Repository: gempy-project/gempy Branch: main Commit: 61b5af5a7ad3 Files: 423 Total size: 5.3 MB Directory structure: gitextract_08koybn1/ ├── .github/ │ ├── FUNDING.yml │ ├── ISSUE_TEMPLATE/ │ │ ├── bug_report.md │ │ ├── feature_request.md │ │ └── question.md │ └── PULL_REQUEST_TEMPLATE.md ├── .gitignore ├── .teamcity/ │ └── Gempy/ │ ├── buildTypes/ │ │ ├── GempyEngine_TestingEngine.xml │ │ ├── Gempy_BuildDocs.xml │ │ ├── Gempy_PublishDocs.xml │ │ ├── Gempy_TestingGemPy.xml │ │ ├── Gempy_TestingViewer.xml │ │ ├── ReleaseGemPy.xml │ │ ├── ReleaseGemPyEngine.xml │ │ └── ReleaseGemPyViewer.xml │ ├── project-config.xml │ └── vcsRoots/ │ ├── GempyEngine_HttpsGithubComGempyProjectGempyEngineRefsHeadsMain.xml │ ├── GempyEngine_HttpsGithubComGempyProjectGempyViewerRefsHeadsMain.xml │ ├── Gempy_HttpsGithubComGempyProjectGempyRefsHeadsMain.xml │ ├── Gempy_HttpsGithubComGempyProjectGempyRefsHeadsMain1.xml │ └── Gempy_HttpsGithubComGempyProjectGempyRefsHeadsMain2.xml ├── .travis.yml ├── AUTHORS.rst ├── CITATION.cff ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── DevelopersGuide.md ├── LICENSE ├── MANIFEST.in ├── README.md ├── WhatsNewGemPy3.md ├── docs/ │ ├── .buildinfo │ ├── .nojekyll │ ├── CNAME │ ├── Makefile │ ├── __init__.py │ ├── developers_notes/ │ │ └── dev_log/ │ │ ├── 2022-2023.md │ │ ├── 2025_05.md │ │ ├── log_2024-05.md │ │ ├── log_2024-06-25.md │ │ └── log_2024-06.md │ ├── index.html │ ├── main_docstrings │ └── source/ │ ├── _static/ │ │ └── nothing.txt │ ├── _templates/ │ │ ├── base.rst │ │ ├── class.rst │ │ └── module.rst │ ├── api_reference.rst │ ├── conf.py │ ├── index.rst │ ├── installation.rst │ └── make_external_gallery.py ├── examples/ │ ├── __init__.py │ ├── data/ │ │ ├── gempy_models/ │ │ │ ├── Greenstone/ │ │ │ │ ├── Greenstone_extent.npy │ │ │ │ ├── Greenstone_faults.csv │ │ │ │ ├── Greenstone_faults_relations.csv │ │ │ │ ├── Greenstone_kriging_data.csv │ │ │ │ ├── Greenstone_options.csv │ │ │ │ ├── Greenstone_orientations.csv │ │ │ │ ├── Greenstone_rescaling_data.csv │ │ │ │ ├── Greenstone_resolution.npy │ │ │ │ ├── Greenstone_series.csv │ │ │ │ ├── Greenstone_surface_points.csv │ │ │ │ └── Greenstone_surfaces.csv │ │ │ ├── Greenstone.gempy │ │ │ ├── Tutorial_ch1-1_Basics/ │ │ │ │ ├── Tutorial_ch1-1_Basics_extent.npy │ │ │ │ ├── Tutorial_ch1-1_Basics_faults.csv │ │ │ │ ├── Tutorial_ch1-1_Basics_faults_relations.csv │ │ │ │ ├── Tutorial_ch1-1_Basics_kriging_data.csv │ │ │ │ ├── Tutorial_ch1-1_Basics_options.csv │ │ │ │ ├── Tutorial_ch1-1_Basics_orientations.csv │ │ │ │ ├── Tutorial_ch1-1_Basics_rescaling_data.csv │ │ │ │ ├── Tutorial_ch1-1_Basics_resolution.npy │ │ │ │ ├── Tutorial_ch1-1_Basics_series.csv │ │ │ │ ├── Tutorial_ch1-1_Basics_surface_points.csv │ │ │ │ └── Tutorial_ch1-1_Basics_surfaces.csv │ │ │ ├── Tutorial_ch1-8_Onlap_relations/ │ │ │ │ ├── Tutorial_ch1-8_Onlap_relations_extent.npy │ │ │ │ ├── Tutorial_ch1-8_Onlap_relations_fault_blocks.npy │ │ │ │ ├── Tutorial_ch1-8_Onlap_relations_faults.csv │ │ │ │ ├── Tutorial_ch1-8_Onlap_relations_faults_relations.csv │ │ │ │ ├── Tutorial_ch1-8_Onlap_relations_gradient.npy │ │ │ │ ├── Tutorial_ch1-8_Onlap_relations_kriging_data.csv │ │ │ │ ├── Tutorial_ch1-8_Onlap_relations_lith_block.npy │ │ │ │ ├── Tutorial_ch1-8_Onlap_relations_options.csv │ │ │ │ ├── Tutorial_ch1-8_Onlap_relations_orientations.csv │ │ │ │ ├── Tutorial_ch1-8_Onlap_relations_rescaling_data.csv │ │ │ │ ├── Tutorial_ch1-8_Onlap_relations_resolution.npy │ │ │ │ ├── Tutorial_ch1-8_Onlap_relations_scalar_field_faults.npy │ │ │ │ ├── Tutorial_ch1-8_Onlap_relations_scalar_field_lith.npy │ │ │ │ ├── Tutorial_ch1-8_Onlap_relations_series.csv │ │ │ │ ├── Tutorial_ch1-8_Onlap_relations_surface_points.csv │ │ │ │ ├── Tutorial_ch1-8_Onlap_relations_surfaces.csv │ │ │ │ └── Tutorial_ch1-8_Onlap_relations_values_block.npy │ │ │ ├── Tutorial_ch1-9a_Fault_relations/ │ │ │ │ ├── Tutorial_ch1-9a_Fault_relations_extent.npy │ │ │ │ ├── Tutorial_ch1-9a_Fault_relations_fault_blocks.npy │ │ │ │ ├── Tutorial_ch1-9a_Fault_relations_faults.csv │ │ │ │ ├── Tutorial_ch1-9a_Fault_relations_faults_relations.csv │ │ │ │ ├── Tutorial_ch1-9a_Fault_relations_gradient.npy │ │ │ │ ├── Tutorial_ch1-9a_Fault_relations_kriging_data.csv │ │ │ │ ├── Tutorial_ch1-9a_Fault_relations_lith_block.npy │ │ │ │ ├── Tutorial_ch1-9a_Fault_relations_options.csv │ │ │ │ ├── Tutorial_ch1-9a_Fault_relations_orientations.csv │ │ │ │ ├── Tutorial_ch1-9a_Fault_relations_rescaling_data.csv │ │ │ │ ├── Tutorial_ch1-9a_Fault_relations_resolution.npy │ │ │ │ ├── Tutorial_ch1-9a_Fault_relations_scalar_field_faults.npy │ │ │ │ ├── Tutorial_ch1-9a_Fault_relations_scalar_field_lith.npy │ │ │ │ ├── Tutorial_ch1-9a_Fault_relations_series.csv │ │ │ │ ├── Tutorial_ch1-9a_Fault_relations_surface_points.csv │ │ │ │ ├── Tutorial_ch1-9a_Fault_relations_surfaces.csv │ │ │ │ └── Tutorial_ch1-9a_Fault_relations_values_block.npy │ │ │ └── Tutorial_ch1-9b_Fault_relations/ │ │ │ ├── Tutorial_ch1-9b_Fault_relations_extent.npy │ │ │ ├── Tutorial_ch1-9b_Fault_relations_fault_blocks.npy │ │ │ ├── Tutorial_ch1-9b_Fault_relations_faults.csv │ │ │ ├── Tutorial_ch1-9b_Fault_relations_faults_relations.csv │ │ │ ├── Tutorial_ch1-9b_Fault_relations_gradient.npy │ │ │ ├── Tutorial_ch1-9b_Fault_relations_kriging_data.csv │ │ │ ├── Tutorial_ch1-9b_Fault_relations_lith_block.npy │ │ │ ├── Tutorial_ch1-9b_Fault_relations_options.csv │ │ │ ├── Tutorial_ch1-9b_Fault_relations_orientations.csv │ │ │ ├── Tutorial_ch1-9b_Fault_relations_rescaling_data.csv │ │ │ ├── Tutorial_ch1-9b_Fault_relations_resolution.npy │ │ │ ├── Tutorial_ch1-9b_Fault_relations_scalar_field_faults.npy │ │ │ ├── Tutorial_ch1-9b_Fault_relations_scalar_field_lith.npy │ │ │ ├── Tutorial_ch1-9b_Fault_relations_series.csv │ │ │ ├── Tutorial_ch1-9b_Fault_relations_surface_points.csv │ │ │ ├── Tutorial_ch1-9b_Fault_relations_surfaces.csv │ │ │ └── Tutorial_ch1-9b_Fault_relations_values_block.npy │ │ └── input_data/ │ │ ├── AlesModel/ │ │ │ ├── 2018_interf.csv │ │ │ ├── 2018_orient_clust_n_init5_0.csv │ │ │ └── _cropped_DEM_coarse.tif │ │ ├── Claudius/ │ │ │ ├── APoints.csv │ │ │ ├── BPoints.csv │ │ │ ├── CPoints.csv │ │ │ ├── DPoints.csv │ │ │ ├── Dips.csv │ │ │ └── Fault.csv │ │ ├── Hecho/ │ │ │ ├── Dips.csv │ │ │ ├── F1Line.csv │ │ │ ├── F2Line.csv │ │ │ ├── F3Line.csv │ │ │ ├── H1.csv │ │ │ ├── H2.csv │ │ │ ├── H3.csv │ │ │ ├── H4.csv │ │ │ ├── H5.csv │ │ │ ├── H6.csv │ │ │ ├── H7.csv │ │ │ ├── H8.csv │ │ │ └── H9.csv │ │ ├── Moureze/ │ │ │ ├── Moureze_Points.csv │ │ │ ├── Sections_EW.csv │ │ │ └── Sections_NS.csv │ │ ├── jan_models/ │ │ │ ├── fixture_model_orientations.csv │ │ │ ├── fixture_model_surfaces.csv │ │ │ ├── model1_orientations.csv │ │ │ ├── model1_surface_points.csv │ │ │ ├── model2_orientations.csv │ │ │ ├── model2_surface_points.csv │ │ │ ├── model3_orientations.csv │ │ │ ├── model3_surface_points.csv │ │ │ ├── model4_orientations.csv │ │ │ ├── model4_surface_points.csv │ │ │ ├── model5_orientations.csv │ │ │ ├── model5_surface_points.csv │ │ │ ├── model6_orientations.csv │ │ │ ├── model6_surface_points.csv │ │ │ ├── model7_orientations.csv │ │ │ ├── model7_surface_points.csv │ │ │ ├── tutorial_model_orientations.csv │ │ │ └── tutorial_model_surface_points.csv │ │ ├── lisa_models/ │ │ │ ├── foliations1.csv │ │ │ ├── foliations2.csv │ │ │ ├── foliations3.csv │ │ │ ├── foliations5.csv │ │ │ ├── foliations6.csv │ │ │ ├── foliations7.csv │ │ │ ├── foliations8.csv │ │ │ ├── foliations9.csv │ │ │ ├── interfaces1.csv │ │ │ ├── interfaces2.csv │ │ │ ├── interfaces3.csv │ │ │ ├── interfaces5.csv │ │ │ ├── interfaces6.csv │ │ │ ├── interfaces7.csv │ │ │ ├── interfaces8.csv │ │ │ └── interfaces9.csv │ │ ├── perth_basin/ │ │ │ ├── Paper_GU2F_sc_faults_topo_Foliations.csv │ │ │ └── Paper_GU2F_sc_faults_topo_Points.csv │ │ ├── striplog_integration/ │ │ │ ├── alpha_strip.tops │ │ │ ├── beta_strip.tops │ │ │ ├── epsilon_strip.tops │ │ │ └── gamma_strip.tops │ │ ├── tests/ │ │ │ ├── fault_relations_test_ori.csv │ │ │ └── fault_relations_test_surf.csv │ │ ├── tut-ch1-4/ │ │ │ ├── tut_ch1-4_orientations.csv │ │ │ └── tut_ch1-4_points.csv │ │ ├── tut-ch1-5/ │ │ │ ├── tut_ch1-5_orientations.csv │ │ │ └── tut_ch1-5_points.csv │ │ ├── tut-ch1-7/ │ │ │ ├── bogota.tif │ │ │ ├── onelayer_interfaces.csv │ │ │ └── onelayer_orient.csv │ │ ├── tut_SandStone/ │ │ │ ├── SandStone_Foliations.csv │ │ │ ├── SandStone_Points.csv │ │ │ ├── Sst_grav_1000.xyz │ │ │ ├── Sst_grav_2000.xyz │ │ │ ├── Sst_grav_500.xyz │ │ │ ├── Uncertainties.xlsx │ │ │ ├── parameters.xlsx │ │ │ └── real_grav.npy │ │ ├── tut_chapter1/ │ │ │ ├── .~lock.fault_unconformity_model_orientations.csv# │ │ │ ├── .~lock.fault_unconformity_model_points.csv# │ │ │ ├── fault_unconformity_model_orientations.csv │ │ │ ├── fault_unconformity_model_points.csv │ │ │ ├── simple_fault_model_orientations.csv │ │ │ ├── simple_fault_model_orientations_geophy.csv │ │ │ ├── simple_fault_model_points.csv │ │ │ └── simple_fault_model_points_geophy.csv │ │ └── tut_chapter6/ │ │ ├── ch6_data_fol.csv │ │ └── ch6_data_interf.csv │ ├── examples/ │ │ ├── README.rst │ │ ├── __init__.py │ │ ├── geometries/ │ │ │ ├── README.rst │ │ │ ├── __init__.py │ │ │ ├── a01_horizontal_stratigraphic.py │ │ │ ├── b02_fold.py │ │ │ ├── c03_recumbent_fold.py │ │ │ ├── d04_pinchout.py │ │ │ ├── e05_fault.py │ │ │ ├── f06_unconformity.py │ │ │ ├── g07_combination.py │ │ │ └── h08_more_examples.py │ │ └── real/ │ │ ├── Alesmodel.py │ │ ├── Claudius.py │ │ ├── Greenstone.py │ │ ├── Hecho.py │ │ ├── Moureze.py │ │ ├── Perth_basin.py │ │ ├── README.rst │ │ ├── __init__.py │ │ ├── mik.py │ │ └── moureze_sf_gempy │ ├── integrations/ │ │ ├── README.rst │ │ ├── __init__.py │ │ ├── gempy_export_MOOSE.py │ │ ├── gempy_subsurface.py │ │ ├── geo_model_units_moose_input.i │ │ └── pflotran_perth_bassin.in │ └── tutorials/ │ ├── README.rst │ ├── __init__.py │ ├── a_getting_started/ │ │ ├── README.rst │ │ ├── __init__.py │ │ └── get_started.py │ ├── ch1_fundamentals/ │ │ ├── README.rst │ │ ├── __init__.py │ │ ├── ch1_1_basics.py │ │ ├── ch1_3a_grids.py │ │ ├── ch1_3b_cross_sections.py │ │ ├── ch1_3c_topography.py │ │ ├── ch1_4_onlap_relations.py │ │ ├── ch1_5_fault_relations.py │ │ ├── ch1_6_2d_visualization.py │ │ └── ch1_7_3d_visualization.py │ ├── ch2-Geophysics/ │ │ ├── README.rst │ │ ├── ch2_1_gravity.py │ │ └── ch2_2_cell_selection.py │ ├── ch3-Interpolations/ │ │ ├── README.rst │ │ └── ch3_1_kriging_interpolation_and_simulation.py │ ├── ch4-Topology/ │ │ ├── README.rst │ │ ├── __init__.py │ │ └── ch4-1-Topology.py │ └── z_other_tutorials/ │ ├── README.rst │ ├── __init__.py │ ├── json_io/ │ │ ├── 01_surface_points_io.py │ │ ├── 02_horizontal_stratigraphic.py │ │ ├── 03_multiple_series_faults.py │ │ ├── 04_combination_model.py │ │ ├── 05_minimal_json.py │ │ ├── 05c_minimal_comparison.py │ │ ├── horizontal_stratigraphic.json │ │ ├── minimal_model.json │ │ ├── minimal_model_loaded.json │ │ ├── multiple_series_faults.json │ │ └── sample_surface_points.json │ ├── video_tutorial_model_1.py │ ├── video_tutorial_model_2_faults.py │ └── video_tutorial_model_3_onlap.py ├── gempy/ │ ├── API/ │ │ ├── __init__.py │ │ ├── _version.py │ │ ├── compute_API.py │ │ ├── examples_generator.py │ │ ├── faults_API.py │ │ ├── gp2_gp3_compatibility/ │ │ │ ├── __init__.py │ │ │ ├── gp3_to_gp2_input.py │ │ │ └── gp3_to_gp2_output.py │ │ ├── grid_API.py │ │ ├── implicit_functions.py │ │ ├── initialization_API.py │ │ ├── io_API.py │ │ └── map_stack_to_surfaces_API.py │ ├── __init__.py │ ├── _version.py │ ├── config.py │ ├── core/ │ │ ├── __init__.py │ │ ├── color_generator.py │ │ ├── data/ │ │ │ ├── DEP_xsolution.py │ │ │ ├── __init__.py │ │ │ ├── _data_points_helpers.py │ │ │ ├── core_utils.py │ │ │ ├── encoders/ │ │ │ │ ├── __init__.py │ │ │ │ ├── binary_encoder.py │ │ │ │ ├── converters.py │ │ │ │ └── json_geomodel_encoder.py │ │ │ ├── enumerators.py │ │ │ ├── gempy_engine_config.py │ │ │ ├── geo_model.py │ │ │ ├── grid.py │ │ │ ├── grid_modules/ │ │ │ │ ├── __init__.py │ │ │ │ ├── custom_grid.py │ │ │ │ ├── diamond_square.py │ │ │ │ ├── regular_grid.py │ │ │ │ ├── sections_grid.py │ │ │ │ └── topography.py │ │ │ ├── importer_helper.py │ │ │ ├── options.py │ │ │ ├── orientations.py │ │ │ ├── structural_element.py │ │ │ ├── structural_frame.py │ │ │ ├── structural_group.py │ │ │ └── surface_points.py │ │ └── engine.rst │ ├── modules/ │ │ ├── __init__.py │ │ ├── advance_pile/ │ │ │ ├── __init__.py │ │ │ └── strat_definition.py │ │ ├── custom_implicit_functions/ │ │ │ ├── __init__.py │ │ │ └── ellipsoid_implicit_function.py │ │ ├── data_manipulation/ │ │ │ ├── __init__.py │ │ │ ├── _engine_factory.py │ │ │ ├── manipulate_points.py │ │ │ ├── manipulate_structural_frame.py │ │ │ └── orientations_from_surface_points.py │ │ ├── grids/ │ │ │ ├── __init__.py │ │ │ └── create_topography.py │ │ ├── json_io/ │ │ │ ├── __init__.py │ │ │ ├── json_operations.py │ │ │ └── schema.py │ │ ├── mesh_extranction/ │ │ │ ├── __init__.py │ │ │ └── marching_cubes.py │ │ ├── optimize_nuggets/ │ │ │ ├── __init__.py │ │ │ ├── _ops.py │ │ │ └── _optimizer.py │ │ └── serialization/ │ │ ├── __init__.py │ │ └── save_load.py │ └── optional_dependencies.py ├── requirements/ │ ├── base-requirements.txt │ ├── dev-requirements.txt │ ├── docs_requirements.txt │ ├── optional-requirements.txt │ └── requirements.txt ├── scripts/ │ ├── build_docs.sh │ ├── deploy.sh │ └── update_doc_elements.sh ├── setup.cfg ├── setup.py └── test/ ├── Test Results - pytest_in_test.xml ├── __init__.py ├── conftest.py ├── context.py ├── pytest.ini ├── test_api/ │ ├── __init__.py │ ├── test_backends.py │ ├── test_fault_api.py │ ├── test_gempy_legacy_comp.py │ ├── test_initialization_and_compute_api.py │ ├── test_model_construction_granular.py │ ├── test_modify_input_data.py │ └── test_show_input_data.py ├── test_community_bugs/ │ ├── __init__.py │ └── test_community_bugs.py ├── test_core/ │ ├── __init__.py │ ├── test_color_generator.py │ ├── test_installation.py │ └── test_transfoms.py ├── test_model_types/ │ ├── __init__.py │ ├── test_example_models_I.py │ ├── test_example_models_I.test_generate_combination_model.Combination Scalar Field.approved.txt │ ├── test_example_models_I.test_generate_fault_model.Fault Scalar Field.approved.txt │ ├── test_example_models_I.test_generate_fold_model.Anticline Scalar Field.approved.txt │ ├── test_example_models_I.test_generate_horizontal_stratigraphic_model.Horizontal Stratigraphic Scalar Field.approved.txt │ └── test_subduction.py ├── test_modules/ │ ├── __init__.py │ ├── test_cg/ │ │ ├── __init__.py │ │ └── test_cg_solver.py │ ├── test_compute_times_for_grids.py │ ├── test_faults/ │ │ ├── __init__.py │ │ ├── test_fault_relations.py │ │ ├── test_finite_faults.py │ │ └── test_finite_faults.test_finite_fault_scalar_field_on_fault.verify/ │ │ └── fault.approved.txt │ ├── test_gempy_subsurface.py │ ├── test_geophysics/ │ │ ├── __init__.py │ │ ├── test_gravity.py │ │ └── test_gravity.test_gravity.verify/ │ │ └── 2-layers.approved.txt │ ├── test_grids/ │ │ ├── __init__.py │ │ ├── test_custom_grid.py │ │ ├── test_custom_grid.test_custom_grid.verify/ │ │ │ └── fold.approved.txt │ │ ├── test_diamond_square.py │ │ ├── test_grids_I.py │ │ ├── test_grids_sections.py │ │ ├── test_grids_sections.test_section_grids.verify/ │ │ │ └── fold.approved.txt │ │ └── test_grids_sections.test_topography_II.verify/ │ │ └── Model1.approved.txt │ ├── test_json_io.py │ ├── test_marching_cubes.py │ ├── test_marching_cubes_pytorch.py │ ├── test_outliers.py │ ├── test_pile/ │ │ ├── __init__.py │ │ └── test_stratigraphic_pile.py │ ├── test_serialize_model.py │ ├── test_serialize_model.test_generate_horizontal_stratigraphic_model.verify/ │ │ └── Horizontal Stratigraphic Model serialization.approved.txt │ └── test_transformed_space.py ├── test_private/ │ ├── __init__.py │ └── test_terranigma/ │ ├── __init__.py │ └── test_nuggets/ │ ├── __init__.py │ ├── _aux_func.py │ └── test_nugget_effect_optimization.py └── verify_helper.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .github/FUNDING.yml ================================================ # These are supported funding model platforms github: Leguark ================================================ 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. Include error messages / tracebacks. If useful include screenshots. **To Reproduce** Provide detailed steps to reproduce the behavior: 1. 2. 3. ... **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) * GemPy Version * if installed via pip: provide GemPy version (e.g. 2.0.1) * if cloned from GitHub: provide Git commit id (e.g. 839bf85f) * Jupyter Version (if applicable) **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. 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Relates to # Checklist - [ ] My code uses type hinting for function and method arguments and return values. - [ ] I have created tests which cover my code. - [ ] The test code either 1. demonstrates at least one valuable use case (e.g. integration tests) or 2. verifies that outputs are as expected for given inputs (e.g. unit tests). - [ ] New tests pass locally with my changes. ================================================ FILE: .gitignore ================================================ .vscode .idea .DS_Store .vs .cache/ GeMpy/__pycache__/__init__.cpython-36.pyc GeMpy/__pycache__/geomodeller_xml_obj.cpython-36.pyc GeMpy/geomodeller_xml_obj.py.bak GeMpy/qgrid/__pycache__/ ../gempy_plugins/gempy_plugins/addons/RexCloud_Api_key.txt docs/source/Notebooks/Erosion.rst docs/source/Notebooks/Example_1_Sandstone_files/Example_1_Sandstone_39_0.png docs/source/Notebooks/Example_1_Sandstone_files/Example_1_Sandstone_41_0.png docs/source/Notebooks/Example_1_Sandstone_files/Example_1_Sandstone_46_1.png docs/source/Notebooks/Example_2_Simple_PyMC3_files/ docs/source/Notebooks/Example_3_Faults_files/ docs/source/Notebooks/OptimizationNotebook.rst docs/source/Notebooks/legacy/ docs/source/index_legacy.rst input_data/Erosion_Foliations.csv input_data/Erosion_Points.csv input_data/FabLessPoints_Foliations.csv input_data/FabLessPoints_Points.csv input_data/Fab_Model_MorePoints_Foliations.csv input_data/Fab_Model_MorePoints_Points.csv input_data/Intrusion-test/ legacy/.ipynb_checkpoints/ test/.ipynb_checkpoints/ test/__pycache__/ test/legacy/ test/log test/log2 /legacy/geomodeller_xml_obj.py.bak !/notebooks/input_data/FabLessPoints_Foliations.csv !