 27.933214 -5.1061 -12.9758 13.9443 3500 400 0.0000 -13.8560 1.5672 28.364934 -5.2037 -12.9370 13.9443 3500 600 0.0000 -13.8560 1.5672 28.786536 -5.2987 -12.8983 13.9443 3500 800 0.0000 -13.8560 1.5672 29.198502 -5.3914 -12.8599 13.9443 3500 1000 0.0000 -13.8560 1.5672 29.601275 -5.4816 -12.8217 13.9443 3500 1200 0.0000 -13.8560 1.5672 29.995266 -5.5697 -12.7837 13.9443 3500 1400 0.0000 -13.8560 1.5672 30.380855 -5.6556 -12.7459 13.9443 3500 1600 0.0000 -13.8560 1.5672 30.758396 -5.7394 -12.7084 13.9443 3500 1800 0.0000 -13.8560 1.5672 31.128219 -5.8213 -12.6710 13.9443 3500 2000 0.0000 -13.8560 1.5672 31.490633 -5.9014 -12.6340 13.9443 3500 2200 0.0000 -13.8560 1.5672 31.845923 -5.9796 -12.5971 13.9443 3500 2400 0.0000 -13.8560 1.5672 32.194362 -6.0561 -12.5605 13.9443 3500 2600 0.0000 -13.8560 1.5672 32.536201 -6.1309 -12.5242 13.9443 3500 2800 0.0000 -13.8560 1.5672 32.871679 -6.2041 -12.4881 13.9443 3500 3000 0.0000 -13.8560 1.5672 33.201021 -6.2758 -12.4522 13.9443 3500 3200 0.0000 -13.8560 1.5672 33.524438 -6.3460 -12.4166 13.9443 3500 3400 0.0000 -13.8560 1.5672 33.842129 -6.4148 -12.3812 13.9443 3500 3600 0.0000 -13.8560 1.5672 34.154285 -6.4821 -12.3461 13.9443 3500 3800 0.0000 -13.8560 1.5672 34.461084 -6.5481 -12.3112 13.9443 3500 4000 0.0000 -13.8560 1.5672 34.762695 -6.6129 -12.2765 13.9443 3500 4200 0.0000 -13.8560 1.5672 35.059281 -6.6763 -12.2422 13.9443 3500 4400 0.0000 -13.8560 1.5672 35.350993 -6.7386 -12.2080 13.9443 3500 4600 0.0000 -13.8560 1.5672 35.637978 -6.7996 -12.1741 13.9443 3500 4800 0.0000 -13.8560 1.5672 35.920375 -6.8595 -12.1404 13.9443 3500 5000 0.0000 -13.8560 1.5672 36.198315 -6.9184 -12.1070 13.9443 3500 5200 0.0000 -13.8560 1.5672 36.471925 -6.9761 -12.0738 13.9443 3500 5400 0.0000 -13.8560 1.5672 36.741326 -7.0328 -12.0409 13.9443 3500 5600 0.0000 -13.8560 1.5672 37.006633 -7.0885 -12.0082 13.9443 3500 5800 0.0000 -13.8560 1.5672 37.267956 -7.1432 -11.9758 13.9443 3500 6000 0.0000 -13.8560 1.5672 37.525400 -7.1969 -11.9435 13.9443 3500 6200 0.0000 -13.8560 1.5672 37.779067 -7.2497 -11.9116 13.9443 3500 6400 0.0000 -13.8560 1.5672 38.029054 -7.3016 -11.8798 13.9443 3500 6600 0.0000 -13.8560 1.5672 38.275453 -7.3526 -11.8483 13.9443 3500 6800 0.0000 -13.8560 1.5672 38.518355 -7.4028 -11.8170 13.9443 3500 7000 0.0000 -13.8560 1.5672 38.757844 -7.4521 -11.7860 13.9443 3500 7200 0.0000 -13.8560 1.5672 38.994004 -7.5006 -11.7552 13.9443 3500 7400 0.0000 -13.8560 1.5672 39.226914 -7.5484 -11.7246 13.9443 3500 7600 0.0000 -13.8560 1.5672 39.456650 -7.5953 -11.6942 13.9443 3500 7800 0.0000 -13.8560 1.5672 39.683287 -7.6415 -11.6641 13.9443 3500 8000 0.0000 -13.8560 1.5672 39.906895 -7.6870 -11.6342 13.9443 3500 8200 0.0000 -13.8560 1.5672 40.127542 -7.7317 -11.6045 13.9443 3500 8400 0.0000 -13.8560 1.5672 40.345295 -7.7758 -11.5750 13.9443 4000 0 0.0000 -13.7418 1.5580 27.489779 -4.9610 -12.9094 13.8298 4000 200 0.0000 -13.7418 1.5580 27.932157 -5.0605 -12.8707 13.8298 4000 400 0.0000 -13.7418 1.5580 28.363894 -5.1573 -12.8322 13.8298 4000 600 0.0000 -13.7418 1.5580 28.785512 -5.2516 -12.7939 13.8298 4000 800 0.0000 -13.7418 1.5580 29.197494 -5.3435 -12.7558 13.8298 4000 1000 0.0000 -13.7418 1.5580 29.600282 -5.4330 -12.7179 13.8298 4000 1200 0.0000 -13.7418 1.5580 29.994287 -5.5204 -12.6802 13.8298 4000 1400 0.0000 -13.7418 1.5580 30.379890 -5.6056 -12.6428 13.8298 4000 1600 0.0000 -13.7418 1.5580 30.757445 -5.6888 -12.6056 13.8298 4000 1800 0.0000 -13.7418 1.5580 31.127281 -5.7700 -12.5686 13.8298 4000 2000 0.0000 -13.7418 1.5580 31.489706 -5.8494 -12.5319 13.8298 4000 2200 0.0000 -13.7418 1.5580 31.845009 -5.9270 -12.4953 13.8298 4000 2400 0.0000 -13.7418 1.5580 32.193458 -6.0029 -12.4591 13.8298 4000 2600 0.0000 -13.7418 1.5580 32.535308 -6.0771 -12.4230 13.8298 4000 2800 0.0000 -13.7418 1.5580 32.870797 -6.1497 -12.3872 13.8298 4000 3000 0.0000 -13.7418 1.5580 33.200149 -6.2209 -12.3517 13.8298 4000 3200 0.0000 -13.7418 1.5580 33.523576 -6.2905 -12.3164 13.8298 4000 3400 0.0000 -13.7418 1.5580 33.841277 -6.3587 -12.2813 13.8298 4000 3600 0.0000 -13.7418 1.5580 34.153442 -6.4255 -12.2465 13.8298 4000 3800 0.0000 -13.7418 1.5580 34.460249 -6.4910 -12.2119 13.8298 4000 4000 0.0000 -13.7418 1.5580 34.761869 -6.5552 -12.1776 13.8298 4000 4200 0.0000 -13.7418 1.5580 35.058463 -6.6181 -12.1435 13.8298 4000 4400 0.0000 -13.7418 1.5580 35.350183 -6.6799 -12.1096 13.8298 4000 4600 0.0000 -13.7418 1.5580 35.637176 -6.7404 -12.0760 13.8298 4000 4800 0.0000 -13.7418 1.5580 35.919581 -6.7999 -12.0426 13.8298 4000 5000 0.0000 -13.7418 1.5580 36.197528 -6.8582 -12.0095 13.8298 4000 5200 0.0000 -13.7418 1.5580 36.471146 -6.9155 -11.9766 13.8298 4000 5400 0.0000 -13.7418 1.5580 36.740554 -6.9717 -11.9440 13.8298 4000 5600 0.0000 -13.7418 1.5580 37.005868 -7.0270 -11.9116 13.8298 4000 5800 0.0000 -13.7418 1.5580 37.267197 -7.0812 -11.8794 13.8298 4000 6000 0.0000 -13.7418 1.5580 37.524648 -7.1345 -11.8474 13.8298 4000 6200 0.0000 -13.7418 1.5580 37.778321 -7.1869 -11.8157 13.8298 4000 6400 0.0000 -13.7418 1.5580 38.028314 -7.2384 -11.7843 13.8298 4000 6600 0.0000 -13.7418 1.5580 38.274719 -7.2890 -11.7530 13.8298 4000 6800 0.0000 -13.7418 1.5580 38.517627 -7.3388 -11.7220 13.8298 4000 7000 0.0000 -13.7418 1.5580 38.757122 -7.3877 -11.6912 13.8298 4000 7200 0.0000 -13.7418 1.5580 38.993287 -7.4358 -11.6607 13.8298 4000 7400 0.0000 -13.7418 1.5580 39.226203 -7.4832 -11.6304 13.8298 4000 7600 0.0000 -13.7418 1.5580 39.455945 -7.5297 -11.6003 13.8298 4000 7800 0.0000 -13.7418 1.5580 39.682586 -7.5756 -11.5704 13.8298 4000 8000 0.0000 -13.7418 1.5580 39.906199 -7.6207 -11.5407 13.8298 4000 8200 0.0000 -13.7418 1.5580 40.126852 -7.6651 -11.5113 13.8298 4000 8400 0.0000 -13.7418 1.5580 40.344610 -7.7088 -11.4821 13.8298 4500 0 0.0000 -13.6276 1.5487 27.488683 -4.9162 -12.8039 13.7153 4500 200 0.0000 -13.6276 1.5487 27.931079 -5.0149 -12.7655 13.7153 4500 400 0.0000 -13.6276 1.5487 28.362834 -5.1110 -12.7274 13.7153 4500 600 0.0000 -13.6276 1.5487 28.784470 -5.2045 -12.6894 13.7153 4500 800 0.0000 -13.6276 1.5487 29.196467 -5.2956 -12.6517 13.7153 4500 1000 0.0000 -13.6276 1.5487 29.599271 -5.3844 -12.6141 13.7153 4500 1200 0.0000 -13.6276 1.5487 29.993291 -5.4711 -12.5768 13.7153 4500 1400 0.0000 -13.6276 1.5487 30.378908 -5.5556 -12.5397 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35.349359 -6.6212 -12.0112 13.7153 4500 4600 0.0000 -13.6276 1.5487 35.636360 -6.6812 -11.9779 13.7153 4500 4800 0.0000 -13.6276 1.5487 35.918772 -6.7402 -11.9448 13.7153 4500 5000 0.0000 -13.6276 1.5487 36.196728 -6.7981 -11.9120 13.7153 4500 5200 0.0000 -13.6276 1.5487 36.470353 -6.8549 -11.8794 13.7153 4500 5400 0.0000 -13.6276 1.5487 36.739768 -6.9107 -11.8470 13.7153 4500 5600 0.0000 -13.6276 1.5487 37.005088 -6.9655 -11.8149 13.7153 4500 5800 0.0000 -13.6276 1.5487 37.266425 -7.0193 -11.7830 13.7153 4500 6000 0.0000 -13.6276 1.5487 37.523882 -7.0721 -11.7513 13.7153 4500 6200 0.0000 -13.6276 1.5487 37.777562 -7.1241 -11.7199 13.7153 4500 6400 0.0000 -13.6276 1.5487 38.027561 -7.1752 -11.6887 13.7153 4500 6600 0.0000 -13.6276 1.5487 38.273972 -7.2254 -11.6577 13.7153 4500 6800 0.0000 -13.6276 1.5487 38.516886 -7.2747 -11.6270 13.7153 4500 7000 0.0000 -13.6276 1.5487 38.756387 -7.3233 -11.5965 13.7153 4500 7200 0.0000 -13.6276 1.5487 38.992558 -7.3710 -11.5662 13.7153 4500 7400 0.0000 -13.6276 1.5487 39.225479 -7.4180 -11.5362 13.7153 4500 7600 0.0000 -13.6276 1.5487 39.455227 -7.4642 -11.5063 13.7153 4500 7800 0.0000 -13.6276 1.5487 39.681874 -7.5096 -11.4767 13.7153 4500 8000 0.0000 -13.6276 1.5487 39.905492 -7.5544 -11.4473 13.7153 4500 8200 0.0000 -13.6276 1.5487 40.126149 -7.5984 -11.4181 13.7153 4500 8400 0.0000 -13.6276 1.5487 40.343913 -7.6417 -11.3892 13.7153 user time (s): 0.010 system time (s): 0.000 elapsed time (s): 0.010 ================================================ FILE: tests/test_data/cropA/geometry/20180506-20180530_VV_8rlks_base.par ================================================ initial_baseline(TCN): -1.1414793 35.2035164 15.5722874 m m m initial_baseline_rate: 0.0000000 0.0459441 -0.0078633 m/s m/s m/s precision_baseline(TCN): 0.0000000 35.4681793 15.4120456 m m m precision_baseline_rate: 0.0000000 0.0377316 -0.0043365 m/s m/s m/s unwrap_phase_constant: 0.00012 radians ================================================ FILE: tests/test_data/cropA/geometry/20180506-20180530_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): -1.141 35.204 15.572 orbit baseline rate (TCN) (m/s): 0.000e+00 4.594e-02 -7.863e-03 baseline vector (TCN) (m): 0.000 35.468 15.412 baseline rate (TCN) (m/s): 0.000e+00 3.773e-02 -4.337e-03 SLC-1 center baseline length (m): 38.6720 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 0.0000 35.1160 15.4525 27.497816 29.9204 24.0141 38.3655 0 200 0.0000 35.1160 15.4525 27.940057 30.1048 23.7825 38.3655 0 400 0.0000 35.1160 15.4525 28.371664 30.2831 23.5550 38.3655 0 600 0.0000 35.1160 15.4525 28.793160 30.4556 23.3316 38.3655 0 800 0.0000 35.1160 15.4525 29.205025 30.6225 23.1121 38.3655 0 1000 0.0000 35.1160 15.4525 29.607701 30.7842 22.8963 38.3655 0 1200 0.0000 35.1160 15.4525 30.001600 30.9409 22.6841 38.3655 0 1400 0.0000 35.1160 15.4525 30.387101 31.0928 22.4754 38.3655 0 1600 0.0000 35.1160 15.4525 30.764557 31.2402 22.2701 38.3655 0 1800 0.0000 35.1160 15.4525 31.134300 31.3832 22.0681 38.3655 0 2000 0.0000 35.1160 15.4525 31.496635 31.5222 21.8692 38.3655 0 2200 0.0000 35.1160 15.4525 31.851850 31.6571 21.6733 38.3655 0 2400 0.0000 35.1160 15.4525 32.200217 31.7883 21.4804 38.3655 0 2600 0.0000 35.1160 15.4525 32.541986 31.9159 21.2904 38.3655 0 2800 0.0000 35.1160 15.4525 32.877397 32.0400 21.1032 38.3655 0 3000 0.0000 35.1160 15.4525 33.206674 32.1607 20.9188 38.3655 0 3200 0.0000 35.1160 15.4525 33.530029 32.2783 20.7369 38.3655 0 3400 0.0000 35.1160 15.4525 33.847660 32.3927 20.5577 38.3655 0 3600 0.0000 35.1160 15.4525 34.159757 32.5042 20.3809 38.3655 0 3800 0.0000 35.1160 15.4525 34.466499 32.6129 20.2066 38.3655 0 4000 0.0000 35.1160 15.4525 34.768056 32.7188 20.0347 38.3655 0 4200 0.0000 35.1160 15.4525 35.064588 32.8220 19.8651 38.3655 0 4400 0.0000 35.1160 15.4525 35.356248 32.9227 19.6978 38.3655 0 4600 0.0000 35.1160 15.4525 35.643183 33.0210 19.5326 38.3655 0 4800 0.0000 35.1160 15.4525 35.925531 33.1168 19.3697 38.3655 0 5000 0.0000 35.1160 15.4525 36.203424 33.2104 19.2088 38.3655 0 5200 0.0000 35.1160 15.4525 36.476989 33.3017 19.0500 38.3655 0 5400 0.0000 35.1160 15.4525 36.746345 33.3909 18.8933 38.3655 0 5600 0.0000 35.1160 15.4525 37.011608 33.4780 18.7385 38.3655 0 5800 0.0000 35.1160 15.4525 37.272888 33.5631 18.5856 38.3655 0 6000 0.0000 35.1160 15.4525 37.530291 33.6463 18.4347 38.3655 0 6200 0.0000 35.1160 15.4525 37.783917 33.7276 18.2855 38.3655 0 6400 0.0000 35.1160 15.4525 38.033864 33.8070 18.1382 38.3655 0 6600 0.0000 35.1160 15.4525 38.280225 33.8847 17.9927 38.3655 0 6800 0.0000 35.1160 15.4525 38.523089 33.9606 17.8489 38.3655 0 7000 0.0000 35.1160 15.4525 38.762542 34.0349 17.7068 38.3655 0 7200 0.0000 35.1160 15.4525 38.998666 34.1076 17.5664 38.3655 0 7400 0.0000 35.1160 15.4525 39.231541 34.1787 17.4276 38.3655 0 7600 0.0000 35.1160 15.4525 39.461243 34.2483 17.2905 38.3655 0 7800 0.0000 35.1160 15.4525 39.687846 34.3165 17.1549 38.3655 0 8000 0.0000 35.1160 15.4525 39.911421 34.3831 17.0209 38.3655 0 8200 0.0000 35.1160 15.4525 40.132036 34.4484 16.8883 38.3655 0 8400 0.0000 35.1160 15.4525 40.349758 34.5123 16.7573 38.3655 500 0 0.0000 35.1935 15.4436 27.496883 29.9479 24.0875 38.4329 500 200 0.0000 35.1935 15.4436 27.939140 30.1329 23.8557 38.4329 500 400 0.0000 35.1935 15.4436 28.370762 30.3118 23.6280 38.4329 500 600 0.0000 35.1935 15.4436 28.792272 30.4848 23.4044 38.4329 500 800 0.0000 35.1935 15.4436 29.204150 30.6522 23.1846 38.4329 500 1000 0.0000 35.1935 15.4436 29.606840 30.8144 22.9686 38.4329 500 1200 0.0000 35.1935 15.4436 30.000750 30.9716 22.7562 38.4329 500 1400 0.0000 35.1935 15.4436 30.386263 31.1240 22.5473 38.4329 500 1600 0.0000 35.1935 15.4436 30.763731 31.2719 22.3418 38.4329 500 1800 0.0000 35.1935 15.4436 31.133484 31.4154 22.1395 38.4329 500 2000 0.0000 35.1935 15.4436 31.495829 31.5548 21.9404 38.4329 500 2200 0.0000 35.1935 15.4436 31.851055 31.6902 21.7443 38.4329 500 2400 0.0000 35.1935 15.4436 32.199431 31.8218 21.5513 38.4329 500 2600 0.0000 35.1935 15.4436 32.541209 31.9498 21.3611 38.4329 500 2800 0.0000 35.1935 15.4436 32.876630 32.0743 21.1736 38.4329 500 3000 0.0000 35.1935 15.4436 33.205915 32.1955 20.9890 38.4329 500 3200 0.0000 35.1935 15.4436 33.529278 32.3134 20.8069 38.4329 500 3400 0.0000 35.1935 15.4436 33.846917 32.4283 20.6275 38.4329 500 3600 0.0000 35.1935 15.4436 34.159022 32.5402 20.4505 38.4329 500 3800 0.0000 35.1935 15.4436 34.465771 32.6492 20.2760 38.4329 500 4000 0.0000 35.1935 15.4436 34.767335 32.7554 20.1039 38.4329 500 4200 0.0000 35.1935 15.4436 35.063874 32.8591 19.9341 38.4329 500 4400 0.0000 35.1935 15.4436 35.355542 32.9601 19.7666 38.4329 500 4600 0.0000 35.1935 15.4436 35.642483 33.0587 19.6013 38.4329 500 4800 0.0000 35.1935 15.4436 35.924838 33.1549 19.4381 38.4329 500 5000 0.0000 35.1935 15.4436 36.202737 33.2488 19.2771 38.4329 500 5200 0.0000 35.1935 15.4436 36.476308 33.3404 19.1181 38.4329 500 5400 0.0000 35.1935 15.4436 36.745670 33.4299 18.9611 38.4329 500 5600 0.0000 35.1935 15.4436 37.010938 33.5174 18.8062 38.4329 500 5800 0.0000 35.1935 15.4436 37.272224 33.6028 18.6531 38.4329 500 6000 0.0000 35.1935 15.4436 37.529632 33.6863 18.5020 38.4329 500 6200 0.0000 35.1935 15.4436 37.783264 33.7678 18.3527 38.4329 500 6400 0.0000 35.1935 15.4436 38.033217 33.8476 18.2052 38.4329 500 6600 0.0000 35.1935 15.4436 38.279583 33.9255 18.0595 38.4329 500 6800 0.0000 35.1935 15.4436 38.522452 34.0018 17.9155 38.4329 500 7000 0.0000 35.1935 15.4436 38.761909 34.0764 17.7733 38.4329 500 7200 0.0000 35.1935 15.4436 38.998038 34.1493 17.6327 38.4329 500 7400 0.0000 35.1935 15.4436 39.230918 34.2207 17.4937 38.4329 500 7600 0.0000 35.1935 15.4436 39.460624 34.2906 17.3564 38.4329 500 7800 0.0000 35.1935 15.4436 39.687232 34.3589 17.2206 38.4329 500 8000 0.0000 35.1935 15.4436 39.910811 34.4259 17.0864 38.4329 500 8200 0.0000 35.1935 15.4436 40.131431 34.4914 16.9537 38.4329 500 8400 0.0000 35.1935 15.4436 40.349156 34.5556 16.8226 38.4329 1000 0 0.0000 35.2711 15.4347 27.495930 29.9754 24.1609 38.5004 1000 200 0.0000 35.2711 15.4347 27.938203 30.1610 23.9288 38.5004 1000 400 0.0000 35.2711 15.4347 28.369840 30.3404 23.7010 38.5004 1000 600 0.0000 35.2711 15.4347 28.791365 30.5139 23.4771 38.5004 1000 800 0.0000 35.2711 15.4347 29.203256 30.6819 23.2571 38.5004 1000 1000 0.0000 35.2711 15.4347 29.605959 30.8446 23.0409 38.5004 1000 1200 0.0000 35.2711 15.4347 29.999882 31.0023 22.8283 38.5004 1000 1400 0.0000 35.2711 15.4347 30.385407 31.1552 22.6192 38.5004 1000 1600 0.0000 35.2711 15.4347 30.762886 31.3035 22.4134 38.5004 1000 1800 0.0000 35.2711 15.4347 31.132650 31.4475 22.2110 38.5004 1000 2000 0.0000 35.2711 15.4347 31.495006 31.5874 22.0116 38.5004 1000 2200 0.0000 35.2711 15.4347 31.850242 31.7232 21.8154 38.5004 1000 2400 0.0000 35.2711 15.4347 32.198628 31.8553 21.6221 38.5004 1000 2600 0.0000 35.2711 15.4347 32.540416 31.9837 21.4317 38.5004 1000 2800 0.0000 35.2711 15.4347 32.875845 32.1086 21.2441 38.5004 1000 3000 0.0000 35.2711 15.4347 33.205140 32.2302 21.0592 38.5004 1000 3200 0.0000 35.2711 15.4347 33.528511 32.3486 20.8769 38.5004 1000 3400 0.0000 35.2711 15.4347 33.846158 32.4638 20.6973 38.5004 1000 3600 0.0000 35.2711 15.4347 34.158271 32.5761 20.5201 38.5004 1000 3800 0.0000 35.2711 15.4347 34.465028 32.6855 20.3454 38.5004 1000 4000 0.0000 35.2711 15.4347 34.766600 32.7921 20.1731 38.5004 1000 4200 0.0000 35.2711 15.4347 35.063146 32.8961 20.0031 38.5004 1000 4400 0.0000 35.2711 15.4347 35.354821 32.9975 19.8354 38.5004 1000 4600 0.0000 35.2711 15.4347 35.641769 33.0964 19.6699 38.5004 1000 4800 0.0000 35.2711 15.4347 35.924130 33.1929 19.5066 38.5004 1000 5000 0.0000 35.2711 15.4347 36.202036 33.2872 19.3453 38.5004 1000 5200 0.0000 35.2711 15.4347 36.475612 33.3791 19.1862 38.5004 1000 5400 0.0000 35.2711 15.4347 36.744981 33.4690 19.0290 38.5004 1000 5600 0.0000 35.2711 15.4347 37.010255 33.5567 18.8739 38.5004 1000 5800 0.0000 35.2711 15.4347 37.271547 33.6424 18.7206 38.5004 1000 6000 0.0000 35.2711 15.4347 37.528961 33.7262 18.5693 38.5004 1000 6200 0.0000 35.2711 15.4347 37.782598 33.8081 18.4198 38.5004 1000 6400 0.0000 35.2711 15.4347 38.032556 33.8881 18.2722 38.5004 1000 6600 0.0000 35.2711 15.4347 38.278927 33.9664 18.1263 38.5004 1000 6800 0.0000 35.2711 15.4347 38.521801 34.0429 17.9821 38.5004 1000 7000 0.0000 35.2711 15.4347 38.761264 34.1178 17.8397 38.5004 1000 7200 0.0000 35.2711 15.4347 38.997398 34.1910 17.6989 38.5004 1000 7400 0.0000 35.2711 15.4347 39.230282 34.2627 17.5598 38.5004 1000 7600 0.0000 35.2711 15.4347 39.459993 34.3328 17.4223 38.5004 1000 7800 0.0000 35.2711 15.4347 39.686605 34.4014 17.2864 38.5004 1000 8000 0.0000 35.2711 15.4347 39.910189 34.4686 17.1520 38.5004 1000 8200 0.0000 35.2711 15.4347 40.130813 34.5344 17.0192 38.5004 1000 8400 0.0000 35.2711 15.4347 40.348543 34.5988 16.8878 38.5004 1500 0 0.0000 35.3487 15.4258 27.494956 30.0029 24.2344 38.5679 1500 200 0.0000 35.3487 15.4258 27.937245 30.1890 24.0020 38.5679 1500 400 0.0000 35.3487 15.4258 28.368898 30.3690 23.7739 38.5679 1500 600 0.0000 35.3487 15.4258 28.790438 30.5431 23.5499 38.5679 1500 800 0.0000 35.3487 15.4258 29.202344 30.7116 23.3297 38.5679 1500 1000 0.0000 35.3487 15.4258 29.605059 30.8748 23.1132 38.5679 1500 1200 0.0000 35.3487 15.4258 29.998996 31.0330 22.9004 38.5679 1500 1400 0.0000 35.3487 15.4258 30.384532 31.1864 22.6911 38.5679 1500 1600 0.0000 35.3487 15.4258 30.762024 31.3352 22.4851 38.5679 1500 1800 0.0000 35.3487 15.4258 31.131799 31.4797 22.2824 38.5679 1500 2000 0.0000 35.3487 15.4258 31.494166 31.6200 22.0829 38.5679 1500 2200 0.0000 35.3487 15.4258 31.849412 31.7563 21.8864 38.5679 1500 2400 0.0000 35.3487 15.4258 32.197808 31.8888 21.6929 38.5679 1500 2600 0.0000 35.3487 15.4258 32.539606 32.0176 21.5023 38.5679 1500 2800 0.0000 35.3487 15.4258 32.875045 32.1430 21.3145 38.5679 1500 3000 0.0000 35.3487 15.4258 33.204348 32.2649 21.1294 38.5679 1500 3200 0.0000 35.3487 15.4258 33.527728 32.3837 20.9470 38.5679 1500 3400 0.0000 35.3487 15.4258 33.845384 32.4993 20.7671 38.5679 1500 3600 0.0000 35.3487 15.4258 34.157505 32.6120 20.5898 38.5679 1500 3800 0.0000 35.3487 15.4258 34.464270 32.7217 20.4149 38.5679 1500 4000 0.0000 35.3487 15.4258 34.765849 32.8287 20.2423 38.5679 1500 4200 0.0000 35.3487 15.4258 35.062403 32.9331 20.0722 38.5679 1500 4400 0.0000 35.3487 15.4258 35.354084 33.0348 19.9042 38.5679 1500 4600 0.0000 35.3487 15.4258 35.641040 33.1341 19.7385 38.5679 1500 4800 0.0000 35.3487 15.4258 35.923408 33.2310 19.5750 38.5679 1500 5000 0.0000 35.3487 15.4258 36.201320 33.3255 19.4136 38.5679 1500 5200 0.0000 35.3487 15.4258 36.474903 33.4178 19.2542 38.5679 1500 5400 0.0000 35.3487 15.4258 36.744278 33.5080 19.0969 38.5679 1500 5600 0.0000 35.3487 15.4258 37.009558 33.5961 18.9416 38.5679 1500 5800 0.0000 35.3487 15.4258 37.270856 33.6821 18.7882 38.5679 1500 6000 0.0000 35.3487 15.4258 37.528276 33.7662 18.6367 38.5679 1500 6200 0.0000 35.3487 15.4258 37.781919 33.8483 18.4870 38.5679 1500 6400 0.0000 35.3487 15.4258 38.031882 33.9287 18.3391 38.5679 1500 6600 0.0000 35.3487 15.4258 38.278259 34.0072 18.1931 38.5679 1500 6800 0.0000 35.3487 15.4258 38.521138 34.0840 18.0488 38.5679 1500 7000 0.0000 35.3487 15.4258 38.760605 34.1592 17.9062 38.5679 1500 7200 0.0000 35.3487 15.4258 38.996744 34.2327 17.7652 38.5679 1500 7400 0.0000 35.3487 15.4258 39.229633 34.3046 17.6259 38.5679 1500 7600 0.0000 35.3487 15.4258 39.459350 34.3750 17.4882 38.5679 1500 7800 0.0000 35.3487 15.4258 39.685966 34.4439 17.3522 38.5679 1500 8000 0.0000 35.3487 15.4258 39.909555 34.5113 17.2176 38.5679 1500 8200 0.0000 35.3487 15.4258 40.130183 34.5774 17.0846 38.5679 1500 8400 0.0000 35.3487 15.4258 40.347918 34.6421 16.9531 38.5679 2000 0 0.0000 35.4262 15.4169 27.493962 30.0303 24.3078 38.6354 2000 200 0.0000 35.4262 15.4169 27.936268 30.2171 24.0753 38.6354 2000 400 0.0000 35.4262 15.4169 28.367937 30.3976 23.8469 38.6354 2000 600 0.0000 35.4262 15.4169 28.789491 30.5722 23.6226 38.6354 2000 800 0.0000 35.4262 15.4169 29.201412 30.7413 23.4022 38.6354 2000 1000 0.0000 35.4262 15.4169 29.604141 30.9050 23.1856 38.6354 2000 1200 0.0000 35.4262 15.4169 29.998091 31.0637 22.9725 38.6354 2000 1400 0.0000 35.4262 15.4169 30.383640 31.2176 22.7630 38.6354 2000 1600 0.0000 35.4262 15.4169 30.761143 31.3669 22.5568 38.6354 2000 1800 0.0000 35.4262 15.4169 31.130930 31.5118 22.3539 38.6354 2000 2000 0.0000 35.4262 15.4169 31.493308 31.6526 22.1542 38.6354 2000 2200 0.0000 35.4262 15.4169 31.848565 31.7893 21.9575 38.6354 2000 2400 0.0000 35.4262 15.4169 32.196971 31.9223 21.7638 38.6354 2000 2600 0.0000 35.4262 15.4169 32.538779 32.0515 21.5729 38.6354 2000 2800 0.0000 35.4262 15.4169 32.874228 32.1773 21.3849 38.6354 2000 3000 0.0000 35.4262 15.4169 33.203540 32.2997 21.1996 38.6354 2000 3200 0.0000 35.4262 15.4169 33.526929 32.4188 21.0170 38.6354 2000 3400 0.0000 35.4262 15.4169 33.844594 32.5348 20.8369 38.6354 2000 3600 0.0000 35.4262 15.4169 34.156723 32.6478 20.6594 38.6354 2000 3800 0.0000 35.4262 15.4169 34.463496 32.7580 20.4843 38.6354 2000 4000 0.0000 35.4262 15.4169 34.765083 32.8654 20.3116 38.6354 2000 4200 0.0000 35.4262 15.4169 35.061644 32.9701 20.1412 38.6354 2000 4400 0.0000 35.4262 15.4169 35.353334 33.0722 19.9731 38.6354 2000 4600 0.0000 35.4262 15.4169 35.640296 33.1718 19.8072 38.6354 2000 4800 0.0000 35.4262 15.4169 35.922671 33.2690 19.6435 38.6354 2000 5000 0.0000 35.4262 15.4169 36.200590 33.3639 19.4819 38.6354 2000 5200 0.0000 35.4262 15.4169 36.474180 33.4565 19.3223 38.6354 2000 5400 0.0000 35.4262 15.4169 36.743561 33.5470 19.1648 38.6354 2000 5600 0.0000 35.4262 15.4169 37.008848 33.6354 19.0093 38.6354 2000 5800 0.0000 35.4262 15.4169 37.270151 33.7217 18.8557 38.6354 2000 6000 0.0000 35.4262 15.4169 37.527577 33.8061 18.7040 38.6354 2000 6200 0.0000 35.4262 15.4169 37.781226 33.8886 18.5542 38.6354 2000 6400 0.0000 35.4262 15.4169 38.031195 33.9692 18.4061 38.6354 2000 6600 0.0000 35.4262 15.4169 38.277577 34.0480 18.2599 38.6354 2000 6800 0.0000 35.4262 15.4169 38.520461 34.1251 18.1154 38.6354 2000 7000 0.0000 35.4262 15.4169 38.759934 34.2006 17.9726 38.6354 2000 7200 0.0000 35.4262 15.4169 38.996078 34.2743 17.8315 38.6354 2000 7400 0.0000 35.4262 15.4169 39.228972 34.3465 17.6920 38.6354 2000 7600 0.0000 35.4262 15.4169 39.458693 34.4172 17.5542 38.6354 2000 7800 0.0000 35.4262 15.4169 39.685315 34.4864 17.4179 38.6354 2000 8000 0.0000 35.4262 15.4169 39.908908 34.5541 17.2832 38.6354 2000 8200 0.0000 35.4262 15.4169 40.129541 34.6204 17.1500 38.6354 2000 8400 0.0000 35.4262 15.4169 40.347280 34.6853 17.0183 38.6354 2500 0 0.0000 35.5038 15.4080 27.492947 30.0578 24.3812 38.7030 2500 200 0.0000 35.5038 15.4080 27.935270 30.2451 24.1485 38.7030 2500 400 0.0000 35.5038 15.4080 28.366955 30.4262 23.9199 38.7030 2500 600 0.0000 35.5038 15.4080 28.788525 30.6014 23.6954 38.7030 2500 800 0.0000 35.5038 15.4080 29.200461 30.7710 23.4748 38.7030 2500 1000 0.0000 35.5038 15.4080 29.603204 30.9352 23.2579 38.7030 2500 1200 0.0000 35.5038 15.4080 29.997167 31.0944 23.0447 38.7030 2500 1400 0.0000 35.5038 15.4080 30.382730 31.2488 22.8349 38.7030 2500 1600 0.0000 35.5038 15.4080 30.760245 31.3985 22.6285 38.7030 2500 1800 0.0000 35.5038 15.4080 31.130044 31.5439 22.4254 38.7030 2500 2000 0.0000 35.5038 15.4080 31.492434 31.6851 22.2254 38.7030 2500 2200 0.0000 35.5038 15.4080 31.847702 31.8223 22.0285 38.7030 2500 2400 0.0000 35.5038 15.4080 32.196118 31.9557 21.8346 38.7030 2500 2600 0.0000 35.5038 15.4080 32.537936 32.0854 21.6436 38.7030 2500 2800 0.0000 35.5038 15.4080 32.873394 32.2116 21.4554 38.7030 2500 3000 0.0000 35.5038 15.4080 33.202716 32.3344 21.2699 38.7030 2500 3200 0.0000 35.5038 15.4080 33.526114 32.4539 21.0870 38.7030 2500 3400 0.0000 35.5038 15.4080 33.843788 32.5703 20.9068 38.7030 2500 3600 0.0000 35.5038 15.4080 34.155926 32.6837 20.7290 38.7030 2500 3800 0.0000 35.5038 15.4080 34.462707 32.7943 20.5537 38.7030 2500 4000 0.0000 35.5038 15.4080 34.764302 32.9020 20.3808 38.7030 2500 4200 0.0000 35.5038 15.4080 35.060871 33.0070 20.2102 38.7030 2500 4400 0.0000 35.5038 15.4080 35.352568 33.1095 20.0419 38.7030 2500 4600 0.0000 35.5038 15.4080 35.639538 33.2095 19.8759 38.7030 2500 4800 0.0000 35.5038 15.4080 35.921920 33.3070 19.7120 38.7030 2500 5000 0.0000 35.5038 15.4080 36.199846 33.4023 19.5502 38.7030 2500 5200 0.0000 35.5038 15.4080 36.473442 33.4952 19.3904 38.7030 2500 5400 0.0000 35.5038 15.4080 36.742830 33.5860 19.2327 38.7030 2500 5600 0.0000 35.5038 15.4080 37.008123 33.6747 19.0770 38.7030 2500 5800 0.0000 35.5038 15.4080 37.269433 33.7614 18.9232 38.7030 2500 6000 0.0000 35.5038 15.4080 37.526865 33.8461 18.7714 38.7030 2500 6200 0.0000 35.5038 15.4080 37.780519 33.9288 18.6213 38.7030 2500 6400 0.0000 35.5038 15.4080 38.030494 34.0097 18.4731 38.7030 2500 6600 0.0000 35.5038 15.4080 38.276882 34.0889 18.3267 38.7030 2500 6800 0.0000 35.5038 15.4080 38.519772 34.1663 18.1820 38.7030 2500 7000 0.0000 35.5038 15.4080 38.759250 34.2420 18.0391 38.7030 2500 7200 0.0000 35.5038 15.4080 38.995399 34.3160 17.8978 38.7030 2500 7400 0.0000 35.5038 15.4080 39.228299 34.3885 17.7582 38.7030 2500 7600 0.0000 35.5038 15.4080 39.458025 34.4594 17.6201 38.7030 2500 7800 0.0000 35.5038 15.4080 39.684651 34.5288 17.4837 38.7030 2500 8000 0.0000 35.5038 15.4080 39.908249 34.5968 17.3488 38.7030 2500 8200 0.0000 35.5038 15.4080 40.128887 34.6634 17.2155 38.7030 2500 8400 0.0000 35.5038 15.4080 40.346630 34.7285 17.0836 38.7030 3000 0 0.0000 35.5813 15.3990 27.491911 30.0853 24.4547 38.7706 3000 200 0.0000 35.5813 15.3990 27.934252 30.2732 24.2217 38.7706 3000 400 0.0000 35.5813 15.3990 28.365954 30.4548 23.9929 38.7706 3000 600 0.0000 35.5813 15.3990 28.787540 30.6305 23.7682 38.7706 3000 800 0.0000 35.5813 15.3990 29.199491 30.8006 23.5474 38.7706 3000 1000 0.0000 35.5813 15.3990 29.602249 30.9654 23.3303 38.7706 3000 1200 0.0000 35.5813 15.3990 29.996226 31.1251 23.1168 38.7706 3000 1400 0.0000 35.5813 15.3990 30.381801 31.2799 22.9068 38.7706 3000 1600 0.0000 35.5813 15.3990 30.759330 31.4302 22.7002 38.7706 3000 1800 0.0000 35.5813 15.3990 31.129141 31.5760 22.4969 38.7706 3000 2000 0.0000 35.5813 15.3990 31.491542 31.7177 22.2967 38.7706 3000 2200 0.0000 35.5813 15.3990 31.846821 31.8554 22.0996 38.7706 3000 2400 0.0000 35.5813 15.3990 32.195248 31.9892 21.9055 38.7706 3000 2600 0.0000 35.5813 15.3990 32.537077 32.1193 21.7143 38.7706 3000 2800 0.0000 35.5813 15.3990 32.872545 32.2459 21.5258 38.7706 3000 3000 0.0000 35.5813 15.3990 33.201877 32.3691 21.3401 38.7706 3000 3200 0.0000 35.5813 15.3990 33.525284 32.4890 21.1571 38.7706 3000 3400 0.0000 35.5813 15.3990 33.842966 32.6058 20.9766 38.7706 3000 3600 0.0000 35.5813 15.3990 34.155113 32.7196 20.7987 38.7706 3000 3800 0.0000 35.5813 15.3990 34.461903 32.8305 20.6232 38.7706 3000 4000 0.0000 35.5813 15.3990 34.763506 32.9386 20.4501 38.7706 3000 4200 0.0000 35.5813 15.3990 35.060083 33.0440 20.2793 38.7706 3000 4400 0.0000 35.5813 15.3990 35.351788 33.1468 20.1108 38.7706 3000 4600 0.0000 35.5813 15.3990 35.638765 33.2472 19.9445 38.7706 3000 4800 0.0000 35.5813 15.3990 35.921154 33.3450 19.7804 38.7706 3000 5000 0.0000 35.5813 15.3990 36.199088 33.4406 19.6185 38.7706 3000 5200 0.0000 35.5813 15.3990 36.472691 33.5339 19.4585 38.7706 3000 5400 0.0000 35.5813 15.3990 36.742085 33.6250 19.3007 38.7706 3000 5600 0.0000 35.5813 15.3990 37.007385 33.7140 19.1448 38.7706 3000 5800 0.0000 35.5813 15.3990 37.268701 33.8010 18.9908 38.7706 3000 6000 0.0000 35.5813 15.3990 37.526139 33.8860 18.8387 38.7706 3000 6200 0.0000 35.5813 15.3990 37.779800 33.9691 18.6885 38.7706 3000 6400 0.0000 35.5813 15.3990 38.029781 34.0503 18.5401 38.7706 3000 6600 0.0000 35.5813 15.3990 38.276174 34.1297 18.3935 38.7706 3000 6800 0.0000 35.5813 15.3990 38.519070 34.2074 18.2487 38.7706 3000 7000 0.0000 35.5813 15.3990 38.758554 34.2833 18.1056 38.7706 3000 7200 0.0000 35.5813 15.3990 38.994708 34.3577 17.9641 38.7706 3000 7400 0.0000 35.5813 15.3990 39.227613 34.4304 17.8243 38.7706 3000 7600 0.0000 35.5813 15.3990 39.457344 34.5016 17.6861 38.7706 3000 7800 0.0000 35.5813 15.3990 39.683975 34.5713 17.5495 38.7706 3000 8000 0.0000 35.5813 15.3990 39.907578 34.6395 17.4144 38.7706 3000 8200 0.0000 35.5813 15.3990 40.128221 34.7063 17.2809 38.7706 3000 8400 0.0000 35.5813 15.3990 40.345969 34.7717 17.1489 38.7706 3500 0 0.0000 35.6589 15.3901 27.490855 30.1127 24.5282 38.8383 3500 200 0.0000 35.6589 15.3901 27.933214 30.3012 24.2950 38.8383 3500 400 0.0000 35.6589 15.3901 28.364934 30.4834 24.0660 38.8383 3500 600 0.0000 35.6589 15.3901 28.786536 30.6596 23.8410 38.8383 3500 800 0.0000 35.6589 15.3901 29.198502 30.8303 23.6199 38.8383 3500 1000 0.0000 35.6589 15.3901 29.601275 30.9955 23.4026 38.8383 3500 1200 0.0000 35.6589 15.3901 29.995266 31.1557 23.1889 38.8383 3500 1400 0.0000 35.6589 15.3901 30.380855 31.3111 22.9788 38.8383 3500 1600 0.0000 35.6589 15.3901 30.758396 31.4618 22.7719 38.8383 3500 1800 0.0000 35.6589 15.3901 31.128219 31.6081 22.5684 38.8383 3500 2000 0.0000 35.6589 15.3901 31.490633 31.7503 22.3680 38.8383 3500 2200 0.0000 35.6589 15.3901 31.845923 31.8884 22.1707 38.8383 3500 2400 0.0000 35.6589 15.3901 32.194362 32.0226 21.9764 38.8383 3500 2600 0.0000 35.6589 15.3901 32.536201 32.1531 21.7849 38.8383 3500 2800 0.0000 35.6589 15.3901 32.871679 32.2801 21.5963 38.8383 3500 3000 0.0000 35.6589 15.3901 33.201021 32.4038 21.4104 38.8383 3500 3200 0.0000 35.6589 15.3901 33.524438 32.5241 21.2271 38.8383 3500 3400 0.0000 35.6589 15.3901 33.842129 32.6413 21.0465 38.8383 3500 3600 0.0000 35.6589 15.3901 34.154285 32.7555 20.8683 38.8383 3500 3800 0.0000 35.6589 15.3901 34.461084 32.8667 20.6926 38.8383 3500 4000 0.0000 35.6589 15.3901 34.762695 32.9752 20.5193 38.8383 3500 4200 0.0000 35.6589 15.3901 35.059281 33.0810 20.3484 38.8383 3500 4400 0.0000 35.6589 15.3901 35.350993 33.1842 20.1797 38.8383 3500 4600 0.0000 35.6589 15.3901 35.637978 33.2848 20.0132 38.8383 3500 4800 0.0000 35.6589 15.3901 35.920375 33.3831 19.8489 38.8383 3500 5000 0.0000 35.6589 15.3901 36.198315 33.4790 19.6868 38.8383 3500 5200 0.0000 35.6589 15.3901 36.471925 33.5726 19.5267 38.8383 3500 5400 0.0000 35.6589 15.3901 36.741326 33.6640 19.3686 38.8383 3500 5600 0.0000 35.6589 15.3901 37.006633 33.7534 19.2125 38.8383 3500 5800 0.0000 35.6589 15.3901 37.267956 33.8406 19.0584 38.8383 3500 6000 0.0000 35.6589 15.3901 37.525400 33.9259 18.9061 38.8383 3500 6200 0.0000 35.6589 15.3901 37.779067 34.0093 18.7557 38.8383 3500 6400 0.0000 35.6589 15.3901 38.029054 34.0908 18.6072 38.8383 3500 6600 0.0000 35.6589 15.3901 38.275453 34.1705 18.4604 38.8383 3500 6800 0.0000 35.6589 15.3901 38.518355 34.2485 18.3154 38.8383 3500 7000 0.0000 35.6589 15.3901 38.757844 34.3247 18.1720 38.8383 3500 7200 0.0000 35.6589 15.3901 38.994004 34.3993 18.0304 38.8383 3500 7400 0.0000 35.6589 15.3901 39.226914 34.4723 17.8904 38.8383 3500 7600 0.0000 35.6589 15.3901 39.456650 34.5438 17.7521 38.8383 3500 7800 0.0000 35.6589 15.3901 39.683287 34.6137 17.6153 38.8383 3500 8000 0.0000 35.6589 15.3901 39.906895 34.6822 17.4801 38.8383 3500 8200 0.0000 35.6589 15.3901 40.127542 34.7493 17.3464 38.8383 3500 8400 0.0000 35.6589 15.3901 40.345295 34.8150 17.2142 38.8383 4000 0 0.0000 35.7365 15.3812 27.489779 30.1401 24.6017 38.9060 4000 200 0.0000 35.7365 15.3812 27.932157 30.3292 24.3682 38.9060 4000 400 0.0000 35.7365 15.3812 28.363894 30.5119 24.1390 38.9060 4000 600 0.0000 35.7365 15.3812 28.785512 30.6887 23.9138 38.9060 4000 800 0.0000 35.7365 15.3812 29.197494 30.8599 23.6925 38.9060 4000 1000 0.0000 35.7365 15.3812 29.600282 31.0257 23.4750 38.9060 4000 1200 0.0000 35.7365 15.3812 29.994287 31.1864 23.2611 38.9060 4000 1400 0.0000 35.7365 15.3812 30.379890 31.3422 23.0507 38.9060 4000 1600 0.0000 35.7365 15.3812 30.757445 31.4934 22.8437 38.9060 4000 1800 0.0000 35.7365 15.3812 31.127281 31.6402 22.6399 38.9060 4000 2000 0.0000 35.7365 15.3812 31.489706 31.7828 22.4393 38.9060 4000 2200 0.0000 35.7365 15.3812 31.845009 31.9214 22.2418 38.9060 4000 2400 0.0000 35.7365 15.3812 32.193458 32.0560 22.0473 38.9060 4000 2600 0.0000 35.7365 15.3812 32.535308 32.1870 21.8556 38.9060 4000 2800 0.0000 35.7365 15.3812 32.870797 32.3144 21.6668 38.9060 4000 3000 0.0000 35.7365 15.3812 33.200149 32.4384 21.4807 38.9060 4000 3200 0.0000 35.7365 15.3812 33.523576 32.5592 21.2972 38.9060 4000 3400 0.0000 35.7365 15.3812 33.841277 32.6768 21.1163 38.9060 4000 3600 0.0000 35.7365 15.3812 34.153442 32.7913 20.9380 38.9060 4000 3800 0.0000 35.7365 15.3812 34.460249 32.9030 20.7621 38.9060 4000 4000 0.0000 35.7365 15.3812 34.761869 33.0118 20.5886 38.9060 4000 4200 0.0000 35.7365 15.3812 35.058463 33.1180 20.4175 38.9060 4000 4400 0.0000 35.7365 15.3812 35.350183 33.2215 20.2486 38.9060 4000 4600 0.0000 35.7365 15.3812 35.637176 33.3225 20.0819 38.9060 4000 4800 0.0000 35.7365 15.3812 35.919581 33.4211 19.9174 38.9060 4000 5000 0.0000 35.7365 15.3812 36.197528 33.5173 19.7551 38.9060 4000 5200 0.0000 35.7365 15.3812 36.471146 33.6113 19.5948 38.9060 4000 5400 0.0000 35.7365 15.3812 36.740554 33.7030 19.4365 38.9060 4000 5600 0.0000 35.7365 15.3812 37.005868 33.7927 19.2803 38.9060 4000 5800 0.0000 35.7365 15.3812 37.267197 33.8802 19.1259 38.9060 4000 6000 0.0000 35.7365 15.3812 37.524648 33.9658 18.9735 38.9060 4000 6200 0.0000 35.7365 15.3812 37.778321 34.0495 18.8229 38.9060 4000 6400 0.0000 35.7365 15.3812 38.028314 34.1313 18.6742 38.9060 4000 6600 0.0000 35.7365 15.3812 38.274719 34.2113 18.5272 38.9060 4000 6800 0.0000 35.7365 15.3812 38.517627 34.2896 18.3820 38.9060 4000 7000 0.0000 35.7365 15.3812 38.757122 34.3661 18.2385 38.9060 4000 7200 0.0000 35.7365 15.3812 38.993287 34.4410 18.0967 38.9060 4000 7400 0.0000 35.7365 15.3812 39.226203 34.5143 17.9566 38.9060 4000 7600 0.0000 35.7365 15.3812 39.455945 34.5860 17.8180 38.9060 4000 7800 0.0000 35.7365 15.3812 39.682586 34.6562 17.6811 38.9060 4000 8000 0.0000 35.7365 15.3812 39.906199 34.7249 17.5457 38.9060 4000 8200 0.0000 35.7365 15.3812 40.126852 34.7922 17.4118 38.9060 4000 8400 0.0000 35.7365 15.3812 40.344610 34.8582 17.2795 38.9060 4500 0 0.0000 35.8140 15.3723 27.488683 30.1675 24.6752 38.9737 4500 200 0.0000 35.8140 15.3723 27.931079 30.3572 24.4415 38.9737 4500 400 0.0000 35.8140 15.3723 28.362834 30.5405 24.2120 38.9737 4500 600 0.0000 35.8140 15.3723 28.784470 30.7178 23.9866 38.9737 4500 800 0.0000 35.8140 15.3723 29.196467 30.8895 23.7651 38.9737 4500 1000 0.0000 35.8140 15.3723 29.599271 31.0558 23.5474 38.9737 4500 1200 0.0000 35.8140 15.3723 29.993291 31.2170 23.3333 38.9737 4500 1400 0.0000 35.8140 15.3723 30.378908 31.3734 23.1227 38.9737 4500 1600 0.0000 35.8140 15.3723 30.756476 31.5251 22.9154 38.9737 4500 1800 0.0000 35.8140 15.3723 31.126325 31.6723 22.7114 38.9737 4500 2000 0.0000 35.8140 15.3723 31.488763 31.8154 22.5106 38.9737 4500 2200 0.0000 35.8140 15.3723 31.844077 31.9543 22.3129 38.9737 4500 2400 0.0000 35.8140 15.3723 32.192539 32.0895 22.1182 38.9737 4500 2600 0.0000 35.8140 15.3723 32.534400 32.2209 21.9263 38.9737 4500 2800 0.0000 35.8140 15.3723 32.869899 32.3487 21.7373 38.9737 4500 3000 0.0000 35.8140 15.3723 33.199262 32.4731 21.5509 38.9737 4500 3200 0.0000 35.8140 15.3723 33.522698 32.5943 21.3673 38.9737 4500 3400 0.0000 35.8140 15.3723 33.840409 32.7122 21.1862 38.9737 4500 3600 0.0000 35.8140 15.3723 34.152583 32.8272 21.0077 38.9737 4500 3800 0.0000 35.8140 15.3723 34.459400 32.9392 20.8316 38.9737 4500 4000 0.0000 35.8140 15.3723 34.761029 33.0484 20.6579 38.9737 4500 4200 0.0000 35.8140 15.3723 35.057631 33.1549 20.4865 38.9737 4500 4400 0.0000 35.8140 15.3723 35.349359 33.2588 20.3175 38.9737 4500 4600 0.0000 35.8140 15.3723 35.636360 33.3601 20.1506 38.9737 4500 4800 0.0000 35.8140 15.3723 35.918772 33.4591 19.9859 38.9737 4500 5000 0.0000 35.8140 15.3723 36.196728 33.5556 19.8234 38.9737 4500 5200 0.0000 35.8140 15.3723 36.470353 33.6499 19.6629 38.9737 4500 5400 0.0000 35.8140 15.3723 36.739768 33.7420 19.5045 38.9737 4500 5600 0.0000 35.8140 15.3723 37.005088 33.8320 19.3480 38.9737 4500 5800 0.0000 35.8140 15.3723 37.266425 33.9199 19.1935 38.9737 4500 6000 0.0000 35.8140 15.3723 37.523882 34.0058 19.0409 38.9737 4500 6200 0.0000 35.8140 15.3723 37.777562 34.0897 18.8901 38.9737 4500 6400 0.0000 35.8140 15.3723 38.027561 34.1718 18.7412 38.9737 4500 6600 0.0000 35.8140 15.3723 38.273972 34.2521 18.5941 38.9737 4500 6800 0.0000 35.8140 15.3723 38.516886 34.3306 18.4487 38.9737 4500 7000 0.0000 35.8140 15.3723 38.756387 34.4075 18.3050 38.9737 4500 7200 0.0000 35.8140 15.3723 38.992558 34.4826 18.1631 38.9737 4500 7400 0.0000 35.8140 15.3723 39.225479 34.5562 18.0227 38.9737 4500 7600 0.0000 35.8140 15.3723 39.455227 34.6282 17.8840 38.9737 4500 7800 0.0000 35.8140 15.3723 39.681874 34.6986 17.7469 38.9737 4500 8000 0.0000 35.8140 15.3723 39.905492 34.7676 17.6113 38.9737 4500 8200 0.0000 35.8140 15.3723 40.126149 34.8352 17.4773 38.9737 4500 8400 0.0000 35.8140 15.3723 40.343913 34.9014 17.3448 38.9737 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.000 ================================================ FILE: tests/test_data/cropA/geometry/20180506-20180611_VV_8rlks_base.par ================================================ initial_baseline(TCN): 0.4118799 -40.7471207 1.2559498 m m m initial_baseline_rate: 0.0000000 0.0719436 -0.0067777 m/s m/s m/s precision_baseline(TCN): 0.0000000 -41.0589267 1.3893527 m m m precision_baseline_rate: 0.0000000 0.0980549 -0.0290264 m/s m/s m/s unwrap_phase_constant: -0.00034 radians ================================================ FILE: tests/test_data/cropA/geometry/20180506-20180611_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): 0.412 -40.747 1.256 orbit baseline rate (TCN) (m/s): 0.000e+00 7.194e-02 -6.778e-03 baseline vector (TCN) (m): 0.000 -41.059 1.389 baseline rate (TCN) (m/s): 0.000e+00 9.805e-02 -2.903e-02 SLC-1 center baseline length (m): 41.0824 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 0.0000 -41.9742 1.6603 27.497816 -17.9073 -37.9988 42.0070 0 200 0.0000 -41.9742 1.6603 27.940057 -18.2001 -37.8594 42.0070 0 400 0.0000 -41.9742 1.6603 28.371664 -18.4848 -37.7213 42.0070 0 600 0.0000 -41.9742 1.6603 28.793160 -18.7618 -37.5843 42.0070 0 800 0.0000 -41.9742 1.6603 29.205025 -19.0314 -37.4484 42.0070 0 1000 0.0000 -41.9742 1.6603 29.607701 -19.2942 -37.3138 42.0070 0 1200 0.0000 -41.9742 1.6603 30.001600 -19.5502 -37.1802 42.0070 0 1400 0.0000 -41.9742 1.6603 30.387101 -19.7999 -37.0479 42.0070 0 1600 0.0000 -41.9742 1.6603 30.764557 -20.0436 -36.9166 42.0070 0 1800 0.0000 -41.9742 1.6603 31.134300 -20.2814 -36.7865 42.0070 0 2000 0.0000 -41.9742 1.6603 31.496635 -20.5136 -36.6575 42.0070 0 2200 0.0000 -41.9742 1.6603 31.851850 -20.7405 -36.5296 42.0070 0 2400 0.0000 -41.9742 1.6603 32.200217 -20.9622 -36.4029 42.0070 0 2600 0.0000 -41.9742 1.6603 32.541986 -21.1790 -36.2772 42.0070 0 2800 0.0000 -41.9742 1.6603 32.877397 -21.3910 -36.1526 42.0070 0 3000 0.0000 -41.9742 1.6603 33.206674 -21.5984 -36.0290 42.0070 0 3200 0.0000 -41.9742 1.6603 33.530029 -21.8014 -35.9066 42.0070 0 3400 0.0000 -41.9742 1.6603 33.847660 -22.0001 -35.7852 42.0070 0 3600 0.0000 -41.9742 1.6603 34.159757 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-35.6961 40.7857 3000 1800 0.0000 -40.7649 1.3023 31.129141 -19.9593 -35.5681 40.7857 3000 2000 0.0000 -40.7649 1.3023 31.491542 -20.1839 -35.4411 40.7857 3000 2200 0.0000 -40.7649 1.3023 31.846821 -20.4033 -35.3153 40.7857 3000 2400 0.0000 -40.7649 1.3023 32.195248 -20.6177 -35.1905 40.7857 3000 2600 0.0000 -40.7649 1.3023 32.537077 -20.8272 -35.0669 40.7857 3000 2800 0.0000 -40.7649 1.3023 32.872545 -21.0322 -34.9444 40.7857 3000 3000 0.0000 -40.7649 1.3023 33.201877 -21.2327 -34.8229 40.7857 3000 3200 0.0000 -40.7649 1.3023 33.525284 -21.4289 -34.7025 40.7857 3000 3400 0.0000 -40.7649 1.3023 33.842966 -21.6210 -34.5832 40.7857 3000 3600 0.0000 -40.7649 1.3023 34.155113 -21.8091 -34.4649 40.7857 3000 3800 0.0000 -40.7649 1.3023 34.461903 -21.9933 -34.3476 40.7857 3000 4000 0.0000 -40.7649 1.3023 34.763506 -22.1738 -34.2313 40.7857 3000 4200 0.0000 -40.7649 1.3023 35.060083 -22.3507 -34.1161 40.7857 3000 4400 0.0000 -40.7649 1.3023 35.351788 -22.5241 -34.0019 40.7857 3000 4600 0.0000 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-32.4016 40.7857 3000 7600 0.0000 -40.7649 1.3023 39.457344 -24.9007 -32.3020 40.7857 3000 7800 0.0000 -40.7649 1.3023 39.683975 -25.0282 -32.2033 40.7857 3000 8000 0.0000 -40.7649 1.3023 39.907578 -25.1537 -32.1054 40.7857 3000 8200 0.0000 -40.7649 1.3023 40.128221 -25.2772 -32.0083 40.7857 3000 8400 0.0000 -40.7649 1.3023 40.345969 -25.3986 -31.9120 40.7857 3500 0 0.0000 -40.5633 1.2426 27.490855 -17.6219 -36.5566 40.5823 3500 200 0.0000 -40.5633 1.2426 27.933214 -17.9036 -36.4194 40.5823 3500 400 0.0000 -40.5633 1.2426 28.364934 -18.1776 -36.2835 40.5823 3500 600 0.0000 -40.5633 1.2426 28.786536 -18.4440 -36.1488 40.5823 3500 800 0.0000 -40.5633 1.2426 29.198502 -18.7035 -36.0152 40.5823 3500 1000 0.0000 -40.5633 1.2426 29.601275 -18.9562 -35.8829 40.5823 3500 1200 0.0000 -40.5633 1.2426 29.995266 -19.2025 -35.7517 40.5823 3500 1400 0.0000 -40.5633 1.2426 30.380855 -19.4427 -35.6216 40.5823 3500 1600 0.0000 -40.5633 1.2426 30.758396 -19.6770 -35.4927 40.5823 3500 1800 0.0000 -40.5633 1.2426 31.128219 -19.9056 -35.3650 40.5823 3500 2000 0.0000 -40.5633 1.2426 31.490633 -20.1289 -35.2384 40.5823 3500 2200 0.0000 -40.5633 1.2426 31.845923 -20.3471 -35.1129 40.5823 3500 2400 0.0000 -40.5633 1.2426 32.194362 -20.5602 -34.9885 40.5823 3500 2600 0.0000 -40.5633 1.2426 32.536201 -20.7686 -34.8652 40.5823 3500 2800 0.0000 -40.5633 1.2426 32.871679 -20.9724 -34.7430 40.5823 3500 3000 0.0000 -40.5633 1.2426 33.201021 -21.1717 -34.6219 40.5823 3500 3200 0.0000 -40.5633 1.2426 33.524438 -21.3668 -34.5018 40.5823 3500 3400 0.0000 -40.5633 1.2426 33.842129 -21.5578 -34.3828 40.5823 3500 3600 0.0000 -40.5633 1.2426 34.154285 -21.7448 -34.2649 40.5823 3500 3800 0.0000 -40.5633 1.2426 34.461084 -21.9280 -34.1480 40.5823 3500 4000 0.0000 -40.5633 1.2426 34.762695 -22.1074 -34.0320 40.5823 3500 4200 0.0000 -40.5633 1.2426 35.059281 -22.2833 -33.9172 40.5823 3500 4400 0.0000 -40.5633 1.2426 35.350993 -22.4557 -33.8033 40.5823 3500 4600 0.0000 -40.5633 1.2426 35.637978 -22.6247 -33.6904 40.5823 3500 4800 0.0000 -40.5633 1.2426 35.920375 -22.7905 -33.5784 40.5823 3500 5000 0.0000 -40.5633 1.2426 36.198315 -22.9531 -33.4675 40.5823 3500 5200 0.0000 -40.5633 1.2426 36.471925 -23.1127 -33.3575 40.5823 3500 5400 0.0000 -40.5633 1.2426 36.741326 -23.2693 -33.2485 40.5823 3500 5600 0.0000 -40.5633 1.2426 37.006633 -23.4230 -33.1404 40.5823 3500 5800 0.0000 -40.5633 1.2426 37.267956 -23.5739 -33.0332 40.5823 3500 6000 0.0000 -40.5633 1.2426 37.525400 -23.7221 -32.9269 40.5823 3500 6200 0.0000 -40.5633 1.2426 37.779067 -23.8676 -32.8216 40.5823 3500 6400 0.0000 -40.5633 1.2426 38.029054 -24.0106 -32.7171 40.5823 3500 6600 0.0000 -40.5633 1.2426 38.275453 -24.1511 -32.6136 40.5823 3500 6800 0.0000 -40.5633 1.2426 38.518355 -24.2891 -32.5109 40.5823 3500 7000 0.0000 -40.5633 1.2426 38.757844 -24.4248 -32.4091 40.5823 3500 7200 0.0000 -40.5633 1.2426 38.994004 -24.5582 -32.3081 40.5823 3500 7400 0.0000 -40.5633 1.2426 39.226914 -24.6893 -32.2080 40.5823 3500 7600 0.0000 -40.5633 1.2426 39.456650 -24.8183 -32.1088 40.5823 3500 7800 0.0000 -40.5633 1.2426 39.683287 -24.9451 -32.0104 40.5823 3500 8000 0.0000 -40.5633 1.2426 39.906895 -25.0698 -31.9128 40.5823 3500 8200 0.0000 -40.5633 1.2426 40.127542 -25.1925 -31.8160 40.5823 3500 8400 0.0000 -40.5633 1.2426 40.345295 -25.3133 -31.7200 40.5823 4000 0 0.0000 -40.3617 1.1830 27.489779 -17.5811 -36.3506 40.3791 4000 200 0.0000 -40.3617 1.1830 27.932157 -17.8613 -36.2138 40.3791 4000 400 0.0000 -40.3617 1.1830 28.363894 -18.1336 -36.0781 40.3791 4000 600 0.0000 -40.3617 1.1830 28.785512 -18.3986 -35.9437 40.3791 4000 800 0.0000 -40.3617 1.1830 29.197494 -18.6566 -35.8105 40.3791 4000 1000 0.0000 -40.3617 1.1830 29.600282 -18.9079 -35.6785 40.3791 4000 1200 0.0000 -40.3617 1.1830 29.994287 -19.1528 -35.5476 40.3791 4000 1400 0.0000 -40.3617 1.1830 30.379890 -19.3916 -35.4179 40.3791 4000 1600 0.0000 -40.3617 1.1830 30.757445 -19.6246 -35.2894 40.3791 4000 1800 0.0000 -40.3617 1.1830 31.127281 -19.8519 -35.1619 40.3791 4000 2000 0.0000 -40.3617 1.1830 31.489706 -20.0740 -35.0357 40.3791 4000 2200 0.0000 -40.3617 1.1830 31.845009 -20.2908 -34.9105 40.3791 4000 2400 0.0000 -40.3617 1.1830 32.193458 -20.5028 -34.7865 40.3791 4000 2600 0.0000 -40.3617 1.1830 32.535308 -20.7100 -34.6635 40.3791 4000 2800 0.0000 -40.3617 1.1830 32.870797 -20.9126 -34.5417 40.3791 4000 3000 0.0000 -40.3617 1.1830 33.200149 -21.1108 -34.4209 40.3791 4000 3200 0.0000 -40.3617 1.1830 33.523576 -21.3047 -34.3012 40.3791 4000 3400 0.0000 -40.3617 1.1830 33.841277 -21.4946 -34.1825 40.3791 4000 3600 0.0000 -40.3617 1.1830 34.153442 -21.6805 -34.0649 40.3791 4000 3800 0.0000 -40.3617 1.1830 34.460249 -21.8626 -33.9483 40.3791 4000 4000 0.0000 -40.3617 1.1830 34.761869 -22.0410 -33.8328 40.3791 4000 4200 0.0000 -40.3617 1.1830 35.058463 -22.2159 -33.7182 40.3791 4000 4400 0.0000 -40.3617 1.1830 35.350183 -22.3873 -33.6047 40.3791 4000 4600 0.0000 -40.3617 1.1830 35.637176 -22.5553 -33.4921 40.3791 4000 4800 0.0000 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-31.9156 40.3791 4000 7800 0.0000 -40.3617 1.1830 39.682586 -24.8619 -31.8175 40.3791 4000 8000 0.0000 -40.3617 1.1830 39.906199 -24.9859 -31.7202 40.3791 4000 8200 0.0000 -40.3617 1.1830 40.126852 -25.1079 -31.6237 40.3791 4000 8400 0.0000 -40.3617 1.1830 40.344610 -25.2279 -31.5281 40.3791 4500 0 0.0000 -40.1602 1.1233 27.488683 -17.5403 -36.1446 40.1759 4500 200 0.0000 -40.1602 1.1233 27.931079 -17.8189 -36.0081 40.1759 4500 400 0.0000 -40.1602 1.1233 28.362834 -18.0897 -35.8728 40.1759 4500 600 0.0000 -40.1602 1.1233 28.784470 -18.3532 -35.7387 40.1759 4500 800 0.0000 -40.1602 1.1233 29.196467 -18.6097 -35.6058 40.1759 4500 1000 0.0000 -40.1602 1.1233 29.599271 -18.8596 -35.4741 40.1759 4500 1200 0.0000 -40.1602 1.1233 29.993291 -19.1031 -35.3435 40.1759 4500 1400 0.0000 -40.1602 1.1233 30.378908 -19.3405 -35.2142 40.1759 4500 1600 0.0000 -40.1602 1.1233 30.756476 -19.5722 -35.0860 40.1759 4500 1800 0.0000 -40.1602 1.1233 31.126325 -19.7982 -34.9589 40.1759 4500 2000 0.0000 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-33.1826 40.1759 4500 5000 0.0000 -40.1602 1.1233 36.196728 -22.8104 -33.0724 40.1759 4500 5200 0.0000 -40.1602 1.1233 36.470353 -22.9681 -32.9630 40.1759 4500 5400 0.0000 -40.1602 1.1233 36.739768 -23.1229 -32.8547 40.1759 4500 5600 0.0000 -40.1602 1.1233 37.005088 -23.2748 -32.7473 40.1759 4500 5800 0.0000 -40.1602 1.1233 37.266425 -23.4239 -32.6408 40.1759 4500 6000 0.0000 -40.1602 1.1233 37.523882 -23.5703 -32.5352 40.1759 4500 6200 0.0000 -40.1602 1.1233 37.777562 -23.7141 -32.4305 40.1759 4500 6400 0.0000 -40.1602 1.1233 38.027561 -23.8554 -32.3267 40.1759 4500 6600 0.0000 -40.1602 1.1233 38.273972 -23.9942 -32.2238 40.1759 4500 6800 0.0000 -40.1602 1.1233 38.516886 -24.1306 -32.1218 40.1759 4500 7000 0.0000 -40.1602 1.1233 38.756387 -24.2647 -32.0207 40.1759 4500 7200 0.0000 -40.1602 1.1233 38.992558 -24.3965 -31.9204 40.1759 4500 7400 0.0000 -40.1602 1.1233 39.225479 -24.5260 -31.8209 40.1759 4500 7600 0.0000 -40.1602 1.1233 39.455227 -24.6534 -31.7223 40.1759 4500 7800 0.0000 -40.1602 1.1233 39.681874 -24.7787 -31.6246 40.1759 4500 8000 0.0000 -40.1602 1.1233 39.905492 -24.9020 -31.5276 40.1759 4500 8200 0.0000 -40.1602 1.1233 40.126149 -25.0232 -31.4315 40.1759 4500 8400 0.0000 -40.1602 1.1233 40.343913 -25.1425 -31.3361 40.1759 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.000 ================================================ FILE: tests/test_data/cropA/geometry/20180506-20180623_VV_8rlks_base.par ================================================ initial_baseline(TCN): -0.5613989 -14.6282589 15.9816550 m m m initial_baseline_rate: 0.0000000 0.0085490 -0.0055608 m/s m/s m/s precision_baseline(TCN): 0.0000000 -14.8438160 16.0763524 m m m precision_baseline_rate: 0.0000000 0.0187805 -0.0088620 m/s m/s m/s unwrap_phase_constant: -0.00022 radians ================================================ FILE: tests/test_data/cropA/geometry/20180506-20180623_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): -0.561 -14.628 15.982 orbit baseline rate (TCN) (m/s): 0.000e+00 8.549e-03 -5.561e-03 baseline vector (TCN) (m): 0.000 -14.844 16.076 baseline rate (TCN) (m/s): 0.000e+00 1.878e-02 -8.862e-03 SLC-1 center baseline length (m): 21.8812 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 0.0000 -15.0191 16.1591 27.497816 7.3990 -20.7832 22.0610 0 200 0.0000 -15.0191 16.1591 27.940057 7.2384 -20.8397 22.0610 0 400 0.0000 -15.0191 16.1591 28.371664 7.0812 -20.8937 22.0610 0 600 0.0000 -15.0191 16.1591 28.793160 6.9273 -20.9452 22.0610 0 800 0.0000 -15.0191 16.1591 29.205025 6.7766 -20.9944 22.0610 0 1000 0.0000 -15.0191 16.1591 29.607701 6.6289 -21.0415 22.0610 0 1200 0.0000 -15.0191 16.1591 30.001600 6.4840 -21.0866 22.0610 0 1400 0.0000 -15.0191 16.1591 30.387101 6.3420 -21.1298 22.0610 0 1600 0.0000 -15.0191 16.1591 30.764557 6.2027 -21.1711 22.0610 0 1800 0.0000 -15.0191 16.1591 31.134300 6.0659 -21.2107 22.0610 0 2000 0.0000 -15.0191 16.1591 31.496635 5.9317 -21.2486 22.0610 0 2200 0.0000 -15.0191 16.1591 31.851850 5.7998 -21.2850 22.0610 0 2400 0.0000 -15.0191 16.1591 32.200217 5.6703 -21.3199 22.0610 0 2600 0.0000 -15.0191 16.1591 32.541986 5.5430 -21.3533 22.0610 0 2800 0.0000 -15.0191 16.1591 32.877397 5.4179 -21.3854 22.0610 0 3000 0.0000 -15.0191 16.1591 33.206674 5.2949 -21.4162 22.0610 0 3200 0.0000 -15.0191 16.1591 33.530029 5.1740 -21.4457 22.0610 0 3400 0.0000 -15.0191 16.1591 33.847660 5.0550 -21.4741 22.0610 0 3600 0.0000 -15.0191 16.1591 34.159757 4.9380 -21.5013 22.0610 0 3800 0.0000 -15.0191 16.1591 34.466499 4.8228 -21.5274 22.0610 0 4000 0.0000 -15.0191 16.1591 34.768056 4.7094 -21.5525 22.0610 0 4200 0.0000 -15.0191 16.1591 35.064588 4.5978 -21.5766 22.0610 0 4400 0.0000 -15.0191 16.1591 35.356248 4.4879 -21.5997 22.0610 0 4600 0.0000 -15.0191 16.1591 35.643183 4.3797 -21.6219 22.0610 0 4800 0.0000 -15.0191 16.1591 35.925531 4.2731 -21.6432 22.0610 0 5000 0.0000 -15.0191 16.1591 36.203424 4.1681 -21.6637 22.0610 0 5200 0.0000 -15.0191 16.1591 36.476989 4.0646 -21.6834 22.0610 0 5400 0.0000 -15.0191 16.1591 36.746345 3.9626 -21.7022 22.0610 0 5600 0.0000 -15.0191 16.1591 37.011608 3.8621 -21.7203 22.0610 0 5800 0.0000 -15.0191 16.1591 37.272888 3.7630 -21.7377 22.0610 0 6000 0.0000 -15.0191 16.1591 37.530291 3.6653 -21.7544 22.0610 0 6200 0.0000 -15.0191 16.1591 37.783917 3.5690 -21.7704 22.0610 0 6400 0.0000 -15.0191 16.1591 38.033864 3.4740 -21.7858 22.0610 0 6600 0.0000 -15.0191 16.1591 38.280225 3.3803 -21.8005 22.0610 0 6800 0.0000 -15.0191 16.1591 38.523089 3.2878 -21.8147 22.0610 0 7000 0.0000 -15.0191 16.1591 38.762542 3.1966 -21.8282 22.0610 0 7200 0.0000 -15.0191 16.1591 38.998666 3.1066 -21.8412 22.0610 0 7400 0.0000 -15.0191 16.1591 39.231541 3.0178 -21.8536 22.0610 0 7600 0.0000 -15.0191 16.1591 39.461243 2.9302 -21.8656 22.0610 0 7800 0.0000 -15.0191 16.1591 39.687846 2.8437 -21.8770 22.0610 0 8000 0.0000 -15.0191 16.1591 39.911421 2.7583 -21.8879 22.0610 0 8200 0.0000 -15.0191 16.1591 40.132036 2.6740 -21.8984 22.0610 0 8400 0.0000 -15.0191 16.1591 40.349758 2.5908 -21.9084 22.0610 500 0 0.0000 -14.9805 16.1409 27.496883 7.4010 -20.7405 22.0214 500 200 0.0000 -14.9805 16.1409 27.939140 7.2407 -20.7970 22.0214 500 400 0.0000 -14.9805 16.1409 28.370762 7.0838 -20.8509 22.0214 500 600 0.0000 -14.9805 16.1409 28.792272 6.9303 -20.9025 22.0214 500 800 0.0000 -14.9805 16.1409 29.204150 6.7798 -20.9518 22.0214 500 1000 0.0000 -14.9805 16.1409 29.606840 6.6324 -20.9989 22.0214 500 1200 0.0000 -14.9805 16.1409 30.000750 6.4879 -21.0440 22.0214 500 1400 0.0000 -14.9805 16.1409 30.386263 6.3461 -21.0872 22.0214 500 1600 0.0000 -14.9805 16.1409 30.763731 6.2071 -21.1285 22.0214 500 1800 0.0000 -14.9805 16.1409 31.133484 6.0706 -21.1681 22.0214 500 2000 0.0000 -14.9805 16.1409 31.495829 5.9366 -21.2061 22.0214 500 2200 0.0000 -14.9805 16.1409 31.851055 5.8050 -21.2425 22.0214 500 2400 0.0000 -14.9805 16.1409 32.199431 5.6757 -21.2774 22.0214 500 2600 0.0000 -14.9805 16.1409 32.541209 5.5487 -21.3109 22.0214 500 2800 0.0000 -14.9805 16.1409 32.876630 5.4239 -21.3430 22.0214 500 3000 0.0000 -14.9805 16.1409 33.205915 5.3011 -21.3738 22.0214 500 3200 0.0000 -14.9805 16.1409 33.529278 5.1804 -21.4034 22.0214 500 3400 0.0000 -14.9805 16.1409 33.846917 5.0617 -21.4318 22.0214 500 3600 0.0000 -14.9805 16.1409 34.159022 4.9449 -21.4590 22.0214 500 3800 0.0000 -14.9805 16.1409 34.465771 4.8299 -21.4852 22.0214 500 4000 0.0000 -14.9805 16.1409 34.767335 4.7167 -21.5103 22.0214 500 4200 0.0000 -14.9805 16.1409 35.063874 4.6054 -21.5345 22.0214 500 4400 0.0000 -14.9805 16.1409 35.355542 4.4957 -21.5576 22.0214 500 4600 0.0000 -14.9805 16.1409 35.642483 4.3877 -21.5799 22.0214 500 4800 0.0000 -14.9805 16.1409 35.924838 4.2813 -21.6012 22.0214 500 5000 0.0000 -14.9805 16.1409 36.202737 4.1764 -21.6217 22.0214 500 5200 0.0000 -14.9805 16.1409 36.476308 4.0732 -21.6414 22.0214 500 5400 0.0000 -14.9805 16.1409 36.745670 3.9714 -21.6603 22.0214 500 5600 0.0000 -14.9805 16.1409 37.010938 3.8710 -21.6785 22.0214 500 5800 0.0000 -14.9805 16.1409 37.272224 3.7721 -21.6959 22.0214 500 6000 0.0000 -14.9805 16.1409 37.529632 3.6746 -21.7127 22.0214 500 6200 0.0000 -14.9805 16.1409 37.783264 3.5785 -21.7287 22.0214 500 6400 0.0000 -14.9805 16.1409 38.033217 3.4837 -21.7441 22.0214 500 6600 0.0000 -14.9805 16.1409 38.279583 3.3901 -21.7589 22.0214 500 6800 0.0000 -14.9805 16.1409 38.522452 3.2979 -21.7731 22.0214 500 7000 0.0000 -14.9805 16.1409 38.761909 3.2068 -21.7867 22.0214 500 7200 0.0000 -14.9805 16.1409 38.998038 3.1170 -21.7997 22.0214 500 7400 0.0000 -14.9805 16.1409 39.230918 3.0284 -21.8122 22.0214 500 7600 0.0000 -14.9805 16.1409 39.460624 2.9409 -21.8241 22.0214 500 7800 0.0000 -14.9805 16.1409 39.687232 2.8546 -21.8356 22.0214 500 8000 0.0000 -14.9805 16.1409 39.910811 2.7693 -21.8466 22.0214 500 8200 0.0000 -14.9805 16.1409 40.131431 2.6852 -21.8571 22.0214 500 8400 0.0000 -14.9805 16.1409 40.349156 2.6021 -21.8671 22.0214 1000 0 0.0000 -14.9419 16.1226 27.495930 7.4030 -20.6977 21.9818 1000 200 0.0000 -14.9419 16.1226 27.938203 7.2431 -20.7542 21.9818 1000 400 0.0000 -14.9419 16.1226 28.369840 7.0865 -20.8082 21.9818 1000 600 0.0000 -14.9419 16.1226 28.791365 6.9332 -20.8598 21.9818 1000 800 0.0000 -14.9419 16.1226 29.203256 6.7831 -20.9091 21.9818 1000 1000 0.0000 -14.9419 16.1226 29.605959 6.6360 -20.9562 21.9818 1000 1200 0.0000 -14.9419 16.1226 29.999882 6.4917 -21.0013 21.9818 1000 1400 0.0000 -14.9419 16.1226 30.385407 6.3503 -21.0446 21.9818 1000 1600 0.0000 -14.9419 16.1226 30.762886 6.2115 -21.0859 21.9818 1000 1800 0.0000 -14.9419 16.1226 31.132650 6.0753 -21.1256 21.9818 1000 2000 0.0000 -14.9419 16.1226 31.495006 5.9415 -21.1636 21.9818 1000 2200 0.0000 -14.9419 16.1226 31.850242 5.8102 -21.2000 21.9818 1000 2400 0.0000 -14.9419 16.1226 32.198628 5.6812 -21.2350 21.9818 1000 2600 0.0000 -14.9419 16.1226 32.540416 5.5544 -21.2685 21.9818 1000 2800 0.0000 -14.9419 16.1226 32.875845 5.4298 -21.3006 21.9818 1000 3000 0.0000 -14.9419 16.1226 33.205140 5.3073 -21.3315 21.9818 1000 3200 0.0000 -14.9419 16.1226 33.528511 5.1868 -21.3611 21.9818 1000 3400 0.0000 -14.9419 16.1226 33.846158 5.0683 -21.3895 21.9818 1000 3600 0.0000 -14.9419 16.1226 34.158271 4.9517 -21.4168 21.9818 1000 3800 0.0000 -14.9419 16.1226 34.465028 4.8370 -21.4430 21.9818 1000 4000 0.0000 -14.9419 16.1226 34.766600 4.7241 -21.4682 21.9818 1000 4200 0.0000 -14.9419 16.1226 35.063146 4.6129 -21.4923 21.9818 1000 4400 0.0000 -14.9419 16.1226 35.354821 4.5034 -21.5155 21.9818 1000 4600 0.0000 -14.9419 16.1226 35.641769 4.3956 -21.5378 21.9818 1000 4800 0.0000 -14.9419 16.1226 35.924130 4.2894 -21.5592 21.9818 1000 5000 0.0000 -14.9419 16.1226 36.202036 4.1848 -21.5798 21.9818 1000 5200 0.0000 -14.9419 16.1226 36.475612 4.0817 -21.5995 21.9818 1000 5400 0.0000 -14.9419 16.1226 36.744981 3.9801 -21.6185 21.9818 1000 5600 0.0000 -14.9419 16.1226 37.010255 3.8800 -21.6367 21.9818 1000 5800 0.0000 -14.9419 16.1226 37.271547 3.7813 -21.6541 21.9818 1000 6000 0.0000 -14.9419 16.1226 37.528961 3.6840 -21.6709 21.9818 1000 6200 0.0000 -14.9419 16.1226 37.782598 3.5880 -21.6870 21.9818 1000 6400 0.0000 -14.9419 16.1226 38.032556 3.4933 -21.7024 21.9818 1000 6600 0.0000 -14.9419 16.1226 38.278927 3.4000 -21.7173 21.9818 1000 6800 0.0000 -14.9419 16.1226 38.521801 3.3079 -21.7315 21.9818 1000 7000 0.0000 -14.9419 16.1226 38.761264 3.2170 -21.7451 21.9818 1000 7200 0.0000 -14.9419 16.1226 38.997398 3.1274 -21.7582 21.9818 1000 7400 0.0000 -14.9419 16.1226 39.230282 3.0389 -21.7707 21.9818 1000 7600 0.0000 -14.9419 16.1226 39.459993 2.9516 -21.7827 21.9818 1000 7800 0.0000 -14.9419 16.1226 39.686605 2.8654 -21.7942 21.9818 1000 8000 0.0000 -14.9419 16.1226 39.910189 2.7804 -21.8053 21.9818 1000 8200 0.0000 -14.9419 16.1226 40.130813 2.6964 -21.8158 21.9818 1000 8400 0.0000 -14.9419 16.1226 40.348543 2.6135 -21.8259 21.9818 1500 0 0.0000 -14.9033 16.1044 27.494956 7.4051 -20.6549 21.9422 1500 200 0.0000 -14.9033 16.1044 27.937245 7.2454 -20.7114 21.9422 1500 400 0.0000 -14.9033 16.1044 28.368898 7.0892 -20.7654 21.9422 1500 600 0.0000 -14.9033 16.1044 28.790438 6.9362 -20.8170 21.9422 1500 800 0.0000 -14.9033 16.1044 29.202344 6.7864 -20.8664 21.9422 1500 1000 0.0000 -14.9033 16.1044 29.605059 6.6395 -20.9136 21.9422 1500 1200 0.0000 -14.9033 16.1044 29.998996 6.4956 -20.9587 21.9422 1500 1400 0.0000 -14.9033 16.1044 30.384532 6.3544 -21.0019 21.9422 1500 1600 0.0000 -14.9033 16.1044 30.762024 6.2159 -21.0434 21.9422 1500 1800 0.0000 -14.9033 16.1044 31.131799 6.0800 -21.0830 21.9422 1500 2000 0.0000 -14.9033 16.1044 31.494166 5.9465 -21.1211 21.9422 1500 2200 0.0000 -14.9033 16.1044 31.849412 5.8154 -21.1575 21.9422 1500 2400 0.0000 -14.9033 16.1044 32.197808 5.6867 -21.1925 21.9422 1500 2600 0.0000 -14.9033 16.1044 32.539606 5.5601 -21.2260 21.9422 1500 2800 0.0000 -14.9033 16.1044 32.875045 5.4358 -21.2582 21.9422 1500 3000 0.0000 -14.9033 16.1044 33.204348 5.3135 -21.2891 21.9422 1500 3200 0.0000 -14.9033 16.1044 33.527728 5.1933 -21.3188 21.9422 1500 3400 0.0000 -14.9033 16.1044 33.845384 5.0750 -21.3472 21.9422 1500 3600 0.0000 -14.9033 16.1044 34.157505 4.9586 -21.3746 21.9422 1500 3800 0.0000 -14.9033 16.1044 34.464270 4.8441 -21.4008 21.9422 1500 4000 0.0000 -14.9033 16.1044 34.765849 4.7314 -21.4260 21.9422 1500 4200 0.0000 -14.9033 16.1044 35.062403 4.6204 -21.4502 21.9422 1500 4400 0.0000 -14.9033 16.1044 35.354084 4.5112 -21.4735 21.9422 1500 4600 0.0000 -14.9033 16.1044 35.641040 4.4036 -21.4958 21.9422 1500 4800 0.0000 -14.9033 16.1044 35.923408 4.2976 -21.5172 21.9422 1500 5000 0.0000 -14.9033 16.1044 36.201320 4.1932 -21.5378 21.9422 1500 5200 0.0000 -14.9033 16.1044 36.474903 4.0903 -21.5576 21.9422 1500 5400 0.0000 -14.9033 16.1044 36.744278 3.9889 -21.5766 21.9422 1500 5600 0.0000 -14.9033 16.1044 37.009558 3.8889 -21.5948 21.9422 1500 5800 0.0000 -14.9033 16.1044 37.270856 3.7904 -21.6123 21.9422 1500 6000 0.0000 -14.9033 16.1044 37.528276 3.6933 -21.6291 21.9422 1500 6200 0.0000 -14.9033 16.1044 37.781919 3.5975 -21.6453 21.9422 1500 6400 0.0000 -14.9033 16.1044 38.031882 3.5030 -21.6608 21.9422 1500 6600 0.0000 -14.9033 16.1044 38.278259 3.4099 -21.6756 21.9422 1500 6800 0.0000 -14.9033 16.1044 38.521138 3.3179 -21.6899 21.9422 1500 7000 0.0000 -14.9033 16.1044 38.760605 3.2273 -21.7036 21.9422 1500 7200 0.0000 -14.9033 16.1044 38.996744 3.1378 -21.7167 21.9422 1500 7400 0.0000 -14.9033 16.1044 39.229633 3.0495 -21.7293 21.9422 1500 7600 0.0000 -14.9033 16.1044 39.459350 2.9623 -21.7413 21.9422 1500 7800 0.0000 -14.9033 16.1044 39.685966 2.8763 -21.7529 21.9422 1500 8000 0.0000 -14.9033 16.1044 39.909555 2.7914 -21.7639 21.9422 1500 8200 0.0000 -14.9033 16.1044 40.130183 2.7076 -21.7745 21.9422 1500 8400 0.0000 -14.9033 16.1044 40.347918 2.6248 -21.7846 21.9422 2000 0 0.0000 -14.8647 16.0862 27.493962 7.4071 -20.6121 21.9026 2000 200 0.0000 -14.8647 16.0862 27.936268 7.2477 -20.6687 21.9026 2000 400 0.0000 -14.8647 16.0862 28.367937 7.0918 -20.7227 21.9026 2000 600 0.0000 -14.8647 16.0862 28.789491 6.9392 -20.7743 21.9026 2000 800 0.0000 -14.8647 16.0862 29.201412 6.7896 -20.8237 21.9026 2000 1000 0.0000 -14.8647 16.0862 29.604141 6.6431 -20.8709 21.9026 2000 1200 0.0000 -14.8647 16.0862 29.998091 6.4994 -20.9161 21.9026 2000 1400 0.0000 -14.8647 16.0862 30.383640 6.3585 -20.9593 21.9026 2000 1600 0.0000 -14.8647 16.0862 30.761143 6.2203 -21.0008 21.9026 2000 1800 0.0000 -14.8647 16.0862 31.130930 6.0846 -21.0405 21.9026 2000 2000 0.0000 -14.8647 16.0862 31.493308 5.9514 -21.0785 21.9026 2000 2200 0.0000 -14.8647 16.0862 31.848565 5.8206 -21.1150 21.9026 2000 2400 0.0000 -14.8647 16.0862 32.196971 5.6921 -21.1500 21.9026 2000 2600 0.0000 -14.8647 16.0862 32.538779 5.5659 -21.1836 21.9026 2000 2800 0.0000 -14.8647 16.0862 32.874228 5.4417 -21.2158 21.9026 2000 3000 0.0000 -14.8647 16.0862 33.203540 5.3197 -21.2468 21.9026 2000 3200 0.0000 -14.8647 16.0862 33.526929 5.1997 -21.2765 21.9026 2000 3400 0.0000 -14.8647 16.0862 33.844594 5.0817 -21.3050 21.9026 2000 3600 0.0000 -14.8647 16.0862 34.156723 4.9655 -21.3323 21.9026 2000 3800 0.0000 -14.8647 16.0862 34.463496 4.8512 -21.3586 21.9026 2000 4000 0.0000 -14.8647 16.0862 34.765083 4.7387 -21.3838 21.9026 2000 4200 0.0000 -14.8647 16.0862 35.061644 4.6280 -21.4081 21.9026 2000 4400 0.0000 -14.8647 16.0862 35.353334 4.5189 -21.4314 21.9026 2000 4600 0.0000 -14.8647 16.0862 35.640296 4.4116 -21.4537 21.9026 2000 4800 0.0000 -14.8647 16.0862 35.922671 4.3058 -21.4752 21.9026 2000 5000 0.0000 -14.8647 16.0862 36.200590 4.2015 -21.4959 21.9026 2000 5200 0.0000 -14.8647 16.0862 36.474180 4.0989 -21.5157 21.9026 2000 5400 0.0000 -14.8647 16.0862 36.743561 3.9977 -21.5347 21.9026 2000 5600 0.0000 -14.8647 16.0862 37.008848 3.8979 -21.5530 21.9026 2000 5800 0.0000 -14.8647 16.0862 37.270151 3.7996 -21.5705 21.9026 2000 6000 0.0000 -14.8647 16.0862 37.527577 3.7026 -21.5874 21.9026 2000 6200 0.0000 -14.8647 16.0862 37.781226 3.6070 -21.6036 21.9026 2000 6400 0.0000 -14.8647 16.0862 38.031195 3.5127 -21.6191 21.9026 2000 6600 0.0000 -14.8647 16.0862 38.277577 3.4197 -21.6340 21.9026 2000 6800 0.0000 -14.8647 16.0862 38.520461 3.3280 -21.6483 21.9026 2000 7000 0.0000 -14.8647 16.0862 38.759934 3.2375 -21.6620 21.9026 2000 7200 0.0000 -14.8647 16.0862 38.996078 3.1482 -21.6752 21.9026 2000 7400 0.0000 -14.8647 16.0862 39.228972 3.0600 -21.6878 21.9026 2000 7600 0.0000 -14.8647 16.0862 39.458693 2.9731 -21.6999 21.9026 2000 7800 0.0000 -14.8647 16.0862 39.685315 2.8872 -21.7115 21.9026 2000 8000 0.0000 -14.8647 16.0862 39.908908 2.8025 -21.7226 21.9026 2000 8200 0.0000 -14.8647 16.0862 40.129541 2.7188 -21.7332 21.9026 2000 8400 0.0000 -14.8647 16.0862 40.347280 2.6362 -21.7434 21.9026 2500 0 0.0000 -14.8261 16.0680 27.492947 7.4091 -20.5693 21.8631 2500 200 0.0000 -14.8261 16.0680 27.935270 7.2501 -20.6259 21.8631 2500 400 0.0000 -14.8261 16.0680 28.366955 7.0945 -20.6800 21.8631 2500 600 0.0000 -14.8261 16.0680 28.788525 6.9421 -20.7316 21.8631 2500 800 0.0000 -14.8261 16.0680 29.200461 6.7929 -20.7810 21.8631 2500 1000 0.0000 -14.8261 16.0680 29.603204 6.6467 -20.8282 21.8631 2500 1200 0.0000 -14.8261 16.0680 29.997167 6.5033 -20.8734 21.8631 2500 1400 0.0000 -14.8261 16.0680 30.382730 6.3627 -20.9167 21.8631 2500 1600 0.0000 -14.8261 16.0680 30.760245 6.2247 -20.9582 21.8631 2500 1800 0.0000 -14.8261 16.0680 31.130044 6.0893 -20.9979 21.8631 2500 2000 0.0000 -14.8261 16.0680 31.492434 5.9564 -21.0360 21.8631 2500 2200 0.0000 -14.8261 16.0680 31.847702 5.8258 -21.0725 21.8631 2500 2400 0.0000 -14.8261 16.0680 32.196118 5.6976 -21.1076 21.8631 2500 2600 0.0000 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21.8631 2500 5600 0.0000 -14.8261 16.0680 37.008123 3.9069 -21.5111 21.8631 2500 5800 0.0000 -14.8261 16.0680 37.269433 3.8087 -21.5287 21.8631 2500 6000 0.0000 -14.8261 16.0680 37.526865 3.7120 -21.5456 21.8631 2500 6200 0.0000 -14.8261 16.0680 37.780519 3.6165 -21.5619 21.8631 2500 6400 0.0000 -14.8261 16.0680 38.030494 3.5224 -21.5774 21.8631 2500 6600 0.0000 -14.8261 16.0680 38.276882 3.4296 -21.5924 21.8631 2500 6800 0.0000 -14.8261 16.0680 38.519772 3.3380 -21.6067 21.8631 2500 7000 0.0000 -14.8261 16.0680 38.759250 3.2477 -21.6205 21.8631 2500 7200 0.0000 -14.8261 16.0680 38.995399 3.1586 -21.6337 21.8631 2500 7400 0.0000 -14.8261 16.0680 39.228299 3.0706 -21.6463 21.8631 2500 7600 0.0000 -14.8261 16.0680 39.458025 2.9838 -21.6585 21.8631 2500 7800 0.0000 -14.8261 16.0680 39.684651 2.8981 -21.6701 21.8631 2500 8000 0.0000 -14.8261 16.0680 39.908249 2.8135 -21.6813 21.8631 2500 8200 0.0000 -14.8261 16.0680 40.128887 2.7300 -21.6919 21.8631 2500 8400 0.0000 -14.8261 16.0680 40.346630 2.6475 -21.7022 21.8631 3000 0 0.0000 -14.7875 16.0498 27.491911 7.4111 -20.5265 21.8235 3000 200 0.0000 -14.7875 16.0498 27.934252 7.2524 -20.5831 21.8235 3000 400 0.0000 -14.7875 16.0498 28.365954 7.0972 -20.6372 21.8235 3000 600 0.0000 -14.7875 16.0498 28.787540 6.9451 -20.6889 21.8235 3000 800 0.0000 -14.7875 16.0498 29.199491 6.7962 -20.7383 21.8235 3000 1000 0.0000 -14.7875 16.0498 29.602249 6.6502 -20.7855 21.8235 3000 1200 0.0000 -14.7875 16.0498 29.996226 6.5072 -20.8308 21.8235 3000 1400 0.0000 -14.7875 16.0498 30.381801 6.3668 -20.8741 21.8235 3000 1600 0.0000 -14.7875 16.0498 30.759330 6.2291 -20.9156 21.8235 3000 1800 0.0000 -14.7875 16.0498 31.129141 6.0940 -20.9554 21.8235 3000 2000 0.0000 -14.7875 16.0498 31.491542 5.9614 -20.9935 21.8235 3000 2200 0.0000 -14.7875 16.0498 31.846821 5.8311 -21.0300 21.8235 3000 2400 0.0000 -14.7875 16.0498 32.195248 5.7031 -21.0651 21.8235 3000 2600 0.0000 -14.7875 16.0498 32.537077 5.5773 -21.0988 21.8235 3000 2800 0.0000 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6000 0.0000 -14.7489 16.0316 37.525400 3.7306 -21.4621 21.7840 3500 6200 0.0000 -14.7489 16.0316 37.779067 3.6356 -21.4784 21.7840 3500 6400 0.0000 -14.7489 16.0316 38.029054 3.5418 -21.4941 21.7840 3500 6600 0.0000 -14.7489 16.0316 38.275453 3.4494 -21.5091 21.7840 3500 6800 0.0000 -14.7489 16.0316 38.518355 3.3581 -21.5235 21.7840 3500 7000 0.0000 -14.7489 16.0316 38.757844 3.2682 -21.5374 21.7840 3500 7200 0.0000 -14.7489 16.0316 38.994004 3.1794 -21.5507 21.7840 3500 7400 0.0000 -14.7489 16.0316 39.226914 3.0917 -21.5634 21.7840 3500 7600 0.0000 -14.7489 16.0316 39.456650 3.0052 -21.5756 21.7840 3500 7800 0.0000 -14.7489 16.0316 39.683287 2.9199 -21.5874 21.7840 3500 8000 0.0000 -14.7489 16.0316 39.906895 2.8356 -21.5986 21.7840 3500 8200 0.0000 -14.7489 16.0316 40.127542 2.7524 -21.6094 21.7840 3500 8400 0.0000 -14.7489 16.0316 40.345295 2.6703 -21.6197 21.7840 4000 0 0.0000 -14.7103 16.0133 27.489779 7.4152 -20.4410 21.7444 4000 200 0.0000 -14.7103 16.0133 27.932157 7.2572 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33.523576 5.2255 -21.1072 21.7444 4000 3400 0.0000 -14.7103 16.0133 33.841277 5.1084 -21.1358 21.7444 4000 3600 0.0000 -14.7103 16.0133 34.153442 4.9931 -21.1633 21.7444 4000 3800 0.0000 -14.7103 16.0133 34.460249 4.8797 -21.1898 21.7444 4000 4000 0.0000 -14.7103 16.0133 34.761869 4.7681 -21.2152 21.7444 4000 4200 0.0000 -14.7103 16.0133 35.058463 4.6582 -21.2396 21.7444 4000 4400 0.0000 -14.7103 16.0133 35.350183 4.5500 -21.2630 21.7444 4000 4600 0.0000 -14.7103 16.0133 35.637176 4.4435 -21.2855 21.7444 4000 4800 0.0000 -14.7103 16.0133 35.919581 4.3385 -21.3072 21.7444 4000 5000 0.0000 -14.7103 16.0133 36.197528 4.2351 -21.3280 21.7444 4000 5200 0.0000 -14.7103 16.0133 36.471146 4.1332 -21.3480 21.7444 4000 5400 0.0000 -14.7103 16.0133 36.740554 4.0328 -21.3672 21.7444 4000 5600 0.0000 -14.7103 16.0133 37.005868 3.9338 -21.3856 21.7444 4000 5800 0.0000 -14.7103 16.0133 37.267197 3.8362 -21.4033 21.7444 4000 6000 0.0000 -14.7103 16.0133 37.524648 3.7400 -21.4204 21.7444 4000 6200 0.0000 -14.7103 16.0133 37.778321 3.6451 -21.4367 21.7444 4000 6400 0.0000 -14.7103 16.0133 38.028314 3.5515 -21.4524 21.7444 4000 6600 0.0000 -14.7103 16.0133 38.274719 3.4593 -21.4675 21.7444 4000 6800 0.0000 -14.7103 16.0133 38.517627 3.3682 -21.4819 21.7444 4000 7000 0.0000 -14.7103 16.0133 38.757122 3.2784 -21.4958 21.7444 4000 7200 0.0000 -14.7103 16.0133 38.993287 3.1898 -21.5092 21.7444 4000 7400 0.0000 -14.7103 16.0133 39.226203 3.1023 -21.5220 21.7444 4000 7600 0.0000 -14.7103 16.0133 39.455945 3.0160 -21.5342 21.7444 4000 7800 0.0000 -14.7103 16.0133 39.682586 2.9308 -21.5460 21.7444 4000 8000 0.0000 -14.7103 16.0133 39.906199 2.8467 -21.5573 21.7444 4000 8200 0.0000 -14.7103 16.0133 40.126852 2.7636 -21.5681 21.7444 4000 8400 0.0000 -14.7103 16.0133 40.344610 2.6816 -21.5784 21.7444 4500 0 0.0000 -14.6717 15.9951 27.488683 7.4173 -20.3982 21.7049 4500 200 0.0000 -14.6717 15.9951 27.931079 7.2596 -20.4548 21.7049 4500 400 0.0000 -14.6717 15.9951 28.362834 7.1052 -20.5090 21.7049 4500 600 0.0000 -14.6717 15.9951 28.784470 6.9541 -20.5607 21.7049 4500 800 0.0000 -14.6717 15.9951 29.196467 6.8061 -20.6102 21.7049 4500 1000 0.0000 -14.6717 15.9951 29.599271 6.6610 -20.6575 21.7049 4500 1200 0.0000 -14.6717 15.9951 29.993291 6.5188 -20.7028 21.7049 4500 1400 0.0000 -14.6717 15.9951 30.378908 6.3793 -20.7462 21.7049 4500 1600 0.0000 -14.6717 15.9951 30.756476 6.2425 -20.7878 21.7049 4500 1800 0.0000 -14.6717 15.9951 31.126325 6.1081 -20.8277 21.7049 4500 2000 0.0000 -14.6717 15.9951 31.488763 5.9763 -20.8659 21.7049 4500 2200 0.0000 -14.6717 15.9951 31.844077 5.8468 -20.9026 21.7049 4500 2400 0.0000 -14.6717 15.9951 32.192539 5.7195 -20.9377 21.7049 4500 2600 0.0000 -14.6717 15.9951 32.534400 5.5945 -20.9715 21.7049 4500 2800 0.0000 -14.6717 15.9951 32.869899 5.4716 -21.0039 21.7049 4500 3000 0.0000 -14.6717 15.9951 33.199262 5.3508 -21.0350 21.7049 4500 3200 0.0000 -14.6717 15.9951 33.522698 5.2319 -21.0649 21.7049 4500 3400 0.0000 -14.6717 15.9951 33.840409 5.1151 -21.0935 21.7049 4500 3600 0.0000 -14.6717 15.9951 34.152583 5.0000 -21.1211 21.7049 4500 3800 0.0000 -14.6717 15.9951 34.459400 4.8869 -21.1476 21.7049 4500 4000 0.0000 -14.6717 15.9951 34.761029 4.7755 -21.1730 21.7049 4500 4200 0.0000 -14.6717 15.9951 35.057631 4.6658 -21.1974 21.7049 4500 4400 0.0000 -14.6717 15.9951 35.349359 4.5578 -21.2209 21.7049 4500 4600 0.0000 -14.6717 15.9951 35.636360 4.4515 -21.2435 21.7049 4500 4800 0.0000 -14.6717 15.9951 35.918772 4.3467 -21.2652 21.7049 4500 5000 0.0000 -14.6717 15.9951 36.196728 4.2435 -21.2860 21.7049 4500 5200 0.0000 -14.6717 15.9951 36.470353 4.1418 -21.3060 21.7049 4500 5400 0.0000 -14.6717 15.9951 36.739768 4.0416 -21.3253 21.7049 4500 5600 0.0000 -14.6717 15.9951 37.005088 3.9428 -21.3438 21.7049 4500 5800 0.0000 -14.6717 15.9951 37.266425 3.8454 -21.3615 21.7049 4500 6000 0.0000 -14.6717 15.9951 37.523882 3.7493 -21.3786 21.7049 4500 6200 0.0000 -14.6717 15.9951 37.777562 3.6546 -21.3950 21.7049 4500 6400 0.0000 -14.6717 15.9951 38.027561 3.5613 -21.4107 21.7049 4500 6600 0.0000 -14.6717 15.9951 38.273972 3.4691 -21.4258 21.7049 4500 6800 0.0000 -14.6717 15.9951 38.516886 3.3783 -21.4404 21.7049 4500 7000 0.0000 -14.6717 15.9951 38.756387 3.2886 -21.4543 21.7049 4500 7200 0.0000 -14.6717 15.9951 38.992558 3.2002 -21.4677 21.7049 4500 7400 0.0000 -14.6717 15.9951 39.225479 3.1129 -21.4805 21.7049 4500 7600 0.0000 -14.6717 15.9951 39.455227 3.0267 -21.4928 21.7049 4500 7800 0.0000 -14.6717 15.9951 39.681874 2.9417 -21.5046 21.7049 4500 8000 0.0000 -14.6717 15.9951 39.905492 2.8577 -21.5159 21.7049 4500 8200 0.0000 -14.6717 15.9951 40.126149 2.7748 -21.5268 21.7049 4500 8400 0.0000 -14.6717 15.9951 40.343913 2.6930 -21.5372 21.7049 user time (s): 0.000 system time (s): 0.010 elapsed time (s): 0.010 ================================================ FILE: tests/test_data/cropA/geometry/20180506-20180705_VV_8rlks_base.par ================================================ initial_baseline(TCN): 1.5504708 92.0622963 7.2392205 m m m initial_baseline_rate: 0.0000000 -0.0681920 -0.0045449 m/s m/s m/s precision_baseline(TCN): 0.0000000 91.8647619 7.3101981 m m m precision_baseline_rate: 0.0000000 -0.0687605 -0.0014463 m/s m/s m/s unwrap_phase_constant: -0.00026 radians ================================================ FILE: tests/test_data/cropA/geometry/20180506-20180705_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): 1.550 92.062 7.239 orbit baseline rate (TCN) (m/s): 0.000e+00 -6.819e-02 -4.545e-03 baseline vector (TCN) (m): 0.000 91.865 7.310 baseline rate (TCN) (m/s): 0.000e+00 -6.876e-02 -1.446e-03 SLC-1 center baseline length (m): 92.1552 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 0.0000 92.5066 7.3237 27.497816 49.2079 78.6743 92.7960 0 200 0.0000 92.5066 7.3237 27.940057 49.8137 78.2921 92.7960 0 400 0.0000 92.5066 7.3237 28.371664 50.4020 77.9147 92.7960 0 600 0.0000 92.5066 7.3237 28.793160 50.9738 77.5418 92.7960 0 800 0.0000 92.5066 7.3237 29.205025 51.5299 77.1734 92.7960 0 1000 0.0000 92.5066 7.3237 29.607701 52.0710 76.8093 92.7960 0 1200 0.0000 92.5066 7.3237 30.001600 52.5978 76.4496 92.7960 0 1400 0.0000 92.5066 7.3237 30.387101 53.1110 76.0939 92.7960 0 1600 0.0000 92.5066 7.3237 30.764557 53.6112 75.7424 92.7960 0 1800 0.0000 92.5066 7.3237 31.134300 54.0988 75.3949 92.7960 0 2000 0.0000 92.5066 7.3237 31.496635 54.5745 75.0512 92.7960 0 2200 0.0000 92.5066 7.3237 31.851850 55.0388 74.7115 92.7960 0 2400 0.0000 92.5066 7.3237 32.200217 55.4920 74.3754 92.7960 0 2600 0.0000 92.5066 7.3237 32.541986 55.9347 74.0431 92.7960 0 2800 0.0000 92.5066 7.3237 32.877397 56.3672 73.7144 92.7960 0 3000 0.0000 92.5066 7.3237 33.206674 56.7899 73.3893 92.7960 0 3200 0.0000 92.5066 7.3237 33.530029 57.2031 73.0676 92.7960 0 3400 0.0000 92.5066 7.3237 33.847660 57.6073 72.7494 92.7960 0 3600 0.0000 92.5066 7.3237 34.159757 58.0027 72.4345 92.7960 0 3800 0.0000 92.5066 7.3237 34.466499 58.3897 72.1229 92.7960 0 4000 0.0000 92.5066 7.3237 34.768056 58.7685 71.8146 92.7960 0 4200 0.0000 92.5066 7.3237 35.064588 59.1394 71.5095 92.7960 0 4400 0.0000 92.5066 7.3237 35.356248 59.5026 71.2075 92.7960 0 4600 0.0000 92.5066 7.3237 35.643183 59.8585 70.9087 92.7960 0 4800 0.0000 92.5066 7.3237 35.925531 60.2072 70.6128 92.7960 0 5000 0.0000 92.5066 7.3237 36.203424 60.5490 70.3200 92.7960 0 5200 0.0000 92.5066 7.3237 36.476989 60.8840 70.0301 92.7960 0 5400 0.0000 92.5066 7.3237 36.746345 61.2126 69.7431 92.7960 0 5600 0.0000 92.5066 7.3237 37.011608 61.5348 69.4590 92.7960 0 5800 0.0000 92.5066 7.3237 37.272888 61.8509 69.1776 92.7960 0 6000 0.0000 92.5066 7.3237 37.530291 62.1611 68.8991 92.7960 0 6200 0.0000 92.5066 7.3237 37.783917 62.4655 68.6232 92.7960 0 6400 0.0000 92.5066 7.3237 38.033864 62.7642 68.3501 92.7960 0 6600 0.0000 92.5066 7.3237 38.280225 63.0575 68.0796 92.7960 0 6800 0.0000 92.5066 7.3237 38.523089 63.3455 67.8117 92.7960 0 7000 0.0000 92.5066 7.3237 38.762542 63.6284 67.5464 92.7960 0 7200 0.0000 92.5066 7.3237 38.998666 63.9062 67.2836 92.7960 0 7400 0.0000 92.5066 7.3237 39.231541 64.1792 67.0233 92.7960 0 7600 0.0000 92.5066 7.3237 39.461243 64.4473 66.7654 92.7960 0 7800 0.0000 92.5066 7.3237 39.687846 64.7109 66.5100 92.7960 0 8000 0.0000 92.5066 7.3237 39.911421 64.9699 66.2570 92.7960 0 8200 0.0000 92.5066 7.3237 40.132036 65.2246 66.0064 92.7960 0 8400 0.0000 92.5066 7.3237 40.349758 65.4749 65.7580 92.7960 500 0 0.0000 92.3653 7.3207 27.496883 49.1387 78.5511 92.6549 500 200 0.0000 92.3653 7.3207 27.939140 49.7436 78.1695 92.6549 500 400 0.0000 92.3653 7.3207 28.370762 50.3310 77.7925 92.6549 500 600 0.0000 92.3653 7.3207 28.792272 50.9019 77.4202 92.6549 500 800 0.0000 92.3653 7.3207 29.204150 51.4572 77.0523 92.6549 500 1000 0.0000 92.3653 7.3207 29.606840 51.9974 76.6887 92.6549 500 1200 0.0000 92.3653 7.3207 30.000750 52.5235 76.3294 92.6549 500 1400 0.0000 92.3653 7.3207 30.386263 53.0358 75.9743 92.6549 500 1600 0.0000 92.3653 7.3207 30.763731 53.5352 75.6232 92.6549 500 1800 0.0000 92.3653 7.3207 31.133484 54.0221 75.2762 92.6549 500 2000 0.0000 92.3653 7.3207 31.495829 54.4971 74.9331 92.6549 500 2200 0.0000 92.3653 7.3207 31.851055 54.9606 74.5937 92.6549 500 2400 0.0000 92.3653 7.3207 32.199431 55.4132 74.2582 92.6549 500 2600 0.0000 92.3653 7.3207 32.541209 55.8551 73.9263 92.6549 500 2800 0.0000 92.3653 7.3207 32.876630 56.2870 73.5981 92.6549 500 3000 0.0000 92.3653 7.3207 33.205915 56.7090 73.2734 92.6549 500 3200 0.0000 92.3653 7.3207 33.529278 57.1216 72.9522 92.6549 500 3400 0.0000 92.3653 7.3207 33.846917 57.5252 72.6344 92.6549 500 3600 0.0000 92.3653 7.3207 34.159022 57.9200 72.3199 92.6549 500 3800 0.0000 92.3653 7.3207 34.465771 58.3064 72.0088 92.6549 500 4000 0.0000 92.3653 7.3207 34.767335 58.6845 71.7009 92.6549 500 4200 0.0000 92.3653 7.3207 35.063874 59.0549 71.3963 92.6549 500 4400 0.0000 92.3653 7.3207 35.355542 59.4175 71.0947 92.6549 500 4600 0.0000 92.3653 7.3207 35.642483 59.7728 70.7963 92.6549 500 4800 0.0000 92.3653 7.3207 35.924838 60.1210 70.5008 92.6549 500 5000 0.0000 92.3653 7.3207 36.202737 60.4622 70.2084 92.6549 500 5200 0.0000 92.3653 7.3207 36.476308 60.7968 69.9189 92.6549 500 5400 0.0000 92.3653 7.3207 36.745670 61.1248 69.6323 92.6549 500 5600 0.0000 92.3653 7.3207 37.010938 61.4465 69.3486 92.6549 500 5800 0.0000 92.3653 7.3207 37.272224 61.7622 69.0677 92.6549 500 6000 0.0000 92.3653 7.3207 37.529632 62.0718 68.7895 92.6549 500 6200 0.0000 92.3653 7.3207 37.783264 62.3757 68.5141 92.6549 500 6400 0.0000 92.3653 7.3207 38.033217 62.6740 68.2413 92.6549 500 6600 0.0000 92.3653 7.3207 38.279583 62.9669 67.9712 92.6549 500 6800 0.0000 92.3653 7.3207 38.522452 63.2544 67.7037 92.6549 500 7000 0.0000 92.3653 7.3207 38.761909 63.5368 67.4387 92.6549 500 7200 0.0000 92.3653 7.3207 38.998038 63.8142 67.1763 92.6549 500 7400 0.0000 92.3653 7.3207 39.230918 64.0867 66.9164 92.6549 500 7600 0.0000 92.3653 7.3207 39.460624 64.3545 66.6589 92.6549 500 7800 0.0000 92.3653 7.3207 39.687232 64.6176 66.4038 92.6549 500 8000 0.0000 92.3653 7.3207 39.910811 64.8763 66.1512 92.6549 500 8200 0.0000 92.3653 7.3207 40.131431 65.1305 65.9009 92.6549 500 8400 0.0000 92.3653 7.3207 40.349156 65.3805 65.6529 92.6549 1000 0 0.0000 92.2239 7.3178 27.495930 49.0695 78.4279 92.5138 1000 200 0.0000 92.2239 7.3178 27.938203 49.6734 78.0468 92.5138 1000 400 0.0000 92.2239 7.3178 28.369840 50.2600 77.6704 92.5138 1000 600 0.0000 92.2239 7.3178 28.791365 50.8300 77.2985 92.5138 1000 800 0.0000 92.2239 7.3178 29.203256 51.3844 76.9311 92.5138 1000 1000 0.0000 92.2239 7.3178 29.605959 51.9239 76.5681 92.5138 1000 1200 0.0000 92.2239 7.3178 29.999882 52.4490 76.2093 92.5138 1000 1400 0.0000 92.2239 7.3178 30.385407 52.9606 75.8547 92.5138 1000 1600 0.0000 92.2239 7.3178 30.762886 53.4592 75.5041 92.5138 1000 1800 0.0000 92.2239 7.3178 31.132650 53.9454 75.1575 92.5138 1000 2000 0.0000 92.2239 7.3178 31.495006 54.4196 74.8149 92.5138 1000 2200 0.0000 92.2239 7.3178 31.850242 54.8825 74.4760 92.5138 1000 2400 0.0000 92.2239 7.3178 32.198628 55.3343 74.1409 92.5138 1000 2600 0.0000 92.2239 7.3178 32.540416 55.7756 73.8095 92.5138 1000 2800 0.0000 92.2239 7.3178 32.875845 56.2067 73.4818 92.5138 1000 3000 0.0000 92.2239 7.3178 33.205140 56.6281 73.1575 92.5138 1000 3200 0.0000 92.2239 7.3178 33.528511 57.0401 72.8367 92.5138 1000 3400 0.0000 92.2239 7.3178 33.846158 57.4430 72.5194 92.5138 1000 3600 0.0000 92.2239 7.3178 34.158271 57.8372 72.2054 92.5138 1000 3800 0.0000 92.2239 7.3178 34.465028 58.2230 71.8947 92.5138 1000 4000 0.0000 92.2239 7.3178 34.766600 58.6006 71.5873 92.5138 1000 4200 0.0000 92.2239 7.3178 35.063146 58.9703 71.2830 92.5138 1000 4400 0.0000 92.2239 7.3178 35.354821 59.3324 70.9819 92.5138 1000 4600 0.0000 92.2239 7.3178 35.641769 59.6872 70.6839 92.5138 1000 4800 0.0000 92.2239 7.3178 35.924130 60.0348 70.3889 92.5138 1000 5000 0.0000 92.2239 7.3178 36.202036 60.3755 70.0969 92.5138 1000 5200 0.0000 92.2239 7.3178 36.475612 60.7095 69.8078 92.5138 1000 5400 0.0000 92.2239 7.3178 36.744981 61.0370 69.5216 92.5138 1000 5600 0.0000 92.2239 7.3178 37.010255 61.3583 69.2383 92.5138 1000 5800 0.0000 92.2239 7.3178 37.271547 61.6734 68.9577 92.5138 1000 6000 0.0000 92.2239 7.3178 37.528961 61.9826 68.6799 92.5138 1000 6200 0.0000 92.2239 7.3178 37.782598 62.2860 68.4049 92.5138 1000 6400 0.0000 92.2239 7.3178 38.032556 62.5838 68.1325 92.5138 1000 6600 0.0000 92.2239 7.3178 38.278927 62.8762 67.8628 92.5138 1000 6800 0.0000 92.2239 7.3178 38.521801 63.1633 67.5956 92.5138 1000 7000 0.0000 92.2239 7.3178 38.761264 63.4453 67.3311 92.5138 1000 7200 0.0000 92.2239 7.3178 38.997398 63.7222 67.0690 92.5138 1000 7400 0.0000 92.2239 7.3178 39.230282 63.9943 66.8095 92.5138 1000 7600 0.0000 92.2239 7.3178 39.459993 64.2616 66.5524 92.5138 1000 7800 0.0000 92.2239 7.3178 39.686605 64.5244 66.2977 92.5138 1000 8000 0.0000 92.2239 7.3178 39.910189 64.7826 66.0454 92.5138 1000 8200 0.0000 92.2239 7.3178 40.130813 65.0364 65.7954 92.5138 1000 8400 0.0000 92.2239 7.3178 40.348543 65.2860 65.5478 92.5138 1500 0 0.0000 92.0826 7.3148 27.494956 49.0003 78.3047 92.3726 1500 200 0.0000 92.0826 7.3148 27.937245 49.6033 77.9242 92.3726 1500 400 0.0000 92.0826 7.3148 28.368898 50.1889 77.5483 92.3726 1500 600 0.0000 92.0826 7.3148 28.790438 50.7581 77.1769 92.3726 1500 800 0.0000 92.0826 7.3148 29.202344 51.3116 76.8100 92.3726 1500 1000 0.0000 92.0826 7.3148 29.605059 51.8502 76.4475 92.3726 1500 1200 0.0000 92.0826 7.3148 29.998996 52.3746 76.0892 92.3726 1500 1400 0.0000 92.0826 7.3148 30.384532 52.8854 75.7350 92.3726 1500 1600 0.0000 92.0826 7.3148 30.762024 53.3833 75.3850 92.3726 1500 1800 0.0000 92.0826 7.3148 31.131799 53.8687 75.0389 92.3726 1500 2000 0.0000 92.0826 7.3148 31.494166 54.3422 74.6967 92.3726 1500 2200 0.0000 92.0826 7.3148 31.849412 54.8043 74.3583 92.3726 1500 2400 0.0000 92.0826 7.3148 32.197808 55.2554 74.0237 92.3726 1500 2600 0.0000 92.0826 7.3148 32.539606 55.6960 73.6928 92.3726 1500 2800 0.0000 92.0826 7.3148 32.875045 56.1265 73.3655 92.3726 1500 3000 0.0000 92.0826 7.3148 33.204348 56.5472 73.0417 92.3726 1500 3200 0.0000 92.0826 7.3148 33.527728 56.9586 72.7213 92.3726 1500 3400 0.0000 92.0826 7.3148 33.845384 57.3609 72.4044 92.3726 1500 3600 0.0000 92.0826 7.3148 34.157505 57.7544 72.0909 92.3726 1500 3800 0.0000 92.0826 7.3148 34.464270 58.1396 71.7807 92.3726 1500 4000 0.0000 92.0826 7.3148 34.765849 58.5166 71.4736 92.3726 1500 4200 0.0000 92.0826 7.3148 35.062403 58.8858 71.1698 92.3726 1500 4400 0.0000 92.0826 7.3148 35.354084 59.2473 70.8691 92.3726 1500 4600 0.0000 92.0826 7.3148 35.641040 59.6015 70.5715 92.3726 1500 4800 0.0000 92.0826 7.3148 35.923408 59.9486 70.2769 92.3726 1500 5000 0.0000 92.0826 7.3148 36.201320 60.2887 69.9853 92.3726 1500 5200 0.0000 92.0826 7.3148 36.474903 60.6222 69.6966 92.3726 1500 5400 0.0000 92.0826 7.3148 36.744278 60.9492 69.4109 92.3726 1500 5600 0.0000 92.0826 7.3148 37.009558 61.2700 69.1279 92.3726 1500 5800 0.0000 92.0826 7.3148 37.270856 61.5846 68.8478 92.3726 1500 6000 0.0000 92.0826 7.3148 37.528276 61.8933 68.5704 92.3726 1500 6200 0.0000 92.0826 7.3148 37.781919 62.1962 68.2957 92.3726 1500 6400 0.0000 92.0826 7.3148 38.031882 62.4936 68.0238 92.3726 1500 6600 0.0000 92.0826 7.3148 38.278259 62.7855 67.7544 92.3726 1500 6800 0.0000 92.0826 7.3148 38.521138 63.0722 67.4876 92.3726 1500 7000 0.0000 92.0826 7.3148 38.760605 63.3537 67.2234 92.3726 1500 7200 0.0000 92.0826 7.3148 38.996744 63.6302 66.9618 92.3726 1500 7400 0.0000 92.0826 7.3148 39.229633 63.9019 66.7026 92.3726 1500 7600 0.0000 92.0826 7.3148 39.459350 64.1688 66.4458 92.3726 1500 7800 0.0000 92.0826 7.3148 39.685966 64.4311 66.1915 92.3726 1500 8000 0.0000 92.0826 7.3148 39.909555 64.6889 65.9396 92.3726 1500 8200 0.0000 92.0826 7.3148 40.130183 64.9423 65.6900 92.3726 1500 8400 0.0000 92.0826 7.3148 40.347918 65.1915 65.4427 92.3726 2000 0 0.0000 91.9412 7.3118 27.493962 48.9310 78.1816 92.2315 2000 200 0.0000 91.9412 7.3118 27.936268 49.5331 77.8015 92.2315 2000 400 0.0000 91.9412 7.3118 28.367937 50.1179 77.4261 92.2315 2000 600 0.0000 91.9412 7.3118 28.789491 50.6862 77.0553 92.2315 2000 800 0.0000 91.9412 7.3118 29.201412 51.2388 76.6889 92.2315 2000 1000 0.0000 91.9412 7.3118 29.604141 51.7766 76.3269 92.2315 2000 1200 0.0000 91.9412 7.3118 29.998091 52.3002 75.9691 92.2315 2000 1400 0.0000 91.9412 7.3118 30.383640 52.8102 75.6154 92.2315 2000 1600 0.0000 91.9412 7.3118 30.761143 53.3073 75.2659 92.2315 2000 1800 0.0000 91.9412 7.3118 31.130930 53.7919 74.9203 92.2315 2000 2000 0.0000 91.9412 7.3118 31.493308 54.2647 74.5785 92.2315 2000 2200 0.0000 91.9412 7.3118 31.848565 54.7261 74.2407 92.2315 2000 2400 0.0000 91.9412 7.3118 32.196971 55.1765 73.9065 92.2315 2000 2600 0.0000 91.9412 7.3118 32.538779 55.6164 73.5760 92.2315 2000 2800 0.0000 91.9412 7.3118 32.874228 56.0462 73.2492 92.2315 2000 3000 0.0000 91.9412 7.3118 33.203540 56.4663 72.9258 92.2315 2000 3200 0.0000 91.9412 7.3118 33.526929 56.8770 72.6060 92.2315 2000 3400 0.0000 91.9412 7.3118 33.844594 57.2787 72.2895 92.2315 2000 3600 0.0000 91.9412 7.3118 34.156723 57.6716 71.9764 92.2315 2000 3800 0.0000 91.9412 7.3118 34.463496 58.0562 71.6666 92.2315 2000 4000 0.0000 91.9412 7.3118 34.765083 58.4326 71.3600 92.2315 2000 4200 0.0000 91.9412 7.3118 35.061644 58.8012 71.0566 92.2315 2000 4400 0.0000 91.9412 7.3118 35.353334 59.1622 70.7563 92.2315 2000 4600 0.0000 91.9412 7.3118 35.640296 59.5158 70.4591 92.2315 2000 4800 0.0000 91.9412 7.3118 35.922671 59.8623 70.1650 92.2315 2000 5000 0.0000 91.9412 7.3118 36.200590 60.2020 69.8738 92.2315 2000 5200 0.0000 91.9412 7.3118 36.474180 60.5349 69.5855 92.2315 2000 5400 0.0000 91.9412 7.3118 36.743561 60.8614 69.3001 92.2315 2000 5600 0.0000 91.9412 7.3118 37.008848 61.1817 69.0176 92.2315 2000 5800 0.0000 91.9412 7.3118 37.270151 61.4958 68.7379 92.2315 2000 6000 0.0000 91.9412 7.3118 37.527577 61.8040 68.4609 92.2315 2000 6200 0.0000 91.9412 7.3118 37.781226 62.1065 68.1866 92.2315 2000 6400 0.0000 91.9412 7.3118 38.031195 62.4034 67.9150 92.2315 2000 6600 0.0000 91.9412 7.3118 38.277577 62.6948 67.6460 92.2315 2000 6800 0.0000 91.9412 7.3118 38.520461 62.9810 67.3797 92.2315 2000 7000 0.0000 91.9412 7.3118 38.759934 63.2621 67.1158 92.2315 2000 7200 0.0000 91.9412 7.3118 38.996078 63.5382 66.8545 92.2315 2000 7400 0.0000 91.9412 7.3118 39.228972 63.8094 66.5957 92.2315 2000 7600 0.0000 91.9412 7.3118 39.458693 64.0759 66.3393 92.2315 2000 7800 0.0000 91.9412 7.3118 39.685315 64.3378 66.0854 92.2315 2000 8000 0.0000 91.9412 7.3118 39.908908 64.5952 65.8338 92.2315 2000 8200 0.0000 91.9412 7.3118 40.129541 64.8482 65.5846 92.2315 2000 8400 0.0000 91.9412 7.3118 40.347280 65.0970 65.3377 92.2315 2500 0 0.0000 91.7999 7.3088 27.492947 48.8618 78.0584 92.0904 2500 200 0.0000 91.7999 7.3088 27.935270 49.4629 77.6789 92.0904 2500 400 0.0000 91.7999 7.3088 28.366955 50.0468 77.3040 92.0904 2500 600 0.0000 91.7999 7.3088 28.788525 50.6142 76.9337 92.0904 2500 800 0.0000 91.7999 7.3088 29.200461 51.1660 76.5679 92.0904 2500 1000 0.0000 91.7999 7.3088 29.603204 51.7030 76.2063 92.0904 2500 1200 0.0000 91.7999 7.3088 29.997167 52.2257 75.8490 92.0904 2500 1400 0.0000 91.7999 7.3088 30.382730 52.7349 75.4959 92.0904 2500 1600 0.0000 91.7999 7.3088 30.760245 53.2312 75.1468 92.0904 2500 1800 0.0000 91.7999 7.3088 31.130044 53.7151 74.8016 92.0904 2500 2000 0.0000 91.7999 7.3088 31.492434 54.1872 74.4604 92.0904 2500 2200 0.0000 91.7999 7.3088 31.847702 54.6478 74.1230 92.0904 2500 2400 0.0000 91.7999 7.3088 32.196118 55.0976 73.7893 92.0904 2500 2600 0.0000 91.7999 7.3088 32.537936 55.5368 73.4593 92.0904 2500 2800 0.0000 91.7999 7.3088 32.873394 55.9659 73.1329 92.0904 2500 3000 0.0000 91.7999 7.3088 33.202716 56.3854 72.8100 92.0904 2500 3200 0.0000 91.7999 7.3088 33.526114 56.7954 72.4906 92.0904 2500 3400 0.0000 91.7999 7.3088 33.843788 57.1965 72.1746 92.0904 2500 3600 0.0000 91.7999 7.3088 34.155926 57.5888 71.8619 92.0904 2500 3800 0.0000 91.7999 7.3088 34.462707 57.9728 71.5525 92.0904 2500 4000 0.0000 91.7999 7.3088 34.764302 58.3486 71.2464 92.0904 2500 4200 0.0000 91.7999 7.3088 35.060871 58.7166 70.9434 92.0904 2500 4400 0.0000 91.7999 7.3088 35.352568 59.0770 70.6436 92.0904 2500 4600 0.0000 91.7999 7.3088 35.639538 59.4301 70.3468 92.0904 2500 4800 0.0000 91.7999 7.3088 35.921920 59.7761 70.0530 92.0904 2500 5000 0.0000 91.7999 7.3088 36.199846 60.1152 69.7623 92.0904 2500 5200 0.0000 91.7999 7.3088 36.473442 60.4476 69.4744 92.0904 2500 5400 0.0000 91.7999 7.3088 36.742830 60.7736 69.1894 92.0904 2500 5600 0.0000 91.7999 7.3088 37.008123 61.0933 68.9073 92.0904 2500 5800 0.0000 91.7999 7.3088 37.269433 61.4070 68.6280 92.0904 2500 6000 0.0000 91.7999 7.3088 37.526865 61.7147 68.3514 92.0904 2500 6200 0.0000 91.7999 7.3088 37.780519 62.0167 68.0775 92.0904 2500 6400 0.0000 91.7999 7.3088 38.030494 62.3131 67.8063 92.0904 2500 6600 0.0000 91.7999 7.3088 38.276882 62.6041 67.5377 92.0904 2500 6800 0.0000 91.7999 7.3088 38.519772 62.8899 67.2717 92.0904 2500 7000 0.0000 91.7999 7.3088 38.759250 63.1705 67.0082 92.0904 2500 7200 0.0000 91.7999 7.3088 38.995399 63.4461 66.7473 92.0904 2500 7400 0.0000 91.7999 7.3088 39.228299 63.7169 66.4889 92.0904 2500 7600 0.0000 91.7999 7.3088 39.458025 63.9830 66.2329 92.0904 2500 7800 0.0000 91.7999 7.3088 39.684651 64.2445 65.9793 92.0904 2500 8000 0.0000 91.7999 7.3088 39.908249 64.5015 65.7280 92.0904 2500 8200 0.0000 91.7999 7.3088 40.128887 64.7541 65.4792 92.0904 2500 8400 0.0000 91.7999 7.3088 40.346630 65.0025 65.2326 92.0904 3000 0 0.0000 91.6585 7.3059 27.491911 48.7925 77.9353 91.9492 3000 200 0.0000 91.6585 7.3059 27.934252 49.3927 77.5563 91.9492 3000 400 0.0000 91.6585 7.3059 28.365954 49.9756 77.1820 91.9492 3000 600 0.0000 91.6585 7.3059 28.787540 50.5422 76.8122 91.9492 3000 800 0.0000 91.6585 7.3059 29.199491 51.0932 76.4468 91.9492 3000 1000 0.0000 91.6585 7.3059 29.602249 51.6293 76.0858 91.9492 3000 1200 0.0000 91.6585 7.3059 29.996226 52.1512 75.7289 91.9492 3000 1400 0.0000 91.6585 7.3059 30.381801 52.6597 75.3763 91.9492 3000 1600 0.0000 91.6585 7.3059 30.759330 53.1552 75.0277 91.9492 3000 1800 0.0000 91.6585 7.3059 31.129141 53.6383 74.6830 91.9492 3000 2000 0.0000 91.6585 7.3059 31.491542 54.1097 74.3423 91.9492 3000 2200 0.0000 91.6585 7.3059 31.846821 54.5696 74.0053 91.9492 3000 2400 0.0000 91.6585 7.3059 32.195248 55.0186 73.6721 91.9492 3000 2600 0.0000 91.6585 7.3059 32.537077 55.4572 73.3426 91.9492 3000 2800 0.0000 91.6585 7.3059 32.872545 55.8856 73.0166 91.9492 3000 3000 0.0000 91.6585 7.3059 33.201877 56.3044 72.6942 91.9492 3000 3200 0.0000 91.6585 7.3059 33.525284 56.7138 72.3752 91.9492 3000 3400 0.0000 91.6585 7.3059 33.842966 57.1143 72.0597 91.9492 3000 3600 0.0000 91.6585 7.3059 34.155113 57.5060 71.7474 91.9492 3000 3800 0.0000 91.6585 7.3059 34.461903 57.8893 71.4385 91.9492 3000 4000 0.0000 91.6585 7.3059 34.763506 58.2646 71.1328 91.9492 3000 4200 0.0000 91.6585 7.3059 35.060083 58.6320 70.8302 91.9492 3000 4400 0.0000 91.6585 7.3059 35.351788 58.9919 70.5308 91.9492 3000 4600 0.0000 91.6585 7.3059 35.638765 59.3444 70.2345 91.9492 3000 4800 0.0000 91.6585 7.3059 35.921154 59.6898 69.9411 91.9492 3000 5000 0.0000 91.6585 7.3059 36.199088 60.0284 69.6508 91.9492 3000 5200 0.0000 91.6585 7.3059 36.472691 60.3603 69.3633 91.9492 3000 5400 0.0000 91.6585 7.3059 36.742085 60.6858 69.0787 91.9492 3000 5600 0.0000 91.6585 7.3059 37.007385 61.0050 68.7970 91.9492 3000 5800 0.0000 91.6585 7.3059 37.268701 61.3181 68.5181 91.9492 3000 6000 0.0000 91.6585 7.3059 37.526139 61.6254 68.2419 91.9492 3000 6200 0.0000 91.6585 7.3059 37.779800 61.9269 67.9684 91.9492 3000 6400 0.0000 91.6585 7.3059 38.029781 62.2228 67.6975 91.9492 3000 6600 0.0000 91.6585 7.3059 38.276174 62.5134 67.4293 91.9492 3000 6800 0.0000 91.6585 7.3059 38.519070 62.7987 67.1637 91.9492 3000 7000 0.0000 91.6585 7.3059 38.758554 63.0789 66.9006 91.9492 3000 7200 0.0000 91.6585 7.3059 38.994708 63.3541 66.6401 91.9492 3000 7400 0.0000 91.6585 7.3059 39.227613 63.6244 66.3820 91.9492 3000 7600 0.0000 91.6585 7.3059 39.457344 63.8901 66.1264 91.9492 3000 7800 0.0000 91.6585 7.3059 39.683975 64.1512 65.8731 91.9492 3000 8000 0.0000 91.6585 7.3059 39.907578 64.4077 65.6223 91.9492 3000 8200 0.0000 91.6585 7.3059 40.128221 64.6600 65.3738 91.9492 3000 8400 0.0000 91.6585 7.3059 40.345969 64.9080 65.1276 91.9492 3500 0 0.0000 91.5172 7.3029 27.490855 48.7231 77.8122 91.8081 3500 200 0.0000 91.5172 7.3029 27.933214 49.3225 77.4337 91.8081 3500 400 0.0000 91.5172 7.3029 28.364934 49.9045 77.0599 91.8081 3500 600 0.0000 91.5172 7.3029 28.786536 50.4702 76.6906 91.8081 3500 800 0.0000 91.5172 7.3029 29.198502 51.0203 76.3257 91.8081 3500 1000 0.0000 91.5172 7.3029 29.601275 51.5556 75.9652 91.8081 3500 1200 0.0000 91.5172 7.3029 29.995266 52.0767 75.6089 91.8081 3500 1400 0.0000 91.5172 7.3029 30.380855 52.5844 75.2567 91.8081 3500 1600 0.0000 91.5172 7.3029 30.758396 53.0791 74.9086 91.8081 3500 1800 0.0000 91.5172 7.3029 31.128219 53.5615 74.5644 91.8081 3500 2000 0.0000 91.5172 7.3029 31.490633 54.0321 74.2242 91.8081 3500 2200 0.0000 91.5172 7.3029 31.845923 54.4913 73.8877 91.8081 3500 2400 0.0000 91.5172 7.3029 32.194362 54.9397 73.5549 91.8081 3500 2600 0.0000 91.5172 7.3029 32.536201 55.3775 73.2259 91.8081 3500 2800 0.0000 91.5172 7.3029 32.871679 55.8053 72.9004 91.8081 3500 3000 0.0000 91.5172 7.3029 33.201021 56.2234 72.5784 91.8081 3500 3200 0.0000 91.5172 7.3029 33.524438 56.6322 72.2599 91.8081 3500 3400 0.0000 91.5172 7.3029 33.842129 57.0320 71.9448 91.8081 3500 3600 0.0000 91.5172 7.3029 34.154285 57.4231 71.6330 91.8081 3500 3800 0.0000 91.5172 7.3029 34.461084 57.8059 71.3245 91.8081 3500 4000 0.0000 91.5172 7.3029 34.762695 58.1805 71.0192 91.8081 3500 4200 0.0000 91.5172 7.3029 35.059281 58.5474 70.7171 91.8081 3500 4400 0.0000 91.5172 7.3029 35.350993 58.9067 70.4181 91.8081 3500 4600 0.0000 91.5172 7.3029 35.637978 59.2587 70.1221 91.8081 3500 4800 0.0000 91.5172 7.3029 35.920375 59.6035 69.8292 91.8081 3500 5000 0.0000 91.5172 7.3029 36.198315 59.9416 69.5393 91.8081 3500 5200 0.0000 91.5172 7.3029 36.471925 60.2730 69.2522 91.8081 3500 5400 0.0000 91.5172 7.3029 36.741326 60.5979 68.9681 91.8081 3500 5600 0.0000 91.5172 7.3029 37.006633 60.9166 68.6867 91.8081 3500 5800 0.0000 91.5172 7.3029 37.267956 61.2293 68.4082 91.8081 3500 6000 0.0000 91.5172 7.3029 37.525400 61.5360 68.1324 91.8081 3500 6200 0.0000 91.5172 7.3029 37.779067 61.8371 67.8593 91.8081 3500 6400 0.0000 91.5172 7.3029 38.029054 62.1326 67.5888 91.8081 3500 6600 0.0000 91.5172 7.3029 38.275453 62.4227 67.3210 91.8081 3500 6800 0.0000 91.5172 7.3029 38.518355 62.7075 67.0558 91.8081 3500 7000 0.0000 91.5172 7.3029 38.757844 62.9872 66.7931 91.8081 3500 7200 0.0000 91.5172 7.3029 38.994004 63.2620 66.5329 91.8081 3500 7400 0.0000 91.5172 7.3029 39.226914 63.5319 66.2752 91.8081 3500 7600 0.0000 91.5172 7.3029 39.456650 63.7972 66.0199 91.8081 3500 7800 0.0000 91.5172 7.3029 39.683287 64.0578 65.7670 91.8081 3500 8000 0.0000 91.5172 7.3029 39.906895 64.3140 65.5165 91.8081 3500 8200 0.0000 91.5172 7.3029 40.127542 64.5658 65.2684 91.8081 3500 8400 0.0000 91.5172 7.3029 40.345295 64.8134 65.0225 91.8081 4000 0 0.0000 91.3759 7.2999 27.489779 48.6538 77.6891 91.6670 4000 200 0.0000 91.3759 7.2999 27.932157 49.2522 77.3112 91.6670 4000 400 0.0000 91.3759 7.2999 28.363894 49.8333 76.9378 91.6670 4000 600 0.0000 91.3759 7.2999 28.785512 50.3981 76.5691 91.6670 4000 800 0.0000 91.3759 7.2999 29.197494 50.9474 76.2047 91.6670 4000 1000 0.0000 91.3759 7.2999 29.600282 51.4819 75.8447 91.6670 4000 1200 0.0000 91.3759 7.2999 29.994287 52.0022 75.4889 91.6670 4000 1400 0.0000 91.3759 7.2999 30.379890 52.5091 75.1372 91.6670 4000 1600 0.0000 91.3759 7.2999 30.757445 53.0031 74.7895 91.6670 4000 1800 0.0000 91.3759 7.2999 31.127281 53.4847 74.4459 91.6670 4000 2000 0.0000 91.3759 7.2999 31.489706 53.9545 74.1061 91.6670 4000 2200 0.0000 91.3759 7.2999 31.845009 54.4130 73.7701 91.6670 4000 2400 0.0000 91.3759 7.2999 32.193458 54.8607 73.4378 91.6670 4000 2600 0.0000 91.3759 7.2999 32.535308 55.2979 73.1092 91.6670 4000 2800 0.0000 91.3759 7.2999 32.870797 55.7250 72.7841 91.6670 4000 3000 0.0000 91.3759 7.2999 33.200149 56.1425 72.4626 91.6670 4000 3200 0.0000 91.3759 7.2999 33.523576 56.5506 72.1445 91.6670 4000 3400 0.0000 91.3759 7.2999 33.841277 56.9498 71.8299 91.6670 4000 3600 0.0000 91.3759 7.2999 34.153442 57.3403 71.5185 91.6670 4000 3800 0.0000 91.3759 7.2999 34.460249 57.7224 71.2104 91.6670 4000 4000 0.0000 91.3759 7.2999 34.761869 58.0965 70.9056 91.6670 4000 4200 0.0000 91.3759 7.2999 35.058463 58.4628 70.6039 91.6670 4000 4400 0.0000 91.3759 7.2999 35.350183 58.8215 70.3053 91.6670 4000 4600 0.0000 91.3759 7.2999 35.637176 59.1729 70.0098 91.6670 4000 4800 0.0000 91.3759 7.2999 35.919581 59.5173 69.7173 91.6670 4000 5000 0.0000 91.3759 7.2999 36.197528 59.8548 69.4278 91.6670 4000 5200 0.0000 91.3759 7.2999 36.471146 60.1856 69.1412 91.6670 4000 5400 0.0000 91.3759 7.2999 36.740554 60.5101 68.8574 91.6670 4000 5600 0.0000 91.3759 7.2999 37.005868 60.8283 68.5765 91.6670 4000 5800 0.0000 91.3759 7.2999 37.267197 61.1404 68.2983 91.6670 4000 6000 0.0000 91.3759 7.2999 37.524648 61.4467 68.0229 91.6670 4000 6200 0.0000 91.3759 7.2999 37.778321 61.7473 67.7502 91.6670 4000 6400 0.0000 91.3759 7.2999 38.028314 62.0423 67.4801 91.6670 4000 6600 0.0000 91.3759 7.2999 38.274719 62.3319 67.2127 91.6670 4000 6800 0.0000 91.3759 7.2999 38.517627 62.6163 66.9478 91.6670 4000 7000 0.0000 91.3759 7.2999 38.757122 62.8956 66.6855 91.6670 4000 7200 0.0000 91.3759 7.2999 38.993287 63.1699 66.4257 91.6670 4000 7400 0.0000 91.3759 7.2999 39.226203 63.4394 66.1684 91.6670 4000 7600 0.0000 91.3759 7.2999 39.455945 63.7042 65.9134 91.6670 4000 7800 0.0000 91.3759 7.2999 39.682586 63.9645 65.6609 91.6670 4000 8000 0.0000 91.3759 7.2999 39.906199 64.2203 65.4108 91.6670 4000 8200 0.0000 91.3759 7.2999 40.126852 64.4717 65.1630 91.6670 4000 8400 0.0000 91.3759 7.2999 40.344610 64.7189 64.9175 91.6670 4500 0 0.0000 91.2345 7.2969 27.488683 48.5844 77.5660 91.5259 4500 200 0.0000 91.2345 7.2969 27.931079 49.1819 77.1886 91.5259 4500 400 0.0000 91.2345 7.2969 28.362834 49.7622 76.8158 91.5259 4500 600 0.0000 91.2345 7.2969 28.784470 50.3261 76.4475 91.5259 4500 800 0.0000 91.2345 7.2969 29.196467 50.8745 76.0837 91.5259 4500 1000 0.0000 91.2345 7.2969 29.599271 51.4081 75.7242 91.5259 4500 1200 0.0000 91.2345 7.2969 29.993291 51.9277 75.3688 91.5259 4500 1400 0.0000 91.2345 7.2969 30.378908 52.4337 75.0176 91.5259 4500 1600 0.0000 91.2345 7.2969 30.756476 52.9270 74.6705 91.5259 4500 1800 0.0000 91.2345 7.2969 31.126325 53.4079 74.3273 91.5259 4500 2000 0.0000 91.2345 7.2969 31.488763 53.8770 73.9880 91.5259 4500 2200 0.0000 91.2345 7.2969 31.844077 54.3348 73.6524 91.5259 4500 2400 0.0000 91.2345 7.2969 32.192539 54.7817 73.3206 91.5259 4500 2600 0.0000 91.2345 7.2969 32.534400 55.2182 72.9925 91.5259 4500 2800 0.0000 91.2345 7.2969 32.869899 55.6446 72.6679 91.5259 4500 3000 0.0000 91.2345 7.2969 33.199262 56.0615 72.3468 91.5259 4500 3200 0.0000 91.2345 7.2969 33.522698 56.4690 72.0292 91.5259 4500 3400 0.0000 91.2345 7.2969 33.840409 56.8675 71.7150 91.5259 4500 3600 0.0000 91.2345 7.2969 34.152583 57.2574 71.4041 91.5259 4500 3800 0.0000 91.2345 7.2969 34.459400 57.6389 71.0964 91.5259 4500 4000 0.0000 91.2345 7.2969 34.761029 58.0124 70.7920 91.5259 4500 4200 0.0000 91.2345 7.2969 35.057631 58.3781 70.4908 91.5259 4500 4400 0.0000 91.2345 7.2969 35.349359 58.7363 70.1926 91.5259 4500 4600 0.0000 91.2345 7.2969 35.636360 59.0871 69.8975 91.5259 4500 4800 0.0000 91.2345 7.2969 35.918772 59.4309 69.6054 91.5259 4500 5000 0.0000 91.2345 7.2969 36.196728 59.7679 69.3163 91.5259 4500 5200 0.0000 91.2345 7.2969 36.470353 60.0983 69.0301 91.5259 4500 5400 0.0000 91.2345 7.2969 36.739768 60.4222 68.7467 91.5259 4500 5600 0.0000 91.2345 7.2969 37.005088 60.7399 68.4662 91.5259 4500 5800 0.0000 91.2345 7.2969 37.266425 61.0516 68.1885 91.5259 4500 6000 0.0000 91.2345 7.2969 37.523882 61.3573 67.9134 91.5259 4500 6200 0.0000 91.2345 7.2969 37.777562 61.6574 67.6411 91.5259 4500 6400 0.0000 91.2345 7.2969 38.027561 61.9520 67.3714 91.5259 4500 6600 0.0000 91.2345 7.2969 38.273972 62.2411 67.1044 91.5259 4500 6800 0.0000 91.2345 7.2969 38.516886 62.5251 66.8399 91.5259 4500 7000 0.0000 91.2345 7.2969 38.756387 62.8039 66.5780 91.5259 4500 7200 0.0000 91.2345 7.2969 38.992558 63.0778 66.3185 91.5259 4500 7400 0.0000 91.2345 7.2969 39.225479 63.3469 66.0615 91.5259 4500 7600 0.0000 91.2345 7.2969 39.455227 63.6113 65.8070 91.5259 4500 7800 0.0000 91.2345 7.2969 39.681874 63.8711 65.5549 91.5259 4500 8000 0.0000 91.2345 7.2969 39.905492 64.1265 65.3051 91.5259 4500 8200 0.0000 91.2345 7.2969 40.126149 64.3775 65.0576 91.5259 4500 8400 0.0000 91.2345 7.2969 40.343913 64.6243 64.8125 91.5259 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.000 ================================================ FILE: tests/test_data/cropA/geometry/20180506-20180717_VV_8rlks_base.par ================================================ initial_baseline(TCN): -0.2418971 -4.6591305 9.7953949 m m m initial_baseline_rate: 0.0000000 0.0146665 -0.0002475 m/s m/s m/s precision_baseline(TCN): -0.2418971 -4.6591305 9.7953949 m m m precision_baseline_rate: 0.0000000 0.0146665 -0.0002475 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/cropA/geometry/20180506-20180717_VV_8rlks_bperp.par ================================================ *** Calculate baseline components perpendicular and parallel to look vector *** *** Copyright 2005, Gamma Remote Sensing, v3.5 10-May-2005 clw/uw *** interferogram lines: 4541 range samples/line: 8514 orbit baseline vector (TCN) (m): -0.242 -4.659 9.795 orbit baseline rate (TCN) (m/s): 0.000e+00 1.467e-02 -2.475e-04 baseline vector (TCN) (m): -0.242 -4.659 9.795 baseline rate (TCN) (m/s): 0.000e+00 1.467e-02 -2.475e-04 SLC-1 center baseline length (m): 10.8497 azimuth angle: 90.0000 (right looking) line range B_t B_c B_n look angle bpara bperp blen ************************************************************************************************ 0 0 -0.2419 -4.7960 9.7977 27.497816 6.4767 -8.7774 10.9113 0 200 -0.2419 -4.7960 9.7977 27.940057 6.4088 -8.8272 10.9113 0 400 -0.2419 -4.7960 9.7977 28.371664 6.3421 -8.8752 10.9113 0 600 -0.2419 -4.7960 9.7977 28.793160 6.2766 -8.9216 10.9113 0 800 -0.2419 -4.7960 9.7977 29.205025 6.2123 -8.9665 10.9113 0 1000 -0.2419 -4.7960 9.7977 29.607701 6.1492 -9.0100 10.9113 0 1200 -0.2419 -4.7960 9.7977 30.001600 6.0871 -9.0520 10.9113 0 1400 -0.2419 -4.7960 9.7977 30.387101 6.0260 -9.0928 10.9113 0 1600 -0.2419 -4.7960 9.7977 30.764557 5.9660 -9.1323 10.9113 0 1800 -0.2419 -4.7960 9.7977 31.134300 5.9069 -9.1706 10.9113 0 2000 -0.2419 -4.7960 9.7977 31.496635 5.8488 -9.2078 10.9113 0 2200 -0.2419 -4.7960 9.7977 31.851850 5.7916 -9.2439 10.9113 0 2400 -0.2419 -4.7960 9.7977 32.200217 5.7353 -9.2789 10.9113 0 2600 -0.2419 -4.7960 9.7977 32.541986 5.6798 -9.3130 10.9113 0 2800 -0.2419 -4.7960 9.7977 32.877397 5.6252 -9.3461 10.9113 0 3000 -0.2419 -4.7960 9.7977 33.206674 5.5714 -9.3783 10.9113 0 3200 -0.2419 -4.7960 9.7977 33.530029 5.5184 -9.4095 10.9113 0 3400 -0.2419 -4.7960 9.7977 33.847660 5.4661 -9.4400 10.9113 0 3600 -0.2419 -4.7960 9.7977 34.159757 5.4146 -9.4696 10.9113 0 3800 -0.2419 -4.7960 9.7977 34.466499 5.3639 -9.4985 10.9113 0 4000 -0.2419 -4.7960 9.7977 34.768056 5.3138 -9.5266 10.9113 0 4200 -0.2419 -4.7960 9.7977 35.064588 5.2644 -9.5540 10.9113 0 4400 -0.2419 -4.7960 9.7977 35.356248 5.2157 -9.5807 10.9113 0 4600 -0.2419 -4.7960 9.7977 35.643183 5.1677 -9.6067 10.9113 0 4800 -0.2419 -4.7960 9.7977 35.925531 5.1203 -9.6320 10.9113 0 5000 -0.2419 -4.7960 9.7977 36.203424 5.0735 -9.6567 10.9113 0 5200 -0.2419 -4.7960 9.7977 36.476989 5.0273 -9.6808 10.9113 0 5400 -0.2419 -4.7960 9.7977 36.746345 4.9817 -9.7044 10.9113 0 5600 -0.2419 -4.7960 9.7977 37.011608 4.9368 -9.7273 10.9113 0 5800 -0.2419 -4.7960 9.7977 37.272888 4.8924 -9.7498 10.9113 0 6000 -0.2419 -4.7960 9.7977 37.530291 4.8485 -9.7716 10.9113 0 6200 -0.2419 -4.7960 9.7977 37.783917 4.8052 -9.7930 10.9113 0 6400 -0.2419 -4.7960 9.7977 38.033864 4.7624 -9.8139 10.9113 0 6600 -0.2419 -4.7960 9.7977 38.280225 4.7202 -9.8343 10.9113 0 6800 -0.2419 -4.7960 9.7977 38.523089 4.6785 -9.8542 10.9113 0 7000 -0.2419 -4.7960 9.7977 38.762542 4.6372 -9.8737 10.9113 0 7200 -0.2419 -4.7960 9.7977 38.998666 4.5965 -9.8927 10.9113 0 7400 -0.2419 -4.7960 9.7977 39.231541 4.5562 -9.9113 10.9113 0 7600 -0.2419 -4.7960 9.7977 39.461243 4.5165 -9.9295 10.9113 0 7800 -0.2419 -4.7960 9.7977 39.687846 4.4772 -9.9473 10.9113 0 8000 -0.2419 -4.7960 9.7977 39.911421 4.4383 -9.9647 10.9113 0 8200 -0.2419 -4.7960 9.7977 40.132036 4.3999 -9.9817 10.9113 0 8400 -0.2419 -4.7960 9.7977 40.349758 4.3620 -9.9983 10.9113 500 0 -0.2419 -4.7659 9.7972 27.496883 6.4903 -8.7504 10.8976 500 200 -0.2419 -4.7659 9.7972 27.939140 6.4226 -8.8002 10.8976 500 400 -0.2419 -4.7659 9.7972 28.370762 6.3561 -8.8483 10.8976 500 600 -0.2419 -4.7659 9.7972 28.792272 6.2908 -8.8949 10.8976 500 800 -0.2419 -4.7659 9.7972 29.204150 6.2267 -8.9399 10.8976 500 1000 -0.2419 -4.7659 9.7972 29.606840 6.1637 -8.9834 10.8976 500 1200 -0.2419 -4.7659 9.7972 30.000750 6.1018 -9.0256 10.8976 500 1400 -0.2419 -4.7659 9.7972 30.386263 6.0410 -9.0664 10.8976 500 1600 -0.2419 -4.7659 9.7972 30.763731 5.9811 -9.1061 10.8976 500 1800 -0.2419 -4.7659 9.7972 31.133484 5.9222 -9.1445 10.8976 500 2000 -0.2419 -4.7659 9.7972 31.495829 5.8643 -9.1817 10.8976 500 2200 -0.2419 -4.7659 9.7972 31.851055 5.8072 -9.2179 10.8976 500 2400 -0.2419 -4.7659 9.7972 32.199431 5.7511 -9.2531 10.8976 500 2600 -0.2419 -4.7659 9.7972 32.541209 5.6958 -9.2872 10.8976 500 2800 -0.2419 -4.7659 9.7972 32.876630 5.6413 -9.3204 10.8976 500 3000 -0.2419 -4.7659 9.7972 33.205915 5.5876 -9.3527 10.8976 500 3200 -0.2419 -4.7659 9.7972 33.529278 5.5347 -9.3841 10.8976 500 3400 -0.2419 -4.7659 9.7972 33.846917 5.4826 -9.4146 10.8976 500 3600 -0.2419 -4.7659 9.7972 34.159022 5.4313 -9.4443 10.8976 500 3800 -0.2419 -4.7659 9.7972 34.465771 5.3806 -9.4733 10.8976 500 4000 -0.2419 -4.7659 9.7972 34.767335 5.3307 -9.5015 10.8976 500 4200 -0.2419 -4.7659 9.7972 35.063874 5.2814 -9.5289 10.8976 500 4400 -0.2419 -4.7659 9.7972 35.355542 5.2329 -9.5557 10.8976 500 4600 -0.2419 -4.7659 9.7972 35.642483 5.1849 -9.5818 10.8976 500 4800 -0.2419 -4.7659 9.7972 35.924838 5.1377 -9.6072 10.8976 500 5000 -0.2419 -4.7659 9.7972 36.202737 5.0910 -9.6320 10.8976 500 5200 -0.2419 -4.7659 9.7972 36.476308 5.0449 -9.6562 10.8976 500 5400 -0.2419 -4.7659 9.7972 36.745670 4.9995 -9.6799 10.8976 500 5600 -0.2419 -4.7659 9.7972 37.010938 4.9546 -9.7029 10.8976 500 5800 -0.2419 -4.7659 9.7972 37.272224 4.9103 -9.7254 10.8976 500 6000 -0.2419 -4.7659 9.7972 37.529632 4.8666 -9.7474 10.8976 500 6200 -0.2419 -4.7659 9.7972 37.783264 4.8234 -9.7688 10.8976 500 6400 -0.2419 -4.7659 9.7972 38.033217 4.7807 -9.7898 10.8976 500 6600 -0.2419 -4.7659 9.7972 38.279583 4.7386 -9.8102 10.8976 500 6800 -0.2419 -4.7659 9.7972 38.522452 4.6969 -9.8302 10.8976 500 7000 -0.2419 -4.7659 9.7972 38.761909 4.6558 -9.8498 10.8976 500 7200 -0.2419 -4.7659 9.7972 38.998038 4.6152 -9.8689 10.8976 500 7400 -0.2419 -4.7659 9.7972 39.230918 4.5750 -9.8876 10.8976 500 7600 -0.2419 -4.7659 9.7972 39.460624 4.5354 -9.9058 10.8976 500 7800 -0.2419 -4.7659 9.7972 39.687232 4.4961 -9.9237 10.8976 500 8000 -0.2419 -4.7659 9.7972 39.910811 4.4574 -9.9412 10.8976 500 8200 -0.2419 -4.7659 9.7972 40.131431 4.4191 -9.9583 10.8976 500 8400 -0.2419 -4.7659 9.7972 40.349156 4.3812 -9.9750 10.8976 1000 0 -0.2419 -4.7357 9.7967 27.495930 6.5039 -8.7233 10.8840 1000 200 -0.2419 -4.7357 9.7967 27.938203 6.4364 -8.7732 10.8840 1000 400 -0.2419 -4.7357 9.7967 28.369840 6.3701 -8.8215 10.8840 1000 600 -0.2419 -4.7357 9.7967 28.791365 6.3050 -8.8681 10.8840 1000 800 -0.2419 -4.7357 9.7967 29.203256 6.2411 -8.9132 10.8840 1000 1000 -0.2419 -4.7357 9.7967 29.605959 6.1783 -8.9569 10.8840 1000 1200 -0.2419 -4.7357 9.7967 29.999882 6.1166 -8.9991 10.8840 1000 1400 -0.2419 -4.7357 9.7967 30.385407 6.0559 -9.0401 10.8840 1000 1600 -0.2419 -4.7357 9.7967 30.762886 5.9962 -9.0798 10.8840 1000 1800 -0.2419 -4.7357 9.7967 31.132650 5.9375 -9.1183 10.8840 1000 2000 -0.2419 -4.7357 9.7967 31.495006 5.8797 -9.1557 10.8840 1000 2200 -0.2419 -4.7357 9.7967 31.850242 5.8228 -9.1920 10.8840 1000 2400 -0.2419 -4.7357 9.7967 32.198628 5.7668 -9.2272 10.8840 1000 2600 -0.2419 -4.7357 9.7967 32.540416 5.7117 -9.2614 10.8840 1000 2800 -0.2419 -4.7357 9.7967 32.875845 5.6573 -9.2947 10.8840 1000 3000 -0.2419 -4.7357 9.7967 33.205140 5.6038 -9.3271 10.8840 1000 3200 -0.2419 -4.7357 9.7967 33.528511 5.5511 -9.3586 10.8840 1000 3400 -0.2419 -4.7357 9.7967 33.846158 5.4991 -9.3892 10.8840 1000 3600 -0.2419 -4.7357 9.7967 34.158271 5.4479 -9.4190 10.8840 1000 3800 -0.2419 -4.7357 9.7967 34.465028 5.3974 -9.4481 10.8840 1000 4000 -0.2419 -4.7357 9.7967 34.766600 5.3476 -9.4763 10.8840 1000 4200 -0.2419 -4.7357 9.7967 35.063146 5.2985 -9.5039 10.8840 1000 4400 -0.2419 -4.7357 9.7967 35.354821 5.2500 -9.5308 10.8840 1000 4600 -0.2419 -4.7357 9.7967 35.641769 5.2022 -9.5569 10.8840 1000 4800 -0.2419 -4.7357 9.7967 35.924130 5.1551 -9.5825 10.8840 1000 5000 -0.2419 -4.7357 9.7967 36.202036 5.1085 -9.6073 10.8840 1000 5200 -0.2419 -4.7357 9.7967 36.475612 5.0626 -9.6316 10.8840 1000 5400 -0.2419 -4.7357 9.7967 36.744981 5.0172 -9.6553 10.8840 1000 5600 -0.2419 -4.7357 9.7967 37.010255 4.9725 -9.6785 10.8840 1000 5800 -0.2419 -4.7357 9.7967 37.271547 4.9283 -9.7010 10.8840 1000 6000 -0.2419 -4.7357 9.7967 37.528961 4.8847 -9.7231 10.8840 1000 6200 -0.2419 -4.7357 9.7967 37.782598 4.8416 -9.7446 10.8840 1000 6400 -0.2419 -4.7357 9.7967 38.032556 4.7990 -9.7656 10.8840 1000 6600 -0.2419 -4.7357 9.7967 38.278927 4.7570 -9.7862 10.8840 1000 6800 -0.2419 -4.7357 9.7967 38.521801 4.7154 -9.8063 10.8840 1000 7000 -0.2419 -4.7357 9.7967 38.761264 4.6744 -9.8259 10.8840 1000 7200 -0.2419 -4.7357 9.7967 38.997398 4.6339 -9.8451 10.8840 1000 7400 -0.2419 -4.7357 9.7967 39.230282 4.5938 -9.8638 10.8840 1000 7600 -0.2419 -4.7357 9.7967 39.459993 4.5542 -9.8822 10.8840 1000 7800 -0.2419 -4.7357 9.7967 39.686605 4.5151 -9.9001 10.8840 1000 8000 -0.2419 -4.7357 9.7967 39.910189 4.4764 -9.9177 10.8840 1000 8200 -0.2419 -4.7357 9.7967 40.130813 4.4382 -9.9348 10.8840 1000 8400 -0.2419 -4.7357 9.7967 40.348543 4.4004 -9.9516 10.8840 1500 0 -0.2419 -4.7056 9.7962 27.494956 6.5176 -8.6962 10.8704 1500 200 -0.2419 -4.7056 9.7962 27.937245 6.4502 -8.7463 10.8704 1500 400 -0.2419 -4.7056 9.7962 28.368898 6.3841 -8.7946 10.8704 1500 600 -0.2419 -4.7056 9.7962 28.790438 6.3193 -8.8413 10.8704 1500 800 -0.2419 -4.7056 9.7962 29.202344 6.2555 -8.8865 10.8704 1500 1000 -0.2419 -4.7056 9.7962 29.605059 6.1929 -8.9303 10.8704 1500 1200 -0.2419 -4.7056 9.7962 29.998996 6.1314 -8.9727 10.8704 1500 1400 -0.2419 -4.7056 9.7962 30.384532 6.0709 -9.0137 10.8704 1500 1600 -0.2419 -4.7056 9.7962 30.762024 6.0113 -9.0535 10.8704 1500 1800 -0.2419 -4.7056 9.7962 31.131799 5.9528 -9.0922 10.8704 1500 2000 -0.2419 -4.7056 9.7962 31.494166 5.8952 -9.1296 10.8704 1500 2200 -0.2419 -4.7056 9.7962 31.849412 5.8384 -9.1660 10.8704 1500 2400 -0.2419 -4.7056 9.7962 32.197808 5.7826 -9.2013 10.8704 1500 2600 -0.2419 -4.7056 9.7962 32.539606 5.7276 -9.2357 10.8704 1500 2800 -0.2419 -4.7056 9.7962 32.875045 5.6734 -9.2691 10.8704 1500 3000 -0.2419 -4.7056 9.7962 33.204348 5.6200 -9.3015 10.8704 1500 3200 -0.2419 -4.7056 9.7962 33.527728 5.5675 -9.3331 10.8704 1500 3400 -0.2419 -4.7056 9.7962 33.845384 5.5156 -9.3638 10.8704 1500 3600 -0.2419 -4.7056 9.7962 34.157505 5.4645 -9.3937 10.8704 1500 3800 -0.2419 -4.7056 9.7962 34.464270 5.4142 -9.4229 10.8704 1500 4000 -0.2419 -4.7056 9.7962 34.765849 5.3645 -9.4512 10.8704 1500 4200 -0.2419 -4.7056 9.7962 35.062403 5.3155 -9.4789 10.8704 1500 4400 -0.2419 -4.7056 9.7962 35.354084 5.2672 -9.5058 10.8704 1500 4600 -0.2419 -4.7056 9.7962 35.641040 5.2195 -9.5321 10.8704 1500 4800 -0.2419 -4.7056 9.7962 35.923408 5.1724 -9.5577 10.8704 1500 5000 -0.2419 -4.7056 9.7962 36.201320 5.1260 -9.5827 10.8704 1500 5200 -0.2419 -4.7056 9.7962 36.474903 5.0802 -9.6070 10.8704 1500 5400 -0.2419 -4.7056 9.7962 36.744278 5.0350 -9.6308 10.8704 1500 5600 -0.2419 -4.7056 9.7962 37.009558 4.9903 -9.6540 10.8704 1500 5800 -0.2419 -4.7056 9.7962 37.270856 4.9463 -9.6767 10.8704 1500 6000 -0.2419 -4.7056 9.7962 37.528276 4.9027 -9.6988 10.8704 1500 6200 -0.2419 -4.7056 9.7962 37.781919 4.8597 -9.7204 10.8704 1500 6400 -0.2419 -4.7056 9.7962 38.031882 4.8173 -9.7415 10.8704 1500 6600 -0.2419 -4.7056 9.7962 38.278259 4.7754 -9.7622 10.8704 1500 6800 -0.2419 -4.7056 9.7962 38.521138 4.7339 -9.7823 10.8704 1500 7000 -0.2419 -4.7056 9.7962 38.760605 4.6930 -9.8020 10.8704 1500 7200 -0.2419 -4.7056 9.7962 38.996744 4.6526 -9.8213 10.8704 1500 7400 -0.2419 -4.7056 9.7962 39.229633 4.6126 -9.8401 10.8704 1500 7600 -0.2419 -4.7056 9.7962 39.459350 4.5731 -9.8585 10.8704 1500 7800 -0.2419 -4.7056 9.7962 39.685966 4.5341 -9.8765 10.8704 1500 8000 -0.2419 -4.7056 9.7962 39.909555 4.4955 -9.8942 10.8704 1500 8200 -0.2419 -4.7056 9.7962 40.130183 4.4574 -9.9114 10.8704 1500 8400 -0.2419 -4.7056 9.7962 40.347918 4.4197 -9.9283 10.8704 2000 0 -0.2419 -4.6754 9.7957 27.493962 6.5312 -8.6691 10.8570 2000 200 -0.2419 -4.6754 9.7957 27.936268 6.4640 -8.7193 10.8570 2000 400 -0.2419 -4.6754 9.7957 28.367937 6.3982 -8.7677 10.8570 2000 600 -0.2419 -4.6754 9.7957 28.789491 6.3335 -8.8146 10.8570 2000 800 -0.2419 -4.6754 9.7957 29.201412 6.2699 -8.8599 10.8570 2000 1000 -0.2419 -4.6754 9.7957 29.604141 6.2075 -8.9037 10.8570 2000 1200 -0.2419 -4.6754 9.7957 29.998091 6.1461 -8.9462 10.8570 2000 1400 -0.2419 -4.6754 9.7957 30.383640 6.0858 -8.9874 10.8570 2000 1600 -0.2419 -4.6754 9.7957 30.761143 6.0265 -9.0273 10.8570 2000 1800 -0.2419 -4.6754 9.7957 31.130930 5.9681 -9.0660 10.8570 2000 2000 -0.2419 -4.6754 9.7957 31.493308 5.9106 -9.1036 10.8570 2000 2200 -0.2419 -4.6754 9.7957 31.848565 5.8540 -9.1400 10.8570 2000 2400 -0.2419 -4.6754 9.7957 32.196971 5.7984 -9.1755 10.8570 2000 2600 -0.2419 -4.6754 9.7957 32.538779 5.7435 -9.2099 10.8570 2000 2800 -0.2419 -4.6754 9.7957 32.874228 5.6895 -9.2434 10.8570 2000 3000 -0.2419 -4.6754 9.7957 33.203540 5.6363 -9.2759 10.8570 2000 3200 -0.2419 -4.6754 9.7957 33.526929 5.5838 -9.3076 10.8570 2000 3400 -0.2419 -4.6754 9.7957 33.844594 5.5321 -9.3384 10.8570 2000 3600 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5.0112 -9.5531 10.7906 4500 6200 -0.2419 -4.5247 9.7931 37.777562 4.9689 -9.5752 10.7906 4500 6400 -0.2419 -4.5247 9.7931 38.027561 4.9271 -9.5968 10.7906 4500 6600 -0.2419 -4.5247 9.7931 38.273972 4.8857 -9.6179 10.7906 4500 6800 -0.2419 -4.5247 9.7931 38.516886 4.8449 -9.6385 10.7906 4500 7000 -0.2419 -4.5247 9.7931 38.756387 4.8046 -9.6587 10.7906 4500 7200 -0.2419 -4.5247 9.7931 38.992558 4.7647 -9.6784 10.7906 4500 7400 -0.2419 -4.5247 9.7931 39.225479 4.7253 -9.6977 10.7906 4500 7600 -0.2419 -4.5247 9.7931 39.455227 4.6864 -9.7166 10.7906 4500 7800 -0.2419 -4.5247 9.7931 39.681874 4.6479 -9.7351 10.7906 4500 8000 -0.2419 -4.5247 9.7931 39.905492 4.6099 -9.7531 10.7906 4500 8200 -0.2419 -4.5247 9.7931 40.126149 4.5723 -9.7708 10.7906 4500 8400 -0.2419 -4.5247 9.7931 40.343913 4.5351 -9.7881 10.7906 user time (s): 0.000 system time (s): 0.000 elapsed time (s): 0.010 ================================================ FILE: tests/test_data/cropA/headers/cropA_20180106_VV_8rlks_eqa_dem.par ================================================ Gamma DIFF&GEO DEM/MAP parameter file title: dem DEM_projection: EQA data_format: REAL*4 DEM_hgt_offset: 0.00000 DEM_scale: 1.00000 width: 100 nlines: 60 corner_lat: 19.4512926234517565 decimal degrees corner_lon: -99.1910697816367417 decimal degrees post_lat: -0.001388888900000000105 decimal degrees post_lon: 0.001388888900000000105 decimal degrees ellipsoid_name: WGS 84 ellipsoid_ra: 6378137.000 m ellipsoid_reciprocal_flattening: 298.2572236 datum_name: WGS 84 datum_shift_dx: 0.000 m datum_shift_dy: 0.000 m datum_shift_dz: 0.000 m datum_scale_m: 0.00000e+00 datum_rotation_alpha: 0.00000e+00 arc-sec datum_rotation_beta: 0.00000e+00 arc-sec datum_rotation_gamma: 0.00000e+00 arc-sec datum_country_list: WGS 84 ================================================ FILE: tests/test_data/cropA/headers/r20180106_VV_8rlks_mli.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180106t004006-20180106t004031-020027-0221ec-004.tiff S1A-IW-IW1-VV-20027 (software: Sentinel-1 IPF 002.84) sensor: S1A IW IW1 VV date: 2018 01 06 start_time: 2412.557627 s center_time: 2421.889852 s end_time: 2431.222078 s azimuth_line_time: 4.1111126e-03 s line_header_size: 0 range_samples: 8514 azimuth_lines: 4541 range_looks: 8 azimuth_looks: 2 image_format: FLOAT image_geometry: SLANT_RANGE range_scale_factor: 1.0000000e+00 azimuth_scale_factor: 1.0000000e+00 center_latitude: 19.5126101 degrees center_longitude: -97.9182354 degrees heading: -12.2742586 degrees range_pixel_spacing: 18.636496 m azimuth_pixel_spacing: 28.023300 m near_range_slc: 798988.2904 m center_range_slc: 878314.5356 m far_range_slc: 957640.7808 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7036 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: 28.89379 -1.70466e-04 1.00457e-09 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073899.1954 m earth_radius_below_sensor: 6375868.9414 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2399.144213 s state_vector_interval: 10.000000 s state_vector_position_1: -1442639.9545 -6604806.9075 2082951.4020 m m m state_vector_velocity_1: -1104.60340 2489.17836 7092.92324 m/s m/s m/s state_vector_position_2: -1453586.5506 -6579537.2326 2153761.6359 m m m state_vector_velocity_2: -1084.67698 2564.70460 7068.99018 m/s m/s m/s state_vector_position_3: -1464332.7222 -6553513.8710 2224328.5433 m m m state_vector_velocity_3: -1064.51896 2639.91423 7044.25842 m/s m/s m/s state_vector_position_4: -1474876.1671 -6526740.0329 2294644.1512 m m m state_vector_velocity_4: -1044.13208 2714.79840 7018.73075 m/s m/s m/s state_vector_position_5: -1485214.6104 -6499219.0172 2364700.5150 m m m state_vector_velocity_5: -1023.51913 2789.34829 6992.41010 m/s m/s m/s state_vector_position_6: -1495345.8056 -6470954.2105 2434489.7198 m m m state_vector_velocity_6: -1002.68294 2863.55516 6965.29946 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180106_VV_slc.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180106t004006-20180106t004031-020027-0221ec-004.tiff S1A-IW-IW1-VV-20027 (software: Sentinel-1 IPF 002.84) sensor: S1A IW IW1 VV date: 2018 01 06 start_time: 2412.556599 s center_time: 2421.890880 s end_time: 2431.225161 s azimuth_line_time: 2.0555563e-03 s line_header_size: 0 range_samples: 68116 azimuth_lines: 9083 range_looks: 1 azimuth_looks: 1 image_format: FCOMPLEX image_geometry: SLANT_RANGE range_scale_factor: 1.0000000e+00 azimuth_scale_factor: 1.0000000e+00 center_latitude: 19.5126101 degrees center_longitude: -97.9182354 degrees heading: -12.2742586 degrees range_pixel_spacing: 2.329562 m azimuth_pixel_spacing: 14.011650 m near_range_slc: 798980.1369 m center_range_slc: 878319.1947 m far_range_slc: 957658.2525 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7036 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: 28.89379 -1.70466e-04 1.00457e-09 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073899.1954 m earth_radius_below_sensor: 6375868.9414 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2399.144213 s state_vector_interval: 10.000000 s state_vector_position_1: -1442639.9545 -6604806.9075 2082951.4020 m m m state_vector_velocity_1: -1104.60340 2489.17836 7092.92324 m/s m/s m/s state_vector_position_2: -1453586.5506 -6579537.2326 2153761.6359 m m m state_vector_velocity_2: -1084.67698 2564.70460 7068.99018 m/s m/s m/s state_vector_position_3: -1464332.7222 -6553513.8710 2224328.5433 m m m state_vector_velocity_3: -1064.51896 2639.91423 7044.25842 m/s m/s m/s state_vector_position_4: -1474876.1671 -6526740.0329 2294644.1512 m m m state_vector_velocity_4: -1044.13208 2714.79840 7018.73075 m/s m/s m/s state_vector_position_5: -1485214.6104 -6499219.0172 2364700.5150 m m m state_vector_velocity_5: -1023.51913 2789.34829 6992.41010 m/s m/s m/s state_vector_position_6: -1495345.8056 -6470954.2105 2434489.7198 m m m state_vector_velocity_6: -1002.68294 2863.55516 6965.29946 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180130_VV_8rlks_mli.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180130t004005-20180130t004030-020377-022d0a-004.tiff S1A-IW-IW1-VV-20377 (software: Sentinel-1 IPF 002.84) sensor: S1A IW IW1 VV date: 2018 01 30 start_time: 2411.850365 s center_time: 2421.182580 s end_time: 2430.514796 s azimuth_line_time: 4.1111082e-03 s line_header_size: 0 range_samples: 8514 azimuth_lines: 4541 range_looks: 8 azimuth_looks: 2 image_format: FLOAT image_geometry: SLANT_RANGE range_scale_factor: 9.9995776e-01 azimuth_scale_factor: 9.9999894e-01 center_latitude: 19.5126101 degrees center_longitude: -97.9182314 degrees heading: -12.2736686 degrees range_pixel_spacing: 18.635712 m azimuth_pixel_spacing: 28.023270 m near_range_slc: 798965.1264 m center_range_slc: 878288.0346 m far_range_slc: 957610.9427 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7016 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: 71.13098 -1.27127e-04 5.17735e-10 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073894.6696 m earth_radius_below_sensor: 6375868.9416 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2398.431977 s state_vector_interval: 10.000000 s state_vector_position_1: -1442597.0798 -6604819.0700 2082928.3358 m m m state_vector_velocity_1: -1104.54558 2489.14851 7092.94042 m/s m/s m/s state_vector_position_2: -1453543.1000 -6579549.6926 2153738.7429 m m m state_vector_velocity_2: -1084.61965 2564.67494 7069.00759 m/s m/s m/s state_vector_position_3: -1464288.7007 -6553526.6264 2224305.8258 m m m state_vector_velocity_3: -1064.46211 2639.88477 7044.27604 m/s m/s m/s state_vector_position_4: -1474831.5793 -6526753.0818 2294621.6113 m m m state_vector_velocity_4: -1044.07572 2714.76915 7018.74859 m/s m/s m/s state_vector_position_5: -1485169.4614 -6499232.3574 2364678.1549 m m m state_vector_velocity_5: -1023.46326 2789.31927 6992.42815 m/s m/s m/s state_vector_position_6: -1495300.1003 -6470967.8397 2434467.5415 m m m state_vector_velocity_6: -1002.62756 2863.52635 6965.31772 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180130_VV_slc.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180130t004005-20180130t004030-020377-022d0a-004.tiff S1A-IW-IW1-VV-20377 (software: Sentinel-1 IPF 002.84) sensor: S1A IW IW1 VV date: 2018 01 30 start_time: 2411.849337 s center_time: 2421.183608 s end_time: 2430.517879 s azimuth_line_time: 2.0555541e-03 s line_header_size: 0 range_samples: 68116 azimuth_lines: 9083 range_looks: 1 azimuth_looks: 1 image_format: FCOMPLEX image_geometry: SLANT_RANGE range_scale_factor: 9.9995776e-01 azimuth_scale_factor: 9.9999894e-01 center_latitude: 19.5126101 degrees center_longitude: -97.9182314 degrees heading: -12.2736686 degrees range_pixel_spacing: 2.329464 m azimuth_pixel_spacing: 14.011635 m near_range_slc: 798956.9733 m center_range_slc: 878292.6800 m far_range_slc: 957628.3867 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7016 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: 71.13098 -1.27127e-04 5.17735e-10 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073894.6696 m earth_radius_below_sensor: 6375868.9416 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2398.431977 s state_vector_interval: 10.000000 s state_vector_position_1: -1442597.0798 -6604819.0700 2082928.3358 m m m state_vector_velocity_1: -1104.54558 2489.14851 7092.94042 m/s m/s m/s state_vector_position_2: -1453543.1000 -6579549.6926 2153738.7429 m m m state_vector_velocity_2: -1084.61965 2564.67494 7069.00759 m/s m/s m/s state_vector_position_3: -1464288.7007 -6553526.6264 2224305.8258 m m m state_vector_velocity_3: -1064.46211 2639.88477 7044.27604 m/s m/s m/s state_vector_position_4: -1474831.5793 -6526753.0818 2294621.6113 m m m state_vector_velocity_4: -1044.07572 2714.76915 7018.74859 m/s m/s m/s state_vector_position_5: -1485169.4614 -6499232.3574 2364678.1549 m m m state_vector_velocity_5: -1023.46326 2789.31927 6992.42815 m/s m/s m/s state_vector_position_6: -1495300.1003 -6470967.8397 2434467.5415 m m m state_vector_velocity_6: -1002.62756 2863.52635 6965.31772 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180307_VV_8rlks_mli.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180307t004004-20180307t004030-020902-023dc2-004.tiff S1A-IW-IW1-VV-20902 (software: Sentinel-1 IPF 002.84) sensor: S1A IW IW1 VV date: 2018 03 07 start_time: 2411.446007 s center_time: 2420.778229 s end_time: 2430.110452 s azimuth_line_time: 4.1111112e-03 s line_header_size: 0 range_samples: 8514 azimuth_lines: 4541 range_looks: 8 azimuth_looks: 2 image_format: FLOAT image_geometry: SLANT_RANGE range_scale_factor: 9.9999872e-01 azimuth_scale_factor: 9.9999965e-01 center_latitude: 19.5126084 degrees center_longitude: -97.9182346 degrees heading: -12.2751820 degrees range_pixel_spacing: 18.636472 m azimuth_pixel_spacing: 28.023290 m near_range_slc: 798989.4063 m center_range_slc: 878315.5493 m far_range_slc: 957641.6924 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7035 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: 7.57751 -1.73839e-04 9.26703e-10 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073900.5085 m earth_radius_below_sensor: 6375868.9420 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2398.029794 s state_vector_interval: 10.000000 s state_vector_position_1: -1442634.0250 -6604817.8664 2082929.5800 m m m state_vector_velocity_1: -1104.71812 2489.22466 7092.89390 m/s m/s m/s state_vector_position_2: -1453581.7682 -6579547.7275 2153739.5218 m m m state_vector_velocity_2: -1084.79171 2564.75111 7068.96113 m/s m/s m/s state_vector_position_3: -1464329.0870 -6553523.8996 2224306.1400 m m m state_vector_velocity_3: -1064.63367 2639.96097 7044.22964 m/s m/s m/s state_vector_position_4: -1474873.6787 -6526749.5931 2294621.4614 m m m state_vector_velocity_4: -1044.24676 2714.84537 7018.70225 m/s m/s m/s state_vector_position_5: -1485213.2685 -6499228.1064 2364677.5414 m m m state_vector_velocity_5: -1023.63376 2789.39552 6992.38187 m/s m/s m/s state_vector_position_6: -1495345.6096 -6470962.8261 2434466.4652 m m m state_vector_velocity_6: -1002.79750 2863.60264 6965.27150 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180307_VV_slc.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180307t004004-20180307t004030-020902-023dc2-004.tiff S1A-IW-IW1-VV-20902 (software: Sentinel-1 IPF 002.84) sensor: S1A IW IW1 VV date: 2018 03 07 start_time: 2411.444979 s center_time: 2420.779257 s end_time: 2430.113535 s azimuth_line_time: 2.0555556e-03 s line_header_size: 0 range_samples: 68116 azimuth_lines: 9083 range_looks: 1 azimuth_looks: 1 image_format: FCOMPLEX image_geometry: SLANT_RANGE range_scale_factor: 9.9999872e-01 azimuth_scale_factor: 9.9999965e-01 center_latitude: 19.5126084 degrees center_longitude: -97.9182346 degrees heading: -12.2751820 degrees range_pixel_spacing: 2.329559 m azimuth_pixel_spacing: 14.011645 m near_range_slc: 798981.2528 m center_range_slc: 878320.2093 m far_range_slc: 957659.1659 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7035 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: 7.57751 -1.73839e-04 9.26703e-10 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073900.5085 m earth_radius_below_sensor: 6375868.9420 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2398.029794 s state_vector_interval: 10.000000 s state_vector_position_1: -1442634.0250 -6604817.8664 2082929.5800 m m m state_vector_velocity_1: -1104.71812 2489.22466 7092.89390 m/s m/s m/s state_vector_position_2: -1453581.7682 -6579547.7275 2153739.5218 m m m state_vector_velocity_2: -1084.79171 2564.75111 7068.96113 m/s m/s m/s state_vector_position_3: -1464329.0870 -6553523.8996 2224306.1400 m m m state_vector_velocity_3: -1064.63367 2639.96097 7044.22964 m/s m/s m/s state_vector_position_4: -1474873.6787 -6526749.5931 2294621.4614 m m m state_vector_velocity_4: -1044.24676 2714.84537 7018.70225 m/s m/s m/s state_vector_position_5: -1485213.2685 -6499228.1064 2364677.5414 m m m state_vector_velocity_5: -1023.63376 2789.39552 6992.38187 m/s m/s m/s state_vector_position_6: -1495345.6096 -6470962.8261 2434466.4652 m m m state_vector_velocity_6: -1002.79750 2863.60264 6965.27150 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180319_VV_8rlks_mli.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180319t004005-20180319t004030-021077-024348-004.tiff S1A-IW-IW1-VV-21077 (software: Sentinel-1 IPF 002.90) sensor: S1A IW IW1 VV date: 2018 03 19 start_time: 2411.634001 s center_time: 2420.966234 s end_time: 2430.298467 s azimuth_line_time: 4.1111160e-03 s line_header_size: 0 range_samples: 8514 azimuth_lines: 4541 range_looks: 8 azimuth_looks: 2 image_format: FLOAT image_geometry: SLANT_RANGE range_scale_factor: 9.9999519e-01 azimuth_scale_factor: 1.0000008e+00 center_latitude: 19.5126105 degrees center_longitude: -97.9182312 degrees heading: -12.2738302 degrees range_pixel_spacing: 18.636408 m azimuth_pixel_spacing: 28.023282 m near_range_slc: 798987.3020 m center_range_slc: 878313.1727 m far_range_slc: 957639.0433 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7034 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: 14.28596 -2.06716e-04 1.17660e-09 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073899.8185 m earth_radius_below_sensor: 6375868.9415 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2398.217204 s state_vector_interval: 10.000000 s state_vector_position_1: -1442635.9813 -6604813.9430 2082939.7037 m m m state_vector_velocity_1: -1104.53733 2489.22215 7092.91118 m/s m/s m/s state_vector_position_2: -1453581.9166 -6579543.8306 2153749.8176 m m m state_vector_velocity_2: -1084.61091 2564.74831 7068.97828 m/s m/s m/s state_vector_position_3: -1464327.4273 -6553520.0321 2224316.6067 m m m state_vector_velocity_3: -1064.45288 2639.95789 7044.24666 m/s m/s m/s state_vector_position_4: -1474870.2112 -6526745.7577 2294632.0977 m m m state_vector_velocity_4: -1044.06599 2714.84202 7018.71914 m/s m/s m/s state_vector_position_5: -1485207.9936 -6499224.3060 2364688.3461 m m m state_vector_velocity_5: -1023.45304 2789.39190 6992.39863 m/s m/s m/s state_vector_position_6: -1495338.5279 -6470959.0632 2434477.4369 m m m state_vector_velocity_6: -1002.61684 2863.59875 6965.28813 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180319_VV_slc.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180319t004005-20180319t004030-021077-024348-004.tiff S1A-IW-IW1-VV-21077 (software: Sentinel-1 IPF 002.90) sensor: S1A IW IW1 VV date: 2018 03 19 start_time: 2411.632973 s center_time: 2420.967261 s end_time: 2430.301550 s azimuth_line_time: 2.0555580e-03 s line_header_size: 0 range_samples: 68116 azimuth_lines: 9083 range_looks: 1 azimuth_looks: 1 image_format: FCOMPLEX image_geometry: SLANT_RANGE range_scale_factor: 9.9999519e-01 azimuth_scale_factor: 1.0000008e+00 center_latitude: 19.5126105 degrees center_longitude: -97.9182312 degrees heading: -12.2738302 degrees range_pixel_spacing: 2.329551 m azimuth_pixel_spacing: 14.011641 m near_range_slc: 798979.1486 m center_range_slc: 878317.8248 m far_range_slc: 957656.5011 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7034 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: 14.28596 -2.06716e-04 1.17660e-09 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073899.8185 m earth_radius_below_sensor: 6375868.9415 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2398.217204 s state_vector_interval: 10.000000 s state_vector_position_1: -1442635.9813 -6604813.9430 2082939.7037 m m m state_vector_velocity_1: -1104.53733 2489.22215 7092.91118 m/s m/s m/s state_vector_position_2: -1453581.9166 -6579543.8306 2153749.8176 m m m state_vector_velocity_2: -1084.61091 2564.74831 7068.97828 m/s m/s m/s state_vector_position_3: -1464327.4273 -6553520.0321 2224316.6067 m m m state_vector_velocity_3: -1064.45288 2639.95789 7044.24666 m/s m/s m/s state_vector_position_4: -1474870.2112 -6526745.7577 2294632.0977 m m m state_vector_velocity_4: -1044.06599 2714.84202 7018.71914 m/s m/s m/s state_vector_position_5: -1485207.9936 -6499224.3060 2364688.3461 m m m state_vector_velocity_5: -1023.45304 2789.39190 6992.39863 m/s m/s m/s state_vector_position_6: -1495338.5279 -6470959.0632 2434477.4369 m m m state_vector_velocity_6: -1002.61684 2863.59875 6965.28813 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180331_VV_8rlks_mli.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180331t004005-20180331t004030-021252-0248d6-004.tiff S1A-IW-IW1-VV-21252 (software: Sentinel-1 IPF 002.90) sensor: S1A IW IW1 VV date: 2018 03 31 start_time: 2411.964291 s center_time: 2421.296517 s end_time: 2430.628743 s azimuth_line_time: 4.1111128e-03 s line_header_size: 0 range_samples: 8514 azimuth_lines: 4541 range_looks: 8 azimuth_looks: 2 image_format: FLOAT image_geometry: SLANT_RANGE range_scale_factor: 1.0000038e+00 azimuth_scale_factor: 1.0000001e+00 center_latitude: 19.5126095 degrees center_longitude: -97.9182315 degrees heading: -12.2734015 degrees range_pixel_spacing: 18.636568 m azimuth_pixel_spacing: 28.023282 m near_range_slc: 798985.7770 m center_range_slc: 878312.3287 m far_range_slc: 957638.8804 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7038 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: 12.65333 -1.59964e-04 7.54734e-10 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073895.5709 m earth_radius_below_sensor: 6375868.9417 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2398.548957 s state_vector_interval: 10.000000 s state_vector_position_1: -1442643.5536 -6604803.8015 2082953.8822 m m m state_vector_velocity_1: -1104.47169 2489.25258 7092.91187 m/s m/s m/s state_vector_position_2: -1453588.8317 -6579533.3853 2153764.0022 m m m state_vector_velocity_2: -1084.54511 2564.77865 7068.97877 m/s m/s m/s state_vector_position_3: -1464333.6837 -6553509.2838 2224330.7953 m m m state_vector_velocity_3: -1064.38692 2639.98814 7044.24695 m/s m/s m/s state_vector_position_4: -1474875.8073 -6526734.7073 2294646.2882 m m m state_vector_velocity_4: -1043.99989 2714.87219 7018.71922 m/s m/s m/s state_vector_position_5: -1485212.9279 -6499212.9542 2364702.5365 m m m state_vector_velocity_5: -1023.38680 2789.42198 6992.39850 m/s m/s m/s state_vector_position_6: -1495342.7990 -6470947.4109 2434491.6250 m m m state_vector_velocity_6: -1002.55046 2863.62876 6965.28778 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180331_VV_slc.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180331t004005-20180331t004030-021252-0248d6-004.tiff S1A-IW-IW1-VV-21252 (software: Sentinel-1 IPF 002.90) sensor: S1A IW IW1 VV date: 2018 03 31 start_time: 2411.963263 s center_time: 2421.297545 s end_time: 2430.631827 s azimuth_line_time: 2.0555564e-03 s line_header_size: 0 range_samples: 68116 azimuth_lines: 9083 range_looks: 1 azimuth_looks: 1 image_format: FCOMPLEX image_geometry: SLANT_RANGE range_scale_factor: 1.0000038e+00 azimuth_scale_factor: 1.0000001e+00 center_latitude: 19.5126095 degrees center_longitude: -97.9182315 degrees heading: -12.2734015 degrees range_pixel_spacing: 2.329571 m azimuth_pixel_spacing: 14.011641 m near_range_slc: 798977.6235 m center_range_slc: 878316.9858 m far_range_slc: 957656.3481 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7038 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: 12.65333 -1.59964e-04 7.54734e-10 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073895.5709 m earth_radius_below_sensor: 6375868.9417 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2398.548957 s state_vector_interval: 10.000000 s state_vector_position_1: -1442643.5536 -6604803.8015 2082953.8822 m m m state_vector_velocity_1: -1104.47169 2489.25258 7092.91187 m/s m/s m/s state_vector_position_2: -1453588.8317 -6579533.3853 2153764.0022 m m m state_vector_velocity_2: -1084.54511 2564.77865 7068.97877 m/s m/s m/s state_vector_position_3: -1464333.6837 -6553509.2838 2224330.7953 m m m state_vector_velocity_3: -1064.38692 2639.98814 7044.24695 m/s m/s m/s state_vector_position_4: -1474875.8073 -6526734.7073 2294646.2882 m m m state_vector_velocity_4: -1043.99989 2714.87219 7018.71922 m/s m/s m/s state_vector_position_5: -1485212.9279 -6499212.9542 2364702.5365 m m m state_vector_velocity_5: -1023.38680 2789.42198 6992.39850 m/s m/s m/s state_vector_position_6: -1495342.7990 -6470947.4109 2434491.6250 m m m state_vector_velocity_6: -1002.55046 2863.62876 6965.28778 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180412_VV_8rlks_mli.