/notebooks/input_data/FabLessPoints_Points.csv ### Python template # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python env/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ .installed.cfg *.egg # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *,cover .hypothesis/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # SageMath parsed files *.sage.py # dotenv .env # virtualenv .venv venv/ ENV/ # Spyder project settings .spyderproject # Rope project settings .ropeproject notebooks/prototype notebooks/vtks notebooks/prototype notebooks/vtk_res_model_adjusted_1 notebooks/prototype notebooks/vtk_exp_smooth_trnasitions notebooks/prototype notebooks/plots # Remove the built document files - but not the static ones, which are required for the builds docs/build # The following files are generated locally during sphinx updates - they don't have to be part of the repo docs/source/examples docs/source/tutorials docs/source/Modeling\ Classes docs/source/GemPy\ API docs/source/Helper\ Classes docs/source/Plot docs/source/external docs/source/sg_execution_times.rst test/figs Profiles examples/data/input_data/Claudius/Results_ /docs/source/gen_modules/ #examples folders examples/integrations/*.vs examples/integrations/*.ugi examples/integrations/*.h5 examples/integrations/*.xmf examples/integrations/*.ss examples/integrations/*.out # Approval tests *.received.txt *.Identifier /.obsidian/app.json /.obsidian/appearance.json /.obsidian/core-plugins.json /.obsidian/core-plugins-migration.json /.obsidian/hotkeys.json /.obsidian/workspace.json # Ignore generated tutorial files examples/tutorials/z_other_tutorials/json_io/04_simple_layer_stack.py examples/tutorials/z_other_tutorials/json_io/fault_scalar_field.png examples/tutorials/z_other_tutorials/json_io/initial_model_y.png examples/tutorials/z_other_tutorials/json_io/model_with_data.png examples/tutorials/z_other_tutorials/json_io/multiple_series_faults_computed.json # Generated JSON files from examples examples/tutorials/z_other_tutorials/json_io/combination_model.json examples/tutorials/z_other_tutorials/json_io/combination_model_computed.json /test/temp/ test/test_modules/run_test.py ================================================ FILE: .teamcity/Gempy/buildTypes/GempyEngine_TestingEngine.xml ================================================ TestingEngine ================================================ FILE: .teamcity/Gempy/buildTypes/Gempy_BuildDocs.xml ================================================ BuildDocs ]]> ================================================ FILE: .teamcity/Gempy/buildTypes/Gempy_PublishDocs.xml ================================================ Publish Docs /dev/null || true # 4. Cleanup build artifacts before commit rm -rf build docs_site.zip # 3. Prevent Jekyll from ignoring files touch .nojekyll # after mv build/html/* . and before git add echo "docs.gempy.org" > CNAME # 4. Commit & push git add -A git commit -m "Publish docs from build %build.counter%" git push origin gh-pages]]> ================================================ FILE: .teamcity/Gempy/buildTypes/Gempy_TestingGemPy.xml ================================================ TestingGemPy ================================================ FILE: .teamcity/Gempy/buildTypes/Gempy_TestingViewer.xml ================================================ TestingViewer ================================================ FILE: .teamcity/Gempy/buildTypes/ReleaseGemPy.xml ================================================ Release GemPy ================================================ FILE: .teamcity/Gempy/buildTypes/ReleaseGemPyEngine.xml ================================================ Release GemPy Engine ================================================ FILE: .teamcity/Gempy/buildTypes/ReleaseGemPyViewer.xml ================================================ Release GemPy Viewer ================================================ FILE: .teamcity/Gempy/project-config.xml ================================================ Gempy ================================================ FILE: .teamcity/Gempy/vcsRoots/GempyEngine_HttpsGithubComGempyProjectGempyEngineRefsHeadsMain.xml ================================================ https://github.com/gempy-project/gempy_engine#refs/heads/main ================================================ FILE: .teamcity/Gempy/vcsRoots/GempyEngine_HttpsGithubComGempyProjectGempyViewerRefsHeadsMain.xml ================================================ https://github.com/gempy-project/gempy_viewer#refs/heads/main ================================================ FILE: .teamcity/Gempy/vcsRoots/Gempy_HttpsGithubComGempyProjectGempyRefsHeadsMain.xml ================================================ GemPy Main ================================================ FILE: .teamcity/Gempy/vcsRoots/Gempy_HttpsGithubComGempyProjectGempyRefsHeadsMain1.xml ================================================ Caching docs vcs ================================================ FILE: .teamcity/Gempy/vcsRoots/Gempy_HttpsGithubComGempyProjectGempyRefsHeadsMain2.xml ================================================ GemPy - GH Pages ================================================ FILE: .travis.yml ================================================ language: python os: linux dist: xenial branches: only: - master - release python: - '3.8' before_install: # configure a headless display - git clone --depth 1 git://github.com/pyvista/gl-ci-helpers.git - source ./gl-ci-helpers/travis/setup_headless_display.sh install: - sudo apt-get update # We do this conditionally because it saves us some downloading if the # version is the same. - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh; - bash miniconda.sh -b -p $HOME/miniconda - export PATH="$HOME/miniconda/bin:$PATH" - hash -r - conda config --set always_yes yes --set changeps1 no - conda update --all # Useful for debugging any issues with conda - conda info -a # Install and activate environment, install packages - conda create -q -n tenv python=$TRAVIS_PYTHON_VERSION - source activate tenv # Installs from conda-forge - conda install -c conda-forge python-graphviz - conda install -c conda-forge pip matplotlib ipython six pytest pytest-cov ipywidgets - conda install -c conda-forge recommonmark networkx panel setuptools mkl-service # Installs from anaconda - conda install -c anaconda libffi - conda install aesara # Installs from pip - pip install welly pymc3 - pip install scikit-image>=0.17 sklearn - pip install pandas seaborn>=0.9 sphinx-gallery # Install - pip install -e . # Pyvista should have been fixed- pip install pyvista # Arviz broke with xarray update - pip install git+https://github.com/arviz-devs/arviz --force-reinstall # Install subsurface - pip install subsurface - conda install -c conda-forge gdal - python -c "import pyvista as pv;print(pv.Report())" - pip install pooch stages: - test jobs: include: - stage: test script: - pytest -v cache: pip ================================================ FILE: AUTHORS.rst ================================================ .. _authors_ref: Authors ------- .. image:: https://img.shields.io/github/contributors/gempy-project/gempy_engine.svg?logo=github&logoColor=white :target: https://github.com/gempy-projects/gempy_engine/graphs/contributors/ The following is a list of authors who have made substantial contributions to the conception or design of this software; or the creation of new code used in this software; or have drafted the work or substantively revised it: - Miguel de la Varga, (`@leguark `_) - Alexander Zimmerman - Elisa Heim - Alexander Schaaf - Fabian Stamm - Florian Wellmann ================================================ FILE: CITATION.cff ================================================ cff-version: 1.2.0 message: "If you use GemPy, please cite it as below." authors: - family-names: "de la Varga" given-names: "Miguel" orcid: https://orcid.org/0000-0001-6941-2685 - family-names: "Schaaf" given-names: "Alexander" - family-names: "Wellmann" given-names: "Florian" orcid: https://orcid.org/0000-0003-2552-1876 title: "GemPy 1.0: open-source stochastic geological modeling and inversion" version: 1.0 doi: https://doi.org/10.5194/gmd-12-1-2019 date-released: 2019-01-02 url: "https://github.com/cgre-aachen/gempy" ================================================ FILE: CODE_OF_CONDUCT.md ================================================ # Contributor Covenant Code of Conduct ## Our Pledge In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. ## Our Standards Examples of behavior that contributes to creating a positive environment include: * Using welcoming and inclusive language * Being respectful of differing viewpoints and experiences * Gracefully accepting constructive criticism * Focusing on what is best for the community * Showing empathy towards other community members Examples of unacceptable behavior by participants include: * The use of sexualized language or imagery and unwelcome sexual attention or advances * Trolling, insulting/derogatory comments, and personal or political attacks * Public or private harassment * Publishing others' private information, such as a physical or electronic address, without explicit permission * Other conduct which could reasonably be considered inappropriate in a professional setting ## Our Responsibilities Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior. Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful. ## Scope This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers. ## Enforcement Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at florian.wellmann@cgre.rwth-aachen.de. All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately. Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership. ## Attribution This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html [homepage]: https://www.contributor-covenant.org For answers to common questions about this code of conduct, see https://www.contributor-covenant.org/faq ================================================ FILE: CONTRIBUTING.md ================================================ # Contributing We welcome and encoruage everyone to contribute to GemPy! Contributions can be questions, bug reports, feature requests and new code. Here is how to get started. ## Issues ### Questions For questions about GemPy (e.g. its applications, functionality, and usage), please [search the existing issues for related questions](https://github.com/cgre-aachen/gempy/issues). If your question has not already been asked, then [make a new issue](https://github.com/cgre-aachen/gempy/issues/new/choose). ### Reporting Bugs Please report bugs on the [issue page using the bug report template](https://github.com/cgre-aachen/gempy/issues/new?assignees=&labels=&template=bug_report.md&title=) and label the issue as a bug. The template asks essential questions for you to answer so that we can to understand, reproduce, and fix the bug. Be verbose! Whenever possible, provide tracebacks and/or error messages, screenshots, and sample code or other files. ### Feature Requests We encourage users to submit ideas for improvements to the GemPy project. For this please create an issue on the [issue page](https://github.com/cgre-aachen/gempy/issues) with the *Feature Request* template and label. Please make sure to use a descriptive title and to provide ample background information to help us implement that functionality in the future. ## Contributing New Code Any code contributions are welcome, whether fixing a typo or bug, adding new post-processing/plotting functionality, improve core functionality, or anything that you think should be in the repository. Contributions should address an open issue (either a bug or a feature request). If you have found a new bug or have an idea or a new feature, then please [open the issue](https://github.com/cgre-aachen/gempy/issues/new/choose) for discussion and link to that issue in your pull request. ### Python code guidelines We aim to follow particular Python coding guidelines to improve the sustainability and positive impact of this community project: - Follow [The Zen of Python](https://www.python.org/dev/peps/pep-0020/), most importantly "readability counts" when writing Python code. - Adhere to the [Style Guide for Python Code (PEP8)](https://www.python.org/dev/peps/pep-0008/). - Write thorough and effective documentation: Make a docstring for each module, function, class, and method, all following [PEP 257](https://www.python.org/dev/peps/pep-0257/) for high-level guidelines and [Google Python Style Guidelines](http://google.github.io/styleguide/pyguide.html) for Syntax. **Example function documentation:** ```python def func(arg1: int, arg2: float) -> int: """A concise one line summary of the function. Additional information and description of the function, if necessary. This can be as long and verbose as you think is necessary for other users and developers to understand your functionality. Args: arg1 (int): Description of the first argument. arg2 (float): Description of the second argument. Please use hanging indentation for multi-line argument descriptions. Returns: (int) Description of the return value(s) """ return 42 ``` - The code should explain the *what* and *how*. Add inline comments to explain the *why*. If an inline comment seems to be needed, consider first making the code more readable. For all comments, follow [PEP 8](https://www.python.org/dev/peps/pep-0008/). - Test every line of code. Untested code is dead code. ### Licensing All contributed code will be licensed under [a EUPL-1.2 license](https://github.com/cgre-aachen/gempy/blob/master/LICENSE). If you did not write the code yourself, it is your responsibility to ensure that the existing license is compatible and included in the contributed files. In general we discourage contributing third party code. ### Testing Our test suite uses [`pytest`](https://docs.pytest.org/). You should be familiar with `pytest` before contributing. Please run all tests locally before creating a pull request. You can do this by running `pytest` via your terminal in your GemPy folder: ```bash cd ./path/to/gempy pytest ``` All tests are located in the `test` folder and its subfolders. All contributed code must include test code in the pull request. ### Pull Requests All contributions are made via pull requests (PR's) to the master branch. You must complete the checklist in the [PR template](https://github.com/cgre-aachen/gempy/blob/master/.github/PULL_REQUEST_TEMPLATE.md) before we will review the PR and consider merging your contribution. ================================================ FILE: DevelopersGuide.md ================================================ Before a release. ---------------- # set version number in setup.py, also in the config file of the documentation and init of the package - [ ] setup.py - [ ] gempy.__init__ > Note: in the config for sphinx~ this is taken from gempy.__init__ Requirements version -------------------- - requirements.txt, optional_requirements.txt, dev_requirements.txt should either set minimum version and reject versions Github release -------------- # add new tag $ git tag X.X -m "Add X.X tag for PyPI" # push git tag $ git push --tags origin master PyPi release: ------------ New ``` # First create the dist python -m build # Second upload the distributions twine upload dist/* ``` ### Type of commits: - ENH: Enhancement, new functionality - BUG: Bug fix - DOC: Additions/updates to documentation - TST: Additions/updates to tests - BLD: Updates to the build process/scripts - PERF: Performance improvement - CLN: Code cleanup ## Documentation update The documentation is created using the [Sphinx documentation framework](https://www.sphinx-doc.org/en/master/) and the [Sphinx Gallery extension](https://sphinx-gallery.github.io/stable/index.html), which builds the documentation on basis of the repository code and the added examples. The documentation includes the tutorials and example models, which are part of the main repository, as well as additional external files. Also, opposed to the standard approach with GitHub Actions, the documentation is created offline, to avoid an unnecessary bloating of the repository and to reduce downtimes during update. Creating an update of the documentation can therefore be quite involved (depending on the level of changes). Please follow the steps below carefully. ### General case: using a fork #### Create fork and perform changes - If you don't already have one, create a fork of the main GemPy repository to your own GitHub account. Make sure to tick the box to fork _all_ branches (not only the _main_ branch). - Clone the repository to your local computer. - Create a new branch of the `main` repository for the subsequent pull request. - Perform the changes, e.g.: - Update text in the documentation - Update examples (and/ or add new ones) - Perform changes in code and/ or function docstrings - etc. - If you have performed changes before, then you can also copy these changes into the branch (Note: if you have changes in a separate branch, then they can also be copied with `git restore` or `git checkout` - please see git documentation for details). #### Update documentation using Sphinx - Update the documentation using sphinx in the branch where the changes were made (`main` branch or new branch based on `main`): - Check that you have `sphinx` and `sphinx-gallery` installed; - Change to folder with documentation from main repo with `cd docs`; - Run update with `make html`; - Wait... (can take some hours when run for the first time) - The updated documentation is now in the subfolder `build/html`. Preview the generated documentation locally: open `build/html/index.html` in a web browser. #### Add updated documentation to branch `gh-pages` - If everything is fine, first commit all changes that were made in the main repo: - `git add .` - `git commit -m "update message"` - Copy updated documentation to a temporary directory,for example `/tmp/docs-html` with: - `mkdir /tmp/docs-html` - `cp -R docs/build/html/* /tmp/docs-html/` - checkout the documentation branch: `git checkout gh-pages` - Copy the updated files into the main folder of the `gh-pages` branch: `cp -R /tmp/docs-html/* .` - To be sure: check updated documentation, open `index.html`, now in the main folder of the repository. - Commit all changes: - `git add .` - `git commit -m "Updated documentation in gh-pages branch` #### Contribute updates back to main repository - Push _both_ branches `main` and `gh-pages` back to your own remote GitHub profile - Create a pull request for both branches (make sure to create the pull request for the `gh-pages` branch also to the `gh-pages` branch of the original gempy repository) - Please include meaningful descriptions about your changes (both, in the main repo, as well as in the documnetation). ### Special case: documentation update for maintainers with repository write rights - `git worktree add ../gempy_docs gh-pages` This will create a new folder called `gempy_docs` in the parent directory of the current repo. This folder will contain the `gh-pages` branch of the repo. This is where the docs will be pushed to. - `cp -r --force ./docs/build/html/* ../gempy_docs/` This will copy the contents of the `docs/build/html` folder to the `VisualBayesicDocs` folder. - `cd ../gempy_docs` This will change the current directory to the `gempy_docs` folder. - `git add .` This will add all the files in the current directory to the staging area. - `git commit -m "Update docs"` This will commit the changes to the `gh-pages` branch. - `git push origin gh-pages` This will push the changes to the `gh-pages` branch of the repo. - `cd ../gempy` This will change the current directory back to the `gempy` folder. ================================================ FILE: LICENSE ================================================ Copyright (c) 2020-2023 The GemPy Engine Developers EUROPEAN UNION PUBLIC LICENCE v. 1.2 EUPL © the European Union 2007, 2016 This European Union Public Licence (the ‘EUPL’) applies to the Work (as defined below) which is provided under the terms of this Licence. 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[![GitHub Stars](https://img.shields.io/github/stars/cgre-aachen/gempy.svg)](https://github.com/cgre-aachen/gempy/stargazers) [![GitHub Forks](https://img.shields.io/github/forks/cgre-aachen/gempy.svg)](https://github.com/cgre-aachen/gempy/network) [![Documentation Status](https://assets.readthedocs.org/static/projects/badges/passing-flat.svg)](http://docs.gempy.org) [![Build Status](http://terranigma-solutions.teamcity.com/app/rest/builds/buildType:(id:Gempy_TestingGemPy)/statusIcon)](http://terranigma-solutions.teamcity.com/viewType.html?buildTypeId=Gempy_TestingGemPy&guest=1) [![PyPI](https://img.shields.io/badge/python-3.10%20%7C%203.11%20%7C%203.12-blue)](https://www.python.org/downloads/) [![PyPI](https://img.shields.io/badge/pypi-1.0-blue.svg)](https://pypi.org/project/gempy/) [![license: EUPL v1.2](https://img.shields.io/badge/license-EUPL%20v1.2-blue.svg)](https://github.com/cgre-aachen/gempy/blob/master/LICENSE) [![DOI](https://zenodo.org/badge/96211155.svg)](https://zenodo.org/badge/latestdoi/96211155) ## What's New: GemPy 2024.1 (a.k.a GemPy v3) Release! Welcome to the era of GemPy v3! We are thrilled to announce the release of the latest version, a product of meticulous planning, redesign, and rigorous testing. While the core essence remains intact, v3 brings significant enhancements and novelties that promise to revolutionize your geomodeling experience. Delve into the exciting new features in the [What's New in GemPy v3](WhatsNewGemPy3.md). The journey from GemPy v2 to v3 has been transformative. To ensure that our users don't lose out on any previous functionalities, we've shifted v2 to a package named [gempy_legacy](https://github.com/gempy-project/gempy_legacy). While the core team will not develop any new features for this version, we'll continue maintaining it based on community requests. ## Overview [GemPy](https://www.gempy.org/) is a Python-based, **open-source geomodeling library**. It is capable of constructing complex **3D geological models** of folded structures, fault networks and unconformities, based on the underlying powerful **implicit representation** approach. ## Installation We provide the latest release version of GemPy via PyPi package services. We highly recommend using PyPi, `$ pip install gempy[base]` ## Resources After installation, you can either check the [notebook tutorials](https://docs.gempy.org/getting_started/get_started.html#sphx-glr-getting-started-get-started-py) or the [video introduction](https://www.youtube.com/watch?v=n0btC5Zilyc) to get started. Go to the [documentation site](http://docs.gempy.org/) for further information and enjoy the [tutorials and examples](https://www.gempy.org/tutorials). For questions and support, please use [discussions](https://github.com/cgre-aachen/gempy/discussions). If you find a bug or have a feature request, create an [issue](https://github.com/cgre-aachen/gempy/issues). Follow these [guidelines](https://github.com/cgre-aachen/gempy/blob/WIP_readme-update-march21/CONTRIBUTING.md) to contribute to GemPy. ## Gallery ### Geometries

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### Features

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### Case studies

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## Publications using GemPy * Marquetto, L., Jüstel, A., Troian, G.C., Reginato, P.A.R & Simões, J.C. (2024). [Developing a 3D hydrostratigraphical model of the emerged part of the Pelotas Basin along the northern coast of Rio Grande do Sul state, Brazil](https://link.springer.com/article/10.1007/s12665-024-11609-y). Environmental Earth Sciences, 83, 329. * Brisson, S., Wellmann, F., Chudalla, N., von Harten, J., & von Hagke, C. (2023). [Estimating uncertainties in 3-D models of complex fold-and-thrust belts: A case study of the Eastern Alps triangle zone](https://www.sciencedirect.com/science/article/pii/S2590197423000046). Applied Computing and Geosciences, 18, 100115. * Liang, Z., de la Varga, M., & Wellmann, F. (2023). [Kernel method for gravity forward simulation in implicit probabilistic geologic modeling](https://pubs.geoscienceworld.org/geophysics/article/88/3/G43/621596/Kernel-method-for-gravity-forward-simulation-in?casa_token=VjCR7rYOkKoAAAAA:W81L1AXgW_j9GiYPciBvLIdL8Zo66IzYVYiU6Ri8xLgIjbzTmpcDE74rzmAwnokX_71_XKg). Geophysics, 88(3), G43-G55. * Kong, S., Oh, J., Yoon, D., Ryu, D. W., & Kwon, H. S. (2023). [Integrating Deep Learning and Deterministic Inversion for Enhancing Fault Detection in Electrical Resistivity Surveys](https://www.mdpi.com/2076-3417/13/10/6250). Applied Sciences, 13(10), 6250. * Thomas, A. T., Micallef, A., Duan, S., & Zou, Z. (2023). [Characteristics and controls of an offshore freshened groundwater system in the Shengsi region, East China Sea](https://www.frontiersin.org/articles/10.3389/feart.2023.1198215/full). Frontiers in Earth Science, 11, 1198215. * Haehnel, P., Freund, H., Greskowiak, J. & Massmann, G. (2023) [Development of a three-dimensional hydrogeological model for the island of Norderney (Germany) using GemPy](https://doi.org/10.1002/gdj3.208). Geoscience Data Journal, 00, 1–17. * Jüstel, A., de la Varga, M., Chudalla, N., Wagner, J. D., Back, S., & Wellmann, F. (2023). [From Maps to Models-Tutorials for structural geological modeling using GemPy and GemGIS](https://jose.theoj.org/papers/10.21105/jose.00185). Journal of Open Source Education, 6(66), 185. * Thomas, A. T., von Harten, J., Jusri, T., Reiche, S., Wellmann, F. (2022). [An integrated modeling scheme for characterizing 3D hydrogeological heterogeneity of the New Jersey shelf](https://link.springer.com/article/10.1007/s11001-022-09475-z). Marine Geophysical Research, 43, 11. * Sehsah, H., Eldosouky, A. M., & Pham, L. T. (2022). [Incremental Emplacement of the Sierra Nevada Batholith Constrained by U-Pb Ages and Potential Field Data](https://www.journals.uchicago.edu/doi/full/10.1086/722724?casa_token=pkl8XXrtyokAAAAA:YeIh1t-qwt6AT8yz_vTj4OQapaR1_nZUjS3Az_77VZXlpyfGu0cN5DSzrz6NNjoj4Qv5iud4rdc). The Journal of Geology, 130(5), 381-391. * von Harten, J., de la Varga, M., Hillier, M., Wellmann, F. (2021). [Informed Local Smoothing in 3D Implicit Geological Modeling](https://www.mdpi.com/2075-163X/11/11/1281). Minerals 2021, 11, 1281. * Schaaf, A., de la Varga, M., Wellmann, F., & Bond, C. E. (2021). [Constraining stochastic 3-D structural geological models with topology information using approximate Bayesian computation in GemPy 2.1](https://gmd.copernicus.org/articles/14/3899/2021/gmd-14-3899-2021.html). Geosci. Model Dev., 14(6), 3899-3913. doi:10.5194/gmd-14-3899-2021 * Güdük, N., de la Varga, M. Kaukolinna, J. and Wellmann, F. (2021). [Model-Based Probabilistic Inversion Using Magnetic Data: A Case Study on the Kevitsa Deposit](https://www.mdpi.com/2076-3263/11/4/150), _Geosciences_, 11(4):150. * Wu, J., & Sun, B. (2021). [Discontinuous mechanical analysis of manifold element strain of rock slope based on open source Gempy](https://www.e3s-conferences.org/articles/e3sconf/abs/2021/24/e3sconf_caes2021_03084/e3sconf_caes2021_03084.html). In E3S Web of Conferences (Vol. 248, p. 03084). EDP Sciences. * Stamm, F. A., de la Varga, M., and Wellmann, F. (2019). [Actors, actions, and uncertainties: optimizing decision-making based on 3-D structural geological models](https://se.copernicus.org/articles/10/2015/2019/se-10-2015-2019.html), Solid Earth, 10, 2015–2043. * Wellmann, F., Schaaf, A., de la Varga, M., & von Hagke, C. (2019). [From Google Earth to 3D Geology Problem 2: Seeing Below the Surface of the Digital Earth]( https://www.sciencedirect.com/science/article/pii/B9780128140482000156). In Developments in Structural Geology and Tectonics (Vol. 5, pp. 189-204). Elsevier. Please let us know if your publication is missing! A continuously growing list of gempy-applications (e.g. listing real-world models) can be found [here](https://hackmd.io/@Japhiolite/B1juPvCxc). ## References * de la Varga, M., Schaaf, A., and Wellmann, F. (2019). [GemPy 1.0: open-source stochastic geological modeling and inversion](https://gmd.copernicus.org/articles/12/1/2019/gmd-12-1-2019.pdf), Geosci. Model Dev., 12, 1-32. * Wellmann, F., & Caumon, G. (2018). [3-D Structural geological models: Concepts, methods, and uncertainties.](https://hal.univ-lorraine.fr/hal-01921494/file/structural_models_for_geophysicsHAL.pdf) In Advances in Geophysics (Vol. 59, pp. 1-121). Elsevier. * Calcagno, P., Chilès, J. P., Courrioux, G., & Guillen, A. (2008). [Geological modelling from field data and geological knowledge: Part I. Modelling method coupling 3D potential-field interpolation and geological rules](https://www.sciencedirect.com/science/article/abs/pii/S0031920108001258). Physics of the Earth and Planetary Interiors, 171(1-4), 147-157. * Lajaunie, C., Courrioux, G., & Manuel, L. (1997). [Foliation fields and 3D cartography in geology: principles of a method based on potential interpolation](https://link.springer.com/article/10.1007/BF02775087). Mathematical Geology, 29(4), 571-584. ================================================ FILE: WhatsNewGemPy3.md ================================================ ## **What's New: GemPy v3 Release!** **Introducing GemPy Version 3: Future-Proofing GemPy and its Role in the Open-Source Geoscience Ecosystem** We are excited to announce the release of GemPy v3, which has been meticulously reworked to enhance robustness, reliability, and ease of installation. With this release, we aim to strengthen GemPy’s position as one of the cornerstones in the open-source geoscience ecosystem. Our ongoing commitment is to develop and provide a reliable and essential tool that promotes transparency and openness in geological modeling and beyond - thereby empowering users to realize their projects and implement novel scientific approaches. **1. Transition from GemPy v2 to v3: Significant Changes** While the core essence of GemPy remains unchanged, the upgrade from v2 to v3 introduces substantial changes to workflow steps and function names, potentially affecting familiar processes. To ensure that our users retain full access to previous functionalities and can continue running their existing GemPy v2-based projects, we have transitioned v2 to a package named [gempy_legacy](https://github.com/gempy-project/gempy_legacy). We have updated our documentation and tutorials to reflect the changes in GemPy v3 and are preparing additional support materials, including short videos that showcase key functionalities of the new version. Although the core GemPy team will not develop new features for the legacy version, we are committed to maintaining it based on community requests. **2. Refined API** The GemPy API has been extensively reworked to increase robustness and readiness for production environments. We've redesigned the API and data classes to optimize utility, minimize code repetition, and boost performance. **3. Transitioning from Theano/Aesara to NumPy & PyTorch** Due to discontinued support and frequent dependency issues with Theano/Aesara, we have decisively moved away from using these frameworks in GemPy (this transition also means that PyMC is no longer supported). Instead, to ensure GemPy's future-proofness, v3 incorporates a flexible tensor library framework that currently utilizes **`numpy`** and has optional dependencies on **`PyTorch`**. Our restructured code makes it easy to integrate additional tensor libraries in the backend. Additionally, similar functionalities to those provided by PyMC will be part of the upcoming **`gempy_probability`** module, which will enhance GemPy’s capabilities in probabilistic modeling. **4. General Refactoring for Greater Robustness** We've undertaken a refactoring of GemPy to enhance its robustness and reliability. Key improvements include: - **Enhanced State Management:** We’ve made improvements to ensure consistently valid states in GemPy. Before, invalid states could arise during data adjustments. - **Optimized Dependency Management:** We've streamlined the handling of dependencies. Most dependencies are now optional, which simplifies setup and integration for users. - **Modular Design:** We've divided GemPy into several distinct libraries. This modular approach allows users to leverage specific functionalities according to their needs: - **`gempy_engine`** [here](https://github.com/gempy-project/gempy_engine): Handles core computational algorithms. - **`gempy_viewer`** [here](https://github.com/gempy-project/gempy_viewer): Provides visualization capabilities. - **`gempy_plugins`** [here](https://github.com/gempy-project/gempy_plugins): Supports additional functionalities through plugins. - **`gempy_probability`**: Focuses on probabilistic modeling and uncertainty analysis (Coming soon). - **`gempy`**: Now leaner and primarily focused on documentation and managing the API. **5. Octree Implementation for Efficient Model Computation** We have introduced an octree-based approach in GemPy v3 to optimize model computation and iteration times. **6. Dual Contouring for High-Quality Meshing** To ensure compatibility with our new octree approach, we have implemented dual contouring in GemPy v3. This method guarantees high-quality mesh generation, enhancing both the precision and visual appeal of 3D geological models. **7. Initial Steps in LiquidEarth Integration** We have begun integrating GemPy with the [LiquidEarth app](https://www.terranigma-solutions.com/liquid-earth-one) to significantly extend GemPy's applicability in practical scenarios, enhancing workflow integration and data visualization capabilities. LiquidEarth, developed by the same main developers as GemPy, is a commercial, cloud-based software solution. It empowers experts to visualize, edit, and communicate geological data and models in intuitive 3D, facilitating real-time collaboration across multiple devices, independent of location. This integration aims to leverage LiquidEarth's commercial platform, which utilizes emerging technologies to provide novel environments for working with 3D geoscience data and focuses on ease of use and on offering a low entry barrier. Simultaneously, GemPy will continue to serve as a completely free and open-source tool, offering a flexible, reliable, and transparent solution for 3D geological modeling. Together, this direct integration between GemPy and LiquidEarth is designed to maximize the strengths of both systems: enhancing the visual representation of 3D geoscience projects, connecting them to the broader open-source geoscience ecosystem, and enhancing the utility of GemPy for companies and industry applications. 1. **Other Changes, Prototypes, and Future Developments** GemPy v3 introduces numerous other smaller changes and feature prototypes. These include optimizations to the nugget effect for improved model stability, the inclusion of external implicit functions for modeling features such as dykes, and a prototype to accommodate fault zone thickness. Additionally, improved caching mechanisms have been implemented to enhance performance. One of the most requested features, which we plan to address in the near future, is the implementation of finite faults. Stay tuned for future updates as we continue to expand the GemPy documentation, publish additional examples, and progress with developments. --- **In Conclusion** With the release of v3, we are reinforcing the role of GemPy as a key contributing piece within the larger open-source geoscience ecosystem. This update not only improves core functionalities but also introduces important integrations and enhancements that make it more robust and pave the way for future developments. Dedicated to advancing GemPy as a valuable tool for both academic research and industry applications, our team values your ongoing support and feedback. We invite you to explore the new features and join us in shaping the future of open-source geosciences. Looking forward to the results and innovative applications you achieve with this new version of GemPy! ================================================ FILE: docs/.buildinfo ================================================ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. config: 6b4aef288104d0a5b66327c5a7278fec tags: 645f666f9bcd5a90fca523b33c5a78b7 ================================================ FILE: docs/.nojekyll ================================================ ================================================ FILE: docs/CNAME ================================================ gempy.rocks ================================================ FILE: docs/Makefile ================================================ # Minimal makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build SPHINXPROJ = gempy SOURCEDIR = source 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) html-noplot: export SPHX_GLR_THEME = $(theme) html-noplot: $(SPHINXBUILD) -D plot_gallery=0 -b html $(ALLSPHINXOPTS) $(SOURCEDIR) $(BUILDDIR)/html @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." ================================================ FILE: docs/__init__.py ================================================ ================================================ FILE: docs/developers_notes/dev_log/2022-2023.md ================================================ ## Refactoring for v3 notes: - I want to make proper subpackages of the library - Once the subpackages are done we can start moving them out to different repos - First proposition: - integration tests - notebooks - scripts - examples - GemPy - Plugins: - Addons - Assets - Bayesian - API - API Modules - gempy_api - Plotting - Core - Data - Interpolation - Theano - GemPy Engine > I think Pluggins and addons have to be split depending on how couple are they to the interpolator? ## Refactoring notes: Doing: - [-] Refactoring interpolation (Aug-Sep 2023) - TODO: - [ ] Saving and loading models (Sep-Oct 2023) - [ ] Refactoring Geophysics (Oct-Nov 2023) - [ ] Refactoring topology - [ ] Orientations from surface points - Move to gempy_plugins ================================================ FILE: docs/developers_notes/dev_log/2025_05.md ================================================ ### From gempy_prob notes: **Saving models** - ? Do we want to try to save the full input as versioning so that we can just have one system to go back and forth? - A way to save the model already with the correct **nugget effects** instead of having to "load nuggets" # TODO: -[ ] Saving and loading models - [x] Make tests passing for InterpOptions serializable - [x] Dealing with large numpy arrays - [x] Trying to have a better implementation for deserializing complex fields - [ ] Make save and load function -[ ] Better api for nugget effect optimization ## Saving models - From gempy - StructuralFrame - OrientationsTable - SurfacePointsTable - From Engine - Input - EngineGrid - GeophysicsInput - InterpolationOptions - InputDataDescriptor - InterpolatorInput - Solutions (Not sure if I want to save them) - RawArraysSolutions - Solutions - Testing - [ ] 10 Test to go in modules - [ ] test api ## What do I have in the engine server logic? ================================================ FILE: docs/developers_notes/dev_log/log_2024-05.md ================================================ ## Optimizing solver ### Debugging Vector model 1 #### Questions - [x] How many times do the system of equations gets solved? - Without caching is 1 per scalar field x 2 (centers and corners) x n_octree levels - [x] How many iterations we need to reach the solution? - Model 1 takes 5000 iterations (actually starts to diverge after 4k) - [ ] Has Pykeops become slower with the latest additions to the gradient? - [x] Is the Jacobi pre-conditioner bad for all cases? - Model 1: - Numpy Jacobi: Converge in - 1602 iterations and reach 5000 - time 150 sec - More or less good results: Mesh closed - Numpy No jacobi - 1018 iterations and reach 5000 - time 150 sec too - Results similar? I would say maybe a bit worse - Pykeosp No jacobi - 92 iterations and reach 5000 - time 324 (all on running the solver) - [ ] Can be that pykeops is faster than invert the matrix but still slower that any other CG - Since the matrix is already constructed, it makes sense - [ ] Then which cases is worth using pykeops? The number of iterations will need to be rather small Will it make sense on edits reusing the previous weights? This is going to need a hell of orchestrator - [ ] How much memory takes numpy direct, numpy cg and pykeops cg - numpy direct: 1.5 or so - numpy cg: 1.25 - pykeops cg: 1.1 - **Note** Now the exporting takes quite a lot of memory ### Experiments conclusions - Using pykeops CG to solve a ill posed problem is not realistic - if the model fits in memory probably the best thing to do is make the first run in torch an use the weights as initial point - if the model does not fit in memory we should try to construct the sparse version of it and try to run the incomplete cholesky decomposition - I didn't find any improvement using the Jacobi pre-conditioner. Mostly the opposite - With ichol we get different results with numpy or pykeops - Pykeops - ichol does not give super good results either at least before the convergence happens - Model 1 converge **without** ichol on 1290 and 8518 - Model 1 converge **With** ichol 496 (506 in pykeops) 5080 (4995 in pykeops) ### TODO: - [x] Clean solver interface - [ ] Add citation for pymatting ### Things to try: - [x] Pykeops as linear operator to use exact solver - Does not look good. Probably is not even possible mathematically - [x] Falkon ### Optimal solver - Tools we have: - keops - CG - Incomplete cholesky decomposition - passing the weights as initial point - Variables to choose what to use: - Size of the covariance matrix - Number of CG iterations we are going to need. This depends on: - Conditional number - Do we have weights - **Strategy**: - If the Number of CG iterations is a handful -> **use keops** - If the number of CG iterations is large: - Use **direct solver** if the matrix fits in memory - Then for changes hopefully we can use the weights as initial point and use **keops** - If the matrix does not fit in memory: - Construct the sparse version of the matrix - Use **incomplete cholesky decomposition** - Once we have the decomposition is better to use **keops** to solve the system (except if the number of iterations has to be very large) - Optimizing nugget effects still seems the best way to do this - If memory is not an issue. **Direct solver with torch and keops for exporting seems to be the best option** #### Notes - [ ] I need to make similar default env file for gempy itself and using the default when we call the engine or whatever ================================================ FILE: docs/developers_notes/dev_log/log_2024-06-25.md ================================================ ## Goals - [ ] 1. Fixing 2024.2.0 for whatever version of python that is not working ## TODO: - [x] Analyze error: - [x] Set up environment 3.11 - [x] Fixing the lines of code - [x] Test the code - [ ] Release hot fix ## Extra points: - [ ] Setting up github actions - [ ] Categories for the tests like I did in subsurface - [ ] Run tests in python 3.10, 3.11 and 3.12 ================================================ FILE: docs/developers_notes/dev_log/log_2024-06.md ================================================ # Refactoring grid to add transform ## TODO: - [x] Rotation of pyvista grid - [x] Rotation of pyvista meshes - [ ] Run something with octrees again - [ ] **We do need settings presets** ## Release Notes: - (Beta) Pivot grids - Analysis tools: - Eigenvalues and condition numbers - Transformed data plots - Octree refinement evaluation - Improved octree testing - LiquidEarth integration - Chunking for large grids ## Notes: ### Optimizing interpolation June 2024 - Create_grad_kernel is call 27 times ### Wholes issue - Spremberg with 1.5 std of extra refinement: Number of voxels marked by stats: 424 of torch.Size([512]). Number of voxels marked total by corners: 281 Dense Grid would be 512 voxels Number of voxels marked by stats: 2535 of torch.Size([3568]). Number of voxels marked total by corners: 1492 Dense Grid would be 4096 voxels Number of voxels marked by stats: 12148 of torch.Size([21152]). Number of voxels marked total by corners: 7614 Dense Grid would be 32768 voxels Number of voxels marked by stats: 69619 of torch.Size([101432]). Number of voxels marked total by corners: 37423 Dense Grid would be 262144 voxels Chunking done: 12 chunks - Spremberg with 1 Number of voxels marked by stats: 408 of torch.Size([512]). Number of voxels marked total by corners: 281 Dense Grid would be 512 voxels Number of voxels marked by stats: 1953 of torch.Size([3504]). Number of voxels marked total by corners: 1492 Dense Grid would be 4096 voxels Number of voxels marked by stats: 8632 of torch.Size([16856]). Number of voxels marked total by corners: 7612 Dense Grid would be 32768 voxels Chunking done: 13 chunks Number of voxels marked by stats: 47653 of torch.Size([79080]). Number of voxels marked total by corners: 37408 Dense Grid would be 262144 voxels Chunking done: 10 chunks ### Other: {'_cached_pivot': [200, 200, 0], '_is_default_transform': False, 'position': array([0., 0., 0.]), 'rotation': array([ 0., -0., -45.]), 'scale': array([1., 1., 1.])