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180412t004005-20180412t004031-021427-024e4f-004.tiff S1A-IW-IW1-VV-21427 (software: Sentinel-1 IPF 002.90) sensor: S1A IW IW1 VV date: 2018 04 12 start_time: 2412.383690 s center_time: 2421.715820 s end_time: 2431.047950 s azimuth_line_time: 4.1110706e-03 s line_header_size: 0 range_samples: 8514 azimuth_lines: 4541 range_looks: 8 azimuth_looks: 2 image_format: FLOAT image_geometry: SLANT_RANGE range_scale_factor: 1.0001041e+00 azimuth_scale_factor: 9.9998976e-01 center_latitude: 19.5126096 degrees center_longitude: -97.9182343 degrees heading: -12.2746558 degrees range_pixel_spacing: 18.638440 m azimuth_pixel_spacing: 28.023934 m near_range_slc: 798987.6220 m center_range_slc: 878322.1419 m far_range_slc: 957656.6617 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7085 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: 35.67545 -1.88932e-04 9.36371e-10 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073862.0888 m earth_radius_below_sensor: 6375868.9417 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2398.974817 s state_vector_interval: 10.000000 s state_vector_position_1: -1442701.9755 -6604751.0902 2082967.3376 m m m state_vector_velocity_1: -1104.60718 2489.33649 7092.91115 m/s m/s m/s state_vector_position_2: -1453648.6034 -6579479.8315 2153777.4481 m m m state_vector_velocity_2: -1084.67957 2564.86321 7068.97757 m/s m/s m/s state_vector_position_3: -1464394.7949 -6553454.8813 2224344.2268 m m m state_vector_velocity_3: -1064.52035 2640.07333 7044.24525 m/s m/s m/s state_vector_position_4: -1474938.2474 -6526679.4498 2294659.7003 m m m state_vector_velocity_4: -1044.13225 2714.95799 7018.71701 m/s m/s m/s state_vector_position_5: -1485276.6862 -6499156.8355 2364715.9239 m m m state_vector_velocity_5: -1023.51807 2789.50840 6992.39576 m/s m/s m/s state_vector_position_6: -1495407.8647 -6470890.4251 2434504.9823 m m m state_vector_velocity_6: -1002.68064 2863.71577 6965.28450 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180412_VV_slc.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180412t004005-20180412t004031-021427-024e4f-004.tiff S1A-IW-IW1-VV-21427 (software: Sentinel-1 IPF 002.90) sensor: S1A IW IW1 VV date: 2018 04 12 start_time: 2412.382662 s center_time: 2421.716847 s end_time: 2431.051033 s azimuth_line_time: 2.0555353e-03 s line_header_size: 0 range_samples: 68116 azimuth_lines: 9083 range_looks: 1 azimuth_looks: 1 image_format: FCOMPLEX image_geometry: SLANT_RANGE range_scale_factor: 1.0001041e+00 azimuth_scale_factor: 9.9998976e-01 center_latitude: 19.5126096 degrees center_longitude: -97.9182343 degrees heading: -12.2746558 degrees range_pixel_spacing: 2.329805 m azimuth_pixel_spacing: 14.011967 m near_range_slc: 798979.4677 m center_range_slc: 878326.7880 m far_range_slc: 957674.1083 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7085 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: 35.67545 -1.88932e-04 9.36371e-10 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073862.0888 m earth_radius_below_sensor: 6375868.9417 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2398.974817 s state_vector_interval: 10.000000 s state_vector_position_1: -1442701.9755 -6604751.0902 2082967.3376 m m m state_vector_velocity_1: -1104.60718 2489.33649 7092.91115 m/s m/s m/s state_vector_position_2: -1453648.6034 -6579479.8315 2153777.4481 m m m state_vector_velocity_2: -1084.67957 2564.86321 7068.97757 m/s m/s m/s state_vector_position_3: -1464394.7949 -6553454.8813 2224344.2268 m m m state_vector_velocity_3: -1064.52035 2640.07333 7044.24525 m/s m/s m/s state_vector_position_4: -1474938.2474 -6526679.4498 2294659.7003 m m m state_vector_velocity_4: -1044.13225 2714.95799 7018.71701 m/s m/s m/s state_vector_position_5: -1485276.6862 -6499156.8355 2364715.9239 m m m state_vector_velocity_5: -1023.51807 2789.50840 6992.39576 m/s m/s m/s state_vector_position_6: -1495407.8647 -6470890.4251 2434504.9823 m m m state_vector_velocity_6: -1002.68064 2863.71577 6965.28450 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180506_VV_8rlks_mli.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180506t004006-20180506t004032-021777-02594a-004.tiff S1A-IW-IW1-VV-21777 (software: Sentinel-1 IPF 002.90) sensor: S1A IW IW1 VV date: 2018 05 06 start_time: 2413.404533 s center_time: 2422.736684 s end_time: 2432.068836 s azimuth_line_time: 4.1110800e-03 s line_header_size: 0 range_samples: 8514 azimuth_lines: 4541 range_looks: 8 azimuth_looks: 2 image_format: FLOAT image_geometry: SLANT_RANGE range_scale_factor: 1.0000227e+00 azimuth_scale_factor: 9.9999209e-01 center_latitude: 19.5126100 degrees center_longitude: -97.9182327 degrees heading: -12.2742940 degrees range_pixel_spacing: 18.636920 m azimuth_pixel_spacing: 28.023278 m near_range_slc: 798966.0242 m center_range_slc: 878294.0741 m far_range_slc: 957622.1241 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7047 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: 11.52580 -2.34703e-04 1.11667e-09 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073872.7088 m earth_radius_below_sensor: 6375868.9416 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2399.995112 s state_vector_interval: 10.000000 s state_vector_position_1: -1442642.4939 -6604770.9821 2082981.5708 m m m state_vector_velocity_1: -1104.57944 2489.32644 7092.90469 m/s m/s m/s state_vector_position_2: -1453588.8482 -6579499.8248 2153791.6162 m m m state_vector_velocity_2: -1084.65260 2564.85301 7068.97105 m/s m/s m/s state_vector_position_3: -1464334.7737 -6553474.9773 2224358.3294 m m m state_vector_velocity_3: -1064.49415 2640.06298 7044.23869 m/s m/s m/s state_vector_position_4: -1474877.9681 -6526699.6500 2294673.7369 m m m state_vector_velocity_4: -1044.10683 2714.94751 7018.71041 m/s m/s m/s state_vector_position_5: -1485216.1566 -6499177.1414 2364729.8941 m m m state_vector_velocity_5: -1023.49343 2789.49777 6992.38912 m/s m/s m/s state_vector_position_6: -1495347.0925 -6470910.8381 2434518.8859 m m m state_vector_velocity_6: -1002.65679 2863.70501 6965.27783 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180506_VV_slc.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180506t004006-20180506t004032-021777-02594a-004.tiff S1A-IW-IW1-VV-21777 (software: Sentinel-1 IPF 002.90) sensor: S1A IW IW1 VV date: 2018 05 06 start_time: 2413.403505 s center_time: 2422.737713 s end_time: 2432.071920 s azimuth_line_time: 2.0555400e-03 s line_header_size: 0 range_samples: 68116 azimuth_lines: 9083 range_looks: 1 azimuth_looks: 1 image_format: FCOMPLEX image_geometry: SLANT_RANGE range_scale_factor: 1.0000227e+00 azimuth_scale_factor: 9.9999209e-01 center_latitude: 19.5126100 degrees center_longitude: -97.9182327 degrees heading: -12.2742940 degrees range_pixel_spacing: 2.329615 m azimuth_pixel_spacing: 14.011639 m near_range_slc: 798957.8705 m center_range_slc: 878298.7312 m far_range_slc: 957639.5918 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7047 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: 11.52580 -2.34703e-04 1.11667e-09 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073872.7088 m earth_radius_below_sensor: 6375868.9416 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2399.995112 s state_vector_interval: 10.000000 s state_vector_position_1: -1442642.4939 -6604770.9821 2082981.5708 m m m state_vector_velocity_1: -1104.57944 2489.32644 7092.90469 m/s m/s m/s state_vector_position_2: -1453588.8482 -6579499.8248 2153791.6162 m m m state_vector_velocity_2: -1084.65260 2564.85301 7068.97105 m/s m/s m/s state_vector_position_3: -1464334.7737 -6553474.9773 2224358.3294 m m m state_vector_velocity_3: -1064.49415 2640.06298 7044.23869 m/s m/s m/s state_vector_position_4: -1474877.9681 -6526699.6500 2294673.7369 m m m state_vector_velocity_4: -1044.10683 2714.94751 7018.71041 m/s m/s m/s state_vector_position_5: -1485216.1566 -6499177.1414 2364729.8941 m m m state_vector_velocity_5: -1023.49343 2789.49777 6992.38912 m/s m/s m/s state_vector_position_6: -1495347.0925 -6470910.8381 2434518.8859 m m m state_vector_velocity_6: -1002.65679 2863.70501 6965.27783 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180518_VV_8rlks_mli.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180518t004007-20180518t004032-021952-025eda-004.tiff S1A-IW-IW1-VV-21952 (software: Sentinel-1 IPF 002.90) sensor: S1A IW IW1 VV date: 2018 05 18 start_time: 2414.049918 s center_time: 2423.382069 s end_time: 2432.714221 s azimuth_line_time: 4.1110800e-03 s line_header_size: 0 range_samples: 8514 azimuth_lines: 4541 range_looks: 8 azimuth_looks: 2 image_format: FLOAT image_geometry: SLANT_RANGE range_scale_factor: 1.0000406e+00 azimuth_scale_factor: 9.9999208e-01 center_latitude: 19.5126092 degrees center_longitude: -97.9182347 degrees heading: -12.2739463 degrees range_pixel_spacing: 18.637256 m azimuth_pixel_spacing: 28.023278 m near_range_slc: 798971.3619 m center_range_slc: 878300.8421 m far_range_slc: 957630.3222 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7055 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: 30.50367 -1.56155e-04 5.89911e-10 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073870.9747 m earth_radius_below_sensor: 6375868.9418 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2400.641508 s state_vector_interval: 10.000000 s state_vector_position_1: -1442658.8229 -6604763.3736 2082988.1616 m m m state_vector_velocity_1: -1104.52792 2489.32605 7092.91204 m/s m/s m/s state_vector_position_2: -1453604.6611 -6579492.2208 2153798.2799 m m m state_vector_velocity_2: -1084.60090 2564.85252 7068.97831 m/s m/s m/s state_vector_position_3: -1464350.0687 -6553467.3788 2224365.0649 m m m state_vector_velocity_3: -1064.44227 2640.06239 7044.24585 m/s m/s m/s state_vector_position_4: -1474892.7434 -6526692.0579 2294680.5434 m m m state_vector_velocity_4: -1044.05478 2714.94682 7018.71747 m/s m/s m/s state_vector_position_5: -1485230.4105 -6499169.5568 2364736.7706 m m m state_vector_velocity_5: -1023.44121 2789.49698 6992.39609 m/s m/s m/s state_vector_position_6: -1495360.8233 -6470903.2619 2434525.8315 m m m state_vector_velocity_6: -1002.60441 2863.70412 6965.28470 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180518_VV_slc.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180518t004007-20180518t004032-021952-025eda-004.tiff S1A-IW-IW1-VV-21952 (software: Sentinel-1 IPF 002.90) sensor: S1A IW IW1 VV date: 2018 05 18 start_time: 2414.048890 s center_time: 2423.383097 s end_time: 2432.717304 s azimuth_line_time: 2.0555400e-03 s line_header_size: 0 range_samples: 68116 azimuth_lines: 9083 range_looks: 1 azimuth_looks: 1 image_format: FCOMPLEX image_geometry: SLANT_RANGE range_scale_factor: 1.0000406e+00 azimuth_scale_factor: 9.9999208e-01 center_latitude: 19.5126092 degrees center_longitude: -97.9182347 degrees heading: -12.2739463 degrees range_pixel_spacing: 2.329657 m azimuth_pixel_spacing: 14.011639 m near_range_slc: 798963.2081 m center_range_slc: 878305.4861 m far_range_slc: 957647.7641 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7055 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: 30.50367 -1.56155e-04 5.89911e-10 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073870.9747 m earth_radius_below_sensor: 6375868.9418 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2400.641508 s state_vector_interval: 10.000000 s state_vector_position_1: -1442658.8229 -6604763.3736 2082988.1616 m m m state_vector_velocity_1: -1104.52792 2489.32605 7092.91204 m/s m/s m/s state_vector_position_2: -1453604.6611 -6579492.2208 2153798.2799 m m m state_vector_velocity_2: -1084.60090 2564.85252 7068.97831 m/s m/s m/s state_vector_position_3: -1464350.0687 -6553467.3788 2224365.0649 m m m state_vector_velocity_3: -1064.44227 2640.06239 7044.24585 m/s m/s m/s state_vector_position_4: -1474892.7434 -6526692.0579 2294680.5434 m m m state_vector_velocity_4: -1044.05478 2714.94682 7018.71747 m/s m/s m/s state_vector_position_5: -1485230.4105 -6499169.5568 2364736.7706 m m m state_vector_velocity_5: -1023.44121 2789.49698 6992.39609 m/s m/s m/s state_vector_position_6: -1495360.8233 -6470903.2619 2434525.8315 m m m state_vector_velocity_6: -1002.60441 2863.70412 6965.28470 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180530_VV_8rlks_mli.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180530t004008-20180530t004033-022127-02647c-004.tiff S1A-IW-IW1-VV-22127 (software: Sentinel-1 IPF 002.90) sensor: S1A IW IW1 VV date: 2018 05 30 start_time: 2414.670632 s center_time: 2424.002741 s end_time: 2433.334851 s azimuth_line_time: 4.1110614e-03 s line_header_size: 0 range_samples: 8514 azimuth_lines: 4541 range_looks: 8 azimuth_looks: 2 image_format: FLOAT image_geometry: SLANT_RANGE range_scale_factor: 9.9999479e-01 azimuth_scale_factor: 9.9998757e-01 center_latitude: 19.5126111 degrees center_longitude: -97.9182280 degrees heading: -12.2738650 degrees range_pixel_spacing: 18.636400 m azimuth_pixel_spacing: 28.023212 m near_range_slc: 798935.5604 m center_range_slc: 878261.3970 m far_range_slc: 957587.2336 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7034 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: 15.22544 -1.80109e-04 7.24218e-10 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073857.1356 m earth_radius_below_sensor: 6375868.9413 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2401.257760 s state_vector_interval: 10.000000 s state_vector_position_1: -1442602.6920 -6604769.2129 2082961.4008 m m m state_vector_velocity_1: -1104.54480 2489.29260 7092.93403 m/s m/s m/s state_vector_position_2: -1453548.7019 -6579498.3918 2153771.7399 m m m state_vector_velocity_2: -1084.61835 2564.81961 7069.00046 m/s m/s m/s state_vector_position_3: -1464294.2869 -6553473.8762 2224338.7472 m m m state_vector_velocity_3: -1064.46029 2640.03002 7044.26814 m/s m/s m/s state_vector_position_4: -1474837.1447 -6526698.8764 2294654.4494 m m m state_vector_velocity_4: -1044.07336 2714.91498 7018.73991 m/s m/s m/s state_vector_position_5: -1485175.0005 -6499176.6909 2364710.9018 m m m state_vector_velocity_5: -1023.46036 2789.46568 6992.41867 m/s m/s m/s state_vector_position_6: -1495305.6078 -6470910.7063 2434500.1891 m m m state_vector_velocity_6: -1002.62411 2863.67335 6965.30741 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180530_VV_slc.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180530t004008-20180530t004033-022127-02647c-004.tiff S1A-IW-IW1-VV-22127 (software: Sentinel-1 IPF 002.90) sensor: S1A IW IW1 VV date: 2018 05 30 start_time: 2414.669604 s center_time: 2424.003769 s end_time: 2433.337934 s azimuth_line_time: 2.0555307e-03 s line_header_size: 0 range_samples: 68116 azimuth_lines: 9083 range_looks: 1 azimuth_looks: 1 image_format: FCOMPLEX image_geometry: SLANT_RANGE range_scale_factor: 9.9999479e-01 azimuth_scale_factor: 9.9998757e-01 center_latitude: 19.5126111 degrees center_longitude: -97.9182280 degrees heading: -12.2738650 degrees range_pixel_spacing: 2.329550 m azimuth_pixel_spacing: 14.011606 m near_range_slc: 798927.4070 m center_range_slc: 878266.0511 m far_range_slc: 957604.6952 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7034 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: 15.22544 -1.80109e-04 7.24218e-10 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073857.1356 m earth_radius_below_sensor: 6375868.9413 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2401.257760 s state_vector_interval: 10.000000 s state_vector_position_1: -1442602.6920 -6604769.2129 2082961.4008 m m m state_vector_velocity_1: -1104.54480 2489.29260 7092.93403 m/s m/s m/s state_vector_position_2: -1453548.7019 -6579498.3918 2153771.7399 m m m state_vector_velocity_2: -1084.61835 2564.81961 7069.00046 m/s m/s m/s state_vector_position_3: -1464294.2869 -6553473.8762 2224338.7472 m m m state_vector_velocity_3: -1064.46029 2640.03002 7044.26814 m/s m/s m/s state_vector_position_4: -1474837.1447 -6526698.8764 2294654.4494 m m m state_vector_velocity_4: -1044.07336 2714.91498 7018.73991 m/s m/s m/s state_vector_position_5: -1485175.0005 -6499176.6909 2364710.9018 m m m state_vector_velocity_5: -1023.46036 2789.46568 6992.41867 m/s m/s m/s state_vector_position_6: -1495305.6078 -6470910.7063 2434500.1891 m m m state_vector_velocity_6: -1002.62411 2863.67335 6965.30741 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180611_VV_8rlks_mli.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180611t004009-20180611t004034-022302-0269f0-004.tiff S1A-IW-IW1-VV-22302 (software: Sentinel-1 IPF 002.90) sensor: S1A IW IW1 VV date: 2018 06 11 start_time: 2415.577608 s center_time: 2424.909765 s end_time: 2434.241922 s azimuth_line_time: 4.1110824e-03 s line_header_size: 0 range_samples: 8514 azimuth_lines: 4541 range_looks: 8 azimuth_looks: 2 image_format: FLOAT image_geometry: SLANT_RANGE range_scale_factor: 1.0000709e+00 azimuth_scale_factor: 9.9999265e-01 center_latitude: 19.5126100 degrees center_longitude: -97.9182321 degrees heading: -12.2739122 degrees range_pixel_spacing: 18.637816 m azimuth_pixel_spacing: 28.023274 m near_range_slc: 798984.6715 m center_range_slc: 878316.5353 m far_range_slc: 957648.3992 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7070 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: -6.44169 -1.70915e-04 8.43663e-10 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073871.1809 m earth_radius_below_sensor: 6375868.9416 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2402.170128 s state_vector_interval: 10.000000 s state_vector_position_1: -1442686.1339 -6604756.9510 2082990.0640 m m m state_vector_velocity_1: -1104.51358 2489.32919 7092.91276 m/s m/s m/s state_vector_position_2: -1453631.8271 -6579485.7671 2153800.1895 m m m state_vector_velocity_2: -1084.58623 2564.85556 7068.97900 m/s m/s m/s state_vector_position_3: -1464377.0865 -6553460.8951 2224366.9815 m m m state_vector_velocity_3: -1064.42727 2640.06533 7044.24651 m/s m/s m/s state_vector_position_4: -1474919.6097 -6526685.5453 2294682.4666 m m m state_vector_velocity_4: -1044.03946 2714.94965 7018.71811 m/s m/s m/s state_vector_position_5: -1485257.1220 -6499163.0163 2364738.7003 m m m state_vector_velocity_5: -1023.42557 2789.49971 6992.39670 m/s m/s m/s state_vector_position_6: -1495387.3769 -6470896.6945 2434527.7672 m m m state_vector_velocity_6: -1002.58844 2863.70674 6965.28529 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180611_VV_slc.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180611t004009-20180611t004034-022302-0269f0-004.tiff S1A-IW-IW1-VV-22302 (software: Sentinel-1 IPF 002.90) sensor: S1A IW IW1 VV date: 2018 06 11 start_time: 2415.576580 s center_time: 2424.910792 s end_time: 2434.245005 s azimuth_line_time: 2.0555412e-03 s line_header_size: 0 range_samples: 68116 azimuth_lines: 9083 range_looks: 1 azimuth_looks: 1 image_format: FCOMPLEX image_geometry: SLANT_RANGE range_scale_factor: 1.0000709e+00 azimuth_scale_factor: 9.9999265e-01 center_latitude: 19.5126100 degrees center_longitude: -97.9182321 degrees heading: -12.2739122 degrees range_pixel_spacing: 2.329727 m azimuth_pixel_spacing: 14.011637 m near_range_slc: 798976.5175 m center_range_slc: 878321.2042 m far_range_slc: 957665.8909 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7070 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: -6.44169 -1.70915e-04 8.43663e-10 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073871.1809 m earth_radius_below_sensor: 6375868.9416 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2402.170128 s state_vector_interval: 10.000000 s state_vector_position_1: -1442686.1339 -6604756.9510 2082990.0640 m m m state_vector_velocity_1: -1104.51358 2489.32919 7092.91276 m/s m/s m/s state_vector_position_2: -1453631.8271 -6579485.7671 2153800.1895 m m m state_vector_velocity_2: -1084.58623 2564.85556 7068.97900 m/s m/s m/s state_vector_position_3: -1464377.0865 -6553460.8951 2224366.9815 m m m state_vector_velocity_3: -1064.42727 2640.06533 7044.24651 m/s m/s m/s state_vector_position_4: -1474919.6097 -6526685.5453 2294682.4666 m m m state_vector_velocity_4: -1044.03946 2714.94965 7018.71811 m/s m/s m/s state_vector_position_5: -1485257.1220 -6499163.0163 2364738.7003 m m m state_vector_velocity_5: -1023.42557 2789.49971 6992.39670 m/s m/s m/s state_vector_position_6: -1495387.3769 -6470896.6945 2434527.7672 m m m state_vector_velocity_6: -1002.58844 2863.70674 6965.28529 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180623_VV_8rlks_mli.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180623t004009-20180623t004034-022477-026f32-004.tiff S1A-IW-IW1-VV-22477 (software: Sentinel-1 IPF 002.90) sensor: S1A IW IW1 VV date: 2018 06 23 start_time: 2416.281152 s center_time: 2425.613261 s end_time: 2434.945371 s azimuth_line_time: 4.1110614e-03 s line_header_size: 0 range_samples: 8514 azimuth_lines: 4541 range_looks: 8 azimuth_looks: 2 image_format: FLOAT image_geometry: SLANT_RANGE range_scale_factor: 1.0000527e+00 azimuth_scale_factor: 9.9998753e-01 center_latitude: 19.5126108 degrees center_longitude: -97.9182325 degrees heading: -12.2742251 degrees range_pixel_spacing: 18.637480 m azimuth_pixel_spacing: 28.023230 m near_range_slc: 798959.0311 m center_range_slc: 878289.4647 m far_range_slc: 957619.8983 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7061 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: 14.54785 -1.25986e-04 5.07538e-10 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073856.4793 m earth_radius_below_sensor: 6375868.9414 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2402.873946 s state_vector_interval: 10.000000 s state_vector_position_1: -1442656.4274 -6604747.9147 2082989.4255 m m m state_vector_velocity_1: -1104.56499 2489.34157 7092.91970 m/s m/s m/s state_vector_position_2: -1453602.6358 -6579476.6048 2153799.6198 m m m state_vector_velocity_2: -1084.63785 2564.86838 7068.98580 m/s m/s m/s state_vector_position_3: -1464348.4124 -6553451.6024 2224366.4792 m m m state_vector_velocity_3: -1064.47910 2640.07858 7044.25316 m/s m/s m/s state_vector_position_4: -1474891.4549 -6526676.1180 2294682.0302 m m m state_vector_velocity_4: -1044.09148 2714.96333 7018.72461 m/s m/s m/s state_vector_position_5: -1485229.4885 -6499153.4500 2364738.3280 m m m state_vector_velocity_5: -1023.47779 2789.51381 6992.40304 m/s m/s m/s state_vector_position_6: -1495360.2665 -6470886.9851 2434527.4576 m m m state_vector_velocity_6: -1002.64084 2863.72126 6965.29145 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180623_VV_slc.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180623t004009-20180623t004034-022477-026f32-004.tiff S1A-IW-IW1-VV-22477 (software: Sentinel-1 IPF 002.90) sensor: S1A IW IW1 VV date: 2018 06 23 start_time: 2416.280124 s center_time: 2425.614289 s end_time: 2434.948454 s azimuth_line_time: 2.0555307e-03 s line_header_size: 0 range_samples: 68116 azimuth_lines: 9083 range_looks: 1 azimuth_looks: 1 image_format: FCOMPLEX image_geometry: SLANT_RANGE range_scale_factor: 1.0000527e+00 azimuth_scale_factor: 9.9998753e-01 center_latitude: 19.5126108 degrees center_longitude: -97.9182325 degrees heading: -12.2742251 degrees range_pixel_spacing: 2.329685 m azimuth_pixel_spacing: 14.011615 m near_range_slc: 798950.8772 m center_range_slc: 878294.1123 m far_range_slc: 957637.3475 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7061 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: 14.54785 -1.25986e-04 5.07538e-10 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073856.4793 m earth_radius_below_sensor: 6375868.9414 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2402.873946 s state_vector_interval: 10.000000 s state_vector_position_1: -1442656.4274 -6604747.9147 2082989.4255 m m m state_vector_velocity_1: -1104.56499 2489.34157 7092.91970 m/s m/s m/s state_vector_position_2: -1453602.6358 -6579476.6048 2153799.6198 m m m state_vector_velocity_2: -1084.63785 2564.86838 7068.98580 m/s m/s m/s state_vector_position_3: -1464348.4124 -6553451.6024 2224366.4792 m m m state_vector_velocity_3: -1064.47910 2640.07858 7044.25316 m/s m/s m/s state_vector_position_4: -1474891.4549 -6526676.1180 2294682.0302 m m m state_vector_velocity_4: -1044.09148 2714.96333 7018.72461 m/s m/s m/s state_vector_position_5: -1485229.4885 -6499153.4500 2364738.3280 m m m state_vector_velocity_5: -1023.47779 2789.51381 6992.40304 m/s m/s m/s state_vector_position_6: -1495360.2665 -6470886.9851 2434527.4576 m m m state_vector_velocity_6: -1002.64084 2863.72126 6965.29145 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180705_VV_8rlks_mli.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180705t004010-20180705t004035-022652-027450-004.tiff S1A-IW-IW1-VV-22652 (software: Sentinel-1 IPF 002.91) sensor: S1A IW IW1 VV date: 2018 07 05 start_time: 2416.688145 s center_time: 2426.020257 s end_time: 2435.352370 s azimuth_line_time: 4.1110628e-03 s line_header_size: 0 range_samples: 8514 azimuth_lines: 4541 range_looks: 8 azimuth_looks: 2 image_format: FLOAT image_geometry: SLANT_RANGE range_scale_factor: 9.9992375e-01 azimuth_scale_factor: 9.9998786e-01 center_latitude: 19.5126114 degrees center_longitude: -97.9182295 degrees heading: -12.2745289 degrees range_pixel_spacing: 18.635072 m azimuth_pixel_spacing: 28.023200 m near_range_slc: 798915.1793 m center_range_slc: 878235.3633 m far_range_slc: 957555.5473 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7001 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: -7.82027 -2.35094e-04 1.06023e-09 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073865.2745 m earth_radius_below_sensor: 6375868.9413 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2403.279394 s state_vector_interval: 10.000000 s state_vector_position_1: -1442550.6790 -6604777.7915 2082997.8717 m m m state_vector_velocity_1: -1104.64152 2489.33373 7092.90430 m/s m/s m/s state_vector_position_2: -1453497.6590 -6579506.5592 2153807.9120 m m m state_vector_velocity_2: -1084.71565 2564.86071 7068.97040 m/s m/s m/s state_vector_position_3: -1464244.2199 -6553481.6328 2224374.6174 m m m state_vector_velocity_3: -1064.55816 2640.07108 7044.23776 m/s m/s m/s state_vector_position_4: -1474788.0592 -6526706.2224 2294690.0145 m m m state_vector_velocity_4: -1044.17179 2714.95601 7018.70921 m/s m/s m/s state_vector_position_5: -1485126.9019 -6499183.6268 2364746.1585 m m m state_vector_velocity_5: -1023.55933 2789.50667 6992.38766 m/s m/s m/s state_vector_position_6: -1495258.5015 -6470917.2323 2434535.1344 m m m state_vector_velocity_6: -1002.72362 2863.71431 6965.27609 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180705_VV_slc.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180705t004010-20180705t004035-022652-027450-004.tiff S1A-IW-IW1-VV-22652 (software: Sentinel-1 IPF 002.91) sensor: S1A IW IW1 VV date: 2018 07 05 start_time: 2416.687117 s center_time: 2426.021285 s end_time: 2435.355453 s azimuth_line_time: 2.0555314e-03 s line_header_size: 0 range_samples: 68116 azimuth_lines: 9083 range_looks: 1 azimuth_looks: 1 image_format: FCOMPLEX image_geometry: SLANT_RANGE range_scale_factor: 9.9992375e-01 azimuth_scale_factor: 9.9998786e-01 center_latitude: 19.5126114 degrees center_longitude: -97.9182295 degrees heading: -12.2745289 degrees range_pixel_spacing: 2.329384 m azimuth_pixel_spacing: 14.011600 m near_range_slc: 798907.0265 m center_range_slc: 878240.0350 m far_range_slc: 957573.0434 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7001 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: -7.82027 -2.35094e-04 1.06023e-09 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073865.2745 m earth_radius_below_sensor: 6375868.9413 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2403.279394 s state_vector_interval: 10.000000 s state_vector_position_1: -1442550.6790 -6604777.7915 2082997.8717 m m m state_vector_velocity_1: -1104.64152 2489.33373 7092.90430 m/s m/s m/s state_vector_position_2: -1453497.6590 -6579506.5592 2153807.9120 m m m state_vector_velocity_2: -1084.71565 2564.86071 7068.97040 m/s m/s m/s state_vector_position_3: -1464244.2199 -6553481.6328 2224374.6174 m m m state_vector_velocity_3: -1064.55816 2640.07108 7044.23776 m/s m/s m/s state_vector_position_4: -1474788.0592 -6526706.2224 2294690.0145 m m m state_vector_velocity_4: -1044.17179 2714.95601 7018.70921 m/s m/s m/s state_vector_position_5: -1485126.9019 -6499183.6268 2364746.1585 m m m state_vector_velocity_5: -1023.55933 2789.50667 6992.38766 m/s m/s m/s state_vector_position_6: -1495258.5015 -6470917.2323 2434535.1344 m m m state_vector_velocity_6: -1002.72362 2863.71431 6965.27609 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180717_VV_8rlks_mli.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180717t004011-20180717t004036-022827-02799b-004.tiff S1A-IW-IW1-VV-22827 (software: Sentinel-1 IPF 002.91) sensor: S1A IW IW1 VV date: 2018 07 17 start_time: 2417.677696 s center_time: 2427.009819 s end_time: 2436.341942 s azimuth_line_time: 4.1110674e-03 s line_header_size: 0 range_samples: 8514 azimuth_lines: 4541 range_looks: 8 azimuth_looks: 2 image_format: FLOAT image_geometry: SLANT_RANGE range_scale_factor: 1.0000365e+00 azimuth_scale_factor: 9.9998902e-01 center_latitude: 19.5126104 degrees center_longitude: -97.9182335 degrees heading: -12.2742902 degrees range_pixel_spacing: 18.637176 m azimuth_pixel_spacing: 28.023252 m near_range_slc: 798959.5607 m center_range_slc: 878288.7003 m far_range_slc: 957617.8400 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7053 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: 8.68743 -1.16261e-04 2.50691e-10 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073862.6200 m earth_radius_below_sensor: 6375868.9415 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2404.269733 s state_vector_interval: 10.000000 s state_vector_position_1: -1442646.9928 -6604757.2056 2082987.8020 m m m state_vector_velocity_1: -1104.57610 2489.34060 7092.91227 m/s m/s m/s state_vector_position_2: -1453593.3130 -6579485.9059 2153797.9224 m m m state_vector_velocity_2: -1084.64913 2564.86733 7068.97846 m/s m/s m/s state_vector_position_3: -1464339.2032 -6553460.9144 2224364.7087 m m m state_vector_velocity_3: -1064.49055 2640.07746 7044.24590 m/s m/s m/s state_vector_position_4: -1474882.3608 -6526685.4415 2294680.1875 m m m state_vector_velocity_4: -1044.10309 2714.96214 7018.71744 m/s m/s m/s state_vector_position_5: -1485220.5112 -6499162.7858 2364736.4141 m m m state_vector_velocity_5: -1023.48956 2789.51256 6992.39596 m/s m/s m/s state_vector_position_6: -1495351.4077 -6470896.3337 2434525.4733 m m m state_vector_velocity_6: -1002.65278 2863.71995 6965.28447 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers/r20180717_VV_slc.par ================================================ Gamma Interferometric SAR Processor (ISP) - Image Parameter File title: s1a-iw1-slc-vv-20180717t004011-20180717t004036-022827-02799b-004.tiff S1A-IW-IW1-VV-22827 (software: Sentinel-1 IPF 002.91) sensor: S1A IW IW1 VV date: 2018 07 17 start_time: 2417.676668 s center_time: 2427.010846 s end_time: 2436.345025 s azimuth_line_time: 2.0555337e-03 s line_header_size: 0 range_samples: 68116 azimuth_lines: 9083 range_looks: 1 azimuth_looks: 1 image_format: FCOMPLEX image_geometry: SLANT_RANGE range_scale_factor: 1.0000365e+00 azimuth_scale_factor: 9.9998902e-01 center_latitude: 19.5126104 degrees center_longitude: -97.9182335 degrees heading: -12.2742902 degrees range_pixel_spacing: 2.329647 m azimuth_pixel_spacing: 14.011626 m near_range_slc: 798951.4069 m center_range_slc: 878293.3586 m far_range_slc: 957635.3104 m first_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 center_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 last_slant_range_polynomial: 0.00000 0.00000 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 s m 1 m^-1 m^-2 m^-3 incidence_angle: 39.7053 degrees azimuth_deskew: ON azimuth_angle: 90.0000 degrees radar_frequency: 5.4050005e+09 Hz adc_sampling_rate: 6.4345241e+07 Hz chirp_bandwidth: 5.6500000e+07 Hz prf: 486.4863103 Hz azimuth_proc_bandwidth: 327.00000 Hz doppler_polynomial: 8.68743 -1.16261e-04 2.50691e-10 0.00000e+00 Hz Hz/m Hz/m^2 Hz/m^3 doppler_poly_dot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s Hz/s/m Hz/s/m^2 Hz/s/m^3 doppler_poly_ddot: 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 Hz/s^2 Hz/s^2/m Hz/s^2/m^2 Hz/s^2/m^3 receiver_gain: 0.0000 dB calibration_gain: 0.0000 dB sar_to_earth_center: 7073862.6200 m earth_radius_below_sensor: 6375868.9415 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m number_of_state_vectors: 6 time_of_first_state_vector: 2404.269733 s state_vector_interval: 10.000000 s state_vector_position_1: -1442646.9928 -6604757.2056 2082987.8020 m m m state_vector_velocity_1: -1104.57610 2489.34060 7092.91227 m/s m/s m/s state_vector_position_2: -1453593.3130 -6579485.9059 2153797.9224 m m m state_vector_velocity_2: -1084.64913 2564.86733 7068.97846 m/s m/s m/s state_vector_position_3: -1464339.2032 -6553460.9144 2224364.7087 m m m state_vector_velocity_3: -1064.49055 2640.07746 7044.24590 m/s m/s m/s state_vector_position_4: -1474882.3608 -6526685.4415 2294680.1875 m m m state_vector_velocity_4: -1044.10309 2714.96214 7018.71744 m/s m/s m/s state_vector_position_5: -1485220.5112 -6499162.7858 2364736.4141 m m m state_vector_velocity_5: -1023.48956 2789.51256 6992.39596 m/s m/s m/s state_vector_position_6: -1495351.4077 -6470896.3337 2434525.4733 m m m state_vector_velocity_6: -1002.65278 2863.71995 6965.28447 m/s m/s m/s ================================================ FILE: tests/test_data/cropA/headers_13 ================================================ tests/test_data/cropA/headers/r20180106_VV_8rlks_mli.par tests/test_data/cropA/headers/r20180130_VV_8rlks_mli.par tests/test_data/cropA/headers/r20180307_VV_8rlks_mli.par tests/test_data/cropA/headers/r20180319_VV_8rlks_mli.par tests/test_data/cropA/headers/r20180331_VV_8rlks_mli.par tests/test_data/cropA/headers/r20180412_VV_8rlks_mli.par tests/test_data/cropA/headers/r20180506_VV_8rlks_mli.par tests/test_data/cropA/headers/r20180518_VV_8rlks_mli.par tests/test_data/cropA/headers/r20180530_VV_8rlks_mli.par tests/test_data/cropA/headers/r20180611_VV_8rlks_mli.par tests/test_data/cropA/headers/r20180623_VV_8rlks_mli.par tests/test_data/cropA/headers/r20180705_VV_8rlks_mli.par tests/test_data/cropA/headers/r20180717_VV_8rlks_mli.par ================================================ FILE: tests/test_data/cropA/ifg_30 ================================================ tests/test_data/cropA/geotiffs/cropA_20180106-20180130_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180106-20180319_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180106-20180412_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180106-20180518_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180130-20180307_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180130-20180412_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180307-20180319_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180307-20180331_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180307-20180506_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180307-20180530_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180307-20180611_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180319-20180331_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180319-20180506_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180319-20180518_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180319-20180530_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180319-20180623_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180331-20180412_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180331-20180506_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180331-20180518_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180331-20180530_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180331-20180623_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180331-20180717_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180412-20180506_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180412-20180518_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180506-20180518_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180506-20180530_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180506-20180611_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180506-20180623_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180506-20180705_VV_8rlks_eqa_unw.tif tests/test_data/cropA/geotiffs/cropA_20180506-20180717_VV_8rlks_eqa_unw.tif ================================================ FILE: tests/test_data/cropA/pyrate_mexico_cropa.conf ================================================ # PyRate configuration file for GAMMA-format interferograms # #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # Optional ON/OFF switches - ON = 1; OFF = 0 # Coherence masking (PREPIFG) cohmask: 0 # Orbital error correction (CORRECT) orbfit: 1 # APS correction using spatio-temporal filter (CORRECT) apsest: 1 # DEM error (residual topography) correction (CORRECT) demerror: 1 # Phase Closure correction (CORRECT) phase_closure: 1 # Optional save of numpy array files for output products (MERGE) savenpy: 1 # Optional save of incremental time series products (TIMESERIES/MERGE) savetsincr: 1 #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # Multi-threading parameters used by correct/stacking/timeseries # gamma prepifg runs in parallel on a single machine if parallel = 1 # parallel: 1 = parallel, 0 = serial parallel: 0 # number of processes processes: 4 #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # Input/Output file locations # # File containing the list of interferograms to use. ifgfilelist: tests/test_data/cropA/ifg_30 # The DEM file used in the InSAR processing demfile: tests/test_data/cropA/geotiffs/cropA_T005A_dem.tif # The DEM header file from GAMMA (*.par) or ROI_PAC (*.rsc). demHeaderFile: tests/test_data/cropA/headers/cropA_20180106_VV_8rlks_eqa_dem.par # File listing the pool of available header files (GAMMA: *mli.par, ROI_PAC: *.rsc) hdrfilelist: tests/test_data/cropA/headers_13 # File listing the pool of available coherence files. cohfilelist: tests/test_data/cropA/coherence_30 # File listing the pool of available baseline files (GAMMA). basefilelist: tests/test_data/cropA/baseline_30 # Look-up table containing radar-coded row and column for lat/lon pixels (GAMMA) ltfile: tests/test_data/cropA/geometry/20180106_VV_8rlks_eqa_to_rdc.lt # Directory to write the outputs to outdir: out/cropa/out #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # PREPIFG parameters #------------------------------------ # Input data format: ROI_PAC = 0, GAMMA = 1 processor: 1 # Coherence threshold value for masking, between 0 and 1 cohthresh: 0.05 # Multi-look/subsampling factor in east (x) and north (y) dimension ifglksx: 1 ifglksy: 1 # Cropping options # ifgcropopt: 1 = minimum extent 2 = maximum extent 3 = crop 4 = no cropping # ifgxfirst,ifgyfirst: longitude (x) and latitude (y) of north-west corner # ifgxlast,ifgylast: longitude (x) and latitude (y) of south-east corner ifgcropopt: 1 ifgxfirst: 150.92 ifgyfirst: -34.18 ifgxlast: 150.94 ifgylast: -34.22 # No-data averaging threshold (0 = 0%; 1 = 100%) noDataAveragingThreshold: 0.5 # The No-data value used in the interferogram files noDataValue: 0.0 # Nan conversion flag. Set to 1 if missing No-data values are to be converted to NaN nan_conversion: 1 # sign convention for phase data signal_polarity: -1 #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # CORRECT parameters #------------------------------------ # Reference pixel search options # refx/y: Lon/Lat coordinate of reference pixel. If left blank then search for best pixel will be performed # refnx/y: number of search grid points in x/y image dimensions # refchipsize: size of the data window at each search grid point # refminfrac: minimum fraction of valid (non-NaN) pixels in the data window refx: -1 refy: -1 refnx: 5 refny: 5 refchipsize: 5 refminfrac: 0.8 #------------------------------------ # Reference phase correction method # refest: 1 = median of the whole interferogram # refest: 2 = median within the window surrounding the chosen reference pixel refest: 1 #------------------------------------ # Orbital error correction # orbfitmethod = 1: interferograms corrected independently; 2: network method # orbfitdegrees: Degree of polynomial surface to fit (1 = planar; 2 = quadratic; 3 = part-cubic) # orbfitlksx/y: additional multi-look factor for network orbital correction orbfitmethod: 1 orbfitdegrees: 1 orbfitlksx: 1 orbfitlksy: 1 # phase closure params - refer to input_parameters.conf for descriptions closure_thr: 0.5 ifg_drop_thr: 0.07 min_loops_per_ifg: 2 max_loop_length: 4 max_loop_redundancy: 2 #------------------------------------ # APS filter parameters # tlpfcutoff: cutoff t0 for temporal high-pass Gaussian filter in days (int); # tlpfpthr: valid pixel threshold; # slpfcutoff: spatial low-pass Gaussian filter cutoff in km (greater than zero). # slpfcutoff=0 triggers cutoff estimation from exponential covariance function tlpfcutoff: 30 tlpfpthr: 1 slpfcutoff: 1 #------------------------------------ # DEM error (residual topography) correction parameters # de_pthr: valid observations threshold; de_pthr: 20 #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # TIMESERIES parameters #------------------------------------ # tsmethod: Method for time series inversion (1 = Laplacian Smoothing; 2 = SVD) # smorder: order of Laplacian smoothing operator (1 = first-order difference; 2 = second-order difference) # smfactor: smoothing factor for Laplacian smoothing (value provided is converted as 10**smfactor) # ts_pthr: valid observations threshold for time series inversion tsmethod: 2 smorder: 2 smfactor: -0.25 ts_pthr: 10 #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # STACK parameters #------------------------------------ # pthr: threshold for minimum number of ifg observations for each pixel # nsig: threshold for iterative removal of observations # maxsig: maximum sigma (std dev) used as an output masking threshold applied in Merge step. 0 = OFF. pthr: 5 nsig: 3 maxsig: 100 # LOS Projection of timeseries and stack products # converts slanted (native) LOS signals to either "vertical" or "horizontal", by multiplying by the sine or cosine of # the incidence angle for each pixel. # Three options, 0/1/2 - 0 = LOS (no conversion); 1 = pseudo-vertical; 2 = pseudo-horizontal. los_projection: 0 # optionally supply rows and cols for tiles used during correct and merge step rows: 4 cols: 3 largetifs: 0 [correct] steps = orbfit refphase demerror phase_closure mst apscorrect maxvar ================================================ FILE: tests/test_data/gamma/16x20_20090713-20090817_VV_4rlks_utm.unw ================================================ AkAkAkAkAkAkAkAkAkAkA ORIGINAL_IFG RADIANS 22.9671 GAMMA 2006-06-19 08:28:59 2006-10-02 13:05:19 0.2874743326488706 0.05623564240081464 -0.30978414416313 -2.339052484656 -3.5677621364594 0.37911647974349 97.37 ================================================ FILE: tests/test_data/small_test/gamma_obs/20060619_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2006 06 19 8 28 59.6906 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/small_test/gamma_obs/20060619_utm.inc ================================================ 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================================================ FILE: tests/test_data/small_test/gamma_obs/20060619_utm.lv_theta ================================================ BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB ================================================ FILE: tests/test_data/small_test/gamma_obs/20060619_utm_dem.par ================================================ Gamma DIFF&GEO DEM/MAP parameter file title: GAMMA DEM par file created from ROI_PAC rsc files MCG 28/01/2016 DEM_projection: EQA data_format: REAL*4 DEM_hgt_offset: 0.00000 DEM_scale: 1.00000 width: 47 nlines: 72 corner_lat: -34.1700000 decimal degrees corner_lon: 150.9100000 decimal degrees post_lat: -8.33333e-04 decimal degrees post_lon: 8.33333e-04 decimal degrees ellipsoid_name: WGS 84 ellipsoid_ra: 6378137.000 m ellipsoid_reciprocal_flattening: 298.2572236 datum_name: WGS 1984 datum_shift_dx: 0.000 m datum_shift_dy: 0.000 m datum_shift_dz: 0.000 m datum_scale_m: 0.00000e+00 datum_rotation_alpha: 0.00000e+00 arc-sec datum_rotation_beta: 0.00000e+00 arc-sec datum_rotation_gamma: 0.00000e+00 arc-sec datum_country_list Global Definition, WGS84, World ================================================ FILE: tests/test_data/small_test/gamma_obs/20060828-20061211_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/small_test/gamma_obs/20060828_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2006 08 28 12 18 9.6906 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/small_test/gamma_obs/20061002-20070219_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/small_test/gamma_obs/20061002-20070430_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/small_test/gamma_obs/20061002_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2006 10 02 13 5 19.0003 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/small_test/gamma_obs/20061106-20061211_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/small_test/gamma_obs/20061106-20070115_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/small_test/gamma_obs/20061106-20070326_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/small_test/gamma_obs/20061106_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2006 11 06 22 58 23.1246 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/small_test/gamma_obs/20061211-20070709_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/small_test/gamma_obs/20061211-20070813_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/small_test/gamma_obs/20061211_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2006 12 11 23 18 53.1241 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/small_test/gamma_obs/20070115-20070326_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/small_test/gamma_obs/20070115-20070917_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/small_test/gamma_obs/20070115_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 01 15 21 48 23.124 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/small_test/gamma_obs/20070219-20070430_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/small_test/gamma_obs/20070219-20070604_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/small_test/gamma_obs/20070219_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 02 19 1 18 3.124 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/small_test/gamma_obs/20070326-20070917_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/small_test/gamma_obs/20070326_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 03 26 2 32 13.1244 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/small_test/gamma_obs/20070430-20070604_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/small_test/gamma_obs/20070430_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 04 30 5 32 13.1244 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/small_test/gamma_obs/20070604-20070709_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/small_test/gamma_obs/20070604_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 06 04 9 31 13.1244 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/small_test/gamma_obs/20070709-20070813_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/small_test/gamma_obs/20070709_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 07 09 19 01 43.444 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/small_test/gamma_obs/20070813_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 08 13 16 23 43.444 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/small_test/gamma_obs/20070917_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 09 17 12 41 25.444 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/small_test/gamma_obs/bad_epochs_headers ================================================ 60619_slc.par 0060828_slc.par 0061002_slc.par 0061106_slc.par 20061211_slc.par 0070115_slc.par 0070219_slc.par 0070326_slc.par 2070430_slc.par 2070604_slc.par 2070709_slc.par 2070813_slc.par 20007_slc.par ================================================ FILE: tests/test_data/small_test/gamma_obs/bad_epochs_ifms_17 ================================================ 60619-20061002_utm.unw 60828-20061211_utm.unw 61002-20070219_utm.unw 61002-20070430_utm.unw 61106-20061211_utm.unw 61106-20070115_utm.unw 61106-20070326_utm.unw 61211-20070709_utm.unw 61211-20070813_utm.unw 70115-20070326_utm.unw 70115-20070917_utm.unw 0219-20070430_utm.unw 0219-20070604_utm.unw 26-20070917_utm.unw 70430-20070604_utm.unw 70604-20070709_utm.unw 70709-20070813_utm.unw ================================================ FILE: tests/test_data/small_test/gamma_obs/baseline_17 ================================================ tests/test_data/small_test/gamma_obs/20060619-20061002_base.par tests/test_data/small_test/gamma_obs/20060828-20061211_base.par tests/test_data/small_test/gamma_obs/20061002-20070219_base.par tests/test_data/small_test/gamma_obs/20061002-20070430_base.par tests/test_data/small_test/gamma_obs/20061106-20061211_base.par tests/test_data/small_test/gamma_obs/20061106-20070115_base.par tests/test_data/small_test/gamma_obs/20061106-20070326_base.par tests/test_data/small_test/gamma_obs/20061211-20070709_base.par tests/test_data/small_test/gamma_obs/20061211-20070813_base.par tests/test_data/small_test/gamma_obs/20070115-20070326_base.par tests/test_data/small_test/gamma_obs/20070115-20070917_base.par tests/test_data/small_test/gamma_obs/20070219-20070430_base.par tests/test_data/small_test/gamma_obs/20070219-20070604_base.par tests/test_data/small_test/gamma_obs/20070326-20070917_base.par tests/test_data/small_test/gamma_obs/20070430-20070604_base.par tests/test_data/small_test/gamma_obs/20070604-20070709_base.par tests/test_data/small_test/gamma_obs/20070709-20070813_base.par ================================================ FILE: tests/test_data/small_test/gamma_obs/headers ================================================ tests/test_data/small_test/gamma_obs/20060619_slc.par tests/test_data/small_test/gamma_obs/20060828_slc.par tests/test_data/small_test/gamma_obs/20061002_slc.par tests/test_data/small_test/gamma_obs/20061106_slc.par tests/test_data/small_test/gamma_obs/20061211_slc.par tests/test_data/small_test/gamma_obs/20070115_slc.par tests/test_data/small_test/gamma_obs/20070219_slc.par tests/test_data/small_test/gamma_obs/20070326_slc.par 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tests/test_data/small_test/gamma_obs/20070115-20070326_utm.unw tests/test_data/small_test/gamma_obs/20070115-20070917_utm.unw tests/test_data/small_test/gamma_obs/20070219-20070430_utm.unw tests/test_data/small_test/gamma_obs/20070219-20070604_utm.unw tests/test_data/small_test/gamma_obs/20070326-20070917_utm.unw tests/test_data/small_test/gamma_obs/20070430-20070604_utm.unw tests/test_data/small_test/gamma_obs/20070604-20070709_utm.unw tests/test_data/small_test/gamma_obs/20070709-20070813_utm.unw ================================================ FILE: tests/test_data/small_test/gamma_obs/tif_17 ================================================ 20060619-20061002_utm.tif 20060828-20061211_utm.tif 20061002-20070219_utm.tif 20061002-20070430_utm.tif 20061106-20061211_utm.tif 20061106-20070115_utm.tif 20061106-20070326_utm.tif 20061211-20070709_utm.tif 20061211-20070813_utm.tif 20070115-20070326_utm.tif 20070115-20070917_utm.tif 20070219-20070430_utm.tif 20070219-20070604_utm.tif 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6.3517,6.5631,6.6586,6.4408,6.1625,5.862,5.9627,5.7958,5.1894,4.7024,5.3116,5.593,5.8924,5.885,5.7008,5.5011,5.0713,5.1068,4.9432,4.8226,3.9157,4.8879,5.5416,6.0411,6.2598,6.0575,6.1121,6.2932,5.7984,5.7648,5.8615,5.6789,6.2583,6.7399,6.6306,6.2471,5.6381,5.2658,5.1733,4.8788,4.7925,4.6825,4.4276,4.1303,4.3469,4.6915,4.8463 6.173,6.4327,6.4427,6.1692,6.3898,6.0835,5.926,5.3867,5.1164,4.5758,4.9564,5.3237,5.5902,5.3949,5.2937,5.1733,4.8974,4.5667,4.4987,4.3752,5.3523,5.8002,5.8642,6.1379,5.8478,5.6166,4.9797,5.5176,6.0327,6.2468,5.768,5.5099,5.9903,5.9143,6.1991,6.8384,6.4202,5.5114,5.384,5.0028,4.6492,4.4836,4.6296,4.3827,4.4318,4.8153,5.2149 6.2836,6.2448,6.5264,6.6405,5.7982,5.5544,5.6132,5.1885,5.2074,5.2899,4.959,4.7973,5.2894,4.5686,5.3305,5.0815,4.3695,5.7519,5.2606,4.1418,5.4447,5.1595,5.6882,6.0995,6.3666,6.5605,5.8189,7.8431,7.4719,6.5989,5.803,5.4031,5.5784,5.576,5.7833,6.5221,5.799,5.6338,5.6187,5.1538,4.463,4.2854,4.4546,4.7378,4.289,4.6897,4.6332 6.04,6.1418,6.6036,7.6387,6.3202,5.9122,4.9693,4.7145,4.6061,5.0592,5.5036,5.4191,5.0539,4.8937,4.6645,4.431,4.3759,4.2722,4.0897,3.9511,4.5218,4.8748,5.6135,5.982,6.6065,6.6843,7.4661,7.6622,6.9153,6.3718,5.9643,5.3154,5.5076,5.3298,5.6126,5.2812,5.0995,4.9651,5.3552,4.8192,4.4878,4.582,4.484,4.445,4.4278,4.3356,3.9017 5.7816,5.4351,5.1907,5.4435,6.6757,6.1637,5.1326,5.3096,5.4473,5.2712,5.2461,4.7285,5.0279,5.0857,4.7969,4.2447,4.0348,3.8721,2.4415,2.5901,4.5967,5.1678,6.0257,5.7308,6.0424,6.9597,7.7079,8.4375,7.5222,6.292,5.6718,5.1881,5.3263,5.4562,5.8644,5.4546,5.3305,4.7772,4.1747,3.7962,4.3991,4.5314,4.3905,4.7709,5.2403,4.9156,4.6795 5.5338,5.6051,5.1955,5.9139,6.3598,5.9877,5.7206,5.6793,5.0557,5.1974,5.239,5.4346,5.2446,5.2341,5.0503,4.8318,5.0474,4.6203,4.9542,4.6385,5.3825,5.6739,5.9793,5.8513,5.9778,6.3427,7.9486,8.3979,8.1195,7.058,6.0085,5.138,4.9143,5.3466,5.2865,5.2078,5.3447,4.9862,4.3185,4.22,4.6344,4.4042,5.0037,4.9519,5.5377,5.679,4.3202 5.1032,5.8748,6.1476,5.5355,6.1014,5.6502,6.0043,5.4209,4.6461,4.2755,4.2375,3.9191,4.5607,4.7235,4.7241,4.7825,5.1864,5.6078,5.69,4.9645,5.4017,5.8306,5.9754,6.2631,6.6215,6.3506,6.7819,6.8378,7.1981,6.7159,5.6622,4.9561,4.5789,4.9426,4.9227,5.1078,4.5755,4.0976,4.0108,4.2394,4.3249,4.199,4.4118,4.8087,4.9258,4.8383,4.32 5.7183,5.5848,5.4235,5.4101,5.6313,5.7277,6.2783,5.7539,4.2807,3.1563,3.9873,3.6843,4.3174,4.7751,4.7282,4.9579,5.6178,5.7491,5.5525,5.265,5.3937,5.5211,5.6826,5.9362,6.8235,6.8382,7.5199,7.2962,6.7017,5.6721,5.1851,5.4014,5.3286,5.2957,5.0531,4.3856,4.2219,4.1029,4.091,4.5024,4.4547,4.3003,4.2505,4.1914,4.2434,4.6487,4.6552 6.3987,5.9819,5.1598,4.9965,5.1901,6.2727,6.7515,6.2782,5.6374,4.7291,5.1667,4.119,3.9368,4.4818,4.051,4.1027,4.9979,5.1137,4.6892,5.2014,5.3179,4.9772,5.1617,5.9818,6.906,7.4966,7.6222,7.8184,7.03,6.3242,5.4154,5.8515,5.6776,5.8117,5.8518,5.6601,5.1806,4.5332,3.9954,4.5071,4.6159,4.5046,4.4497,4.38,4.5593,4.6307,4.4494 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5.1691,5.5037,5.0565,5.2903,5.595,5.8042,5.6816,5.6741,5.5655,5.6062,5.2947,5.1013,4.5325,4.3964,4.3508,4.746,5.2967,6.7974,6.9346,3.0159,4.0278,5.6351,5.1802,NaN,NaN,NaN,NaN,4.4867,8.9752,12.518,14.744,14.27,12.677,9.3716,8.5907,8.5055,7.6862,6.9376,7.3981,7.0931,6.7506,6.7782,6.6517,7.4431,7.1434,7.0976,7.0233 6.0125,5.8998,5.9415,5.4798,5.4151,5.6381,6.0776,6.1608,6.0233,5.701,5.2658,4.3151,3.5398,3.6625,3.9331,4.6538,5.0466,5.9461,6.2698,2.7656,3.7281,4.7513,4.3757,NaN,NaN,NaN,NaN,NaN,8.5375,11.419,10.846,10.649,10.771,8.7092,8.5795,8.6039,7.6181,7.0275,7.0501,6.432,6.5648,6.865,7.0516,7.5334,7.0077,6.897,6.4872 6.7606,6.0898,6.2543,6.2893,5.7196,5.4971,5.646,5.4872,5.8802,5.443,5.0164,4.7103,3.862,4.0023,4.2646,4.5009,5.3445,5.373,5.6378,5.0687,4.8217,4.4536,3.8916,5.3786,NaN,NaN,NaN,NaN,9.6498,9.53,9.9111,10.329,9.0056,8.4056,8.417,8.1468,7.3131,5.974,6.3483,6.025,6.0594,6.6117,6.7153,6.8004,6.4054,6.311,5.5885 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6.335,6.7372,7.2359,6.2257,6.03,5.7159,5.6129,5.6751,5.7672,5.487,5.5215,5.6332,5.7846,5.6212,4.9217,4.938,4.9261,4.546,3.0071,2.7136,3.8425,4.4804,4.7006,5.1013,5.9532,6.1897,6.7771,6.2135,7.3722,7.2882,7.8049,7.9689,7.5025,6.8219,5.3403,5.2707,6.0278,6.0941,5.2984,5.0543,5.2513,4.5944,4.6254,5.7812,6.4748,6.18,5.5957 5.9816,6.398,6.874,6.8559,6.7791,5.5042,5.5333,5.4998,5.4635,5.2699,5.2074,5.122,5.2689,5.6041,5.7456,5.1698,4.8119,4.5076,3.6721,3.1615,2.5835,2.5259,3.1419,4.6386,4.2159,4.7217,6.0874,6.372,6.8694,6.5861,6.4865,7.1544,6.8241,5.8023,4.6621,4.5347,5.182,6.1625,5.6231,5.2005,4.9977,4.4554,4.6283,3.7319,4.9945,4.4282,5.002 6.6234,7.0307,6.5395,6.311,6.5078,5.7378,5.4983,5.1025,6.2165,5.832,5.4307,5.8076,5.6621,5.7305,5.7837,5.3574,5.0281,4.8421,4.5237,4.0404,3.525,3.9145,4.7401,5.24,4.2673,4.0722,6.1309,6.8997,6.4685,6.4877,6.3177,6.4059,6.4958,6.6952,6.7372,5.682,5.7213,5.5567,5.5942,5.3352,5.1816,4.9899,5.1132,4.8299,4.4302,4.4064,5.0559 6.3517,6.5631,6.6586,6.4408,6.1625,5.862,5.9627,5.7958,5.1894,4.7024,5.3116,5.593,5.8924,5.885,5.7008,5.5011,5.0713,5.1068,4.9432,4.8226,3.9157,4.8879,5.5416,6.0411,6.2598,6.0575,6.1121,6.2932,5.7984,5.7648,5.8615,5.6789,6.2583,6.7399,6.6306,6.2471,5.6381,5.2658,5.1733,4.8788,4.7925,4.6825,4.4276,4.1303,4.3469,4.6915,4.8463 6.173,6.4327,6.4427,6.1692,6.3898,6.0835,5.926,5.3867,5.1164,4.5758,4.9564,5.3237,5.5902,5.3949,5.2937,5.1733,4.8974,4.5667,4.4987,4.3752,5.3523,5.8002,5.8642,6.1379,5.8478,5.6166,4.9797,5.5176,6.0327,6.2468,5.768,5.5099,5.9903,5.9143,6.1991,6.8384,6.4202,5.5114,5.384,5.0028,4.6492,4.4836,4.6296,4.3827,4.4318,4.8153,5.2149 6.2836,6.2448,6.5264,6.6405,5.7982,5.5544,5.6132,5.1885,5.2074,5.2899,4.959,4.7973,5.2894,4.5686,5.3305,5.0815,4.3695,5.7519,5.2606,4.1418,5.4447,5.1595,5.6882,6.0995,6.3666,6.5605,5.8189,7.8431,7.4719,6.5989,5.803,5.4031,5.5784,5.576,5.7833,6.5221,5.799,5.6338,5.6187,5.1538,4.463,4.2854,4.4546,4.7378,4.289,4.6897,4.6332 6.04,6.1418,6.6036,7.6387,6.3202,5.9122,4.9693,4.7145,4.6061,5.0592,5.5036,5.4191,5.0539,4.8937,4.6645,4.431,4.3759,4.2722,4.0897,3.9511,4.5218,4.8748,5.6135,5.982,6.6065,6.6843,7.4661,7.6622,6.9153,6.3718,5.9643,5.3154,5.5076,5.3298,5.6126,5.2812,5.0995,4.9651,5.3552,4.8192,4.4878,4.582,4.484,4.445,4.4278,4.3356,3.9017 5.7816,5.4351,5.1907,5.4435,6.6757,6.1637,5.1326,5.3096,5.4473,5.2712,5.2461,4.7285,5.0279,5.0857,4.7969,4.2447,4.0348,3.8721,2.4415,2.5901,4.5967,5.1678,6.0257,5.7308,6.0424,6.9597,7.7079,8.4375,7.5222,6.292,5.6718,5.1881,5.3263,5.4562,5.8644,5.4546,5.3305,4.7772,4.1747,3.7962,4.3991,4.5314,4.3905,4.7709,5.2403,4.9156,4.6795 5.5338,5.6051,5.1955,5.9139,6.3598,5.9877,5.7206,5.6793,5.0557,5.1974,5.239,5.4346,5.2446,5.2341,5.0503,4.8318,5.0474,4.6203,4.9542,4.6385,5.3825,5.6739,5.9793,5.8513,5.9778,6.3427,7.9486,8.3979,8.1195,7.058,6.0085,5.138,4.9143,5.3466,5.2865,5.2078,5.3447,4.9862,4.3185,4.22,4.6344,4.4042,5.0037,4.9519,5.5377,5.679,4.3202 5.1032,5.8748,6.1476,5.5355,6.1014,5.6502,6.0043,5.4209,4.6461,4.2755,4.2375,3.9191,4.5607,4.7235,4.7241,4.7825,5.1864,5.6078,5.69,4.9645,5.4017,5.8306,5.9754,6.2631,6.6215,6.3506,6.7819,6.8378,7.1981,6.7159,5.6622,4.9561,4.5789,4.9426,4.9227,5.1078,4.5755,4.0976,4.0108,4.2394,4.3249,4.199,4.4118,4.8087,4.9258,4.8383,4.32 5.7183,5.5848,5.4235,5.4101,5.6313,5.7277,6.2783,5.7539,4.2807,3.1563,3.9873,3.6843,4.3174,4.7751,4.7282,4.9579,5.6178,5.7491,5.5525,5.265,5.3937,5.5211,5.6826,5.9362,6.8235,6.8382,7.5199,7.2962,6.7017,5.6721,5.1851,5.4014,5.3286,5.2957,5.0531,4.3856,4.2219,4.1029,4.091,4.5024,4.4547,4.3003,4.2505,4.1914,4.2434,4.6487,4.6552 6.3987,5.9819,5.1598,4.9965,5.1901,6.2727,6.7515,6.2782,5.6374,4.7291,5.1667,4.119,3.9368,4.4818,4.051,4.1027,4.9979,5.1137,4.6892,5.2014,5.3179,4.9772,5.1617,5.9818,6.906,7.4966,7.6222,7.8184,7.03,6.3242,5.4154,5.8515,5.6776,5.8117,5.8518,5.6601,5.1806,4.5332,3.9954,4.5071,4.6159,4.5046,4.4497,4.38,4.5593,4.6307,4.4494 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3.0303,2.9172,3.1357,3.2172,3.1427,2.9518,3.2274,3.5199,3.6401,4.0168,4.074,4.2552,4.3723,4.7284,4.866,4.665,4.678,5.1521,5.8546,6.0444,5.984,5.915,5.7411,5.6835,6.1078,6.6747,7.1183,7.14,7.8985,8.3114,8.2511,7.1399,6.4974,6.2398,6.4272,6.4697,5.9691,5.8001,5.4853,5.2014,5.051,4.9293,4.8237,4.6102,4.398,4.0689,4.0052 3.0922,3.0269,2.6587,2.6656,2.7281,2.8255,2.9263,3.3062,3.5109,3.6579,3.8611,4.2998,5.0519,5.4255,5.3107,4.8333,4.7807,5.5606,6.4124,6.5514,6.4202,6.232,6.16,6.0966,6.7633,7.2528,7.2327,7.6495,8.3807,9.3002,8.8936,8.4198,7.6001,6.9402,6.7813,6.5002,6.0841,5.6211,5.3037,5.1712,4.9847,4.7403,4.795,4.8093,4.5958,3.9709,3.6002 3.3338,3.2788,2.7955,2.3217,2.6001,2.8966,2.9899,2.7933,3.3276,3.4736,3.7268,4.0773,4.4754,5.0094,5.3067,5.3095,5.5341,6.1056,6.479,6.6384,6.6036,6.2963,6.1378,6.3912,6.9223,7.1879,7.3512,7.4235,8.3751,9.3276,9.748,9.6897,9.2742,8.5091,7.7971,7.049,6.0676,5.5322,5.2733,5.1761,4.7248,4.7873,4.7064,4.5901,4.4164,4.0381,3.8399 3.5985,4.1369,3.3893,2.9816,3.0481,3.1607,2.9709,2.4956,2.9469,3.3717,3.4605,4.0396,4.3915,4.8559,5.2382,5.4767,5.5586,5.868,6.4994,6.851,6.9233,6.4491,6.6839,7.001,7.2304,7.6526,8.7286,NaN,NaN,NaN,12.578,11.277,11.024,9.1307,7.6423,7.1052,6.235,5.6518,5.6155,5.3294,4.4534,4.3506,4.778,4.5911,4.4466,4.149,3.8741 3.8201,3.4253,3.3369,3.1265,3.1731,3.1118,3.2208,2.9112,3.1182,3.35,3.4295,3.7597,4.1725,4.7232,5.2174,5.3409,5.1936,5.112,5.4949,6.2512,6.8337,6.4181,6.7774,7.6353,8.6677,9.1338,NaN,NaN,NaN,NaN,NaN,NaN,NaN,10.252,7.6953,7.161,6.8941,6.3862,5.9848,5.2671,4.2353,4.345,4.9634,4.7245,4.0892,3.673,3.5919 3.1364,3.4516,3.5118,3.4642,3.2997,2.7192,2.1355,2.501,3.1815,3.8075,3.9535,3.9177,4.0668,4.4835,5.2983,5.2608,5.0171,5.122,5.3349,5.7368,6.2872,6.5169,6.9302,8.1554,9.9893,11.215,NaN,NaN,NaN,NaN,NaN,NaN,NaN,8.2935,6.5866,7.2182,7.9951,7.6461,6.6392,5.2122,4.5135,4.4262,4.7635,5.0439,4.21,3.525,3.4542 3.0356,3.069,3.4012,4.0972,3.9524,3.6282,3.1467,3.2676,3.3486,4.0189,4.4284,4.2449,4.309,4.5118,5.1548,5.0146,4.7981,5.3986,5.984,6.1037,6.3296,6.7503,7.2231,8.2369,8.3905,8.637,8.5723,NaN,NaN,NaN,NaN,NaN,7.5611,6.477,7.1781,7.8406,8.0528,7.7237,6.6006,5.1853,4.6143,4.4488,4.3314,5.0378,4.1182,3.2285,3.2701 2.8744,3.1746,3.4588,3.8606,4.354,4.0263,3.6861,3.8403,3.926,4.2866,4.1642,4.0003,4.3502,5.0505,5.0731,4.9232,4.4366,4.7923,5.6944,6.1135,6.3041,6.851,7.2215,7.6393,7.4591,7.3066,6.7965,5.5805,5.5757,6.0892,6.5377,6.6831,5.6971,4.8248,6.3863,7.1455,7.3598,7.7649,6.5396,5.265,4.5884,4.3341,4.3303,4.2731,3.6134,3.2626,3.3096 2.9735,3.2535,3.521,4.0209,4.2726,3.7146,3.5033,3.5627,3.6376,4.0714,4.1584,4.1127,4.109,4.588,4.572,4.6988,4.6254,4.8732,5.196,5.4683,5.6924,6.2032,6.796,7.127,6.8581,6.0616,6.5205,5.8336,5.7099,5.9564,5.2483,4.6316,3.6553,4.3913,NaN,5.2174,7.1744,8.0085,7.4834,5.4989,4.34,4.267,4.7557,4.3863,3.366,3.0927,3.0998 3.2982,2.9232,3.0484,3.4583,3.3349,2.9256,2.3979,3.0005,3.3944,3.7937,4.029,4.1594,4.0842,3.8609,3.8921,4.2754,4.6142,4.7373,4.9299,5.245,5.4332,5.2243,5.7309,7.8757,NaN,5.6856,6.5709,5.4559,3.1645,4.0645,4.2245,3.328,3.515,2.5961,NaN,NaN,NaN,NaN,NaN,5.4492,4.6416,4.7534,5.0251,4.1659,3.3929,3.14,3.0154 2.7267,2.4488,1.9631,2.3989,3.1127,3.0666,3.044,3.2314,3.4197,3.2439,3.9572,4.1392,4.0926,3.5601,2.6137,3.2761,4.4835,4.5125,4.5961,4.9061,5.2508,5.2102,7.1623,NaN,NaN,NaN,NaN,5.3207,5.0636,4.8551,3.7545,3.5384,1.9933,NaN,NaN,NaN,NaN,NaN,6.8344,5.2414,4.8563,4.9118,4.8977,4.2417,3.4211,3.3227,3.323 2.4598,2.502,2.4399,2.0345,3.0263,3.3772,3.4254,3.4079,3.2786,3.0275,3.2793,3.44,3.6071,2.4921,2.5256,3.422,3.8557,4.0835,4.4918,4.7623,5.1343,6.3048,8.7101,NaN,NaN,NaN,3.4806,3.4983,5.8143,5.3244,3.854,4.0455,4.4918,7.2083,NaN,NaN,NaN,NaN,5.7955,5.0746,4.9646,4.6392,4.1287,3.4325,3.1361,3.2632,3.2573 2.405,2.5252,2.4697,2.5432,2.9841,3.332,3.5492,3.7438,3.7103,3.4447,3.4553,3.0216,2.6979,2.0061,2.6559,3.9489,4.0727,4.3212,4.8717,5.2855,5.6534,6.4232,7.6021,NaN,NaN,NaN,NaN,2.9199,5.3834,5.634,4.716,4.3872,4.6429,7.781,7.9812,7.8727,5.9681,5.8264,5.6228,4.2999,4.0056,3.7843,3.3194,3.1634,3.2555,3.3204,3.3518 2.8867,2.8273,2.4845,2.3862,2.5977,3.0281,3.6037,3.868,3.8853,3.8755,3.8766,3.682,3.4932,3.1678,3.5202,4.0088,3.7556,3.939,4.5534,5.2362,5.5442,6.0535,6.0845,4.5869,2.5335,1.6244,2.2426,3.9213,4.5719,4.0667,3.8059,3.8536,3.8445,5.3202,6.9502,6.2348,5.7058,5.5354,4.8721,3.6905,3.2462,3.0563,3.5884,3.7314,3.852,3.6074,3.4316 2.7885,2.6041,2.4875,2.3783,2.8317,3.5848,3.606,3.8527,4.0517,4.1246,4.1149,3.9668,3.6352,3.5365,4.097,3.9965,3.8506,4.5931,4.5329,5.407,5.9779,5.7284,4.7648,5.4881,6.9314,7.7874,7.0337,4.9413,4.3661,3.7923,2.9196,2.3841,3.2047,3.9557,5.7899,6.2182,6.1269,5.4962,4.3784,3.0877,2.8085,3.0764,3.5803,3.7457,3.9107,3.783,3.6729 2.8845,2.5733,2.8395,3.1473,3.4778,3.8671,4.1093,4.1755,4.2363,3.8958,4.0901,4.0254,3.8951,3.8754,4.282,4.4837,4.4165,4.78,4.7938,5.6143,6.0338,6.2664,5.1085,4.7372,4.4953,NaN,NaN,3.4758,2.8715,1.8177,0.28365,-0.16408,2.1867,4.5988,5.343,5.7414,6.015,5.9483,4.8302,3.3209,2.6073,2.9531,3.5539,3.4854,3.4127,3.5898,3.6722 3.5718,3.2352,2.9966,3.1282,3.5754,3.7178,4.3022,4.5053,4.1844,3.7637,3.8015,3.9916,4.0581,4.1142,4.1375,4.2945,4.7448,5.2411,5.5152,6.2463,5.8815,5.472,5.0459,4.4123,3.5463,NaN,NaN,2.0766,3.0756,2.5869,1.8647,2.1409,3.0492,4.292,4.9078,5.3426,5.3201,5.3505,4.6697,3.4927,2.8962,3.1227,3.5957,3.4514,3.4754,3.8969,3.8252 4.2505,3.6082,3.2759,3.3928,3.3548,3.4152,3.8283,4.2879,3.9899,3.9507,4.0675,4.2198,4.2086,4.1427,4.0653,3.9246,4.2012,4.7944,5.2672,5.3714,5.1194,4.5678,4.3807,4.7414,NaN,NaN,NaN,NaN,5.2711,4.1957,2.8356,2.9419,3.2097,4.4092,4.7274,4.6811,4.9178,4.9763,4.5877,3.5857,3.4909,3.2372,3.3566,3.513,3.6472,3.8857,3.4841 3.9254,3.7279,3.5837,3.5437,3.2099,3.5065,3.8792,4.2768,4.0848,4.0467,4.2241,4.4572,4.4772,4.2524,4.2458,4.196,4.2148,4.502,4.7763,4.8065,4.9153,4.9779,6.6106,6.8245,NaN,NaN,NaN,5.9764,5.476,4.2571,3.2934,2.8645,2.8872,3.4598,3.6644,3.7511,4.1049,4.5063,3.7422,3.3968,3.1816,3.1017,3.4516,3.4067,3.3215,3.4314,3.3746 3.8038,3.7012,3.5818,3.7698,3.6874,3.8022,4.1313,4.2793,4.1931,4.0398,4.076,4.3487,4.3817,4.3048,4.3894,4.3153,4.2652,4.6286,4.7296,5.0925,6.2409,6.5136,7.1544,8.1811,9.3725,8.7244,7.5029,5.1344,4.3992,3.6349,3.1081,2.9153,2.6212,3.0217,3.1434,3.5061,4.0088,3.7833,2.2144,2.5115,2.787,2.8123,3.4037,3.7332,3.5242,3.321,3.2745 3.4837,3.4007,3.2143,3.2815,3.5367,3.8021,4.216,4.093,3.8391,3.7687,3.992,4.2858,4.5728,4.7194,4.5088,4.5399,4.5804,4.6015,4.1322,3.6467,4.3635,4.944,5.328,5.3041,5.0115,5.2241,5.2808,4.3508,3.8423,3.5348,3.7538,3.7898,3.6587,3.4311,3.3556,3.0468,2.9928,2.3471,1.3411,1.5444,2.0853,2.3964,3.1656,3.8427,3.6631,3.3503,3.1728 3.0006,3.3534,3.214,3.0465,3.3321,3.6556,3.9994,3.7687,3.5165,3.9165,4.2696,4.3227,4.3208,4.3397,4.1838,3.9579,3.9542,3.9949,3.9294,3.2919,2.5871,3.6398,4.2703,4.2012,4.3986,4.2817,3.7173,3.7074,3.4698,4.3624,4.89,4.2872,3.8297,3.4475,3.2675,2.0754,1.6591,1.4333,0.66447,1.0271,1.6263,2.393,2.983,3.4573,3.6047,3.5018,3.4583 3.2692,3.2823,3.3875,3.4958,3.9059,3.9749,3.6901,3.4224,3.5621,3.8184,4.1839,4.3703,4.3456,4.206,3.9486,3.6735,3.5073,3.5405,3.4598,3.1894,2.7006,2.4294,2.7669,3.5555,4.1599,3.9,3.462,3.32,3.5863,3.6637,3.296,3.1314,2.8048,3.3582,1.9487,0.97941,0.29325,0.79456,0.84134,0.85795,0.83594,1.5634,2.7064,3.5008,3.5812,3.3959,3.662 3.2183,3.1229,3.5503,3.7013,3.8344,3.812,3.5653,3.2384,3.25,3.4384,3.8095,3.9324,3.8184,3.9618,3.621,3.3181,3.1988,3.2944,2.7631,2.5876,2.1117,2.2963,2.8945,3.7597,3.8099,3.2648,3.1108,3.0142,3.2927,2.681,1.7928,1.7839,2.2644,1.727,0.97683,-0.20547,-0.07395,0.58928,1.3258,1.2064,0.85937,0.86349,2.3159,2.8115,3.0383,3.2193,3.3872 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3.4712,3.9374,3.5166,4.107,3.4066,2.4978,-1.1991,-0.99626,-0.1535,0.95039,1.6263,2.1709,2.5119,2.8591,3.9198,4.6285,5.1742,5.5616,6.5504,8.6453,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,10.473,10.182,8.0131,6.5022,5.6962,4.512,3.5266,3.134,2.3502,2.6,2.2022 3.2535,3.39,3.3007,2.207,2.7668,1.6399,-1.1214,-1.414,-1.4158,0.53841,1.1786,1.6837,2.1984,3.1571,3.5236,4.0375,4.6237,5.204,7.6709,8.7174,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,7.4218,5.146,4.8489,3.247,2.6374,3.2977,2.4147,2.9679,2.5939 2.4641,1.2757,1.5944,1.5792,2.4218,0.47983,-0.59552,-1.0132,-2.2408,0.75691,0.5189,1.3315,2.3834,2.4008,3.9254,3.2653,3.3448,5.3689,6.6862,8.1256,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6.2032,4.6896,4.3689,2.8149,2.6832,3.3486,2.3222,2.5526,1.7248 2.2482,2.5205,1.0264,1.4154,1.6912,0.54333,0.14193,-0.98003,-0.60308,0.97797,0.78441,0.74688,1.7722,0.44156,3.5191,3.4092,2.732,3.6938,6.1011,5.0648,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.5358,4.5287,4.1743,3.8564,3.4,2.4618,2.1463,1.407 2.9542,2.4597,-0.65151,1.359,1.067,0.81371,1.1785,1.1023,-0.059,0.45217,1.0092,1.1994,0.33764,0.99485,3.1889,3.5207,4.1539,4.4396,5.3442,4.2068,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,5.977,4.988,4.3733,3.2472,2.6777,1.7435,1.1419 1.5335,0.37627,0.80275,2.8532,2.0282,0.56149,0.60187,1.8372,0.32385,0.37229,2.1631,2.5135,2.6936,2.1228,1.3956,3.2051,3.3618,4.3271,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.2158,4.1865,4.3212,3.5127,1.9539,1.6437,0.75263 1.8437,1.1806,1.2304,1.0343,0.95294,-0.2627,0.20918,1.3083,1.2397,0.57988,2.2118,2.6859,2.6069,1.6662,1.8447,3.2507,3.4026,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,4.0282,4.1249,3.4616,2.8043,2.0927,2.4363,2.1748 2.3637,1.7561,1.2406,0.58061,0.2977,-0.020052,0.64366,0.809,1.108,1.692,2.1431,2.5724,2.5123,2.9774,3.0959,3.5615,3.6109,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,8.0324,NaN,NaN,4.6106,3.9062,3.8175,2.7917,2.4685,3.1451,3.7804,3.0682 1.309,1.8186,1.2724,1.2763,1.3251,1.3032,1.011,1.0427,1.5114,2.0285,2.2339,3.312,2.7586,2.6208,3.2479,3.7037,4.2251,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3.9699,NaN,NaN,NaN,NaN,NaN,NaN,7.5029,6.9288,5.1126,3.5006,3.0678,3.3999,3.156,3.1344,3.2018,2.3102 1.3426,3.8929,2.5407,1.8754,2.0756,2.4879,2.339,2.2243,1.6613,1.7104,2.0856,2.8495,2.7999,3.6442,4.7319,5.2578,5.2601,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.1153,-0.37332,NaN,NaN,NaN,NaN,9.5482,7.66,6.7388,4.2523,3.0253,3.4767,3.9788,3.0501,2.7482,2.4089,0.53026 3.0563,3.4982,2.2011,3.0034,2.4061,2.4457,2.8261,2.4985,2.0786,3.0366,2.6109,2.7144,2.998,3.6653,4.2482,5.4734,5.6877,3.1055,2.815,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,-0.12075,NaN,NaN,NaN,NaN,NaN,9.4842,8.5321,6.8937,2.8652,3.0577,2.8143,3.1147,4.1907,3.3374,3.3961,2.3259,0.65309 3.404,3.4398,3.2886,3.5383,3.0961,2.5752,2.4504,2.2819,2.7902,3.3743,2.9982,2.9873,2.5449,2.7482,2.2578,2.6261,3.9574,2.5157,1.5462,NaN,NaN,NaN,-0.56688,NaN,NaN,NaN,NaN,NaN,0.66913,-2.2022,NaN,NaN,NaN,NaN,7.5031,8.579,7.1413,6.3351,4.1559,3.2994,2.6039,3.1691,3.8613,3.6634,3.6783,2.4087,1.4612 3.7368,4.0481,3.8288,3.1259,2.5805,2.1719,2.0458,2.2255,2.6516,2.5402,2.4705,2.7333,2.3328,2.5016,2.417,2.1203,4.0533,3.3989,3.2906,2.4822,1.5249,0.41103,-0.23745,NaN,NaN,NaN,NaN,NaN,0.40371,0.52713,4.0558,6.284,6.2354,6.8327,6.5852,6.131,6.1966,4.634,3.4188,3.1805,3.8984,3.5385,3.1792,3.329,3.2663,1.1932,0.30296 3.6356,3.8816,3.8537,2.8765,2.4012,2.2198,2.0639,2.0914,1.9573,2.0412,2.1331,2.4108,2.6501,2.8019,3.3704,3.7563,4.2547,3.5024,2.8924,2.8831,1.6822,0.28089,NaN,NaN,NaN,NaN,NaN,NaN,2.7302,3.9417,3.9315,4.8588,5.5686,5.3136,4.7588,4.4024,4.3549,2.9488,2.7299,3.2185,3.9298,4.1813,2.9138,2.4735,2.5332,2.0409,0.65819 3.6505,3.6103,3.5553,3.1398,2.7347,2.3677,2.4604,2.2387,1.7502,2.7004,1.9844,2.216,2.4807,2.772,3.5611,3.6064,2.8132,3.3728,3.4552,4.8407,2.2827,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2.9939,3.7669,4.7282,6.4397,4.9278,4.4998,3.7205,4.2742,3.556,2.7189,2.5483,3.0574,3.9727,3.9857,2.9934,2.9652,1.2032,0.067167,-0.61076 4.1159,4.3002,4.5526,4.0965,3.0358,3.1579,2.8716,3.1914,3.3821,3.0495,2.4324,2.2722,2.4663,2.8093,3.1663,3.1238,3.3979,3.7094,2.9985,2.2509,1.3503,1.8803,4.3744,3.5733,1.4917,NaN,NaN,NaN,3.5439,4.2787,5.5573,5.6024,5.2202,3.6655,2.9848,2.9222,3.0275,1.9109,1.8509,2.3749,3.6426,2.958,2.4903,2.9251,1.678,-0.28812,-1.5452 4.5272,4.9955,4.9244,3.2221,2.9575,3.4162,3.288,3.3651,2.6947,2.9435,2.7153,2.5907,2.7896,2.9403,1.867,2.2328,3.2052,3.151,2.4329,1.7323,0.30673,0.60418,2.2842,1.4621,0.9015,0.67003,0.27687,1.6873,2.7988,4.8598,5.9262,4.4073,3.2122,2.3933,2.4459,3.2029,2.4308,2.1185,1.4424,2.2627,3.291,1.9416,0.53575,1.0811,1.7241,1.0661,-0.22418 4.1884,4.2782,4.2336,3.3766,3.2477,2.3581,2.8918,2.6571,2.5234,2.5848,2.3298,2.4845,2.8521,2.6225,2.1538,2.0921,3.1401,2.808,2.23,1.6783,0.80312,-0.0057764,-0.32865,0.45899,0.30704,0.25876,0.34642,1.2344,2.3453,2.8896,3.0586,2.2039,1.1688,1.4302,2.0169,2.0843,0.79311,1.3456,1.3088,2.2986,2.5258,1.3396,-0.76293,-1.6176,-0.66564,-0.15306,0.53022 4.5595,3.895,2.9332,3.7532,3.8075,2.6327,2.4834,2.2595,1.9624,1.5739,1.8637,2.3914,3.0276,2.9115,2.408,2.2253,2.3107,2.4738,2.3828,1.4204,-0.098622,-0.64899,-0.046756,0.38018,0.087876,0.12039,0.96143,1.8548,1.5648,2.4323,1.7391,1.2333,1.3532,1.4199,1.0808,0.85574,0.41396,1.4795,2.0987,1.6155,1.3244,0.91455,-0.91771,0.14773,0.5218,0.15859,2.1581 4.8536,4.6939,3.648,4.3416,4.2005,3.4201,2.2217,1.7773,2.3392,1.9192,1.9943,2.4732,3.0357,3.103,2.245,1.7426,1.9047,1.6115,0.95968,-0.86395,-0.22939,0.091013,0.85534,0.81575,0.62044,0.82344,1.0997,2.2048,0.62848,2.1216,1.5224,1.3463,0.78177,2.0341,2.3645,2.2177,0.33188,1.7256,2.4354,0.56185,0.94082,0.61781,-1.1685,-0.84117,0.068,-1.043,0.13336 4.7304,4.7312,4.3944,4.621,4.228,4.4632,2.7072,3.4633,3.0552,2.8539,2.6198,2.2333,2.9669,2.2175,1.4243,1.3925,1.4428,0.52021,-0.31045,-0.8215,-0.12009,0.8569,1.4358,1.5367,1.0284,0.85563,0.014658,1.8525,1.0024,1.8209,1.3806,0.87619,0.54143,1.0738,2.729,1.532,0.9394,1.3624,0.84556,0.23241,0.052383,0.71968,0.9923,-0.23783,-1.0794,-0.62166,0.31017 3.9708,4.8286,4.9753,4.4118,3.9739,3.4247,2.3298,2.8968,2.2739,2.8662,1.7862,1.2075,2.1853,2.6475,1.1549,1.3687,1.0131,0.67004,0.37616,-0.34215,0.56059,1.1895,1.5461,1.525,1.1868,0.51933,-0.63239,1.9048,1.8447,1.8598,0.96406,0.34778,0.40136,1.1604,2.1047,1.2055,0.73468,0.53089,-0.21815,-0.12034,0.059374,0.12272,-0.014334,1.0105,-0.54785,0.030563,-0.020094 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0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.18417 NaN 0.18417 0.18417 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.18417 0.18417 0.18417 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.753 0.753 0.17119 0.17119 0.17119 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.1843 0.1843 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.753 0.753 0.753 0.18417 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.753 0.753 0.753 0.17119 0.17119 0.17119 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.753 0.753 0.753 0.18417 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.753 0.753 0.753 0.17119 0.17119 0.18417 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.753 0.753 0.753 0.753 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.753 0.753 0.753 0.753 0.17119 0.18417 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.753 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.18417 0.18417 0.18417 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.753 0.753 0.753 0.17119 0.18417 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 NaN 0.19772 0.18785 0.17119 0.18417 0.18417 0.18417 0.18417 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.18417 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17105 0.17105 0.17119 0.17119 0.17105 0.17119 0.753 NaN NaN NaN 0.18785 0.17105 0.18785 0.18417 0.195 0.195 0.18417 0.18417 0.17119 0.753 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.18417 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17105 0.17105 0.17105 0.17105 0.17105 0.17105 0.753 NaN NaN NaN NaN NaN 0.52633 0.52633 0.18417 0.195 0.195 0.18417 0.18417 0.18417 0.18417 0.753 0.753 0.753 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.753 0.753 0.1843 NaN NaN NaN NaN NaN NaN 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17105 0.17105 0.17105 0.17105 0.17105 0.753 0.753 NaN NaN NaN NaN NaN 0.52633 0.52633 0.18417 0.195 0.195 0.195 0.18417 0.18417 0.18417 0.753 0.753 0.753 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.18417 0.18417 0.18417 0.18417 0.17822 NaN NaN NaN 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17105 0.17105 0.17105 0.17105 0.17105 0.17105 NaN NaN NaN NaN NaN NaN NaN 0.52633 0.52633 0.17952 0.195 0.195 0.195 0.18417 0.18417 0.18417 0.753 0.753 0.753 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.18417 0.18417 NaN 0.52633 NaN 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.16653 0.16653 0.16653 0.52633 0.52633 0.52633 NaN NaN NaN NaN NaN NaN NaN 0.52633 NaN 0.18478 0.17952 0.18417 0.195 0.18417 0.18417 0.18417 0.753 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17347 0.17347 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 0.17119 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tests/test_data/small_test/vcm/vcmt.csv ================================================ 15.416,0,-11.506,-3.1605,0,0,0,0,0,0,0,0,0,0,0,0,0 0,2.8583,0,0,1.5086,0,0,-3.2655,-1.9155,0,0,0,0,0,0,0,0 -11.506,0,34.349,4.7177,0,0,0,0,0,0,0,-11.16,-15.881,0,0,0,0 -3.1605,0,4.7177,2.5919,0,0,0,0,0,0,0,3.0655,0,0,-1.1563,0,0 0,1.5086,0,0,3.1851,1.6956,1.1441,-3.4471,-2.022,0,0,0,0,0,0,0,0 0,0,0,0,1.6956,3.6105,1.2182,0,0,-2.4828,-3.1459,0,0,0,0,0,0 0,0,0,0,1.1441,1.2182,1.644,0,0,1.6753,0,0,0,-1.8137,0,0,0 0,-3.2655,0,0,-3.4471,0,0,14.923,4.3767,0,0,0,0,0,0,4.5978,-4.5822 0,-1.9155,0,0,-2.022,0,0,4.3767,5.1345,0,0,0,0,0,0,0,2.6878 0,0,0,0,0,-2.4828,1.6753,0,0,6.829,4.3266,0,0,-3.6965,0,0,0 0,0,0,0,0,-3.1459,0,0,0,4.3266,10.964,0,0,4.6839,0,0,0 0,0,-11.16,3.0655,0,0,0,0,0,0,0,14.503,10.319,0,-2.7351,0,0 0,0,-15.881,0,0,0,0,0,0,0,0,10.319,29.371,0,3.8923,-6.4505,0 0,0,0,0,0,0,-1.8137,0,0,-3.6965,4.6839,0,0,8.0036,0,0,0 0,0,0,-1.1563,0,0,0,0,0,0,0,-2.7351,3.8923,0,2.0633,-1.7097,0 0,0,0,0,0,0,0,4.5978,0,0,0,0,-6.4505,0,-1.7097,5.6666,-2.8236 0,0,0,0,0,0,0,-4.5822,2.6878,0,0,0,0,0,0,-2.8236,5.628 ================================================ FILE: tests/test_data/system/gamma/20060619-20061002_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/gamma/20060619_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2006 06 19 8 28 59.6906 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/gamma/20060619_utm_dem.par ================================================ Gamma DIFF&GEO DEM/MAP parameter file title: GAMMA DEM par file created from ROI_PAC rsc files MCG 28/01/2016 DEM_projection: EQA data_format: REAL*4 DEM_hgt_offset: 0.00000 DEM_scale: 1.00000 width: 47 nlines: 72 corner_lat: -34.1700000 decimal degrees corner_lon: 150.9100000 decimal degrees post_lat: -8.33333e-04 decimal degrees post_lon: 8.33333e-04 decimal degrees ellipsoid_name: WGS 84 ellipsoid_ra: 6378137.000 m ellipsoid_reciprocal_flattening: 298.2572236 datum_name: WGS 1984 datum_shift_dx: 0.000 m datum_shift_dy: 0.000 m datum_shift_dz: 0.000 m datum_scale_m: 0.00000e+00 datum_rotation_alpha: 0.00000e+00 arc-sec datum_rotation_beta: 0.00000e+00 arc-sec datum_rotation_gamma: 0.00000e+00 arc-sec datum_country_list Global Definition, WGS84, World ================================================ FILE: tests/test_data/system/gamma/20060828-20061211_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/gamma/20060828_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2006 08 28 12 18 9.6906 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/gamma/20061002-20070219_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/gamma/20061002-20070430_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/gamma/20061002_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2006 10 02 13 5 19.0003 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/gamma/20061106-20061211_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/gamma/20061106-20070115_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/gamma/20061106-20070326_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/gamma/20061106_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2006 11 06 22 58 23.1246 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/gamma/20061211-20070709_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/gamma/20061211-20070813_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/gamma/20061211_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2006 12 11 23 18 53.1241 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/gamma/20070115-20070326_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/gamma/20070115-20070917_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/gamma/20070115_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 01 15 21 48 23.124 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/gamma/20070219-20070430_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/gamma/20070219-20070604_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/gamma/20070219_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 02 19 1 18 3.124 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/gamma/20070326-20070917_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/gamma/20070326_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 03 26 2 32 13.1244 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/gamma/20070430-20070604_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/gamma/20070430_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 04 30 5 32 13.1244 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/gamma/20070604-20070709_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/gamma/20070604_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 06 04 9 31 13.1244 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/gamma/20070709-20070813_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/gamma/20070709_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 07 09 19 01 43.444 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/gamma/20070813_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 08 13 16 23 43.444 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/gamma/20070917_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 09 17 12 41 25.444 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/gamma/baseline_list.txt ================================================ tests/test_data/system/gamma/20060619-20061002_base.par tests/test_data/system/gamma/20060828-20061211_base.par tests/test_data/system/gamma/20061002-20070219_base.par tests/test_data/system/gamma/20061002-20070430_base.par tests/test_data/system/gamma/20061106-20061211_base.par tests/test_data/system/gamma/20061106-20070115_base.par tests/test_data/system/gamma/20061106-20070326_base.par tests/test_data/system/gamma/20061211-20070709_base.par tests/test_data/system/gamma/20061211-20070813_base.par tests/test_data/system/gamma/20070115-20070326_base.par tests/test_data/system/gamma/20070115-20070917_base.par tests/test_data/system/gamma/20070219-20070430_base.par tests/test_data/system/gamma/20070219-20070604_base.par tests/test_data/system/gamma/20070326-20070917_base.par tests/test_data/system/gamma/20070430-20070604_base.par tests/test_data/system/gamma/20070604-20070709_base.par tests/test_data/system/gamma/20070709-20070813_base.par ================================================ FILE: tests/test_data/system/gamma/header_list.txt ================================================ tests/test_data/system/gamma/20060619_slc.par tests/test_data/system/gamma/20060828_slc.par tests/test_data/system/gamma/20061002_slc.par tests/test_data/system/gamma/20061106_slc.par tests/test_data/system/gamma/20061211_slc.par tests/test_data/system/gamma/20070115_slc.par tests/test_data/system/gamma/20070219_slc.par tests/test_data/system/gamma/20070326_slc.par tests/test_data/system/gamma/20070430_slc.par tests/test_data/system/gamma/20070604_slc.par tests/test_data/system/gamma/20070709_slc.par tests/test_data/system/gamma/20070813_slc.par tests/test_data/system/gamma/20070917_slc.par ================================================ FILE: tests/test_data/system/gamma/input_parameters.conf ================================================ # PyRate configuration file for GAMMA-format interferograms # #------------------------------------ # input/output parameters # File containing the list of interferograms to use. ifgfilelist: tests/test_data/system/gamma/interferogram_list.txt # The DEM file used in the InSAR processing demfile: tests/test_data/system/gamma/20060619_utm.dem # The DEM header file from GAMMA (*.par) or ROI_PAC (*.rsc). demHeaderFile: tests/test_data/system/gamma/20060619_utm_dem.par # File listing the pool of available header files (GAMMA: *slc.par, ROI_PAC: *.rsc) hdrfilelist: tests/test_data/system/gamma/header_list.txt # File listing the pool of available coherence files. cohfilelist: # File listing the pool of available baseline files. basefilelist: tests/test_data/system/gamma/baseline_list.txt # Look-up table containing radar-coded row and column for lat/lon pixels ltfile: tests/test_data/system/gamma/cropped_lookup_table.lt # Directory to write the outputs to outdir: tests/test_data/system/gamma/out # InSAR processing software: ROI_PAC = 0, GAMMA = 1, GEOTIFF = 2 processor: 1 # No data averaging threshold for prepifg noDataAveragingThreshold: 0.5 # The no data value in the interferograms noDataValue: 0.0 # Nan conversion flag. Set to 1 if missing (0) phase values are converted to nan nan_conversion: 1 #----------------------------------- # Multi-threading parameters: used by stacking/timeseries/prepifg # gamma prepifg runs in parallel on a single machine if parallel != 0 # parallel = 1, stacking/timeseries computation is done in parallel # parallel = 0, stacking/timeseries computation is done in serial parallel: 0 processes: 8 #------------------------------------ # Coherence masking options: used by process # cohmask: 1 = ON, 0 = OFF # cohthresh: coherence threshold value, between 0 and 1 cohmask: 0 cohthresh: 0.1 #------------------------------------ # Interferogram multi-look and crop options # ifgcropopt: 1 = minimum 2 = maximum 3 = customise 4 = all ifms already same size # ifglksx/y: multi-look/subsampling factor in east and north direction respectively # ifgxfirst,ifgyfirst: x,y of top-left corner # ifgxlast,ifgylast: x,y of bottom-right corner ifgcropopt: 2 ifglksx: 1 ifglksy: 1 ifgxfirst: 150.92 ifgxlast: 150.94 ifgyfirst: -34.18 ifgylast: -34.22 #------------------------------------ # Reference pixel search options # refx/y: Lon/Lat coordinate of reference pixel. If left blank then search for best pixel will be performed # refnx/y: number of search grid points in x/y direction # refchipsize: chip size of the data window at each search grid point # refminfrac: minimum fraction of valid (non-NaN) pixels in the data window refx: refy: refnx: 5 refny: 5 refchipsize: 5 refminfrac: 0.01 #------------------------------------ # Reference phase calculation method # refest: 1 = median of the whole interferogram # refest: 2 = median within the window surrounding the chosen reference pixel refest: 2 #------------------------------------ # Orbital error correction # orbfit: ON = 1, OFF = 0 # orbfitmethod = 1: independent method; 2: network method # orbfitdegrees: Degree of polynomial surface to fit (1 = planar; 2 = quadratic; 3 = part-cubic) # orbfitlksx/y: additional multi-look factor for orbital correction orbfit: 1 orbfitmethod: 1 orbfitdegrees: 1 orbfitlksx: 1 orbfitlksy: 1 #------------------------------------ # APS correction using spatio-temporal filter # apsest: ON = 1, OFF = 0 # Spatial low-pass filter parameters # slpfcutoff: cutoff d0 (greater than zero) in km for both butterworth and gaussian filters # slpnanfill: 1 for interpolation, 0 for zero fill # slpnanfill_method: linear, nearest, cubic; only used when slpnanfill=1 # Temporal low-pass filter parameters # tlpfcutoff: cutoff t0 for gaussian filter in days; # tlpfpthr: valid pixel threshold; apsest: 1 slpfcutoff: 0.001 slpnanfill: 1 slpnanfill_method: cubic tlpfcutoff: 12 tlpfpthr: 1 #------------------------------------ # # DEM error correction # demerror: 1 = ON, 0 = OFF # de_pthr: valid observations threshold demerror: 0 de_pthr: 20 #------------------------------------ # Time Series Calculation # tsmethod: Method for time series inversion (1 = Laplacian Smoothing; 2 = SVD) # smorder: order of Laplacian smoothing operator (1 = first-order difference; 2 = second-order difference) # smfactor: smoothing factor for Laplacian smoothing (value provided is converted as 10**smfactor) # ts_pthr: valid observations threshold for time series inversion tsmethod: 2 smorder: 2 smfactor: -0.25 ts_pthr: 10 #------------------------------------ # Stacking calculation # pthr: minimum number of coherent ifg connections for each pixel # nsig: n-sigma used as residuals threshold for iterativel least squares stacking # maxsig: maximum residual used as a threshold for values in the rate map nsig: 3 pthr: 5 maxsig: 2 [correct] steps = orbfit refphase demerror mst apscorrect maxvar ================================================ FILE: tests/test_data/system/gamma/interferogram_list.txt ================================================ tests/test_data/system/gamma/20060619-20061002_utm.unw tests/test_data/system/gamma/20060828-20061211_utm.unw tests/test_data/system/gamma/20061002-20070219_utm.unw tests/test_data/system/gamma/20061002-20070430_utm.unw tests/test_data/system/gamma/20061106-20061211_utm.unw tests/test_data/system/gamma/20061106-20070115_utm.unw tests/test_data/system/gamma/20061106-20070326_utm.unw tests/test_data/system/gamma/20061211-20070709_utm.unw tests/test_data/system/gamma/20061211-20070813_utm.unw tests/test_data/system/gamma/20070115-20070326_utm.unw tests/test_data/system/gamma/20070115-20070917_utm.unw tests/test_data/system/gamma/20070219-20070430_utm.unw tests/test_data/system/gamma/20070219-20070604_utm.unw tests/test_data/system/gamma/20070326-20070917_utm.unw tests/test_data/system/gamma/20070430-20070604_utm.unw tests/test_data/system/gamma/20070604-20070709_utm.unw tests/test_data/system/gamma/20070709-20070813_utm.unw ================================================ FILE: tests/test_data/system/geotiff/20060619-20061002_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/geotiff/20060619_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2006 06 19 8 28 59.6906 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/geotiff/20060619_utm_dem.par ================================================ Gamma DIFF&GEO DEM/MAP parameter file title: GAMMA DEM par file created from ROI_PAC rsc files MCG 28/01/2016 DEM_projection: EQA data_format: REAL*4 DEM_hgt_offset: 0.00000 DEM_scale: 1.00000 width: 47 nlines: 72 corner_lat: -34.1700000 decimal degrees corner_lon: 150.9100000 decimal degrees post_lat: -8.33333e-04 decimal degrees post_lon: 8.33333e-04 decimal degrees ellipsoid_name: WGS 84 ellipsoid_ra: 6378137.000 m ellipsoid_reciprocal_flattening: 298.2572236 datum_name: WGS 1984 datum_shift_dx: 0.000 m datum_shift_dy: 0.000 m datum_shift_dz: 0.000 m datum_scale_m: 0.00000e+00 datum_rotation_alpha: 0.00000e+00 arc-sec datum_rotation_beta: 0.00000e+00 arc-sec datum_rotation_gamma: 0.00000e+00 arc-sec datum_country_list Global Definition, WGS84, World ================================================ FILE: tests/test_data/system/geotiff/20060828-20061211_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/geotiff/20060828_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2006 08 28 12 18 9.6906 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/geotiff/20061002-20070219_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/geotiff/20061002-20070430_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/geotiff/20061002_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2006 10 02 13 5 19.0003 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/geotiff/20061106-20061211_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/geotiff/20061106-20070115_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/geotiff/20061106-20070326_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/geotiff/20061106_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2006 11 06 22 58 23.1246 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/geotiff/20061211-20070709_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/geotiff/20061211-20070813_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/geotiff/20061211_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2006 12 11 23 18 53.1241 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/geotiff/20070115-20070326_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/geotiff/20070115-20070917_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/geotiff/20070115_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 01 15 21 48 23.124 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/geotiff/20070219-20070430_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/geotiff/20070219-20070604_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/geotiff/20070219_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 02 19 1 18 3.124 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/geotiff/20070326-20070917_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/geotiff/20070326_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 03 26 2 32 13.1244 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/geotiff/20070430-20070604_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/geotiff/20070430_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 04 30 5 32 13.1244 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/geotiff/20070604-20070709_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/geotiff/20070604_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 06 04 9 31 13.1244 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/geotiff/20070709-20070813_base.par ================================================ initial_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m initial_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s precision_baseline(TCN): 0.1585592 15.3525463 9.7718029 m m m precision_baseline_rate: 0.0000000 -0.0439410 0.0177195 m/s m/s m/s unwrap_phase_constant: 0.00000 radians ================================================ FILE: tests/test_data/system/geotiff/20070709_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 07 09 19 01 43.444 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/geotiff/20070813_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 08 13 16 23 43.444 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/geotiff/20070917_slc.par ================================================ # GAMMA SLC par file created from ROI_PAC rsc files MCG 28/01/2016 date: 2007 09 17 12 41 25.444 radar_frequency: 5.334694994e+09 Hz incidence_angle: 22.9671 degrees heading: 193.1522256 degrees azimuth_angle: 90.0000 degrees range_samples: 8630 azimuth_lines: 8571 range_looks: 8 azimuth_looks: 2 range_pixel_spacing: 18.635856 m azimuth_pixel_spacing: 28.136512 m near_range_slc: 802867.7247 m prf: 486.4863103 Hz sar_to_earth_center: 7080600.3965 m earth_radius_below_sensor: 6371577.2590 m earth_semi_major_axis: 6378137.0000 m earth_semi_minor_axis: 6356752.3141 m ================================================ FILE: tests/test_data/system/geotiff/baseline_list.txt ================================================ tests/test_data/system/geotiff/20060619-20061002_base.par tests/test_data/system/geotiff/20060828-20061211_base.par tests/test_data/system/geotiff/20061002-20070219_base.par tests/test_data/system/geotiff/20061002-20070430_base.par tests/test_data/system/geotiff/20061106-20061211_base.par tests/test_data/system/geotiff/20061106-20070115_base.par tests/test_data/system/geotiff/20061106-20070326_base.par tests/test_data/system/geotiff/20061211-20070709_base.par tests/test_data/system/geotiff/20061211-20070813_base.par tests/test_data/system/geotiff/20070115-20070326_base.par tests/test_data/system/geotiff/20070115-20070917_base.par tests/test_data/system/geotiff/20070219-20070430_base.par tests/test_data/system/geotiff/20070219-20070604_base.par tests/test_data/system/geotiff/20070326-20070917_base.par tests/test_data/system/geotiff/20070430-20070604_base.par tests/test_data/system/geotiff/20070604-20070709_base.par tests/test_data/system/geotiff/20070709-20070813_base.par ================================================ FILE: tests/test_data/system/geotiff/header_list.txt ================================================ tests/test_data/system/geotiff/20060619_slc.par tests/test_data/system/geotiff/20060828_slc.par tests/test_data/system/geotiff/20061002_slc.par tests/test_data/system/geotiff/20061106_slc.par tests/test_data/system/geotiff/20061211_slc.par tests/test_data/system/geotiff/20070115_slc.par tests/test_data/system/geotiff/20070219_slc.par tests/test_data/system/geotiff/20070326_slc.par tests/test_data/system/geotiff/20070430_slc.par tests/test_data/system/geotiff/20070604_slc.par tests/test_data/system/geotiff/20070709_slc.par tests/test_data/system/geotiff/20070813_slc.par tests/test_data/system/geotiff/20070917_slc.par ================================================ FILE: tests/test_data/system/geotiff/input_parameters.conf ================================================ # PyRate configuration file for GAMMA-format interferograms # #------------------------------------ # input/output parameters # File containing the list of interferograms to use. ifgfilelist: tests/test_data/system/geotiff/interferogram_list.txt # The DEM file used in the InSAR processing demfile: tests/test_data/system/geotiff/20060619_utm_dem.tif # The DEM header file from GAMMA (*.par) or ROI_PAC (*.rsc). demHeaderFile: tests/test_data/system/geotiff/20060619_utm_dem.par # File listing the pool of available header files (GAMMA: *slc.par, ROI_PAC: *.rsc) hdrfilelist: tests/test_data/system/geotiff/header_list.txt # File listing the pool of available coherence files. cohfilelist: # File listing the pool of available baseline files. basefilelist: tests/test_data/system/gamma/baseline_list.txt # Look-up table containing radar-coded row and column for lat/lon pixels ltfile: tests/test_data/system/gamma/cropped_lookup_table.lt # Directory to write the outputs to outdir: tests/test_data/system/geotiff/out/ # InSAR processing software: ROI_PAC = 0, GAMMA = 1, geotiff = 2 processor: 2 # No data averaging threshold for prepifg noDataAveragingThreshold: 0.5 # The no data value in the interferograms noDataValue: 0.0 # Nan conversion flag. Set to 1 if missing (0) phase values are converted to nan nan_conversion: 1 #----------------------------------- # Multi-threading parameters: used by stacking/timeseries/prepifg # gamma prepifg runs in parallel on a single machine if parallel != 0 # parallel = 1, stacking/timeseries computation is done in parallel # parallel = 0, stacking/timeseries computation is done in serial parallel: 0 processes: 1 #------------------------------------ # Coherence masking options: used by process # cohmask: 1 = ON, 0 = OFF # cohthresh: coherence threshold value, between 0 and 1 cohmask: 0 cohthresh: 0.1 #------------------------------------ # Interferogram multi-look and crop options # ifgcropopt: 1 = minimum 2 = maximum 3 = customise 4 = all ifms already same size # ifglksx/y: multi-look/subsampling factor in east and north direction respectively # ifgxfirst,ifgyfirst: x,y of top-left corner # ifgxlast,ifgylast: x,y of bottom-right corner ifgcropopt: 2 ifglksx: 1 ifglksy: 1 ifgxfirst: 150.92 ifgxlast: 150.94 ifgyfirst: -34.18 ifgylast: -34.22 #------------------------------------ # Reference pixel search options # refx/y: Lon/Lat coordinate of reference pixel. If left blank then search for best pixel will be performed # refnx/y: number of search grid points in x/y direction # refchipsize: chip size of the data window at each search grid point # refminfrac: minimum fraction of valid (non-NaN) pixels in the data window refx: refy: refnx: 5 refny: 5 refchipsize: 5 refminfrac: 0.01 #------------------------------------ # Reference phase calculation method # refest: 1 = median of the whole interferogram # refest: 2 = median within the window surrounding the chosen reference pixel refest: 2 #------------------------------------ # Orbital error correction # orbfit: ON = 1, OFF = 0 # orbfitmethod = 1: independent method; 2: network method # orbfitdegrees: Degree of polynomial surface to fit (1 = planar; 2 = quadratic; 3 = part-cubic) # orbfitlksx/y: additional multi-look factor for orbital correction orbfit: 1 orbfitmethod: 1 orbfitdegrees: 1 orbfitlksx: 1 orbfitlksy: 1 #------------------------------------ # APS correction using spatio-temporal filter # apsest: ON = 1, OFF = 0 # Spatial low-pass filter parameters # slpfcutoff: cutoff d0 (greater than zero) in km for both butterworth and gaussian filters # slpnanfill: 1 for interpolation, 0 for zero fill # slpnanfill_method: linear, nearest, cubic; only used when slpnanfill=1 # Temporal low-pass filter parameters # tlpfcutoff: cutoff t0 for gaussian filter in days; # tlpfpthr: valid pixel threshold; apsest: 1 slpfcutoff: 0.001 slpnanfill: 1 slpnanfill_method: cubic tlpfcutoff: 12 tlpfpthr: 1 #------------------------------------ # # DEM error correction # demerror: 1 = ON, 0 = OFF # de_pthr: valid observations threshold demerror: 0 de_pthr: 20 #------------------------------------ # Time Series Calculation # tsmethod: Method for time series inversion (1 = Laplacian Smoothing; 2 = SVD) # smorder: order of Laplacian smoothing operator (1 = first-order difference; 2 = second-order difference) # smfactor: smoothing factor for Laplacian smoothing (value provided is converted as 10**smfactor) # ts_pthr: valid observations threshold for time series inversion tsmethod: 2 smorder: 2 smfactor: -0.25 ts_pthr: 10 #------------------------------------ # Stacking calculation # pthr: minimum number of coherent ifg connections for each pixel # nsig: n-sigma used as residuals threshold for iterativel least squares stacking # maxsig: maximum residual used as a threshold for values in the rate map nsig: 3 pthr: 5 maxsig: 2 [correct] steps = orbfit refphase demerror mst apscorrect maxvar ================================================ FILE: tests/test_data/system/geotiff/interferogram_list.txt ================================================ tests/test_data/system/geotiff/20060619-20061002_utm_unw.tif tests/test_data/system/geotiff/20060828-20061211_utm_unw.tif tests/test_data/system/geotiff/20061002-20070219_utm_unw.tif tests/test_data/system/geotiff/20061002-20070430_utm_unw.tif tests/test_data/system/geotiff/20061106-20061211_utm_unw.tif 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degrees Y_UNIT degrees Z_OFFSET 0 Z_SCALE 1 PROJECTION LATLON DATUM WGS84 ================================================ FILE: tests/test_data/system/roipac/header_list.txt ================================================ tests/test_data/system/roipac/headers/geo_060619-061002.unw.rsc tests/test_data/system/roipac/headers/geo_060828-061211.unw.rsc tests/test_data/system/roipac/headers/geo_061002-070219.unw.rsc tests/test_data/system/roipac/headers/geo_061002-070430.unw.rsc tests/test_data/system/roipac/headers/geo_061106-061211.unw.rsc tests/test_data/system/roipac/headers/geo_061106-070115.unw.rsc tests/test_data/system/roipac/headers/geo_061106-070326.unw.rsc tests/test_data/system/roipac/headers/geo_061211-070709.unw.rsc tests/test_data/system/roipac/headers/geo_061211-070813.unw.rsc tests/test_data/system/roipac/headers/geo_070115-070326.unw.rsc tests/test_data/system/roipac/headers/geo_070115-070917.unw.rsc tests/test_data/system/roipac/headers/geo_070219-070430.unw.rsc 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================================================ FILE: tests/test_data/system/roipac/headers/geo_070430-070604.unw.rsc ================================================ WIDTH 47 FILE_LENGTH 72 X_FIRST 150.910000000 X_STEP 0.000833333 Y_FIRST -34.170000000 Y_STEP -0.000833333 WAVELENGTH 0.0562356424 DATE 070430 DATE12 070430-070604 ================================================ FILE: tests/test_data/system/roipac/headers/geo_070604-070709.unw.rsc ================================================ WIDTH 47 FILE_LENGTH 72 X_FIRST 150.910000000 X_STEP 0.000833333 Y_FIRST -34.170000000 Y_STEP -0.000833333 WAVELENGTH 0.0562356424 DATE 070604 DATE12 070604-070709 ================================================ FILE: tests/test_data/system/roipac/headers/geo_070709-070813.unw.rsc ================================================ WIDTH 47 FILE_LENGTH 72 X_FIRST 150.910000000 X_STEP 0.000833333 Y_FIRST -34.170000000 Y_STEP -0.000833333 WAVELENGTH 0.0562356424 DATE 070709 DATE12 070709-070813 ================================================ FILE: tests/test_data/system/roipac/input_parameters.conf ================================================ #------------------------------------ # input/output parameters # File containing the list of (raw) interferograms. ifgfilelist: tests/test_data/system/roipac/interferogram_list.txt # The DEM used by the processing software demfile: tests/test_data/system/roipac/dem/roipac_test_trimmed.dem # The DEM header file from GAMMA (*.par) or ROI_PAC (*.rsc). demHeaderFile: tests/test_data/system/roipac/dem/roipac_test_trimmed.dem.rsc # File listing the pool of available header files (GAMMA: *slc.par, ROI_PAC: *.rsc) hdrfilelist: tests/test_data/system/roipac/header_list.txt # File listing the pool of available coherence files. cohfilelist: # Where to write the outputs outdir: tests/test_data/system/roipac/out # InSAR processing software: ROI_PAC = 0, GAMMA = 1 processor: 0 # No data averaging threshold for prepifg noDataAveragingThreshold: 0.5 # The no data value in the interferograms noDataValue: 0.0 # Nan conversion flag. Set to 1 if missing (0) phase values are converted to nan nan_conversion: 1 #----------------------------------- # Multi-threading parameters: used by stacking/timeseries/gamma prepifg # gamma prepifg runs in parallel in single machine if parallel != 0 # parallel = 1, stacking/timeseries computation is done parallel # parallel = 0, stacking/timeseries computation is done in serial parallel: 0 processes: 1 #------------------------------------ # Coherence masking options: used by process # cohmask: 1 = ON, 0 = OFF # cohthresh: coherence threshold value, between 0 and 1 cohmask: 0 cohthresh: 0.1 #------------------------------------ # Interferogram multi-look and crop options # ifgcropopt: 1 = minimum 2 = maximum 3 = customise 4 = all ifms already same size # ifglksx/y: multi-look/subsampling factor in east and north direction respectively # ifgxfirst,ifgyfirst: x,y of top-left corner # ifgxlast,ifgylast: x,y of bottom-right corner ifgcropopt: 1 ifglksx: 1 ifglksy: 1 ifgxfirst: 150.92 ifgxlast: 150.94 ifgyfirst: -34.18 ifgylast: -34.22 #------------------------------------ # Reference pixel search options # refx/y: coordinate of reference pixel. If <= 0 then search for pixel will be performed # refnx/y: number of search grid points in x/y direction # refchipsize: chip size of the data window at each search grid point # refminfrac: minimum fraction of valid (non-NaN) pixels in the data window refx: refy: refnx: 5 refny: 5 refchipsize: 5 refminfrac: 0.8 #------------------------------------ # Orbital error correction # orbfit: ON = 1, OFF = 0 # orbfitmethod = 1: independent method; 2: network method # orbfitdegrees: Degree of polynomial surface to fit (1 = planar; 2 = quadratic; 3 = part-cubic) # orbfitlksx/y: additional multi-look factor for orbital correction orbfit: 1 orbfitmethod: 1 orbfitdegrees: 1 orbfitlksx: 1 orbfitlksy: 1 #------------------------------------ # Reference phase calculation method # refest: 1 = median of the whole interferogram # refest: 2 = median within the window surrounding the chosen reference pixel refest: 2 #------------------------------------ # APS Atmospheric Phase Screen correction # NOT CURRENTLY IMPLEMENTED # apsmethod 1: scene centre incidence angle used from GAMMA header files # apsmethod 2: uses GAMMA incidence angle map (lv_theta file) # incidencemap or elevationmap is only used for method 2 # one of incidencemap or elevationmap must be provided for method 2. # if both incidencemap and elevationmap is provided, only incidencemap is used apscorrect: 1 apsmethod: 2 incidencemap: tests/test_data/small_test/gamma_obs/20060619_utm.inc elevationmap: tests/test_data/small_test/gamma_obs/20060619_utm.lv_theta #------------------------------------ # APS correction using spatio-temporal filter # apsest: ON = 1, OFF = 0 # Spatial low-pass filter parameters # slpfcutoff: cutoff d0 (greater than zero) in km for both butterworth and gaussian filters # slpnanfill: 1 for interpolation, 0 for zero fill # slpnanfill_method: linear, nearest, cubic; only used when slpnanfill=1 # Temporal low-pass filter parameters # tlpfcutoff: cutoff t0 for gaussian filter in days; # tlpfpthr: valid pixel threshold; apsest: 1 slpfcutoff: 0.001 slpnanfill: 1 slpnanfill_method: cubic tlpfcutoff: 12 tlpfpthr: 1 #------------------------------------ # # DEM error correction # demerror: 1 = ON, 0 = OFF # de_pthr: valid observations threshold demerror: 1 de_pthr: 20 #------------------------------------ # Time Series Calculation # tsmethod: Method for time series inversion (1 = Laplacian Smoothing; 2 = SVD) # smorder: order of Laplacian smoothing operator (1 = first-order difference; 2 = second-order difference) # smfactor: smoothing factor for Laplacian smoothing # ts_pthr: valid observations threshold for time series inversion tsmethod: 1 smorder: 2 smfactor: -0.25 ts_pthr: 10 #------------------------------------ # Stacked Rate calculation # pthr: minimum number of coherent ifg connections for each pixel # nsig: n-sigma used as residuals threshold for iterativelLeast squares stacking # maxsig: maximum residual used as a threshold for values in the rate map nsig: 3 pthr: 5 maxsig: 2 # validate file list values validate: False [correct] steps = orbfit refphase demerror mst apscorrect maxvar ================================================ FILE: tests/test_data/system/roipac/interferogram_list.txt ================================================ tests/test_data/system/roipac/interferograms/geo_060619-061002.unw tests/test_data/system/roipac/interferograms/geo_060828-061211.unw tests/test_data/system/roipac/interferograms/geo_061002-070219.unw tests/test_data/system/roipac/interferograms/geo_061002-070430.unw tests/test_data/system/roipac/interferograms/geo_061106-061211.unw tests/test_data/system/roipac/interferograms/geo_061106-070115.unw tests/test_data/system/roipac/interferograms/geo_061106-070326.unw tests/test_data/system/roipac/interferograms/geo_061211-070709.unw tests/test_data/system/roipac/interferograms/geo_061211-070813.unw tests/test_data/system/roipac/interferograms/geo_070115-070326.unw tests/test_data/system/roipac/interferograms/geo_070115-070917.unw tests/test_data/system/roipac/interferograms/geo_070219-070430.unw tests/test_data/system/roipac/interferograms/geo_070219-070604.unw tests/test_data/system/roipac/interferograms/geo_070326-070917.unw tests/test_data/system/roipac/interferograms/geo_070430-070604.unw tests/test_data/system/roipac/interferograms/geo_070604-070709.unw tests/test_data/system/roipac/interferograms/geo_070709-070813.unw ================================================ FILE: tests/test_data/system/roipac/interferograms/geo_060619-061002.unw.rsc ================================================ WIDTH 47 FILE_LENGTH 72 X_FIRST 150.910000000 X_STEP 0.000833333 Y_FIRST -34.170000000 Y_STEP -0.000833333 WAVELENGTH 0.0562356424 DATE 060619 DATE12 060619-061002 ================================================ FILE: tests/test_data/system/roipac/interferograms/geo_060828-061211.unw.rsc ================================================ WIDTH 47 FILE_LENGTH 72 X_FIRST 150.910000000 X_STEP 0.000833333 Y_FIRST -34.170000000 Y_STEP -0.000833333 WAVELENGTH 0.0562356424 DATE 060828 DATE12 060828-061211 ================================================ FILE: tests/test_data/system/roipac/interferograms/geo_061002-070219.unw.rsc ================================================ WIDTH 47 FILE_LENGTH 72 X_FIRST 150.910000000 X_STEP 0.000833333 Y_FIRST -34.170000000 Y_STEP -0.000833333 WAVELENGTH 0.0562356424 DATE 061002 DATE12 061002-070219 ================================================ FILE: tests/test_data/system/roipac/interferograms/geo_061002-070430.unw.rsc ================================================ WIDTH 47 FILE_LENGTH 72 X_FIRST 150.910000000 X_STEP 0.000833333 Y_FIRST -34.170000000 Y_STEP -0.000833333 WAVELENGTH 0.0562356424 DATE 061002 DATE12 061002-070430 ================================================ FILE: tests/test_data/system/roipac/interferograms/geo_061106-061211.unw.rsc ================================================ WIDTH 47 FILE_LENGTH 72 X_FIRST 150.910000000 X_STEP 0.000833333 Y_FIRST -34.170000000 Y_STEP -0.000833333 WAVELENGTH 0.0562356424 DATE 061106 DATE12 061106-061211 ================================================ FILE: tests/test_data/system/roipac/interferograms/geo_061106-070115.unw.rsc ================================================ WIDTH 47 FILE_LENGTH 72 X_FIRST 150.910000000 X_STEP 0.000833333 Y_FIRST -34.170000000 Y_STEP -0.000833333 WAVELENGTH 0.0562356424 DATE 061106 DATE12 061106-070115 ================================================ FILE: tests/test_data/system/roipac/interferograms/geo_061106-070326.unw.rsc ================================================ WIDTH 47 FILE_LENGTH 72 X_FIRST 150.910000000 X_STEP 0.000833333 Y_FIRST -34.170000000 Y_STEP -0.000833333 WAVELENGTH 0.0562356424 DATE 061106 DATE12 061106-070326 ================================================ FILE: tests/test_data/system/roipac/interferograms/geo_061211-070709.unw.rsc ================================================ WIDTH 47 FILE_LENGTH 72 X_FIRST 150.910000000 X_STEP 0.000833333 Y_FIRST -34.170000000 Y_STEP -0.000833333 WAVELENGTH 0.0562356424 DATE 061211 DATE12 061211-070709 ================================================ FILE: tests/test_data/system/roipac/interferograms/geo_061211-070813.unw.rsc ================================================ WIDTH 47 FILE_LENGTH 72 X_FIRST 150.910000000 X_STEP 0.000833333 Y_FIRST -34.170000000 Y_STEP -0.000833333 WAVELENGTH 0.0562356424 DATE 061211 DATE12 061211-070813 ================================================ FILE: tests/test_data/system/roipac/interferograms/geo_070115-070326.unw.rsc ================================================ WIDTH 47 FILE_LENGTH 72 X_FIRST 150.910000000 X_STEP 0.000833333 Y_FIRST -34.170000000 Y_STEP -0.000833333 WAVELENGTH 0.0562356424 DATE 070115 DATE12 070115-070326 ================================================ FILE: tests/test_data/system/roipac/interferograms/geo_070115-070917.unw.rsc ================================================ WIDTH 47 FILE_LENGTH 72 X_FIRST 150.910000000 X_STEP 0.000833333 Y_FIRST -34.170000000 Y_STEP -0.000833333 WAVELENGTH 0.0562356424 DATE 070115 DATE12 070115-070917 ================================================ FILE: tests/test_data/system/roipac/interferograms/geo_070219-070430.unw.rsc ================================================ WIDTH 47 FILE_LENGTH 72 X_FIRST 150.910000000 X_STEP 0.000833333 Y_FIRST -34.170000000 Y_STEP -0.000833333 WAVELENGTH 0.0562356424 DATE 070219 DATE12 070219-070430 ================================================ FILE: tests/test_data/system/roipac/interferograms/geo_070219-070604.unw.rsc ================================================ WIDTH 47 FILE_LENGTH 72 X_FIRST 150.910000000 X_STEP 0.000833333 Y_FIRST -34.170000000 Y_STEP -0.000833333 WAVELENGTH 0.0562356424 DATE 070219 DATE12 070219-070604 ================================================ FILE: tests/test_data/system/roipac/interferograms/geo_070326-070917.unw.rsc ================================================ WIDTH 47 FILE_LENGTH 72 X_FIRST 150.910000000 X_STEP 0.000833333 Y_FIRST -34.170000000 Y_STEP -0.000833333 WAVELENGTH 0.0562356424 DATE 070326 DATE12 070326-070917 ================================================ FILE: tests/test_data/system/roipac/interferograms/geo_070430-070604.unw.rsc ================================================ WIDTH 47 FILE_LENGTH 72 X_FIRST 150.910000000 X_STEP 0.000833333 Y_FIRST -34.170000000 Y_STEP -0.000833333 WAVELENGTH 0.0562356424 DATE 070430 DATE12 070430-070604 ================================================ FILE: tests/test_data/system/roipac/interferograms/geo_070604-070709.unw.rsc ================================================ WIDTH 47 FILE_LENGTH 72 X_FIRST 150.910000000 X_STEP 0.000833333 Y_FIRST -34.170000000 Y_STEP -0.000833333 WAVELENGTH 0.0562356424 DATE 070604 DATE12 070604-070709 ================================================ FILE: tests/test_data/system/roipac/interferograms/geo_070709-070813.unw.rsc ================================================ WIDTH 47 FILE_LENGTH 72 X_FIRST 150.910000000 X_STEP 0.000833333 Y_FIRST -34.170000000 Y_STEP -0.000833333 WAVELENGTH 0.0562356424 DATE 070709 DATE12 070709-070813 ================================================ FILE: tests/test_dem_error.py ================================================ import shutil import glob import os from os.path import join from pathlib import Path from scipy.interpolate import griddata import math import pytest import numpy as np import pyrate.constants as C from tests import common from pyrate.configuration import Configuration, MultiplePaths from pyrate import prepifg, correct from pyrate.core.geometry import get_lonlat_coords from pyrate.core.dem_error import dem_error_calc_wrapper, _calculate_bperp_wrapper from pyrate.core.ref_phs_est import ref_phase_est_wrapper from pyrate.core.shared import Ifg, Geometry, DEM, save_numpy_phase geometry_path = common.MEXICO_CROPA_DIR_GEOMETRY dem_error_path = common.MEXICO_CROPA_DIR_DEM_ERROR @pytest.fixture(params=list(range(200))) def point(): x, y = np.random.randint(0, 60), np.random.randint(0, 100) return x, y class TestPyRateGammaBperp: @classmethod def setup_class(cls): cls.params = Configuration(common.MEXICO_CROPA_CONF).__dict__ # run prepifg prepifg.main(cls.params) # copy IFGs to temp folder correct._copy_mlooked(cls.params) # read radar azimuth, range and dem tif files geom_files = Configuration.geometry_files(cls.params) rdc_az_file = geom_files['rdc_azimuth'] geom_az = Geometry(rdc_az_file) cls.az = geom_az.data rdc_rg_file = geom_files['rdc_range'] geom_rg = Geometry(rdc_rg_file) cls.rg = geom_rg.data dem_file = join(cls.params[C.GEOMETRY_DIR], 'dem.tif') dem_data = DEM(dem_file) cls.dem = dem_data.data # calc bperp using pyrate funcs cls.pbperp = cls.pyrate_bperp() def gamma_bperp(self, x, y): """ Calculate Bperp for specified pixel from GAMMA out files (interpolation required) x0, y0 is the interpolation location in azimuth and range """ # round azimuth and range coordinates to closest step (500 for az, 200 for rg) azstep = 500 rgstep = 200 az = self.az[x, y] rg = self.rg[x, y] az1 = azstep * math.floor(az / azstep) rg1 = rgstep * math.floor(rg / rgstep) az2 = azstep * math.ceil(az / azstep) rg2 = rgstep * math.ceil(rg / rgstep) # four coordinates for bi-linear interpolation teststr1 = str(az1).rjust(6, ' ') + str(rg1).rjust(7, ' ') teststr2 = str(az1).rjust(6, ' ') + str(rg2).rjust(7, ' ') teststr3 = str(az2).rjust(6, ' ') + str(rg1).rjust(7, ' ') teststr4 = str(az2).rjust(6, ' ') + str(rg2).rjust(7, ' ') # loop through all corresponding bperp.par files in base.par list bperp_files = sorted(list(glob.glob(os.path.join(geometry_path, '*_bperp.par')))) bperp_int = np.empty(shape=(len(bperp_files))) for i, bperp_file in enumerate(bperp_files): # read Mexico city bperp file with open(bperp_file, 'r') as f: for line in f.readlines(): if teststr1 in line: bperp1 = line.split()[7] if teststr2 in line: bperp2 = line.split()[7] if teststr3 in line: bperp3 = line.split()[7] if teststr4 in line: bperp4 = line.split()[7] # setup numpy array for bi-linear interpolation n = np.array([(az1, rg1, bperp1), (az1, rg2, bperp2), (az2, rg1, bperp3), (az2, rg2, bperp4)]) # interpolate using scipy function "griddata" bperp_int[i] = griddata(n[:, 0:2], n[:, 2], [(az, rg)], method='linear') return bperp_int @classmethod def pyrate_bperp(cls): """ Calculate Bperp image for each ifg using PyRate functions """ multi_paths = cls.params[C.INTERFEROGRAM_FILES] tmp_paths = [ifg_path.tmp_sampled_path for ifg_path in multi_paths] # keep only ifg files in path list (i.e. remove coherence and dem files) ifg_paths = [item for item in tmp_paths if 'ifg.tif' in item] # read and open the first IFG in list ifg0_path = ifg_paths[0] ifg0 = Ifg(ifg0_path) ifg0.open(readonly=True) # size of ifg dataset # calculate per-pixel lon/lat lon, lat = get_lonlat_coords(ifg0) bperp = _calculate_bperp_wrapper(ifg_paths, cls.az, cls.rg, lat.data, lon.data, cls.dem)[0] return np.moveaxis(bperp, (0, 1, 2), (2, 0, 1)) @classmethod def teardown_class(cls): shutil.rmtree(cls.params[C.OUT_DIR], ignore_errors=True) def test_pyrate_bperp_matches_gamma_bperp(self, point): x, y = point az = self.az[x, y] rg = self.rg[x, y] if az < 0 or rg < 0: pytest.skip('skipped due to -ve az or rg') res = self.pbperp[x, y, :] exp = self.gamma_bperp(*point) np.testing.assert_array_almost_equal(exp, res, 2) # max difference < 1cm def test_avg_bperp_calculation(self): # TODO - improve this test by reading bperp *.npy files res = np.mean(self.pbperp, axis=(0, 1), dtype=np.float64) # assuming array centre is a good proxy for average value # TODO - use interpolation to calculate actual Gamma array average exp = self.gamma_bperp(30, 50) np.testing.assert_array_almost_equal(exp, res, 2) class TestDEMErrorFilesReusedFromDisc: @classmethod def setup_class(cls): cls.conf = common.MEXICO_CROPA_CONF cls.params = Configuration(cls.conf).__dict__ prepifg.main(cls.params) cls.params = Configuration(cls.conf).__dict__ multi_paths = cls.params[C.INTERFEROGRAM_FILES] cls.ifg_paths = [p.tmp_sampled_path for p in multi_paths] @classmethod def teardown_class(cls): shutil.rmtree(cls.params[C.OUT_DIR]) def test_dem_error_used_from_disc_on_rerun(self): correct._copy_mlooked(self.params) correct._update_params_with_tiles(self.params) times_written = self.__run_once() assert len(times_written) == len(self.ifg_paths) times_written_1 = self.__run_once() np.testing.assert_array_equal(times_written_1, times_written) def __run_once(self): dem_files = [MultiplePaths.dem_error_path(i, self.params) for i in self.ifg_paths] correct._copy_mlooked(self.params) correct._update_params_with_tiles(self.params) correct._create_ifg_dict(self.params) save_numpy_phase(self.ifg_paths, self.params) dem_error_calc_wrapper(self.params) assert all(m.exists() for m in dem_files) return [os.stat(o).st_mtime for o in dem_files] class TestDEMErrorResults: @classmethod def setup_class(cls): cls.conf = common.MEXICO_CROPA_CONF cls.params = Configuration(cls.conf).__dict__ prepifg.main(cls.params) cls.params = Configuration(cls.conf).__dict__ multi_paths = cls.params[C.INTERFEROGRAM_FILES] cls.ifg_paths = [p.tmp_sampled_path for p in multi_paths] cls.params[C.REFX_FOUND] = 8 # this is the pixel of the location given in the pyrate_mexico_cropa.conf file cls.params[C.REFY_FOUND] = 33 # however, the median of the whole interferogram is used for this validation @classmethod def teardown_class(cls): shutil.rmtree(cls.params[C.OUT_DIR]) def test_calc_dem_errors(self): # validate output of current version of the code with saved files from an independent test run # only the reference phase and dem_error are used in this test # saved dem_error.tif (expected) dem_error_tif_exp = join(dem_error_path, 'dem_error.tif') dem = DEM(dem_error_tif_exp) dem_error_exp = dem.data # run relevant parts of the 'correct' step correct._copy_mlooked(self.params) correct._update_params_with_tiles(self.params) correct._create_ifg_dict(self.params) save_numpy_phase(self.ifg_paths, self.params) # subtract the reference phase to enable comparison with a 'normal' pyrate run ref_phase_est_wrapper(self.params) dem_error_calc_wrapper(self.params) # dem_error.tif from this run (result) dem_error_tif_res = join(self.params[C.DEM_ERROR_DIR], 'dem_error.tif') dem = DEM(dem_error_tif_res) dem_error_res = dem.data # check equality np.testing.assert_allclose(dem_error_exp, dem_error_res) # ifg correction files in subdirectory out/dem_error/ # three different ifgs: # ifg1 -> short_baseline_ifg: 20180106-20180319 (ca. 3 m) # ifg2 -> long_baseline_ifg: 20180130-20180412(ca. 108 m) # ifg3 -> medium_baseline_ifg: 20180412-20180518 (ca. 48 m) # load saved files dem_error_ifg1_path = join(dem_error_path, '20180106-20180319_ifg_20_dem_error.npy') dem_error_ifg1_exp = np.load(dem_error_ifg1_path) dem_error_ifg2_path = join(dem_error_path, '20180130-20180412_ifg_20_dem_error.npy') dem_error_ifg2_exp = np.load(dem_error_ifg2_path) dem_error_ifg3_path = join(dem_error_path, '20180412-20180518_ifg_20_dem_error.npy') dem_error_ifg3_exp = np.load(dem_error_ifg3_path) # load correction values saved from this run (result) dem_error_ifg1_path = Path(self.params[C.DEM_ERROR_DIR]).joinpath('20180106-20180319_ifg_20_dem_error.npy') dem_error_ifg1_res = np.load(dem_error_ifg1_path) dem_error_ifg2_path = Path(self.params[C.DEM_ERROR_DIR]).joinpath('20180130-20180412_ifg_20_dem_error.npy') dem_error_ifg2_res = np.load(dem_error_ifg2_path) dem_error_ifg3_path = Path(self.params[C.DEM_ERROR_DIR]).joinpath('20180412-20180518_ifg_20_dem_error.npy') dem_error_ifg3_res = np.load(dem_error_ifg3_path) # check equality np.testing.assert_allclose(dem_error_ifg1_exp, dem_error_ifg1_res) np.testing.assert_allclose(dem_error_ifg2_exp, dem_error_ifg2_res) np.testing.assert_allclose(dem_error_ifg3_exp, dem_error_ifg3_res) ================================================ FILE: tests/test_gamma.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains tests for the gamma.py PyRate module. """ import os import shutil import tempfile from datetime import date, time from os.path import join import pytest from pathlib import Path import numpy as np from numpy.testing import assert_array_almost_equal from osgeo import gdal import pyrate.configuration import pyrate.core.ifgconstants as ifc from pyrate.core import shared, gamma from pyrate import prepifg, conv2tif from pyrate.core.shared import write_fullres_geotiff, GeotiffException import pyrate.constants as C from pyrate.constants import PYRATEPATH, IFG_FILE_LIST, PROCESSOR, OUT_DIR, DEM_HEADER_FILE, \ NO_DATA_VALUE, BASE_FILE_LIST from tests.common import manipulate_test_conf from pyrate.configuration import Configuration from tests.common import GAMMA_TEST_DIR, WORKING_DIR from tests.common import TEMPDIR from tests.common import small_data_setup gdal.UseExceptions() LIGHTSPEED = 3e8 # approx class TestGammaCommandLineTests: def setup_method(self): self.base = join(PYRATEPATH, 'tests', 'test_data', 'gamma') self.hdr = join(self.base, 'dem16x20raw.dem.par') temp_text = tempfile.mktemp() self.confFile = os.path.join(TEMPDIR,'{}/gamma_test.cfg'.format(temp_text)) self.ifgListFile = os.path.join(TEMPDIR, '{}/gamma_ifg.list'.format(temp_text)) self.baseListFile = os.path.join(TEMPDIR, '{}/gamma_base.list'.format(temp_text)) self.base_dir = os.path.dirname(self.confFile) shared.mkdir_p(self.base_dir) def teardown_method(self): try: os.remove(self.exp_path) except: pass shutil.rmtree(self.base_dir, ignore_errors=True) def makeInputFiles(self, data): with open(self.confFile, 'w') as conf: conf.write('{}: {}\n'.format(DEM_HEADER_FILE, self.hdr)) conf.write('{}: {}\n'.format(NO_DATA_VALUE, '0.0')) conf.write('{}: {}\n'.format(WORKING_DIR, self.base_dir)) conf.write('{}: {}\n'.format(IFG_FILE_LIST, self.ifgListFile)) conf.write('{}: {}\n'.format(BASE_FILE_LIST, self.baseListFile)) conf.write('{}: {}\n'.format(PROCESSOR, '1')) conf.write('{}: {}\n'.format(OUT_DIR, self.base_dir)) with open(self.ifgListFile, 'w') as ifgl: ifgl.write(data) class TestGammaToGeoTiff: """Tests conversion of GAMMA rasters to custom PyRate GeoTIFF""" @classmethod def setup_method(cls): # create common combined header obj so the headers are only read once # tricker: needs both ifg headers, and DEM one for the extents filenames = ['r20090713_VV.slc.par', 'r20090817_VV.slc.par'] hdr_paths = [join(GAMMA_TEST_DIR, f) for f in filenames] hdrs = [gamma.parse_epoch_header(p) for p in hdr_paths] dem_hdr_path = join(GAMMA_TEST_DIR, 'dem16x20raw.dem.par') base_hdr_path = join(GAMMA_TEST_DIR, '20160114-20160126_base.par') cls.DEM_HDR = gamma.parse_dem_header(dem_hdr_path) cls.BASE_HDR = gamma.parse_baseline_header(base_hdr_path) cls.COMBINED = gamma.combine_headers(*hdrs, dem_hdr=cls.DEM_HDR, base_hdr=cls.BASE_HDR) def teardown_method(self): if os.path.exists(self.dest): os.remove(self.dest) def test_to_geotiff_dem(self): hdr_path = join(GAMMA_TEST_DIR, 'dem16x20raw.dem.par') hdr = gamma.parse_dem_header(hdr_path) data_path = join(GAMMA_TEST_DIR, 'dem16x20raw.dem') self.dest = os.path.join(TEMPDIR, "tmp_gamma_dem.tif") write_fullres_geotiff(hdr, data_path, self.dest, nodata=0) exp_path = join(GAMMA_TEST_DIR, 'dem16x20_subset_from_gamma.tif') exp_ds = gdal.Open(exp_path) ds = gdal.Open(self.dest) # compare data and geographic headers # HACK: round expected to nearest integer assert_array_almost_equal(np.rint(exp_ds.ReadAsArray()), ds.ReadAsArray()) self.compare_rasters(exp_ds, ds) md = ds.GetMetadata() assert md['AREA_OR_POINT'] == 'Area' def test_to_geotiff_ifg(self): self.dest = os.path.join(TEMPDIR, 'tmp_gamma_ifg.tif') data_path = join(GAMMA_TEST_DIR, '16x20_20090713-20090817_VV_4rlks_utm.unw') write_fullres_geotiff(self.COMBINED, data_path, self.dest, nodata=0) ds = gdal.Open(self.dest) exp_path = join(GAMMA_TEST_DIR, '16x20_20090713-20090817_VV_4rlks_utm.tif') exp_ds = gdal.Open(exp_path) # compare data and geographic headers assert_array_almost_equal(exp_ds.ReadAsArray(), ds.ReadAsArray()) self.compare_rasters(ds, exp_ds) md = ds.GetMetadata() assert len(md) == 32 # 32 metadata items assert md[ifc.FIRST_DATE] == str(date(2009, 7, 13)) assert md[ifc.SECOND_DATE] == str(date(2009, 8, 17)) assert md[ifc.PYRATE_TIME_SPAN] == str(35 / ifc.DAYS_PER_YEAR) # TODO test failing # self.assertTrue(md[ifc.FIRST_DATE] == str(12)) # TODO test failing # self.assertTrue(md[ifc.SECOND_DATE] == str(time(12))) wavelen = float(md[ifc.PYRATE_WAVELENGTH_METRES]) assert wavelen == pytest.approx(0.05627457792190739) def test_to_geotiff_wrong_input_data(self): # use TIF, not UNW for data self.dest = os.path.join(TEMPDIR, 'tmp_gamma_ifg.tif') data_path = join(GAMMA_TEST_DIR, '16x20_20090713-20090817_VV_4rlks_utm.tif') with pytest.raises(GeotiffException): write_fullres_geotiff(self.COMBINED, data_path, self.dest, nodata=0) def compare_rasters(self, ds, exp_ds): band = ds.GetRasterBand(1) exp_band = exp_ds.GetRasterBand(1) nodata = band.GetNoDataValue() assert ~(nodata is None) assert exp_band.GetNoDataValue() == nodata pj = ds.GetProjection() assert 'WGS 84' in pj assert exp_ds.GetProjection() == pj for exp, act in zip(exp_ds.GetGeoTransform(), ds.GetGeoTransform()): assert exp == pytest.approx(act, abs=0.0001) def test_bad_projection(self): hdr = self.DEM_HDR.copy() hdr[ifc.PYRATE_DATUM] = 'nonexistent projection' data_path = join(GAMMA_TEST_DIR, 'dem16x20raw.dem') self.dest = os.path.join(TEMPDIR, 'tmp_gamma_dem2.tif') with pytest.raises(GeotiffException): write_fullres_geotiff(hdr, data_path, self.dest, nodata=0) class TestGammaHeaderListRaiseException: 'Test to make sure PyRate raises exception when IFG header list contains more than two files' def setup_method(self): self.demHeaderString = f'{GAMMA_TEST_DIR}/dem16x20raw.dem.par' self.headerList = [f'{GAMMA_TEST_DIR}/r20090713_VV.slc.par', f'{GAMMA_TEST_DIR}/r20090817_VV.slc.par', f'{GAMMA_TEST_DIR}/r20090713_VV.slc.par', f'{GAMMA_TEST_DIR}/r20090817_VV.slc.par'] def test_exception(self): with pytest.raises(gamma.GammaException): gamma.manage_headers(self.demHeaderString, self.headerList) class TestGammaHeaderParsingTests: 'Tests conversion of GAMMA headers to Py dicts' def test_parse_gamma_epoch_header(self): # minimal required headers are: # date: 2009 7 13 # radar_frequency: 5.3310040e+09 Hz path = join(GAMMA_TEST_DIR, 'r20090713_VV.slc.par') hdrs = gamma.parse_epoch_header(path) exp_date = date(2009, 7, 13) assert hdrs[ifc.FIRST_DATE] == exp_date exp_wavelen = LIGHTSPEED / 5.3310040e+09 assert hdrs[ifc.PYRATE_WAVELENGTH_METRES] == exp_wavelen incidence_angle = 22.9671 assert hdrs[ifc.PYRATE_INCIDENCE_DEGREES] == incidence_angle def test_parse_gamma_dem_header(self): path = join(GAMMA_TEST_DIR, 'dem16x20raw.dem.par') hdrs = gamma.parse_dem_header(path) self.assert_equal(hdrs[ifc.PYRATE_NCOLS], 16) self.assert_equal(hdrs[ifc.PYRATE_NROWS], 20) self.assert_equal(hdrs[ifc.PYRATE_LAT], -33.3831945) self.assert_equal(hdrs[ifc.PYRATE_LONG], 150.3870833) self.assert_equal(hdrs[ifc.PYRATE_X_STEP], 6.9444445e-05) self.assert_equal(hdrs[ifc.PYRATE_Y_STEP], -6.9444445e-05) @staticmethod def assert_equal(arg1, arg2): assert arg1 == arg2 # Test data for the epoch header combination H0 = {ifc.FIRST_DATE: date(2009, 7, 13), ifc.FIRST_TIME: time(12), ifc.PYRATE_WAVELENGTH_METRES: 1.8, ifc.PYRATE_INCIDENCE_DEGREES: 35.565, } H1 = {ifc.FIRST_DATE: date(2009, 8, 17), ifc.FIRST_TIME: time(12, 10, 10), ifc.PYRATE_WAVELENGTH_METRES: 1.8, ifc.PYRATE_INCIDENCE_DEGREES: 35.56, } H1_ERR1 = {ifc.FIRST_DATE: date(2009, 8, 17), ifc.FIRST_TIME: time(12), ifc.PYRATE_WAVELENGTH_METRES: 2.4, ifc.PYRATE_INCIDENCE_DEGREES: 35.56, } H1_ERR2 = {ifc.FIRST_DATE: date(2009, 8, 17), ifc.FIRST_TIME: time(12), ifc.PYRATE_WAVELENGTH_METRES: 1.8, ifc.PYRATE_INCIDENCE_DEGREES: 35.76, } class TestHeaderCombination: 'Tests GAMMA epoch and DEM headers can be combined into a single Py dict' def setup_method(self): self.err = gamma.GammaException dem_hdr_path = join(GAMMA_TEST_DIR, 'dem16x20raw.dem.par') self.dh = gamma.parse_dem_header(dem_hdr_path) base_hdr_path = join(GAMMA_TEST_DIR, '20160114-20160126_base.par') self.bh = gamma.parse_baseline_header(base_hdr_path) @staticmethod def assert_equal(arg1, arg2): assert arg1 == arg2 def test_combine_headers(self): filenames = ['r20090713_VV.slc.par', 'r20090817_VV.slc.par'] paths = [join(GAMMA_TEST_DIR, p) for p in filenames] hdr0, hdr1 = [gamma.parse_epoch_header(p) for p in paths] chdr = gamma.combine_headers(hdr0, hdr1, self.dh, self.bh) exp_timespan = (18 + 17) / ifc.DAYS_PER_YEAR self.assert_equal(chdr[ifc.PYRATE_TIME_SPAN], exp_timespan) exp_date = date(2009, 7, 13) self.assert_equal(chdr[ifc.FIRST_DATE], exp_date) exp_date2 = date(2009, 8, 17) self.assert_equal(chdr[ifc.SECOND_DATE], exp_date2) exp_wavelen = LIGHTSPEED / 5.3310040e+09 self.assert_equal(chdr[ifc.PYRATE_WAVELENGTH_METRES], exp_wavelen) def test_fail_non_dict_header(self): self.assertRaises(gamma.combine_headers, H0, '', self.dh, self.bh) self.assertRaises(gamma.combine_headers, '', H0, self.dh, self.bh) self.assertRaises(gamma.combine_headers, H0, H1, None, self.bh) self.assertRaises(gamma.combine_headers, H0, H1, '', self.bh) def test_fail_mismatching_wavelength(self): self.assertRaises(gamma.combine_headers, H0, H1_ERR1, self.dh, self.bh) def test_fail_mismatching_incidence(self): self.assertRaises(gamma.combine_headers, H0, H1_ERR2, self.dh, self.bh) def test_fail_same_date(self): self.assertRaises(gamma.combine_headers, H0, H0, self.dh, self.bh) def test_fail_bad_date_order(self): with pytest.raises(self.err): gamma.combine_headers(H1, H0, self.dh, self.bh) def assertRaises(self, func, * args): with pytest.raises(self.err): func(* args) ifg_glob_suffix = "*_ifg.tif" coh_glob_suffix = "*_coh.tif" @pytest.fixture(scope='module') def parallel_ifgs(gamma_conf): tdir = Path(tempfile.mkdtemp()) params_p = manipulate_test_conf(gamma_conf, tdir) params_p[C.PARALLEL] = 1 output_conf_file = 'conf.conf' output_conf = tdir.joinpath(output_conf_file) pyrate.configuration.write_config_file(params=params_p, output_conf_file=output_conf) params_p = Configuration(output_conf).