} ## Questions: ================================================ FILE: docs/index.html ================================================ Title ================================================ FILE: docs/main_docstrings ================================================ :class:`gempy.interpolator.InterpolatorData` :class:`gempy.data_management.InputData` :class:`gempy.data_management.GridClass` :class:`_pn.core.frame.DataFrame` :class:`_np.ndarray` :class:`matplotlib.pyplot.Figure` :class:`DataFrame` :class:`ndarray` itype(str {'all', 'interfaces', 'orientaions', 'formations', 'series', 'faults', 'fautls_relations'}): input data type to be retrieved. ================================================ FILE: docs/source/_static/nothing.txt ================================================ ================================================ FILE: docs/source/_templates/base.rst ================================================ {{ fullname | escape | underline}} .. currentmodule:: {{ module }} .. auto{{ objtype }}:: {{ objname }} .. include:: ../gen_modules/backreferences/{{fullname}}.{{item}}examples .. raw:: html

================================================ FILE: docs/source/_templates/class.rst ================================================ {{ fullname | escape | underline}} .. currentmodule:: {{ module }} .. autoclass:: {{ objname }} :no-undoc-members: {% block examples%} .. include:: ../gen_modules/backreferences/{{fullname}}.examples .. raw:: html
{% endblock %} {% block methods %} {% if methods %} .. rubric:: Methods .. autosummary:: {% for item in methods %} ~{{ name }}.{{ item }} {%- endfor %} {% endif %} {% endblock %} {% block attributes %} {% if attributes %} .. rubric:: Attributes .. autosummary:: {% for item in attributes %} ~{{ name }}.{{ item }} {%- endfor %} {% endif %} {% endblock %} ================================================ FILE: docs/source/_templates/module.rst ================================================ {{ fullname | escape | underline}} .. automodule:: {{ fullname }} {% block functions %} {% if functions %} .. rubric:: Functions .. autosummary:: {% for item in functions %} {{ item }} {%- endfor %} {% endif %} {% endblock %} {% block classes %} {% if classes %} .. rubric:: Classes .. autosummary:: {% for item in classes %} {{ item }} {%- endfor %} {% endif %} {% endblock %} {% block exceptions %} {% if exceptions %} .. rubric:: Exceptions .. autosummary:: {% for item in exceptions %} {{ item }} {%- endfor %} {% endif %} {% endblock %} ================================================ FILE: docs/source/api_reference.rst ================================================ Code ==== .. toctree:: :maxdepth: 3 GemPy API --------- .. currentmodule:: gempy .. autosummary:: :toctree: GemPy API :template: base.rst compute_model create_geomodel map_stack_to_surfaces structural_elements_from_borehole_set set_section_grid set_active_grid set_topography_from_random set_topography_from_file set_topography_from_subsurface_structured_grid set_topography_from_arrays set_custom_grid set_centered_grid generate_example_model set_fault_relation set_is_fault set_is_finite_fault add_surface_points add_orientations delete_surface_points delete_orientations create_orientations_from_surface_points_coords modify_surface_points modify_orientations add_structural_group remove_structural_group_by_index remove_structural_group_by_name remove_element_by_name calculate_gravity_gradient Plot ---- .. currentmodule:: gempy_viewer .. autosummary:: :toctree: Plot :template: base.rst plot_2d plot_3d Data Classes ============ .. toctree:: :maxdepth: 3 Modeling Classes ---------------- .. module:: gempy.core.data .. autosummary:: :toctree: Modeling Classes :template: class.rst GeoModel StructuralFrame StructuralGroup StructuralElement SurfacePointsTable OrientationsTable InterpolationOptions Grid Topography Solutions RawArraysSolution FaultsData GeophysicsInput Transform Helper Classes -------------- .. currentmodule:: gempy.core.data .. autosummary:: :toctree: Helper Classes :template: class.rst ImporterHelper GemPyEngineConfig ColorsGenerator Enumerators ----------- .. currentmodule:: gempy.core.data .. autosummary:: :toctree: Modeling Classes :template: class.rst StackRelationType GlobalAnisotropy AvailableBackends FaultsRelationSpecialCase ================================================ FILE: docs/source/conf.py ================================================ #!/usr/bin/env python3 # -*- coding: utf-8 -*- # # GemPy documentation build configuration file, created by # sphinx-quickstart on Tue Jul 4 10:28:52 2017. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # import datetime import os import sys import warnings import pyvista from sphinx_gallery.sorting import FileNameSortKey import gempy # External examples: sys.path.insert(0, os.path.abspath('.')) import make_external_gallery make_external_gallery.make_example_gallery() # region PyVista Configuration pyvista.set_error_output_file('errors.txt') pyvista.OFF_SCREEN = True # Not necessary - simply an insurance policy. Ensure that offscreen rendering is used for docs generation pyvista.set_plot_theme('document') # Preferred plotting style for documentation pyvista.FIGURE_PATH = os.path.join(os.path.abspath('_images/'), 'auto-generated/') # Save figures in specified directory pyvista.BUILDING_GALLERY = True if not os.path.exists(pyvista.FIGURE_PATH): os.makedirs(pyvista.FIGURE_PATH) sys.path.insert(0, os.path.abspath('.')) # -- 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.autosummary', 'sphinx.ext.doctest', 'sphinx.ext.intersphinx', 'sphinx.ext.todo', 'sphinx.ext.coverage', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'sphinx.ext.githubpages', 'sphinx.ext.napoleon', 'sphinx_gallery.gen_gallery', "pyvista.ext.plot_directive", "sphinx_design", 'sphinx_automodapi.automodapi', 'sphinx_automodapi.smart_resolver', ] if run_intersphinx := False: # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = { 'numpy' : ('https://numpy.org/doc/stable/', None), 'python' : ('https://docs.python.org/{.major}'.format(sys.version_info), None), 'matplotlib': ('https://matplotlib.org/stable/', None), 'mayavi' : ('http://docs.enthought.com/mayavi/mayavi', None), 'sklearn' : ('https://scikit-learn.org/stable', None), 'skimage' : ('https://scikit-image.org/docs/dev/', None), 'pyvista' : ('https://docs.pyvista.org/', None), 'sphinx' : ('https://www.sphinx-doc.org/en/master/', None), 'pandas' : ('https://pandas.pydata.org/pandas-docs/stable/', None), } napoleon_google_docstring = True autodoc_default_options = { 'autodoc_default_flags': ['members'], 'members' : None, 'member-order' : 'bysource', 'special-members' : '__init__', 'undoc-members' : True, 'exclude-members' : '__weakref__' } autosummary_generate = True autosummary_imported_members = True # 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: # source_suffix = ['.rst'] # The master toctree document. master_doc = 'index' # region General information about the project. project = 'GemPy' year = datetime.date.today().year copyright = u'2017-{}, Gempy Developers'.format(year) with open(os.path.join(os.path.dirname(__file__), '../../AUTHORS.rst'), 'r') as f: author = f.read() version = gempy.__version__ release = gempy.__version__ # The full version, including alpha/beta/rc tags. today_fmt = '%d %B %Y' language = 'en' # endregion # region building the documentation configuration # 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', '**.ipynb_checkpoints', 'Thumbs.db', '.DS_Store', 'errors.txt', '../test'] # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' highlight_language = 'python3' todo_include_todos = True # If true, `todo` and `todoList` produce output, else they produce nothing. linkcheck_retries = 3 linkcheck_timeout = 500 # * -- Sphinx Gallery Options sphinx_gallery_conf = { # path to your examples scripts "examples_dirs" : [ "../../examples/tutorials", "../../examples/examples", # "../../examples/integrations", ], # path where to save gallery generated examples "gallery_dirs" : [ 'tutorials', "examples", # 'integrations' ], "filename_pattern" : r"\.py", # "ignore_pattern": r"(__init__\.py|ch1_7_3d_visualization\.py|mik\.py)$", # Exclude specific files: uncomment for faster build as long as examples are broken "download_all_examples" : False, # Remove the "Download all examples" button from the top level gallery "within_subsection_order": FileNameSortKey, # Sort gallery example by file name instead of number of lines (default) "backreferences_dir" : 'gen_modules/backreferences', # directory where function granular galleries are stored "doc_module" : ("gempy", "gempy_viewer", 'numpy', 'pandas', 'matplotlib'), # Modules for which function level galleries are created. In "image_scrapers" : ('pyvista', 'matplotlib'), 'first_notebook_cell' : ("%matplotlib inline\n" "from pyvista import set_plot_theme\n" "set_plot_theme('document')"), 'reference_url' : { 'gempy': None, # The module you locally document uses None 'numpy': 'https://numpy.org/doc/stable/' }, } # endregion # region -- 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 = 'alabaster' html_theme_options = { 'github_user' : 'gempy-project', 'github_repo' : 'gempy', 'github_type' : 'star', 'logo' : 'logos/gempy.png', 'logo_name' : False, 'travis_button' : False, 'page_width' : '1200px', 'fixed_sidebar' : False, 'show_related' : True, 'sidebar_collapse': True, } # Custom sidebar templates, maps document names to template names. # html_sidebars = { '**': ['globaltoc.html', 'relations.html', 'sourcelink.html', 'searchbox.html']} html_sidebars = {'**': ['about.html', 'navigation.html', 'relations.html', 'searchbox.html', 'donate.html', ]} # 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 = ['_static'] html_favicon = '_static/logos/favicon.ico' # endregion # -- Options for HTMLHelp output ------------------------------------------ # Output file base name for HTML help builder. htmlhelp_basename = 'GemPydoc' # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [(master_doc, 'gempy', 'GemPy Documentation', [author], 1)] # region other options # Remove matplotlib agg warnings from generated doc when using plt.show warnings.filterwarnings("ignore", category=UserWarning, message='Matplotlib is currently using agg, which is a' ' non-GUI backend, so cannot show the figure.') # endregion ================================================ FILE: docs/source/index.rst ================================================ .. GeMpy documentation master file, created by sphinx-quickstart on Wed Dec 14 12:44:40 2016. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. .. ../logos/gempy1.png :width: 30% About ===== Open-source software for implicit 3D structural geological modeling in Python. ****************************************************************************** Overview -------- ``GemPy`` is a Python-based, community-driven, **open-source geomodeling library**. It is capable of constructing complex **3D geological models** including various features such as fold structures, fault networks and unconformities, based on an underlying powerful **implicit** approach. From the ground up, ``GemPy`` was designed to be easily embedded in probabilistic frameworks for conducting uncertainty analysis regarding subsurface structures. .. Check out the documentation either in `gempy.org `_ (better option), or `read the docs `_. 3D models created with GemPy may look like this: .. image:: ./_images/AlesModel_example.png Contents: .. toctree:: :maxdepth: 1 self installation .. toctree:: :maxdepth: 1 tutorials/index examples/index .. toctree:: :maxdepth: 1 external/external_examples .. toctree:: :maxdepth: 1 api_reference Features -------- Geological features ^^^^^^^^^^^^^^^^^^^ ``GemPy`` is capable of modeling complex 3D geological scenarios, including: * Multiple conformal layers (e.g. sequences of sedimentary layers) * Several sequences of layers, with conformal continuation or unconformities * Magmatic bodies of (almost) arbitrary shapes * Faults (offset calculated automatically from affected geological objects) * Full fault networks (faults affecting faults) * Folds (affecting single layers or entire layer stacks, including overturned and recumbent folds) Combining these elements in GemPy allows for the generation of realistic 3D geological models, on a par with most commercial geomodeling software. Interpolation approach ^^^^^^^^^^^^^^^^^^^^^^ The generation of complex structural settings is based on the powerful interpolation algorithm underlying ``GemPy``\ , a unviersal cokriging method devised by `Lajaunie et al. (1997)` and extended by `Calcagno et al. (2008)`\ . This method is used to interpolate a 3D scalar field, such that geologically significant interfaces are isosurfces in this field. The algorithm allows for a direct integration of two of the most relevant geological input data types: * **Surface contact points**\ : 3D coordinates of points marking the boundaries between different features (e.g. layer interfaces, fault planes, unconformities). * **Orientation measurements**\ : Orientation of the poles perpendicular to the dipping of surfaces at any point in 3D space. ``GemPy`` also allows for the definition of topological elements such as combining multiple stratigraphic sequences and complex fault networks to be considered in the modeling process. .. image:: ./_images/data_to_model.png Integrated visualization ^^^^^^^^^^^^^^^^^^^^^^^^ Models generated with ``GemPy`` can be visualized in several ways: * direct visualization of 2D model sections (or geological maps) using `matplotlib `_, including hillshading and other options for intuitive representation of results; * 3D visualization using `Pyvista `_, including interactive plots .. image:: _images/3D_view_pyvista_example.png :target: https://cgre-aachen.github.io/gempy/_images/sphx_glr_ch1_1_basics_009.png :width: 70% For a more detailed elaboration of the theory behind ``GemPy``\ , we refer to the **open access scientific publication**\ : `\ "GemPy 1.0: open-source stochastic geological modeling and inversion" by de la Varga et al. (2019) `_. Publications using GemPy ------------------------ - Marquetto, L., Jüstel, A., Troian, G.C., Reginato, P.A.R & Simões, J.C. (2024). `Developing a 3D hydrostratigraphical model of the emerged part of the Pelotas Basin along the northern coast of Rio Grande do Sul state, Brazil `_. Environmental Earth Sciences, 83, 329. - Brisson, S., Wellmann, F., Chudalla, N., von Harten, J., & von Hagke, C. (2023). `Estimating uncertainties in 3-D models of complex fold-and-thrust belts: A case study of the Eastern Alps triangle zone `_. Applied Computing and Geosciences, 18, 100115. - Liang, Z., de la Varga, M., & Wellmann, F. (2023). `Kernel method for gravity forward simulation in implicit probabilistic geologic modeling `_. Geophysics, 88(3), G43-G55. - Kong, S., Oh, J., Yoon, D., Ryu, D. W., & Kwon, H. S. (2023). `Integrating Deep Learning and Deterministic Inversion for Enhancing Fault Detection in Electrical Resistivity Surveys `_. Applied Sciences, 13(10), 6250. - Thomas, A. T., Micallef, A., Duan, S., & Zou, Z. (2023). `Characteristics and controls of an offshore freshened groundwater system in the Shengsi region, East China Sea `_. Frontiers in Earth Science, 11, 1198215. - Haehnel, P., Freund, H., Greskowiak, J., & Massmann, G. (2023). `Development of a three-dimensional hydrogeological model for the island of Norderney (Germany) using GemPy `_. Geoscience Data Journal, 00, 1–17. - Jüstel, A., de la Varga, M., Chudalla, N., Wagner, J. D., Back, S., & Wellmann, F. (2023). `From Maps to Models-Tutorials for structural geological modeling using GemPy and GemGIS `_. Journal of Open Source Education, 6(66), 185. - Thomas, A. T., von Harten, J., Jusri, T., Reiche, S., & Wellmann, F. (2022). `An integrated modeling scheme for characterizing 3D hydrogeological heterogeneity of the New Jersey shelf `_. Marine Geophysical Research, 43, 11. - Sehsah, H., Eldosouky, A. M., & Pham, L. T. (2022). `Incremental Emplacement of the Sierra Nevada Batholith Constrained by U-Pb Ages and Potential Field Data `_. The Journal of Geology, 130(5), 381-391. - von Harten, J., de la Varga, M., Hillier, M., & Wellmann, F. (2021). `Informed Local Smoothing in 3D Implicit Geological Modeling `_. Minerals 2021, 11, 1281. - Schaaf, A., de la Varga, M., Wellmann, F., & Bond, C. E. (2021). `Constraining stochastic 3-D structural geological models with topology information using approximate Bayesian computation in GemPy 2.1 `_. Geosci. Model Dev., 14(6), 3899-3913. doi:10.5194/gmd-14-3899-2021. - Güdük, N., de la Varga, M., Kaukolinna, J., & Wellmann, F. (2021). `Model-Based Probabilistic Inversion Using Magnetic Data: A Case Study on the Kevitsa Deposit `_. Geosciences, 11(4):150. - Wu, J., & Sun, B. (2021). `Discontinuous mechanical analysis of manifold element strain of rock slope based on open source Gempy `_. In E3S Web of Conferences (Vol. 248, p. 03084). EDP Sciences. - Stamm, F. A., de la Varga, M., & Wellmann, F. (2019). `Actors, actions, and uncertainties: optimizing decision-making based on 3-D structural geological models `_. Solid Earth, 10, 2015–2043. - Wellmann, F., Schaaf, A., de la Varga, M., & von Hagke, C. (2019). `From Google Earth to 3D Geology Problem 2: Seeing Below the Surface of the Digital Earth `_. In Developments in Structural Geology and Tectonics (Vol. 5, pp. 189-204). Elsevier. References ---------- * de la Varga, M., Schaaf, A., and Wellmann, F.: GemPy 1.0: `open-source stochastic geological modeling and inversion,` Geosci. Model Dev., 12, 1–32, https://doi.org/10.5194/gmd-12-1-2019, 2019. * Calcagno, P., Chilès, J. P., Courrioux, G., & Guillen, A. (2008). `Geological modelling from field data and geological knowledge: Part I. Modelling method coupling 3D potential-field interpolation and geological rules.