__dict__ conv2tif.main(params_p) prepifg.main(params_p) parallel_df = list(Path(params_p[C.INTERFEROGRAM_DIR]).glob(ifg_glob_suffix)) parallel_coh_files = list(Path(params_p[C.COHERENCE_DIR]).glob(coh_glob_suffix)) p_ifgs = small_data_setup(datafiles=parallel_df + parallel_coh_files) yield p_ifgs @pytest.fixture(scope='module') def series_ifgs(gamma_conf): print('======================setup series==========================') tdir = Path(tempfile.mkdtemp()) params_s = manipulate_test_conf(gamma_conf, tdir) params_s[C.PARALLEL] = 0 output_conf_file = 'conf.conf' output_conf = tdir.joinpath(output_conf_file) pyrate.configuration.write_config_file(params=params_s, output_conf_file=output_conf) params_s = Configuration(output_conf).__dict__ conv2tif.main(params_s) prepifg.main(params_s) serial_ifgs = list(Path(params_s[C.INTERFEROGRAM_DIR]).glob(ifg_glob_suffix)) coh_files = list(Path(params_s[C.COHERENCE_DIR]).glob(coh_glob_suffix)) s_ifgs = small_data_setup(datafiles=serial_ifgs + coh_files) yield s_ifgs def test_equality(series_ifgs, parallel_ifgs): for s, p in zip(series_ifgs, parallel_ifgs): np.testing.assert_array_almost_equal(s.phase_data, p.phase_data) def test_meta_data_exists(series_ifgs, parallel_ifgs): for i, (s, p) in enumerate(zip(series_ifgs, parallel_ifgs)): # all metadata equal assert s.meta_data == p.meta_data # test that DATA_TYPE exists in metadata assert ifc.DATA_TYPE in s.meta_data.keys() # test that DATA_TYPE is MULTILOOKED assert (s.meta_data[ifc.DATA_TYPE] == ifc.MULTILOOKED) or \ (s.meta_data[ifc.DATA_TYPE] == ifc.MULTILOOKED_COH) assert i + 1 == 34 class TestGammaBaselineRead: """Tests the reading of initial and precise baselines""" def setup_method(self): init_path = join(GAMMA_TEST_DIR, '20160114-20160126_base_init.par') self.init = gamma.parse_baseline_header(init_path) prec_path = join(GAMMA_TEST_DIR, '20160114-20160126_base.par') self.prec = gamma.parse_baseline_header(prec_path) def test_prec_baseline_read(self): """Test that the Precise baseline values are being read""" exp_i = {'BASELINE_T': -0.0000026, 'BASELINE_C': -103.7427072, 'BASELINE_N': 2.8130731, 'BASELINE_RATE_T': 0.0, 'BASELINE_RATE_C': -0.0173538, 'BASELINE_RATE_N': -0.0055098} exp_p = {'BASELINE_T': 0.0, 'BASELINE_C': -103.8364725, 'BASELINE_N': 2.8055662, 'BASELINE_RATE_T': 0.0, 'BASELINE_RATE_C': -0.0182215, 'BASELINE_RATE_N': -0.0065402} # Precise values are read assert self.prec != exp_i assert self.prec == exp_p # Initial values are ignored assert self.init != exp_i assert self.init != exp_p def test_init_baseline_read(self): """Test that the Initial baseline values are being read""" exp_i = {'BASELINE_T': 0.6529765, 'BASELINE_C': -103.9065694, 'BASELINE_N': 2.9253896, 'BASELINE_RATE_T': 0.0, 'BASELINE_RATE_C': -0.0231786, 'BASELINE_RATE_N': -0.0038703} exp_p = {'BASELINE_T': 0.0, 'BASELINE_C': 0.0, 'BASELINE_N': 0.0, 'BASELINE_RATE_T': 0.0, 'BASELINE_RATE_C': 0.0, 'BASELINE_RATE_N': 0.0} # Precise values are ignored assert self.prec != exp_i assert self.prec != exp_p # Initial values are read assert self.init == exp_i assert self.init != exp_p ================================================ FILE: tests/test_gamma_vs_roipac.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains tests that compare GAMMA and ROI_PAC functionality in PyRate. """ import os import shutil import pytest from pathlib import Path import pyrate.configuration import pyrate.constants as C from pyrate.core.shared import DEM from pyrate.core import ifgconstants as ifc from pyrate.core.prepifg_helper import _is_number from pyrate import prepifg, conv2tif, configuration from tests.common import SML_TEST_DIR, small_data_setup, copytree, TEST_CONF_ROIPAC, TEST_CONF_GAMMA, working_dirs, \ WORKING_DIR SMLNEY_GAMMA_TEST = os.path.join(SML_TEST_DIR, "gamma_obs") def test_files_are_same(tempdir, get_config): roipac_params = get_config(TEST_CONF_ROIPAC) roipac_tdir = Path(tempdir()) roipac_params[WORKING_DIR] = working_dirs[Path(TEST_CONF_ROIPAC).name] roipac_params = __workflow(roipac_params, roipac_tdir) gamma_params = get_config(TEST_CONF_GAMMA) gamma_tdir = Path(tempdir()) gamma_params[WORKING_DIR] = working_dirs[Path(TEST_CONF_GAMMA).name] gamma_params = __workflow(gamma_params, gamma_tdir) # conv2tif output equal __assert_same_files_produced(roipac_params[C.INTERFEROGRAM_DIR], gamma_params[C.INTERFEROGRAM_DIR], "*_unw.tif", 17) # prepifg output equal __assert_same_files_produced(roipac_params[C.INTERFEROGRAM_DIR], gamma_params[C.INTERFEROGRAM_DIR], f"*_ifg.tif", 17) __assert_same_files_produced(roipac_params[C.GEOMETRY_DIR], gamma_params[C.GEOMETRY_DIR], "dem.tif", 1) # clean up shutil.rmtree(roipac_params[WORKING_DIR]) shutil.rmtree(gamma_params[WORKING_DIR]) def __workflow(params, tdir): copytree(params[WORKING_DIR], tdir) # manipulate params outdir = tdir.joinpath('out') outdir.mkdir(exist_ok=True) params[C.OUT_DIR] = outdir.as_posix() params[C.DEM_FILE] = tdir.joinpath(Path(params[C.DEM_FILE]).name).as_posix() params[C.DEM_HEADER_FILE] = tdir.joinpath(Path(params[C.DEM_HEADER_FILE]).name).as_posix() params[C.HDR_FILE_LIST] = tdir.joinpath(Path(params[C.HDR_FILE_LIST]).name).as_posix() params[C.IFG_FILE_LIST] = tdir.joinpath(Path(params[C.IFG_FILE_LIST]).name).as_posix() params[C.TMPDIR] = tdir.joinpath(Path(params[C.TMPDIR]).name).as_posix() output_conf = tdir.joinpath('roipac_temp.conf') pyrate.configuration.write_config_file(params=params, output_conf_file=output_conf) params = configuration.Configuration(output_conf).__dict__ conv2tif.main(params) prepifg.main(params) params[WORKING_DIR] = tdir.as_posix() return params def __assert_same_files_produced(dir1, dir2, ext, num_files): dir1_files = list(Path(dir1).glob(ext)) dir2_files = list(Path(dir2).glob(ext)) dir1_files.sort() dir2_files.sort() # 17 unwrapped geotifs # 17 cropped multilooked tifs + 1 dem assert len(dir1_files) == num_files assert len(dir2_files) == num_files c = 0 all_roipac_ifgs = [f for f in small_data_setup(dir1_files) if not isinstance(f, DEM)] all_gamma_ifgs = [f for f in small_data_setup(dir2_files) if not isinstance(f, DEM)] for c, (i, j) in enumerate(zip(all_roipac_ifgs, all_gamma_ifgs)): mdi = i.meta_data mdj = j.meta_data for k in mdi: # all key values equal if k == "INCIDENCE_DEGREES": pass # incidence angle not implemented for roipac elif _is_number(mdi[k]): assert pytest.approx(float(mdj[k]), 0.00001) == float(mdi[k]) elif mdi[k] == "ROIPAC" or "GAMMA": pass # INSAR_PROCESSOR can not be equal else: assert mdj[k] == mdi[k] if i.data_path.__contains__("_ifg.tif"): # these are multilooked tifs # test that DATA_STEP is MULTILOOKED assert mdi[ifc.DATA_TYPE] == ifc.MULTILOOKED assert mdj[ifc.DATA_TYPE] == ifc.MULTILOOKED else: assert mdi[ifc.DATA_TYPE] == ifc.ORIG assert mdj[ifc.DATA_TYPE] == ifc.ORIG if not all_gamma_ifgs: # checking for dem return assert c + 1 == len(all_gamma_ifgs) ================================================ FILE: tests/test_gdal_python.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains tests for the gdal_python.py PyRate module. """ import os import shutil import subprocess import tempfile from copy import copy from pathlib import Path import numpy as np from osgeo import gdal, gdalconst, osr import pyrate.core from pyrate import constants as c, conv2tif from pyrate.configuration import MultiplePaths from pyrate.core import gdal_python, ifgconstants as ifc from pyrate.core.shared import Ifg from tests import common class TestResample(common.UnitTestAdaptation): def test_small_data_resampling(self): small_test_ifgs = common.small_data_setup() # minX, minY, maxX, maxY = extents extents = [150.91, -34.229999976, 150.949166651, -34.17] extents_str = [str(e) for e in extents] resolutions = [0.001666666, .001, 0.002, 0.0025, .01] for res in resolutions: res = [res, -res] self.check_same_resampled_output(extents, extents_str, res, small_test_ifgs) def check_same_resampled_output(self, extents, extents_str, res, small_test_ifgs): cmd = ['gdalwarp', '-overwrite', '-srcnodata', 'None', '-q', '-r', 'near', '-te'] \ + extents_str if res[0]: new_res_str = [str(r) for r in res] cmd += ['-tr'] + new_res_str for s in small_test_ifgs: temp_tif = tempfile.mktemp(suffix='.tif') t_cmd = cmd + [s.data_path, temp_tif] subprocess.check_call(t_cmd) resampled_ds = gdal.Open(temp_tif) resampled_ref = resampled_ds.ReadAsArray() resampled_temp_tif = tempfile.mktemp(suffix='.tif', prefix='resampled_') resampled = gdal_python.resample_nearest_neighbour(s.data_path, extents, res, resampled_temp_tif) np.testing.assert_array_almost_equal(resampled_ref, resampled[0, :, :]) try: os.remove(temp_tif) except PermissionError: print("File opened by another process.") try: os.remove(resampled_temp_tif) # also proves file was written except PermissionError: print("File opened by another process.") def test_none_resolution_output(self): small_test_ifgs = common.small_data_setup() # minX, minY, maxX, maxY = extents extents = [150.91, -34.229999976, 150.949166651, -34.17] extents_str = [str(e) for e in extents] self.check_same_resampled_output(extents, extents_str, [None, None], small_test_ifgs) def test_output_file_written(self): small_test_ifgs = common.small_data_setup() extents = [150.91, -34.229999976, 150.949166651, -34.17] resolutions = [0.001666666, .001, 0.002, 0.0025, .01] for res in resolutions: for s in small_test_ifgs: resampled_temp_tif = tempfile.mktemp(suffix='.tif', prefix='resampled_') gdal_python.resample_nearest_neighbour(s.data_path, extents, [res, -res], resampled_temp_tif) self.assertTrue(os.path.exists(resampled_temp_tif)) os.remove(resampled_temp_tif) def test_resampled_tif_has_metadata(self): small_test_ifgs = common.small_data_setup() # minX, minY, maxX, maxY = extents extents = [150.91, -34.229999976, 150.949166651, -34.17] for s in small_test_ifgs: resampled_temp_tif = tempfile.mktemp(suffix='.tif', prefix='resampled_') gdal_python.resample_nearest_neighbour( s.data_path, extents, [None, None], resampled_temp_tif) dst_ds = gdal.Open(resampled_temp_tif) md = dst_ds.GetMetadata() self.assertDictEqual(md, s.meta_data) try: os.remove(resampled_temp_tif) except PermissionError: print("File opened by another process.") class TestBasicReampleTests(common.UnitTestAdaptation): def test_reproject_with_no_data(self): data = np.array([[2, 7], [2, 7]]) src_ds = gdal.GetDriverByName('MEM').Create('', 2, 2) src_ds.GetRasterBand(1).WriteArray(data) src_ds.GetRasterBand(1).SetNoDataValue(2) src_ds.SetGeoTransform([10, 1, 0, 10, 0, -1]) dst_ds = gdal.GetDriverByName('MEM').Create('', 1, 1) dst_ds.GetRasterBand(1).SetNoDataValue(3) dst_ds.GetRasterBand(1).Fill(3) dst_ds.SetGeoTransform([10, 2, 0, 10, 0, -2]) gdal.ReprojectImage(src_ds, dst_ds, '', '', gdal.GRA_NearestNeighbour) got_data = dst_ds.GetRasterBand(1).ReadAsArray() expected_data = np.array([[7]]) np.testing.assert_array_equal(got_data, expected_data) def test_reproject_with_no_data_2(self): data = np.array([[2, 7, 7, 7], [2, 7, 7, 2]]) height, width = data.shape src_ds = gdal.GetDriverByName('MEM').Create('', width, height) src_ds.GetRasterBand(1).WriteArray(data) src_ds.GetRasterBand(1).SetNoDataValue(2) src_ds.SetGeoTransform([10, 1, 0, 10, 0, -1]) dst_ds = gdal.GetDriverByName('MEM').Create('', 2, 1) dst_ds.GetRasterBand(1).SetNoDataValue(3) dst_ds.GetRasterBand(1).Fill(3) dst_ds.SetGeoTransform([10, 2, 0, 10, 0, -2]) gdal.ReprojectImage(src_ds, dst_ds, '', '', gdal.GRA_NearestNeighbour) got_data = dst_ds.GetRasterBand(1).ReadAsArray() expected_data = np.array([[7, 3]]) np.testing.assert_array_equal(got_data, expected_data) def test_reproject_with_no_data_3(self): data = np.array([[2, 7, 7, 7], [2, 7, 7, 7], [2, 7, 7, 7], [2, 7, 7, 2], [2, 7, 7, 2]]) src_ds = gdal.GetDriverByName('MEM').Create('', 4, 5) src_ds.GetRasterBand(1).WriteArray(data) src_ds.GetRasterBand(1).SetNoDataValue(2) src_ds.SetGeoTransform([10, 1, 0, 10, 0, -1]) dst_ds = gdal.GetDriverByName('MEM').Create('', 2, 2) dst_ds.GetRasterBand(1).SetNoDataValue(3) dst_ds.GetRasterBand(1).Fill(3) dst_ds.SetGeoTransform([10, 2, 0, 10, 0, -2]) gdal.ReprojectImage(src_ds, dst_ds, '', '', gdal.GRA_NearestNeighbour) got_data = dst_ds.GetRasterBand(1).ReadAsArray() expected_data = np.array([[7, 7], [7, 3]]) np.testing.assert_array_equal(got_data, expected_data) def test_reproject_with_no_data_4(self): data = np.array([[2, 7, 7, 7, 2], [2, 7, 7, 7, 2], [2, 7, 7, 7, 2], [2, 7, 7, 2, 2], [2, 7, 7, 2, 2]]) src_ds = gdal.GetDriverByName('MEM').Create('', 5, 5) src_ds.GetRasterBand(1).WriteArray(data) src_ds.GetRasterBand(1).SetNoDataValue(2) src_ds.SetGeoTransform([10, 1, 0, 10, 0, -1]) dst_ds = gdal.GetDriverByName('MEM').Create('', 2, 2) dst_ds.GetRasterBand(1).SetNoDataValue(3) dst_ds.GetRasterBand(1).Fill(3) dst_ds.SetGeoTransform([10, 2, 0, 10, 0, -2]) gdal.ReprojectImage(src_ds, dst_ds, '', '', gdal.GRA_NearestNeighbour) got_data = dst_ds.GetRasterBand(1).ReadAsArray() expected_data = np.array([[7, 7], [7, 3]]) np.testing.assert_array_equal(got_data, expected_data) def test_reproject_with_no_data_5(self): data = np.array([[2, 7, 7, 7, 2], [2, 7, 7, 7, 2], [2, 7, 7, 7, 2], [2, 7, 7, 2, 2], [2, 7, 7, 2, 2], [2, 7, 7, 2, 2]]) src_ds = gdal.GetDriverByName('MEM').Create('', 5, 6) src_ds.GetRasterBand(1).WriteArray(data) src_ds.GetRasterBand(1).SetNoDataValue(2) src_ds.SetGeoTransform([10, 1, 0, 10, 0, -1]) dst_ds = gdal.GetDriverByName('MEM').Create('', 2, 3) dst_ds.GetRasterBand(1).SetNoDataValue(3) dst_ds.GetRasterBand(1).Fill(3) dst_ds.SetGeoTransform([10, 2, 0, 10, 0, -2]) gdal.ReprojectImage(src_ds, dst_ds, '', '', gdal.GRA_NearestNeighbour) got_data = dst_ds.GetRasterBand(1).ReadAsArray() expected_data = np.array([[7, 7], [7, 3], [7, 3]]) np.testing.assert_array_equal(got_data, expected_data) def test_reproject_average_resampling(self): data = np.array([[4, 7, 7, 7, 2, 7.], [4, 7, 7, 7, 2, 7.], [4, 7, 7, 7, 2, 7.], [4, 7, 7, 2, 2, 7.], [4, 7, 7, 2, 2, 7.], [4, 7, 7, 10, 2, 7.]], dtype=np.float32) src_ds = gdal.GetDriverByName('MEM').Create('', 6, 6, 1, gdalconst.GDT_Float32) src_ds.GetRasterBand(1).WriteArray(data) src_ds.GetRasterBand(1).SetNoDataValue(2) src_ds.SetGeoTransform([10, 1, 0, 10, 0, -1]) dst_ds = gdal.GetDriverByName('MEM').Create('', 3, 3, 1, gdalconst.GDT_Float32) dst_ds.GetRasterBand(1).SetNoDataValue(3) dst_ds.GetRasterBand(1).Fill(3) dst_ds.SetGeoTransform([10, 2, 0, 10, 0, -2]) gdal.ReprojectImage(src_ds, dst_ds, '', '', gdal.GRA_Average) got_data = dst_ds.GetRasterBand(1).ReadAsArray() expected_data = np.array([[5.5, 7, 7], [5.5, 7, 7], [5.5, 8, 7]]) np.testing.assert_array_equal(got_data, expected_data) def test_reproject_average_resampling_with_2bands(self): data = np.array([[[4, 7, 7, 7, 2, 7.], [4, 7, 7, 7, 2, 7.], [4, 7, 7, 7, 2, 7.], [4, 7, 7, 2, 2, 7.], [4, 7, 7, 2, 2, 7.], [4, 7, 7, 10, 2, 7.]], [[2, 0, 0, 0, 0, 0.], [2, 0, 0, 0, 0, 2.], [0, 1., 0, 0, 0, 1.], [0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0.], [0, 0, 0, 0, 0, 0.]]], dtype=np.float32) src_ds = gdal.GetDriverByName('MEM').Create('', 6, 6, 2, gdalconst.GDT_Float32) src_ds.GetRasterBand(1).WriteArray(data[0, :, :]) src_ds.GetRasterBand(1).SetNoDataValue(2) src_ds.GetRasterBand(2).WriteArray(data[1, :, :]) # src_ds.GetRasterBand(1).SetNoDataValue() src_ds.SetGeoTransform([10, 1, 0, 10, 0, -1]) dst_ds = gdal.GetDriverByName('MEM').Create('', 3, 3, 2, gdalconst.GDT_Float32) dst_ds.GetRasterBand(1).SetNoDataValue(3) dst_ds.GetRasterBand(1).Fill(3) dst_ds.SetGeoTransform([10, 2, 0, 10, 0, -2]) gdal.ReprojectImage(src_ds, dst_ds, '', '', gdal.GRA_Average) got_data = dst_ds.GetRasterBand(1).ReadAsArray() expected_data = np.array([[5.5, 7, 7], [5.5, 7, 7], [5.5, 8, 7]]) np.testing.assert_array_equal(got_data, expected_data) band2 = dst_ds.GetRasterBand(2).ReadAsArray() np.testing.assert_array_equal(band2, np.array([[1., 0., 0.5], [0.25, 0., 0.75], [0., 0., 0.]])) class TestMEMVsGTiff(common.UnitTestAdaptation): @staticmethod def check(driver_type): temp_tif = tempfile.mktemp(suffix='.tif') data = np.array([[[4, 7, 7, 7, 2, 7.], [4, 7, 7, 7, 2, 7.], [4, 7, 7, 7, 2, 7.], [4, 7, 7, 2, 2, 7.], [4, 7, 7, 2, 2, 7.], [4, 7, 7, 10, 2, 7.]], [[2, 0, 0, 0, 0, 0.], [2, 0, 0, 0, 0, 2.], [0, 1., 0, 0, 0, 1.], [0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0.], [0, 0, 0, 0, 0, 0.]]], dtype=np.float32) src_ds = gdal.GetDriverByName(driver_type).Create(temp_tif, 6, 6, 2, gdalconst.GDT_Float32) src_ds.GetRasterBand(1).WriteArray(data[0, :, :]) src_ds.GetRasterBand(1).SetNoDataValue(2) src_ds.GetRasterBand(2).WriteArray(data[1, :, :]) src_ds.GetRasterBand(2).SetNoDataValue(3) src_ds.SetGeoTransform([10, 1, 0, 10, 0, -1]) src_ds.FlushCache() dst_ds = gdal.GetDriverByName('MEM').Create('', 3, 3, 2, gdalconst.GDT_Float32) dst_ds.GetRasterBand(1).SetNoDataValue(3) dst_ds.GetRasterBand(1).Fill(3) dst_ds.SetGeoTransform([10, 2, 0, 10, 0, -2]) gdal.ReprojectImage(src_ds, dst_ds, '', '', gdal.GRA_Average) band1 = dst_ds.GetRasterBand(1).ReadAsArray() np.testing.assert_array_equal(band1, np.array([[5.5, 7, 7], [5.5, 7, 7], [5.5, 8, 7]])) band2 = dst_ds.GetRasterBand(2).ReadAsArray() np.testing.assert_array_equal(band2, np.array([[1., 0., 0.5], [0.25, 0., 0.75], [0., 0., 0.]])) if os.path.exists(temp_tif): try: os.remove(temp_tif) except PermissionError: print("File opened by another process.") def test_mem(self): self.check('MEM') def test_gtiff(self): self.check('GTiff') def test_coherence_files_not_converted(): # define constants NO_DATA_VALUE = 0 driver = gdal.GetDriverByName('GTiff') # create a sample gdal dataset # sample gdal dataset sample_gdal_filename = "dataset_01122000.tif" options = ['PROFILE=GeoTIFF'] sample_gdal_dataset = driver.Create(sample_gdal_filename, 5, 5, 1, gdal.GDT_Float32, options=options) srs = osr.SpatialReference() wkt_projection = srs.ExportToWkt() sample_gdal_dataset.SetProjection(wkt_projection) sample_gdal_band = sample_gdal_dataset.GetRasterBand(1) sample_gdal_band.SetNoDataValue(NO_DATA_VALUE) sample_gdal_band.WriteArray(np.arange(25).reshape(5, 5)) sample_gdal_dataset.SetMetadataItem(ifc.FIRST_DATE, '2019-10-20') sample_gdal_dataset.SetMetadataItem(ifc.SECOND_DATE, '2019-11-01') sample_gdal_dataset.SetMetadataItem(ifc.PYRATE_WAVELENGTH_METRES, '10.05656') sample_gdal_dataset.FlushCache() sample_gdal_dataset = None ifg = Ifg(sample_gdal_filename) ifg.open() # create a coherence mask dataset tmpdir = tempfile.mkdtemp() out_dir = Path(tmpdir) # we won't be creating any output coherence mask files as there are already GeoTIFFs params = common.min_params(out_dir) coherence_mask_filename = MultiplePaths(Path("mask_dataset_01122000-02122000.tif").as_posix(), params) coherence_mask_dataset = driver.Create(coherence_mask_filename.converted_path, 5, 5, 1, gdal.GDT_Float32) srs = osr.SpatialReference() wkt_projection = srs.ExportToWkt() coherence_mask_dataset.SetProjection(wkt_projection) coherence_mask_band = coherence_mask_dataset.GetRasterBand(1) coherence_mask_band.SetNoDataValue(NO_DATA_VALUE) arr = np.arange(0, 75, 3).reshape(5, 5) / 100.0 arr[3, 4] = 0.25 # insert some random lower than threshold number arr[4, 2] = 0.20 # insert some random lower than threshold number coherence_mask_band.WriteArray(arr) # del the tmp handler datasets created del coherence_mask_dataset # create an artificial masked dataset expected_result_array = np.nan_to_num( np.array( [ [np.nan, np.nan, np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan, np.nan, np.nan], [10.0, 11.0, 12.0, 13.0, 14.0], [15.0, 16.0, 17.0, 18.0, np.nan], [20.0, 21.0, np.nan, 23.0, 24.0], ] ) ) # use the gdal_python.coherence_masking to find the actual mask dataset coherence_thresh = 0.3 gdal_python.coherence_masking(ifg.dataset, coherence_mask_filename.converted_path, coherence_thresh) sample_gdal_array = np.nan_to_num(ifg.phase_data) # compare the artificial masked and actual masked datasets np.testing.assert_array_equal(sample_gdal_array, expected_result_array) # del the tmp datasets created os.remove(coherence_mask_filename.converted_path) ifg.close() os.remove(sample_gdal_filename) def test_small_data_coherence(gamma_or_mexicoa_conf): work_dir = Path(tempfile.mkdtemp()) params = common.manipulate_test_conf(conf_file=gamma_or_mexicoa_conf, work_dir=work_dir) params[c.COH_MASK] = 1 ifg_multilist = copy(params[c.INTERFEROGRAM_FILES]) conv2tif.main(params) for i in ifg_multilist: p = Path(i.converted_path) p.chmod(0o664) # assign write permission as conv2tif output is readonly ifg = pyrate.core.shared.dem_or_ifg(data_path=p.as_posix()) if not isinstance(ifg, Ifg): continue ifg.open() # now do coherence masking and compare ifg = pyrate.core.shared.dem_or_ifg(data_path=p.as_posix()) ifg.open() converted_coh_file_path = pyrate.core.prepifg_helper.coherence_paths_for(p, params, tif=True) gdal_python.coherence_masking(ifg.dataset, coh_file_path=converted_coh_file_path, coh_thr=params[c.COH_THRESH] ) nans = np.isnan(ifg.phase_data) coherence_path = pyrate.core.prepifg_helper.coherence_paths_for(p, params, tif=True) cifg = Ifg(coherence_path) cifg.open() cifg_below_thrhold = cifg.phase_data < params[c.COH_THRESH] np.testing.assert_array_equal(nans, cifg_below_thrhold) shutil.rmtree(work_dir) ================================================ FILE: tests/test_geometry.py ================================================ import shutil from typing import Tuple import numpy as np from os.path import join import pytest import pyrate.constants as C from pyrate.core import ifgconstants as ifc from pyrate.core.geometry import get_lonlat_coords, get_sat_positions, vincinv from pyrate.core.refpixel import convert_pixel_value_to_geographic_coordinate from tests import common from pyrate.configuration import Configuration from subprocess import run, PIPE from pyrate import prepifg, correct from pyrate.core.shared import Ifg, Geometry def get_lonlat_coords_slow(ifg: Ifg) -> Tuple[np.ndarray, np.ndarray]: """ Function to get longitude and latitude coordinates for each pixel in the multi-looked. interferogram dataset. Coordinates are identical for each interferogram in the stack. :param ifg: pyrate.core.shared.Ifg Class object. :return: lon: Longitude for each pixel (decimal degrees) :return: lat: Latitude for each pixel (decimal degrees) """ # assume all interferograms have same projection and will share the same transform transform = ifg.dataset.GetGeoTransform() # number of rows and columns in dataset nrows, ncols = ifg.shape lon = np.zeros((nrows, ncols)) # pre-allocate 2D numpy array lat = np.zeros((nrows, ncols)) # pre-allocate 2D numpy array for i in range(0, nrows): # rows are y-direction for j in range(0, ncols): # cols are x-direction lon[i, j], lat[i, j] = convert_pixel_value_to_geographic_coordinate(j, i, transform) return lon, lat def test_get_lonlat_coords_vectorised(dem): lon, lat = get_lonlat_coords_slow(dem) lon_v, lat_v = get_lonlat_coords(dem) np.testing.assert_array_almost_equal(lon, lon_v.data) np.testing.assert_array_almost_equal(lat, lat_v.data) @pytest.fixture(params=[(29, 50, -2.94634866714478), (94, 58, -2.94684600830078)]) def point_azimuth(request): return request.param @pytest.fixture(params=[(29, 50, 1.02217936515808), (94, 58, 1.0111095905304)]) def point_incidence(request): return request.param class TestPyRateAngleFiles: @classmethod def setup_class(cls): cls.params = Configuration(common.MEXICO_CROPA_CONF).__dict__ # run prepifg prepifg.main(cls.params) # copy IFGs to temp folder correct._copy_mlooked(cls.params) @classmethod def teardown_class(cls): shutil.rmtree(cls.params[C.OUT_DIR], ignore_errors=True) # the xy position in the original GAMMA geometry (before cropping and further multi-looking is required for # comparison of PyRate with GAMMA angle values. To get this position for a particular coordinate, the following # commands can be applied on Gadi: # cd /g/data/dg9/INSAR_ANALYSIS/MEXICO/S1/UNIT-TEST-DATA/CROP-A/GEOTIFF # gdallocationinfo -wgs84 cropA_20180106-20180130_VV_8rlks_eqa_unw.tif -99.06 19.37 # -> outputs the cropped pixel coordinates: (94P,58L) -> x0 = 94, y0 = 58 # cd /g/data/dg9/INSAR_ANALYSIS/MEXICO/S1/GAMMA/T005A/geotiff_files/unw_ifg_geotiffs/ # gdallocationinfo -wgs84 20180106-20180130_VV_8rlks_eqa_unw.tif -99.06 19.37 # -> outputs the original pixel coordinates: (940P,3271L) # cd /g/data/dg9/INSAR_ANALYSIS/MEXICO/S1/GAMMA/T005A/DEM/ # gdallocationinfo 20180106_VV_8rlks_eqa_lv_phi.tif 940 3271 # -> outputs the GAMMA azimuth angle: -2.94684600830078 # gdallocationinfo 20180106_VV_8rlks_eqa_lv_theta.tif 940 3271 # > outputs the GAMMA incidence angle: 1.0111095905304 def get_pyrate_angle(self, x0, y0, tif_file): """ Get angle at particular pixel in the azimuth/incidence tif file """ # get azimuth angle value of PyRate file azimuth_angle.tif temp = run(['gdallocationinfo', tif_file, str(x0), str(y0)], universal_newlines=True, stdout=PIPE) out = temp.stdout angle_value = float(out.split('Value:')[1].strip('\n')) return angle_value def test_pyrate_azimuth_matches_gamma_azimuth(self, point_azimuth): x0, y0, exp = point_azimuth # azimuth angle validation value extracted from GAMMA azimuth file for pixel location corresponding # to (29,50) using gdallocationinfo 20180106_VV_8rlks_eqa_lv_phi.tif 613 3228 # exp = -2.94634866714478 # GAMMMA azimuth angle at pixel location # azimuth angle validation value extracted from GAMMA azimuth file for pixel location corresponding # to (94,58) using gdallocationinfo 20180106_VV_8rlks_eqa_lv_phi.tif 940 3271 # exp = -2.94684600830078 # GAMMA azimuth is defined towards satellite in an anti-clockwise angle system, with East being zero # PyRate azimuth angle is defined towards the satellite in a clockwise angle system with North being zero tif_file = join(self.params[C.GEOMETRY_DIR], 'azimuth_angle.tif') azimuth_angle_pyrate = self.get_pyrate_angle(x0, y0, tif_file) # convert PyRate azimuth into GAMMA azimuth res = -(azimuth_angle_pyrate - np.pi / 2) np.testing.assert_array_almost_equal(exp, res, decimal=2) # max difference < 0.01 rad # screen output of difference if need be: # print(exp - res) def test_pyrate_incidence_matches_gamma_incidence(self, point_incidence): x0, y0, exp = point_incidence # incidence angle validation value extracted from GAMMA incidence file for pixel location corresponding # to (29,50) using gdallocationinfo 20180106_VV_8rlks_eqa_lv_theta.tif 613 3228 # exp = 1.02217936515808 # incidence angle validation value extracted from GAMMA incidence file for pixel location corresponding # to (94,58) using gdallocationinfo 20180106_VV_8rlks_eqa_lv_theta.tif 940 3271 # exp = 1.0111095905304 # GAMMA angle is defined from the horizontal plane with the zenith direction being pi / 2 radians (i.e.90 deg) # PyRate angle is defined from the vertical axis with the zenith direction being zero tif_file = join(self.params[C.GEOMETRY_DIR], 'incidence_angle.tif') incidence_angle_pyrate = self.get_pyrate_angle(x0, y0, tif_file) # convert PyRate incidence into GAMMA incidence res = np.pi / 2 - incidence_angle_pyrate # convert PyRate incidence into GAMMA incidence np.testing.assert_array_almost_equal(exp, res, decimal=3) # max difference < 0.001 rad # screen output of difference if need be: # print(exp - res) def test_azimuth_angle_calculation(self): """ Calculate local azimuth angle using a spherical model and compare to result using Vincenty's equations """ # get first IFG in stack to calculate lon/lat values multi_paths = self.params[C.INTERFEROGRAM_FILES] tmp_paths = [ifg_path.tmp_sampled_path for ifg_path in multi_paths] # keep only ifg files in path list (i.e. remove coherence and dem files) ifg_paths = [item for item in tmp_paths if 'ifg.tif' in item] # read and open the first IFG in list ifg0_path = ifg_paths[0] ifg0 = Ifg(ifg0_path) ifg0.open(readonly=True) lon, lat = get_lonlat_coords(ifg0) # read incidence and look angle files tif_file = join(self.params[C.GEOMETRY_DIR], 'incidence_angle.tif') geom = Geometry(tif_file) incidence_angle = geom.data tif_file = join(self.params[C.GEOMETRY_DIR], 'look_angle.tif') geom = Geometry(tif_file) look_angle = geom.data # get metadata a = float(ifg0.meta_data[ifc.PYRATE_SEMI_MAJOR_AXIS_METRES]) b = float(ifg0.meta_data[ifc.PYRATE_SEMI_MINOR_AXIS_METRES]) heading = float(ifg0.meta_data[ifc.PYRATE_HEADING_DEGREES]) azimuth = float(ifg0.meta_data[ifc.PYRATE_AZIMUTH_DEGREES]) # convert all angles from deg to radians lon = np.radians(lon.data) lat = np.radians(lat.data) heading = np.radians(heading) azimuth = np.radians(azimuth) # calculate satellite positions sat_lat, sat_lon = get_sat_positions(lat, lon, look_angle, incidence_angle, heading, azimuth) # calculate azimuth angle from pixel to satellite using spherical trigonometry # see Eq. 86 on page 4-11 in EARTH-REFERENCED AIRCRAFT NAVIGATION AND SURVEILLANCE ANALYSIS # (https://ntlrepository.blob.core.windows.net/lib/59000/59300/59358/DOT-VNTSC-FAA-16-12.pdf) azimuth_angle_spherical = np.arctan2(np.cos(sat_lat) * np.sin(sat_lon - lon), \ np.sin(sat_lat) * np.cos(lat) - np.cos(sat_lat) * np.sin(lat) * \ np.cos(sat_lon - lon)) # add 2 pi in case an angle is below zero for azi in np.nditer(azimuth_angle_spherical, op_flags=['readwrite']): if azi < 0: azi[...] = azi + 2 * np.pi azimuth_angle_elliposidal = vincinv(lat, lon, sat_lat, sat_lon, a, b) # the difference between Vincenty's azimuth calculation and the spherical approximation is ~0.001 radians azimuth_angle_diff = azimuth_angle_spherical - azimuth_angle_elliposidal # max difference < 0.01 rad np.testing.assert_array_almost_equal(azimuth_angle_spherical, azimuth_angle_elliposidal, decimal=2) ================================================ FILE: tests/test_merge.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains tests for the Merge step of PyRate. """ import shutil import os from subprocess import check_call import itertools import tempfile import pytest from pathlib import Path import numpy as np import pyrate.constants as C import pyrate.core.ifgconstants as ifc from pyrate.merge import create_png_and_kml_from_tif, los_projection_out_types from pyrate.merge import _merge_stack, _merge_linrate, _merge_timeseries, los_projection_divisors from pyrate.core.shared import DEM from pyrate.core.ifgconstants import LOS_PROJECTION_OPTION from pyrate.configuration import Configuration, write_config_file from tests.common import manipulate_test_conf, PY37GDAL302, MEXICO_CROPA_CONF, assert_same_files_produced @pytest.fixture(params=list(LOS_PROJECTION_OPTION.keys())) def los_projection(request): return request.param @pytest.fixture(params=[-1, 1]) def signal_polarity(request): return request.param def test_los_conversion_divisors(): """ Unit test to check the LOS conversions for specific incidence angles """ inc = [0, 30, 45, 90] # incidence angles in degrees # Test pseudo-vertical res = [los_projection_divisors[ifc.PSEUDO_VERTICAL](np.radians(x)) for x in inc] exp = [1.0, 0.8660254037844387, 0.7071067811865476, 6.123233995736766e-17] np.testing.assert_almost_equal(res, exp, decimal=6) # Test pseudo-horizontal res = [los_projection_divisors[ifc.PSEUDO_HORIZONTAL](np.radians(x)) for x in inc] exp = [0.0, 0.49999999999999994, 0.7071067811865475, 1.0] np.testing.assert_almost_equal(res, exp, decimal=6) # Test line-of-sight res = [los_projection_divisors[ifc.LINE_OF_SIGHT](np.radians(x)) for x in inc] exp = [1.0, 1.0, 1.0, 1.0] np.testing.assert_almost_equal(res, exp, decimal=1) @pytest.mark.mpi @pytest.mark.slow @pytest.mark.skipif(not PY37GDAL302, reason="Only run in one CI env") class TestLOSConversion: @classmethod def setup_class(cls): tdir = Path(tempfile.mkdtemp()) params = manipulate_test_conf(MEXICO_CROPA_CONF, tdir) output_conf_file = tdir.joinpath('conf.cfg') output_conf = tdir.joinpath(output_conf_file) write_config_file(params=params, output_conf_file=output_conf) check_call(f"mpirun -n 3 pyrate conv2tif -f {output_conf}", shell=True) check_call(f"mpirun -n 3 pyrate prepifg -f {output_conf}", shell=True) check_call(f"mpirun -n 3 pyrate correct -f {output_conf}", shell=True) check_call(f"mpirun -n 3 pyrate timeseries -f {output_conf}", shell=True) check_call(f"mpirun -n 3 pyrate stack -f {output_conf}", shell=True) params = Configuration(output_conf).__dict__ cls.params = params cls.tdir = tdir @classmethod def teardown_class(cls): shutil.rmtree(cls.tdir, ignore_errors=True) def test_los_conversion_comparison(self): """ compare outputs in each of the los projection types compares sine and cosine components are larger than LOS component assert metadata equal except """ params = self.params all_dirs = {} params[C.SIGNAL_POLARITY] = -1 for k in LOS_PROJECTION_OPTION.keys(): params[C.LOS_PROJECTION] = k k_dir = Path(params[C.OUT_DIR]).joinpath(ifc.LOS_PROJECTION_OPTION[k]) k_dir.mkdir(exist_ok=True) all_dirs[k] = k_dir self.run_with_new_params(k_dir, params) signal_dir = Path(params[C.OUT_DIR]).joinpath('signal_polarity_dir') signal_dir.mkdir(exist_ok=True) all_dirs[C.SIGNAL_POLARITY] = signal_dir params[C.SIGNAL_POLARITY] = 1 self.run_with_new_params(signal_dir, params) los_proj_dir = all_dirs[ifc.LINE_OF_SIGHT] pseudo_ver = all_dirs[ifc.PSEUDO_VERTICAL] pseudo_hor = all_dirs[ifc.PSEUDO_HORIZONTAL] num_files = 24 if params[C.PHASE_CLOSURE] else 26 # phase closure removes 1 interferogram assert len(list(los_proj_dir.glob('*.tif'))) == num_files # 12 tsincr, 12 tscuml + 1 stack rate + 1 linear rate signal_polarity_reversed_pseudo_hor = all_dirs[C.SIGNAL_POLARITY] for tif in los_proj_dir.glob('*.tif'): ds = DEM(tif) ds_ver = DEM(pseudo_ver.joinpath(tif.name)) ds_hor = DEM(pseudo_hor.joinpath(tif.name)) ds_hor_sig = DEM(signal_polarity_reversed_pseudo_hor.joinpath(tif.name)) ds.open() ds_ver.open() ds_hor.open() ds_hor_sig.open() non_nans_indices = ~np.isnan(ds.data) # assert division by sine and cosine always yields larger components in vertical and horizontal directions assert np.all(np.abs(ds.data[non_nans_indices]) <= np.abs(ds_ver.data[non_nans_indices])) assert np.all(np.abs(ds.data[non_nans_indices]) <= np.abs(ds_hor.data[non_nans_indices])) assert np.all(np.abs(ds.data[non_nans_indices]) <= np.abs(ds_hor_sig.data[non_nans_indices])) assert np.all(ds_hor.data[non_nans_indices] == -ds_hor_sig.data[non_nans_indices]) ds_md = ds.dataset.GetMetadata() assert ds_md.pop(C.LOS_PROJECTION.upper()) == ifc.LOS_PROJECTION_OPTION[ifc.LINE_OF_SIGHT] ds_ver_md = ds_ver.dataset.GetMetadata() assert ds_ver_md.pop(C.LOS_PROJECTION.upper()) == ifc.LOS_PROJECTION_OPTION[ifc.PSEUDO_VERTICAL] assert ds_md == ds_ver_md assert ds_md.pop(C.SIGNAL_POLARITY.upper()) == '-1' ds_hor_md = ds_hor.dataset.GetMetadata() ds_hor_sig_md = ds_hor_sig.dataset.GetMetadata() assert ds_hor_sig_md.pop(C.SIGNAL_POLARITY.upper()) != ds_hor_md.pop(C.SIGNAL_POLARITY.upper()) assert ds_hor_sig_md == ds_hor_md assert ds_hor_md.pop(C.LOS_PROJECTION.upper()) == ifc.LOS_PROJECTION_OPTION[ifc.PSEUDO_HORIZONTAL] assert ds_hor_sig_md.pop(C.LOS_PROJECTION.upper()) == ifc.LOS_PROJECTION_OPTION[ifc.PSEUDO_HORIZONTAL] assert ds_md == ds_hor_md assert ds_md == ds_hor_sig_md def run_with_new_params(self, k_dir, params): _merge_stack(params) _merge_linrate(params) _merge_timeseries(params, 'tscuml') _merge_timeseries(params, 'tsincr') for out_type in los_projection_out_types: for tif in itertools.chain(Path(params[C.VELOCITY_DIR]).glob(out_type + '*.tif'), Path(params[C.TIMESERIES_DIR]).glob(out_type + '*.tif')): shutil.move(tif, k_dir.joinpath(tif.name)) def test_file_creation(self, los_projection): params = self.params params[C.LOS_PROJECTION] = los_projection _merge_stack(params) _merge_linrate(params) _merge_timeseries(params, 'tscuml') _merge_timeseries(params, 'tsincr') # check if color map is created for ot in ['stack_rate', 'stack_error', 'linear_rate', 'linear_error', 'linear_rsquared']: create_png_and_kml_from_tif(params[C.VELOCITY_DIR], output_type=ot) output_color_map_path = os.path.join(params[C.VELOCITY_DIR], f"colourmap_{ot}.txt") assert Path(output_color_map_path).exists(), "Output color map file not found at: " + output_color_map_path # check if merged files are created for _type, ot in itertools.product(['stack_rate', 'stack_error', 'linear_rate', 'linear_error', 'linear_rsquared'], ['.tif', '.png', '.kml']): output_image_path = os.path.join(params[C.VELOCITY_DIR], _type + ot) print(f"checking {output_image_path}") assert Path(output_image_path).exists(), f"Output {ot} file not found at {output_image_path}" # check los_projection metadata for out_type in los_projection_out_types: for tif in Path(params[C.TIMESERIES_DIR]).glob(out_type + '*.tif'): self.__check_md(los_projection, tif.as_posix()) @staticmethod def __check_md(los_projection, output_image_path): ifg = DEM(output_image_path) ifg.open() assert ifg.dataset.GetMetadataItem(C.LOS_PROJECTION.upper()) == LOS_PROJECTION_OPTION[los_projection] ================================================ FILE: tests/test_mpi.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains tests for mpi operations in PyRate. Tun this module as 'mpirun -n 4 pytest tests/test_mpi.py' """ import shutil import numpy as np import os from pathlib import Path import pytest import pyrate.configuration import pyrate.constants as C import pyrate.core.covariance import pyrate.core.orbital import pyrate.core.ref_phs_est import pyrate.core.refpixel from pyrate import correct, prepifg, conv2tif, configuration from pyrate.core import mpiops from tests import common from tests.common import SML_TEST_DIR from tests.test_covariance import legacy_maxvar @pytest.mark.mpi def test_vcm_legacy_vs_mpi(mpisync, tempdir, roipac_or_gamma_conf): params = configuration.Configuration(roipac_or_gamma_conf).__dict__ LEGACY_VCM_DIR = os.path.join(SML_TEST_DIR, 'vcm') legacy_vcm = np.genfromtxt(os.path.join(LEGACY_VCM_DIR, 'vcmt.csv'), delimiter=',') tmpdir = Path(mpiops.run_once(tempdir)) params[C.OUT_DIR] = tmpdir.joinpath('out') params[C.PARALLEL] = 0 output_conf = Path(tmpdir).joinpath('conf.cfg') pyrate.configuration.write_config_file(params=params, output_conf_file=output_conf) params = configuration.Configuration(output_conf).__dict__ # dest_paths = [p.sampled_path for p in params[cf.INTERFEROGRAM_FILES]] # run conv2tif and prepifg, create the dest_paths files conv2tif.main(params) params = configuration.Configuration(output_conf).__dict__ prepifg.main(params) params = configuration.Configuration(output_conf).__dict__ params[C.ORBFIT_OFFSET] = True correct._copy_mlooked(params=params) correct._update_params_with_tiles(params) correct._create_ifg_dict(params=params) pyrate.core.refpixel.ref_pixel_calc_wrapper(params) pyrate.core.orbital.orb_fit_calc_wrapper(params) pyrate.core.ref_phs_est.ref_phase_est_wrapper(params) maxvar, vcmt = pyrate.core.covariance.maxvar_vcm_calc_wrapper(params) # phase data after ref pixel has changed due to commit bf2f7ebd # Legacy tests won't match anymore np.testing.assert_array_almost_equal(maxvar, legacy_maxvar, decimal=4) np.testing.assert_array_almost_equal(legacy_vcm, vcmt, decimal=3) mpiops.run_once(shutil.rmtree, tmpdir) ================================================ FILE: tests/test_mpi_vs_multiprocess_vs_single_process.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains regression tests for comparing output from serial, parallel and MPI PyRate runs. """ import shutil import pytest from pathlib import Path from subprocess import check_call, CalledProcessError, run import numpy as np import pyrate.constants as C from pyrate.configuration import Configuration, write_config_file from tests.common import ( assert_same_files_produced, assert_two_dirs_equal, manipulate_test_conf, MEXICO_CROPA_CONF, TEST_CONF_GAMMA, PY37GDAL304, PY37GDAL302, PYTHON3P8, PYTHON3P7, WORKING_DIR ) @pytest.fixture(params=[0, 1]) def parallel(request): return request.param @pytest.fixture(params=[1, 4]) def local_crop(request): return request.param @pytest.fixture() def modified_config(tempdir, get_lks, get_crop, orbfit_lks, orbfit_method, orbfit_degrees, ref_est_method): def modify_params(conf_file, parallel_vs_serial, output_conf_file): tdir = Path(tempdir()) params = manipulate_test_conf(conf_file, tdir) if params[C.PROCESSOR] == 1: # turn on coherence for gamma params[C.COH_MASK] = 1 params[C.PARALLEL] = parallel_vs_serial params[C.PROCESSES] = 4 params[C.APSEST] = 1 params[C.IFG_LKSX], params[C.IFG_LKSY] = get_lks, get_lks params[C.REFNX], params[C.REFNY] = 2, 2 params[C.IFG_CROP_OPT] = get_crop params[C.ORBITAL_FIT_LOOKS_X], params[C.ORBITAL_FIT_LOOKS_Y] = orbfit_lks, orbfit_lks params[C.ORBITAL_FIT] = 1 params[C.ORBITAL_FIT_METHOD] = orbfit_method params[C.ORBITAL_FIT_DEGREE] = orbfit_degrees params[C.REF_EST_METHOD] = ref_est_method params[C.MAX_LOOP_LENGTH] = 3 params[C.LR_MAXSIG] = 0 # turn off pixel masking for these tests params["rows"], params["cols"] = 3, 2 params["savenpy"] = 1 params["notiles"] = params["rows"] * params["cols"] # number of tiles print(params) # write new temp config output_conf = tdir.joinpath(output_conf_file) write_config_file(params=params, output_conf_file=output_conf) return output_conf, params return modify_params @pytest.mark.mpi @pytest.mark.slow @pytest.mark.skipif(not PYTHON3P8, reason="Only run in one CI env") def test_pipeline_parallel_vs_mpi(modified_config, gamma_or_mexicoa_conf=TEST_CONF_GAMMA): """ Tests proving single/multiprocess/mpi produce same output """ gamma_conf = gamma_or_mexicoa_conf if np.random.rand() > 0.1: # skip 90% of tests randomly pytest.skip("Randomly skipping as part of 85 percent") if gamma_conf == MEXICO_CROPA_CONF: # skip cropA conf 95% time if np.random.rand() > 0.5: pytest.skip('skipped in mexicoA') print("\n\n") print("===x==="*10) mpi_conf, params = modified_config(gamma_conf, 0, 'mpi_conf.conf') run(f"mpirun -n 3 pyrate conv2tif -f {mpi_conf}", shell=True, check=True) run(f"mpirun -n 3 pyrate prepifg -f {mpi_conf}", shell=True, check=True) try: run(f"mpirun -n 3 pyrate correct -f {mpi_conf}", shell=True, check=True) run(f"mpirun -n 3 pyrate timeseries -f {mpi_conf}", shell=True, check=True) run(f"mpirun -n 3 pyrate stack -f {mpi_conf}", shell=True, check=True) run(f"mpirun -n 3 pyrate merge -f {mpi_conf}", shell=True, check=True) except CalledProcessError as e: print(e) pytest.skip("Skipping as part of correction error") mr_conf, params_m = modified_config(gamma_conf, 1, 'multiprocess_conf.conf') run(f"pyrate workflow -f {mr_conf}", shell=True, check=True) sr_conf, params_s = modified_config(gamma_conf, 0, 'singleprocess_conf.conf') run(f"pyrate workflow -f {sr_conf}", shell=True, check=True) # convert2tif tests, 17 interferograms if not gamma_conf == MEXICO_CROPA_CONF: assert_same_files_produced(params[C.INTERFEROGRAM_DIR], params_m[C.INTERFEROGRAM_DIR], params_s[C.INTERFEROGRAM_DIR], "*_unw.tif", 17) # dem assert_same_files_produced(params[C.GEOMETRY_DIR], params_m[C.GEOMETRY_DIR], params_s[C.GEOMETRY_DIR], "*_dem.tif", 1) # if coherence masking, comprare coh files were converted if params[C.COH_FILE_LIST] is not None: assert_same_files_produced(params[C.COHERENCE_DIR], params_m[C.COHERENCE_DIR], params_s[C.COHERENCE_DIR], "*_cc.tif", 17) print("coherence files compared") # prepifg checks num_of_ifgs = 30 if gamma_conf == MEXICO_CROPA_CONF else 17 num_of_coh = 30 if gamma_conf == MEXICO_CROPA_CONF else 17 # check geom files if params[C.DEMERROR]: # check files required by dem error correction are produced assert_same_files_produced( params[C.GEOMETRY_DIR], params_m[C.GEOMETRY_DIR], params_s[C.GEOMETRY_DIR], [ft + '.tif' for ft in C.GEOMETRY_OUTPUT_TYPES] + ['*dem.tif'], 7 if gamma_conf == MEXICO_CROPA_CONF else 8 ) # ifgs assert_same_files_produced(params[C.INTERFEROGRAM_DIR], params_m[C.INTERFEROGRAM_DIR], params_s[C.INTERFEROGRAM_DIR], ["*_ifg.tif"], num_of_ifgs) # coherence assert_same_files_produced(params[C.COHERENCE_DIR], params_m[C.COHERENCE_DIR], params_s[C.COHERENCE_DIR], ["*_coh.tif"], num_of_coh) # coherence stats assert_same_files_produced(params[C.COHERENCE_DIR], params_m[C.COHERENCE_DIR], params_s[C.COHERENCE_DIR], ["coh_*.tif"], 3) num_files = 30 if gamma_conf == MEXICO_CROPA_CONF else 17 # cf.TEMP_MLOOKED_DIR will contain the temp files that can be potentially deleted later assert_same_files_produced(params[C.TEMP_MLOOKED_DIR], params_m[C.TEMP_MLOOKED_DIR], params_s[C.TEMP_MLOOKED_DIR], "*_ifg.tif", num_files) # prepifg + correct steps that overwrite tifs test # ifg phase checking in the previous step checks the correct pipeline upto APS correction # 2 x because of aps files assert_same_files_produced(params[C.TMPDIR], params_m[C.TMPDIR], params_s[ C.TMPDIR], "tsincr_*.npy", params['notiles'] * 2) assert_same_files_produced(params[C.TMPDIR], params_m[C.TMPDIR], params_s[ C.TMPDIR], "tscuml_*.npy", params['notiles']) assert_same_files_produced(params[C.TMPDIR], params_m[C.TMPDIR], params_s[ C.TMPDIR], "linear_rate_*.npy", params['notiles']) assert_same_files_produced(params[C.TMPDIR], params_m[C.TMPDIR], params_s[ C.TMPDIR], "linear_error_*.npy", params['notiles']) assert_same_files_produced(params[C.TMPDIR], params_m[C.TMPDIR], params_s[ C.TMPDIR], "linear_intercept_*.npy", params['notiles']) assert_same_files_produced(params[C.TMPDIR], params_m[C.TMPDIR], params_s[ C.TMPDIR], "linear_rsquared_*.npy", params['notiles']) assert_same_files_produced(params[C.TMPDIR], params_m[C.TMPDIR], params_s[ C.TMPDIR], "linear_samples_*.npy", params['notiles']) assert_same_files_produced(params[C.TMPDIR], params_m[C.TMPDIR], params_s[ C.TMPDIR], "stack_rate_*.npy", params['notiles']) assert_same_files_produced(params[C.TMPDIR], params_m[C.TMPDIR], params_s[ C.TMPDIR], "stack_error_*.npy", params['notiles']) assert_same_files_produced(params[C.TMPDIR], params_m[C.TMPDIR], params_s[ C.TMPDIR], "stack_samples_*.npy", params['notiles']) # compare merge step assert_same_files_produced(params[C.VELOCITY_DIR], params_m[C.VELOCITY_DIR], params_s[ C.VELOCITY_DIR], "stack*.tif", 3) assert_same_files_produced(params[C.VELOCITY_DIR], params_m[C.VELOCITY_DIR], params_s[ C.VELOCITY_DIR], "stack*.kml", 2) assert_same_files_produced(params[C.VELOCITY_DIR], params_m[C.VELOCITY_DIR], params_s[ C.VELOCITY_DIR], "stack*.png", 2) assert_same_files_produced(params[C.VELOCITY_DIR], params_m[C.VELOCITY_DIR], params_s[ C.VELOCITY_DIR], "stack*.npy", 3) assert_same_files_produced(params[C.VELOCITY_DIR], params_m[C.VELOCITY_DIR], params_s[ C.VELOCITY_DIR], "linear_*.tif", 5) assert_same_files_produced(params[C.VELOCITY_DIR], params_m[C.VELOCITY_DIR], params_s[ C.VELOCITY_DIR], "linear_*.kml", 3) assert_same_files_produced(params[C.VELOCITY_DIR], params_m[C.VELOCITY_DIR], params_s[ C.VELOCITY_DIR], "linear_*.png", 3) assert_same_files_produced(params[C.VELOCITY_DIR], params_m[C.VELOCITY_DIR], params_s[ C.VELOCITY_DIR], "linear_*.npy", 5) if params[C.PHASE_CLOSURE]: # only in cropA __check_equality_of_phase_closure_outputs(mpi_conf, sr_conf) __check_equality_of_phase_closure_outputs(mpi_conf, mr_conf) assert_same_files_produced(params[C.TIMESERIES_DIR], params_m[C.TIMESERIES_DIR], params_s[ C.TIMESERIES_DIR], "tscuml*.tif", 11) # phase closure removes one tif assert_same_files_produced(params[C.TIMESERIES_DIR], params_m[C.TIMESERIES_DIR], params_s[ C.TIMESERIES_DIR], "tsincr*.tif", 11) else: assert_same_files_produced(params[C.TIMESERIES_DIR], params_m[C.TIMESERIES_DIR], params_s[ C.TIMESERIES_DIR], "tscuml*.tif", 12) assert_same_files_produced(params[C.TIMESERIES_DIR], params_m[C.TIMESERIES_DIR], params_s[ C.TIMESERIES_DIR], "tsincr*.tif", 12) print("==========================xxx===========================") shutil.rmtree(params[WORKING_DIR]) shutil.rmtree(params_m[WORKING_DIR]) shutil.rmtree(params_s[WORKING_DIR]) def __check_equality_of_phase_closure_outputs(mpi_conf, sr_conf): m_config = Configuration(mpi_conf) s_config = Configuration(sr_conf) m_close = m_config.closure() s_close = s_config.closure() m_closure = np.load(m_close.closure) s_closure = np.load(s_close.closure) # loops m_loops = np.load(m_close.loops, allow_pickle=True) s_loops = np.load(s_close.loops, allow_pickle=True) m_weights = [m.weight for m in m_loops] s_weights = [m.weight for m in s_loops] np.testing.assert_array_equal(m_weights, s_weights) for i, (m, s) in enumerate(zip(m_loops, s_loops)): assert all(m_e == s_e for m_e, s_e in zip(m.edges, s.edges)) # closure np.testing.assert_array_almost_equal(np.abs(m_closure), np.abs(s_closure), decimal=4) # num_occurrences_each_ifg m_num_occurences_each_ifg = np.load(m_close.num_occurences_each_ifg, allow_pickle=True) s_num_occurences_each_ifg = np.load(s_close.num_occurences_each_ifg, allow_pickle=True) np.testing.assert_array_equal(m_num_occurences_each_ifg, s_num_occurences_each_ifg) # check ps m_ifgs_breach_count = np.load(m_close.ifgs_breach_count) s_ifgs_breach_count = np.load(s_close.ifgs_breach_count) np.testing.assert_array_equal(m_ifgs_breach_count, s_ifgs_breach_count) @pytest.fixture(params=[0, 1]) def coh_mask(request): return request.param @pytest.fixture() def modified_config_short(tempdir, local_crop, get_lks, coh_mask): orbfit_lks = 1 orbfit_method = 1 orbfit_degrees = 1 ref_est_method = 1 ref_pixel = (150.941666654, -34.218333314) def modify_params(conf_file, parallel, output_conf_file, largetifs): tdir = Path(tempdir()) params = manipulate_test_conf(conf_file, tdir) params[C.COH_MASK] = coh_mask params[C.PARALLEL] = parallel params[C.PROCESSES] = 4 params[C.APSEST] = 1 params[C.LARGE_TIFS] = largetifs params[C.IFG_LKSX], params[C.IFG_LKSY] = get_lks, get_lks params[C.REFX], params[C.REFY] = ref_pixel params[C.REFNX], params[C.REFNY] = 4, 4 params[C.IFG_CROP_OPT] = local_crop params[C.ORBITAL_FIT_LOOKS_X], params[ C.ORBITAL_FIT_LOOKS_Y] = orbfit_lks, orbfit_lks params[C.ORBITAL_FIT] = 1 params[C.ORBITAL_FIT_METHOD] = orbfit_method params[C.ORBITAL_FIT_DEGREE] = orbfit_degrees params[C.REF_EST_METHOD] = ref_est_method params["rows"], params["cols"] = 3, 2 params["savenpy"] = 1 params["notiles"] = params["rows"] * params["cols"] # number of tiles # print(params) # write new temp config output_conf = tdir.joinpath(output_conf_file) write_config_file(params=params, output_conf_file=output_conf) return output_conf, params return modify_params @pytest.fixture def create_mpi_files(): def _create(modified_config_short, gamma_conf): mpi_conf, params = modified_config_short(gamma_conf, 0, 'mpi_conf.conf', 1) check_call(f"mpirun -n 3 pyrate conv2tif -f {mpi_conf}", shell=True) check_call(f"mpirun -n 3 pyrate prepifg -f {mpi_conf}", shell=True) try: check_call(f"mpirun -n 3 pyrate correct -f {mpi_conf}", shell=True) check_call(f"mpirun -n 3 pyrate timeseries -f {mpi_conf}", shell=True) check_call(f"mpirun -n 3 pyrate stack -f {mpi_conf}", shell=True) except CalledProcessError as c: print(c) pytest.skip("Skipping as we encountered a process error during CI") check_call(f"mpirun -n 3 pyrate merge -f {mpi_conf}", shell=True) return params return _create @pytest.mark.mpi @pytest.mark.slow @pytest.mark.skipif(not PY37GDAL304, reason="Only run in one CI env") def test_stack_and_ts_mpi_vs_parallel_vs_serial(modified_config_short, gamma_conf, create_mpi_files, parallel): """ Checks performed: 1. mpi vs single process pipeline 2. mpi vs parallel (python multiprocess) pipeline. 3. Doing 1 and 2 means we have checked single vs parallel python multiprocess pipelines 4. This also checks the entire pipeline using largetifs (new prepifg) vs old perpifg (python based) """ if np.random.randint(0, 1000) > 300: # skip 90% of tests randomly pytest.skip("Randomly skipping as part of 60 percent") print("\n\n") print("===x==="*10) params = create_mpi_files(modified_config_short, gamma_conf) sr_conf, params_p = modified_config_short(gamma_conf, parallel, 'parallel_conf.conf', 0) check_call(f"pyrate workflow -f {sr_conf}", shell=True) # convert2tif tests, 17 interferograms assert_two_dirs_equal(params[C.INTERFEROGRAM_DIR], params_p[C.INTERFEROGRAM_DIR], "*_unw.tif", 17) # if coherence masking, compare coh files were converted if params[C.COH_FILE_LIST] is not None: assert_two_dirs_equal(params[C.COHERENCE_DIR], params_p[C.COHERENCE_DIR], "*_cc.tif", 17) print("coherence files compared") assert_two_dirs_equal(params[C.INTERFEROGRAM_DIR], params_p[C.INTERFEROGRAM_DIR], ["*_ifg.tif"], 17) # one original dem, another multilooked dem assert_two_dirs_equal(params[C.GEOMETRY_DIR], params_p[C.GEOMETRY_DIR], ['*dem.tif'], 2) assert_two_dirs_equal(params[C.GEOMETRY_DIR], params_p[C.GEOMETRY_DIR], [t + "*.tif" for t in C.GEOMETRY_OUTPUT_TYPES], 6) # 2 dems, 6 geom assert_two_dirs_equal(params[C.TEMP_MLOOKED_DIR], params_p[C.TEMP_MLOOKED_DIR], "*_ifg.tif", 17) # ifg phase checking in the previous step checks the correct pipeline upto APS correction assert_two_dirs_equal(params[C.TMPDIR], params_p[C.TMPDIR], "tsincr_*.npy", params['notiles'] * 2) assert_two_dirs_equal(params[C.TMPDIR], params_p[C.TMPDIR], "tscuml_*.npy", params['notiles']) assert_two_dirs_equal(params[C.TMPDIR], params_p[C.TMPDIR], "linear_rate_*.npy", params['notiles']) assert_two_dirs_equal(params[C.TMPDIR], params_p[C.TMPDIR], "linear_error_*.npy", params['notiles']) assert_two_dirs_equal(params[C.TMPDIR], params_p[C.TMPDIR], "linear_samples_*.npy", params['notiles']) assert_two_dirs_equal(params[C.TMPDIR], params_p[C.TMPDIR], "linear_intercept_*.npy", params['notiles']) assert_two_dirs_equal(params[C.TMPDIR], params_p[C.TMPDIR], "linear_rsquared_*.npy", params['notiles']) assert_two_dirs_equal(params[C.TMPDIR], params_p[C.TMPDIR], "stack_rate_*.npy", params['notiles']) assert_two_dirs_equal(params[C.TMPDIR], params_p[C.TMPDIR], "stack_error_*.npy", params['notiles']) assert_two_dirs_equal(params[C.TMPDIR], params_p[C.TMPDIR], "stack_samples_*.npy", params['notiles']) # compare merge step assert_two_dirs_equal(params[C.VELOCITY_DIR], params_p[C.VELOCITY_DIR], "stack*.tif", 3) assert_two_dirs_equal(params[C.VELOCITY_DIR], params_p[C.VELOCITY_DIR], "stack*.kml", 2) assert_two_dirs_equal(params[C.VELOCITY_DIR], params_p[C.VELOCITY_DIR], "stack*.png", 2) assert_two_dirs_equal(params[C.VELOCITY_DIR], params_p[C.VELOCITY_DIR], "stack*.npy", 3) assert_two_dirs_equal(params[C.VELOCITY_DIR], params_p[C.VELOCITY_DIR], "linear*.tif", 5) assert_two_dirs_equal(params[C.VELOCITY_DIR], params_p[C.VELOCITY_DIR], "linear*.kml", 3) assert_two_dirs_equal(params[C.VELOCITY_DIR], params_p[C.VELOCITY_DIR], "linear*.png", 3) assert_two_dirs_equal(params[C.VELOCITY_DIR], params_p[C.VELOCITY_DIR], "linear*.npy", 5) assert_two_dirs_equal(params[C.TIMESERIES_DIR], params_p[C.TIMESERIES_DIR], "tscuml*.tif") assert_two_dirs_equal(params[C.TIMESERIES_DIR], params_p[C.TIMESERIES_DIR], "tsincr*.tif") assert_two_dirs_equal(params[C.TIMESERIES_DIR], params_p[C.TIMESERIES_DIR], "tscuml*.npy") assert_two_dirs_equal(params[C.TIMESERIES_DIR], params_p[C.TIMESERIES_DIR], "tsincr*.npy") print("==========================xxx===========================") shutil.rmtree(params[WORKING_DIR]) shutil.rmtree(params_p[WORKING_DIR]) ================================================ FILE: tests/test_mst.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This module contains tests for the mst.py PyRate module. """ import os import shutil from itertools import product from numpy import empty, array, nan, isnan, sum as nsum import numpy as np import pyrate.constants as C from pyrate.core import algorithm, mst from pyrate.core.shared import IfgPart, Tile, Ifg, save_numpy_phase from pyrate.configuration import Configuration from pyrate import conv2tif, prepifg, correct from tests import common from tests.common import UnitTestAdaptation, TEST_CONF_GAMMA, MockIfg, small5_mock_ifgs, small_data_setup class TestMST(UnitTestAdaptation): """Basic verification of minimum spanning tree (MST) functionality.""" def setup_method(self): self.ifgs = small_data_setup() def test_mst_matrix_as_array(self): # Verifies MST matrix func returns array with dict/trees in each cell for i in self.ifgs[3:]: i.phase_data[0, 1] = 0 # partial stack of NODATA to one cell for i in self.ifgs: i.convert_to_nans() # zeros to NaN/NODATA epochs = algorithm.get_epochs(self.ifgs)[0] res = mst._mst_matrix_as_array(self.ifgs) ys, xs = res.shape for y, x in product(range(ys), range(xs)): r = res[y, x] num_nodes = len(r) self.assertTrue(num_nodes < len(epochs.dates)) stack = array([i.phase_data[y, x] for i in self.ifgs]) # 17 ifg stack self.assertTrue(0 == nsum(stack == 0)) # all 0s should be converted nc = nsum(isnan(stack)) exp_count = len(epochs.dates) - 1 if nc == 0: self.assertEqual(num_nodes, exp_count) elif nc > 5: # rough test: too many nans must reduce the total tree size self.assertTrue(num_nodes <= (17-nc)) def test_mst_matrix_as_ifgs(self): # ensure only ifgs are returned, not individual MST graphs ifgs = small5_mock_ifgs() nifgs = len(ifgs) ys, xs = ifgs[0].shape result = mst._mst_matrix_ifgs_only(ifgs) for coord in product(range(ys), range(xs)): stack = (i.phase_data[coord] for i in self.ifgs) nc = nsum([isnan(n) for n in stack]) check = len(result[coord]) <= (nifgs - nc) self.assertTrue(check) # HACK: type testing here is a bit grubby self.assertTrue(all([isinstance(i, MockIfg) for i in ifgs])) def test_partial_nan_pixel_stack(self): # Ensure a limited # of coherent cells results in a smaller MST tree num_coherent = 3 def assert_equal(): res = mst._mst_matrix_as_array(mock_ifgs) self.assertEqual(len(res[0,0]), num_coherent) mock_ifgs = [MockIfg(i, 1, 1) for i in self.ifgs] for m in mock_ifgs[num_coherent:]: m.phase_data[:] = nan assert_equal() # fill in more nans leaving only one ifg for m in mock_ifgs[1:num_coherent]: m.phase_data[:] = nan num_coherent = 1 assert_equal() def test_all_nan_pixel_stack(self): # ensure full stack of NaNs in an MST pixel classifies to NaN mock_ifgs = [MockIfg(i, 1, 1) for i in self.ifgs] for m in mock_ifgs: m.phase_data[:] = nan res = mst._mst_matrix_as_array(mock_ifgs) exp = empty((1, 1)) # , dtype=object) exp[:] = nan shape = (mock_ifgs[0].nrows, mock_ifgs[0].ncols) self.assertTrue(res.shape == shape) self.assertTrue(res.shape == exp.shape) self.assertTrue(isnan(res[0][0]) and isnan(exp[0][0])) class TestDefaultMST(UnitTestAdaptation): def test_default_mst(self): # default MST from full set of Ifgs shouldn't drop any nodes ifgs = small5_mock_ifgs() dates = [(i.first, i.second) for i in ifgs] res = mst.mst_from_ifgs(ifgs)[0] num_edges = len(res) self.assertEqual(num_edges, len(ifgs)) # test edges, note node order can be reversed for edge in res: self.assertTrue(edge in dates or (edge[1], edge[0]) in dates) # check all nodes exist in this default tree mst_dates = set(res) mst_dates = list(sum(mst_dates, ())) for i in ifgs: for node in (i.first, i.second): self.assertIn(node, mst_dates) class TestNetworkxMSTTreeCheck(UnitTestAdaptation): @classmethod def setup_class(cls): cls.ifgs = small_data_setup() def test_assert_is_not_tree(self): non_overlapping = [1, 2, 5, 6, 12, 13, 14, 15, 16, 17] ifgs_non_overlapping = [ifg for i, ifg in enumerate(self.ifgs) if i + 1 in non_overlapping] edges, is_tree, ntrees, _ = mst.mst_from_ifgs(ifgs_non_overlapping) self.assertFalse(is_tree) self.assertEqual(4, ntrees) def test_small_data_tree(self): self.assertTrue(mst.mst_from_ifgs(self.ifgs)[1]) def test_assert_is_tree(self): overlapping = [1, 2, 3, 4, 6, 7, 10, 11, 16, 17] ifgs_overlapping = [ifg for i, ifg in enumerate(self.ifgs) if (i + 1 in overlapping)] edges, is_tree, ntrees, _ = mst.mst_from_ifgs(ifgs_overlapping) self.assertFalse(is_tree) self.assertEqual(4, ntrees) def test_assert_two_trees_overlapping(self): overlapping = [3, 4, 5, 6, 7, 8, 9, 10, 11, 16, 17] ifgs_overlapping = [ifg for i, ifg in enumerate(self.ifgs) if (i + 1 in overlapping)] edges, is_tree, ntrees, _ = mst.mst_from_ifgs(ifgs_overlapping) self.assertFalse(is_tree) self.assertEqual(2, ntrees) def test_assert_two_trees_non_overlapping(self): non_overlapping = [2, 5, 6, 12, 13, 15] ifgs_non_overlapping = [ifg for i, ifg in enumerate(self.ifgs) if i + 1 in non_overlapping] edges, is_tree, ntrees, _ = mst.mst_from_ifgs(ifgs_non_overlapping) self.assertFalse(is_tree) self.assertEqual(2, ntrees) class TestIfgPart(UnitTestAdaptation): def setup_method(self): self.ifgs = small_data_setup() self.params = Configuration(common.TEST_CONF_ROIPAC).__dict__ def test_ifg_part_shape_and_slice(self): r_start = 0 r_end = 10 for i in self.ifgs: tile = Tile(0, top_left=(r_start, 0), bottom_right=(r_end, i.ncols)) ifg_part = IfgPart(i.data_path, tile, params=self.params) self.assertEqual(ifg_part.phase_data.shape, (r_end-r_start, i.phase_data.shape[1])) np.testing.assert_array_equal(ifg_part.phase_data, i.phase_data[r_start:r_end, :]) def test_mst_multiprocessing_serial(self): self.params[C.PARALLEL] = False original_mst = mst.mst_boolean_array(self.ifgs) parallel_mst = mst.mst_parallel(self.ifgs, params=self.params) np.testing.assert_array_equal(original_mst, parallel_mst) def test_mst_multiprocessing(self): self.params[C.PARALLEL] = True original_mst = mst.mst_boolean_array(self.ifgs) parallel_mst = mst.mst_parallel(self.ifgs, params=self.params) np.testing.assert_array_equal(original_mst, parallel_mst) class TestMSTFilesReusedFromDisc: @classmethod def setup_class(cls): cls.conf = TEST_CONF_GAMMA cls.params = Configuration(cls.conf).__dict__ conv2tif.main(cls.params) cls.params = Configuration(cls.conf).__dict__ prepifg.main(cls.params) cls.params = Configuration(cls.conf).__dict__ multi_paths = cls.params[C.INTERFEROGRAM_FILES] cls.ifg_paths = [p.tmp_sampled_path for p in multi_paths] @classmethod def teardown_class(cls): shutil.rmtree(cls.params[C.OUT_DIR]) def test_mst_used_from_disc_on_rerun(self): correct._copy_mlooked(self.params) correct._update_params_with_tiles(self.params) times_written = self.__run_once() times_written_1 = self.__run_once() np.testing.assert_array_equal(times_written_1, times_written) def __run_once(self): tiles = self.params[C.TILES] mst_files = [Configuration.mst_path(self.params, t.index) for t in tiles] correct._copy_mlooked(self.params) correct._create_ifg_dict(self.params) save_numpy_phase(self.ifg_paths, self.params) mst.mst_calc_wrapper(self.params) assert all(m.exists() for m in mst_files) return [os.stat(o).st_mtime for o in mst_files] ================================================ FILE: tests/test_orbital.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains tests for the orbital.py PyRate module. """ import os import shutil import tempfile import pytest from itertools import product from numpy import empty, dot, concatenate, float32 from numpy import nan, isnan, array from os.path import join from pathlib import Path from datetime import date import numpy as np from numpy.linalg import pinv, inv from numpy.testing import assert_array_equal, assert_array_almost_equal, assert_allclose from scipy.linalg import lstsq from osgeo import gdal, gdalconst import pyrate.constants as C import pyrate.core.orbital from tests.common import small5_mock_ifgs, MockIfg from pyrate.core.algorithm import first_second_ids, get_all_epochs from pyrate.core.orbital import INDEPENDENT_METHOD, NETWORK_METHOD, PLANAR, \ QUADRATIC, PART_CUBIC from pyrate.core.orbital import OrbitalError, __orb_correction, __orb_inversion from pyrate.core.orbital import get_design_matrix, get_network_design_matrix, orb_fit_calc_wrapper from pyrate.core.orbital import _get_num_params, remove_orbital_error, network_orbital_correction from pyrate.core.orbital import calc_network_orb_correction from pyrate.core.shared import Ifg, mkdir_p from pyrate.core.shared import nanmedian from pyrate.core import roipac from pyrate import correct, conv2tif, prepifg from pyrate.configuration import Configuration, MultiplePaths from pyrate.constants import ORB_ERROR_DIR from tests import common from tests.common import IFMS16, TEST_CONF_GAMMA from tests.common import SML_TEST_LEGACY_ORBITAL_DIR from tests.common import SML_TEST_TIF, PY37GDAL302 from tests.common import small_ifg_file_list # TODO: Purpose of this variable? Degrees are 1, 2 and 3 respectively DEG_LOOKUP = { 2: PLANAR, 5: QUADRATIC, 6: PART_CUBIC} NUM_COEF_LOOKUP = { PLANAR: 2, QUADRATIC: 5, PART_CUBIC: 6} class TestSingleDesignMatrixTests: """ Tests to verify correctness of basic planar & quadratic design matrices or DMs. This class serves two purposes, ensuring the independent method DMs are produced correctly. Secondly, these indivdual DMs are subsets of the larger DM 'grid' required for the networked orbital correction method. """ def setup_class(cls): # faked cell sizes cls.xs = 0.75 cls.ys = 0.8 cls.ifg = Ifg(join(SML_TEST_TIF, 'geo_060619-061002_unw.tif')) cls.ifg.open() cls.ifg.nodata_value = 0 cls.m = MockIfg(cls.ifg, 3, 4) cls.m.x_size = cls.xs cls.m.y_size = cls.ys # tests for planar model def test_create_planar_dm(self): act = get_design_matrix(self.m, PLANAR, intercept=False, scale=100) assert act.shape == (self.m.num_cells, 2) exp = unittest_dm(self.m, INDEPENDENT_METHOD, PLANAR, offset=False) assert_array_equal(act, exp) def test_create_planar_dm_offsets(self): act = get_design_matrix(self.m, PLANAR, intercept=True, scale=100) assert act.shape == (self.m.num_cells, 3) exp = unittest_dm(self.m, INDEPENDENT_METHOD, PLANAR, offset=True) assert_array_almost_equal(act, exp) # tests for quadratic model def test_create_quadratic_dm(self): act = get_design_matrix(self.m, QUADRATIC, intercept=False, scale=100) assert act.shape == (self.m.num_cells, 5) exp = unittest_dm(self.m, INDEPENDENT_METHOD, QUADRATIC, offset=False) assert_array_equal(act, exp) def test_create_quadratic_dm_offsets(self): act = get_design_matrix(self.m, QUADRATIC, intercept=True, scale=100) assert act.shape == (self.m.num_cells, 6) exp = unittest_dm(self.m, INDEPENDENT_METHOD, QUADRATIC, offset=True) assert_array_equal(act, exp) # tests for partial cubic model def test_create_partcubic_dm(self): act = get_design_matrix(self.m, PART_CUBIC, intercept=False, scale=100) assert act.shape == (self.m.num_cells, 6) exp = unittest_dm(self.m, INDEPENDENT_METHOD, PART_CUBIC, offset=False) assert_array_equal(act, exp) def test_create_partcubic_dm_offsets(self): act = get_design_matrix(self.m, PART_CUBIC, intercept=True, scale=100) assert act.shape == (self.m.num_cells, 7) exp = unittest_dm(self.m, INDEPENDENT_METHOD, PART_CUBIC, offset=True) assert_array_equal(act, exp) # tests for unittest_dm() assuming network method def test_create_planar_dm_network(self): # networked method planar version should not have offsets col ncol_exp = 2 exp = unittest_dm(self.m, NETWORK_METHOD, PLANAR, False) assert exp.shape == (self.m.num_cells, ncol_exp) exp2 = unittest_dm(self.m, NETWORK_METHOD, PLANAR, True) assert exp2.shape == (self.m.num_cells, ncol_exp) assert_array_equal(exp, exp2) def test_create_quadratic_dm_network(self): # quadratic version with networked method does not have offsets col ncol_exp = 5 exp = unittest_dm(self.m, NETWORK_METHOD, QUADRATIC, False) assert exp.shape == (self.m.num_cells, ncol_exp) exp2 = unittest_dm(self.m, NETWORK_METHOD, QUADRATIC, True) assert exp2.shape == (self.m.num_cells, ncol_exp) assert_array_equal(exp, exp2) def test_create_partcubic_dm_network(self): # partial cubic version with networked method does not have offsets col ncol_exp = 6 exp = unittest_dm(self.m, NETWORK_METHOD, PART_CUBIC, False) assert exp.shape == (self.m.num_cells, ncol_exp) exp2 = unittest_dm(self.m, NETWORK_METHOD, PART_CUBIC, True) assert exp2.shape == (self.m.num_cells, ncol_exp) assert_array_equal(exp, exp2) class TestIndependentCorrection: """Test cases for the orbital correction component of PyRate.""" @classmethod def setup_class(cls): cls.ifgs = small5_mock_ifgs() _add_nodata(cls.ifgs) for ifg in cls.ifgs: ifg.x_size = 90.0 ifg.y_size = 89.5 ifg.open() def alt_orbital_correction(self, ifg, deg, offset, scale): data = ifg.phase_data.reshape(ifg.num_cells) dm = get_design_matrix(ifg, deg, intercept=True, scale=scale)[~isnan(data)] fd = data[~isnan(data)].reshape((dm.shape[0], 1)) dmt = dm.T invNbb = inv(dmt.dot(dm)) orbparams = invNbb.dot(dmt.dot(fd)) alt_params = lstsq(dm, fd)[0] # FIXME: precision assert_array_almost_equal(orbparams, alt_params, decimal=1) dm2 = get_design_matrix(ifg, deg, intercept=True, scale=scale) fullorb = np.reshape(np.dot(dm2, orbparams), ifg.phase_data.shape) if offset: offset_removal = nanmedian(np.ravel(ifg.phase_data - fullorb)) else: offset_removal = 0 fwd_correction = fullorb - offset_removal # ifg.phase_data -= (fullorb - offset_removal) return ifg.phase_data - fwd_correction def check_correction(self, degree, method, offset, decimal=2): orig = array([c.phase_data.copy() for c in self.ifgs]) exp = [self.alt_orbital_correction(i, degree, offset, scale=100) for i in self.ifgs] params = dict() params[C.ORBITAL_FIT_METHOD] = method params[C.ORBITAL_FIT_DEGREE] = degree params[C.ORBFIT_OFFSET] = offset params[C.ORBFIT_INTERCEPT] = 1 params[C.ORBFIT_SCALE] = 100 params[C.PARALLEL] = False params[C.NO_DATA_VALUE] = 0 params[C.NAN_CONVERSION] = False params[C.OUT_DIR] = tempfile.mkdtemp() params[C.ORBITAL_FIT_LOOKS_X] = 1 params[C.ORBITAL_FIT_LOOKS_Y] = 1 params[C.TEMP_MLOOKED_DIR] = tempfile.mkdtemp() for i in self.ifgs: i.mm_converted = True remove_orbital_error(self.ifgs, params) corrected = array([c.phase_data for c in self.ifgs]) assert ~(orig == corrected).all() self.check_results(self.ifgs, orig) # test shape, data is non zero # FIXME: is decimal=2 close enough? for i, (e, a) in enumerate(zip(exp, corrected)): assert_array_almost_equal(e, a, decimal=decimal) def check_results(self, ifgs, corrections): """Helper method for result verification""" for i, c in zip(ifgs, corrections): ys, xs = c.shape assert i.nrows == ys assert i.ncols == xs # ensure there is real data assert ~ isnan(i.phase_data).all() assert ~ isnan(c).all() assert c.ptp() != 0 # ensure range of values in grid def test_independent_correction_planar(self): self.check_correction(PLANAR, INDEPENDENT_METHOD, False) def test_independent_correction_planar_offsets(self): self.check_correction(PLANAR, INDEPENDENT_METHOD, True) def test_independent_correction_quadratic(self): self.check_correction(QUADRATIC, INDEPENDENT_METHOD, False) def test_independent_correction_quadratic_offsets(self): self.check_correction(QUADRATIC, INDEPENDENT_METHOD, True) def test_independent_correction_partcubic(self): self.check_correction(PART_CUBIC, INDEPENDENT_METHOD, False) def test_independent_correction_partcubic_offsets(self): self.check_correction(PART_CUBIC, INDEPENDENT_METHOD, True, decimal=1) class TestError: """Tests for the networked correction method""" @classmethod def setup_method(cls): out_dir = tempfile.mkdtemp() cls.params = common.min_params(out_dir) cls.ifgs = small5_mock_ifgs() def test_invalid_ifgs_arg(self): # min requirement is 1 ifg, can still subtract one epoch from the other with pytest.raises(OrbitalError): get_network_design_matrix([], PLANAR, 100, True) def test_invalid_degree_arg(self): # test failure of a few different args for 'degree' for d in range(-5, 1): with pytest.raises(OrbitalError): get_network_design_matrix(self.ifgs, d, 100, True) for d in range(4, 7): with pytest.raises(OrbitalError): get_network_design_matrix(self.ifgs, d, 100, True) def test_invalid_method(self): # test failure of a few different args for 'method' for m in [None, 5, -1, -3, 45.8]: self.params[C.ORBITAL_FIT_METHOD] = m with pytest.raises(OrbitalError): remove_orbital_error(self.ifgs, self.params) def test_different_looks_raise(self): # different x/y looks factors should be accepted self.params[C.ORBITAL_FIT_LOOKS_X] = 1 self.params[C.ORBITAL_FIT_LOOKS_Y] = 5 try: remove_orbital_error(self.ifgs, self.params) except: pytest.fail def test_looks_as_int(self): self.params[C.ORBITAL_FIT_LOOKS_X] = 1.1 self.params[C.ORBITAL_FIT_LOOKS_Y] = 5 with pytest.raises(OrbitalError): remove_orbital_error(self.ifgs, self.params) self.params[C.ORBITAL_FIT_LOOKS_X] = 1 self.params[C.ORBITAL_FIT_LOOKS_Y] = '5' with pytest.raises(OrbitalError): remove_orbital_error(self.ifgs, self.params) class TestNetworkDesignMatrixTests: """Contains tests verifying creation of sparse network design matrix.""" def setup_class(self): self.ifgs = small5_mock_ifgs() _add_nodata(self.ifgs) self.nifgs = len(self.ifgs) self.ncells = self.ifgs[0].num_cells self.date_ids = get_date_ids(self.ifgs) self.nepochs = len(self.date_ids) assert self.nepochs == 6 for ifg in self.ifgs: ifg.X_SIZE = 90.0 ifg.Y_SIZE = 89.5 def test_planar_network_dm(self): ncoef = 2 offset = False act = get_network_design_matrix(self.ifgs, PLANAR, 100, intercept=offset) assert act.shape == (self.ncells * self.nifgs, ncoef * self.nepochs) assert act.ptp() != 0 self.check_equality(ncoef, act, self.ifgs, offset) def test_planar_network_dm_offset(self): ncoef = 2 # NB: doesn't include offset col offset = True act = get_network_design_matrix(self.ifgs, PLANAR, 100, intercept=offset) assert act.shape[0] == self.ncells * self.nifgs assert act.shape[1] == (self.nepochs * (ncoef + offset)) assert act.ptp() != 0 self.check_equality(ncoef, act, self.ifgs, offset) def test_quadratic_network_dm(self): ncoef = 5 offset = False act = get_network_design_matrix(self.ifgs, QUADRATIC, 100, intercept=offset) assert act.shape == (self.ncells * self.nifgs, ncoef * self.nepochs) assert act.ptp() != 0 self.check_equality(ncoef, act, self.ifgs, offset) def test_quadratic_network_dm_offset(self): ncoef = 5 offset = True act = get_network_design_matrix(self.ifgs, QUADRATIC, 100, intercept=offset) assert act.shape[0] == self.ncells * self.nifgs assert act.shape[1] == (self.nepochs * (ncoef + offset)) assert act.ptp() != 0 self.check_equality(ncoef, act, self.ifgs, offset) def test_partcubic_network_dm(self): ncoef = 6 offset = False act = get_network_design_matrix(self.ifgs, PART_CUBIC, 100, intercept=offset) assert act.shape == (self.ncells * self.nifgs, ncoef * self.nepochs) assert act.ptp() != 0 self.check_equality(ncoef, act, self.ifgs, offset) def test_partcubic_network_dm_offset(self): ncoef = 6 offset = True act = get_network_design_matrix(self.ifgs, PART_CUBIC, 100, intercept=offset) assert act.shape[0] == self.ncells * self.nifgs assert act.shape[1] == (self.nepochs * (ncoef + offset)) assert act.ptp() != 0 self.check_equality(ncoef, act, self.ifgs, offset) def check_equality(self, ncoef, dm, ifgs, offset): """ Internal test function to check subsets against network design matrix ncoef - base number of coefficients, without extra col for offsets dm - network design matrix to check the results ifgs - sequence of Ifg objs offset - boolean to include extra parameters for model offsets """ deg = DEG_LOOKUP[ncoef] np = ncoef * self.nepochs # index of 1st offset col for i, ifg in enumerate(ifgs): exp = unittest_dm(ifg, NETWORK_METHOD, deg, offset) assert exp.shape == (ifg.num_cells, ncoef) ib1, ib2 = [x * self.ncells for x in (i, i + 1)] # row start/end jbm = (ncoef + offset) * self.date_ids[ifg.first] # starting col index for first image jbs = (ncoef + offset) * self.date_ids[ifg.second] # col start for second image assert_array_almost_equal(-exp, dm[ib1:ib2, jbm:jbm + ncoef]) assert_array_almost_equal(exp, dm[ib1:ib2, jbs:jbs + ncoef]) # ensure remaining rows/cols are zero for this ifg NOT inc offsets assert_array_equal(0, dm[ib1:ib2, :jbm]) # all cols leading up to first image assert_array_equal(0, dm[ib1:ib2, jbm + ncoef + offset:jbs]) # cols btwn mas/slv assert_array_equal(0, dm[ib1:ib2, jbs + ncoef + offset:np]) # to end of non offsets # components for network correction testing def network_correction(ifgs, deg, intercept, ml_ifgs=None, tol=1e-6): """ Compares results of orbital_correction() to alternate implementation. deg - PLANAR, QUADRATIC or PART_CUBIC off - True/False to calculate correction with offsets """ ncells = ifgs[0].num_cells if ml_ifgs: ml_nc = ml_ifgs[0].num_cells ml_data = concatenate([i.phase_data.reshape(ml_nc) for i in ml_ifgs]) dm = get_network_design_matrix(ml_ifgs, deg, 100, intercept)[~isnan(ml_data)] fd = ml_data[~isnan(ml_data)].reshape((dm.shape[0], 1)) else: data = concatenate([i.phase_data.reshape(ncells) for i in ifgs]) dm = get_network_design_matrix(ifgs, deg, 100, intercept)[~isnan(data)] fd = data[~isnan(data)].reshape((dm.shape[0], 1)) params = pinv(dm, tol).dot(fd) assert params.shape == (dm.shape[1], 1) # calculate forward correction sdm = unittest_dm(ifgs[0], NETWORK_METHOD, deg) ncoef = _get_num_params(deg, intercept=False) # NB: ignore offsets for network method assert sdm.shape == (ncells, ncoef) orbs = _expand_corrections(ifgs, sdm, params, ncoef, intercept) # tricky: get expected result before orbital_correction() modifies ifg phase return [i.phase_data - orb for i, orb in zip(ifgs, orbs)] def _expand_corrections(ifgs, dm, params, ncoef, offset): """ Convenience func returns model converted to data points. dm: design matrix (do not filter/remove nan cells) params: model parameters array from pinv() * dm ncoef: number of model coefficients (2 planar, 5 quadratic) offsets: True/False to calculate correction with offsets """ # NB: cannot work on singular ifgs due to date ID id/indexing requirement date_ids = get_date_ids(ifgs) corrections = [] for ifg in ifgs: jbm = date_ids[ifg.first] * ncoef # starting row index for first image jbs = date_ids[ifg.second] * ncoef # row start for second image par = params[jbs:jbs + ncoef] - params[jbm:jbm + ncoef] # estimate orbital correction effects # corresponds to "fullorb = B*parm + offset" in orbfwd.m cor = dm.dot(par).reshape(ifg.phase_data.shape) if offset: off = np.ravel(ifg.phase_data - cor) # bring all ifgs to same base level cor -= nanmedian(off) corrections.append(cor) return corrections class TestNetworkCorrectionTests: """Verifies orbital correction using network method and no multilooking""" def setup_class(cls): # fake some real ifg data by adding nans cls.ifgs = small5_mock_ifgs() _add_nodata(cls.ifgs) # use different sizes to differentiate axes results for ifg in cls.ifgs: ifg.X_SIZE = 90.0 ifg.Y_SIZE = 89.5 cls.nc_tol = 1e-6 """ this test checks that the network orbital fit will return the same parameters if we add a constant to every interferogram. The current network method actually uses the constant parameters, which are assigned per epoch rather than per interferogram, so this test will fail (and should fail). """ @pytest.mark.skip(reason="legacy test against old network method") def test_offset_inversion(self): """ Ensure pinv(DM)*obs gives equal results given constant change to fd """ def get_orbital_params(): """Returns pseudo-inverse of the DM""" ncells = self.ifgs[0].num_cells data = concatenate([i.phase_data.reshape(ncells) for i in self.ifgs]) dm = get_network_design_matrix(self.ifgs, PLANAR, 100, True)[~isnan(data)] fd = data[~isnan(data)].reshape((dm.shape[0], 1)) return dot(pinv(dm, self.nc_tol), fd) tol = 1e-5 nifgs = len(self.ifgs) params0 = get_orbital_params() # apply constant change to the observed values (fd) for value in [5.2, -23.5]: for i in self.ifgs: # change ifgs in place i.phase_data += value assert isnan(i.phase_data).any() params = get_orbital_params() diff = params - params0 assert (diff[:-nifgs] < tol).all() assert_array_almost_equal(diff[-nifgs:], value, decimal=5) # reset back to orig data for i in self.ifgs: i.phase_data -= value # These functions test full size data for orbital correction. The options # are separated as the ifg.phase_data arrays are modified in place, allowing # setUp() reset phase data between tests. def test_network_correction_planar(self): deg, intercept = PLANAR, False exp = network_correction(self.ifgs, deg, intercept) self.verify_corrections(self.ifgs, exp, deg, intercept) """ the test_network_correction_{DEGREE}_offset tests check against a method that fits a constant offset to each interferogram but doesn't remove it. This differs from the current implementation of the network correction so these tests will fail. """ @pytest.mark.skip(reason="legacy test against old network method") def test_network_correction_planar_offset(self): deg, intercept = PLANAR, True exp = network_correction(self.ifgs, deg, intercept) self.verify_corrections(self.ifgs, exp, deg, intercept) def test_network_correction_quadratic(self): deg, intercept = QUADRATIC, False offset = intercept exp = network_correction(self.ifgs, deg, intercept) self.verify_corrections(self.ifgs, exp, deg, intercept) @pytest.mark.skip(reason="legacy test against old network method") def test_network_correction_quadratic_offset(self): deg, intercept = QUADRATIC, True exp = network_correction(self.ifgs, deg, intercept) self.verify_corrections(self.ifgs, exp, deg, intercept) def test_network_correction_partcubic(self): deg, intercept = PART_CUBIC, False exp = network_correction(self.ifgs, deg, intercept) self.verify_corrections(self.ifgs, exp, deg, intercept) @pytest.mark.skip(reason="legacy test against old network method") def test_network_correction_partcubic_offset(self): deg, intercept = PART_CUBIC, True exp = network_correction(self.ifgs, deg, intercept) self.verify_corrections(self.ifgs, exp, deg, intercept) @staticmethod def verify_corrections(ifgs, exp, deg, intercept): # checks orbital correction against unit test version params = dict() params[C.ORBITAL_FIT_METHOD] = NETWORK_METHOD params[C.ORBITAL_FIT_DEGREE] = deg params[C.ORBITAL_FIT_LOOKS_X] = 1 params[C.ORBITAL_FIT_LOOKS_Y] = 1 params[C.PARALLEL] = False params[C.OUT_DIR] = tempfile.mkdtemp() params[C.ORBFIT_OFFSET] = intercept params[C.ORBFIT_INTERCEPT] = intercept params[C.ORBFIT_SCALE] = 100 params[C.PREREAD_IFGS] = None mkdir_p(Path(params[C.OUT_DIR]).joinpath(C.ORB_ERROR_DIR)) network_orbital_correction(ifgs, params) act = [i.phase_data for i in ifgs] assert_array_almost_equal(act, exp, decimal=5) class TestNetworkCorrectionTestsMultilooking: 'Verifies orbital correction with multilooking and network method' @classmethod def setup_class(cls): # fake some real ifg data by adding nans cls.ml_ifgs = small5_mock_ifgs() # 2x data of default Small mock cls.ifgs = small5_mock_ifgs(xs=6, ys=8) # use different sizes to differentiate axes results for ifg in cls.ifgs: ifg.X_SIZE = 90.0 ifg.Y_SIZE = 89.5 # add common nodata to all ifgs for i in cls.ifgs + cls.ml_ifgs: i.phase_data[0, :] = nan # These functions test multilooked data for orbital correction. The options # are separated as the ifg.phase_data arrays are modified in place, allowing # setUp() refresh phase data between tests. def test_mlooked_network_correction_planar(self): deg, intercept = PLANAR, False exp = network_correction(self.ifgs, deg, intercept, self.ml_ifgs) self.verify_corrections(self.ifgs, exp, deg, intercept) @pytest.mark.skip(reason="legacy test against old network method") def test_mlooked_network_correction_planar_offset(self): deg, intercept = PLANAR, True exp = network_correction(self.ifgs, deg, intercept, self.ml_ifgs) self.verify_corrections(self.ifgs, exp, deg, intercept) def test_mlooked_network_correction_quadratic(self): deg, intercept = QUADRATIC, False exp = network_correction(self.ifgs, deg, intercept, self.ml_ifgs) self.verify_corrections(self.ifgs, exp, deg, intercept) @pytest.mark.skip(reason="legacy test against old network method") def test_mlooked_network_correction_quadratic_offset(self): deg, intercept = QUADRATIC, True exp = network_correction(self.ifgs, deg, intercept, self.ml_ifgs) self.verify_corrections(self.ifgs, exp, deg, intercept) def test_mlooked_network_correction_partcubic(self): deg, intercept = PART_CUBIC, False exp = network_correction(self.ifgs, deg, intercept, self.ml_ifgs) self.verify_corrections(self.ifgs, exp, deg, intercept) @pytest.mark.skip(reason="legacy test against old network method") def test_mlooked_network_correction_partcubic_offset(self): deg, intercept = PART_CUBIC, True exp = network_correction(self.ifgs, deg, intercept, self.ml_ifgs) self.verify_corrections(self.ifgs, exp, deg, intercept) def verify_corrections(self, ifgs, exp, deg, intercept): # checks orbital correction against unit test version params = dict() params[C.ORBITAL_FIT_METHOD] = NETWORK_METHOD params[C.ORBITAL_FIT_DEGREE] = deg params[C.ORBITAL_FIT_LOOKS_X] = 1 params[C.ORBITAL_FIT_LOOKS_Y] = 1 params[C.PARALLEL] = False params[C.ORBFIT_OFFSET] = intercept params[C.ORBFIT_INTERCEPT] = intercept params[C.ORBFIT_SCALE] = 100 params[C.PREREAD_IFGS] = None params[C.OUT_DIR] = tempfile.mkdtemp() mkdir_p(Path(params[C.OUT_DIR]).joinpath(C.ORB_ERROR_DIR)) network_orbital_correction(ifgs, params, self.ml_ifgs) act = [i.phase_data for i in ifgs] assert_array_almost_equal(act, exp, decimal=4) def unittest_dm(ifg, method, degree, offset=False, scale=100.0): '''Helper/test func to create design matrix segments. Includes handling for making quadratic DM segments for use in network method. ifg - source interferogram to model design matrix on method - INDEPENDENT_METHOD or NETWORK_METHOD degree - PLANAR, QUADRATIC or PART_CUBIC offset - True/False to include additional cols for offsets ''' assert method in [INDEPENDENT_METHOD, NETWORK_METHOD] xlen = ncoef = NUM_COEF_LOOKUP[degree] if offset and method == INDEPENDENT_METHOD: ncoef += 1 else: offset = False # prevent offsets in DM sections for network method # NB: avoids meshgrid to prevent copying production implementation data = empty((ifg.num_cells, ncoef), dtype=float32) rows = iter(data) yr = range(1, ifg.nrows + 1) # simulate meshgrid starting from 1 xr = range(1, ifg.ncols + 1) xsz, ysz = [i / scale for i in [ifg.x_size, ifg.y_size]] if degree == PLANAR: for y, x in product(yr, xr): row = next(rows) row[:xlen] = [x * xsz, y * ysz] elif degree == QUADRATIC: for y, x in product(yr, xr): ys = y * ysz xs = x * xsz row = next(rows) row[:xlen] = [xs ** 2, ys ** 2, xs * ys, xs, ys] else: for y, x in product(yr, xr): ys = y * ysz xs = x * xsz row = next(rows) row[:xlen] = [xs * ys ** 2, xs ** 2, ys ** 2, xs * ys, xs, ys] if offset: data[:, -1] = 1 return data def get_date_ids(ifgs): ''' Returns unique epoch date IDs from the given Ifgs. ''' dates = [] for ifg in ifgs: dates += [ifg.first, ifg.second] return first_second_ids(dates) def _add_nodata(ifgs): """Adds some NODATA/nan cells to the small mock ifgs""" ifgs[0].phase_data[0, :] = nan # 3 error cells ifgs[1].phase_data[2, 1:3] = nan # 2 error cells ifgs[2].phase_data[3, 2:3] = nan # 1 err ifgs[3].phase_data[1, 2] = nan # 1 err ifgs[4].phase_data[1, 1:3] = nan # 2 err class TestLegacyComparisonTestsOrbfitMethod1: """ This is the legacy comparison test of orbital correction functionality. Tests use the following config orbfit: 1 orbfitmethod: 1 orbfitdegrees: 1 orbfitlksx: 1 orbfitlksy: 1 """ @classmethod @pytest.fixture(autouse=True) def setup_class(cls, roipac_params): cls.params = roipac_params cls.BASE_DIR = cls.params[C.OUT_DIR] # change to orbital error correction method 1 cls.params[C.ORBITAL_FIT_METHOD] = INDEPENDENT_METHOD cls.params[C.ORBITAL_FIT_LOOKS_X] = 1 cls.params[C.ORBITAL_FIT_LOOKS_Y] = 1 cls.params[C.PARALLEL] = False cls.params[C.ORBFIT_OFFSET] = True data_paths = [os.path.join(SML_TEST_TIF, p) for p in IFMS16] cls.ifg_paths = [os.path.join(cls.BASE_DIR, os.path.basename(d)) for d in data_paths] for d in data_paths: shutil.copy(d, os.path.join(cls.BASE_DIR, os.path.basename(d))) @classmethod def teardown_class(cls): "roipac_params fixture auto cleans" pass @pytest.mark.skipif(True, reason="Does not work anymore") def test_orbital_correction_legacy_equality(self): from pyrate import correct from pyrate.configuration import MultiplePaths multi_paths = [MultiplePaths(p, params=self.params) for p in self.ifg_paths] for m in multi_paths: # cheat m.sampled_path = m.converted_path self.params[C.INTERFEROGRAM_FILES] = multi_paths self.params['rows'], self.params['cols'] = 2, 3 self.params[C.ORBFIT_OFFSET] = False Path(self.BASE_DIR).joinpath('tmpdir').mkdir(exist_ok=True, parents=True) correct._copy_mlooked(self.params) correct._update_params_with_tiles(self.params) correct._create_ifg_dict(self.params) correct._copy_mlooked(self.params) pyrate.core.orbital.orb_fit_calc_wrapper(self.params) onlyfiles = [f for f in os.listdir(SML_TEST_LEGACY_ORBITAL_DIR) if os.path.isfile(os.path.join(SML_TEST_LEGACY_ORBITAL_DIR, f)) and f.endswith('.csv') and f.__contains__('_method1_')] count = 0 for i, f in enumerate(onlyfiles): ifg_data = np.genfromtxt(os.path.join(SML_TEST_LEGACY_ORBITAL_DIR, f), delimiter=',') for k, j in enumerate([m.tmp_sampled_path for m in multi_paths]): ifg = Ifg(j) ifg.open() if os.path.basename(j).split('_ifg.')[0] == os.path.basename(f).split( '_orb_planar_1lks_method1_geo_')[1].split('.')[0]: count += 1 # all numbers equal np.testing.assert_array_almost_equal(ifg_data, ifg.phase_data, decimal=2) # means must also be equal assert np.nanmean(ifg_data) == pytest.approx(np.nanmean(ifg.phase_data), abs=1e-2) # number of nans must equal assert np.sum(np.isnan(ifg_data)) == np.sum(np.isnan(ifg.phase_data)) ifg.close() # ensure that we have expected number of matches assert count == len(self.ifg_paths) def test_orbfit_treats_process_inputs_as_read_only(self): pass class TestLegacyComparisonTestsOrbfitMethod2: """ This is the legacy comparison test of orbital correction functionality. Tests use the following config orbfit: 1 orbfitmethod: 2 orbfitdegrees: 1 orbfitlksx: 1 orbfitlksy: 1 """ @classmethod def setup_class(cls): # change to orbital error correction method 2 cls.params = Configuration(common.TEST_CONF_ROIPAC).__dict__ cls.BASE_DIR = cls.params[C.OUT_DIR] cls.params[C.ORBITAL_FIT_METHOD] = NETWORK_METHOD cls.params[C.ORBITAL_FIT_LOOKS_X] = 1 cls.params[C.ORBITAL_FIT_LOOKS_Y] = 1 cls.params[C.ORBFIT_OFFSET] = True cls.params[C.OUT_DIR] = cls.BASE_DIR data_paths = [os.path.join(SML_TEST_TIF, p) for p in small_ifg_file_list()] cls.new_data_paths = [os.path.join(cls.BASE_DIR, os.path.basename(d)) for d in data_paths] cls.params[C.INTERFEROGRAM_FILES] = [MultiplePaths(file_name=d, params=cls.params) for d in data_paths] for p in cls.params[C.INTERFEROGRAM_FILES]: p.sampled_path = p.converted_path # copy the files from the dir into temp dir for d in data_paths: d_copy = os.path.join(cls.BASE_DIR, os.path.basename(d)) shutil.copy(d, d_copy) os.chmod(d_copy, 0o660) cls.headers = [roipac.roipac_header(i, cls.params) for i in cls.new_data_paths] cls.orb_error_dir = Path(cls.params[C.OUT_DIR]).joinpath(ORB_ERROR_DIR) cls.orb_error_dir.mkdir(parents=True, exist_ok=True) @classmethod def teardown_class(cls): shutil.rmtree(cls.BASE_DIR, ignore_errors=True) def test_orbital_correction_legacy_equality_orbfit_method_2(self): correct._copy_mlooked(self.params) correct._create_ifg_dict(self.params) remove_orbital_error(self.new_data_paths, self.params) onlyfiles = [f for f in os.listdir(SML_TEST_LEGACY_ORBITAL_DIR) if os.path.isfile(os.path.join(SML_TEST_LEGACY_ORBITAL_DIR, f)) and f.endswith('.csv') and f.__contains__('_method2_')] count = 0 for i, f in enumerate(onlyfiles): legacy_phase_data = np.genfromtxt(os.path.join( SML_TEST_LEGACY_ORBITAL_DIR, f), delimiter=',') for k, j in enumerate(self.new_data_paths): if os.path.basename(j).split('_unw.')[0] == os.path.basename(f).split('_method2_')[1].split('.')[0]: count += 1 ifg = Ifg(j) ifg.open() # all numbers equal # Note this changed as the nodata mask in the gdal_python.gdal_average changed to nan from 0 # np.testing.assert_array_almost_equal(legacy_phase_data, ifg.phase_data, decimal=3) # number of nans must equal assert np.sum(np.isnan(legacy_phase_data)) == np.sum(np.isnan(ifg.phase_data)) # ensure that we have expected number of matches assert count == len(self.new_data_paths) def test_orbital_error_method2_dummy(self): """ does not test anything except that the method is working """ # change to orbital error correction method 2 self.params[C.ORBITAL_FIT_METHOD] = NETWORK_METHOD self.params[C.ORBITAL_FIT_LOOKS_X] = 2 self.params[C.ORBITAL_FIT_LOOKS_Y] = 2 correct._copy_mlooked(self.params) correct._create_ifg_dict(self.params) remove_orbital_error(self.new_data_paths, self.params) onlyfiles = [f for f in os.listdir(SML_TEST_LEGACY_ORBITAL_DIR) if os.path.isfile(os.path.join(SML_TEST_LEGACY_ORBITAL_DIR, f)) and f.endswith('.csv') and f.__contains__('_method2_')] count = 0 for i, f in enumerate(onlyfiles): legacy_phase_data = np.genfromtxt(os.path.join(SML_TEST_LEGACY_ORBITAL_DIR, f), delimiter=',') for k, j in enumerate(self.new_data_paths): if os.path.basename(j).split('_unw.')[0] == os.path.basename(f).split('_method2_')[1].split('.')[0]: count += 1 ifg = Ifg(j) ifg.open() # number of nans must equal assert np.sum(np.isnan(legacy_phase_data)) == np.sum(np.isnan(ifg.phase_data)) # ensure that we have expected number of matches assert count == len(self.new_data_paths) # TODO: Write tests for various looks and degree combinations # TODO: write mpi tests class TestOrbErrorCorrectionsOnDiscReused: @classmethod def setup_class(cls): cls.conf = TEST_CONF_GAMMA params = Configuration(cls.conf).__dict__ conv2tif.main(params) params = Configuration(cls.conf).__dict__ prepifg.main(params) cls.params = Configuration(cls.conf).__dict__ correct._copy_mlooked(cls.params) correct._create_ifg_dict(cls.params) @classmethod def teardown_class(cls): shutil.rmtree(cls.params[C.OUT_DIR]) def test_orb_error(self, orbfit_method, orbfit_degrees): self.params[C.ORBITAL_FIT_METHOD] = orbfit_method self.params[C.ORBITAL_FIT_DEGREE] = orbfit_degrees multi_paths = self.params[C.INTERFEROGRAM_FILES] self.ifg_paths = [p.tmp_sampled_path for p in multi_paths] remove_orbital_error(self.ifg_paths, self.params) # test_orb_errors_written orb_error_files = [MultiplePaths.orb_error_path(i, self.params) for i in self.ifg_paths] assert all(p.exists() for p in orb_error_files) last_mod_times = np.array([os.stat(o).st_mtime for o in orb_error_files]) # run orbit removal again remove_orbital_error(self.ifg_paths, self.params) orb_error_files2 = [MultiplePaths.orb_error_path(i, self.params) for i in self.ifg_paths] # if files are written again - times will change last_mod_times_2 = np.array([os.stat(o).st_mtime for o in orb_error_files2]) # test_orb_error_reused_if_params_unchanged assert all(a == b for a, b in zip(last_mod_times, last_mod_times_2)) # change one of the params _degrees = set(C.ORB_DEGREE_NAMES.keys()) _degrees.discard(orbfit_degrees) # test_orb_errors_recalculated_if_params_change self.params[C.ORBITAL_FIT_DEGREE] = _degrees.pop() import time time.sleep(0.1) remove_orbital_error(self.ifg_paths, self.params) orb_error_files3 = [MultiplePaths.orb_error_path(i, self.params) for i in self.ifg_paths] last_mod_times_3 = np.array([os.stat(o).st_mtime for o in orb_error_files3]) assert all(a != b for a, b in zip(last_mod_times, last_mod_times_3)) class TestOrbErrorCorrectionsReappliedDoesNotChangePhaseData: @classmethod def setup_method(cls): cls.conf = TEST_CONF_GAMMA params = Configuration(cls.conf).__dict__ conv2tif.main(params) params = Configuration(cls.conf).__dict__ prepifg.main(params) cls.params = Configuration(cls.conf).__dict__ correct._copy_mlooked(cls.params) correct._create_ifg_dict(cls.params) multi_paths = cls.params[C.INTERFEROGRAM_FILES] cls.ifg_paths = [p.tmp_sampled_path for p in multi_paths] @classmethod def teardown_method(cls): shutil.rmtree(cls.params[C.OUT_DIR]) def test_orb_error_multiple_run_does_not_change_phase_data(self, orbfit_method, orbfit_degrees): self.params[C.ORBITAL_FIT_METHOD] = orbfit_method self.params[C.ORBITAL_FIT_DEGREE] = orbfit_degrees remove_orbital_error(self.ifg_paths, self.params) ifgs = [Ifg(i) for i in self.ifg_paths] for i in ifgs: i.open() phase_prev = [i.phase_data for i in ifgs] # orb correct once more correct._copy_mlooked(self.params) remove_orbital_error(self.ifg_paths, self.params) # and again correct._copy_mlooked(self.params) remove_orbital_error(self.ifg_paths, self.params) ifgs = [Ifg(i) for i in self.ifg_paths] for i in ifgs: i.open() phase_now = [i.phase_data for i in ifgs] np.testing.assert_array_equal(phase_now, phase_prev) @pytest.fixture(params=[2, 3, 4]) def orbfit_looks(request): x_lk = request.param y_lk = np.random.choice([2, 3, 4]) return x_lk, y_lk class TestOrbfitIndependentMethodWithMultilooking: @classmethod def setup_class(cls): cls.conf = TEST_CONF_GAMMA params = Configuration(cls.conf).__dict__ conv2tif.main(params) params = Configuration(cls.conf).__dict__ prepifg.main(params) cls.params = Configuration(cls.conf).__dict__ correct._copy_mlooked(cls.params) correct._create_ifg_dict(cls.params) @classmethod def teardown_class(cls): shutil.rmtree(cls.params[C.OUT_DIR]) def test_independent_method_works_with_multilooking(self, orbfit_looks, orbfit_degrees, orbfit_method=1): """ tests when multilooking is used in orbfit method 1 correction also tests that multilooking factors in x and y can be different """ xlks, ylks = orbfit_looks self.params[C.ORBITAL_FIT_METHOD] = orbfit_method self.params[C.ORBITAL_FIT_DEGREE] = orbfit_degrees self.params[C.ORBITAL_FIT_LOOKS_Y] = int(ylks) self.params[C.ORBITAL_FIT_LOOKS_X] = int(xlks) multi_paths = self.params[C.INTERFEROGRAM_FILES] self.ifg_paths = [p.tmp_sampled_path for p in multi_paths] remove_orbital_error(self.ifg_paths, self.params) ifgs = [Ifg(p) for p in self.ifg_paths] for i in ifgs: i.open() assert i.shape == (72, 47) # shape should not change from pyrate.core.shared import cell_size class SyntheticIfg: """ This class will generate a mock interferogram whose signal consists entirely of a synthetic orbital error. The orbital error is generated as a 2D polynomial signal with zero noise. """ def __init__(self, orbfit_degrees): self.x_step = 0.001388888900000 # pixel size - same as cropA self.y_step = 0.001388888900000 self.nrows = 100 self.ncols = 100 self.num_cells = self.nrows * self.ncols self.is_open = False self.orbfit_degrees = orbfit_degrees self.first = None self.second = None self._phase_data = None self._phase_data_first = None self._phase_data_second = None self.y_first = 0 self.x_first = 0 self.add_geographic_data() def add_geographic_data(self): """ Determine and add geographic data to object """ # add some geographic data self.x_centre = int(self.ncols / 2) self.y_centre = int(self.nrows / 2) self.lat_centre = self.y_first + (self.y_step * self.y_centre) self.long_centre = self.x_first + (self.x_step * self.x_centre) # use cell size from centre of scene self.x_size, self.y_size = cell_size(self.lat_centre, self.long_centre, self.x_step, self.y_step) @property def phase_data(self): """ Returns phase band as an array. """ if self._phase_data is None: self.open() return self._phase_data def open(self): x, y = np.meshgrid(np.arange(self.nrows) * self.x_step, np.arange(self.ncols) * self.y_step) x += self.x_step y += self.y_step # define some random coefficients, different for each date x_slope, y_slope, x2_slope, y2_slope, x_y_slope, x_y2_slope, const = np.ravel(np.random.rand(1, 7)) x_slope_, y_slope_, x2_slope_, y2_slope_, x_y_slope_, x_y2_slope_, const_ = np.ravel(np.random.rand(1, 7)) # compute the 2D polynomial separately for first and second dates self._phase_data_first = x_slope * x + y_slope * y + const # planar self._phase_data_second = x_slope_ * x + y_slope_ * y + const_ # planar if self.orbfit_degrees == QUADRATIC: self._phase_data_first += x2_slope * x ** 2 + y2_slope * y ** 2 + x_y_slope * x * y self._phase_data_second += x2_slope_ * x ** 2 + y2_slope_ * y ** 2 + x_y_slope_ * x * y elif self.orbfit_degrees == PART_CUBIC: self._phase_data_first += x2_slope * x ** 2 + y2_slope * y ** 2 + x_y_slope * x * y + \ x_y2_slope * x * (y ** 2) self._phase_data_second += x2_slope_ * x ** 2 + y2_slope_ * y ** 2 + x_y_slope_ * x * y + \ x_y2_slope_ * x * (y ** 2) # combine orbit error for first and second dates to give synthetic phase data for this ifg self._phase_data = self._phase_data_first - self._phase_data_second self.is_open = True from pyrate.core.gdal_python import _gdalwarp_width_and_height from pyrate.core.orbital import __orb_inversion # helper function to multilook a synthetic ifg with gdal def mlk_ifg(ifg, nlooks): src = gdal.GetDriverByName('MEM').Create('', ifg.ncols, ifg.nrows, 1, gdalconst.GDT_Float32) gt = (0, ifg.x_step, 0, 0, 0, ifg.y_step) src.SetGeoTransform(gt) src.GetRasterBand(1).WriteArray(ifg.phase_data) resampled_gt = (0, ifg.x_step * nlooks, 0, 0, 0, ifg.y_step * nlooks) min_x, min_y = 0, 0 max_x, max_y = ifg.x_step * ifg.ncols, ifg.y_step * ifg.nrows px_height, px_width = _gdalwarp_width_and_height(max_x, max_y, min_x, min_y, resampled_gt) dst = gdal.GetDriverByName('MEM').Create('', px_height, px_width, 1, gdalconst.GDT_Float32) dst.SetGeoTransform(resampled_gt) gdal.ReprojectImage(src, dst, '', '', gdal.GRA_Average) mlooked = Ifg(dst) mlooked.first = ifg.first mlooked.second = ifg.second return mlooked @pytest.fixture(params=[1, 2, 3, 4]) def orb_lks(request): return request.param def test_single_synthetic_ifg_independent_method(orbfit_degrees, orb_lks, ifg=None): """ These tests are checking that perfect orbital errors, those matching the assumed orbital error model, can be completely removed by the independent orbital correction with and without multilooking. These tests also prove that orbital error estimates using orbfit multilooking is a valid approach and matches the modelled error with acceptable numerical accuracy, with the accuracy depending on the multilooking factor used. These tests also prove that the orbital error parameters can be approximated using multilooking in the independent method. """ if ifg is None: ifg = SyntheticIfg(orbfit_degrees) fullres_dm = get_design_matrix(ifg, orbfit_degrees, intercept=True, scale=1) m_looked_ifg = mlk_ifg(ifg, orb_lks) mlooked_phase = np.reshape(m_looked_ifg.phase_data, m_looked_ifg.num_cells) mlooked_dm = get_design_matrix(m_looked_ifg, orbfit_degrees, intercept=True, scale=1) orb_corr = __orb_correction(fullres_dm, mlooked_dm, ifg.phase_data, mlooked_phase, offset=True) if orb_lks == 1: assert_array_almost_equal(fullres_dm, mlooked_dm) decimal = 4 else: decimal = 2 assert_array_almost_equal(ifg.phase_data, orb_corr, decimal=decimal) @pytest.mark.slow @pytest.mark.skipif((not PY37GDAL302), reason="Only run in one CI env") def test_set_synthetic_ifgs_independent_method(mexico_cropa_params, orbfit_degrees, orb_lks): """ Test that the independent method can generate a set of orbital corrections that matches a set of synthetic ifg for a range of multi-look factors and polynomial degrees. """ # Use the CropA ifg network configuration ifgs = [Ifg(i.converted_path) for i in mexico_cropa_params[C.INTERFEROGRAM_FILES]] for i in ifgs: i.open() test_ifg = SyntheticIfg(orbfit_degrees) test_single_synthetic_ifg_independent_method(orbfit_degrees, orb_lks, test_ifg) # an in-memory "open" interferogram that we can pass to top-level orb correction methods class FakeIfg: def __init__(self, orbfit_deg, model_params, date_first, date_second): self.x_step = 0.001388888900000 # pixel size - same as cropA self.y_step = 0.001388888900000 self.nrows = 100 self.ncols = 100 self.num_cells = self.nrows * self.ncols self.is_open = False self.orbfit_degrees = orbfit_deg self.model_params = model_params self.first = date_first self.second = date_second self._phase_data = None self.y_first = 0 self.x_first = 0 self.nan_fraction = 0 self.add_geographic_data() def add_geographic_data(self): """ Determine and add geographic data to object """ # add some geographic data self.x_centre = int(self.ncols / 2) self.y_centre = int(self.nrows / 2) self.lat_centre = self.y_first + (self.y_step * self.y_centre) self.long_centre = self.x_first + (self.x_step * self.x_centre) # use cell size from centre of scene self.x_size, self.y_size = cell_size(self.lat_centre, self.long_centre, self.x_step, self.y_step) @property def phase_data(self): """ Returns phase band as an array. """ if self._phase_data is None: self.open() return self._phase_data def open(self): x, y = np.meshgrid(np.arange(self.nrows) * self.x_step, np.arange(self.ncols) * self.y_step) x += self.x_step y += self.y_step # use provided coefficients if self.orbfit_degrees == PLANAR: mx, my = self.model_params self._phase_data = mx * x + my * y elif self.orbfit_degrees == QUADRATIC: mx, my, mx2, my2, mxy = self.model_params self._phase_data = mx * x + my * y + mx2 * x ** 2 + my2 * y ** 2 + mxy * x * y else: mx, my, mx2, my2, mxy, mxy2 = self.model_params self._phase_data = mx * x + my * y + mx2 * x ** 2 + my2 * y ** 2 + mxy * x * y + mxy2 * x * y ** 2 self.is_open = True # tests for network method to recover synthetic orbital error class SyntheticNetwork: """ This class will generate a network of synthetic ifgs, based on orbital errors for each epoch. The signal will be purely from the synthetic orbital error with no noise. """ def __init__(self, orbfit_deg, epochs, network, model_params): """ orbfit_deg: synthesise ifgs with planar, quadratic, or part cubic orbit error models. epochs: list of epoch dates in the network network: list of lists, spec of the ifgs to generate for each epoch as primary model_params: list of iterable - model parameters of correct degree for each epoch """ ifgs = [] for i, e1 in enumerate(epochs): for j in network[i]: ifg_err_model = [model_params[j][k] - model_params[i][k] for k in range(len(model_params[0]))] ifgs.append(FakeIfg(orbfit_deg, ifg_err_model, e1, epochs[j])) self.ifgs = ifgs self.epochs = epochs @pytest.mark.skip(reason="test is non-deterministic due to float calculation errors in array comparison") def test_synthetic_network_correction(orbfit_degrees, orb_lks): epochs = [ date(2000, 1, 1), date(2000, 1, 13), date(2000, 1, 25), date(2000, 2, 6), date(2000, 2, 18), date(2000, 3, 1) ] # start with the network as a connected tree so mst does nothing network = [[2], [2], [3], [4, 5], [], []] # six sets of model parameters - one for each epoch model_params = [[-1, 1, -1, 1, -1, 1], [0, 1, 2, 3, 4, 5], [5, 4, 3, 2, 1, 0], [3, 6, 9, 6, 3, 0], [9, 4, 1, 0, 1, 4], [1, 1, 1, 1, 1, 1]] if orbfit_degrees == PLANAR: nparam = 2 elif orbfit_degrees == QUADRATIC: nparam = 5 else: nparam = 6 model_params = [mi[:nparam] for mi in model_params] # network method uses a hard coded scale of 100 scale = 100 syn_data = SyntheticNetwork(orbfit_degrees, epochs, network, model_params) # id_dict = {date: i for date, i in enumerate(epochs)} nepochs = len(epochs) mlk_ifgs = [mlk_ifg(ifg, orb_lks) for ifg in syn_data.ifgs] coeffs = calc_network_orb_correction(mlk_ifgs, orbfit_degrees, scale, nepochs, intercept=True) # reconstruct correction reconstructed = [] # ifgs are built with lat/long metadata, # orbfit modelling is done with metres coordinates csx = syn_data.ifgs[0].x_size csy = syn_data.ifgs[0].y_size x, y = (coord + 1 for coord in np.meshgrid(np.arange(100, dtype=float), np.arange(100, dtype=float))) x *= csx y *= csy x /= scale y /= scale for i, js in enumerate(network): for j in js: cpair = [cj - ci for ci, cj in zip(coeffs[i], coeffs[j])] if orbfit_degrees == PLANAR: reconstructed.append(cpair[0] * x + cpair[1] * y) elif orbfit_degrees == QUADRATIC: reconstructed.append(cpair[0] * x ** 2 + cpair[1] * y ** 2 + cpair[2] * x * y \ + cpair[3] * x + cpair[4] * y) else: reconstructed.append(cpair[0] * x * y ** 2 + cpair[1] * x ** 2 + cpair[2] * y ** 2 + \ cpair[3] * x * y + cpair[4] * x + cpair[5] * y) for orig, recon in zip(syn_data.ifgs, reconstructed): assert_array_almost_equal(orig.phase_data, recon, decimal=2) def test_orbital_inversion(): """Small unit to test the application of numpy pseudoinverse""" A = np.array([[1, 1, 0], [1, 0, 1], [0, 1, 1]]) d = np.array([2, 4, 3]) exp = np.array([1.5, 0.5, 2.5]) res = __orb_inversion(A, d) assert_array_almost_equal(res, exp, decimal=9) ================================================ FILE: tests/test_prepifg.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains tests for the prepifg.py PyRate module. """ import shutil import os from os.path import exists, join import sys import subprocess import tempfile from math import floor from itertools import product from pathlib import Path import pytest import numpy as np from numpy import isnan, nanmax, nanmin, nanmean, ones, nan, reshape, sum as npsum from numpy.testing import assert_array_almost_equal, assert_array_equal from osgeo import gdal import pyrate.configuration import pyrate.constants as C from pyrate.core.logger import pyratelogger as log from pyrate.core.shared import Ifg, DEM, dem_or_ifg from pyrate.core.shared import InputTypes from pyrate.core.prepifg_helper import CUSTOM_CROP, MAXIMUM_CROP, MINIMUM_CROP, ALREADY_SAME_SIZE from pyrate.core import roipac from pyrate.core.prepifg_helper import prepare_ifg, _resample, PreprocessError, CustomExts from pyrate.core.prepifg_helper import get_analysis_extent from pyrate.core import ifgconstants as ifc from pyrate.configuration import Configuration, MultiplePaths from pyrate import conv2tif, prepifg from tests import common from tests.common import SML_TEST_LEGACY_PREPIFG_DIR, PY37GDAL302, BASE_TEST, WORKING_DIR from tests.common import PREP_TEST_TIF, PREP_TEST_OBS, MEXICO_CROPA_CONF, assert_two_dirs_equal from tests.common import SML_TEST_DEM_TIF, SML_TEST_DEM_HDR, manipulate_test_conf, UnitTestAdaptation gdal.UseExceptions() DUMMY_SECTION_NAME = 'pyrate' if not exists(PREP_TEST_TIF): sys.exit("ERROR: Missing 'prepifg' dir for unittests\n") def test_prepifg_treats_inputs_and_outputs_read_only(gamma_conf, tempdir, coh_mask): tdir = Path(tempdir()) params = common.manipulate_test_conf(gamma_conf, tdir) params[C.COH_MASK] = coh_mask output_conf = tdir.joinpath('conf.cfg') pyrate.configuration.write_config_file(params=params, output_conf_file=output_conf) params = Configuration(output_conf.as_posix()).__dict__ conv2tif.main(params) tifs = list(Path(params[C.INTERFEROGRAM_DIR]).glob('*_unw.tif')) assert len(tifs) == 17 params = Configuration(output_conf.as_posix()).__dict__ prepifg.main(params) cropped_ifgs = list(Path(params[C.INTERFEROGRAM_DIR]).glob('*_ifg.tif')) cropped_cohs = list(Path(params[C.COHERENCE_DIR]).glob('*_coh.tif')) cropped_dem = list(Path(params[C.GEOMETRY_DIR]).glob('*_dem.tif')) if params[C.COH_FILE_LIST] is not None: # 17 + 1 dem + 17 coh files assert len(cropped_ifgs) + len(cropped_cohs) + len(cropped_dem) == 35 else: # 17 + 1 dem assert len(cropped_ifgs) + len(cropped_cohs) + len(cropped_dem) == 18 # check all tifs from conv2tif are still readonly for t in tifs: assert t.stat().st_mode == 33060 # check all prepifg outputs are readonly for c in cropped_cohs + cropped_ifgs + cropped_dem: assert c.stat().st_mode == 33060 def test_prepifg_file_types(tempdir, gamma_conf, coh_mask): tdir = Path(tempdir()) params = manipulate_test_conf(gamma_conf, tdir) params[C.COH_MASK] = coh_mask params[C.PARALLEL] = 0 output_conf_file = 'conf.conf' output_conf = tdir.joinpath(output_conf_file) pyrate.configuration.write_config_file(params=params, output_conf_file=output_conf) params_s = Configuration(output_conf).__dict__ conv2tif.main(params_s) # reread params from config params_s = Configuration(output_conf).__dict__ prepifg.main(params_s) ifg_files = list(Path(tdir.joinpath(params_s[C.INTERFEROGRAM_DIR])).glob('*_unw.tif')) assert len(ifg_files) == 17 mlooked_files = list(Path(tdir.joinpath(params_s[C.INTERFEROGRAM_DIR])).glob('*_ifg.tif')) assert len(mlooked_files) == 17 coh_files = list(Path(tdir.joinpath(params_s[C.COHERENCE_DIR])).glob('*_cc.tif')) mlooked_coh_files = list(Path(tdir.joinpath(params_s[C.COHERENCE_DIR])).glob('*_coh.tif')) if coh_mask: assert len(coh_files) == 17 assert len(mlooked_coh_files) == 17 dem_file = list(Path(tdir.joinpath(params_s[C.GEOMETRY_DIR])).glob('*_dem.tif'))[0] mlooked_dem_file = list(Path(tdir.joinpath(params_s[C.GEOMETRY_DIR])).glob('dem.tif'))[0] import itertools # assert coherence and ifgs have correct metadata for i in itertools.chain(*[ifg_files, mlooked_files, coh_files, mlooked_coh_files]): ifg = Ifg(i) ifg.open() md = ifg.meta_data if i.name.endswith('_unw.tif'): assert md[ifc.DATA_TYPE] == ifc.ORIG assert ifc.IFG_LKSX not in md assert ifc.IFG_LKSY not in md assert ifc.IFG_CROP not in md continue if i.name.endswith('_cc.tif'): assert md[ifc.DATA_TYPE] == ifc.COH assert ifc.IFG_LKSX not in md assert ifc.IFG_LKSY not in md assert ifc.IFG_CROP not in md continue if i.name.endswith('_coh.tif'): assert md[ifc.DATA_TYPE] == ifc.MULTILOOKED_COH assert md[ifc.IFG_LKSX] == '1' assert md[ifc.IFG_LKSY] == '1' assert md[ifc.IFG_CROP] == '1' continue if i.name.endswith('_ifg.tif'): if coh_mask: assert md[ifc.DATA_TYPE] == ifc.MLOOKED_COH_MASKED_IFG assert md[ifc.IFG_LKSX] == '1' assert md[ifc.IFG_LKSY] == '1' assert md[ifc.IFG_CROP] == '1' else: assert md[ifc.DATA_TYPE] == ifc.MULTILOOKED assert md[ifc.IFG_LKSX] == '1' assert md[ifc.IFG_LKSY] == '1' assert md[ifc.IFG_CROP] == '1' continue # assert dem has correct metadata dem = DEM(dem_file.as_posix()) dem.open() md = dem.dataset.GetMetadata() assert md[ifc.DATA_TYPE] == ifc.DEM assert ifc.IFG_LKSX not in md assert ifc.IFG_LKSY not in md assert ifc.IFG_CROP not in md dem = DEM(mlooked_dem_file.as_posix()) dem.open() md = dem.dataset.GetMetadata() assert md[ifc.DATA_TYPE] == ifc.MLOOKED_DEM assert ifc.IFG_LKSX in md assert ifc.IFG_LKSY in md assert ifc.IFG_CROP in md shutil.rmtree(tdir) # convenience ifg creation funcs def diff_exts_ifgs(): """Returns pair of test Ifgs with different extents""" bases = ['geo_060619-061002_unw.tif', 'geo_070326-070917_unw.tif'] headers = ['geo_060619-061002.unw.rsc', 'geo_070326-070917.unw.rsc'] random_dir = tempfile.mkdtemp() for p, h in zip(bases, headers): shutil.copy(src=os.path.join(PREP_TEST_TIF, p), dst=os.path.join(random_dir, p)) shutil.copy(src=os.path.join(PREP_TEST_OBS, h), dst=os.path.join(random_dir, h)) return [Ifg(join(random_dir, p)) for p in bases], random_dir def same_exts_ifgs(): """Return pair of Ifgs with same extents""" return [Ifg(join(PREP_TEST_TIF, f)) for f in ('geo_060619-061002.tif', 'geo_070326-070917.tif')] def extents_from_params(params): """Custom extents from supplied parameters""" keys = ( C.IFG_XFIRST, C.IFG_YFIRST, C.IFG_XLAST, C.IFG_YLAST) return CustomExts(*[params[k] for k in keys]) def test_extents_from_params(): xf, yf = 1.0, 2.0 xl, yl = 5.0, 7.0 pars = {C.IFG_XFIRST: xf, C.IFG_XLAST: xl, C.IFG_YFIRST: yf, C.IFG_YLAST: yl} assert extents_from_params(pars) == CustomExts(xf, yf, xl, yl) class TestPrepifgOutput(UnitTestAdaptation): """Tests aspects of the prepifg.py script, such as resampling.""" @staticmethod def assert_geotransform_equal(files): """ Asserts geotransforms for the given files are equivalent. Files can be paths to datasets, or GDAL dataset objects. """ assert len(files) > 1, "Need more than 1 file to compare" if not all([hasattr(f, "GetGeoTransform") for f in files]): datasets = [gdal.Open(f) for f in files] assert all(datasets) else: datasets = files transforms = [ds.GetGeoTransform() for ds in datasets] head = transforms[0] for t in transforms[1:]: assert_array_almost_equal(t, head, decimal=6, err_msg="Extents do not match!") @classmethod def setup_class(cls): cls.xs = 0.000833333 cls.ys = -cls.xs cls.ifgs, cls.random_dir = diff_exts_ifgs() cls.ifg_paths = [i.data_path for i in cls.ifgs] cls.params = Configuration(common.TEST_CONF_ROIPAC).__dict__ cls.params[C.OUT_DIR] = cls.random_dir cls.params[C.GEOMETRY_DIR] = Path(cls.random_dir).joinpath(C.GEOMETRY_DIR) cls.params[C.GEOMETRY_DIR].mkdir(exist_ok=True) cls.params[C.INTERFEROGRAM_DIR] = Path(cls.random_dir).joinpath(C.INTERFEROGRAM_DIR) cls.params[C.INTERFEROGRAM_DIR].mkdir(exist_ok=True) cls.headers = [roipac.roipac_header(i.data_path, cls.params) for i in cls.ifgs] paths = ["060619-061002_ifg.tif", "060619-061002_ifg.tif", "060619-061002_ifg.tif", "060619-061002_ifg.tif", "070326-070917_ifg.tif", "070326-070917_ifg.tif", "070326-070917_ifg.tif", "070326-070917_ifg.tif"] cls.exp_files = [join(cls.random_dir, C.INTERFEROGRAM_DIR, p) for p in paths] @staticmethod def test_mlooked_paths(): test_mlooked_path() @staticmethod def test_extents_from_params(): test_extents_from_params() @classmethod def teardown_class(cls): for exp_file in cls.exp_files: if exists(exp_file): os.remove(exp_file) for ifg in cls.ifgs: ifg.close() shutil.rmtree(cls.random_dir) def _custom_ext_latlons(self): return [150.91 + (7 * self.xs), # xfirst -34.17 + (16 * self.ys), # yfirst 150.91 + (27 * self.xs), # 20 cells from xfirst -34.17 + (44 * self.ys)] # 28 cells from yfirst def _custom_extents_tuple(self): return CustomExts(*self._custom_ext_latlons()) def assert_projection_equal(self, files): """ Asserts preojections for the given files are equivalent. Files can be paths to datasets, or GDAL dataset objects. """ assert len(files) > 1, "Need more than 1 file to compare" if not all([hasattr(f, "GetGeoTransform") for f in files]): datasets = [gdal.Open(f) for f in files] assert all(datasets) else: datasets = files projections = [ds.GetProjection() for ds in datasets] head = projections[0] for t in projections[1:]: self.assertEqual(t, head) def test_multilooked_projection_same_as_geotiff(self): xlooks = ylooks = 1 exts = get_analysis_extent(crop_opt=MAXIMUM_CROP, rasters=self.ifgs, xlooks=xlooks, ylooks=ylooks, user_exts=None) out_dir = tempfile.mkdtemp() params = common.min_params(out_dir) params[C.IFG_LKSX] = xlooks params[C.IFG_LKSY] = ylooks params[C.IFG_CROP_OPT] = MAXIMUM_CROP params[C.GEOMETRY_DIR] = Path(out_dir).joinpath(C.GEOMETRY_DIR) params[C.GEOMETRY_DIR].mkdir(exist_ok=True) params[C.INTERFEROGRAM_DIR] = Path(out_dir).joinpath(C.INTERFEROGRAM_DIR) params[C.INTERFEROGRAM_DIR].mkdir(exist_ok=True) mlooked_paths = [mlooked_path(f, params, input_type=InputTypes.IFG) for f in self.ifg_paths] for i, h, m in zip(self.ifg_paths, self.headers, mlooked_paths): prepare_ifg(i, xlooks, ylooks, exts, thresh=0.5, crop_opt=MAXIMUM_CROP, header=h, write_to_disk=True, out_path=m) self.assert_projection_equal(self.ifg_paths + mlooked_paths) def test_default_max_extents(self): """Test ifgcropopt=2 crops datasets to max bounding box extents.""" xlooks = ylooks = 1 prepare_ifgs(self.ifg_paths, MAXIMUM_CROP, xlooks, ylooks, self.headers, params=self.params) for f in [self.exp_files[1], self.exp_files[5]]: self.assertTrue(exists(f), msg="Output files not created") # output files should have same extents # NB: also verifies gdalwarp correctly copies geotransform across ifg = Ifg(self.exp_files[1]) ifg.open() gt = ifg.dataset.GetGeoTransform() # copied from gdalinfo output exp_gt = (150.91, 0.000833333, 0, -34.17, 0, -0.000833333) for i, j in zip(gt, exp_gt): self.assertAlmostEqual(i, j) self.assert_geotransform_equal([self.exp_files[1], self.exp_files[5]]) ifg.close() for i in self.ifgs: i.close() def test_min_extents(self): """Test ifgcropopt=1 crops datasets to min extents.""" xlooks = ylooks = 1 prepare_ifgs(self.ifg_paths, MINIMUM_CROP, xlooks, ylooks, headers=self.headers, params=self.params) ifg = Ifg(self.exp_files[0]) ifg.open() # output files should have same extents # NB: also verifies gdalwarp correctly copies geotransform across # NB: expected data copied from gdalinfo output gt = ifg.dataset.GetGeoTransform() exp_gt = (150.911666666, 0.000833333, 0, -34.172499999, 0, -0.000833333) for i, j in zip(gt, exp_gt): self.assertAlmostEqual(i, j) self.assert_geotransform_equal([self.exp_files[0], self.exp_files[4]]) ifg.close() for i in self.ifgs: i.close() def test_custom_extents(self): xlooks = ylooks = 1 cext = self._custom_extents_tuple() prepare_ifgs(self.ifg_paths, CUSTOM_CROP, xlooks, ylooks, headers=self.headers, user_exts=cext, params=self.params) ifg = Ifg(self.exp_files[2]) ifg.open() gt = ifg.dataset.GetGeoTransform() exp_gt = (cext.xfirst, self.xs, 0, cext.yfirst, 0, self.ys) for i, j in zip(gt, exp_gt): self.assertAlmostEqual(i, j) self.assert_geotransform_equal([self.exp_files[2], self.exp_files[6]]) # close ifgs ifg.close() for i in self.ifgs: i.close() def test_exception_without_all_4_crop_parameters(self): """Test misaligned cropping extents raise errors.""" xlooks = ylooks = 1 # empty string and none raises exceptio for i in [None, '']: cext = (150.92, -34.18, 150.94, i) self.assertRaises(PreprocessError, prepare_ifgs, self.ifg_paths, CUSTOM_CROP, xlooks, ylooks, self.headers, user_exts=cext, params=self.params) # three parameters provided self.assertRaises(PreprocessError, prepare_ifgs, self.ifg_paths, CUSTOM_CROP, xlooks, ylooks, self.headers, params=self.params, user_exts=(150.92, -34.18, 150.94)) # close ifgs for i in self.ifgs: i.close() def test_custom_extents_misalignment(self): """Test misaligned cropping extents raise errors.""" xlooks = ylooks = 1 latlons = tuple(self._custom_ext_latlons()) for i, _ in enumerate(['xfirst', 'yfirst', 'xlast', 'ylast']): # error = step / pi * [1000 100] for error in [0.265258, 0.026526]: tmp_latlon = list(latlons) tmp_latlon[i] += error cext = CustomExts(*tmp_latlon) self.assertRaises(PreprocessError, prepare_ifgs, self.ifg_paths, CUSTOM_CROP, xlooks, ylooks, user_exts=cext, headers=self.headers, params=self.params) # close ifgs for i in self.ifgs: i.close() def test_nodata(self): """Verify NODATA value copied correctly (amplitude band not copied)""" xlooks = ylooks = 1 prepare_ifgs(self.ifg_paths, MINIMUM_CROP, xlooks, ylooks, self.headers, self.params) for ex in [self.exp_files[0], self.exp_files[4]]: ifg = Ifg(ex) ifg.open() # NB: amplitude band doesn't have a NODATA value self.assertTrue(isnan(ifg.dataset.GetRasterBand(1).GetNoDataValue())) ifg.close() for i in self.ifgs: i.close() def test_nans(self): """Verify NaNs replace 0 in the multilooked phase band""" xlooks = ylooks = 1 prepare_ifgs(self.ifg_paths, MINIMUM_CROP, xlooks, ylooks, self.headers, self.params) for ex in [self.exp_files[0], self.exp_files[4]]: ifg = Ifg(ex) ifg.open() phase = ifg.phase_band.ReadAsArray() self.assertFalse((phase == 0).any()) self.assertTrue((isnan(phase)).any()) ifg.close() self.assertAlmostEqual(nanmax(phase), 4.247, 3) # copied from gdalinfo self.assertAlmostEqual(nanmin(phase), 0.009, 3) # copied from gdalinfo for i in self.ifgs: i.close() def test_multilook(self): """Test resampling method using a scaling factor of 4""" scale = 4 # assumes square cells self.ifgs.append(DEM(SML_TEST_DEM_TIF)) self.ifg_paths = [i.data_path for i in self.ifgs] # append the dem header self.headers.append(SML_TEST_DEM_HDR) cext = self._custom_extents_tuple() xlooks = ylooks = scale prepare_ifgs(self.ifg_paths, CUSTOM_CROP, xlooks, ylooks, thresh=1.0, user_exts=cext, headers=self.headers, params=self.params) for n, ipath in enumerate([self.exp_files[3], self.exp_files[7]]): i = Ifg(ipath) i.open() self.assertEqual(i.dataset.RasterXSize, 20 / scale) self.assertEqual(i.dataset.RasterYSize, 28 / scale) # verify resampling path = join(PREP_TEST_TIF, "geo_%s.tif" % Path(ipath).name.split('_ifg')[0]) ds = gdal.Open(path) src_data = ds.GetRasterBand(2).ReadAsArray() exp_resample = multilooking(src_data, scale, scale, thresh=0) self.assertEqual(exp_resample.shape, (7, 5)) assert_array_almost_equal(exp_resample, i.phase_band.ReadAsArray()) ds = None i.close() os.remove(ipath) # verify DEM has been correctly processed # ignore output values as resampling has already been tested for phase exp_dem_path = join(self.params[C.GEOMETRY_DIR], 'dem.tif') self.assertTrue(exists(exp_dem_path)) orignal_dem = DEM(SML_TEST_DEM_TIF) orignal_dem.open() dem_dtype = orignal_dem.dataset.GetRasterBand(1).DataType orignal_dem.close() dem = DEM(exp_dem_path) dem.open() # test multilooked dem is of the same datatype as the original dem tif self.assertEqual(dem_dtype, dem.dataset.GetRasterBand(1).DataType) self.assertEqual(dem.dataset.RasterXSize, 20 / scale) self.assertEqual(dem.dataset.RasterYSize, 28 / scale) data = dem.data self.assertTrue(data.ptp() != 0) # close ifgs dem.close() for i in self.ifgs: i.close() os.remove(exp_dem_path) def test_output_datatype(self): """Test resampling method using a scaling factor of 4""" scale = 4 # assumes square cells self.ifgs.append(DEM(SML_TEST_DEM_TIF)) ifg_paths = [i.data_path for i in self.ifgs] data_types = [InputTypes.IFG] * len(self.ifg_paths) ifg_paths.append(SML_TEST_DEM_TIF) data_types.append(InputTypes.DEM) self.headers.append(SML_TEST_DEM_HDR) cext = self._custom_extents_tuple() xlooks = ylooks = scale prepare_ifgs(ifg_paths, CUSTOM_CROP, xlooks, ylooks, thresh=1.0, user_exts=cext, headers=self.headers, params=self.params) self.params[C.IFG_LKSX] = xlooks self.params[C.IFG_LKSY] = ylooks self.params[C.IFG_CROP_OPT] = CUSTOM_CROP for i, t in zip(ifg_paths, data_types): mlooked_ifg = mlooked_path(i, self.params, input_type=t) ds1 = DEM(mlooked_ifg) ds1.open() ds2 = DEM(i) ds2.open() self.assertEqual(ds1.dataset.GetRasterBand(1).DataType, ds2.dataset.GetRasterBand(1).DataType) ds1 = ds2 = None def test_invalid_looks(self): """Verify only numeric values can be given for multilooking""" values = [0, -1, -10, -100000.6, ""] for v in values: self.assertRaises(PreprocessError, prepare_ifgs, self.ifg_paths, CUSTOM_CROP, xlooks=v, ylooks=1, headers=self.headers, params=self.params) self.assertRaises(PreprocessError, prepare_ifgs, self.ifg_paths, CUSTOM_CROP, xlooks=1, ylooks=v, headers=self.headers, params=self.params) class TestThresholdTests(UnitTestAdaptation): """Tests for threshold of data -> NaN during resampling.""" def test_nan_threshold_inputs(self): data = ones((1, 1)) for thresh in [-10, -1, -0.5, 1.000001, 10]: self.assertRaises(ValueError, _resample, data, 2, 2, thresh) @staticmethod def test_nan_threshold(): # test threshold based on number of NaNs per averaging tile data = ones((2, 10)) data[0, 3:] = nan data[1, 7:] = nan # key: NaN threshold as a % of pixels, expected result expected = [(0.0, [1, nan, nan, nan, nan]), (0.25, [1, nan, nan, nan, nan]), (0.5, [1, 1, nan, nan, nan]), (0.75, [1, 1, 1, nan, nan]), (1.0, [1, 1, 1, 1, nan])] for thresh, exp in expected: res = _resample(data, xscale=2, yscale=2, thresh=thresh) assert_array_equal(res, reshape(exp, res.shape)) @staticmethod def test_nan_threshold_alt(): # test threshold on odd numbers data = ones((3, 6)) data[0] = nan data[1, 2:5] = nan expected = [(0.4, [nan, nan]), (0.5, [1, nan]), (0.7, [1, 1])] for thresh, exp in expected: res = _resample(data, xscale=3, yscale=3, thresh=thresh) assert_array_equal(res, reshape(exp, res.shape)) class TestSameSizeTests(UnitTestAdaptation): """Tests aspects of the prepifg.py script, such as resampling.""" @classmethod def setup_class(cls): import datetime cls.xs = 0.000833333 cls.ys = -cls.xs cls.headers = [ {'NCOLS': 47, 'NROWS': 72, 'LAT': -34.17, 'LONG': 150.91, 'X_STEP': 0.000833333, 'Y_STEP': -0.000833333, 'WAVELENGTH_METRES': 0.0562356424, 'FIRST_DATE': datetime.date(2007, 3, 26), 'SECOND_DATE': datetime.date(2007, 9, 17), 'TIME_SPAN_YEAR': 0.4791238877481177, 'DATA_UNITS': 'RADIANS', 'INSAR_PROCESSOR': 'ROIPAC', 'X_LAST': 150.94916665099998, 'Y_LAST': -34.229999976, 'DATUM': 'WGS84', 'DATA_TYPE': 'ORIGINAL_IFG'}, {'NCOLS': 47, 'NROWS': 72, 'LAT': -34.17, 'LONG': 150.91, 'X_STEP': 0.000833333, 'Y_STEP': -0.000833333, 'WAVELENGTH_METRES': 0.0562356424, 'FIRST_DATE': datetime.date(2007, 3, 26), 'SECOND_DATE': datetime.date(2007, 9, 17), 'TIME_SPAN_YEAR': 0.4791238877481177, 'DATA_UNITS': 'RADIANS', 'INSAR_PROCESSOR': 'ROIPAC', 'X_LAST': 150.94916665099998, 'Y_LAST': -34.229999976, 'DATUM': 'WGS84', 'DATA_TYPE': 'ORIGINAL_IFG'} ] out_dir = tempfile.mkdtemp() cls.params = common.min_params(out_dir) cls.params[C.GEOMETRY_DIR] = Path(cls.params[C.OUT_DIR]).joinpath(C.GEOMETRY_DIR) cls.params[C.GEOMETRY_DIR].mkdir(exist_ok=True) cls.params[C.INTERFEROGRAM_DIR] = Path(cls.params[C.OUT_DIR]).joinpath(C.INTERFEROGRAM_DIR) cls.params[C.INTERFEROGRAM_DIR].mkdir(exist_ok=True) # TODO: check output files for same extents? # TODO: make prepifg dir readonly to test output to temp dir # TODO: move to class for testing same size option? def test_already_same_size(self): # should do nothing as layers are same size & no multilooking required ifgs = same_exts_ifgs() ifg_data_paths = [d.data_path for d in ifgs] res_tup = prepare_ifgs(ifg_data_paths, ALREADY_SAME_SIZE, 1, 1, self.headers, self.params) res = [r[1] for r in res_tup] self.assertTrue(all(res)) def test_already_same_size_mismatch(self): ifgs, random_dir = diff_exts_ifgs() ifg_data_paths = [d.data_path for d in ifgs] self.assertRaises(PreprocessError, prepare_ifgs, ifg_data_paths, ALREADY_SAME_SIZE, 1, 1, self.headers, self.params) for i in ifgs: i.close() shutil.rmtree(random_dir) # TODO: ensure multilooked files written to output dir def test_same_size_multilooking(self): ifgs = same_exts_ifgs() ifg_data_paths = [d.data_path for d in ifgs] xlooks = ylooks = 2 out_dir = tempfile.mkdtemp() params = common.min_params(out_dir) params[C.IFG_LKSX] = xlooks params[C.IFG_LKSY] = ylooks params[C.IFG_CROP_OPT] = ALREADY_SAME_SIZE params[C.GEOMETRY_DIR] = Path(params[C.OUT_DIR]).joinpath(C.GEOMETRY_DIR) params[C.GEOMETRY_DIR].mkdir(exist_ok=True) params[C.INTERFEROGRAM_DIR] = Path(params[C.OUT_DIR]).joinpath(C.INTERFEROGRAM_DIR) params[C.INTERFEROGRAM_DIR].mkdir(exist_ok=True) prepare_ifgs(ifg_data_paths, ALREADY_SAME_SIZE, xlooks, ylooks, self.headers, params) looks_paths = [mlooked_path(d, params, input_type=InputTypes.IFG) for d in ifg_data_paths] mlooked = [Ifg(i) for i in looks_paths] for m in mlooked: m.open() self.assertEqual(len(mlooked), 2) for ifg in mlooked: self.assertAlmostEqual(ifg.x_step, xlooks * self.xs) self.assertAlmostEqual(ifg.x_step, ylooks * self.xs) os.remove(ifg.data_path) def mlooked_path(path, params, input_type): m = MultiplePaths(path, params=params, input_type=input_type) return m.sampled_path def test_mlooked_path(): path = 'geo_060619-061002_unw.tif' for xlks, ylks, cr, input_type in product([2, 4, 8], [4, 2, 5], [1, 2, 3, 4], [InputTypes.IFG, InputTypes.COH]): out_dir = tempfile.mkdtemp() params = common.min_params(out_dir) params[C.IFG_LKSX] = xlks params[C.IFG_LKSY] = ylks params[C.IFG_CROP_OPT] = cr m = mlooked_path(path, params, input_type=input_type) assert Path(m).name == f'060619-061002_{input_type.value}.tif' # class LineOfSightTests(unittest.TestCase): # def test_los_conversion(self): # TODO: needs LOS matrix # TODO: this needs to work from config and incidence files on disk # TODO: is convflag (see 'ifgconv' setting) used or just defaulted? # TODO: los conversion has 4 options: 1: ignore, 2: vertical, 3: N/S, 4: E/W # also have a 5th option of arbitrary azimuth angle (PyRate doesn't have this) # params = _default_extents_param() # params[IFG_CROP_OPT] = MINIMUM_CROP # params[PROJECTION_FLAG] = None # prepare_ifgs(params) # def test_phase_conversion(self): # TODO: check output data is converted to mm from radians (in prepifg??) # raise NotImplementedError class TestLocalMultilookTests: """Tests for local testing functions""" @staticmethod def test_multilooking_thresh(): data = ones((3, 6)) data[0] = nan data[1, 2:5] = nan expected = [(6, [nan, nan]), (5, [1, nan]), (4, [1, 1])] scale = 3 for thresh, exp in expected: res = multilooking(data, scale, scale, thresh) assert_array_equal(res, reshape(exp, res.shape)) def multilooking(src, xscale, yscale, thresh=0): """ src: numpy array of phase data thresh: min number of non-NaNs required for a valid tile resampling """ thresh = int(thresh) num_cells = xscale * yscale if thresh > num_cells or thresh < 0: msg = "Invalid threshold: %s (need 0 <= thr <= %s" % (thresh, num_cells) raise ValueError(msg) rows, cols = src.shape rows_lowres = int(floor(rows / yscale)) cols_lowres = int(floor(cols / xscale)) dest = ones((rows_lowres, cols_lowres)) * nan size = xscale * yscale for row in range(rows_lowres): for col in range(cols_lowres): ys = row * yscale ye = ys + yscale xs = col * xscale xe = xs + xscale patch = src[ys:ye, xs:xe] num_values = num_cells - npsum(isnan(patch)) if num_values >= thresh and num_values > 0: # nanmean() only works on 1g axis reshaped = patch.reshape(size) dest[row, col] = nanmean(reshaped) return dest class TestLegacyEqualityTestRoipacSmallTestData(UnitTestAdaptation): """ Legacy roipac prepifg equality test for small test data """ def setup_class(cls): from tests.common import small_data_setup cls.ifgs = small_data_setup() cls.ifg_paths = [i.data_path for i in cls.ifgs] params = Configuration(common.TEST_CONF_ROIPAC).__dict__ cls.headers = [roipac.roipac_header(i.data_path, params) for i in cls.ifgs] params[C.IFG_LKSX], params[C.IFG_LKSY], params[ C.IFG_CROP_OPT] = 1, 1, 1 prepare_ifgs(cls.ifg_paths, crop_opt=1, xlooks=1, ylooks=1, headers=cls.headers, params=params) looks_paths = [mlooked_path(d, params, t) for d, t in zip(cls.ifg_paths, [InputTypes.IFG]*len(cls.ifgs))] cls.ifgs_with_nan = [Ifg(i) for i in looks_paths] for ifg in cls.ifgs_with_nan: ifg.open() @classmethod def teardown_class(cls): for i in cls.ifgs_with_nan: if os.path.exists(i.data_path): i.close() os.remove(i.data_path) def test_legacy_prepifg_equality_array(self): """ Legacy prepifg equality test """ # path to csv folders from legacy output onlyfiles = [ fln for fln in os.listdir(SML_TEST_LEGACY_PREPIFG_DIR) if os.path.isfile(os.path.join(SML_TEST_LEGACY_PREPIFG_DIR, fln)) and fln.endswith('.csv') and fln.__contains__('_rad_') ] for fln in onlyfiles: ifg_data = np.genfromtxt(os.path.join( SML_TEST_LEGACY_PREPIFG_DIR, fln), delimiter=',') for k, j in enumerate(self.ifgs): if fln.split('_rad_')[-1].split('.')[0] == \ os.path.split(j.data_path)[-1].split('.')[0]: np.testing.assert_array_almost_equal(ifg_data, self.ifgs_with_nan[ k].phase_data, decimal=2) def test_legacy_prepifg_and_convert_phase(self): """ Legacy data prepifg equality test """ # path to csv folders from legacy output for i in self.ifgs_with_nan: if not i.mm_converted: i.convert_to_mm() onlyfiles = [ f for f in os.listdir(SML_TEST_LEGACY_PREPIFG_DIR) if os.path.isfile(os.path.join(SML_TEST_LEGACY_PREPIFG_DIR, f)) and f.endswith('.csv') and f.__contains__('_mm_')] count = 0 for i, f in enumerate(onlyfiles): ifg_data = np.genfromtxt(os.path.join( SML_TEST_LEGACY_PREPIFG_DIR, f), delimiter=',') for k, j in enumerate(self.ifgs): if f.split('_mm_')[-1].split('.')[0] == \ os.path.split(j.data_path)[-1].split('_unw.')[0]: count += 1 # all numbers equal np.testing.assert_array_almost_equal( ifg_data, self.ifgs_with_nan[k].phase_data, decimal=2) # means must also be equal self.assertAlmostEqual( nanmean(ifg_data), nanmean(self.ifgs_with_nan[k].phase_data), places=4) # number of nans must equal self.assertEqual( np.sum(np.isnan(ifg_data)), np.sum(np.isnan(self.ifgs_with_nan[k].phase_data))) # ensure we have the correct number of matches self.assertEqual(count, len(self.ifgs)) class TestOneIncidenceOrElevationMap(UnitTestAdaptation): @classmethod def setup_class(cls): cls.base_dir = tempfile.mkdtemp() cls.conf_file = tempfile.mktemp(suffix='.conf', dir=cls.base_dir) cls.ifgListFile = os.path.join(common.GAMMA_SML_TEST_DIR, 'ifms_17') cls.baseListFile = os.path.join(common.GAMMA_SML_TEST_DIR, 'baseline_17') @classmethod def teardown_class(cls): params = Configuration(cls.conf_file).__dict__ shutil.rmtree(cls.base_dir) common.remove_tifs(params[WORKING_DIR]) def make_input_files(self, inc='', ele=''): with open(self.conf_file, 'w') as conf: conf.write('{}: {}\n'.format(C.NO_DATA_VALUE, '0.0')) conf.write('{}: {}\n'.format(WORKING_DIR, common.GAMMA_SML_TEST_DIR)) conf.write('{}: {}\n'.format(C.OUT_DIR, self.base_dir)) conf.write('{}: {}\n'.format(C.IFG_FILE_LIST, self.ifgListFile)) conf.write('{}: {}\n'.format(C.BASE_FILE_LIST, self.baseListFile)) conf.write('{}: {}\n'.format(C.PROCESSOR, '1')) conf.write('{}: {}\n'.format( C.DEM_HEADER_FILE, os.path.join( common.GAMMA_SML_TEST_DIR, '20060619_utm_dem.par'))) conf.write('{}: {}\n'.format(C.IFG_LKSX, '1')) conf.write('{}: {}\n'.format(C.IFG_LKSY, '1')) conf.write('{}: {}\n'.format(C.IFG_CROP_OPT, '1')) conf.write('{}: {}\n'.format(C.NO_DATA_AVERAGING_THRESHOLD, '0.5')) conf.write('{}: {}\n'.format(C.HDR_FILE_LIST, common.SML_TEST_GAMMA_HEADER_LIST)) conf.write('{}: {}\n'.format(C.DEM_FILE, common.SML_TEST_DEM_GAMMA)) conf.write('{}: {}\n'.format(C.APS_INCIDENCE_MAP, inc)) conf.write('{}: {}\n'.format(C.APS_ELEVATION_MAP, ele)) conf.write('{}: {}\n'.format(C.APS_CORRECTION, '1')) conf.write('{}: {}\n'.format(C.APS_METHOD, '2')) conf.write('{}: {}\n'.format(C.TIME_SERIES_SM_ORDER, 1)) def common_check(self, ele, inc): import glob from pyrate.configuration import Configuration assert os.path.exists(self.conf_file) params = Configuration(self.conf_file).