` Physics of the Earth and Planetary Interiors, 171(1-4), 147-157. * `Lajaunie, C., Courrioux, G., & Manuel, L. (1997). `Foliation fields and 3D cartography in geology: principles of a method based on potential interpolation.` Mathematical Geology, 29(4), 571-584. Indices and tables ================== * :ref:`genindex` * :ref:`search` .. image:: _static/logos/logo_CGRE.png :width: 40% .. image:: _static/logos/Terranigma.png :width: 40% ================================================ FILE: docs/source/installation.rst ================================================ .. role:: raw-html-m2r(raw) :format: html Installation ------------ Installing ``GemPy`` ^^^^^^^^^^^^^^^^^^^^ The latest release version of ``GemPy`` is available via **PyPi**. To install the core version, simply use pip: .. code-block:: bash $ pip install gempy This will install ``GemPy`` with the minimal required dependencies. GemPy viewer ^^^^^^^^^^^^ For visualization in 2-D and 3-D, the additional package ``gempy-viewer`` can be used and installed with: .. code-block:: bash $ pip install gempy-viewer The 3-D visualization is based on `pyvista`. This package needs to be installed separately. Please check: - PyVista: Installation guide available on the `PyVista installation page `_. Enhanced Installation Options ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ For additional features, ``GemPy`` offers enhanced installation options: - Base Features: For the majority of functionalities, install with base dependencies: .. code-block:: bash $ pip install gempy[base] - Optional Features: To access optional features such as data download support, install with optional dependencies: .. code-block:: bash $ pip install gempy[opt] - Development Version: For development purposes, including testing tools: .. code-block:: bash $ pip install gempy[dev] Note: Some advanced functionalities in ``GemPy`` require `PyTorch`. For installation, please follow the instructions on the `PyTorch installation page `_. Manual Installation ^^^^^^^^^^^^^^^^^^^ For the latest, cutting-edge version of ``GemPy``, you can clone the repository and install it manually: 1. Clone the repository: .. code-block:: bash $ git clone https://github.com/cgre-aachen/gempy.git 2. Navigate to the root of the cloned repository and install using pip: .. code-block:: bash $ pip install -e . Dependencies ^^^^^^^^^^^^ ``GemPy`` relies on numerous open-source libraries. Core dependencies are automatically installed with the package. Enhanced functionalities require additional dependencies, structured as follows: - Core Dependencies: Installed with the basic package. - Base Dependencies: Additional libraries for extended functionalities (`base-requirements.txt`). - Optional Dependencies: Libraries for optional features (`optional-requirements.txt`). - Development Dependencies: Tools needed for development (`dev-requirements.txt`). ================================================ FILE: docs/source/make_external_gallery.py ================================================ """ Modified after https://github.com/pyvista/pyvista/blob/ab70c26edbcfb107286c827bd4914562056219fb/docs/make_external_gallery.py A helper script to generate the external 2-examples gallery. """ import os from io import StringIO def format_icon(title, description, link, image): body = r""" .. grid-item-card:: {} :link: {} :text-align: center :class-title: pyvista-card-title .. image:: {} """ content = body.format(title, link, image) return content class Example(): def __init__(self, title, description, link, image): self.title = title self.description = description self.link = link self.image = image def format(self): return format_icon(self.title, self.description, self.link, self.image) ############################################################################### articles = dict( _1=Example( title="Vector Model 1: Construction", description="Build a basic structural model", link="https://vector-raw-materials.github.io/vector-geology/examples/02_structural_modeling/01_model_1_gempy_step_by_step.html#sphx-glr-examples-02-structural-modeling-01-model-1-gempy-step-by-step-py", image="https://vector-raw-materials.github.io/vector-geology/_images/sphx_glr_01_model_1_gempy_step_by_step_001.png", ), _2=Example( title="Vector Model 1: Forward Gravity", description="Get the gravity response of the model", link="https://vector-raw-materials.github.io/vector-geology/examples/03_forward_engines/02_model_1_gempy_fw_gravity.html#sphx-glr-examples-03-forward-engines-02-model-1-gempy-fw-gravity-py", image="https://vector-raw-materials.github.io/vector-geology/_images/sphx_glr_02_model_1_gempy_fw_gravity_003.png", ), _3=Example( title="Spremberg: Importing Boreholes", description="Import data from well csv files", link="https://vector-raw-materials.github.io/vector-geology/examples/02_structural_modeling/03_model_spremberg_import.html#sphx-glr-examples-02-structural-modeling-03-model-spremberg-import-py", image="https://vector-raw-materials.github.io/vector-geology/_images/sphx_glr_03_model_spremberg_import_003.png", ), _4=Example( title="Spremberg: Constructing the Model", description="Build a structural model from borehole data", link="https://vector-raw-materials.github.io/vector-geology/examples/02_structural_modeling/04_model_spremberg_building.html#sphx-glr-examples-02-structural-modeling-04-model-spremberg-building-py", image="https://vector-raw-materials.github.io/vector-geology/_images/sphx_glr_04_model_spremberg_building_008.png", ), ) ############################################################################### def make_example_gallery(): """Make the example gallery.""" path = "./external/external_examples.rst" with StringIO() as new_fid: new_fid.write( """ .. _external_examples: External Examples ================== Vector Examples --------------- These are examples from the Vector project. .. grid:: 3 :gutter: 1 """ ) # Reverse to put the latest items at the top for example in list(articles.values())[::-1]: new_fid.write(example.format()) new_fid.write( """ .. raw:: html
""" ) new_fid.seek(0) new_text = new_fid.read() # check if it's necessary to overwrite the table existing = "" if os.path.exists(path): with open(path) as existing_fid: existing = existing_fid.read() # write if different or does not exist if new_text != existing: with open(path, "w", encoding="utf-8") as fid: fid.write(new_text) return ================================================ FILE: examples/__init__.py ================================================ ================================================ FILE: examples/data/gempy_models/Greenstone/Greenstone_faults.csv ================================================ ,isFault,isFinite EarlyGranite_Series,False,False BIF_Series,False,False SimpleMafic_Series,False,False Basement,False,False ================================================ FILE: examples/data/gempy_models/Greenstone/Greenstone_faults_relations.csv ================================================ ,EarlyGranite_Series,BIF_Series,SimpleMafic_Series,Basement EarlyGranite_Series,False,False,False,False BIF_Series,False,False,False,False SimpleMafic_Series,False,False,False,False Basement,False,False,False,False ================================================ FILE: examples/data/gempy_models/Greenstone/Greenstone_kriging_data.csv ================================================ ,range,$C_o$,drift equations,nugget grad,nugget scalar values,86591.22357375486,178524761.9047619,[3 3 3 0],0.01,1e-06 ================================================ FILE: examples/data/gempy_models/Greenstone/Greenstone_options.csv ================================================ ,dtype,output,aesara_optimizer,device,verbosity values,float64,geology,fast_compile,cpu,[] ================================================ FILE: examples/data/gempy_models/Greenstone/Greenstone_orientations.csv ================================================ ,X,Y,Z,X_r,Y_r,Z_r,G_x,G_y,G_z,dip,azimuth,polarity,surface,series,id,order_series,smooth 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39,701844.5315,6875137.25,476.658525,0.3010170742254671,0.311087098833438,0.5456918654283743,0.7357966048102152,-0.6545606669689805,0.17364817766793042,80.0,131.656118,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01 40,698466.9375,6871074.25,463.972201,0.26624061550817,0.269253547563399,0.5455612442160971,0.7825830449493052,-0.5978378443618282,0.17364817766793042,80.0,127.377257,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01 0,739426.6276840001,6891935.451483999,75.422691,0.6879706916384385,0.48404511582219845,0.5415606520803518,-0.6040227735540536,-0.7198463103919542,0.3420201433266688,70.0,220.0,1.0,SimpleBIF,BIF_Series,3,2,0.01 1,717311.112372,6891940.971392999,-1497.488309,0.4602644293524706,0.48410195003242695,0.5253656107410191,0.8660254037854386,1.0000530287619361e-12,0.5000000000010001,60.0,90.0,1.0,SimpleBIF,BIF_Series,3,2,0.01 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10,733335.732778,6903150.931884,-1538.6292970000002,0.625257483582673,0.5995221954017079,0.5249420139822699,-0.7587575629491816,-0.19754255501034038,0.6206963022499282,51.633,255.407,1.0,SimpleMafic1,SimpleMafic_Series,4,3,0.01 11,730820.452963,6902599.428688001,-3825.776297,0.5993596034204055,0.593843795887698,0.5013930400008237,-0.36419606058097953,-0.07271406390723276,0.9284793451490311,21.801,258.709,1.0,SimpleMafic1,SimpleMafic_Series,4,3,0.01 12,725461.656,6915002.0,510.686328,0.5441842385428782,0.7215434274064846,0.5460422232529885,0.8435050562949495,-0.19621218108503954,0.5000000000010001,60.0,103.095,1.0,SimpleMafic1,SimpleMafic_Series,4,3,0.01 13,726102.312,6917775.5,499.040304,0.5507805751469789,0.7501,0.5459223132007867,1e-12,-0.8660254037834386,0.5000000000010001,-60.0,0.0,1.0,SimpleMafic1,SimpleMafic_Series,4,3,0.01 14,727390.156,6913486.75,517.5692429999999,0.5640405032793464,0.7059420765421167,0.5461130912760108,-0.8215644704637983,-0.2739193692847363,0.5000000000010001,-60.0,71.561,1.0,SimpleMafic1,SimpleMafic_Series,4,3,0.01 ================================================ FILE: examples/data/gempy_models/Greenstone/Greenstone_rescaling_data.csv ================================================ ,rescaling factor,centers values,97123.0,[ 7.21180062e+05 6.89349475e+06 -3.95136022e+03] ================================================ FILE: examples/data/gempy_models/Greenstone/Greenstone_series.csv ================================================ ,order_series,BottomRelation,isActive EarlyGranite_Series,1,Erosion,True BIF_Series,2,Erosion,True SimpleMafic_Series,3,Erosion,True Basement,4,Erosion,False ================================================ FILE: examples/data/gempy_models/Greenstone/Greenstone_surface_points.csv ================================================ 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56,726319.875,6884389.0,472.41144699999995,0.5530206521627214,0.40634517364578937,0.5456481365690928,EarlyGranite,EarlyGranite_Series,1,1,1e-06 57,723284.938,6882235.0,475.41346100000004,0.5217722660955691,0.3841671107770559,0.5456790459726326,EarlyGranite,EarlyGranite_Series,1,1,1e-06 58,721522.688,6879591.5,471.253042,0.5036277483191414,0.3569490470846246,0.5456362093736807,EarlyGranite,EarlyGranite_Series,1,1,1e-06 59,720935.313,6881941.0,492.156175,0.4975800047362619,0.3811400214161424,0.5458514326781504,EarlyGranite,EarlyGranite_Series,1,1,1e-06 60,720445.813,6883703.5,498.30804000000006,0.49254000391256414,0.39928711324814925,0.5459147736478486,EarlyGranite,EarlyGranite_Series,1,1,1e-06 61,717410.875,6883507.5,507.58461900000003,0.4612916075491902,0.39726905367420695,0.5460102873675649,EarlyGranite,EarlyGranite_Series,1,1,1e-06 62,714571.688,6882039.0,506.53461200000004,0.43205870700040117,0.3821490512031136,0.5459994762620595,EarlyGranite,EarlyGranite_Series,1,1,1e-06 63,711243.063,6879493.5,505.437117,0.39778644399369834,0.35594001729765345,0.5459881762095488,EarlyGranite,EarlyGranite_Series,1,1,1e-06 64,709676.625,6881549.5,515.202397,0.3816580501014178,0.3771090503794158,0.546088721703407,EarlyGranite,EarlyGranite_Series,1,1,1e-06 65,710264.063,6883899.5,524.0556650000001,0.3877064423463028,0.40130517282209155,0.5461798769189584,EarlyGranite,EarlyGranite_Series,1,1,1e-06 66,707914.438,6882724.5,507.24563600000005,0.3635141809869955,0.38920711160075366,0.5460067971232355,EarlyGranite,EarlyGranite_Series,1,1,1e-06 67,703802.563,6877340.0,483.05280700000003,0.3211774018512604,0.333767102540078,0.5457577023773977,EarlyGranite,EarlyGranite_Series,1,1,1e-06 68,699886.5,6872934.5,470.146586,0.2808567465996726,0.28840709512679796,0.5456248170567218,EarlyGranite,EarlyGranite_Series,1,1,1e-06 69,697047.375,6869214.0,456.299059,0.2516244844166675,0.2501,0.5454822398402026,EarlyGranite,EarlyGranite_Series,1,1,1e-06 0,735484.817806,6891936.435338001,-1819.319309,0.647384940807018,0.4840552458017232,0.5220519672168281,SimpleMafic2,BIF_Series,2,2,1e-06 1,729854.915982,6891937.840530001,-1432.263309,0.5894182200096779,0.4840697139709505,0.5260371818415823,SimpleMafic2,BIF_Series,2,2,1e-06 2,724084.2671609999,6891939.280852,-4739.830309,0.5300023368409125,0.4840845438464651,0.4919817366844105,SimpleMafic2,BIF_Series,2,2,1e-06 3,733521.625,6895282.5,521.55524,0.6271714712272067,0.5185070714454867,0.5461541319872738,SimpleMafic2,BIF_Series,2,2,1e-06 4,721933.375,6884591.5,496.66929500000003,0.507856272973446,0.4084301586647859,0.5458979007650093,SimpleMafic2,BIF_Series,2,2,1e-06 5,724251.0,6886909.0,484.550926,0.5317190552186403,0.4322916538821906,0.5457731273436776,SimpleMafic2,BIF_Series,2,2,1e-06 6,727316.313,6886460.5,478.25442300000003,0.5632801993348637,0.42767379817345014,0.5457082971489761,SimpleMafic2,BIF_Series,2,2,1e-06 7,729858.25,6887133.5,484.25957400000004,0.5894525478002121,0.4346031557921399,0.5457701275187133,SimpleMafic2,BIF_Series,2,2,1e-06 8,732699.25,6885040.0,494.52648099999993,0.6187041154000597,0.41304801437352634,0.5458758378756834,SimpleMafic2,BIF_Series,2,2,1e-06 9,716849.5,6887358.0,508.98189400000007,0.45551156574652757,0.4369146577020891,0.5460246740216015,SimpleMafic2,BIF_Series,2,2,1e-06 10,719017.625,6892217.5,508.179387,0.477835062755475,0.4869491500468478,0.5460164112311193,SimpleMafic2,BIF_Series,2,2,1e-06 11,739179.440691,6891935.513181,-552.591309,0.685425599404878,0.4840457510682332,0.535094480318771,SimpleBIF,BIF_Series,3,2,1e-06 12,735564.599804,6891936.415425,-2652.196309,0.6482063939952432,0.4840550407730381,0.5134764804629182,SimpleBIF,BIF_Series,3,2,1e-06 13,730009.0099770001,6891937.802069,-2088.409309,0.5910048060397647,0.48406931796793723,0.519281356753807,SimpleBIF,BIF_Series,3,2,1e-06 14,718795.791326,6891940.600826,-2773.169309,0.47555101393078886,0.4840981345922167,0.5122309155606808,SimpleBIF,BIF_Series,3,2,1e-06 15,724143.38616,6891939.266096001,-5569.907309,0.5306110391977182,0.48408439191541197,0.48343507935298535,SimpleBIF,BIF_Series,3,2,1e-06 16,723877.188,6899768.5,529.1521690000001,0.5278702037622393,0.5646959247552072,0.546232351657177,SimpleBIF,BIF_Series,3,2,1e-06 17,732998.313,6898049.0,521.619609,0.6217833345345589,0.5469915704827899,0.5461547947448081,SimpleBIF,BIF_Series,3,2,1e-06 18,743689.438,6891769.0,512.811278,0.7318615343430492,0.48233129433810734,0.546064102210599,SimpleBIF,BIF_Series,3,2,1e-06 19,712961.813,6882722.5,547.826016,0.4154830760993788,0.3891865191561216,0.5464246217373845,SimpleBIF,BIF_Series,3,2,1e-06 20,716284.875,6891346.5,515.58686,0.4496980612213379,0.4779811404095837,0.5460926802199273,SimpleBIF,BIF_Series,3,2,1e-06 21,718942.875,6897600.5,538.490136,0.4770654201373516,0.5423737147740494,0.5463284974413888,SimpleBIF,BIF_Series,3,2,1e-06 22,722157.625,6882947.0,481.747055,0.5101652008278162,0.39149802106607084,0.5457442580645161,SimpleBIF,BIF_Series,3,2,1e-06 23,723952.0,6885488.5,480.122832,0.5286404847461467,0.4176658700822668,0.5457275347034173,SimpleBIF,BIF_Series,3,2,1e-06 24,728736.813,6885488.5,477.929009,0.5779059831347875,0.4176658700822668,0.5457049466140873,SimpleBIF,BIF_Series,3,2,1e-06 25,738829.813,6878087.0,470.08143099999995,0.6818257549705009,0.3414583806101541,0.5456241462063569,SimpleBIF,BIF_Series,3,2,1e-06 26,715522.708428,6891941.417769,-2810.185309,0.4418506247541775,0.48410654601896497,0.5118497905954306,SimpleMafic1,SimpleMafic_Series,4,3,1e-06 27,722100.017223,6891939.776110001,-5640.110309000001,0.5095720583486912,0.4840896431329439,0.48271225365773296,SimpleMafic1,SimpleMafic_Series,4,3,1e-06 28,728133.723035,6891938.27013,-8458.173309,0.57169643477858,0.4840741372280518,0.4536968505091482,SimpleMafic1,SimpleMafic_Series,4,3,1e-06 29,733022.378883,6891937.049949001,-7358.592309000001,0.6220311222161589,0.4840615739732153,0.4650183809396332,SimpleMafic1,SimpleMafic_Series,4,3,1e-06 30,737972.021729,6891935.814546,-5589.549309,0.6729937453435337,0.4840488539892735,0.4832328409542539,SimpleMafic1,SimpleMafic_Series,4,3,1e-06 31,726443.009736,6905713.435698,-5023.656995,0.5542884747794032,0.6259063043563294,0.4890593940261318,SimpleMafic1,SimpleMafic_Series,4,3,1e-06 32,727109.3352020001,6886862.614432001,-6720.406176,0.5611491099121743,0.4318140577618129,0.47158928724401017,SimpleMafic1,SimpleMafic_Series,4,3,1e-06 33,726266.236469,6901753.8156550005,-5708.065297,0.5524683779228401,0.5851371761065918,0.48201257399380165,SimpleMafic1,SimpleMafic_Series,4,3,1e-06 34,732617.810926,6902964.017319,-3068.116297,0.617865600588944,0.5975976814863659,0.5091940758007887,SimpleMafic1,SimpleMafic_Series,4,3,1e-06 35,709728.25,6885850.5,532.9365769999999,0.38218959257848295,0.4213931025606705,0.5462713167632796,SimpleMafic1,SimpleMafic_Series,4,3,1e-06 36,714271.063,6897386.0,536.8312540000001,0.428963405166644,0.5401651750872605,0.5463114172235207,SimpleMafic1,SimpleMafic_Series,4,3,1e-06 37,722693.188,6907492.5,555.452867,0.5156794765400571,0.6442239459242404,0.5465031494908518,SimpleMafic1,SimpleMafic_Series,4,3,1e-06 38,725092.188,6913005.0,514.864987,0.5403801138762184,0.7009818714413681,0.5460852476550353,SimpleMafic1,SimpleMafic_Series,4,3,1e-06 39,744692.688,6890291.0,496.01971100000003,0.7421912193816085,0.46711347775501166,0.5458912125037324,SimpleMafic1,SimpleMafic_Series,4,3,1e-06 40,738924.813,6900193.5,551.633725,0.682803896090524,0.569071819239521,0.5464638267557633,SimpleMafic1,SimpleMafic_Series,4,3,1e-06 41,729430.813,6908717.5,517.178069,0.5850515614221139,0.6568368182613799,0.5461090636615425,SimpleMafic1,SimpleMafic_Series,4,3,1e-06 42,726929.75,6916017.0,502.161614,0.5593000607477117,0.7319940930572573,0.5459544509024639,SimpleMafic1,SimpleMafic_Series,4,3,1e-06 ================================================ FILE: examples/data/gempy_models/Greenstone/Greenstone_surfaces.csv ================================================ ,surface,series,order_surfaces,isBasement,isFault,isActive,color,vertices,edges,id,value_0 3,EarlyGranite,EarlyGranite_Series,1,False,False,True,#728f02,"[[ 6.96913326e+05 6.86300000e+06 -1.47480000e+04] [ 6.97020000e+05 6.86309351e+06 -1.47480000e+04] [ 6.97020000e+05 6.86300000e+06 -1.49709013e+04] ... [ 7.45781532e+05 6.87774000e+06 -2.04000000e+02] [ 7.45980000e+05 6.87774000e+06 -5.31339188e+02] [ 7.45980000e+05 6.87779400e+06 -2.04000000e+02]]","[[ 2 1 0] [ 0 1 3] [ 3 1 4] ... [3577 3634 3635] [3576 3634 3577] [3636 3637 3635]]",1,2.61 0,SimpleMafic2,BIF_Series,1,False,False,True,#015482,"[[ 7.17372390e+05 6.88444000e+06 -2.04000000e+02] [ 7.17420000e+05 6.88444000e+06 -2.19525421e+02] [ 7.17420000e+05 6.88416750e+06 -2.04000000e+02] ... [ 7.45980000e+05 6.89022786e+06 -1.01200000e+03] [ 7.45980000e+05 6.89058852e+06 -6.08000000e+02] [ 7.45980000e+05 6.89094654e+06 -2.04000000e+02]]","[[ 2 1 0] [ 0 1 3] [ 3 1 4] ... [1049 1050 990] [ 991 1051 992] [1050 1051 991]]",2,2.92 1,SimpleBIF,BIF_Series,2,False,False,True,#9f0052,"[[ 7.14839407e+05 6.88042000e+06 -2.04000000e+02] [ 7.15380000e+05 6.88042000e+06 -2.72209213e+02] [ 7.15380000e+05 6.87984794e+06 -2.04000000e+02] ... [ 7.45980000e+05 6.89114000e+06 -6.54912231e+02] [ 7.45980000e+05 6.89117842e+06 -6.08000000e+02] [ 7.45980000e+05 6.89150733e+06 -2.04000000e+02]]","[[ 2 1 0] [ 0 1 3] [ 3 1 4] ... [1332 1334 1265] [1334 1266 1265] [1334 1335 1266]]",3,3.1 2,SimpleMafic1,SimpleMafic_Series,1,False,False,True,#ffbe00,"[[ 6.96000000e+05 6.86300000e+06 -2.45178632e+03] [ 6.97020000e+05 6.86393305e+06 -2.22400000e+03] [ 6.97020000e+05 6.86300000e+06 -2.48451546e+03] ... [ 7.45267801e+05 6.88176000e+06 -2.04000000e+02] [ 7.45222338e+05 6.88310000e+06 -2.04000000e+02] [ 7.45091231e+05 6.88444000e+06 -2.04000000e+02]]","[[ 2 1 0] [ 0 1 3] [ 4 3 1] ... [3030 3018 3029] [3029 3018 3014] [3030 3021 3018]]",4,2.92 4,basement,Basement,1,True,False,True,#443988,,,5,2.61 ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-1_Basics/Tutorial_ch1-1_Basics_faults.csv ================================================ ,isFault,isFinite Fault_Series,True,False Strat_Series,False,False ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-1_Basics/Tutorial_ch1-1_Basics_faults_relations.csv ================================================ ,Fault_Series,Strat_Series Fault_Series,False,True Strat_Series,False,False ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-1_Basics/Tutorial_ch1-1_Basics_kriging_data.csv ================================================ ,range,$C_o$,drift equations values,3464.1016151377544,285714.2857142857,[3 3] ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-1_Basics/Tutorial_ch1-1_Basics_options.csv ================================================ ,dtype,output,aesara_optimizer,device,verbosity values,float64,geology,fast_compile,cpu,[] ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-1_Basics/Tutorial_ch1-1_Basics_orientations.csv ================================================ ,X,Y,Z,X_r,Y_r,Z_r,G_x,G_y,G_z,dip,azimuth,polarity,surface,series,id,order_series,smooth 2,500,1000,864.602,0.40799473684210524,0.5001,0.4907847368421052,-0.9486830155918029,9.998257297572324e-13,0.31622861338970926,71.565,270.0,1.0,Main_Fault,Fault_Series,1,1,0.01 1,400,1000,1400.0,0.38167894736842106,0.5001,0.631678947368421,0.3162286133897092,1.0000193634179593e-12,0.9486830155938029,18.435,90.0,1.0,Sandstone_2,Strat_Series,2,2,0.01 0,1000,1000,950.0,0.5395736842105263,0.5001,0.5132578947368421,0.3162286133897092,1.0000193634179593e-12,0.9486830155938029,18.435,90.0,1.0,Shale,Strat_Series,4,2,0.01 ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-1_Basics/Tutorial_ch1-1_Basics_rescaling_data.csv ================================================ ,rescaling factor,centers values,3800,[ 850. 