__dict__ conv2tif.main(params) sys.argv = ['dummy', self.conf_file] prepifg.main(params) # test 17 geotiffs created geotifs = glob.glob(os.path.join(params[C.OUT_DIR], '*_unw.tif')) self.assertEqual(17, len(geotifs)) # test dem geotiff created demtif = glob.glob(os.path.join(params[C.OUT_DIR], '*_dem.tif')) self.assertEqual(1, len(demtif)) # mlooked tifs mlooked_tifs = glob.glob(os.path.join(self.base_dir, '*_ifg.tif')) mlooked_tifs.append(os.path.join(self.base_dir, 'dem.tif')) # 19 including 17 ifgs, 1 dem and one incidence self.assertEqual(18, len(mlooked_tifs)) inc = glob.glob(os.path.join(self.base_dir, '*utm_{inc}.tif'.format(inc=inc))) self.assertEqual(0, len(inc)) def prepare_ifgs(raster_data_paths, crop_opt, xlooks, ylooks, headers, params, thresh=0.5, user_exts=None, write_to_disc=True): """ Wrapper function to prepare a sequence of interferogram files for PyRate analysis. See prepifg.prepare_ifg() for full description of inputs and returns. Note: function need refining for crop options :param list raster_data_paths: List of interferogram file paths :param int crop_opt: Crop option :param int xlooks: Number of multi-looks in x; 5 is 5 times smaller, 1 is no change :param int ylooks: Number of multi-looks in y :param float thresh: see thresh in prepare_ifgs() :param tuple user_exts: Tuple of user defined georeferenced extents for new file: (xfirst, yfirst, xlast, ylast)cropping coordinates :param bool write_to_disc: Write new data to disk :return: resampled_data: output cropped and resampled image :rtype: ndarray :return: out_ds: destination gdal dataset object :rtype: List[gdal.Dataset] """ if xlooks != ylooks: log.warning('X and Y multi-look factors are not equal') # use metadata check to check whether it's a dem or ifg rasters = [dem_or_ifg(r) for r in raster_data_paths] exts = get_analysis_extent(crop_opt, rasters, xlooks, ylooks, user_exts) out_paths = [] for r, t in zip(raster_data_paths, rasters): if isinstance(t, DEM): input_type = InputTypes.DEM else: input_type = InputTypes.IFG out_path = MultiplePaths(r, params, input_type).sampled_path out_paths.append(out_path) return [prepare_ifg(d, xlooks, ylooks, exts, thresh, crop_opt, h, write_to_disc, p) for d, h, p in zip(raster_data_paths, headers, out_paths)] @pytest.mark.mpi @pytest.mark.slow @pytest.mark.skipif(not PY37GDAL302, reason="Only run in one CI env") def test_coh_stats_equality(mexico_cropa_params): subprocess.run(f"mpirun -n 3 pyrate prepifg -f {MEXICO_CROPA_CONF.as_posix()}", shell=True) params = mexico_cropa_params mexico_cropa_coh_stats_dir = Path(BASE_TEST).joinpath("cropA", 'coherence_stats') assert_two_dirs_equal(params[C.COHERENCE_DIR], mexico_cropa_coh_stats_dir, ext="coh*.tif", num_files=3) # assert metadata was written from pyrate.prepifg import out_type_md_dict for stat_tif in mexico_cropa_coh_stats_dir.glob("coh*.tif"): ds = gdal.Open(stat_tif.as_posix()) md = ds.GetMetadata() expected_md = out_type_md_dict[stat_tif.stem.upper()] assert ifc.DATA_TYPE in md.keys() assert expected_md in md.values() ================================================ FILE: tests/test_prepifg_system_vs_python.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains tests that compare system vs python prepifg methods. """ import shutil import pytest from pathlib import Path import numpy as np import pyrate.configuration import pyrate.constants as C from pyrate import conv2tif, prepifg from pyrate.configuration import Configuration from tests.common import ( assert_two_dirs_equal, manipulate_test_conf, GITHUB_ACTIONS, PY37GDAL302 ) @pytest.fixture(params=[1, 2, 3, 4]) def local_crop(request): return request.param @pytest.fixture() def modified_config_short(tempdir, local_crop, get_lks, coh_mask): orbfit_lks = 1 orbfit_method = 1 orbfit_degrees = 1 ref_est_method = 1 def modify_params(conf_file, parallel, output_conf_file): tdir = Path(tempdir()) params = manipulate_test_conf(conf_file, tdir) params[C.COH_MASK] = coh_mask params[C.PARALLEL] = parallel params[C.PROCESSES] = 4 params[C.APSEST] = 1 params[C.IFG_LKSX], params[C.IFG_LKSY] = get_lks, get_lks params[C.REFNX], params[C.REFNY] = 4, 4 params[C.IFG_CROP_OPT] = local_crop params[C.ORBITAL_FIT_LOOKS_X], params[ C.ORBITAL_FIT_LOOKS_Y] = orbfit_lks, orbfit_lks params[C.ORBITAL_FIT] = 1 params[C.ORBITAL_FIT_METHOD] = orbfit_method params[C.ORBITAL_FIT_DEGREE] = orbfit_degrees params[C.REF_EST_METHOD] = ref_est_method params["rows"], params["cols"] = 3, 2 params["notiles"] = params["rows"] * params["cols"] # number of tiles print(params) # write new temp config output_conf = tdir.joinpath(output_conf_file) pyrate.configuration.write_config_file(params=params, output_conf_file=output_conf) return output_conf, params return modify_params @pytest.fixture def create_mpi_files(modified_config_short): def _create(conf): mpi_conf, params = modified_config_short(conf, 0, 'mpi_conf.conf') params = Configuration(mpi_conf).__dict__ conv2tif.main(params) params = Configuration(mpi_conf).__dict__ prepifg.main(params) return params # don't need the reamining params return _create @pytest.fixture() def modified_config_largetifs(tempdir, local_crop, get_lks, coh_mask): orbfit_lks = 1 orbfit_method = 1 orbfit_degrees = 1 ref_est_method = 1 def modify_params(conf_file, parallel, output_conf_file): tdir = Path(tempdir()) params = manipulate_test_conf(conf_file, tdir) params[C.COH_MASK] = coh_mask params[C.LARGE_TIFS] = 1 params[C.PARALLEL] = parallel params[C.PROCESSES] = 4 params[C.APSEST] = 1 params[C.IFG_LKSX], params[C.IFG_LKSY] = get_lks, get_lks params[C.REFNX], params[C.REFNY] = 4, 4 params[C.IFG_CROP_OPT] = local_crop params[C.ORBITAL_FIT_LOOKS_X], params[ C.ORBITAL_FIT_LOOKS_Y] = orbfit_lks, orbfit_lks params[C.ORBITAL_FIT] = 1 params[C.ORBITAL_FIT_METHOD] = orbfit_method params[C.ORBITAL_FIT_DEGREE] = orbfit_degrees params[C.REF_EST_METHOD] = ref_est_method params["rows"], params["cols"] = 3, 2 params["notiles"] = params["rows"] * params["cols"] # number of tiles print(params) # write new temp config output_conf = tdir.joinpath(output_conf_file) pyrate.configuration.write_config_file(params=params, output_conf_file=output_conf) return output_conf, params return modify_params @pytest.mark.mpi @pytest.mark.slow @pytest.mark.skipif(not PY37GDAL302, reason="Only run in one CI env") def test_prepifg_largetifs_vs_python(modified_config_largetifs, gamma_conf, create_mpi_files): print("\n\n") print("===x==="*10) if GITHUB_ACTIONS and np.random.randint(0, 1000) > 899: # skip 90% of tests randomly pytest.skip("Randomly skipping as part of 50 percent") params = create_mpi_files(gamma_conf) sr_conf, params_p = modified_config_largetifs(gamma_conf, 1, 'parallel_conf.conf') params_p = Configuration(sr_conf).__dict__ conv2tif.main(params_p) params_p = Configuration(sr_conf).__dict__ prepifg.main(params_p) params_p = Configuration(sr_conf).__dict__ # convert2tif tests, 17 interferograms assert_two_dirs_equal(params[C.INTERFEROGRAM_DIR], params_p[C.INTERFEROGRAM_DIR], "*_unw.tif", 17) # if coherence masking, compare coh files were converted if params[C.COH_FILE_LIST] is not None: assert_two_dirs_equal(params[C.COHERENCE_DIR], params_p[C.COHERENCE_DIR], "*_cc.tif", 17) # 17 ifgs + 1 dem + 17 mlooked file assert_two_dirs_equal(params[C.COHERENCE_DIR], params_p[C.COHERENCE_DIR], "*_coh.tif", 17) assert_two_dirs_equal(params[C.COHERENCE_DIR], params_p[C.COHERENCE_DIR], ["coh_*.tif"], 3) assert_two_dirs_equal(params[C.GEOMETRY_DIR], params_p[C.GEOMETRY_DIR], "*_dem.tif", 1) assert_two_dirs_equal(params[C.GEOMETRY_DIR], params_p[C.GEOMETRY_DIR], [t + '.tif' for t in C.GEOMETRY_OUTPUT_TYPES], 6) # prepifg # 17 ifgs + 1 dem assert_two_dirs_equal(params[C.INTERFEROGRAM_DIR], params_p[C.INTERFEROGRAM_DIR], "*_ifg.tif", 17) assert_two_dirs_equal(params[C.GEOMETRY_DIR], params_p[C.GEOMETRY_DIR], "dem.tif", 1) print("==========================xxx===========================") shutil.rmtree(params[C.OUT_DIR]) shutil.rmtree(params_p[C.OUT_DIR]) ================================================ FILE: tests/test_pyrate.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains system integration tests for the PyRate workflow. """ import glob import os import shutil import tempfile import pytest from os.path import join from pathlib import Path import numpy as np import pyrate.configuration import pyrate.constants as C import pyrate.core.prepifg_helper import pyrate.core.shared import pyrate.main import tests.common from pyrate.core import shared, prepifg_helper from pyrate.core.shared import dem_or_ifg from pyrate import correct from pyrate.configuration import MultiplePaths, Configuration from tests import common from tests.common import PY37GDAL304 # taken from # http://stackoverflow.com/questions/6260149/os-symlink-support-in-windows if os.name == "nt": def symlink_ms(source, link_name): import ctypes csl = ctypes.windll.kernel32.CreateSymbolicLinkW csl.argtypes = (ctypes.c_wchar_p, ctypes.c_wchar_p, ctypes.c_uint32) csl.restype = ctypes.c_ubyte flags = 1 if os.path.isdir(source) else 0 try: if csl(link_name, source.replace('/', '\\'), flags) == 0: raise ctypes.WinError() except: pass os.symlink = symlink_ms CURRENT_DIR = os.getcwd() def test_transform_params(): params = {C.IFG_LKSX: 3, C.IFG_LKSY: 2, C.IFG_CROP_OPT: 1} assert pyrate.core.prepifg_helper.transform_params(params) == (3, 2, 1) def test_warp_required(): nocrop = prepifg_helper.ALREADY_SAME_SIZE assert shared.warp_required(xlooks=2, ylooks=1, crop=nocrop) assert shared.warp_required(xlooks=1, ylooks=2, crop=nocrop) assert shared.warp_required(xlooks=1, ylooks=1, crop=nocrop) assert not shared.warp_required(xlooks=1, ylooks=1, crop=None) for c in prepifg_helper.CROP_OPTIONS[:-1]: assert shared.warp_required(xlooks=1, ylooks=1, crop=c) def test_original_ifg_paths(): ifgdir = common.SML_TEST_TIF ifglist_path = join(ifgdir, 'ifms_17') paths = tests.common.original_ifg_paths(ifglist_path, ifgdir) assert paths[0] == join(ifgdir, 'geo_060619-061002_unw.tif'), str(paths[0]) assert paths[-1] == join(ifgdir, 'geo_070709-070813_unw.tif') def dest_ifg_paths(ifg_paths, outdir): """ Returns paths to out/dest ifgs. """ bases = [os.path.basename(p) for p in ifg_paths] return [join(outdir, p) for p in bases] def test_dest_ifg_paths(): # given source ifgs to process, get paths of ifgs in out dir src_paths = ['tif/ifg0.tif', 'tif/ifg1.tif'] dest_paths = dest_ifg_paths(src_paths, outdir='out') assert dest_paths == [os.path.join('out', i) for i in ['ifg0.tif', 'ifg1.tif']] # FIXME: change to read output ifgs def get_ifgs(out_dir, _open=True): paths = glob.glob(join(out_dir, 'geo_*-*_unw.tif')) ifgs = [shared.Ifg(p) for p in paths] assert len(ifgs) == 17, 'Got %s' % ifgs if _open: for i in ifgs: i.open(readonly=False) return ifgs class TestPyRate: # Initialise & run workflow from class setup, ignoring multilooking as it is # a separate step. Unit tests verify different steps have completed @classmethod def setup_class(cls): # testing constants2 cls.BASE_DIR = tempfile.mkdtemp() cls.BASE_OUT_DIR = join(cls.BASE_DIR, 'out') cls.BASE_DEM_DIR = join(cls.BASE_DIR, 'dem') cls.BASE_DEM_FILE = join(cls.BASE_DEM_DIR, 'roipac_test_trimmed.tif') try: # copy source data (treat as prepifg already run) os.makedirs(cls.BASE_OUT_DIR) for path in glob.glob(join(common.SML_TEST_TIF, '*')): dest = join(cls.BASE_OUT_DIR, os.path.basename(path)) shutil.copy(path, dest) os.chmod(dest, 0o660) config = Configuration(common.TEST_CONF_ROIPAC) params = config.__dict__ params['correct'] = ['orbfit', 'refphase', 'mst', 'apscorrect', 'maxvar'] params[C.OUT_DIR] = cls.BASE_OUT_DIR params[C.PROCESSOR] = 0 # roipac params[C.APS_CORRECTION] = 0 paths = glob.glob(join(cls.BASE_OUT_DIR, 'geo_*-*.tif')) paths = sorted(paths) params[C.PARALLEL] = False params[C.ORBFIT_OFFSET] = True params[C.TEMP_MLOOKED_DIR] = cls.BASE_OUT_DIR.join(C.TEMP_MLOOKED_DIR) params[C.INTERFEROGRAM_FILES] = [MultiplePaths(p, params) for p in paths] for p in params[C.INTERFEROGRAM_FILES]: # cheat p.sampled_path = p.converted_path p.tmp_sampled_path = p.converted_path params["rows"], params["cols"] = 2, 2 params[C.REF_PIXEL_FILE] = Configuration.ref_pixel_path(params) Path(params[C.OUT_DIR]).joinpath(C.APS_ERROR_DIR).mkdir(exist_ok=True, parents=True) Path(params[C.OUT_DIR]).joinpath(C.MST_DIR).mkdir(exist_ok=True, parents=True) correct.correct_ifgs(config) if not hasattr(cls, 'ifgs'): cls.ifgs = get_ifgs(out_dir=cls.BASE_OUT_DIR) except: # revert working dir & avoid paths busting other tests os.chdir(CURRENT_DIR) raise @classmethod def teardown_class(cls): shutil.rmtree(cls.BASE_DIR, ignore_errors=True) os.chdir(CURRENT_DIR) def key_check(self, ifg, key, value): 'Helper to check for metadata flags' md = ifg.dataset.GetMetadata() assert key in md, 'Missing %s in %s' % (key, ifg.data_path) assert md[key] == value def test_basic_outputs(self): assert os.path.exists(self.BASE_OUT_DIR) for i in self.ifgs: assert ~i.is_read_only # log_path = self.get_logfile_path() # st = os.stat(log_path) # self.assertTrue(st.st_size > 0) def test_phase_conversion(self): # ensure phase has been converted from radians to millimetres key = 'DATA_UNITS' value = 'MILLIMETRES' for i in self.ifgs: self.key_check(i, key, value) def test_orbital_correction(self): key = 'ORBITAL_ERROR' value = 'REMOVED' for i in self.ifgs: self.key_check(i, key, value) @pytest.mark.slow @pytest.mark.skipif(not PY37GDAL304, reason="Only run in one CI env") class TestParallelPyRate: """ parallel vs serial pyrate tests verifying results from all steps equal """ @classmethod 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 @classmethod def teardown_class(cls): shutil.rmtree(cls.params[C.OUT_DIR]) def test_orbital_correction(self): key = 'ORBITAL_ERROR' value = 'REMOVED' ifgs = [dem_or_ifg(i) for i in self.sampled_paths] for i in ifgs: i.open() i.nodata_value = 0 self.key_check(i, key, value) def key_check(self, ifg, key, value): 'Helper to check for metadata flags' md = ifg.dataset.GetMetadata() assert key in md, 'Missing %s in %s' % (key, ifg.data_path) assert md[key] == value def test_phase_conversion(self): # ensure phase has been converted from radians to millimetres key = 'DATA_UNITS' value = 'MILLIMETRES' ifgs = [dem_or_ifg(i) for i in self.sampled_paths] for i in ifgs: i.open() i.nodata_value = 0 self.key_check(i, key, value) def test_mst_equal(self): np.testing.assert_array_equal(self.mst, self.mst_p) def test_refpixel_equal(self): np.testing.assert_array_equal(self.refpixel, self.refpixel_p) def test_maxvar_equal(self): np.testing.assert_array_almost_equal(self.maxvar, self.maxvar_p, decimal=4) def test_vcmt_equal(self): np.testing.assert_array_almost_equal(self.vcmt, self.vcmt_p, decimal=4) def test_stack_rate_equal(self): np.testing.assert_array_almost_equal(self.rate, self.rate_p, decimal=4) np.testing.assert_array_almost_equal(self.error, self.error_p, decimal=4) np.testing.assert_array_almost_equal(self.samples, self.samples_p, decimal=4) @pytest.mark.slow @pytest.mark.skipif(not PY37GDAL304, reason="Only run in one CI env") class TestPrePrepareIfgs: @classmethod def setup_class(cls): params = Configuration(common.TEST_CONF_ROIPAC).__dict__ cls.tmp_dir = tempfile.mkdtemp() common.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 = common.pre_prepare_ifgs(ifg_paths, params=params) for i in cls.ifg_ret: i.close() nan_conversion = params[C.NAN_CONVERSION] # prepare a second set cls.tmp_dir2 = tempfile.mkdtemp() common.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: i.open(readonly=False) if nan_conversion: # nan conversion happens here in networkx mst i.nodata_value = params[C.NO_DATA_VALUE] i.convert_to_nans() if not i.mm_converted: i.convert_to_mm() i.close() @classmethod def teardown_class(cls): shutil.rmtree(cls.tmp_dir2) shutil.rmtree(cls.tmp_dir) def test_small_data_prep_phase_equality(self): for i, j in zip(self.ifgs, self.ifg_ret): np.testing.assert_array_almost_equal(i.phase_data, j.phase_data) assert ~(i.phase_data == 0).any() # if there was any 0 still present i.phase_data[4, 2] = 0 assert (i.phase_data == 0).any() def test_small_data_prep_metadata_equality(self): for i, j in zip(self.ifgs, self.ifg_ret): assert i.meta_data == j.meta_data ================================================ FILE: tests/test_ref_phs_est.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # coding: utf-8 """ This Python module contains tests for the ref_phs_est.py PyRate module. """ import glob import os from pathlib import Path import shutil import tempfile import pytest import numpy as np import pyrate.constants as C from pyrate.core import ifgconstants as ifc from pyrate.core.ref_phs_est import ReferencePhaseError, ref_phase_est_wrapper from pyrate.core.refpixel import ref_pixel_calc_wrapper from pyrate.core.orbital import remove_orbital_error from pyrate.core.shared import CorrectionStatusError, Ifg from pyrate import prepifg, correct, conv2tif from pyrate.configuration import MultiplePaths, Configuration from tests import common from tests.common import TEST_CONF_GAMMA legacy_ref_phs_method1 = [-18.2191658020020, 27.7119445800781, -18.4944229125977, -2.92210483551025, 31.1168708801270, 21.2123012542725, 9.01810073852539, 6.08130645751953, -3.79313516616821, -11.3826837539673, -7.28352737426758, 17.6365375518799, -12.8673439025879, 5.46325922012329, -35.4149475097656, -13.5371961593628, -12.7864856719971] legacy_ref_phs_method2 = [-21.4459648132324, 27.1714553833008, -20.8264484405518, -3.47468209266663, 30.4519863128662, 22.3201427459717, 9.58487224578857, 4.81979084014893, -3.89160847663879, -12.0131330490112, -8.64702987670898, 19.2060871124268, -9.92049789428711, 4.38952684402466, -34.9590339660645, -14.3167810440063, -11.9066228866577] class TestRefPhsTests: """Basic reference phase estimation tests""" def setup_method(self): self.params = Configuration(common.TEST_CONF_GAMMA).__dict__ self.tmp_dir = tempfile.mkdtemp() self.params[C.OUT_DIR] = self.tmp_dir self.params[C.REF_EST_METHOD] = 1 self.params[C.PARALLEL] = False self.params[C.TMPDIR] = self.tmp_dir common.copytree(common.SML_TEST_TIF, self.tmp_dir) self.small_tifs = glob.glob(os.path.join(self.tmp_dir, "*.tif")) for s in self.small_tifs: os.chmod(s, 0o644) self.ifgs = common.small_data_setup(self.tmp_dir, is_dir=True) self.params[C.INTERFEROGRAM_FILES] = [MultiplePaths(p, self.params) for p in self.small_tifs] for p in self.params[C.INTERFEROGRAM_FILES]: p.sampled_path = p.converted_path p.tmp_sampled_path = p.sampled_path for ifg in self.ifgs: ifg.close() self.params[C.REFX], self.params[C.REFY] = -1, -1 self.params[C.REFNX], self.params[C.REFNY] = 10, 10 self.params[C.REF_CHIP_SIZE], self.params[C.REF_MIN_FRAC] = 21, 0.5 self.params['rows'], self.params['cols'] = 3, 2 self.params[C.REF_PIXEL_FILE] = Configuration.ref_pixel_path(self.params) correct._update_params_with_tiles(self.params) correct.ref_pixel_calc_wrapper(self.params) def teardown_method(self): shutil.rmtree(self.params[C.OUT_DIR]) def test_need_at_least_two_ifgs(self): self.params[C.INTERFEROGRAM_FILES] = [MultiplePaths(p, self.params) for p in self.small_tifs[:1]] for p in self.params[C.INTERFEROGRAM_FILES]: p.sampled_path = p.converted_path p.tmp_sampled_path = p.sampled_path with pytest.raises(ReferencePhaseError): ref_phase_est_wrapper(self.params) def test_metadata(self): for ifg in self.ifgs: ifg.open() assert ifc.PYRATE_REF_PHASE not in ifg.dataset.GetMetadata() ifg.close() ref_phase_est_wrapper(self.params) for ifg in self.ifgs: ifg.open() assert ifg.dataset.GetMetadataItem(ifc.PYRATE_REF_PHASE) == ifc.REF_PHASE_REMOVED ifg.close() def test_mixed_metadata_raises(self): # change config to 5 ifgs self.params[C.INTERFEROGRAM_FILES] = [MultiplePaths(p, self.params) for p in self.small_tifs[:5]] for p in self.params[C.INTERFEROGRAM_FILES]: p.sampled_path = p.converted_path p.tmp_sampled_path = p.sampled_path # correct reference phase for some of the ifgs ref_phase_est_wrapper(self.params) for ifg in self.ifgs: ifg.open() # change config to all ifgs self.params[C.INTERFEROGRAM_FILES] = [MultiplePaths(p, self.params) for p in self.small_tifs] for p in self.params[C.INTERFEROGRAM_FILES]: p.sampled_path = p.converted_path p.tmp_sampled_path = p.sampled_path # now it should raise exception if we want to correct refernece phase again on all of them with pytest.raises(CorrectionStatusError): ref_phase_est_wrapper(self.params) class TestRefPhsEstimationLegacyTestMethod1Serial: """ Reference phase estimation method 1 is tested vs legacy output """ @classmethod def setup_class(cls): # start with a clean output dir params = Configuration(common.TEST_CONF_ROIPAC).__dict__ conv2tif.main(params) prepifg.main(params) for p in params[C.INTERFEROGRAM_FILES]: # hack p.tmp_sampled_path = p.sampled_path Path(p.sampled_path).chmod(0o664) # assign write permission as conv2tif output is readonly params[C.REF_EST_METHOD] = 1 params[C.PARALLEL] = False params[C.ORBFIT_OFFSET] = True dest_paths, headers = common.repair_params_for_correct_tests(params[C.INTERFEROGRAM_DIR], params) # start run_pyrate copy ifgs = common.pre_prepare_ifgs(dest_paths, params) mst_grid = common.mst_calculation(dest_paths, params) # Estimate reference pixel location refx, refy = ref_pixel_calc_wrapper(params) # Estimate and remove orbit errors remove_orbital_error(ifgs, params) for i in ifgs: i.close() ifgs = common.pre_prepare_ifgs(dest_paths, params) for ifg in ifgs: ifg.close() for p in params[C.INTERFEROGRAM_FILES]: p.tmp_sampled_path = p.sampled_path params[C.REFX], params[C.REFY] = refx, refy params['rows'], params['cols'] = 3, 2 correct._update_params_with_tiles(params) cls.ref_phs, cls.ifgs = ref_phase_est_wrapper(params) cls.params = params @classmethod def teardown_class(cls): shutil.rmtree(cls.params[C.OUT_DIR]) @pytest.mark.skip(True, reason='Orbfit correction update') def test_estimate_reference_phase(self): np.testing.assert_array_almost_equal(legacy_ref_phs_method1, self.ref_phs, decimal=3) def test_ifgs_after_ref_phs_est(self): for ifg in self.ifgs: if not ifg.is_open: ifg.open() LEGACY_REF_PHASE_DIR = os.path.join(common.SML_TEST_DIR, 'ref_phase_est') onlyfiles = [f for f in os.listdir(LEGACY_REF_PHASE_DIR) if os.path.isfile(os.path.join(LEGACY_REF_PHASE_DIR, f)) and f.endswith('.csv') and f.__contains__('_ref_phase_')] count = 0 for i, f in enumerate(onlyfiles): ifg_data = np.genfromtxt(os.path.join(LEGACY_REF_PHASE_DIR, f), delimiter=',') for k, j in enumerate(self.ifgs): if f.split('_correctedgeo_')[-1].split('.')[0] == \ os.path.split(j.data_path)[-1].split('_ifg.tif')[0]: count += 1 # all numbers equal np.testing.assert_array_almost_equal(ifg_data, self.ifgs[k].phase_data, decimal=3) # means must also be equal assert np.nanmean(ifg_data) == pytest.approx(np.nanmean(self.ifgs[k].phase_data), abs=0.001) # number of nans must equal assert np.sum(np.isnan(ifg_data)) == np.sum(np.isnan(self.ifgs[k].phase_data)) # ensure we have the correct number of matches assert count == len(self.ifgs) class TestRefPhsEstimationLegacyTestMethod1Parallel: """ Reference phase estimation method 1 is tested vs legacy output """ @classmethod def setup_class(cls): params = Configuration(common.TEST_CONF_ROIPAC).__dict__ conv2tif.main(params) prepifg.main(params) for p in params[C.INTERFEROGRAM_FILES]: # hack p.tmp_sampled_path = p.sampled_path Path(p.sampled_path).chmod(0o664) # assign write permission as conv2tif output is readonly params[C.REF_EST_METHOD] = 1 params[C.PARALLEL] = True params[C.ORBFIT_OFFSET] = True dest_paths, headers = common.repair_params_for_correct_tests(params[C.INTERFEROGRAM_DIR], params) # start run_pyrate copy ifgs = common.pre_prepare_ifgs(dest_paths, params) mst_grid = common.mst_calculation(dest_paths, params) # Estimate reference pixel location refx, refy = ref_pixel_calc_wrapper(params) # Estimate and remove orbit errors remove_orbital_error(ifgs, params) for i in ifgs: i.close() ifgs = common.pre_prepare_ifgs(dest_paths, params) for i in ifgs: i.close() for p in params[C.INTERFEROGRAM_FILES]: p.tmp_sampled_path = p.sampled_path params[C.REFX], params[C.REFY] = refx, refy params['rows'], params['cols'] = 3, 2 correct._update_params_with_tiles(params) cls.ref_phs, cls.ifgs = ref_phase_est_wrapper(params) cls.params = params @classmethod def teardown_class(cls): shutil.rmtree(cls.params[C.OUT_DIR]) @pytest.mark.skip(True, reason='Orbfit correction update') def test_estimate_reference_phase(self): np.testing.assert_array_almost_equal(legacy_ref_phs_method1, self.ref_phs, decimal=3) def test_ifgs_after_ref_phs_est(self): for ifg in self.ifgs: ifg.open() LEGACY_REF_PHASE_DIR = os.path.join(common.SML_TEST_DIR, 'ref_phase_est') onlyfiles = [f for f in os.listdir(LEGACY_REF_PHASE_DIR) if os.path.isfile(os.path.join(LEGACY_REF_PHASE_DIR, f)) and f.endswith('.csv') and f.__contains__('_ref_phase_')] count = 0 for i, f in enumerate(onlyfiles): ifg_data = np.genfromtxt(os.path.join( LEGACY_REF_PHASE_DIR, f), delimiter=',') for k, j in enumerate(self.ifgs): if f.split('_correctedgeo_')[-1].split('.')[0] == os.path.split(j.data_path)[-1].split( '_ifg.tif')[0]: count += 1 # all numbers equal np.testing.assert_array_almost_equal( ifg_data, self.ifgs[k].phase_data, decimal=3) # means must also be equal assert np.nanmean(ifg_data) == pytest.approx(np.nanmean(self.ifgs[k].phase_data), abs=0.001) # number of nans must equal assert np.sum(np.isnan(ifg_data)) == np.sum(np.isnan(self.ifgs[k].phase_data)) # ensure we have the correct number of matches assert count == len(self.ifgs) class TestRefPhsEstimationLegacyTestMethod2Serial: """ Reference phase estimation method 2 is tested vs legacy output """ @classmethod def setup_class(cls): params = Configuration(common.TEST_CONF_ROIPAC).__dict__ conv2tif.main(params) prepifg.main(params) for p in params[C.INTERFEROGRAM_FILES]: # hack p.tmp_sampled_path = p.sampled_path Path(p.sampled_path).chmod(0o664) # assign write permission as conv2tif output is readonly params[C.REF_EST_METHOD] = 2 params[C.PARALLEL] = False params[C.ORBFIT_OFFSET] = True dest_paths, headers = common.repair_params_for_correct_tests(params[C.INTERFEROGRAM_DIR], params) # start run_pyrate copy ifgs = common.pre_prepare_ifgs(dest_paths, params) mst_grid = common.mst_calculation(dest_paths, params) # Estimate reference pixel location refx, refy = ref_pixel_calc_wrapper(params) # Estimate and remove orbit errors remove_orbital_error(ifgs, params) for i in ifgs: i.close() ifgs = common.pre_prepare_ifgs(dest_paths, params) for i in ifgs: i.close() for p in params[C.INTERFEROGRAM_FILES]: p.tmp_sampled_path = p.sampled_path params[C.REFX], params[C.REFY] = refx, refy params['rows'], params['cols'] = 3, 2 correct._update_params_with_tiles(params) cls.ref_phs, cls.ifgs = ref_phase_est_wrapper(params) cls.params = params @classmethod def teardown_class(cls): shutil.rmtree(cls.params[C.OUT_DIR]) def test_ifgs_after_ref_phs_est(self): for ifg in self.ifgs: ifg.open() LEGACY_REF_PHASE_DIR = os.path.join(common.SML_TEST_DIR, 'ref_phase_est') onlyfiles = [f for f in os.listdir(LEGACY_REF_PHASE_DIR) if os.path.isfile(os.path.join(LEGACY_REF_PHASE_DIR, f)) and f.endswith('.csv') and f.__contains__('_ref_phase_') and f.__contains__('method2')] count = 0 for i, f in enumerate(onlyfiles): ifg_data = np.genfromtxt(os.path.join(LEGACY_REF_PHASE_DIR, f), delimiter=',') for k, j in enumerate(self.ifgs): if f.split('_corrected_method2geo_')[-1].split('.')[0] == \ os.path.split(j.data_path)[-1].split('_ifg.tif')[0]: count += 1 # all numbers equal np.testing.assert_array_almost_equal(ifg_data, self.ifgs[k].phase_data, decimal=3) # means must also be equal assert np.nanmean(ifg_data) == pytest.approx(np.nanmean(self.ifgs[k].phase_data), abs=0.001) # number of nans must equal assert np.sum(np.isnan(ifg_data)) == np.sum(np.isnan(self.ifgs[k].phase_data)) # ensure we have the correct number of matches assert count == len(self.ifgs) @pytest.mark.skip(True, reason='Orbfit correction update') def test_estimate_reference_phase_method2(self): np.testing.assert_array_almost_equal(legacy_ref_phs_method2, self.ref_phs, decimal=3) class TestRefPhsEstimationLegacyTestMethod2Parallel: """ Reference phase estimation method 2 is tested vs legacy output """ # TODO: Improve the parallel tests to remove duplication from serial tests @classmethod def setup_class(cls): params = Configuration(common.TEST_CONF_ROIPAC).__dict__ conv2tif.main(params) prepifg.main(params) for p in params[C.INTERFEROGRAM_FILES]: # hack p.tmp_sampled_path = p.sampled_path Path(p.sampled_path).chmod(0o664) # assign write permission as conv2tif output is readonly params[C.REF_EST_METHOD] = 2 params[C.PARALLEL] = 1 params[C.ORBFIT_OFFSET] = True dest_paths, headers = common.repair_params_for_correct_tests(params[C.INTERFEROGRAM_DIR], params) # start run_pyrate copy ifgs = common.pre_prepare_ifgs(dest_paths, params) # Estimate reference pixel location refx, refy = ref_pixel_calc_wrapper(params) # Estimate and remove orbit errors remove_orbital_error(ifgs, params) for i in ifgs: i.close() ifgs = common.pre_prepare_ifgs(dest_paths, params) for i in ifgs: i.close() for p in params[C.INTERFEROGRAM_FILES]: p.tmp_sampled_path = p.sampled_path params[C.REFX], params[C.REFY] = refx, refy params['rows'], params['cols'] = 3, 2 correct._update_params_with_tiles(params) cls.ref_phs, cls.ifgs = ref_phase_est_wrapper(params) cls.params = params @classmethod def teardown_class(cls): shutil.rmtree(cls.params[C.OUT_DIR]) def test_ifgs_after_ref_phs_est(self): for ifg in self.ifgs: ifg.open() LEGACY_REF_PHASE_DIR = os.path.join(common.SML_TEST_DIR, 'ref_phase_est') onlyfiles = [f for f in os.listdir(LEGACY_REF_PHASE_DIR) if os.path.isfile(os.path.join(LEGACY_REF_PHASE_DIR, f)) and f.endswith('.csv') and f.__contains__('_ref_phase_') and f.__contains__('method2')] count = 0 for i, f in enumerate(onlyfiles): ifg_data = np.genfromtxt(os.path.join(LEGACY_REF_PHASE_DIR, f), delimiter=',') for k, j in enumerate(self.ifgs): if f.split('_corrected_method2geo_')[-1].split('.')[0] == \ os.path.split(j.data_path)[-1].split('_ifg.tif')[0]: count += 1 # all numbers equal np.testing.assert_array_almost_equal( ifg_data, self.ifgs[k].phase_data, decimal=3) # means must also be equal assert np.nanmean(ifg_data) == pytest.approx(np.nanmean(self.ifgs[k].phase_data), abs=0.001) # number of nans must equal assert np.sum(np.isnan(ifg_data)) == np.sum(np.isnan(self.ifgs[k].phase_data)) # ensure we have the correct number of matches assert count == len(self.ifgs) @pytest.mark.skip(True, reason='Orbfit correction update') def test_estimate_reference_phase_method2(self): np.testing.assert_array_almost_equal(legacy_ref_phs_method2, self.ref_phs, decimal=3) class TestRefPhsEstReusedFromDisc: @classmethod def setup_class(cls): cls.conf = TEST_CONF_GAMMA params = Configuration(cls.conf).__dict__ conv2tif.main(params) params = Configuration(cls.conf).__dict__ prepifg.main(params) cls.params = params @classmethod def teardown_class(cls): shutil.rmtree(cls.params[C.OUT_DIR]) def test_ref_phase_used_from_disc_on_rerun(self, ref_est_method): self.params = Configuration(self.conf).__dict__ self.params[C.REF_EST_METHOD] = ref_est_method correct._copy_mlooked(self.params) correct._update_params_with_tiles(self.params) phase_prev, time_written = self.__run_once() # run again phase_now, time_written_1 = self.__run_once() # and once more phase_again, time_written_2 = self.__run_once() # assert no new file was written assert time_written_1 == time_written assert time_written_2 == time_written # assert phase data is unchanged after applying ref_ph correction from disc np.testing.assert_array_equal(phase_now, phase_prev) np.testing.assert_array_equal(phase_now, phase_again) def __run_once(self): ref_phs_file = Configuration.ref_phs_file(self.params) correct._copy_mlooked(self.params) multi_paths = self.params[C.INTERFEROGRAM_FILES] ifg_paths = [p.tmp_sampled_path for p in multi_paths] ifgs = [Ifg(i) for i in ifg_paths] self.params[C.REFX_FOUND], self.params[C.REFY_FOUND] = ref_pixel_calc_wrapper(self.params) correct._create_ifg_dict(self.params) ref_phase_est_wrapper(self.params) for i in ifgs: i.open() phase_prev = [i.phase_data for i in ifgs] # assert ref_ph_file present assert ref_phs_file.exists() time_written = os.stat(ref_phs_file).st_mtime for i in ifgs: i.close() return phase_prev, time_written ================================================ FILE: tests/test_refpixel.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains tests for the refpixel.py PyRate module. """ import os import copy import shutil from subprocess import run, PIPE from pathlib import Path import pytest import itertools import numpy as np from numpy import nan, mean, std, isnan import pyrate.configuration import pyrate.constants as C import pyrate.core.refpixel from pyrate.core.refpixel import ref_pixel, _step, RefPixelError, ref_pixel_calc_wrapper, \ convert_geographic_coordinate_to_pixel_value, convert_pixel_value_to_geographic_coordinate from pyrate.core import shared, ifgconstants as ifc from pyrate import correct, conv2tif, prepifg from pyrate.configuration import Configuration, ConfigException from tests.common import TEST_CONF_ROIPAC, TEST_CONF_GAMMA, SML_TEST_DEM_TIF from tests.common import small_data_setup, MockIfg, copy_small_ifg_file_list, \ copy_and_setup_small_data, manipulate_test_conf, assert_two_dirs_equal, PY37GDAL304 # TODO: figure out how editing resource.setrlimit fixes the error # to fix the open to many files error # https://stackoverflow.com/questions/18280612/ioerror-errno-24-too-many-open-files # default testing values REFNX = 5 REFNY = 7 MIN_FRAC = 0.7 CHIPSIZE = 3 PARALLEL = False class TestReferencePixelInputTests: ''' Verifies error checking capabilities of the reference pixel function ''' @classmethod def setup_method(cls): cls.ifgs = small_data_setup() cls.params = Configuration(TEST_CONF_ROIPAC).__dict__ cls.params[C.REFNX] = REFNX cls.params[C.REFNY] = REFNY cls.params[C.REF_CHIP_SIZE] = CHIPSIZE cls.params[C.REF_MIN_FRAC] = MIN_FRAC cls.params[C.PARALLEL] = PARALLEL def test_missing_chipsize(self): self.params[C.REF_CHIP_SIZE] = None with pytest.raises(ConfigException): ref_pixel(self.ifgs, self.params) def test_chipsize_valid(self): for illegal in [0, -1, -15, 1, 2, self.ifgs[0].ncols+1, 4, 6, 10, 20]: self.params[C.REF_CHIP_SIZE] = illegal with pytest.raises(RefPixelError): ref_pixel(self.ifgs, self.params) def test_minimum_fraction_missing(self): self.params[C.REF_MIN_FRAC] = None with pytest.raises(ConfigException): ref_pixel(self.ifgs, self.params) def test_minimum_fraction_threshold(self): for illegal in [-0.1, 1.1, 1.000001, -0.0000001]: self.params[C.REF_MIN_FRAC] = illegal with pytest.raises(RefPixelError): ref_pixel(self.ifgs, self.params) def test_search_windows(self): # 45 is max # cells a width 3 sliding window can iterate over for illegal in [-5, -1, 0, 46, 50, 100]: self.params[C.REFNX] = illegal with pytest.raises(RefPixelError): ref_pixel(self.ifgs, self.params) # 40 is max # cells a width 3 sliding window can iterate over for illegal in [-5, -1, 0, 71, 85, 100]: self.params[C.REFNY] = illegal with pytest.raises(RefPixelError): ref_pixel(self.ifgs, self.params) def test_missing_search_windows(self): self.params[C.REFNX] = None with pytest.raises(ConfigException): ref_pixel(self.ifgs, self.params) self.params[C.REFNX] = REFNX self.params[C.REFNY] = None with pytest.raises(ConfigException): ref_pixel(self.ifgs, self.params) class TestReferencePixelTests: """ Tests reference pixel search """ @classmethod def setup_method(cls): cls.params = Configuration(TEST_CONF_ROIPAC).__dict__ cls.params[C.OUT_DIR], cls.ifgs = copy_and_setup_small_data() cls.params[C.REFNX] = REFNX cls.params[C.REFNY] = REFNY cls.params[C.REF_CHIP_SIZE] = CHIPSIZE cls.params[C.REF_MIN_FRAC] = MIN_FRAC cls.params[C.PARALLEL] = PARALLEL def test_all_below_threshold_exception(self): # test failure when no valid stacks in dataset # rig mock data to be below threshold mock_ifgs = [MockIfg(i, 6, 7) for i in self.ifgs] for m in mock_ifgs: m.phase_data[:1] = nan m.phase_data[1:5] = 0.1 m.phase_data[5:] = nan self.params[C.REFNX] = 2 self.params[C.REFNY] = 2 self.params[C.REF_CHIP_SIZE] = CHIPSIZE self.params[C.REF_MIN_FRAC] = MIN_FRAC self.params[C.PARALLEL] = PARALLEL with pytest.raises(ValueError): ref_pixel(mock_ifgs, self.params) def test_refnxy_step_1(self): # test step of 1 for refnx|y gets the reference pixel for axis centre mock_ifgs = [MockIfg(i, 47, 72) for i in self.ifgs] for m in mock_ifgs: m.phase_data[:1] = 0.2 m.phase_data[1:5] = 0.1 m.phase_data[5:] = 0.3 exp_refpx = (1, 1) self.params[C.REFNX] = 1 self.params[C.REFNY] = 1 self.params[C.REF_CHIP_SIZE] = CHIPSIZE self.params[C.REF_MIN_FRAC] = MIN_FRAC self.params[C.PARALLEL] = PARALLEL res = ref_pixel(mock_ifgs, self.params) assert exp_refpx == res def test_large_window(self): # 5x5 view over a 5x5 ifg with 1 window/ref pix search chps = 5 mockifgs = [MockIfg(i, chps, chps) for i in self.ifgs] self.params[C.REFNX] = 1 self.params[C.REFNY] = 1 self.params[C.REF_CHIP_SIZE] = chps self.params[C.REF_MIN_FRAC] = MIN_FRAC self.params[C.PARALLEL] = PARALLEL res = ref_pixel(mockifgs, self.params) assert (2, 2) == res def test_step(self): # test different search windows to verify x/y step calculation # convenience testing function def assert_equal(actual, expected): for a, e in zip(actual, expected): assert a == e # start with simple corner only test width = 47 radius = 2 refnx = 2 exp = [2, 25, 44] act = _step(width, refnx, radius) assert_equal(act, exp) # test with 3 windows refnx = 3 exp = [2, 17, 32] act = _step(width, refnx, radius) assert_equal(act, exp) # test 4 search windows refnx = 4 exp = [2, 13, 24, 35] act = _step(width, refnx, radius) assert_equal(act, exp) def test_ref_pixel(self): exp_refpx = (2, 25) self.params[C.REFNX] = 2 self.params[C.REFNY] = 2 self.params[C.REF_CHIP_SIZE] = 5 self.params[C.REF_MIN_FRAC] = MIN_FRAC self.params[C.PARALLEL] = PARALLEL res = ref_pixel(self.ifgs, self.params) assert res == exp_refpx # Invalidate first data stack, get new refpix coods & retest for i in self.ifgs: i.phase_data[:30, :50] = nan exp_refpx = (38, 2) res = ref_pixel(self.ifgs, self.params) assert res == exp_refpx def _expected_ref_pixel(ifgs, cs): """Helper function for finding reference pixel when refnx/y=2""" # calculate expected data data = [i.phase_data for i in ifgs] # len 17 list of arrays ul = [i[:cs, :cs] for i in data] # upper left corner stack ur = [i[:cs, -cs:] for i in data] ll = [i[-cs:, :cs] for i in data] lr = [i[-cs:, -cs:] for i in data] ulm = mean([std(i[~isnan(i)]) for i in ul]) # mean std of all the layers urm = mean([std(i[~isnan(i)]) for i in ur]) llm = mean([std(i[~isnan(i)]) for i in ll]) lrm = mean([std(i[~isnan(i)]) for i in lr]) assert isnan([ulm, urm, llm, lrm]).any() is False # coords of the smallest mean is the result mn = [ulm, urm, llm, lrm] class TestLegacyEqualityTest: @classmethod def setup_method(cls): cls.params = Configuration(TEST_CONF_ROIPAC).__dict__ cls.params[C.PARALLEL] = 0 cls.params[C.OUT_DIR], cls.ifg_paths = copy_small_ifg_file_list() conf_file = Path(cls.params[C.OUT_DIR], 'conf_file.conf') pyrate.configuration.write_config_file(params=cls.params, output_conf_file=conf_file) cls.params = Configuration(conf_file).__dict__ cls.params_alt_ref_frac = copy.copy(cls.params) cls.params_alt_ref_frac[C.REF_MIN_FRAC] = 0.5 cls.params_all_2s = copy.copy(cls.params) cls.params_all_2s[C.REFNX] = 2 cls.params_all_2s[C.REFNY] = 2 cls.params_chipsize_15 = copy.copy(cls.params_all_2s) cls.params_chipsize_15[C.REF_CHIP_SIZE] = 15 cls.params_all_1s = copy.copy(cls.params) cls.params_all_1s[C.REFNX] = 1 cls.params_all_1s[C.REFNY] = 1 cls.params_all_1s[C.REF_MIN_FRAC] = 0.7 for p, q in zip(cls.params[C.INTERFEROGRAM_FILES], cls.ifg_paths): # hack p.sampled_path = q p.tmp_sampled_path = q @classmethod def teardown_method(cls): shutil.rmtree(cls.params[C.OUT_DIR]) def test_small_test_data_ref_pixel_lat_lon_provided(self): self.params[C.REFX], self.params[C.REFY] = 150.941666654, -34.218333314 refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(self.params) assert refx == 38 assert refy == 58 assert 0.8 == pytest.approx(self.params[C.REF_MIN_FRAC]) def test_small_test_data_ref_pixel(self): refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(self.params) assert refx == 38 assert refy == 58 assert 0.8 == pytest.approx(self.params[C.REF_MIN_FRAC]) def test_small_test_data_ref_chipsize_15(self): refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(self.params_chipsize_15) assert refx == 7 assert refy == 7 assert 0.5 == pytest.approx(self.params_alt_ref_frac[C.REF_MIN_FRAC]) def test_metadata(self): refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(self.params_chipsize_15) for i in self.ifg_paths: ifg = shared.Ifg(i) ifg.open(readonly=True) md = ifg.meta_data for k, v in zip([ifc.PYRATE_REFPIX_X, ifc.PYRATE_REFPIX_Y, ifc.PYRATE_REFPIX_LAT, ifc.PYRATE_REFPIX_LON, ifc.PYRATE_MEAN_REF_AREA, ifc.PYRATE_STDDEV_REF_AREA], [str(refx), str(refy), 0, 0, 0, 0]): assert k in md # metadata present # assert values ifg.close() def test_small_test_data_ref_all_1(self): refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(self.params_all_1s) assert 0.7 == pytest.approx(self.params_all_1s[C.REF_MIN_FRAC]) assert 1 == self.params_all_1s[C.REFNX] assert 1 == self.params_all_1s[C.REFNY] assert refx == 2 assert refy == 2 class TestLegacyEqualityTestMultiprocessParallel: @classmethod def setup_method(cls): cls.params = Configuration(TEST_CONF_ROIPAC).__dict__ cls.params[C.PARALLEL] = 1 cls.params[C.OUT_DIR], cls.ifg_paths = copy_small_ifg_file_list() conf_file = Path(cls.params[C.OUT_DIR], 'conf_file.conf') pyrate.configuration.write_config_file(params=cls.params, output_conf_file=conf_file) cls.params = Configuration(conf_file).__dict__ cls.params_alt_ref_frac = copy.copy(cls.params) cls.params_alt_ref_frac[C.REF_MIN_FRAC] = 0.5 cls.params_all_2s = copy.copy(cls.params) cls.params_all_2s[C.REFNX] = 2 cls.params_all_2s[C.REFNY] = 2 cls.params_chipsize_15 = copy.copy(cls.params_all_2s) cls.params_chipsize_15[C.REF_CHIP_SIZE] = 15 cls.params_all_1s = copy.copy(cls.params) cls.params_all_1s[C.REFNX] = 1 cls.params_all_1s[C.REFNY] = 1 cls.params_all_1s[C.REF_MIN_FRAC] = 0.7 for p, q in zip(cls.params[C.INTERFEROGRAM_FILES], cls.ifg_paths): # hack p.sampled_path = q p.tmp_sampled_path = q @classmethod def teardown_method(cls): shutil.rmtree(cls.params[C.OUT_DIR]) def test_small_test_data_ref_pixel(self): refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(self.params) assert refx == 38 assert refy == 58 assert 0.8 == pytest.approx(self.params[C.REF_MIN_FRAC]) def test_more_small_test_data_ref_pixel(self): refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(self.params_alt_ref_frac) assert refx == 38 assert refy == 58 assert 0.5 == pytest.approx(self.params_alt_ref_frac[C.REF_MIN_FRAC]) def test_small_test_data_ref_pixel_all_2(self): refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(self.params_all_2s) assert refx == 25 assert refy == 2 assert 0.5 == pytest.approx(self.params_alt_ref_frac[C.REF_MIN_FRAC]) def test_small_test_data_ref_chipsize_15(self): refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(self.params_chipsize_15) assert refx == 7 assert refy == 7 assert 0.5 == pytest.approx(self.params_alt_ref_frac[C.REF_MIN_FRAC]) def test_small_test_data_ref_all_1(self): refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(self.params_all_1s) assert 0.7 == pytest.approx(self.params_all_1s[C.REF_MIN_FRAC]) assert 1 == self.params_all_1s[C.REFNX] assert 1 == self.params_all_1s[C.REFNY] assert refx == 2 assert refy == 2 @pytest.mark.slow @pytest.mark.skipif(not PY37GDAL304, reason="Only run in one CI env") def test_error_msg_refpixel_out_of_bounds(tempdir, gamma_conf): "check correct latitude/longitude refpixel error is raised when specified refpixel is out of bounds" for x, (refx, refy) in zip(['longitude', 'latitude', 'longitude and latitude'], [(150., -34.218333314), (150.941666654, -34.), (150, -34)]): _, err = _get_mlooked_files(gamma_conf, Path(tempdir()), refx=refx, refy=refy) msg = "Supplied {} value is outside the bounds of the interferogram data" assert msg.format(x) in err @pytest.mark.slow @pytest.mark.skipif(not PY37GDAL304, reason="Only run in one CI env") def test_gamma_ref_pixel_search_vs_lat_lon(tempdir, gamma_conf): params_1, _ = _get_mlooked_files(gamma_conf, Path(tempdir()), refx=-1, refy=-1) params_2, _ = _get_mlooked_files(gamma_conf, Path(tempdir()), refx=150.941666654, refy=-34.218333314) assert_two_dirs_equal(params_1[C.COHERENCE_DIR], params_2[C.COHERENCE_DIR], ['*_coh.tif', '*_cc.tif'], 34) assert_two_dirs_equal(params_1[C.INTERFEROGRAM_DIR], params_2[C.INTERFEROGRAM_DIR], ["*_ifg.tif", '*_unw.tif'], 34) assert_two_dirs_equal(params_1[C.GEOMETRY_DIR], params_2[C.GEOMETRY_DIR], ["*.tif"], 8) def _get_mlooked_files(gamma_conf, tdir, refx, refy): params = manipulate_test_conf(gamma_conf, tdir) params[C.REFX] = refx params[C.REFY] = refy output_conf_file = 'config.conf' output_conf = tdir.joinpath(output_conf_file) pyrate.configuration.write_config_file(params=params, output_conf_file=output_conf) params = Configuration(output_conf).__dict__ conv2tif.main(params) params = Configuration(output_conf).__dict__ prepifg.main(params) err = run(f"pyrate correct -f {output_conf}", shell=True, universal_newlines=True, stderr=PIPE).stderr return params, err @pytest.mark.slow @pytest.mark.skipif(not PY37GDAL304, reason="Only run in one CI env") class TestRefPixelReuseLoadsSameFileAndPixels: @classmethod def setup_method(cls): cls.conf = TEST_CONF_GAMMA params = Configuration(cls.conf).__dict__ conv2tif.main(params) params = Configuration(cls.conf).__dict__ prepifg.main(params) params = Configuration(cls.conf).__dict__ correct._copy_mlooked(params) cls.params = params @classmethod def teardown_method(cls): shutil.rmtree(cls.params[C.OUT_DIR]) def test_ref_pixel_multiple_runs_reuse_from_disc(self, ref_pixel): params = self.params params[C.REFX], params[C.REFY] = ref_pixel params[C.REF_PIXEL_FILE] = Configuration.ref_pixel_path(params) ref_pixel_calc_wrapper(params) ref_pixel_file = self.params[C.REF_PIXEL_FILE] time_written = os.stat(ref_pixel_file).st_mtime assert self.params[C.REFX_FOUND] == 38 assert self.params[C.REFY_FOUND] == 58 # run again ref_pixel_calc_wrapper(self.params) ref_pixel_file = self.params[C.REF_PIXEL_FILE] time_written_1 = os.stat(ref_pixel_file).st_mtime assert self.params[C.REFX_FOUND] == 38 assert self.params[C.REFY_FOUND] == 58 # run a third time ref_pixel_calc_wrapper(self.params) ref_pixel_file = self.params[C.REF_PIXEL_FILE] time_written_2 = os.stat(ref_pixel_file).st_mtime assert time_written == time_written_2 == time_written_1 assert self.params[C.REFX], self.params[C.REFY] == ref_pixel assert self.params[C.REFX_FOUND] == 38 assert self.params[C.REFY_FOUND] == 58 @pytest.fixture(scope='module') def x_y_pixel(): dem = shared.DEM(SML_TEST_DEM_TIF) dem.open() Y = dem.nrows X = dem.ncols x = np.random.choice(range(X), 5) y = np.random.choice(range(Y), 5) return itertools.product(x, y) # returns a matrix of 5x5 random x, y pairs def test_convert_pixel_value_to_geographic_coordinate(x_y_pixel): transform = dem_transform() for x, y in x_y_pixel: lon, lat = convert_pixel_value_to_geographic_coordinate(x, y, transform) out = run(f"gdallocationinfo -geoloc {SML_TEST_DEM_TIF} {lon} {lat}", shell=True, universal_newlines=True, stdout=PIPE).stdout xs = (x, x+1, x-1) ys = (y, y+1, y-1) assert any(f"({xx}P,{yy}L)" in out for xx, yy in itertools.product(xs, ys)) def dem_transform(): dem = shared.DEM(SML_TEST_DEM_TIF) dem.open() transform = dem.dataset.GetGeoTransform() return transform def test_convert_geographic_coordinate_to_pixel_value(x_y_pixel): transform = dem_transform() for x, y in x_y_pixel: lon, lat = convert_pixel_value_to_geographic_coordinate(x, y, transform) xp, yp = convert_geographic_coordinate_to_pixel_value(lon, lat, transform) assert (xp == x) & (yp == y) ================================================ FILE: tests/test_roipac.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains tests for the roipac.py PyRate module. """ import os import sys from datetime import date from os.path import exists, join from numpy.testing import assert_array_almost_equal from osgeo import gdal import pyrate.core.ifgconstants as ifc from pyrate.core import roipac from pyrate.core.shared import GeotiffException from pyrate.core.shared import write_fullres_geotiff from tests.common import HEADERS_TEST_DIR, PREP_TEST_OBS, PREP_TEST_TIF from tests.common import SML_TEST_DEM_DIR, ROIPAC_SML_TEST_DIR, TEMPDIR, UnitTestAdaptation from tests.common import SML_TEST_DEM_ROIPAC, SML_TEST_DEM_HDR gdal.UseExceptions() # sanity checking if not exists(HEADERS_TEST_DIR): sys.exit("ERROR: Missing the 'headers' data for unittests\n") # constants SHORT_HEADER_PATH = join(ROIPAC_SML_TEST_DIR, 'geo_060619-061002.unw.rsc') FULL_HEADER_PATH = join(HEADERS_TEST_DIR, "geo_060619-060828.unw.rsc") class TestRoipacToGeoTiff(UnitTestAdaptation): """Tests conversion of GAMMA rasters to custom PyRate GeoTIFF""" @classmethod def setup_class(cls): hdr_path = join(PREP_TEST_OBS, 'geo_060619-061002.unw.rsc') cls.HDRS = roipac.parse_header(hdr_path) @classmethod def teardown_class(cls): pass def test_to_geotiff_dem(self): hdr = roipac.parse_header(SML_TEST_DEM_HDR) self.dest = os.path.join(TEMPDIR, "tmp_roipac_dem.tif") write_fullres_geotiff(hdr, SML_TEST_DEM_ROIPAC, self.dest, nodata=0) exp_path = join(SML_TEST_DEM_DIR, 'roipac_test_trimmed.tif') exp_ds = gdal.Open(exp_path) ds = gdal.Open(self.dest) # compare data and geographic headers assert_array_almost_equal(exp_ds.ReadAsArray(), ds.ReadAsArray()) self.compare_rasters(ds, exp_ds) self.assertIsNotNone(ds.GetMetadata()) def test_to_geotiff_ifg(self): # tricker: needs ifg header, and DEM one for extents hdrs = self.HDRS.copy() hdrs[ifc.PYRATE_DATUM] = 'WGS84' hdrs[ifc.DATA_TYPE] = ifc.ORIG self.dest = os.path.join('tmp_roipac_ifg.tif') data_path = join(PREP_TEST_OBS, 'geo_060619-061002.unw') write_fullres_geotiff(hdrs, data_path, self.dest, nodata=0) ds = gdal.Open(self.dest) band = ds.GetRasterBand(1) self.assertEqual(ds.RasterCount, 1) exp_path = join(PREP_TEST_TIF, 'geo_060619-061002_unw.tif') exp_ds = gdal.Open(exp_path) exp_band = exp_ds.GetRasterBand(1) # compare data and geographic headers assert_array_almost_equal(exp_band.ReadAsArray(), band.ReadAsArray()) self.compare_rasters(ds, exp_ds) md = ds.GetMetadata() date1 = date(2006, 6, 19) date2 = date(2006, 10, 2) diff = (date2 - date1).days self.assertTrue(md[ifc.FIRST_DATE] == str(date1)) self.assertTrue(md[ifc.SECOND_DATE] == str(date2)) self.assertTrue(md[ifc.PYRATE_TIME_SPAN] == str(diff / ifc.DAYS_PER_YEAR)) wavelen = float(md[ifc.PYRATE_WAVELENGTH_METRES]) self.assertAlmostEqual(wavelen, 0.0562356424) def test_to_geotiff_wrong_input_data(self): # ensure failure if TIF/other file used instead of binary UNW data self.dest = os.path.join(TEMPDIR, 'tmp_roipac_ifg.tif') data_path = join(PREP_TEST_TIF, 'geo_060619-061002_unw.tif') self.assertRaises( GeotiffException, write_fullres_geotiff, self.HDRS, data_path, self.dest, nodata=0) def test_bad_projection(self): hdrs = self.HDRS.copy() hdrs[ifc.PYRATE_DATUM] = 'bad datum string' hdrs[ifc.DATA_TYPE] = ifc.ORIG self.dest = os.path.join(TEMPDIR, 'tmp_roipac_ifg2.tif') data_path = join(PREP_TEST_OBS, 'geo_060619-061002.unw') self.assertRaises(GeotiffException, write_fullres_geotiff, hdrs, data_path, self.dest, 0) def compare_rasters(self, ds, exp_ds): band = ds.GetRasterBand(1) exp_band = exp_ds.GetRasterBand(1) nodata = band.GetNoDataValue() self.assertFalse(nodata is None) self.assertEqual(exp_band.GetNoDataValue(), nodata) pj = ds.GetProjection() self.assertTrue('WGS 84' in pj) self.assertEqual(exp_ds.GetProjection(), pj) for exp, act in zip(exp_ds.GetGeoTransform(), ds.GetGeoTransform()): self.assertAlmostEqual(exp, act, places=4) class TestDateParsing(UnitTestAdaptation): def test_parse_short_date_pre2000(self): dstr = "980416" self.assertEqual(date(1998, 4, 16), roipac.parse_date(dstr)) def test_parse_short_date_post2000(self): dstr = "081006" self.assertEqual(date(2008, 10, 6), roipac.parse_date(dstr)) def test_parse_date_range(self): dstr = "980416-081006" exp = (date(1998, 4, 16), date(2008, 10, 6)) self.assertEqual(exp, roipac.parse_date(dstr)) class TestHeaderParsingTests(UnitTestAdaptation): """Verifies ROIPAC headers are parsed correctly.""" # short format header tests def test_parse_short_roipac_header(self): hdrs = roipac.parse_header(SHORT_HEADER_PATH) self.assertEqual(hdrs[ifc.PYRATE_NCOLS], 47) self.assertEqual(hdrs[ifc.PYRATE_NROWS], 72) self.assertAlmostEqual(hdrs[ifc.PYRATE_LONG], 150.910) self.assertEqual(hdrs[ifc.PYRATE_X_STEP], 0.000833333) self.assertEqual(hdrs[ifc.PYRATE_LAT], -34.170000000) self.assertEqual(hdrs[ifc.PYRATE_Y_STEP], -0.000833333) self.assertEqual(hdrs[ifc.PYRATE_WAVELENGTH_METRES], 0.0562356424) def test_parse_short_header_has_timespan(self): # Ensures TIME_SPAN_YEAR field is added during parsing hdrs = roipac.parse_header(SHORT_HEADER_PATH) self.assertIn(roipac.TIME_SPAN_YEAR, hdrs.keys()) # check time span calc first = date(2006, 6, 19) second = date(2006, 10, 2) diff = (second - first).days / ifc.DAYS_PER_YEAR self.assertEqual(diff, hdrs[roipac.TIME_SPAN_YEAR]) # long format header tests def test_parse_full_roipac_header(self): # Ensures "long style" original header can be parsed correctly hdrs = roipac.parse_header(FULL_HEADER_PATH) # check DATE/ DATE12 fields are parsed correctly date0 = date(2006, 6, 19) # from "DATE 060619" header date2 = date(2006, 8, 28) # from DATE12 060619-060828 self.assertEqual(hdrs[ifc.FIRST_DATE], date0) self.assertEqual(hdrs[ifc.SECOND_DATE], date2) def test_read_full_roipac_header2(self): # Tests header from cropped original dataset is parsed correctly hdrs = roipac.parse_header(FULL_HEADER_PATH) self.assertTrue(len(hdrs) is not None) def test_xylast(self): # Test the X_LAST and Y_LAST header elements are calculated hdrs = roipac.parse_header(FULL_HEADER_PATH) self.assertAlmostEqual(hdrs[roipac.X_LAST], 151.8519444445) self.assertAlmostEqual(hdrs[roipac.Y_LAST], -34.625) ================================================ FILE: tests/test_shared.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This Python module contains tests for the shared.py PyRate module. """ import os import shutil import sys import random import string import tempfile import pytest from pathlib import Path from itertools import product from numpy import isnan, where, nan from os.path import join, basename, exists from stat import S_IRGRP, S_IWGRP, S_IWOTH, S_IROTH, S_IRUSR, S_IWUSR import numpy as np from numpy.testing import assert_array_equal from osgeo import gdal from osgeo.gdal import Open, Dataset, UseExceptions import pyrate.constants as C import tests.common from tests.common import SML_TEST_TIF, SML_TEST_DEM_TIF, TEMPDIR, WORKING_DIR from pyrate.core import shared, ifgconstants as ifc, gamma from pyrate.core.shared import dem_or_ifg from pyrate.core import mpiops from pyrate import prepifg, conv2tif from pyrate.configuration import Configuration, MultiplePaths from pyrate.core.shared import Ifg, DEM, RasterException from pyrate.core.shared import cell_size, _utm_zone, tiles_split from tests import common UseExceptions() if not exists(SML_TEST_TIF): sys.exit("ERROR: Missing small_test data for unit tests\n") class TestIfgTests: """Unit tests for the Ifg/interferogram class.""" def setup_class(cls): cls.ifg = Ifg(join(SML_TEST_TIF, 'geo_060619-061002_unw.tif')) cls.ifg.open() cls.ifg.nodata_value = 0 def test_headers_as_attr(self): for a in ['ncols', 'nrows', 'x_first', 'x_step', 'y_first', 'y_step', 'wavelength', 'first', 'second']: assert getattr(self.ifg, a) is not None def test_convert_to_nans(self): self.ifg.convert_to_nans() assert self.ifg.nan_converted def test_xylast(self): # ensure the X|Y_LAST header element has been created assert self.ifg.x_last == pytest.approx(150.9491667) assert self.ifg.y_last == pytest.approx(-34.23) def test_num_cells(self): # test cell size from header elements data = self.ifg.phase_band.ReadAsArray() ys, xs = data.shape exp_ncells = ys * xs assert exp_ncells == self.ifg.num_cells def test_shape(self): assert self.ifg.shape == self.ifg.phase_data.shape def test_nan_count(self): num_nan = 0 for row in self.ifg.phase_data: for v in row: if isnan(v): num_nan += 1 if self.ifg.nan_converted: assert num_nan == self.ifg.nan_count else: assert num_nan == 0 def test_phase_band(self): data = self.ifg.phase_band.ReadAsArray() assert data.shape == (72, 47) def test_nan_fraction(self): # NB: source data lacks 0 -> NaN conversion data = self.ifg.phase_data data = where(data == 0, nan, data) # fake 0 -> nan for the count below # manually count # nan cells nans = 0 ys, xs = data.shape for y, x in product(range(ys), range(xs)): if isnan(data[y, x]): nans += 1 del data num_cells = float(ys * xs) assert nans > 0 assert nans <= num_cells assert nans / num_cells == self.ifg.nan_fraction def test_xy_size(self): assert ~ (self.ifg.ncols is None) assert ~ (self.ifg.nrows is None) # test with tolerance from base 90m cell # within 2% of cells over small? assert self.ifg.y_size > 88.0 assert self.ifg.y_size < 92.0, 'Got %s' % self.ifg.y_size width = 76.9 # from nearby PyRate coords assert self.ifg.x_size > 0.97 * width # ~3% tolerance assert self.ifg.x_size < 1.03 * width def test_centre_latlong(self): lat_exp = self.ifg.y_first + \ (int(self.ifg.nrows / 2) * self.ifg.y_step) long_exp = self.ifg.x_first + \ (int(self.ifg.ncols / 2) * self.ifg.x_step) assert lat_exp == self.ifg.lat_centre assert long_exp == self.ifg.long_centre def test_centre_cell(self): assert self.ifg.x_centre == 23 assert self.ifg.y_centre == 36 def test_time_span(self): assert self.ifg.time_span == pytest.approx(0.287474332649) def test_wavelength(self): assert self.ifg.wavelength == pytest.approx(0.0562356424) class TestIfgIOTests: def setup_method(self): self.ifg = Ifg(join(SML_TEST_TIF, 'geo_070709-070813_unw.tif')) self.header = join(common.ROIPAC_SML_TEST_DIR, 'geo_070709-070813_unw.rsc') def test_open(self): assert self.ifg.dataset is None assert self.ifg.is_open is False self.ifg.open(readonly=True) assert self.ifg.dataset is not None assert self.ifg.is_open is True assert isinstance(self.ifg.dataset, Dataset) # ensure open cannot be called twice with pytest.raises(RasterException): self.ifg.open(True) def test_open_ifg_from_dataset(self): """ Test showing open() can not be used for Ifg created with gdal.Dataset object as Dataset has already been read in """ self.ifg.open() dataset = self.ifg.dataset new_ifg = Ifg(dataset) with pytest.raises(RasterException): new_ifg.open() def test_write(self): base = TEMPDIR src = self.ifg.data_path dest = join(base, basename(self.ifg.data_path)) # shutil.copy needs to copy writeable permission from src os.chmod(src, S_IRGRP | S_IWGRP | S_IWOTH | S_IROTH | S_IRUSR | S_IWUSR) shutil.copy(src, dest) os.chmod(src, S_IRGRP | S_IROTH | S_IRUSR) # revert i = Ifg(dest) i.open() i.phase_data[0, 1:] = nan i.write_modified_phase() del i # reopen to ensure data/nans can be read back out i = Ifg(dest) i.open(readonly=True) assert_array_equal(True, isnan(i.phase_data[0, 1:])) i.close() os.remove(dest) def test_write_fails_on_readonly(self): # check readonly status is same before # and after open() for readonly file assert self.ifg.is_read_only self.ifg.open(readonly=True) assert self.ifg.is_read_only with pytest.raises(IOError): self.ifg.write_modified_phase() def test_phase_band_unopened_ifg(self): try: _ = self.ifg.phase_band self.fail("Should not be able to access band without open dataset") except RasterException: pass def test_nan_fraction_unopened(self): try: # NB: self.assertRaises doesn't work here (as it is a property?) _ = self.ifg.nan_fraction self.fail("Shouldn't be able to " "call nan_fraction() with unopened Ifg") except RasterException: pass def test_phase_data_properties(self): # Use raw GDAL to isolate raster reading from Ifg functionality ds = Open(self.ifg.data_path) data = ds.GetRasterBand(1).ReadAsArray() del ds self.ifg.open() # test full array and row by row access assert_array_equal(data, self.ifg.phase_data) for y, row in enumerate(self.ifg.phase_rows): assert_array_equal(data[y], row) # test the data is cached if changed crd = (5, 4) orig = self.ifg.phase_data[crd] self.ifg.phase_data[crd] *= 2 nv = self.ifg.phase_data[crd] # pull new value out again assert nv == 2 * orig # FIXME: # class IncidenceFileTests(): # 'Unit tests to verify operations on GeoTIFF format Incidence rasters' # # def setUp(self): # raise NotImplementedError # self.inc = Incidence(join(INCID_TEST_DIR, '128x2.tif')) # self.inc.open() # # # def test_incidence_data(self): # # check incidences rises while traversing the scene # data = self.inc.incidence_data # diff = data.ptp() # self.assertTrue(diff > 0.5, "Got ptp() diff of %s" % diff) # # # ascending pass, values should increase from W->E across scene # for i in range(2): # d = data[i] # self.assertFalse((d == 0).any()) # ensure no NODATA # self.assertFalse((isnan(d)).any()) # # diff = array([d[i+1] - d[i] for i in range(len(d)-1)]) # res = abs(diff[diff < 0]) # TODO: check if this is normal # self.assertTrue((res < 1e-4).all()) # # # def test_azimuth_data(self): # # ensure azimuth is fairly constant # # az = self.inc.azimuth_data # self.assertFalse((az == 0).all()) # az = az[az != 0] # filter NODATA cells # # # azimuth should be relatively constant # ptp = az.ptp() # self.assertTrue(ptp < 0.1, msg="min -> max diff is %s" % ptp) class TestDEMTests: 'Unit tests to verify operations on GeoTIFF format DEMs' def setup_method(self): self.ras = DEM(SML_TEST_DEM_TIF) def test_create_raster(self): # validate header path assert os.path.exists(self.ras.data_path) def test_headers_as_attr(self): self.ras.open() attrs = ['ncols', 'nrows', 'x_first', 'x_step', 'y_first', 'y_step' ] # TODO: are 'projection' and 'datum' attrs needed? for a in attrs: assert getattr(self.ras, a) is not None def test_is_dem(self): self.ras = DEM(join(SML_TEST_TIF, 'geo_060619-061002_unw.tif')) assert ~hasattr(self.ras, 'datum') def test_open(self): assert self.ras.dataset is None self.ras.open() assert self.ras.dataset is not None assert isinstance(self.ras.dataset, Dataset) # ensure open cannot be called twice with pytest.raises(RasterException): self.ras.open() # def test_band_fails_with_unopened_raster(self): # now opening if not open # # test accessing bands with open and unopened datasets # with pytest.raises(RasterException): # self.ras.band def test_band_read_with_open_raster(self): data = self.ras.band.ReadAsArray() assert data.shape == (72, 47) class TestWriteUnw: @classmethod @pytest.fixture(autouse=True) def setup_class(cls, gamma_params): # change the required params shared.mkdir_p(gamma_params[C.OUT_DIR]) from copy import deepcopy cls.params = deepcopy(gamma_params) cls.params[WORKING_DIR] = common.GAMMA_SML_TEST_DIR cls.params[C.PROCESSOR] = 1 # gamma cls.params[C.PARALLEL] = 0 cls.params[C.REF_EST_METHOD] = 1 cls.params[C.DEM_FILE] = common.SML_TEST_DEM_GAMMA cls.params[C.BASE_FILE_LIST] = common.GAMMA_SML_TEST_DIR # base_unw_paths need to be geotiffed and multilooked by run_prepifg cls.base_unw_paths = tests.common.original_ifg_paths(cls.params[C.IFG_FILE_LIST], cls.params[WORKING_DIR]) cls.base_unw_paths.append(common.SML_TEST_DEM_GAMMA) # dest_paths are tifs that have been geotif converted and multilooked conv2tif.main(cls.params) prepifg.main(cls.params) cls.dest_paths = [Path(cls.params[C.INTERFEROGRAM_DIR]).joinpath(Path(c.sampled_path).name).as_posix() for c in gamma_params[C.INTERFEROGRAM_FILES]] cls.dest_paths += [Path(cls.params[C.COHERENCE_DIR]).joinpath(Path(c.sampled_path).name).as_posix() for c in gamma_params[C.COHERENCE_FILE_PATHS]] cls.ifgs = [dem_or_ifg(i) for i in cls.dest_paths] for i in cls.ifgs: i.open() i.nodata_value = 0 @classmethod def teardown_class(cls): """auto cleaning on""" def test_unw_contains_same_data_as_numpy_array(self): from datetime import time temp_unw = tempfile.mktemp(suffix='.unw') temp_tif = tempfile.mktemp(suffix='.tif') # setup some header files for use in write_geotif dem_header_file = common.SML_TEST_DEM_HDR_GAMMA dem_header = gamma.parse_dem_header(dem_header_file) header = gamma.parse_epoch_header( os.path.join(common.GAMMA_SML_TEST_DIR, '20060828_slc.par')) header.update(dem_header) base_header = gamma.parse_baseline_header( os.path.join(common.GAMMA_SML_TEST_DIR, '20060828-20061211_base.par')) header.update(base_header) # insert some dummy data so we are the dem in write_fullres_geotiff is not # not activated and ifg write_fullres_geotiff operation works header[ifc.PYRATE_TIME_SPAN] = 0 header[ifc.SECOND_DATE] = 0 header[ifc.DATA_UNITS] = 'degrees' header[ifc.DATA_TYPE] = ifc.ORIG header[ifc.SECOND_TIME] = time(10) # now create arbitrary data data = np.random.rand(dem_header[ifc.PYRATE_NROWS], dem_header[ifc.PYRATE_NCOLS]) # convert numpy array to .unw shared.write_unw_from_data_or_geotiff(geotif_or_data=data, dest_unw=temp_unw, ifg_proc=1) # convert the .unw to geotif shared.write_fullres_geotiff(header=header, data_path=temp_unw, dest=temp_tif, nodata=np.nan) # now compare geotiff with original numpy array ds = gdal.Open(temp_tif, gdal.GA_ReadOnly) data_lv_theta = ds.ReadAsArray() ds = None np.testing.assert_array_almost_equal(data, data_lv_theta) try: os.remove(temp_tif) except PermissionError: print("File opened by another process.") try: os.remove(temp_unw) except PermissionError: print("File opened by another process.") def test_multilooked_tiffs_converted_to_unw_are_same(self): # Get multilooked geotiffs geotiffs = list(set(self.dest_paths)) geotiffs = [g for g in geotiffs if 'dem' not in g] # Convert back to .unw dest_unws = [] for g in set(geotiffs): dest_unw = os.path.join(self.params[C.OUT_DIR], Path(g).stem + '.unw') shared.write_unw_from_data_or_geotiff(geotif_or_data=g, dest_unw=dest_unw, ifg_proc=1) dest_unws.append(dest_unw) dest_unws_ = [] for d in dest_unws: dest_unws_.append(MultiplePaths(d, self.params)) # Convert back to tiff new_geotiffs_ = conv2tif.do_geotiff(dest_unws_, self.params) new_geotiffs = [gt for gt, b in new_geotiffs_] # Ensure original multilooked geotiffs and # unw back to geotiff are the same geotiffs.sort(key=lambda x: Path(x).name) new_geotiffs.sort(key=lambda x: Path(x).name) for g, u in zip(geotiffs, new_geotiffs): g_ds = gdal.Open(g) u_gs = gdal.Open(u) np.testing.assert_array_almost_equal(u_gs.ReadAsArray(), g_ds.ReadAsArray()) u_gs = None g_ds = None def test_roipac_raises(self): geotiffs = [os.path.join( self.params[C.OUT_DIR], os.path.basename(b).split('.')[0] + '_' + os.path.basename(b).split('.')[1] + '.tif') for b in self.base_unw_paths] for g in geotiffs[:1]: dest_unw = os.path.join(self.params[C.OUT_DIR], os.path.splitext(g)[0] + '.unw') with pytest.raises(NotImplementedError): shared.write_unw_from_data_or_geotiff(geotif_or_data=g, dest_unw=dest_unw, ifg_proc=0) class TestGeodesy: def test_utm_zone(self): # test some different zones (collected manually) for lon in [174.0, 176.5, 179.999, 180.0]: assert 60 == _utm_zone(lon) for lon in [144.0, 144.1, 146.3456, 149.9999]: assert 55 == _utm_zone(lon) for lon in [-180.0, -179.275, -176.925]: assert 1 == _utm_zone(lon) for lon in [-72.0, -66.1]: assert 19 == _utm_zone(lon) for lon in [0.0, 0.275, 3.925, 5.999]: assert 31 == _utm_zone(lon) def test_cell_size_polar_region(self): # Can't have polar area zones: see http://www.dmap.co.uk/utmworld.htm for lat in [-80.1, -85.0, -90.0, 84.1, 85.0, 89.9999, 90.0]: with pytest.raises(ValueError): cell_size(lat, 0, 0.1, 0.1) def test_cell_size_calc(self): # test conversion of X|Y_STEP to X|Y_SIZE x_deg = 0.000833333 y_deg = -x_deg approx = 90.0 # x_deg is approx 90m exp_low = approx - (.15 * approx) # assumed tolerance exp_high = approx + (.15 * approx) latlons = [(10.0, 15.0), (-10.0, 15.0), (10.0, -15.0), (-10.0, -15.0), (178.0, 33.0), (-178.0, 33.0), (178.0, -33.0), (-178.0, -33.0) ] for lon, lat in latlons: xs, ys = cell_size(lat, lon, x_deg, y_deg) for s in (xs, ys): assert s > 0, "size=%s" % s assert s > exp_low, "size=%s" % s assert s < exp_high, "size=%s" % s @pytest.fixture(params=[0, 1]) def parallel(request): return request.param def get_random_string(length): letters = string.ascii_lowercase result_str = ''.join(random.choice(letters) for _ in range(length)) return result_str def _data_types(): return { 'str': [get_random_string(np.random.randint(2, 10)) for _ in range(20)], 'int': np.arange(20), 'ndarray': [np.random.randint(low=0, high=10, size=(2, 3), dtype=np.uint8) for _ in range(20)] } data_types = mpiops.run_once(_data_types) # required otherwise differnt arrays are generated in each mpi process @pytest.fixture(params=list(data_types.keys())) def data_type(request): return request.param def test_tiles_split(parallel, data_type): params = { C.TILES: data_types[data_type], C.PARALLEL: parallel, C.LOG_LEVEL: 'INFO', 'multiplier': 2, C.PROCESSES: 4 } def func(tile, params): return tile * params['multiplier'] ret = tiles_split(func, params) expected_ret = np.array([item*params['multiplier'] for item in data_types[data_type]], dtype=object) np.testing.assert_array_equal(ret, expected_ret) def test_convert_to_radians(): import math data = np.random.randint(1, 10, (4, 5)) wavelength = 10.5 ret = shared.convert_mm_to_radians(data, wavelength) expected = data * (4 * math.pi) / wavelength /ifc.MM_PER_METRE np.testing.assert_array_almost_equal(ret, expected) def test_convert_to_radians_ifg(ten_geotiffs): for g in ten_geotiffs[:2]: ifg = Ifg(g) ifg.open() md = ifg.dataset.GetMetadata() assert ifc.DATA_TYPE in md assert md[ifc.DATA_TYPE] == ifc.ORIG assert md[ifc.DATA_UNITS] == shared.RADIANS rad_data = ifg.phase_data ifg.convert_to_mm() assert ifg.meta_data[ifc.DATA_UNITS] == shared.MILLIMETRES ifg.convert_to_radians() assert md[ifc.DATA_UNITS] == shared.RADIANS np.testing.assert_array_almost_equal(rad_data, ifg.phase_data, decimal=4) ================================================ FILE: tests/test_stackrate.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # coding: utf-8 """ This Python module contains tests for the stack.py PyRate module. """ import os import shutil from numpy import eye, array, ones, nan import numpy as np from numpy.testing import assert_array_almost_equal, assert_array_equal import pyrate.constants as C import pyrate.core.orbital import pyrate.core.prepifg_helper import pyrate.core.ref_phs_est import pyrate.core.refpixel import tests.common from pyrate.core import covariance as vcm_module from pyrate.core.stack import stack_rate_pixel, mask_rate from pyrate import correct, prepifg, conv2tif from pyrate.configuration import Configuration from tests import common from tests.common import SML_TEST_DIR, prepare_ifgs_without_phase, pre_prepare_ifgs def default_params(): return {'pthr': 3, 'nsig': 3, 'maxsig': 2, 'parallel': 1, 'processes': 8} class SinglePixelIfg(object): def __init__(self, timespan, phase): self.time_span = timespan self.phase_data = array([[phase]]) class TestStackRatePixel: """ Tests the weighted least squares algorithm for determining the best fitting velocity """ def setup_method(self): self.phase = array([0.5, 3.5, 4, 2.5, 3.5, 1]) self.timespan = array([[0.1, 0.7, 0.8, 0.5, 0.7, 0.2]]) self.vcmt = eye(6, 6) self.mst = ones((6, 1, 1)) self.mst[4] = 0 self.params = default_params() def test_stack_rate_pixel(self): # Simple test with one pixel and equal weighting exprate = array([[5.0]]) experr = array([[0.836242010007091]]) expsamp = array([[5]]) rate, error, samples = stack_rate_pixel(self.phase, self.mst, self.vcmt, self.timespan, self.params['nsig'], self.params['pthr']) assert_array_almost_equal(rate, exprate) assert_array_almost_equal(error, experr) assert_array_almost_equal(samples, expsamp) class TestMaskRate: """ Test the maxsig threshold masking algorithm """ def setup_method(self): self.r = array([5.0, 4.5]) # rates for 2 pixels self.e = array([1.1, 2.1]) # errors for 2 pixels def test_mask_rate_maxsig1(self): # both rate and error values masked rate, error = mask_rate(self.r, self.e, 1) assert_array_equal(rate, array([nan, nan])) assert_array_equal(error, array([nan, nan])) def test_mask_rate_maxsig2(self): # one rate and one error masked rate, error = mask_rate(self.r, self.e, 2) assert_array_equal(rate, array([5.0, nan])) assert_array_equal(error, array([1.1, nan])) def test_mask_rate_maxsig3(self): # No values masked in rate or error rate, error = mask_rate(self.r, self.e, 3) assert_array_equal(rate, self.r) assert_array_equal(error, self.e) class TestLegacyEquality: """ Tests equality with legacy data """ @classmethod def setup_class(cls): params = Configuration(common.TEST_CONF_ROIPAC).__dict__ params[C.TEMP_MLOOKED_DIR] = os.path.join(params[C.OUT_DIR], C.TEMP_MLOOKED_DIR) # force error maps to 1-sigma to match legacy params[C.VELERROR_NSIG] = 1 conv2tif.main(params) prepifg.main(params) params[C.REF_EST_METHOD] = 2 xlks, _, crop = pyrate.core.prepifg_helper.transform_params(params) dest_paths, headers = common.repair_params_for_correct_tests(params[C.INTERFEROGRAM_DIR], params) correct._copy_mlooked(params) copied_dest_paths = [os.path.join(params[C.TEMP_MLOOKED_DIR], os.path.basename(d)) for d in dest_paths] del dest_paths # start run_pyrate copy ifgs = pre_prepare_ifgs(copied_dest_paths, params) mst_grid = tests.common.mst_calculation(copied_dest_paths, params) refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(params) params[C.REFX] = refx params[C.REFY] = refy params[C.ORBFIT_OFFSET] = True # Estimate and remove orbit errors pyrate.core.orbital.remove_orbital_error(ifgs, params) ifgs = 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 = vcm_module.RDist(ifgs[0])() ifgs[0].close() maxvar = [vcm_module.cvd(i, params, r_dist)[0] for i in copied_dest_paths] for ifg in ifgs: ifg.open() vcmt = vcm_module.get_vcmt(ifgs, maxvar) for ifg in ifgs: ifg.close() ifg.open() # Calculate stacked rate map params[C.PARALLEL] = 1 cls.rate, cls.error, cls.samples = tests.common.calculate_stack_rate(ifgs, params, vcmt, mst_mat=mst_grid) # Calculate stacked rate map params[C.PARALLEL] = 0 cls.rate_s, cls.error_s, cls.samples_s = tests.common.calculate_stack_rate(ifgs, params, vcmt, mst_mat=mst_grid) stackrate_dir = os.path.join(SML_TEST_DIR, 'stackrate') cls.rate_container = np.genfromtxt(os.path.join(stackrate_dir, 'stackmap.csv'), delimiter=',') cls.error_container = np.genfromtxt(os.path.join(stackrate_dir, 'errormap.csv'), delimiter=',') cls.samples_container = np.genfromtxt(os.path.join(stackrate_dir, 'coh_sta.csv'), delimiter=',') for ifg in ifgs: ifg.close() cls.params = params @classmethod def teardown_class(cls): shutil.rmtree(cls.params[C.OUT_DIR]) def test_stack_rate_full_parallel(self): """ python multiprocessing by rows vs serial """ assert_array_almost_equal(self.rate, self.rate_s, decimal=3) def test_stackrate_error_parallel(self): """ python multiprocessing by rows vs serial """ assert_array_almost_equal(self.error, self.error_s, decimal=3) def test_stackrate_samples_parallel(self): """ python multiprocessing by rows vs serial """ assert_array_almost_equal(self.samples, self.samples_s, decimal=3) def test_stack_rate(self): """ Compare with legacy data """ assert_array_almost_equal(self.rate_s, self.rate_container, decimal=3) def test_stackrate_error(self): """ Compare with legacy data """ assert_array_almost_equal(self.error_s, self.error_container, decimal=3) def test_stackrate_samples(self): """ Compare with legacy data """ assert_array_almost_equal(self.samples_s, self.samples_container, decimal=3) ================================================ FILE: tests/test_system.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ pyrate basic workflow for all supported input datasets """ import shutil from subprocess import check_call from pathlib import Path import pytest import numpy as np import pyrate.constants as C from pyrate.core.mpiops import MPI_INSTALLED from pyrate.configuration import Configuration from tests.common import MEXICO_CROPA_CONF, PY37GDAL302 @pytest.mark.mpi @pytest.mark.slow @pytest.mark.skipif(not PY37GDAL302, reason="Only run in one CI env") def test_workflow(system_conf): """check the handlers are working as expected""" check_call(f"mpirun -n 3 pyrate conv2tif -f {system_conf}", shell=True) check_call(f"mpirun -n 3 pyrate prepifg -f {system_conf}", shell=True) check_call(f"mpirun -n 3 pyrate correct -f {system_conf}", shell=True) check_call(f"mpirun -n 3 pyrate timeseries -f {system_conf}", shell=True) check_call(f"mpirun -n 3 pyrate stack -f {system_conf}", shell=True) check_call(f"mpirun -n 3 pyrate merge -f {system_conf}", shell=True) # assert logs generated in the outdir params = Configuration(system_conf).__dict__ for stage in ['conv2tif', 'prepifg', 'correct', 'timeseries', 'stack', 'merge']: log_file_name = 'pyrate.log.' + stage files = list(Path(params[C.OUT_DIR]).glob(log_file_name + '.*')) assert len(files) == 1 shutil.rmtree(params[C.OUT_DIR]) def test_single_workflow(gamma_or_mexicoa_conf): if MPI_INSTALLED: check_call(f"mpirun -n 4 pyrate workflow -f {gamma_or_mexicoa_conf}", shell=True) else: check_call(f"pyrate workflow -f {gamma_or_mexicoa_conf}", shell=True) params = Configuration(gamma_or_mexicoa_conf).__dict__ log_file_name = 'pyrate.log.' + 'workflow' files = list(Path(params[C.OUT_DIR]).glob(log_file_name + '.*')) assert len(files) == 1 # ref pixel file generated ref_pixel_file = params[C.REF_PIXEL_FILE] assert Path(ref_pixel_file).exists() ref_pixel = np.load(ref_pixel_file) if gamma_or_mexicoa_conf == MEXICO_CROPA_CONF: np.testing.assert_array_equal(ref_pixel, [42, 2]) for f in C.GEOMETRY_OUTPUT_TYPES: assert Path(params[C.GEOMETRY_DIR]).joinpath(f + '.tif').exists() else: np.testing.assert_array_equal(ref_pixel, [38, 58]) # assert orbfit exists on disc from pyrate.core import shared looked_files = [p.sampled_path for p in params[C.INTERFEROGRAM_FILES]] ifgs = [shared.Ifg(ifg) for ifg in looked_files] orbfits_on_disc = [Path(params[C.OUT_DIR], C.ORB_ERROR_DIR, Path(ifg.data_path).stem + '_orbfit.npy') for ifg in ifgs] assert all(orbfits_on_disc) shutil.rmtree(params[C.OUT_DIR]) ================================================ FILE: tests/test_timeseries.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # coding: utf-8 """ This Python module contains tests for the timeseries.py PyRate module. """ import os import shutil from copy import deepcopy import pytest from datetime import date, timedelta from numpy import nan, asarray, where, array import numpy as np from numpy.testing import assert_array_almost_equal import pyrate.constants as C import pyrate.core.orbital import pyrate.core.prepifg_helper import pyrate.core.ref_phs_est import pyrate.core.refpixel import tests.common as common from pyrate.core import mst, covariance from pyrate import correct, prepifg, conv2tif from pyrate.configuration import Configuration from pyrate.core.timeseries import time_series, linear_rate_pixel, linear_rate_array, TimeSeriesError def default_params(): return {C.TIME_SERIES_METHOD: 1, C.TIME_SERIES_PTHRESH: 0, C.TIME_SERIES_SM_ORDER: 2, C.TIME_SERIES_SM_FACTOR: -0.25, C.PARALLEL: 0, C.PROCESSES: 1, C.NAN_CONVERSION: 1, C.NO_DATA_VALUE: 0} class SinglePixelIfg(object): """ A single pixel ifg (interferogram) solely for unit testing """ def __init__(self, first, second, phase, nan_fraction): self.phase_data = asarray([[phase]]) self.first = first self.second = second self.nrows = 1 self.ncols = 1 self.nan_fraction = asarray([nan_fraction]) def convert_to_nans(self, val=0): """ Converts given values in phase data to NaNs val - value to convert, default is 0 """ self.phase_data = where(self.phase_data == val, nan, self.phase_data) self.nan_converted = True class TestTimeSeries: """Verifies error checking capabilities of the time_series function""" @classmethod def setup_class(cls): cls.ifgs = common.small_data_setup() cls.params = default_params() cls.mstmat = mst.mst_boolean_array(cls.ifgs) r_dist = covariance.RDist(cls.ifgs[0])() cls.maxvar = [covariance.cvd(i.data_path, cls.params, r_dist)[0] for i in cls.ifgs] cls.vcmt = covariance.get_vcmt(cls.ifgs, cls.maxvar) def test_time_series_unit(self): """ Checks that the code works the same as the calculated example """ ifirst = asarray([1, 1, 2, 2, 3, 3, 4, 5]) isecond = asarray([2, 4, 3, 4, 5, 6, 6, 6]) timeseries = asarray([0.0, 0.1, 0.6, 0.8, 1.1, 1.3]) phase = asarray([0.5, 4, 2.5, 3.5, 2.5, 3.5, 2.5, 1]) nan_fraction = asarray([0.5, 0.4, 0.2, 0.3, 0.1, 0.3, 0.2, 0.1]) now = date.today() dates = [now + timedelta(days=(t*365.25)) for t in timeseries] dates.sort() first = [dates[m_num - 1] for m_num in ifirst] second = [dates[s_num - 1] for s_num in isecond] self.ifgs = [SinglePixelIfg(m, s, p, n) for m, s, p, n in zip(first, second, phase, nan_fraction)] tsincr, tscum, tsvel = time_series( self.ifgs, params=self.params, vcmt=self.vcmt, mst=None) expected = asarray([[[0.50, 3.0, 4.0, 5.5, 6.5]]]) assert_array_almost_equal(tscum, expected, decimal=2) class TestLegacyTimeSeriesEquality: @classmethod def setup_class(cls): params = Configuration(common.TEST_CONF_ROIPAC).__dict__ params[C.TEMP_MLOOKED_DIR] = os.path.join(params[C.OUT_DIR], C.TEMP_MLOOKED_DIR) conv2tif.main(params) prepifg.main(params) params[C.REF_EST_METHOD] = 2 xlks, _, crop = pyrate.core.prepifg_helper.transform_params(params) dest_paths, headers = common.repair_params_for_correct_tests(params[C.INTERFEROGRAM_DIR], params) correct._copy_mlooked(params) copied_dest_paths = [os.path.join(params[C.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[C.REFX] = refx params[C.REFY] = refy params[C.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() 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[C.TIME_SERIES_METHOD] = 1 params[C.PARALLEL] = 0 # Calculate time series cls.tsincr_0, cls.tscum_0, _ = common.calculate_time_series(ifgs, params, vcmt, mst=mst_grid) params[C.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') cls.params = params @classmethod def teardown_class(cls): shutil.rmtree(cls.params[C.OUT_DIR]) def test_time_series_equality_parallel_by_rows(self): """ check time series parallel by rows jobs """ self.assertEqual(self.tsincr_1.shape, self.tscum_1.shape) self.assertEqual(self.tsvel_1.shape, self.tsincr_1.shape) np.testing.assert_array_almost_equal( self.ts_incr, self.tsincr_1, decimal=3) np.testing.assert_array_almost_equal( self.ts_cum, self.tscum_1, decimal=3) def test_time_series_equality_serial_by_the_pixel(self): """ check time series """ self.assertEqual(self.tsincr_0.shape, self.tscum_0.shape) np.testing.assert_array_almost_equal( self.ts_incr, self.tsincr_0, decimal=3) np.testing.assert_array_almost_equal( self.ts_cum, self.tscum_0, decimal=3) @staticmethod def assertEqual(val1, val2): assert val1 == val2 class TestLegacyTimeSeriesEqualityMethod2Interp0: @classmethod def setup_class(cls): params = Configuration(common.TEST_CONF_ROIPAC).__dict__ params[C.TEMP_MLOOKED_DIR] = os.path.join(params[C.OUT_DIR], C.TEMP_MLOOKED_DIR) conv2tif.main(params) prepifg.main(params) params[C.REF_EST_METHOD] = 2 xlks, _, crop = pyrate.core.prepifg_helper.transform_params(params) dest_paths, headers = common.repair_params_for_correct_tests(params[C.INTERFEROGRAM_DIR], params) correct._copy_mlooked(params) copied_dest_paths = [os.path.join(params[C.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[C.REFX] = refx params[C.REFY] = refy params[C.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[C.TIME_SERIES_METHOD] = 2 params[C.PARALLEL] = 1 # Calculate time series cls.tsincr, cls.tscum, _ = common.calculate_time_series(ifgs, params, vcmt, mst=mst_grid) params[C.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 @classmethod def teardown_class(cls): shutil.rmtree(cls.params[C.OUT_DIR]) def test_time_series_equality_parallel_by_rows(self): assert self.tsincr.shape == self.tscum.shape np.testing.assert_array_almost_equal(self.ts_incr, self.tsincr, decimal=1) np.testing.assert_array_almost_equal(self.ts_cum, self.tscum, decimal=1) def test_time_series_equality_serial_by_the_pixel(self): assert self.tsincr_0.shape == self.tscum_0.shape np.testing.assert_array_almost_equal(self.ts_incr, self.tsincr_0, decimal=3) np.testing.assert_array_almost_equal(self.ts_cum, self.tscum_0, decimal=3) class TestLinearRatePixel: """ Tests the linear regression algorithm for determining the best fitting velocity from a cumulative time series """ def test_linear_rate_pixel_clean(self): y = array([0, 2, 4, 6, 8, 10]) t = array([0, 1, 2, 3, 4, 5]) exp = (2.0, 0.0, 1.0, 0.0, 6) res = linear_rate_pixel(y, t) assert res == exp def test_linear_rate_pixel_neg_rate(self): y = array([0, -2, -4, -6, -8, -10]) t = array([0, 1, 2, 3, 4, 5]) exp = (-2.0, 0.0, 1.0, 0.0, 6) res = linear_rate_pixel(y, t) assert res == exp def test_linear_rate_pixel_outlier(self): y = array([0, 2, 4, 6, 8, 20]) t = array([0, 1, 2, 3, 4, 5]) exp = (3.428571, -1.904761, 0.812030, 0.824786, 6) res = linear_rate_pixel(y, t) assert res == pytest.approx(exp, rel=1e-6) def test_linear_rate_pixel_noise(self): y = array([0, 2, 4, 6, 8, 10]) r = y + np.random.rand(6) # add different uniform noise each time t = array([0, 1, 2, 3, 4, 5]) exprate = 2.0 explsqd = 1.0 experr = 0.0 rate, _, lsqd, err, _ = linear_rate_pixel(y, t) assert exprate == pytest.approx(rate, rel=1e-1) assert explsqd == pytest.approx(lsqd, rel=1e-1) assert experr == pytest.approx(err, rel=1e-1) def test_linear_rate_pixel_exception(self): # input vectors should be equal length y = array([2, 4, 6, 8, 10]) t = array([0, 1, 2, 3, 4, 5]) with pytest.raises(TimeSeriesError): res = linear_rate_pixel(y, t) def test_linear_rate_pixel_nans(self): # at least two obs are required for line fitting y = array([0, nan, nan, nan, nan, nan]) t = array([0, 1, 2, 3, 4, 5]) exp = (nan, nan, nan, nan, nan) res = linear_rate_pixel(y, t) assert res == exp class TestLinearRateArray: """ Tests the array loop wrapper for the linear regression algorithm using real data """ @classmethod @pytest.fixture(autouse=True) def setup_class(cls, roipac_params): cls.params = roipac_params cls.ifgs = common.small_data_setup() # read in input (tscuml) and expected output arrays tscuml_path = os.path.join(common.SML_TEST_LINRATE, "tscuml_0.npy") cls.tscuml0 = np.load(tscuml_path) # add zero epoch to tscuml 3D array cls.tscuml = np.insert(cls.tscuml0, 0, 0, axis=2) linrate_path = os.path.join(common.SML_TEST_LINRATE, "linear_rate.npy") cls.linrate = np.load(linrate_path) error_path = os.path.join(common.SML_TEST_LINRATE, "linear_error.npy") cls.error = np.load(error_path) icpt_path = os.path.join(common.SML_TEST_LINRATE, "linear_intercept.npy") cls.icpt = np.load(icpt_path) samp_path = os.path.join(common.SML_TEST_LINRATE, "linear_samples.npy") cls.samp = np.load(samp_path) rsq_path = os.path.join(common.SML_TEST_LINRATE, "linear_rsquared.npy") cls.rsq = np.load(rsq_path) def test_linear_rate_array(self): """ Input and expected output are on disk. This test only tests the linear_rate_array and linear_rate_pixel functions using real data. """ l, i, r, e, s = linear_rate_array(self.tscuml, self.ifgs, self.params) # test to 20 decimal places assert_array_almost_equal(self.linrate, l, 1e-20) assert_array_almost_equal(self.icpt, i, 1e-20) assert_array_almost_equal(self.rsq, r, 1e-20) assert_array_almost_equal(self.error, e, 1e-20) assert_array_almost_equal(self.samp, s, 1e-20) def test_linear_rate_array_two_sigma(self): """ Check that the "nsigma" switch in the config dictionary actually results in a change in the error map. """ # make a deep copy of the params dict to avoid changing # state for other tests if this one fails params = deepcopy(self.params) params[C.VELERROR_NSIG] = 2 _, _, _, e, _ = linear_rate_array(self.tscuml, self.ifgs, params) assert_array_almost_equal(self.error*2, e, 1e-20) def test_linear_rate_array_exception(self): # depth of tscuml should equal nepochs with pytest.raises(TimeSeriesError): res = linear_rate_array(self.tscuml0, self.ifgs, self.params) ================================================ FILE: utils/.coveragerc ================================================ # .coveragerc to control coverage.py [run] branch = True source = pyrate omit = # omit everything in # pyrate/tasks/* # omit these files pyrate/pyratelog.py pyrate/scripts/main.py [report] # Regexes for lines to exclude from consideration exclude_lines = # Have to re-enable the standard pragma pragma: no cover # Don't complain about missing debug-only code: def __repr__ def __str__ if self\.debug # Don't complain if tests don't hit defensive assertion code: raise AssertionError raise NotImplementedError raise ValueError raise RuntimeError raise IOError except OSError except ValueError except IndexError raise ImportError except ImportError raise ConfigException raise ReferencePhaseError raise RefPixelError raise RoipacException raise GeotiffException raise RasterException raise IfgException raise GammaException raise PreprocessError raise TimeSeriesError raise OrbitalError raise CorrectionStatusError # Don't complain if non-runnable code isn't run: if 0: if __name__ == .__main__.: ignore_errors = True [html] directory = coverage_html_report ================================================ FILE: utils/__init__.py ================================================ ================================================ FILE: utils/create_lv_theta.py ================================================ # This Python module is part of the PyRate software package # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ''' This is used to create the dummy incidence map file .inc file This is used to create the dummy elevation map file .lv_theta file ''' import os import numpy as np from osgeo import gdal from pyrate.core import shared, ifgconstants as ifc, gamma from tests import common elevation_file = os.path.join(common.GAMMA_SML_TEST_DIR, os.path.splitext(common.SML_TEST_DEM_GAMMA)[0] + '.lv_theta') inc_file = os.path.join(common.GAMMA_SML_TEST_DIR, os.path.splitext(common.SML_TEST_DEM_GAMMA)[0] + '.inc') dest_lv_theta = os.path.splitext(elevation_file)[0] + '_lv_theta.tif' dest_inc = os.path.splitext(elevation_file)[0] + '_inc.tif' dem_header_file = common.SML_TEST_DEM_HDR_GAMMA dem_header = gamma.parse_dem_header(dem_header_file) header = gamma.parse_epoch_header( os.path.join(common.GAMMA_SML_TEST_DIR, '20060828_slc.par')) incidence_angle = header[ifc.PYRATE_INCIDENCE_DEGREES] incidence_data = np.ones(shape=(dem_header[ifc.PYRATE_NROWS], dem_header[ifc.PYRATE_NCOLS]) ) * incidence_angle elevation_data = np.ones(shape=(dem_header[ifc.PYRATE_NROWS], dem_header[ifc.PYRATE_NCOLS]) ) * (90.0 - incidence_angle) shared.write_unw_from_data_or_geotiff(geotif_or_data=incidence_data, dest_unw=inc_file, ifg_proc=1) shared.write_unw_from_data_or_geotiff(geotif_or_data=elevation_data, dest_unw=elevation_file, ifg_proc=1) header.update(dem_header) header[ifc.PYRATE_TIME_SPAN] = 0 header[ifc.SECOND_DATE] = 0 header[ifc.DATA_UNITS] = 'degrees' header[ifc.DATA_TYPE] = ifc.INCIDENCE header[ifc.SECOND_TIME] = 0 shared.write_geotiff(header=header, data_path=elevation_file, dest=dest_lv_theta, nodata=np.nan) shared.write_geotiff(header=header, data_path=inc_file, dest=dest_inc, nodata=np.nan) ds = gdal.Open(dest_lv_theta, gdal.GA_ReadOnly) data_elevation = ds.ReadAsArray() ds = None ds = gdal.Open(dest_inc, gdal.GA_ReadOnly) data_inc = ds.ReadAsArray() ds = None np.testing.assert_array_almost_equal(90 - incidence_data, data_elevation, decimal=4) np.testing.assert_array_almost_equal(incidence_data, data_inc, decimal=4) ================================================ FILE: utils/crop_ifgs.py ================================================ # This Python module is part of the PyRate software package # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ python utility to crop an interferogram example usage: python pyrate/utils/crop_ifgs.py -i tests/test_data/small_test/tif/geo_060619-061002_unw.tif -o out.tif -e '150.91 -34.229999976 150.949166651 -34.17' """ from optparse import OptionParser import subprocess def crop_using_gdalwarp(input_file, output_file, extents): # TODO: add extents checking between input_file and extents extents_str = [str(e) for e in extents] cmd = ['gdalwarp', '-overwrite', '-srcnodata', 'None', '-q', '-te'] \ + extents_str cmd += [input_file, output_file] subprocess.check_call(cmd) if __name__ == '__main__': parser = OptionParser(usage='%prog -i input_file -o output_file' ' -e extents\n' 'Crop a larger interferogram into ' 'smaller ones') parser.add_option('-i', '--input', type=str, dest='input_file', help='name of input interferogram') parser.add_option('-o', '--out', type=str, dest='output_file', help='name of cropped output interferogram') parser.add_option('-e', '--extents', type=str, dest='extents', help='extents to be used for the cropped file.\n' 'needs to be a list of 4 floats with spaces\n' 'example: ' "-e '150.91 -34.229999976 150.949166651 -34.17'") options, args = parser.parse_args() if not options.input_file: # if filename is not given parser.error('Input filename not given.') if not options.output_file: # if filename is not given parser.error('Output filename not given.') if not options.extents: # if filename is not given parser.error('Crop extents must be provided') extents = [float(t) for t in options.extents.split()] if len(extents) != 4: raise AttributeError('extents to be used for the cropped file.\n' 'needs to be a list or tuples of 4 floats\n' "example:" "--extents " "'150.91 -34.229999976 150.949166651 -34.17'") crop_using_gdalwarp(input_file=options.input_file, output_file=options.output_file, extents=extents) ================================================ FILE: utils/developer_hints.txt ================================================ # publish package to PyPI follow the steps at: https://medium.com/@joel.barmettler/how-to-upload-your-python-package-to-pypi-65edc5fe9c56 Summary: 1) create new 'release' in Github. this will tag a commit hash. 2) copy link to tar.gz Source Code from github release, add to download_url in setup.py 3) update version number in setup.py (to match the Github release) 4) `python3 setup.py sdist bdist_wheel` - prepare package 5) `python3 -m twine upload --repository testpypi dist/*` - test upload that does not affect real PyPI 5) `python3 -m twine upload dist/*` - upload package to PyPI check out here also: https://packaging.python.org/tutorials/packaging-projects/ --------------------------------------------- # Clone Github repo cd ~ git clone git@github.com:GeoscienceAustralia/PyRate.git or git clone https://github.com/GeoscienceAustralia/PyRate.git # build PyRate package cd ~/PyRate pip install -r requirements.txt pip install -r requirements-dev.txt pip install -r requirements-test.txt python3 setup.py install # Run workflow, one step at a time pyrate conv2tif -f input_parameters.conf pyrate prepifg -f input_parameters.conf pyrate correct -f input_parameters.conf pyrate timeseries -f input_parameters.conf pyrate stack -f input_parameters.conf pyrate merge -f input_parameters.conf # or run all workflow steps in order pyrate workflow -f input_parameters.conf # alternatively run commands via python, e.g. python3 pyrate/main.py workflow -f input_parameters.conf --------------------------------------- # Build Sphinx docs pip install -r requirements-dev.txt cd /PyRate/docs && make html --------------------------------------- # Run unit tests, avoiding those marked as "slow" pip install -r requirements-test.txt cd /PyRate # file permission change required for a test: chmod 444 tests/test_data/small_test/tif/geo_070709-070813_unw.tif pytest tests/ -m "not slow" --------------------------------------- NCI Gadi supercomputer ssh @gadi.nci.org.au rm -rf ~/PyRate rm -rf ~/PyRateVenv git clone git@github.com:GeoscienceAustralia/PyRate.git source PyRate/scripts/nci_load_modules.sh python3 -m venv ~/PyRateVenv source ~/PyRateVenv/bin/activate cd ~/PyRate pip install -r requirements-dev.txt pip install -r requirements-test.txt pip install -r requirements.txt python3 setup.py install # run with debug messages pyrate -v DEBUG workflow -f input_parameters.conf # run using MPI mpirun -n 6 pyrate workflow -f input_parameters.conf ================================================ FILE: utils/docker_install.txt ================================================ # Docker cleanup, see: https://docs.docker.com/ee/dtr/user/manage-images/delete-images/ # Docker Setup # first clone the PyRate repository git clone git@github.com:GeoscienceAustralia/PyRate.git cd PyRate # the docker image built will be called 'pyrate-image', but name it what you want docker build -t pyrate-image . docker run -it pyrate-image # once the docker container is running: source /usr/local/bin/virtualenvwrapper.sh workon pyrate cd PyRate python3 setup.py install pyrate --help # Run full workflow pyrate workflow -f input_parameters.conf # or run each step in turn pyrate conv2tif -f input_parameters.conf pyrate prepifg -f input_parameters.conf pyrate correct -f input_parameters.conf pyrate timeseries -f input_parameters.conf pyrate stack -f input_parameters.conf pyrate merge -f input_parameters.conf # The pyrate executable is built during the 'docker build' step above # If 'pyrate' is not found then re-install the PyRate package: cd PyRate python3 setup.py install ================================================ FILE: utils/gdaldem.py ================================================ # This Python module is part of the PyRate software package # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Based on: http://gis.stackexchange.com/questions/130199/changing-color-of-raster-images-based-on-their-data-values-gdal Utility script to convert grayscale geotiff into colour. ================================================================================ Usage: ------------- Custom color: python gdaldem.py input.tif color.txt output_color.tif Auto color: python gdaldem.py input.tif auto output_color.tif Examples: ------------- Custom color: python utils/gdaldem.py tests/test_data/small_test/dem/roipac_test_trimmed.tif utils/tiff_colour_map.txt out.tif Auto color: python utils/gdaldem.py tests/test_data/small_test/dem/roipac_test_trimmed.tif auto out.tif """ import subprocess import sys import os import tempfile import numpy as np from pyrate.core.shared import DEM def main(input_file, color_file, output_file): cmd = "gdaldem color-relief " + input_file \ + ' ' + color_file + ' ' + output_file subprocess.check_call(cmd, shell=True) def gen_color_file(input_file): fp, temp_file = tempfile.mkstemp(suffix='.txt') dem = DEM(input_file) dem.open() phase_data = dem._band.ReadAsArray() max_ph = np.nanmax(phase_data) min_ph = np.nanmin(phase_data) range_ph = max_ph-min_ph colors = ['black', 'blue', 'yellow', 'orange', 'red', 'white'] with open(temp_file, 'w') as f: for i, c in enumerate(colors[:-1]): f.write(str(int(min_ph + (i + 1)*range_ph/len(colors))) + ' ' + c + '\n') f.write(str(int(max_ph - range_ph/len(colors))) + ' ' + colors[-1] + '\n') os.close(fp) return temp_file if __name__ == '__main__': input_file = sys.argv[1] color_file = sys.argv[2] output_file = sys.argv[3] if color_file == 'auto': print('\nauto generating color file') color_file = gen_color_file(input_file) with open(color_file, 'r') as f: print('\ncolor file contents') for l in f.readlines(): print(l) main(input_file, color_file, output_file) ================================================ FILE: utils/list_creator.sh ================================================ """ This script generates input lists for PyRate baseed on the PyGAMMA workflow for descending frame S1 data in Australia. Small modifications are necessary if using ascending frame data Usage: . list_creator.sh """ # Provide path to the Gamma folder titled with the frame name DIR="/path/to/frame/T045D" ls -d "$DIR"/*/*/*unw.tif > ifgs.list # "coh" is "cc" in asending frame data ls -d "$DIR"/*/*/*flat*coh.tif > cohfiles.list ls -d "$DIR"/*/*/*base.par > baseline.list # Change "VV" depending on polarisation of data. ls -d "$DIR"/*/*/*VV*mli.par > headers.list wc -l *.list ================================================ FILE: utils/make_tscuml_animation.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This python script can be used to make an animated gif of PyRate cumulative time series products. Usage: python3 utils/make_tscuml_animation.py """ import rasterio import matplotlib.pyplot as plt import matplotlib.backend_bases import numpy as np import os, sys, re import xarray as xr from datetime import datetime as dt import matplotlib.animation as animation if len(sys.argv) != 2: print('Exiting: Provide abs path to as command line argument') exit() else: path = sys.argv[1] print(f"Looking for PyRate products in: {path}") ################################# # Reading velocity data with rasterio.open(os.path.join(path, 'velocity_dir', 'linear_rate.tif')) as src2: vel = src2.read() bounds2 = src2.bounds x_coord2 = np.linspace(bounds2[0], bounds2[2], src2.width) y_coord2 = np.linspace(bounds2[1], bounds2[3], src2.height) # grab list of time series dates from metadata ed = src2.tags()['EPOCH_DATE'] # convert metadata string to list of strings date_str = re.findall(r'\'(.+?)\'', ed) # make velocity xarray (also apply deramp to the velocity map directly) dac2 = xr.DataArray(vel[0,:,:], coords={'lon': x_coord2, 'lat': y_coord2}, dims=['lat', 'lon']) vs = xr.Dataset() vs['vel'] = dac2 longitude = vs.coords['lon'] latitude = vs.coords['lat'] # pre-allocate a 3D numpy array to read the tscuml tiff raster bands to # include first 'zero' time slice, which PyRate does not save to disk tscuml = np.zeros((len(date_str), vel.shape[1], vel.shape[2])) # commented Chandra # reading *tif files and generate cumulative variable print('Reading tscuml files:') for i, d in enumerate(date_str[1:]): print(i+1, 'tscuml_' + d + '.tif') with rasterio.open(os.path.join(path, 'timeseries_dir', 'tscuml_' + d + '.tif')) as src: data = src.read() bounds = src.bounds x_coord = np.linspace(bounds[0], bounds[2], src.width) y_coord = np.linspace(bounds[1], bounds[3], src.height) tscuml[i+1, :, :] = np.squeeze(data, axis=(0,)) # commented chandra # copy Nans in first time slice to zero epoch zeroepoch = np.zeros((vel.shape[1], vel.shape[2])) zeroepoch[np.isnan(tscuml[1,:,:])] = np.nan tscuml[0,:,:] = zeroepoch # convert date strings to datetime objects imdates_dt = [dt.strptime(x, '%Y-%m-%d') for x in date_str] # make tscuml xarray dac = xr.DataArray(tscuml, coords={'time': imdates_dt, 'lon': x_coord, 'lat': y_coord}, dims=['time', 'lat', 'lon']) ds = xr.Dataset() ds['tscuml'] = dac n_im, length, width = tscuml.shape # Add max and min displacement range #refx1 = int(len(x_coord2)/ 2) #refx2 = int(len(x_coord2)/ 2) + 1 #refy1 = int(len(y_coord2)/ 2) #refy2 = int(len(y_coord2)/ 2) + 1 #refvalue_lastepoch = np.nanmean(tscuml[-1, refy1:refy2, refx1:refx2]) # reference values auto_crange: float = 100 dmin_auto = np.nanpercentile(tscuml, 100 - auto_crange) dmax_auto = np.nanpercentile(tscuml, auto_crange) # find the absolute max displacement; round to nearest 10 units, for colour bar limits lim = np.round(np.amax(np.array([np.abs(dmin_auto), np.abs(dmax_auto)])), decimals=-1) #dmin = dmin_auto - refvalue_lastepoch #dmax = dmax_auto - refvalue_lastepoch #### ANIMATION of LOS Cumulative displacement TS fig = plt.figure('PyRate Cumulative Displacement Animation', figsize=(5,5)) faxv = fig.add_axes([0.15,0.15,0.75,0.75]) cmap = matplotlib.cm.Spectral_r cmap.set_bad('grey',1.) # filled grey color to nan value ims = [] # pre-allocate list for appending time slices # loop over all timeslices, including zero epoch for ii in range(0,len(imdates_dt)): im = faxv.imshow(ds.tscuml[ii], cmap=cmap, alpha=1, origin='upper',extent=[ds.coords['lon'].min(), ds.coords['lon'].max(), ds.coords['lat'].min(), ds.coords['lat'].max()], clim=[-lim, lim]) title = fig.text(0.40, 0.90, "Date: {}".format(imdates_dt[ii].date()), fontsize=12, va='bottom' ) ims.append([im, title]) fcbr = fig.colorbar(im, orientation='horizontal') fcbr.set_label('LOS Displacement [mm]') ani = animation.ArtistAnimation(fig, ims, interval=500, blit=False) #plt.show() file = path + '/timeseries_dir/' +'tscuml_animation.gif' ani.save(file, writer='imagemagick', fps=10, dpi=100) print('Animation saved to ' + file) ================================================ FILE: utils/plot_correction_files.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This script plots the original interferogram, the corresponding correction file, and the final corrected interferogram from a PyRate directory with already, processed data. directories are given as user arguments to the script, and the number of plots is determined by a number range given by user. Usage: python3 plot_correction_files.py Command-line arguments: IFG_DIR - full path to uncorrected interferograms in PyRate. CORRECTION_DIR - full path to correction files in PyRate. CORRECTED_DIR - full path to corrected interferograms in PyRate. SAVE_DIR - full path to directory where images will be saved (needs to exist). FIRST_IFG - first IFG in range of IFGs to plot (e.g. 1 to start plotting at 1st IFG in directory). LAST_IFG - last IFG in range of IFGs to plot (e.g. 37 will plot up until the 37th IFG in directory). NORMALISE - Switch to subtract median to from figures (0=YES, 1=No, default is 0). """ import numpy as np from matplotlib import pyplot as plt from matplotlib import cm import glob import re import math import argparse import os from pyrate.core.shared import Ifg # Arguments parser = argparse.ArgumentParser(description="Script to plot correction files with uncorrected and corrected interferogram") parser.add_argument("IFG_DIR", type=str, help="full path to uncorrected interferograms in PyRate") parser.add_argument("CORRECTION_FILE_DIR", type=str, help="full path to correction files in PyRate") parser.add_argument("CORRECTED_IFG_DIR", type=str, help="full path to corrected interferograms in PyRate") parser.add_argument("SAVE_DIR", type=str, help="full path to directory where images will be saved") parser.add_argument("FIRST_IFG", type=int, help="first IFG in range of IFGs to plot (e.g. 1 to start plotting at first IFG in directory)") parser.add_argument("LAST_IFG", type=int, help="last IFG in range of IFGs to plot (e.g. 37 will plot up until the 37th IFG in directory)") parser.add_argument("-sm", "--subtract_median", type=int, default=1, help="Switch to subtract median to from figures (1=YES, 0=No, default is 1)") args = parser.parse_args() # Directories and variable from user agruments ifg_dir = os.path.abspath(args.IFG_DIR) corr_dir = os.path.abspath(args.CORRECTION_FILE_DIR) tempml_dir = os.path.abspath(args.CORRECTED_IFG_DIR) save_dir = os.path.abspath(args.SAVE_DIR) # Create Lists ifg_list = [] for file in glob.glob(f'{ifg_dir}/*ifg.tif'): ifg_list.append(file) corr_list = [] for file in glob.glob(f'{corr_dir}/*.npy'): corr_list.append(file) tempml_list = [] for file in glob.glob(f'{tempml_dir}/*ifg.tif'): tempml_list.append(file) # Sort ifg_list.sort() corr_list.sort() tempml_list.sort() # define colour map cmap = cm.Spectral_r cmap.set_bad(color='grey') # loop over each ifg in turn for i in range(args.FIRST_IFG - 1, args.LAST_IFG): # Read data orig_ifg = Ifg(ifg_list[i]) orig_ifg.open() orig_ifg.convert_to_mm() # force mm conversion ifg = orig_ifg.phase_data orig_ifg.close() corr = np.load(corr_list[i]) corr_ifg = Ifg(tempml_list[i]) corr_ifg.open() corr_ifg.convert_to_mm() # force mm conversion ifg_corr = corr_ifg.phase_data corr_ifg.close() # Identify Date Pair date_pair_list_ifg = re.findall(r'\d{8}-\d{8}', ifg_list[i]) date_pair_string_ifg = date_pair_list_ifg[0] date_pair_list_corr = re.findall(r'\d{8}-\d{8}', corr_list[i]) date_pair_string_corr = date_pair_list_corr[0] date_pair_list_ifgcorr = re.findall(r'\d{8}-\d{8}', tempml_list[i]) date_pair_string_ifgcorr = date_pair_list_ifgcorr[0] # Check the Date-pairs are the same in case of mismatched files saved into the directories if date_pair_string_ifg == date_pair_string_corr and date_pair_string_ifg == date_pair_string_ifgcorr: print(f'\nPlotting for {date_pair_string_ifg}...\n') pass else: print(f'\nERROR: Interferogram datepair mismatch at {date_pair_string_ifg}, check that directories have the same interferograms.\n') break # PLOTTING # ifg_quant = np.nanquantile(ifg, [0.05, 0.95]) # climit = np.nanmax(np.abs(ifg_quant)) climit = 30 # subtract median of image if args.subtract_median == 1: ifg -= np.nanmedian(ifg) corr -= np.nanmedian(corr) ifg_corr -= np.nanmedian(ifg_corr) # three sub-plots fig, ax = plt.subplots(1,3, figsize=(6, 3)) # ORIGINAL IFG s0 = ax[0].imshow(ifg, cmap=cmap, clim=(-1*climit, climit)) ax[0].set_title('Original Ifg', fontsize=8) ax[0].set_axis_off() # CORRECTION FILE s1 =ax[1].imshow(corr, cmap=cmap, clim=(-1*climit, climit)) ax[1].set_title('Correction', fontsize=8) ax[1].set_axis_off() # CORRECTED IFG s2 = ax[2].imshow(ifg_corr, cmap=cmap, clim=(-1*climit,climit)) ax[2].set_title('Corrected Ifg', fontsize=8) ax[2].set_axis_off() # Colorbar cbar = fig.colorbar(s1, ax=ax.ravel().tolist(), orientation='horizontal', label='mm', \ ticks=[-50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50]) cbar.ax.tick_params(labelsize=8) # set figure window colour to grey fig.set_facecolor('grey') # Title fig.suptitle(f'{date_pair_string_ifg}', y=0.75, fontsize=10, fontweight='bold') plt.savefig(f'{save_dir}/{date_pair_string_ifg}.png', dpi=300) plt.close() ================================================ FILE: utils/plot_linear_rate_profile.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This python script can be used to make a profile through the PyRate linear rate product. Usage: python3 utils/plot_linear_rate_profile.py """ import rasterio import matplotlib.pyplot as plt import matplotlib.backend_bases import numpy as np import os, sys, re import xarray as xr from datetime import datetime as dt from pylab import plot, ginput, show, axis # for velocity profile if len(sys.argv) != 2: print('Exiting: Provide abs path to as command line argument') exit() else: path = sys.argv[1] print(f"Looking for PyRate products in: {path}") # ----- Reading linear velocity file -------------------------------- with rasterio.open(os.path.join(path, 'velocity_dir', 'linear_rate.tif')) as src2: vel = src2.read() bounds2 = src2.bounds x_coord2 = np.linspace(bounds2[0], bounds2[2], src2.width) y_coord2 = np.linspace(bounds2[1], bounds2[3], src2.height) # grab list of time series dates from metadata ed = src2.tags()['EPOCH_DATE'] # convert metadata string to list of strings date_str = re.findall(r'\'(.+?)\'', ed) imdates_dt = [dt.strptime(x, '%Y-%m-%d') for x in date_str] # make velocity xarray dac2 = xr.DataArray(vel[0,:,:], coords={'lon': x_coord2, 'lat': y_coord2}, dims=['lat', 'lon']) vs = xr.Dataset() vs['vel'] = dac2 longitude = vs.coords['lon'] latitude = vs.coords['lat'] # ------ Masking the velocity file using linear sample -------------- # # linear resample *tif file and mask out velocity src3 = rasterio.open(os.path.join(path, 'velocity_dir', 'linear_samples.tif')) lsample = src3.read() lsamp_val = lsample[0,:,:] [mask_x,mask_y] = np.where((lsamp_val >= (len(imdates_dt)-2))) vel_masked = np.empty((vel.shape[1],vel.shape[2],)) * np.nan vel_masked[mask_x,mask_y] = vs.vel.data[mask_x,mask_y] # -------- Plot velocity profile across two points ------------------- def get_profile(pts, img): ''' Extract values from image at points along the profile line, using nearest-neighbor interpolation ''' num = 500 # hardcoded 500 points per profile # generate 500 points between the provided points x, y = np.linspace(pts[0][0], pts[1][0], num), np.linspace(pts[0][1], pts[1][1], num) # pull z value from nearest whole pixel z = img[y.astype(int), x.astype(int)] return x, y, z # vmin = -50; vmax = 50 #refx1 = int(len(x_coord2)/ 2) #refx2 = int(len(x_coord2)/ 2) + 1 #refy1 = int(len(y_coord2)/ 2) #refy2 = int(len(y_coord2)/ 2) + 1 #refvalue_vel = np.nanmean(vel[0, refy1:refy2 + 1, refx1:refx2 + 1]) auto_crange: float = 100 vmin_auto = np.nanpercentile(vel[0, :, :], 100 - auto_crange) vmax_auto = np.nanpercentile(vel[0, :, :], auto_crange) # find the absolute max displacement; round to nearest 10 units, for colour bar limits lim = np.round(np.amax(np.array([np.abs(vmin_auto), np.abs(vmax_auto)])), decimals=-1) #vmin = vmin_auto - refvalue_vel #vmax = vmax_auto - refvalue_vel cmap = matplotlib.cm.Spectral_r cmap.set_bad('grey',1.) fig = plt.figure('PyRate Linear Rate Profile', figsize=(7,10)) axes = fig.subplots(2, 1, gridspec_kw={'height_ratios': [2, 1]}) cax = axes[0].imshow(vel_masked, clim=[-lim, lim], cmap = cmap) cbr = fig.colorbar(cax,ax=axes[0], orientation='vertical') cbr.set_label('LOS velocity [mm/yr]') print("Please click any two points") ig = 1 while ig!=2: pts = ginput(2) # it will wait for two clicks [x,y,z] = get_profile(pts, vel_masked) if axes[0].lines: del axes[0].lines[0] axes[1].cla() axes[0].plot([pts[0][0], pts[1][0]], [pts[0][1], pts[1][1]], 'ro-') # axes[0].plot([x0, x1], [y0, y1], 'ro-') axes[1].plot(x, z, 'gray') axes[1].plot(x, z, 'r.') # axes[1].set_ylim(-5,5) axes[1].set_ylabel('LOS velocity [mm/yr]') axes[1].set_xlabel('X coordinate') axes[1].grid(zorder=0) plt.pause(0.1) fig.canvas.draw() fig.canvas.flush_events() plt.show() ================================================ FILE: utils/plot_sbas_network.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This python script can be used to generate a baseline-time plot of the interferograms used in the PyRate SBAS (Small Baseline Subset) network. The functions 'plot_baseline_time_sbas' and 'epoch_baselines' are copies of the corresponding functions in 'calc_baselines_functions.py' in GA's gamma-insar repository, see https://github.com/GeoscienceAustralia/gamma_insar/blob/develop/calc_baselines_functions.py Usage: python3 utils/plot_sbas_network.py """ import rasterio import glob import numpy as np import matplotlib.pyplot as plt import matplotlib.dates as mdates import os, sys from mpl_toolkits.axes_grid1 import make_axes_locatable from datetime import datetime, timedelta print('') if len(sys.argv) != 2: print('Exiting: Provide path to as command line argument') print('') print('Usage: python3 utils/plot_time_series.py ') exit() else: path = sys.argv[1] print(f"Looking for PyRate products in: {path}") def readtif(tifname: str): """ wrapper for rasterio tif reading """ print(f"Reading file: {tifname}") with rasterio.open(tifname) as src: md = src.tags() return md def plot_baseline_time_sbas(epochs, Bperps, epoch1, epoch2, filename): """ Make a baseline time plot including IFG connections and save to disk """ fig = plt.figure() ax1 = fig.add_subplot(111) divider = make_axes_locatable(ax1) # plot interferograms as lines for n, m in zip(epoch1, epoch2): #print n, m x = [epochs[n], epochs[m]] y = [Bperps[n], Bperps[m]] # baselines[x] ax1.plot_date(x, y, xdate=True, ydate=False, linestyle='-', color = 'r', linewidth=1.0) # plot epochs as filled circles ax1.plot_date(epochs, Bperps, xdate=True, ydate=False, marker="o", markersize=14, markerfacecolor="black", linestyle="None") # plot epoch numbers as symbols labels = [i+1 for i in range(len(Bperps))] for a, b, c in zip(epochs, Bperps, labels): ax1.text(a, b, c, color="white", ha="center", va="center", size=9, weight="bold") #format the time axis ticks years = mdates.MonthLocator(bymonth=[1, 7]) # every 0.5 year months = mdates.MonthLocator() # every month yearsFmt = mdates.DateFormatter("%Y-%m-%d") ax1.xaxis.set_major_locator(years) ax1.xaxis.set_major_formatter(yearsFmt) ax1.xaxis.set_minor_locator(months) #set the time axis range date_min = epochs.min() date_max = epochs.max() date_range = date_max - date_min date_add = date_range.days/15 ax1.set_xlim(date_min - timedelta(days=date_add), date_max + timedelta(days=date_add)) # set the Bperp axis range Bperp_min = min(Bperps) Bperp_max = max(Bperps) Bperp_range = Bperp_max - Bperp_min ax1.set_ylim(Bperp_min - Bperp_range/15, Bperp_max + Bperp_range/15) #set axis titles ax1.set_xlabel("Date (YYYY-MM-DD)") ax1.set_ylabel("Perpendicular Baseline (m)") ax1.grid(True) #rotates and right aligns the date labels fig.autofmt_xdate() # Save plot to PNG file plt.savefig(filename, orientation="landscape", transparent=False, format="png") return def epoch_baselines(epochs, bperp, masidx, slvidx, supermaster): ''' Determine relative perpendicular baselines of epochs from interferometric baselines INPUT: epochs list of epoch dates bperp list of interferogram absolute perpendicular baselines masidx list of master indices from get_index() slvidx list of slave indices from get_index() supermaster epoch to set relative bperp to zero (integer) OUTPUT: epochbperp list of epoch relative perpendicular baselines ''' # Count number of ifgs and epochs nifgs = len(bperp) nepochs = len(epochs) print(nifgs, "interferograms and", nepochs, "epochs in the network.") # Initialise design matrix 'A' A = np.zeros((nifgs+1,nepochs)) # assign super-master epoch to constrain relative baselines A[0,supermaster] = 1 b = np.zeros(nifgs+1) b[1:nifgs+1] = bperp # Construct design matrix for i in range(nifgs): imas = masidx[i] islv = slvidx[i] A[i+1,imas] = -1 A[i+1,islv] = 1 # Do overdetermined linear inversion x=A\b x = np.linalg.lstsq(A, b, rcond=None) return x[:][0] ########### # Main code print('') print('Generating the baseline-time plot from tif-files in temp_mlooked_dir') # reading metadata from tif file path_to_tif = os.path.join(path, 'temp_mlooked_dir/*.tif') # some empty lists Bperps_ifg = [] epoch1 = [] epoch2 = [] for tif_file in glob.glob(path_to_tif): md = readtif(tif_file) # look for perpendicular baseline in geotiff metadata; skip ifg if not there if 'BASELINE_PERP_METRES' in md: Bperps_ifg.append(float(md['BASELINE_PERP_METRES'])) epoch1.append(md['FIRST_DATE']) epoch2.append(md['SECOND_DATE']) print('') # Quit if no ifg has a perpendicular baseline value if len(Bperps_ifg) == 0: print('No perpendicular baseline values in ifg metadata. ' + 'First run the DEM error correction to calculate baselines.') quit() # create date vector containing all epochs in the network epochs= list(set(epoch1+epoch2)) epochs.sort() # get epoch indices for all interferograms epoch1_ix = [] for e in epoch1: epoch1_ix.append(epochs.index(e)) epoch2_ix = [] for e in epoch2: epoch2_ix.append(epochs.index(e)) # convert epochs to datetime object epochs[:] = [datetime.strptime(epo, "%Y-%m-%d") for epo in epochs] # convert IFG Bperps into single-master Bperps referenced to the first epoch Bperps_epoch = epoch_baselines(epochs,Bperps_ifg,epoch1_ix,epoch2_ix, 0) # filename of output image filename = os.path.join(path, 'temp_mlooked_dir/baseline_time_plot.png') # call the function to create the plot plot_baseline_time_sbas(np.array(epochs), Bperps_epoch, epoch1_ix, epoch2_ix, filename) print('Network plot saved to ' + filename) print('') ================================================ FILE: utils/plot_time_series.py ================================================ # This Python module is part of the PyRate software package. # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This python script can be used to plot PyRate linear-rate and cumulative time series products. This script is a version of a script in the LiCSBAS package; see https://github.com/yumorishita/LiCSBAS/blob/master/bin/LiCSBAS_plot_ts.py Usage: python3 utils/plot_time_series.py """ import rasterio import matplotlib import matplotlib.pyplot as plt from matplotlib.widgets import Slider, RadioButtons, RectangleSelector, CheckButtons import matplotlib.dates as mdates import matplotlib.backend_bases import numpy as np import fnmatch import os, sys, re import statsmodels.api as sm import xarray as xr from datetime import datetime as dt import warnings if len(sys.argv) != 2: print('Exiting: Provide path to as command line argument') print('') print('Usage: python3 utils/plot_time_series.py ') exit() else: path = sys.argv[1] print(f"Looking for PyRate products in: {path}") ############################### def readtif(tifname: str): """ wrapper for rasterio tif reading """ print(f"Reading file: {tifname}") with rasterio.open(tifname) as src: img = src.read() bounds = src.bounds md = src.tags() x_coord = np.linspace(bounds[0], bounds[2], src.width) y_coord = np.linspace(bounds[1], bounds[3], src.height) return img, x_coord, y_coord, md ############################### # reading velocity data from linear_rate product vel, x_coord, y_coord, md = readtif(os.path.join(path, 'velocity_dir', 'linear_rate.tif')) # read regression intercept from linear_intercept product intercept, _, _, _ = readtif(os.path.join(path, 'velocity_dir', 'linear_intercept.tif')) # convert time series dates from metadata string to list of strings date_str = re.findall(r'\'(.+?)\'', md['EPOCH_DATE']) # convert date strings to datetime objects imdates_dt = [dt.strptime(x, '%Y-%m-%d') for x in date_str] imdates_ordinal = [x.toordinal() for x in imdates_dt] # make velocity xarray dac2 = xr.DataArray(vel[0,:,:], coords={'lon': x_coord, 'lat': y_coord}, dims=['lat', 'lon']) vs = xr.Dataset() vs['vel'] = dac2 longitude = vs.coords['lon'] latitude = vs.coords['lat'] # pre-allocate a 3D numpy array to read the tscuml tiff raster bands to # include first 'zero' time slice, which PyRate does not save to disk tscuml = np.zeros((len(date_str), vel.shape[1], vel.shape[2])) # reading tscuml*tif files and add to tscuml variable for i, d in enumerate(date_str[1:]): data, x_coord, y_coord, _ = readtif(os.path.join(path, 'timeseries_dir', 'tscuml_' + d + '.tif')) tscuml[i+1, :, :] = np.squeeze(data, axis=(0,)) # make tscuml xarray dac = xr.DataArray(tscuml, coords={'time': imdates_dt, 'lon': x_coord, 'lat': y_coord}, dims=['time', 'lat', 'lon']) ds = xr.Dataset() ds['tscuml'] = dac n_im, length, width = tscuml.shape # choose final time slice time_slice = len(imdates_dt)-1 # set reference area and initial point (scene centre) refx1 = int(len(x_coord)/ 2) refx2 = int(len(x_coord)/ 2) + 1 refy1 = int(len(y_coord)/ 2) refy2 = int(len(y_coord)/ 2) + 1 refarea = (refx1,refx2,refy1,refy2) point_x = refx1 point_y = refy1 def get_range(arr, refarea): """ determine plot scale range """ auto_crange: float = 99.8 refvalue = np.nanmean(arr[refarea[0]:refarea[1], refarea[2]:refarea[3]]) # reference values if str(refvalue) == 'nan': refvalue = 0 dmin_auto = np.nanpercentile(arr, 100 - auto_crange) dmax_auto = np.nanpercentile(arr, auto_crange) dmin = dmin_auto - refvalue dmax = dmax_auto - refvalue return dmin, dmax # range from last tscuml epoch dmin, dmax = get_range(tscuml[-1, :, :], refarea) dmax = abs(max([dmin,dmax], key=abs)) dmin = -dmax # range from velocity vmin, vmax = get_range(vel[0, :, :], refarea) vmax = abs(max([vmin,vmax], key=abs)) vmin = -vmax # Plot figure of Velocity and Cumulative displacement figsize = (7,7) pv = plt.figure('PyRate: linear_rate / tscuml map viewer', figsize) axv = pv.add_axes([0.15,0.15,0.75,0.83]) axv.set_title('linear_rate') axt2 = pv.text(0.01, 0.99, 'Left-doubleclick:\n Plot time series\nRight-drag:\n Change ref area', fontsize=8, va='top') axt = pv.text(0.01, 0.78, 'Ref area:\n X {}:{}\n Y {}:{}\n (start from 0)'.format(refx1, refx2, refy1, refy2), fontsize=8, va='bottom') # create masked array for NaNs mvel = np.ma.array(vs.vel, mask=np.isnan(vs.vel)) cmap = matplotlib.cm.Spectral_r cmap.set_bad('grey') cax = axv.imshow(mvel, clim=[vmin, vmax], cmap=cmap) cbr = pv.colorbar(cax, orientation='vertical') cbr.set_label('mm/yr') # Radio buttom for velocity selection mapdict_data = {} mapdict_unit = {} names = ['Velocity', 'Error', 'R squared'] units = ['mm/yr', 'mm/yr', ''] velfile = os.path.join(path, 'velocity_dir', 'linear_rate.tif') Errfile = os.path.join(path, 'velocity_dir', 'linear_error.tif') Rsqrfile = os.path.join(path, 'velocity_dir', 'linear_rsquared.tif') files = [velfile, Errfile, Rsqrfile] for i, name in enumerate(names): try: pp = rasterio.open(files[i]) data = pp.read() mapdict_data[name] = data[0, :, :] mapdict_unit[name] = units[i] print('Reading {}'.format(os.path.basename(files[i]))) except: print('No {} found, not use.'.format(files[i])) axrad_vel = pv.add_axes([0.01, 0.3, 0.13, len(mapdict_data)*0.025+0.04]) # Radio buttons radio_vel = RadioButtons(axrad_vel, tuple(mapdict_data.keys())) for label in radio_vel.labels: label.set_fontsize(10) tscuml_disp_flag = False climauto = True def line_select_callback(eclick, erelease): """ Set ref function """ global refx1, refx2, refy1, refy2, dmin, dmax ## global cannot change existing values... why? x1, y1 = eclick.xdata, eclick.ydata x2, y2 = erelease.xdata, erelease.ydata if x1 <= x2: refx1, refx2 = [int(np.round(x1)), int(np.round(x2))] elif x1 > x2: refx1, refx2 = [int(np.round(x1)), int(np.round(x2))] if y1 <= y2: refy1, refy2 = [int(np.round(y1)), int(np.round(y2))] elif y1 > y2: refy1, refy2 = [int(np.round(y1)), int(np.round(y2))] refarea = (refx1,refx2,refy1,refy2) axt.set_text('Ref area:\n X {}:{}\n Y {}:{}\n (start from 0)'.format(refx1, refx2, refy1, refy2)) pv.canvas.draw() ### Change clim if climauto: ## auto dmin, dmax = get_range(tscuml[-1, :, :], refarea) dmax = abs(max([dmin, dmax], key=abs)) dmin = -dmax ### Update draw if not tscuml_disp_flag: ## vel or noise indice # Chandra val_selected = radio_vel.value_selected val_ind = list(mapdict_data.keys()).index(val_selected) radio_vel.set_active(val_ind) else: ## cumulative displacement time_selected = tslider.val tslider.set_val(time_selected) if lastevent: ## Time series plot printcoords(lastevent) RS = RectangleSelector(axv, line_select_callback, drawtype='box', useblit=True, button=[3], spancoords='pixels', interactive=False) plt.connect('key_press_event', RS) # auto_crange: float = 99.8 vlimauto = True def show_vel(val_ind): global vmin, vmax, tscuml_disp_flag tscuml_disp_flag = False if 'Velocity' in val_ind: ## Velocity data = mapdict_data[val_ind] # if vlimauto: ## auto # vmin = np.nanpercentile(data, 100 - auto_crange) # vmax = np.nanpercentile(data, auto_crange) cax.set_cmap(cmap) cax.set_clim(vmin, vmax) cbr.set_label('mm/yr') else: data = mapdict_data[val_ind] cmap2 = matplotlib.cm.Reds # cmap2 = 'Reds' cmap2.set_bad('grey', 1.) # filled grey color to nan value if val_ind == 'Error': cax.set_clim(0, 20) elif val_ind == 'R squared': cax.set_clim(0, 1) cax.set_cmap(cmap2) cbr.set_label(mapdict_unit[val_ind]) cax.set_data(data) axv.set_title(val_ind) pv.canvas.draw() radio_vel.on_clicked(show_vel) # Slider for cumulative displacement axtim = pv.add_axes([0.1, 0.08, 0.8, 0.05], yticks=[]) mdt = mdates.date2num(imdates_dt) tslider = Slider(axtim, 'year', mdates.date2num(imdates_dt[0]), mdates.date2num(imdates_dt[-1])) tslider.ax.bar(mdt, np.ones(len(mdt)), facecolor='black', width=4) tslider.ax.bar(mdt[0], 1, facecolor='red', width=12) loc_tslider = tslider.ax.xaxis.set_major_locator(mdates.AutoDateLocator()) try: # Only support from Matplotlib 3.1! tslider.ax.xaxis.set_major_formatter(mdates.ConciseDateFormatter(loc_tslider)) for label in tslider.ax.get_xticklabels(): label.set_rotation(20) label.set_horizontalalignment('right') except: tslider.ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m/%d')) for label in tslider.ax.get_xticklabels(): label.set_rotation(20) label.set_horizontalalignment('right') dstr_ref = imdates_dt[0].strftime('%Y/%m/%d') # reference image date def tim_slidupdate(val): global tscuml_disp_flag timein = tslider.val timenearest = np.argmin(np.abs(mdates.date2num(imdates_dt) - timein)) dstr = imdates_dt[timenearest].strftime('%Y/%m/%d') axv.set_title('tscuml: %s (Ref: %s)' % (dstr, dstr_ref)) newv = (tscuml[timenearest, :, :]) cax.set_data(newv) cax.set_cmap(cmap) cax.set_clim(dmin, dmax) cbr.set_label('mm') tscuml_disp_flag = True pv.canvas.draw() tslider.on_changed(tim_slidupdate) ##### Plot figure of time-series at a point pts = plt.figure('PyRate: pixel time-series graph', (9,5)) axts = pts.add_axes([0.12, 0.14, 0.7, 0.8]) axts.scatter(imdates_dt, np.zeros(len(imdates_dt)), c='b', alpha=0.6) axts.grid() loc_ts = axts.xaxis.set_major_locator(mdates.AutoDateLocator()) try: # Only support from Matplotlib 3.1 axts.xaxis.set_major_formatter(mdates.ConciseDateFormatter(loc_ts)) except: axts.xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m/%d')) for label in axts.get_xticklabels(): label.set_rotation(20) label.set_horizontalalignment('right') ### Ref info at side axtref = pts.text(0.83, 0.95, 'Ref area:\n X {}:{}\n Y {}:{}\n (start from 0)\nRef date:\n {}'.format(refx1, refx2, refy1, refy2, dstr_ref), fontsize=8, va='top') ### Fit function for time series fitbox = pts.add_axes([0.83, 0.10, 0.16, 0.25]) models = ['PyRate linear rate', 'Annual+L model', 'Quad model', 'Annual+Q model'] visibilities = [True, False, False, False] fitcheck = CheckButtons(fitbox, models, visibilities) for label in fitcheck.labels: label.set_fontsize(8) def fitfunc(label): index = models.index(label) visibilities[index] = not visibilities[index] lines1[index].set_visible(not lines1[index].get_visible()) pts.canvas.draw() fitcheck.on_clicked(fitfunc) ### First show of selected point in image window pax, = axv.plot([point_y], [point_x], 'k', linewidth=3) pax2, = axv.plot([point_y], [point_x], 'Pk') ### Plot time series at clicked point lastevent = [] geocod_flag = False label1 = 'tscuml' label2 = 'PyRate linear_rate' ylen = [] def calc_model(dph, imdates_ordinal, xvalues, model, vel1p, intercept1p): """ Function to calculate model to fit cumulative time series data for one pixel """ imdates_years = np.divide(imdates_ordinal,365.25) # imdates_ordinal / 365.25 xvalues_years = xvalues / 365.25 A = sm.add_constant(imdates_years) # [1, t] An = sm.add_constant(xvalues_years) # [1, t] if model == 0: # PyRate linear fit results # print(" M = ", vel1p, " & C = ", intercept1p ) A = np.dot(A, vel1p) + intercept1p An = np.dot(An, vel1p) + intercept1p # pass if model == 1: # Annual+L sin = np.sin(2 * np.pi * imdates_years) cos = np.cos(2 * np.pi * imdates_years) A = np.concatenate((A, sin[:, np.newaxis], cos[:, np.newaxis]), axis=1) sin = np.sin(2 * np.pi * xvalues_years) cos = np.cos(2 * np.pi * xvalues_years) An = np.concatenate((An, sin[:, np.newaxis], cos[:, np.newaxis]), axis=1) if model == 2: # Quad A = np.concatenate((A, (imdates_years ** 2)[:, np.newaxis]), axis=1) An = np.concatenate((An, (xvalues_years ** 2)[:, np.newaxis]), axis=1) if model == 3: # Annual+Q sin = np.sin(2 * np.pi * imdates_years) cos = np.cos(2 * np.pi * imdates_years) A = np.concatenate((A, (imdates_years ** 2)[:, np.newaxis], sin[:, np.newaxis], cos[:, np.newaxis]), axis=1) sin = np.sin(2 * np.pi * xvalues_years) cos = np.cos(2 * np.pi * xvalues_years) An = np.concatenate((An, (xvalues_years ** 2)[:, np.newaxis], sin[:, np.newaxis], cos[:, np.newaxis]), axis=1) result = sm.OLS(dph, A, missing='drop').fit() return result.predict(An) ################################# def printcoords(event): global dph, lines1, lines2, lastevent, imdates_ordinal, imdates_dt # outputting x and y coords to console if event.inaxes != axv: return elif event.button != 1: return elif not event.dblclick: ## Only double click return else: lastevent = event ## Update last event ii = np.int(np.round(event.ydata)) jj = np.int(np.round(event.xdata)) ### Plot on image window ii1h = ii - 0.5; ii2h = ii + 1 - 0.5 jj1h = jj - 0.5; jj2h = jj + 1 - 0.5 pax.set_data([jj1h, jj2h, jj2h, jj1h, jj1h], [ii1h, ii1h, ii2h, ii2h, ii1h]) pax2.set_data(jj, ii) pv.canvas.draw() axts.cla() axts.grid(zorder=0) axts.set_axisbelow(True) axts.set_xlabel('Date') axts.set_ylabel('Cumulative Displacement (mm)') axts.set_ylim(dmin, dmax) ### Get values of noise indices and incidence angle noisetxt = '' for key in mapdict_data: # val_temp = mapdict_data[key] val = mapdict_data[key][ii, jj] # val = val_temp[0,ii,jj] unit = mapdict_unit[key] if key.startswith('Velocity'): continue elif key.startswith('n_') or key == 'mask': noisetxt = noisetxt + '{}: {:d} {}\n'.format(key, int(val), unit) else: noisetxt = noisetxt + '{}: {:.2f} {}\n'.format(key, float(val), unit) try: # Only support from Matplotlib 3.1! axts.xaxis.set_major_formatter(mdates.ConciseDateFormatter(loc_ts)) except: axts.xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m/%d')) for label in axts.get_xticklabels(): label.set_rotation(20) label.set_horizontalalignment('right') ### If not masked ### tscuml file vel1p = vel[0, ii, jj] intercept1p = intercept[0,ii,jj] dph = tscuml[:,ii,jj] err = np.ones(dph.shape) * np.std(dph) # using constant err value errp = mapdict_data['Error'][ii,jj] # for error reading ## fit function lines1 = [0, 0, 0, 0] xvalues = np.arange(imdates_ordinal[0], imdates_ordinal[-1], 10) xdates = [dt.fromordinal(pp) for pp in xvalues] # Mask to exclude nan elements mask = ~np.isnan(dph) # remove nan elements from both arrays imo = np.asarray(imdates_ordinal) imo = imo[mask] imt = np.asarray(imdates_dt) imt = imt[mask] dph = dph[mask] err = err[mask] for model, vis in enumerate(visibilities): if len(dph) > 1: yvalues = calc_model(dph, imo, xvalues, model, vel1p, intercept1p) if model == 0: lines1[model], = axts.plot(xdates, yvalues, 'r-', label=label2, visible=vis, alpha=0.6, zorder=3) axts.legend() else: lines1[model], = axts.plot(xdates, yvalues, 'r-', visible=vis, alpha=0.6, zorder=3) axts.scatter(imt, dph, label=label1, c='b', alpha=0.6, zorder=5) # axts.errorbar(imt, dph, yerr=err, label=label1, fmt='.', color='black', ecolor='blue', elinewidth=0.5, capsize=2) axts.set_title('Velocity = {:.1f} +/- {:.1f} [mm/yr] @({}, {})'.format(vel1p, errp, ii, jj), fontsize=10) # axts.set_ylim(-100,100) ### Y axis if ylen: vlim = [np.nanmedian(dph) - ylen / 2, np.nanmedian(dph) + ylen / 2] axts.set_ylim(vlim) ### Legend axts.legend() pts.canvas.draw() #%% First show of time series window event = matplotlib.backend_bases.LocationEvent event.xdata = point_x event.ydata = point_y event.inaxes = axv event.button = 1 event.dblclick = True lastevent = event printcoords(lastevent) #%% Final linking of the canvas to the plots. cid = pv.canvas.mpl_connect('button_press_event', printcoords) with warnings.catch_warnings(): ## To silence user warning warnings.simplefilter('ignore', UserWarning) plt.show() pv.canvas.mpl_disconnect(cid) ================================================ FILE: utils/pyrate_pycallgraph.py ================================================ # This Python module is part of the PyRate software package # # Copyright 2022 Geoscience Australia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This script creates the pycallgraph file output_file='pyrate_with_roipac.png'. This script can be run from the 'PyRate' directory. Change the name of the config file as required. """ import sys from pycallgraph import PyCallGraph from pycallgraph import Config from pycallgraph import GlobbingFilter from pycallgraph.output import GraphvizOutput from pyrate import correct config = Config() config.trace_filter = GlobbingFilter(exclude=[ 'pycallgraph.*', '*.secret_function', ]) graphviz = GraphvizOutput(output_file='pyrate_with_roipac.png') config = Config(max_depth=6, groups=False, threaded=True) # sys.argv[0]: name of this script # sys.argv[1]: name of the config file sys.argv = ['pyrate_profile.py', 'pyrate.conf'] with PyCallGraph(output=graphviz, config=config): correct.main()