1000. 900.] ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-1_Basics/Tutorial_ch1-1_Basics_series.csv ================================================ ,order_series,BottomRelation,isActive Fault_Series,1,Fault,True Strat_Series,2,Erosion,True ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-1_Basics/Tutorial_ch1-1_Basics_surface_points.csv ================================================ ,X,Y,Z,X_r,Y_r,Z_r,surface,series,id,order_series,smooth 52,700,1000,900,0.46062631578947366,0.5001,0.5001,Main_Fault,Fault_Series,1,1,2e-06 53,600,1000,600,0.4343105263157895,0.5001,0.42115263157894733,Main_Fault,Fault_Series,1,1,2e-06 54,500,1000,300,0.40799473684210524,0.5001,0.34220526315789473,Main_Fault,Fault_Series,1,1,2e-06 55,800,1000,1200,0.4869421052631579,0.5001,0.5790473684210526,Main_Fault,Fault_Series,1,1,2e-06 56,900,1000,1500,0.5132578947368421,0.5001,0.6579947368421053,Main_Fault,Fault_Series,1,1,2e-06 44,0,1000,1500,0.27641578947368417,0.5001,0.6579947368421053,Sandstone_2,Strat_Series,2,2,2e-06 45,300,1000,1400,0.3553631578947368,0.5001,0.631678947368421,Sandstone_2,Strat_Series,2,2,2e-06 46,600,1000,1300,0.4343105263157895,0.5001,0.6053631578947368,Sandstone_2,Strat_Series,2,2,2e-06 47,1100,1000,1300,0.5658894736842105,0.5001,0.6053631578947368,Sandstone_2,Strat_Series,2,2,2e-06 48,1400,1000,1250,0.6448368421052632,0.5001,0.5922052631578947,Sandstone_2,Strat_Series,2,2,2e-06 49,1700,1000,1300,0.7237842105263158,0.5001,0.6053631578947368,Sandstone_2,Strat_Series,2,2,2e-06 50,1500,500,1200,0.6711526315789473,0.36852105263157897,0.5790473684210526,Sandstone_2,Strat_Series,2,2,2e-06 51,1500,1500,1150,0.6711526315789473,0.631678947368421,0.5658894736842105,Sandstone_2,Strat_Series,2,2,2e-06 31,900,150,920,0.5132578947368421,0.27641578947368417,0.5053631578947368,Siltstone,Strat_Series,3,2,2e-06 32,900,300,920,0.5132578947368421,0.3158894736842105,0.5053631578947368,Siltstone,Strat_Series,3,2,2e-06 33,900,1500,920,0.5132578947368421,0.631678947368421,0.5053631578947368,Siltstone,Strat_Series,3,2,2e-06 34,900,1700,820,0.5132578947368421,0.6843105263157895,0.4790473684210526,Siltstone,Strat_Series,3,2,2e-06 35,900,1950,870,0.5132578947368421,0.7501,0.4922052631578947,Siltstone,Strat_Series,3,2,2e-06 36,0,1000,1300,0.27641578947368417,0.5001,0.6053631578947368,Siltstone,Strat_Series,3,2,2e-06 37,300,1000,1200,0.3553631578947368,0.5001,0.5790473684210526,Siltstone,Strat_Series,3,2,2e-06 38,600,1000,1100,0.4343105263157895,0.5001,0.5527315789473684,Siltstone,Strat_Series,3,2,2e-06 39,1100,1000,1100,0.5658894736842105,0.5001,0.5527315789473684,Siltstone,Strat_Series,3,2,2e-06 40,1400,1000,1050,0.6448368421052632,0.5001,0.5395736842105263,Siltstone,Strat_Series,3,2,2e-06 41,1700,1000,1100,0.7237842105263158,0.5001,0.5527315789473684,Siltstone,Strat_Series,3,2,2e-06 42,1500,500,1000,0.6711526315789473,0.36852105263157897,0.5264157894736842,Siltstone,Strat_Series,3,2,2e-06 43,1500,1500,950,0.6711526315789473,0.631678947368421,0.5132578947368421,Siltstone,Strat_Series,3,2,2e-06 0,800,50,750,0.4869421052631579,0.2501,0.46062631578947366,Shale,Strat_Series,4,2,2e-06 1,800,150,700,0.4869421052631579,0.27641578947368417,0.44746842105263157,Shale,Strat_Series,4,2,2e-06 2,800,300,700,0.4869421052631579,0.3158894736842105,0.44746842105263157,Shale,Strat_Series,4,2,2e-06 3,800,500,800,0.4869421052631579,0.36852105263157897,0.4737842105263158,Shale,Strat_Series,4,2,2e-06 4,800,1000,1000,0.4869421052631579,0.5001,0.5264157894736842,Shale,Strat_Series,4,2,2e-06 5,800,1500,700,0.4869421052631579,0.631678947368421,0.44746842105263157,Shale,Strat_Series,4,2,2e-06 6,800,1700,600,0.4869421052631579,0.6843105263157895,0.42115263157894733,Shale,Strat_Series,4,2,2e-06 7,800,1950,650,0.4869421052631579,0.7501,0.4343105263157895,Shale,Strat_Series,4,2,2e-06 8,0,1000,1100,0.27641578947368417,0.5001,0.5527315789473684,Shale,Strat_Series,4,2,2e-06 9,300,1000,1000,0.3553631578947368,0.5001,0.5264157894736842,Shale,Strat_Series,4,2,2e-06 10,450,1000,950,0.39483684210526315,0.5001,0.5132578947368421,Shale,Strat_Series,4,2,2e-06 11,1100,1000,900,0.5658894736842105,0.5001,0.5001,Shale,Strat_Series,4,2,2e-06 12,1400,1000,850,0.6448368421052632,0.5001,0.4869421052631579,Shale,Strat_Series,4,2,2e-06 13,1700,1000,900,0.7237842105263158,0.5001,0.5001,Shale,Strat_Series,4,2,2e-06 14,1500,500,800,0.6711526315789473,0.36852105263157897,0.4737842105263158,Shale,Strat_Series,4,2,2e-06 15,1500,1500,750,0.6711526315789473,0.631678947368421,0.46062631578947366,Shale,Strat_Series,4,2,2e-06 16,800,50,450,0.4869421052631579,0.2501,0.38167894736842106,Sandstone_1,Strat_Series,5,2,2e-06 17,800,150,400,0.4869421052631579,0.27641578947368417,0.36852105263157897,Sandstone_1,Strat_Series,5,2,2e-06 18,800,300,400,0.4869421052631579,0.3158894736842105,0.36852105263157897,Sandstone_1,Strat_Series,5,2,2e-06 19,800,500,500,0.4869421052631579,0.36852105263157897,0.39483684210526315,Sandstone_1,Strat_Series,5,2,2e-06 20,800,1000,700,0.4869421052631579,0.5001,0.44746842105263157,Sandstone_1,Strat_Series,5,2,2e-06 21,800,1500,400,0.4869421052631579,0.631678947368421,0.36852105263157897,Sandstone_1,Strat_Series,5,2,2e-06 22,800,1700,300,0.4869421052631579,0.6843105263157895,0.34220526315789473,Sandstone_1,Strat_Series,5,2,2e-06 23,800,1950,350,0.4869421052631579,0.7501,0.3553631578947368,Sandstone_1,Strat_Series,5,2,2e-06 24,0,1000,800,0.27641578947368417,0.5001,0.4737842105263158,Sandstone_1,Strat_Series,5,2,2e-06 25,300,1000,700,0.3553631578947368,0.5001,0.44746842105263157,Sandstone_1,Strat_Series,5,2,2e-06 26,1100,1000,600,0.5658894736842105,0.5001,0.42115263157894733,Sandstone_1,Strat_Series,5,2,2e-06 27,1400,1000,550,0.6448368421052632,0.5001,0.40799473684210524,Sandstone_1,Strat_Series,5,2,2e-06 28,1700,1000,600,0.7237842105263158,0.5001,0.42115263157894733,Sandstone_1,Strat_Series,5,2,2e-06 29,1500,500,500,0.6711526315789473,0.36852105263157897,0.39483684210526315,Sandstone_1,Strat_Series,5,2,2e-06 30,1500,1500,450,0.6711526315789473,0.631678947368421,0.38167894736842106,Sandstone_1,Strat_Series,5,2,2e-06 ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-1_Basics/Tutorial_ch1-1_Basics_surfaces.csv ================================================ ,surface,series,order_surfaces,isBasement,isFault,isActive,color,vertices,edges,sfai,id 4,Main_Fault,Fault_Series,1,False,True,True,#527682,"[[ 400. 0. 40. ] [ 386.66667938 0. 0. ] [ 400. 40. 40. ] ... [1013.33335876 1960. 1880. ] [1026.66664124 1960. 1920. ] [1040. 1960. 1960. ]]","[[ 2 1 0] [ 3 1 2] [ 0 5 4] ... [3297 3294 3298] [3299 3295 3296] [3298 3295 3299]]",-0.55768591019109,1 3,Sandstone_2,Strat_Series,1,False,False,True,#728f02,"[[ 0. 0. 1399.34921265] [ 0. 40. 1399.55947876] [ 40. 0. 1383.18862915] ... [1960. 1880. 1193.70231628] [1960. 1920. 1189.24514771] [1960. 1960. 1185.27381897]]","[[ 2 1 0] [ 3 1 2] [ 5 1 4] ... [3763 3696 3762] [3763 3697 3696] [3764 3697 3763]]",0.8179798867858623,2 2,Siltstone,Strat_Series,2,False,False,True,#ffbe00,"[[ 31.40995264 40. 1160. ] [ 40. 0. 1156.60209656] [ 32.15043068 0. 1160. ] ... [1960. 1880. 983.82293701] [1960. 1920. 978.76838684] [1960. 1960. 974.21592712]]","[[ 2 1 0] [ 0 1 3] [ 0 5 4] ... [3905 3836 3904] [3905 3838 3836] [3906 3838 3905]]",0.730487102963508,3 0,Shale,Strat_Series,3,False,False,True,#015482,"[[ 0. 0. 949.93782043] [ 0. 40. 949.11277771] [ 40. 0. 931.37695312] ... [1960. 1880. 781.52374268] [1960. 1920. 776.03805542] [1960. 1960. 771.07551575]]","[[ 2 1 0] [ 3 1 2] [ 3 4 1] ... [4047 3978 4046] [4047 3980 3978] [4048 3980 4047]]",0.6496588766460675,4 1,Sandstone_1,Strat_Series,4,False,False,True,#9f0052,"[[ 11.65150642 40. 640. ] [ 40. 0. 628.33042145] [ 17.65681148 0. 640. ] ... [1960. 1880. 512.40386963] [1960. 1920. 507.28397369] [1960. 1960. 502.67974854]]","[[ 2 1 0] [ 0 1 3] [ 0 5 4] ... [4128 4061 4127] [4128 4062 4061] [4129 4062 4128]]",0.5520920084971302,5 5,basement,Strat_Series,5,True,False,True,#ff3f20,,,,6 ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-8_Onlap_relations/Tutorial_ch1-8_Onlap_relations_faults.csv ================================================ ,isFault,isFinite seafloor_series,False,False right_series,False,False onlap_series,False,False left_series,False,False basement,False,False ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-8_Onlap_relations/Tutorial_ch1-8_Onlap_relations_faults_relations.csv ================================================ ,seafloor_series,right_series,onlap_series,left_series,basement seafloor_series,False,False,False,False,False right_series,False,False,False,False,False onlap_series,False,False,False,False,False left_series,False,False,False,False,False basement,False,False,False,False,False ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-8_Onlap_relations/Tutorial_ch1-8_Onlap_relations_kriging_data.csv ================================================ ,range,$C_o$,drift equations,nugget grad,nugget scalar values,1854.7236990991407,81904.76190476191,[3 3 3 3],0.01,1e-06 ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-8_Onlap_relations/Tutorial_ch1-8_Onlap_relations_options.csv ================================================ ,dtype,output,aesara_optimizer,device,verbosity values,float64,geology,fast_compile,cpu,[] ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-8_Onlap_relations/Tutorial_ch1-8_Onlap_relations_orientations.csv ================================================ ,X,Y,Z,X_r,Y_r,Z_r,G_x,G_y,G_z,dip,azimuth,polarity,surface,series,id,order_series 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9,550.0,50.0,-500.0,0.53135,0.5001,0.5626,0.766044443119978,1.0000469066937634e-12,0.6427876096875393,50.0,90.0,1.0,onlap_surface,onlap_series,4,3 10,100.0,50.0,-400.0,0.2501,0.5001,0.6251,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock3,left_series,5,4 11,450.0,50.0,-1000.0,0.46885,0.5001,0.2501,1.000000000001,2.3054614646942163e-12,1.0000612323399569e-12,90.0,89.9999999999252,1.0,rock3,left_series,5,4 12,300.0,50.0,-500.0,0.3751,0.5001,0.5626,0.766044443119978,1.0000469066937634e-12,0.6427876096875393,49.99999999992522,89.9999999999252,1.0,rock3,left_series,5,4 ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-8_Onlap_relations/Tutorial_ch1-8_Onlap_relations_rescaling_data.csv ================================================ ,rescaling factor,centers values,1600.0,[ 500. 50. -600.] ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-8_Onlap_relations/Tutorial_ch1-8_Onlap_relations_series.csv ================================================ ,order_series,BottomRelation seafloor_series,1,Erosion right_series,2,Onlap onlap_series,3,Erosion left_series,4,Erosion basement,5,Erosion ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-8_Onlap_relations/Tutorial_ch1-8_Onlap_relations_surface_points.csv ================================================ ,X,Y,Z,X_r,Y_r,Z_r,surface,series,id,order_series 0,200.0,0.0,-200.0,0.3126,0.46885,0.7501,seafloor,seafloor_series,6,1 1,200.0,100.0,-200.0,0.3126,0.53135,0.7501,seafloor,seafloor_series,6,1 2,500.0,0.0,-200.0,0.5001,0.46885,0.7501,seafloor,seafloor_series,6,1 3,500.0,100.0,-200.0,0.5001,0.53135,0.7501,seafloor,seafloor_series,6,1 4,800.0,0.0,-200.0,0.6876,0.46885,0.7501,seafloor,seafloor_series,6,1 5,800.0,100.0,-200.0,0.6876,0.53135,0.7501,seafloor,seafloor_series,6,1 7,700.0,0.0,-450.0,0.6251,0.46885,0.59385,rock1,right_series,4,2 6,700.0,100.0,-450.0,0.6251,0.53135,0.59385,rock1,right_series,4,2 8,900.0,0.0,-450.0,0.7501,0.46885,0.59385,rock1,right_series,4,2 9,900.0,100.0,-450.0,0.7501,0.53135,0.59385,rock1,right_series,4,2 13,700.0,0.0,-700.0,0.6251,0.46885,0.4376,rock2,right_series,5,2 12,700.0,100.0,-700.0,0.6251,0.53135,0.4376,rock2,right_series,5,2 11,900.0,0.0,-700.0,0.7501,0.46885,0.4376,rock2,right_series,5,2 10,900.0,100.0,-700.0,0.7501,0.53135,0.4376,rock2,right_series,5,2 14,300.0,0.0,-200.0,0.3751,0.46885,0.7501,onlap_surface,onlap_series,3,3 15,300.0,100.0,-200.0,0.3751,0.53135,0.7501,onlap_surface,onlap_series,3,3 16,700.0,0.0,-1000.0,0.6251,0.46885,0.2501,onlap_surface,onlap_series,3,3 17,700.0,100.0,-1000.0,0.6251,0.53135,0.2501,onlap_surface,onlap_series,3,3 18,550.0,0.0,-500.0,0.53135,0.46885,0.5626,onlap_surface,onlap_series,3,3 19,550.0,100.0,-500.0,0.53135,0.53135,0.5626,onlap_surface,onlap_series,3,3 20,100.0,0.0,-400.0,0.2501,0.46885,0.6251,rock3,left_series,2,4 21,100.0,100.0,-400.0,0.2501,0.53135,0.6251,rock3,left_series,2,4 22,450.0,0.0,-1000.0,0.46885,0.46885,0.2501,rock3,left_series,2,4 23,450.0,100.0,-1000.0,0.46885,0.53135,0.2501,rock3,left_series,2,4 24,300.0,0.0,-500.0,0.3751,0.46885,0.5626,rock3,left_series,2,4 25,300.0,100.0,-500.0,0.3751,0.53135,0.5626,rock3,left_series,2,4 ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-8_Onlap_relations/Tutorial_ch1-8_Onlap_relations_surfaces.csv ================================================ ,surface,series,order_surfaces,isBasement,color,vertices,edges,id 4,basement,basement,1,False,#ff3f20,"[[-200. -500. -210.] [-200. -480. -210.] [-176. -500. -210.] ... [ 976. 440. -210.] [ 976. 460. -210.] [ 976. 480. -210.]]","[[ 2 1 0] [ 3 1 2] [ 3 4 1] ... [2498 2448 2497] [2498 2449 2448] [2499 2449 2498]]",1 3,rock3,left_series,1,False,#9f0052,"[[ 400. -500. -460.00003815] [ 400. -480. -460.00003815] [ 424. -500. -460.00003815] ... [ 976. 440. -460.00003815] [ 976. 460. -460.00003815] [ 976. 480. -460.00003815]]","[[ 2 1 0] [ 3 1 2] [ 3 4 1] ... [1111 1061 1110] [1111 1062 1061] [1112 1062 1111]]",2 5,onlap_surface,onlap_series,1,False,#ffbe00,"[[ 544. -500. -710.] [ 544. -480. -710.] [ 568. -500. -710.] ... [ 976. 440. -710.] [ 976. 460. -710.] [ 976. 480. -710.]]","[[ 2 1 0] [ 3 1 2] [ 3 4 1] ... [842 792 841] [842 793 792] [843 793 842]]",3 1,rock1,right_series,1,False,#728f02,"[[ 52.39768219 -480. -220. ] [ 64. -500. -227.81341553] [ 45.62319183 -500. -220. ] ... [ 688.02554321 40. -980. ] [ 688.29644775 40. -1000. ] [ 688.20480347 60. -1000. ]]","[[ 2 1 0] [ 0 1 3] [ 0 3 4] ... [3321 3322 3320] [3320 3173 3161] [3322 3185 3172]]",4 2,rock2,right_series,2,False,#443988,"[[ -184.16520214 -480. -420. ] [ -176. -500. -425.53325653] [ -192.13578463 -500. -420. ] ... [ 427.72203064 280. -1000. ] [ 424.01483154 280. -960. ] [ 425.82922363 300. -1000. ]]","[[ 2 1 0] [ 0 1 3] [ 0 5 4] ... [3199 2969 3201] [3201 2969 2958] [3202 2978 2967]]",5 0,seafloor,seafloor_series,1,True,#015482,"[[-200. -500. -210.] [-200. -480. -210.] [-176. -500. -210.] ... [ 976. 440. -210.] [ 976. 460. -210.] [ 976. 480. -210.]]","[[ 2 1 0] [ 3 1 2] [ 3 4 1] ... [2498 2448 2497] [2498 2449 2448] [2499 2449 2498]]",6 ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-9a_Fault_relations/Tutorial_ch1-9a_Fault_relations_faults.csv ================================================ ,isFault,isFinite fault_series1,True,False series2,False,False series1,False,False basement_series,False,False ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-9a_Fault_relations/Tutorial_ch1-9a_Fault_relations_faults_relations.csv ================================================ ,fault_series1,series2,series1,basement_series fault_series1,False,False,True,True series2,False,False,False,False series1,False,False,False,False basement_series,False,False,False,False ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-9a_Fault_relations/Tutorial_ch1-9a_Fault_relations_kriging_data.csv ================================================ ,range,$C_o$,drift equations,nugget grad,nugget scalar values,1732.0508075688772,71428.57142857142,[3 3 3],0.01,1e-06 ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-9a_Fault_relations/Tutorial_ch1-9a_Fault_relations_options.csv ================================================ ,dtype,output,aesara_optimizer,device,verbosity values,float64,geology,fast_compile,cpu,[0] ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-9a_Fault_relations/Tutorial_ch1-9a_Fault_relations_orientations.csv ================================================ ,X,Y,Z,X_r,Y_r,Z_r,G_x,G_y,G_z,dip,azimuth,polarity,surface,series,id,order_series 10,500.0,500.0,-600.0,0.5001,0.5001,0.46885,0.8949343616030251,1.0000547989250692e-12,0.4461978131108087,63.5,90.0,1.0,fault1,fault_series1,1,1 9,850.0,500.0,-425.0,0.71885,0.5001,0.578225,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock5,series2,2,2 8,150.0,500.0,-400.0,0.28135,0.5001,0.59385,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock5,series2,2,2 7,150.0,500.0,-450.0,0.28135,0.5001,0.5626,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock4,series2,3,2 6,850.0,500.0,-525.0,0.71885,0.5001,0.515725,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock4,series2,3,2 5,150.0,500.0,-500.0,0.28135,0.5001,0.53135,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock3,series1,4,3 4,850.0,500.0,-575.0,0.71885,0.5001,0.484475,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock3,series1,4,3 3,850.0,500.0,-625.0,0.71885,0.5001,0.453225,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock2,series1,5,3 2,150.0,500.0,-550.0,0.28135,0.5001,0.5001,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock2,series1,5,3 0,150.0,500.0,-600.0,0.28135,0.5001,0.46885,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock1,series1,6,3 1,850.0,500.0,-675.0,0.71885,0.5001,0.421975,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock1,series1,6,3 ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-9a_Fault_relations/Tutorial_ch1-9a_Fault_relations_rescaling_data.csv ================================================ ,rescaling factor,centers values,1600.0,[ 500. 500. -550.] ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-9a_Fault_relations/Tutorial_ch1-9a_Fault_relations_series.csv ================================================ ,order_series,BottomRelation fault_series1,1,Fault series2,2,Erosion series1,3,Erosion basement_series,4,Erosion ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-9a_Fault_relations/Tutorial_ch1-9a_Fault_relations_surface_points.csv ================================================ ,X,Y,Z,X_r,Y_r,Z_r,surface,series,id,order_series 57,450.0,200.0,-500.0,0.46885,0.3126,0.53135,fault1,fault_series1,2,1 58,450.0,500.0,-500.0,0.46885,0.5001,0.53135,fault1,fault_series1,2,1 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7,900.0,500.0,-675.0,0.7501,0.5001,0.421975,rock1,series1,5,3 8,900.0,800.0,-675.0,0.7501,0.6876,0.421975,rock1,series1,5,3 9,800.0,200.0,-675.0,0.6876,0.3126,0.421975,rock1,series1,5,3 10,800.0,500.0,-675.0,0.6876,0.5001,0.421975,rock1,series1,5,3 11,800.0,800.0,-675.0,0.6876,0.6876,0.421975,rock1,series1,5,3 ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-9a_Fault_relations/Tutorial_ch1-9a_Fault_relations_surfaces.csv ================================================ ,surface,series,order_surfaces,isBasement,color,vertices,edges,id 4,basement,basement_series,1,False,#325916,,,1 5,fault1,fault_series1,1,False,#015482,,,2 0,rock3,series1,1,False,#728f02,,,3 3,rock2,series1,2,False,#443988,,,4 1,rock1,series1,3,False,#ff3f20,,,5 7,rock5,series2,1,False,#9f0052,,,6 6,rock4,series2,2,True,#ffbe00,,,7 ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-9b_Fault_relations/Tutorial_ch1-9b_Fault_relations_faults.csv ================================================ ,isFault,isFinite fault_series2,True,False fault_series1,True,False series1,False,False basement_series,False,False ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-9b_Fault_relations/Tutorial_ch1-9b_Fault_relations_faults_relations.csv ================================================ ,fault_series2,fault_series1,series1,basement_series fault_series2,False,True,True,True fault_series1,False,False,True,True series1,False,False,False,False basement_series,False,False,False,False ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-9b_Fault_relations/Tutorial_ch1-9b_Fault_relations_kriging_data.csv ================================================ ,range,$C_o$,drift equations,nugget grad,nugget scalar values,1732.0508075688772,71428.57142857142,[3 3 3],0.01,1e-06 ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-9b_Fault_relations/Tutorial_ch1-9b_Fault_relations_options.csv ================================================ ,dtype,output,aesara_optimizer,device,verbosity values,float64,geology,fast_compile,cpu,[0] ================================================ FILE: examples/data/gempy_models/Tutorial_ch1-9b_Fault_relations/Tutorial_ch1-9b_Fault_relations_orientations.csv ================================================ ,X,Y,Z,X_r,Y_r,Z_r,G_x,G_y,G_z,dip,azimuth,polarity,surface,series,id,order_series 13,550.0,500.0,-600.0,0.53135,0.5001,0.53135,-0.8949343616010249,9.576199963080072e-18,0.4461978131108091,63.49999999993597,270.0,1.0,fault2,fault_series2,1,1 10,350.0,500.0,-700.0,0.40635,0.5001,0.46885,0.8949343616030251,1.0000547989250692e-12,0.4461978131108087,63.5,90.0,1.0,fault1,fault_series1,2,2 11,400.0,500.0,-625.0,0.4376,0.5001,0.515725,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock4,series1,3,3 7,150.0,500.0,-550.0,0.28135,0.5001,0.5626,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock4,series1,3,3 6,850.0,500.0,-525.0,0.71885,0.5001,0.578225,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock4,series1,3,3 12,400.0,500.0,-675.0,0.4376,0.5001,0.484475,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock3,series1,4,3 5,150.0,500.0,-600.0,0.28135,0.5001,0.53135,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock3,series1,4,3 4,850.0,500.0,-575.0,0.71885,0.5001,0.546975,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock3,series1,4,3 3,850.0,500.0,-625.0,0.71885,0.5001,0.515725,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock2,series1,5,3 2,150.0,500.0,-650.0,0.28135,0.5001,0.5001,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock2,series1,5,3 0,150.0,500.0,-700.0,0.28135,0.5001,0.46885,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock1,series1,6,3 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,surface,series,order_surfaces,isBasement,color,vertices,edges,id 4,basement,basement_series,1,False,#325916,,,1 5,fault1,fault_series1,1,False,#9f0052,,,2 7,fault2,fault_series2,1,False,#015482,,,3 6,rock4,series1,1,False,#ffbe00,,,4 0,rock3,series1,2,False,#728f02,,,5 3,rock2,series1,3,False,#443988,,,6 1,rock1,series1,4,True,#ff3f20,,,7 ================================================ FILE: examples/data/input_data/AlesModel/2018_interf.csv ================================================ X,Y,Z,X_r,Y_r,Z_r,formation,series,id,order_series,smooth 737866.44,1916440.94,408.293632,0.4736471608999658,0.4589280664098209,0.4929178917721893,fault_left,fault_left,1,1,1e-08 737393.89,1917771.71,321.804233,0.4598361365138869,0.4978219335609481,0.4903901016997599,fault_left,fault_left,1,1,1e-08 737824.59,1917182.81,345.54013599999996,0.4724240281720912,0.4806103956052571,0.4910838211689349,fault_left,fault_left,1,1,1e-08 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248;68;-150.965;13;0.903527552 248;69.277473;-151.277;13;0.222567375 248;70;-151.454;13;0.379324395 248;70.475929;-152;13;0.052385153 248;72;-153.748;13;0.831020082 248;72.219833;-154;13;0.112290255 248;72.25721;-154.257;13;0.003137227 248;72.510468;-156;13;0.597141384 248;72.403862;-156.404;13;0.46224633 248;72;-157.934;13;0.411353157 248;71.96257;-157.963;13;0.187044423 248;71.913696;-158;13;0.165205271 248;70.830017;-158.83;13;0.25203568 ================================================ FILE: examples/data/input_data/jan_models/fixture_model_orientations.csv ================================================ X,Y,Z,azimuth,dip,polarity,formation 0,500,450,90,330,1,rock2 0,500,250,90,345,1,rock1 500,500,800,90,0,1,rock3 2000,500,400,90,55,1,fault ================================================ FILE: examples/data/input_data/jan_models/fixture_model_surfaces.csv ================================================ X,Y,Z,formation 0,0,800,rock3 0,500,800,rock3 0,1000,800,rock3 500,0,800,rock3 500,500,800,rock3 500,1000,800,rock3 1000,0,800,rock3 1000,500,800,rock3 1000,1000,800,rock3 1500,0,800,rock3 1500,500,800,rock3 1500,1000,800,rock3 2000,0,650,rock3 2000,500,650,rock3 2000,1000,650,rock3 0,0,450,rock2 0,500,450,rock2 0,1000,450,rock2 150,0,600,rock2 150,500,600,rock2 150,1000,600,rock2 300,0,750,rock2 300,500,750,rock2 300,1000,750,rock2 450,0,600,rock2 450,500,600,rock2 450,1000,600,rock2 600,0,450,rock2 600,500,450,rock2 600,1000,450,rock2 750,0,600,rock2 750,500,600,rock2 750,1000,600,rock2 850,0,800,rock2 850,500,800,rock2 850,1000,800,rock2 1500,0,800,rock2 1500,500,800,rock2 1500,1000,800,rock2 1600,0,600,rock2 1600,500,600,rock2 1600,1000,600,rock2 1750,0,450,rock2 1750,500,450,rock2 1750,1000,450,rock2 1850,0,550,rock2 1850,500,550,rock2 1850,1000,550,rock2 2200,0,550,rock2 2200,500,550,rock2 2200,1000,550,rock2 2300,0,650,rock2 2300,500,650,rock2 2300,1000,650,rock2 0,0,250,rock1 0,500,250,rock1 0,1000,250,rock1 150,0,400,rock1 150,500,400,rock1 150,1000,400,rock1 300,0,550,rock1 300,500,550,rock1 300,1000,550,rock1 450,0,400,rock1 450,500,400,rock1 450,1000,400,rock1 600,0,250,rock1 600,500,250,rock1 600,1000,250,rock1 750,0,350,rock1 750,500,350,rock1 750,1000,350,rock1 900,0,550,rock1 900,500,550,rock1 900,1000,550,rock1 1450,0,550,rock1 1450,500,550,rock1 1450,1000,550,rock1 1600,0,350,rock1 1600,500,350,rock1 1600,1000,350,rock1 1750,0,250,rock1 1750,500,250,rock1 1750,1000,250,rock1 1850,0,350,rock1 1850,500,350,rock1 1850,1000,350,rock1 2200,0,350,rock1 2200,500,350,rock1 2200,1000,350,rock1 2300,0,450,rock1 2300,500,450,rock1 2300,1000,450,rock1 1050,0,799,rock1 1050,500,799,rock1 1050,1000,799,rock1 1300,0,799,rock1 1300,500,799,rock1 1300,1000,799,rock1 2000,0,400,fault 2000,500,400,fault 2000,1000,400,fault ================================================ FILE: examples/data/input_data/jan_models/model1_orientations.csv ================================================ X,Y,Z,azimuth,dip,polarity,formation 500,500,600,90,0,1,rock2 500,500,400,90,0,1,rock1 ================================================ FILE: examples/data/input_data/jan_models/model1_surface_points.csv ================================================ X,Y,Z,formation 100,200,600,rock2 500,200,600,rock2 900,200,600,rock2 100,800,600,rock2 500,800,600,rock2 900,800,600,rock2 100,200,400,rock1 500,200,400,rock1 900,200,400,rock1 100,800,400,rock1 500,800,400,rock1 900,800,400,rock1 ================================================ FILE: examples/data/input_data/jan_models/model2_orientations.csv ================================================ X,Y,Z,azimuth,dip,polarity,formation 500,500,820,90,0,1,rock2 500,500,620,90,0,1,rock1 ================================================ FILE: examples/data/input_data/jan_models/model2_surface_points.csv ================================================ X,Y,Z,formation 0,100,200,rock1 0,500,200,rock1 0,900,200,rock1 400,100,600,rock1 400,500,600,rock1 400,900,600,rock1 200,100,400,rock1 200,500,400,rock1 200,900,400,rock1 800,100,400,rock1 800,500,400,rock1 800,900,400,rock1 600,100,600,rock1 600,500,600,rock1 600,900,600,rock1 1000,100,200,rock1 1000,500,200,rock1 1000,900,200,rock1 0,100,400,rock2 0,500,400,rock2 0,900,400,rock2 1000,100,400,rock2 1000,500,400,rock2 1000,900,400,rock2 400,100,800,rock2 400,500,800,rock2 400,900,800,rock2 200,100,600,rock2 200,500,600,rock2 200,900,600,rock2 600,100,800,rock2 600,500,800,rock2 600,900,800,rock2 800,100,600,rock2 800,500,600,rock2 800,900,600,rock2 ================================================ FILE: examples/data/input_data/jan_models/model3_orientations.csv ================================================ X,Y,Z,azimuth,dip,polarity,formation 200,500,780,90,185.73,1,rock1 200,500,220,90,354.27,1,rock1 ================================================ FILE: examples/data/input_data/jan_models/model3_surface_points.csv ================================================ X,Y,Z,formation 0,200,800,rock1 0,200,200,rock1 100,200,790,rock1 100,200,210,rock1 200,200,780,rock1 200,200,220,rock1 300,200,770,rock1 300,200,230,rock1 400,200,760,rock1 400,200,240,rock1 700,200,500,rock1 0,0,800,rock1 0,0,200,rock1 100,0,790,rock1 100,0,210,rock1 200,0,780,rock1 200,0,220,rock1 300,0,770,rock1 300,0,230,rock1 400,0,760,rock1 400,0,240,rock1 700,0,500,rock1 0,500,800,rock1 0,500,200,rock1 100,500,790,rock1 100,500,210,rock1 200,500,780,rock1 200,500,220,rock1 300,500,770,rock1 300,500,230,rock1 400,500,760,rock1 400,500,240,rock1 700,500,500,rock1 0,1000,800,rock1 0,1000,200,rock1 100,1000,790,rock1 100,1000,210,rock1 200,1000,780,rock1 200,1000,220,rock1 300,1000,770,rock1 300,1000,230,rock1 400,1000,760,rock1 400,1000,240,rock1 700,1000,500,rock1 700,800,500,rock1 0,800,800,rock1 0,800,200,rock1 100,800,790,rock1 100,800,210,rock1 200,800,780,rock1 200,800,220,rock1 300,800,770,rock1 300,800,230,rock1 400,800,760,rock1 400,800,240,rock1 0,200,600,rock2 0,200,400,rock2 100,200,590,rock2 100,200,410,rock2 200,200,580,rock2 200,200,420,rock2 300,200,570,rock2 300,200,430,rock2 400,200,560,rock2 400,200,440,rock2 0,0,600,rock2 0,0,400,rock2 100,0,590,rock2 100,0,410,rock2 200,0,580,rock2 200,0,420,rock2 300,0,570,rock2 300,0,430,rock2 400,0,560,rock2 400,0,440,rock2 0,500,600,rock2 0,500,400,rock2 100,500,590,rock2 100,500,410,rock2 200,500,580,rock2 200,500,420,rock2 300,500,570,rock2 300,500,430,rock2 400,500,560,rock2 400,500,440,rock2 0,1000,600,rock2 0,1000,400,rock2 100,1000,590,rock2 100,1000,410,rock2 200,1000,580,rock2 200,1000,420,rock2 300,1000,570,rock2 300,1000,430,rock2 400,1000,560,rock2 400,1000,440,rock2 0,800,600,rock2 0,800,400,rock2 100,800,590,rock2 100,800,410,rock2 200,800,580,rock2 200,800,420,rock2 300,800,570,rock2 300,800,430,rock2 400,800,560,rock2 400,800,440,rock2 ================================================ FILE: examples/data/input_data/jan_models/model4_orientations.csv ================================================ X,Y,Z,azimuth,dip,polarity,formation 500,500,625,90,8.54,1,rock2 500,200,625,90,8.54,1,rock2 500,800,625,90,8.54,1,rock2 500,500,375,90,351.46,1,rock1 500,200,375,90,351.46,1,rock1 500,800,375,90,351.46,1,rock1 ================================================ FILE: examples/data/input_data/jan_models/model4_surface_points.csv ================================================ X,Y,Z,formation 0,200,300,rock1 0,800,300,rock1 500,200,375,rock1 500,800,375,rock1 1000,200,450,rock1 1000,800,450,rock1 0,200,700,rock2 0,800,700,rock2 500,200,625,rock2 500,800,625,rock2 1000,200,550,rock2 1000,800,550,rock2 ================================================ FILE: examples/data/input_data/jan_models/model5_orientations.csv ================================================ X,Y,Z,azimuth,dip,polarity,formation 100,500,800,90,0,1,rock2 100,500,600,90,0,1,rock1 900,500,400,90,0,1,rock2 900,500,200,90,0,1,rock1 500,500,500,90,60,1,fault ================================================ FILE: examples/data/input_data/jan_models/model5_surface_points.csv ================================================ X,Y,Z,formation 0,200,600,rock1 0,500,600,rock1 0,800,600,rock1 200,200,600,rock1 200,500,600,rock1 200,800,600,rock1 800,200,200,rock1 800,500,200,rock1 800,800,200,rock1 1000,200,200,rock1 1000,500,200,rock1 1000,800,200,rock1 0,200,800,rock2 0,800,800,rock2 200,200,800,rock2 200,800,800,rock2 800,200,400,rock2 800,800,400,rock2 1000,200,400,rock2 1000,800,400,rock2 500,500,500,fault 450,500,600,fault 500,200,500,fault 450,200,600,fault 500,800,500,fault 450,800,600,fault ================================================ FILE: examples/data/input_data/jan_models/model6_orientations.csv ================================================ X,Y,Z,azimuth,dip,polarity,formation 100,500,700,90,315,1,rock2 900,500,700,90,45,1,rock2 100,500,500,90,315,1,rock1 900,500,500,90,45,1,rock1 500,500,701,90,0,1,rock3 ================================================ FILE: examples/data/input_data/jan_models/model6_surface_points.csv ================================================ X,Y,Z,formation 0,200,400,rock1 0,500,400,rock1 0,800,400,rock1 100,200,500,rock1 100,500,500,rock1 100,800,500,rock1 200,200,600,rock1 200,500,600,rock1 200,800,600,rock1 800,200,600,rock1 800,500,600,rock1 800,800,600,rock1 900,200,500,rock1 900,500,500,rock1 900,800,500,rock1 1000,200,400,rock1 1000,500,400,rock1 1000,800,400,rock1 0,200,600,rock2 0,500,600,rock2 0,800,600,rock2 1000,200,600,rock2 1000,500,600,rock2 1000,800,600,rock2 100,200,700,rock2 100,500,700,rock2 100,800,700,rock2 900,200,700,rock2 900,500,700,rock2 900,800,700,rock2 200,200,701,rock3 200,500,701,rock3 200,800,701,rock3 500,200,701,rock3 500,500,701,rock3 500,800,701,rock3 800,200,701,rock3 800,500,701,rock3 800,800,701,rock3 ================================================ FILE: examples/data/input_data/jan_models/model7_orientations.csv ================================================ X,Y,Z,azimuth,dip,polarity,formation 0,500,450,90,335,1,rock2 0,500,250,90,335,1,rock1 0,0,450,90,335,1,rock2 0,0,250,90,335,1,rock1 0,1000,450,90,335,1,rock2 0,1000,250,90,335,1,rock1 500,500,800,90,0,1,rock3 2000,500,400,90,55,1,fault ================================================ FILE: examples/data/input_data/jan_models/model7_surface_points.csv ================================================ X,Y,Z,formation 0,0,800,rock3 0,500,800,rock3 0,1000,800,rock3 500,0,800,rock3 500,500,800,rock3 500,1000,800,rock3 1000,0,800,rock3 1000,500,800,rock3 1000,1000,800,rock3 1500,0,800,rock3 1500,500,800,rock3 1500,1000,800,rock3 2000,0,650,rock3 2000,500,650,rock3 2000,1000,650,rock3 0,0,450,rock2 0,500,450,rock2 0,1000,450,rock2 150,0,600,rock2 150,500,600,rock2 150,1000,600,rock2 300,0,750,rock2 300,500,750,rock2 300,1000,750,rock2 450,0,600,rock2 450,500,600,rock2 450,1000,600,rock2 600,0,450,rock2 600,500,450,rock2 600,1000,450,rock2 750,0,600,rock2 750,500,600,rock2 750,1000,600,rock2 850,0,800,rock2 850,500,800,rock2 850,1000,800,rock2 1500,0,800,rock2 1500,500,800,rock2 1500,1000,800,rock2 1600,0,600,rock2 1600,500,600,rock2 1600,1000,600,rock2 1750,0,450,rock2 1750,500,450,rock2 1750,1000,450,rock2 1850,0,550,rock2 1850,500,550,rock2 1850,1000,550,rock2 2200,0,550,rock2 2200,500,550,rock2 2200,1000,550,rock2 2300,0,650,rock2 2300,500,650,rock2 2300,1000,650,rock2 0,0,250,rock1 0,500,250,rock1 0,1000,250,rock1 150,0,400,rock1 150,500,400,rock1 150,1000,400,rock1 300,0,550,rock1 300,500,550,rock1 300,1000,550,rock1 450,0,400,rock1 450,500,400,rock1 450,1000,400,rock1 600,0,250,rock1 600,500,250,rock1 600,1000,250,rock1 750,0,350,rock1 750,500,350,rock1 750,1000,350,rock1 900,0,550,rock1 900,500,550,rock1 900,1000,550,rock1 1450,0,550,rock1 1450,500,550,rock1 1450,1000,550,rock1 1600,0,350,rock1 1600,500,350,rock1 1600,1000,350,rock1 1750,0,250,rock1 1750,500,250,rock1 1750,1000,250,rock1 1850,0,350,rock1 1850,500,350,rock1 1850,1000,350,rock1 2200,0,350,rock1 2200,500,350,rock1 2200,1000,350,rock1 2300,0,450,rock1 2300,500,450,rock1 2300,1000,450,rock1 1050,0,799,rock1 1050,500,799,rock1 1050,1000,799,rock1 1300,0,799,rock1 1300,500,799,rock1 1300,1000,799,rock1 2000,0,400,fault 2000,500,400,fault 2000,1000,400,fault ================================================ FILE: examples/data/input_data/jan_models/tutorial_model_orientations.csv ================================================ X,Y,Z,azimuth,dip,polarity,formation 0,500,450,90,335,1,rock2 0,500,250,90,335,1,rock1 0,0,450,90,335,1,rock2 0,0,250,90,335,1,rock1 0,1000,450,90,335,1,rock2 0,1000,250,90,335,1,rock1 500,500,800,90,0,1,rock3 ================================================ FILE: examples/data/input_data/jan_models/tutorial_model_surface_points.csv ================================================ X,Y,Z,formation 0,0,800,rock3 0,500,800,rock3 0,1000,800,rock3 500,0,800,rock3 500,500,800,rock3 500,1000,800,rock3 1000,0,800,rock3 1000,500,800,rock3 1000,1000,800,rock3 1500,0,800,rock3 1500,500,800,rock3 1500,1000,800,rock3 2000,0,800,rock3 2000,500,800,rock3 2000,1000,800,rock3 0,0,450,rock2 0,500,450,rock2 0,1000,450,rock2 150,0,600,rock2 150,500,600,rock2 150,1000,600,rock2 300,0,750,rock2 300,500,750,rock2 300,1000,750,rock2 450,0,600,rock2 450,500,600,rock2 450,1000,600,rock2 600,0,450,rock2 600,500,450,rock2 600,1000,450,rock2 750,0,600,rock2 750,500,600,rock2 750,1000,600,rock2 850,0,800,rock2 850,500,800,rock2 850,1000,800,rock2 1500,0,800,rock2 1500,500,800,rock2 1500,1000,800,rock2 1600,0,600,rock2 1600,500,600,rock2 1600,1000,600,rock2 1750,0,450,rock2 1750,500,450,rock2 1750,1000,450,rock2 1850,0,550,rock2 1850,500,550,rock2 1850,1000,550,rock2 0,0,250,rock1 0,500,250,rock1 0,1000,250,rock1 150,0,400,rock1 150,500,400,rock1 150,1000,400,rock1 300,0,550,rock1 300,500,550,rock1 300,1000,550,rock1 450,0,400,rock1 450,500,400,rock1 450,1000,400,rock1 600,0,250,rock1 600,500,250,rock1 600,1000,250,rock1 750,0,350,rock1 750,500,350,rock1 750,1000,350,rock1 900,0,550,rock1 900,500,550,rock1 900,1000,550,rock1 1450,0,550,rock1 1450,500,550,rock1 1450,1000,550,rock1 1600,0,350,rock1 1600,500,350,rock1 1600,1000,350,rock1 1750,0,250,rock1 1750,500,250,rock1 1750,1000,250,rock1 1850,0,350,rock1 1850,500,350,rock1 1850,1000,350,rock1 1050,0,799,rock1 1050,500,799,rock1 1050,1000,799,rock1 1300,0,799,rock1 1300,500,799,rock1 1300,1000,799,rock1 ================================================ FILE: examples/data/input_data/lisa_models/foliations1.csv ================================================ ,X,Y,Z,G_x,G_y,G_z,dip,azimuth,polarity,formation,series,formation_number,order_series,isFault,formation_value,annotations 0,1000.0,1000.0,1500.0,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,Sandstone,Strat_Series_1,1,1,False,1,"${\bf{x}}_{\beta \,{\bf{1}},0}$" 1,1000.0,1000.0,1400.0,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,Siltstone,Strat_Series_1,2,1,False,2,"${\bf{x}}_{\beta \,{\bf{2}},0}$" 2,1000.0,1000.0,1100.0,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,Shale,Strat_Series_1,3,1,False,3,"${\bf{x}}_{\beta \,{\bf{3}},0}$" 3,1000.0,1000.0,1000.0,0.6427876096875392,1.0000393593894367e-12,0.766044443119978,40.0,90.0,1.0,Sandstone2,Strat_Series_2,4,2,False,4,"${\bf{x}}_{\beta \,{\bf{4}},0}$" 4,1000.0,1000.0,500.0,0.6427876096875392,1.0000393593894367e-12,0.766044443119978,40.0,90.0,1.0,Siltstone2,Strat_Series_2,5,2,False,5,"${\bf{x}}_{\beta \,{\bf{5}},0}$" 5,1000.0,1000.0,100.0,0.6427876096875392,1.0000393593894367e-12,0.766044443119978,40.0,90.0,1.0,Shale2,Strat_Series_2,6,2,False,6,"${\bf{x}}_{\beta \,{\bf{6}},0}$" ================================================ FILE: examples/data/input_data/lisa_models/foliations2.csv ================================================ ,X,Y,Z,G_x,G_y,G_z,dip,azimuth,polarity,formation,series,formation_number,order_series,isFault,formation_value,annotations 2,750.0,1000.0,1300.0,-0.8191520442879918,9.998495242106419e-13,0.573576436352046,55.0,270.0,1.0,Siltstone,Strat_Series,2,2,False,2,"${\bf{x}}_{\beta \,{\bf{2}},0}$" 3,1250.0,1000.0,1300.0,0.8191520442899918,1.0000501585964528e-12,0.573576436352046,55.0,90.0,1.0,Siltstone,Strat_Series,2,2,False,2,"${\bf{x}}_{\beta \,{\bf{2}},1}$" 0,750.0,1000.0,500.0,-0.8191520442879918,9.998495242106419e-13,0.573576436352046,55.0,270.0,1.0,Schist,Strat_Series,5,2,False,5,"${\bf{x}}_{\beta \,{\bf{5}},0}$" 1,1250.0,1000.0,500.0,0.8191520442899918,1.0000501585964528e-12,0.573576436352046,55.0,90.0,1.0,Schist,Strat_Series,5,2,False,5,"${\bf{x}}_{\beta \,{\bf{5}},1}$" ================================================ FILE: examples/data/input_data/lisa_models/foliations3.csv ================================================ ,X,Y,Z,G_x,G_y,G_z,dip,azimuth,polarity,formation,series,formation_number,order_series,isFault,formation_value,annotations 2,1000.0,1250.0,1500.0,0.31816239933811036,-0.4772448176609822,0.8191520442899918,35.0,146.31,1.0,Sandstone,Strat_Series,1,2,False,1,"${\bf{x}}_{\beta \,{\bf{1}},0}$" 1,1000.0,1250.0,700.0,0.3181623993373182,-0.477244817659794,0.8191520442909918,34.99999999990011,146.31,1.0,Schist,Strat_Series,5,2,False,5,"${\bf{x}}_{\beta \,{\bf{5}},0}$" 0,1000.0000000000001,1249.9999999999982,10.00000000000001,0.31816239933811036,-0.4772448176609822,0.8191520442899918,35.0,146.31,1.0,Gneiss,Strat_Series,6,2,False,6,"${\bf{x}}_{\beta \,{\bf{6}},0}$" ================================================ FILE: examples/data/input_data/lisa_models/foliations5.csv ================================================ ,X,Y,Z,G_x,G_y,G_z,dip,azimuth,polarity,formation,series,formation_number,order_series,isFault,formation_value,annotations 0,500.0,1000.0,864.602,-0.9486830155920029,-0.31622861338710945,1.999261954502598e-12,89.99999999994274,251.56500000003624,1.0,Main_Fault,Fault_Series,1,1,True,1,"${\bf{x}}_{\beta \,{\bf{1}},0}$" 1,1000.0,1000.0,1500.0,0.40557978767430714,-0.40557978767030683,0.8191520442903204,34.99999999996717,134.99999999985872,1.0,Sandstone,Strat_Series,2,2,False,2,"${\bf{x}}_{\beta \,{\bf{2}},0}$" 2,1000.0,1000.0,500.0,0.4055797876736288,-0.4055797876696291,0.8191520442909919,34.999999999900105,134.99999999985874,1.0,Schist,Strat_Series,6,2,False,6,"${\bf{x}}_{\beta \,{\bf{6}},0}$" 3,1000.0,1000.0,10.0,0.4055797876736288,-0.4055797876696291,0.8191520442909919,34.999999999900105,134.99999999985874,1.0,Gneiss,Strat_Series,7,2,False,7,"${\bf{x}}_{\beta \,{\bf{7}},0}$" ================================================ FILE: examples/data/input_data/lisa_models/foliations6.csv ================================================ ,X,Y,Z,G_x,G_y,G_z,dip,azimuth,polarity,formation,series,formation_number,order_series,isFault,annotations,formation_value 2,1000,1000,1500,1e-12,1e-12,1.000000000001,0,90,1,Sandstone,Strat_Series,2,2,False,"${\bf{x}}_{\beta \,{\bf{2}},0}$",2 1,1000,1000,1000,1e-12,1e-12,1.000000000001,0,90,1,Siltstone,Strat_Series,3,2,False,"${\bf{x}}_{\beta \,{\bf{3}},0}$",3 0,1000,1000,500,1e-12,1e-12,1.000000000001,0,90,1,Shale,Strat_Series,4,2,False,"${\bf{x}}_{\beta \,{\bf{4}},0}$",4 ================================================ FILE: examples/data/input_data/lisa_models/foliations7.csv ================================================ ,X,Y,Z,G_x,G_y,G_z,dip,azimuth,polarity,formation,series,formation_number,order_series,isFault,formation_value,annotations 0,750.0,1000.0,500.0,0.9659258262900683,1.000059145898569e-12,0.2588190451035207,75.0,90.0,1.0,Fault_1,Fault_1,1,1,True,1,"${\bf{x}}_{\beta \,{\bf{1}},0}$" 1,1250.0,1000.0,500.0,-0.9659258262880683,9.998225623042932e-13,0.2588190451035207,75.0,270.0,1.0,Fault_2,Fault_2,2,2,True,2,"${\bf{x}}_{\beta \,{\bf{2}},0}$" 7,1000.0,1000.0,500.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Schist,Strat_Series,7,3,False,7,"${\bf{x}}_{\beta \,{\bf{7}},2}$" 8,250.0,500.0,700.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Schist,Strat_Series,7,3,False,7,"${\bf{x}}_{\beta \,{\bf{7}},1}$" 9,1750.0,500.0,700.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Schist,Strat_Series,7,3,False,7,"${\bf{x}}_{\beta \,{\bf{7}},0}$" 5,1000.0,1000.0,700.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Sandstone_2,Strat_Series,6,3,False,6,"${\bf{x}}_{\beta \,{\bf{6}},4}$" 6,250.0,500.0,900.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Sandstone_2,Strat_Series,6,3,False,6,"${\bf{x}}_{\beta \,{\bf{6}},3}$" 10,1750.0,500.0,900.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Sandstone_2,Strat_Series,6,3,False,6,"${\bf{x}}_{\beta \,{\bf{6}},2}$" 14,250.0,1500.0,900.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Sandstone_2,Strat_Series,6,3,False,6,"${\bf{x}}_{\beta \,{\bf{6}},1}$" 15,1750.0,1500.0,900.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Sandstone_2,Strat_Series,6,3,False,6,"${\bf{x}}_{\beta \,{\bf{6}},0}$" 3,1000.0,1000.0,1000.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Siltstone,Strat_Series,4,3,False,4,"${\bf{x}}_{\beta \,{\bf{4}},4}$" 4,250.0,500.0,1300.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Siltstone,Strat_Series,4,3,False,4,"${\bf{x}}_{\beta \,{\bf{4}},3}$" 11,1750.0,500.0,1300.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Siltstone,Strat_Series,4,3,False,4,"${\bf{x}}_{\beta \,{\bf{4}},2}$" 12,250.0,1500.0,1300.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Siltstone,Strat_Series,4,3,False,4,"${\bf{x}}_{\beta \,{\bf{4}},1}$" 13,1750.0,1500.0,1300.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Siltstone,Strat_Series,4,3,False,4,"${\bf{x}}_{\beta \,{\bf{4}},0}$" 18,250.0,1500.0,1500.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Sandstone,Strat_Series,3,3,False,3,"${\bf{x}}_{\beta \,{\bf{3}},3}$" 19,250.0,500.0,1500.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Sandstone,Strat_Series,3,3,False,3,"${\bf{x}}_{\beta \,{\bf{3}},2}$" 20,1750.0,500.0,1500.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Sandstone,Strat_Series,3,3,False,3,"${\bf{x}}_{\beta \,{\bf{3}},1}$" 21,1750.0,1500.0,1500.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Sandstone,Strat_Series,3,3,False,3,"${\bf{x}}_{\beta \,{\bf{3}},0}$" 16,250.0,1500.0,700.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Schist,Strat_Series,7,3,False,7,"${\bf{x}}_{\beta \,{\bf{7}},3}$" 17,1750.0,1500.0,700.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Schist,Strat_Series,7,3,False,7,"${\bf{x}}_{\beta \,{\bf{7}},4}$" ================================================ FILE: examples/data/input_data/lisa_models/foliations8.csv ================================================ ,X,Y,Z,G_x,G_y,G_z,dip,azimuth,polarity,formation,series,formation_number,order_series,isFault,formation_value,annotations 0,500.0,1000.0,864.602,-0.9486830155920029,-0.31622861338710945,1.999261954502598e-12,89.99999999994274,251.56500000003624,1.0,Main_Fault,Fault_Series,1,1,True,1,"${\bf{x}}_{\beta \,{\bf{1}},0}$" 1,1500.0,1000.0,1000.0,0.5389855446963152,0.19617469496985063,0.8191520442903204,34.99999999996717,70.0,1.0,Sandstone,Strat_Series,2,2,False,2,"${\bf{x}}_{\beta \,{\bf{2}},0}$" 4,250.0,1000.0,1100.0,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,Sandstone,Strat_Series,2,2,False,2,"${\bf{x}}_{\beta \,{\bf{2}},1}$" 5,250.0,1000.0,800.0,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,Shale,Strat_Series,4,2,False,4,"${\bf{x}}_{\beta \,{\bf{4}},0}$" 6,250.0,1000.0,500.0,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,Sandstone_2,Strat_Series,5,2,False,5,"${\bf{x}}_{\beta \,{\bf{5}},0}$" 2,1500.0,1000.0,200.0,0.5389855446954142,0.19617469496952267,0.8191520442909919,34.999999999900105,70.0,1.0,Schist,Strat_Series,6,2,False,6,"${\bf{x}}_{\beta \,{\bf{6}},0}$" 7,250.0,1000.0,300.0,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,Schist,Strat_Series,6,2,False,6,"${\bf{x}}_{\beta \,{\bf{6}},1}$" 3,1500.0,1000.0,100.0,0.5389855446954142,0.19617469496952267,0.8191520442909919,34.999999999900105,70.0,1.0,Gneiss,Strat_Series,7,2,False,7,"${\bf{x}}_{\beta \,{\bf{7}},0}$" ================================================ FILE: examples/data/input_data/lisa_models/foliations9.csv ================================================ ,X,Y,Z,G_x,G_y,G_z,dip,azimuth,polarity,formation,series,formation_number,order_series,isFault,Unnamed: 0,formation_value,annotations 2,1000.0,1249.9999999999984,10.000000000000009,0.31816239933811036,-0.4772448176609822,0.8191520442899918,35.0,146.31,1.0,Gneiss,Default series,1,1,False,0,1,"${\bf{x}}_{\beta \,{\bf{1}},0}$" 0,1000.0,1250.0,1500.0,0.31816239933811036,-0.4772448176609822,0.8191520442899918,35.0,146.31,1.0,Sandstone,Default series,2,1,False,2,2,"${\bf{x}}_{\beta \,{\bf{2}},0}$" 1,1000.0,1250.0,700.0,0.31816239933731816,-0.4772448176597939,0.8191520442909919,34.999999999900105,146.31,1.0,Schist,Default series,4,1,False,1,4,"${\bf{x}}_{\beta \,{\bf{4}},0}$" ================================================ FILE: examples/data/input_data/lisa_models/interfaces1.csv ================================================ ,X,Y,Z,formation,series,formation_number,order_series,isFault,annotations,formation_value 0,500.0,1000.0,1500.0,Sandstone,Strat_Series_1,1,1,False,"${\bf{x}}_{\alpha \,{\bf{1}},0}$",1 1,1500.0,1000.0,1500.0,Sandstone,Strat_Series_1,1,1,False,"${\bf{x}}_{\alpha \,{\bf{1}},1}$",1 2,500.0,1000.0,1400.0,Siltstone,Strat_Series_1,2,1,False,"${\bf{x}}_{\alpha \,{\bf{2}},0}$",2 3,1500.0,1000.0,1400.0,Siltstone,Strat_Series_1,2,1,False,"${\bf{x}}_{\alpha \,{\bf{2}},1}$",2 4,500.0,1000.0,1100.0,Shale,Strat_Series_1,3,1,False,"${\bf{x}}_{\alpha \,{\bf{3}},0}$",3 5,1500.0,1000.0,1100.0,Shale,Strat_Series_1,3,1,False,"${\bf{x}}_{\alpha \,{\bf{3}},1}$",3 6,1000.0,500.0,1000.0,Sandstone2,Strat_Series_2,4,2,False,"${\bf{x}}_{\alpha \,{\bf{4}},0}$",4 7,1000.0,1500.0,1000.0,Sandstone2,Strat_Series_2,4,2,False,"${\bf{x}}_{\alpha \,{\bf{4}},1}$",4 8,1000.0,500.0,500.0,Siltstone2,Strat_Series_2,5,2,False,"${\bf{x}}_{\alpha \,{\bf{5}},0}$",5 9,1000.0,1500.0,500.0,Siltstone2,Strat_Series_2,5,2,False,"${\bf{x}}_{\alpha \,{\bf{5}},1}$",5 10,1000.0,500.0,100.0,Shale2,Strat_Series_2,6,2,False,"${\bf{x}}_{\alpha \,{\bf{6}},0}$",6 11,1000.0,1500.0,100.0,Shale2,Strat_Series_2,6,2,False,"${\bf{x}}_{\alpha \,{\bf{6}},1}$",6 ================================================ FILE: examples/data/input_data/lisa_models/interfaces2.csv ================================================ ,X,Y,Z,formation,series,formation_number,order_series,isFault,annotations,formation_value 2,1500.0,1000.0,1500.0,Sandstone,Strat_Series,1,2,False,"${\bf{x}}_{\alpha \,{\bf{1}},0}$",1 3,500.0,1000.0,1500.0,Sandstone,Strat_Series,1,2,False,"${\bf{x}}_{\alpha \,{\bf{1}},1}$",1 10,500.0,1000.0,1300.0,Siltstone,Strat_Series,2,2,False,"${\bf{x}}_{\alpha \,{\bf{2}},0}$",2 11,1500.0,1000.0,1300.0,Siltstone,Strat_Series,2,2,False,"${\bf{x}}_{\alpha \,{\bf{2}},1}$",2 8,1500.0,1000.0,1200.0,Shale,Strat_Series,3,2,False,"${\bf{x}}_{\alpha \,{\bf{3}},0}$",3 9,500.0,1000.0,1200.0,Shale,Strat_Series,3,2,False,"${\bf{x}}_{\alpha \,{\bf{3}},1}$",3 4,1500.0,1000.0,800.0,Sandstone_2,Strat_Series,4,2,False,"${\bf{x}}_{\alpha \,{\bf{4}},0}$",4 5,500.0,1000.0,800.0,Sandstone_2,Strat_Series,4,2,False,"${\bf{x}}_{\alpha \,{\bf{4}},1}$",4 6,1500.0,1000.0,500.0,Schist,Strat_Series,5,2,False,"${\bf{x}}_{\alpha \,{\bf{5}},0}$",5 7,500.0,1000.0,500.0,Schist,Strat_Series,5,2,False,"${\bf{x}}_{\alpha \,{\bf{5}},1}$",5 0,1500.0,1000.0,10.0,Gneiss,Strat_Series,6,2,False,"${\bf{x}}_{\alpha \,{\bf{6}},0}$",6 1,500.0,1000.0,10.0,Gneiss,Strat_Series,6,2,False,"${\bf{x}}_{\alpha \,{\bf{6}},1}$",6 ================================================ FILE: examples/data/input_data/lisa_models/interfaces3.csv ================================================ ,Unnamed: 0,X,Y,Z,annotations,formation,formation_number,formation_value,isFault,order_series,series 0,11.0,2000.0,2000.0,1500.0,"${\bf{x}}_{\alpha \,{\bf{1}},0}$",Sandstone,1,1,False,2,Strat_Series 1,12.0,0.0,500.0,1500.0,"${\bf{x}}_{\alpha \,{\bf{1}},1}$",Sandstone,1,1,False,2,Strat_Series 2,0.0,0.0,500.0,1400.0,"${\bf{x}}_{\alpha \,{\bf{2}},0}$",Siltstone,2,2,False,2,Strat_Series 3,10.0,2000.0,2000.0,1400.0,"${\bf{x}}_{\alpha \,{\bf{2}},1}$",Siltstone,2,2,False,2,Strat_Series 4,1.0,2000.0,2000.0,1150.0,"${\bf{x}}_{\alpha \,{\bf{3}},0}$",Shale,3,3,False,2,Strat_Series 5,2.0,0.0,500.0,1150.0,"${\bf{x}}_{\alpha \,{\bf{3}},1}$",Shale,3,3,False,2,Strat_Series 6,3.0,2000.0,2000.0,1000.0,"${\bf{x}}_{\alpha \,{\bf{4}},0}$",Sandstone_2,4,4,False,2,Strat_Series 7,4.0,0.0,500.0,1000.0,"${\bf{x}}_{\alpha \,{\bf{4}},1}$",Sandstone_2,4,4,False,2,Strat_Series 8,5.0,2000.0,2000.0,700.0,"${\bf{x}}_{\alpha \,{\bf{5}},0}$",Schist,5,5,False,2,Strat_Series 9,6.0,0.0,500.0,700.0,"${\bf{x}}_{\alpha \,{\bf{5}},1}$",Schist,5,5,False,2,Strat_Series 10,7.0,2000.0,2000.0,10.0,"${\bf{x}}_{\alpha \,{\bf{6}},0}$",Gneiss,6,6,False,2,Strat_Series 11,8.0,0.0,500.0,10.0,"${\bf{x}}_{\alpha \,{\bf{6}},1}$",Gneiss,6,6,False,2,Strat_Series ================================================ FILE: examples/data/input_data/lisa_models/interfaces5.csv ================================================ ,X,Y,Z,formation,series,formation_number,order_series,isFault,annotations,formation_value 0,600.0,1000.0,600.0,Main_Fault,Fault_Series,1,1,True,"${\bf{x}}_{\alpha \,{\bf{1}},0}$",1 1,700.0,1000.0,900.0,Main_Fault,Fault_Series,1,1,True,"${\bf{x}}_{\alpha \,{\bf{1}},1}$",1 2,800.0,1000.0,1200.0,Main_Fault,Fault_Series,1,1,True,"${\bf{x}}_{\alpha \,{\bf{1}},2}$",1 3,900.0,1000.0,1500.0,Main_Fault,Fault_Series,1,1,True,"${\bf{x}}_{\alpha \,{\bf{1}},3}$",1 4,500.0,1000.0,300.0,Main_Fault,Fault_Series,1,1,True,"${\bf{x}}_{\alpha \,{\bf{1}},4}$",1 16,2000.0,2000.0,100.0,Gneiss,Strat_Series,7,2,False,"${\bf{x}}_{\alpha \,{\bf{7}},0}$",7 14,0.0,0.0,100.0,Schist,Strat_Series,6,2,False,"${\bf{x}}_{\alpha \,{\bf{6}},1}$",6 13,2000.0,2000.0,500.0,Schist,Strat_Series,6,2,False,"${\bf{x}}_{\alpha \,{\bf{6}},0}$",6 12,0.0,0.0,400.0,Sandstone_2,Strat_Series,5,2,False,"${\bf{x}}_{\alpha \,{\bf{5}},1}$",5 11,2000.0,2000.0,800.0,Sandstone_2,Strat_Series,5,2,False,"${\bf{x}}_{\alpha \,{\bf{5}},0}$",5 8,2000.0,2000.0,1300.0,Siltstone,Strat_Series,3,2,False,"${\bf{x}}_{\alpha \,{\bf{3}},1}$",3 10,0.0,0.0,800.0,Shale,Strat_Series,4,2,False,"${\bf{x}}_{\alpha \,{\bf{4}},0}$",4 15,0.0,0.0,0.0,Gneiss,Strat_Series,7,2,False,"${\bf{x}}_{\alpha \,{\bf{7}},1}$",7 7,0.0,0.0,900.0,Siltstone,Strat_Series,3,2,False,"${\bf{x}}_{\alpha \,{\bf{3}},0}$",3 6,0.0,0.0,1100.0,Sandstone,Strat_Series,2,2,False,"${\bf{x}}_{\alpha \,{\bf{2}},1}$",2 5,2000.0,2000.0,1500.0,Sandstone,Strat_Series,2,2,False,"${\bf{x}}_{\alpha \,{\bf{2}},0}$",2 9,2000.0,2000.0,1200.0,Shale,Strat_Series,4,2,False,"${\bf{x}}_{\alpha \,{\bf{4}},1}$",4 ================================================ FILE: examples/data/input_data/lisa_models/interfaces6.csv ================================================ ,X,Y,Z,formation,series,formation_number,order_series,isFault,annotations,formation_value 46,500.0,1000.0,1500.0,Sandstone,Strat_Series,2,2,False,"${\bf{x}}_{\alpha \,{\bf{2}},0}$",2 56,1500.0,1000.0,500.0,Gneiss,Strat_Series,7,2,False,"${\bf{x}}_{\alpha \,{\bf{5}},7}$",7 49,500.0,1000.0,800.0,Sandstone_2,Strat_Series,5,2,False,"${\bf{x}}_{\alpha \,{\bf{5}},6}$",5 50,1500.0,1000.0,800.0,Sandstone_2,Strat_Series,5,2,False,"${\bf{x}}_{\alpha \,{\bf{5}},5}$",5 51,1000.0,1000.0,600.0,Sandstone_2,Strat_Series,5,2,False,"${\bf{x}}_{\alpha \,{\bf{5}},4}$",5 52,500.0,1000.0,700.0,Schist,Strat_Series,6,2,False,"${\bf{x}}_{\alpha \,{\bf{5}},3}$",6 53,1500.0,1000.0,700.0,Schist,Strat_Series,6,2,False,"${\bf{x}}_{\alpha \,{\bf{5}},2}$",6 54,1000.0,1000.0,500.0,Schist,Strat_Series,6,2,False,"${\bf{x}}_{\alpha \,{\bf{5}},1}$",6 57,1000.0,1000.0,300.0,Gneiss,Strat_Series,7,2,False,"${\bf{x}}_{\alpha \,{\bf{5}},8}$",7 55,500.0,1000.0,500.0,Gneiss,Strat_Series,7,2,False,"${\bf{x}}_{\alpha \,{\bf{5}},0}$",7 15,500.0,1000.0,1200.0,Shale,Strat_Series,4,2,False,"${\bf{x}}_{\alpha \,{\bf{4}},1}$",4 12,1500.0,1000.0,1200.0,Shale,Strat_Series,4,2,False,"${\bf{x}}_{\alpha \,{\bf{4}},0}$",4 19,500.0,1000.0,1300.0,Siltstone,Strat_Series,3,2,False,"${\bf{x}}_{\alpha \,{\bf{3}},2}$",3 20,1500.0,1000.0,1300.0,Siltstone,Strat_Series,3,2,False,"${\bf{x}}_{\alpha \,{\bf{3}},1}$",3 21,1000.0,1000.0,1100.0,Siltstone,Strat_Series,3,2,False,"${\bf{x}}_{\alpha \,{\bf{3}},0}$",3 47,1500.0,1000.0,1500.0,Sandstone,Strat_Series,2,2,False,"${\bf{x}}_{\alpha \,{\bf{2}},2}$",2 27,1000.0,1000.0,1200.0,Sandstone,Strat_Series,2,2,False,"${\bf{x}}_{\alpha \,{\bf{2}},1}$",2 9,1000.0,1000.0,1000.0,Shale,Strat_Series,4,2,False,"${\bf{x}}_{\alpha \,{\bf{4}},2}$",4 ================================================ FILE: examples/data/input_data/lisa_models/interfaces7.csv ================================================ ,X,Y,Z,formation,series,formation_number,order_series,isFault,annotations,formation_value 0,750.0,1500.0,500.0,Fault_1,Fault_1,1,1,True,"${\bf{x}}_{\alpha \,{\bf{1}},0}$",1 1,750.0,500.0,500.0,Fault_1,Fault_1,1,1,True,"${\bf{x}}_{\alpha \,{\bf{1}},1}$",1 2,1250.0,1500.0,500.0,Fault_2,Fault_2,2,2,True,"${\bf{x}}_{\alpha \,{\bf{2}},0}$",2 3,1250.0,1500.0,500.0,Fault_2,Fault_2,2,2,True,"${\bf{x}}_{\alpha \,{\bf{2}},1}$",2 13,1000.0,1000.0,300.0,Gneiss,Strat_Series,8,3,False,"${\bf{x}}_{\alpha \,{\bf{8}},1}$",8 5,250.0,1000.0,500.0,Gneiss,Strat_Series,8,3,False,"${\bf{x}}_{\alpha \,{\bf{8}},0}$",8 8,250.0,1000.0,700.0,Schist,Strat_Series,7,3,False,"${\bf{x}}_{\alpha \,{\bf{7}},2}$",7 7,1750.0,1000.0,700.0,Schist,Strat_Series,7,3,False,"${\bf{x}}_{\alpha \,{\bf{7}},1}$",7 6,1000.0,1000.0,500.0,Schist,Strat_Series,7,3,False,"${\bf{x}}_{\alpha \,{\bf{7}},0}$",7 9,250.0,1000.0,900.0,Sandstone_2,Strat_Series,6,3,False,"${\bf{x}}_{\alpha \,{\bf{6}},2}$",6 10,1750.0,1000.0,900.0,Sandstone_2,Strat_Series,6,3,False,"${\bf{x}}_{\alpha \,{\bf{6}},1}$",6 11,1000.0,1000.0,700.0,Sandstone_2,Strat_Series,6,3,False,"${\bf{x}}_{\alpha \,{\bf{6}},0}$",6 12,1750.0,1000.0,1200.0,Shale,Strat_Series,5,3,False,"${\bf{x}}_{\alpha \,{\bf{5}},1}$",5 4,1750.0,1000.0,500.0,Gneiss,Strat_Series,8,3,False,"${\bf{x}}_{\alpha \,{\bf{8}},2}$",8 14,250.0,1000.0,1200.0,Shale,Strat_Series,5,3,False,"${\bf{x}}_{\alpha \,{\bf{5}},0}$",5 17,250.0,1000.0,1300.0,Siltstone,Strat_Series,4,3,False,"${\bf{x}}_{\alpha \,{\bf{4}},2}$",4 16,1750.0,1000.0,1300.0,Siltstone,Strat_Series,4,3,False,"${\bf{x}}_{\alpha \,{\bf{4}},1}$",4 15,1000.0,1000.0,1100.0,Siltstone,Strat_Series,4,3,False,"${\bf{x}}_{\alpha \,{\bf{4}},0}$",4 20,250.0,1000.0,1500.0,Sandstone,Strat_Series,3,3,False,"${\bf{x}}_{\alpha \,{\bf{3}},2}$",3 19,1000.0,1000.0,1200.0,Sandstone,Strat_Series,3,3,False,"${\bf{x}}_{\alpha \,{\bf{3}},1}$",3 18,1750.0,1000.0,1500.0,Sandstone,Strat_Series,3,3,False,"${\bf{x}}_{\alpha \,{\bf{3}},0}$",3 21,1000.0,1000.0,1000.0,Shale,Strat_Series,5,3,False,"${\bf{x}}_{\alpha \,{\bf{5}},2}$",5 ================================================ FILE: examples/data/input_data/lisa_models/interfaces8.csv ================================================ ,X,Y,Z,formation,series,formation_number,order_series,isFault,annotations,formation_value 1,700.0,1000.0,900.0,Main_Fault,Fault_Series,1,1,True,"${\bf{x}}_{\alpha \,{\bf{1}},0}$",1 2,800.0,1000.0,1200.0,Main_Fault,Fault_Series,1,1,True,"${\bf{x}}_{\alpha \,{\bf{1}},1}$",1 3,900.0,1000.0,1500.0,Main_Fault,Fault_Series,1,1,True,"${\bf{x}}_{\alpha \,{\bf{1}},2}$",1 4,500.0,1000.0,300.0,Main_Fault,Fault_Series,1,1,True,"${\bf{x}}_{\alpha \,{\bf{1}},3}$",1 0,600.0,1000.0,600.0,Main_Fault,Fault_Series,1,1,True,"${\bf{x}}_{\alpha \,{\bf{1}},4}$",1 22,250.0,500.0,1100.0,Sandstone,Strat_Series,2,2,False,"${\bf{x}}_{\alpha \,{\bf{2}},0}$",2 23,835.0,450.0,1100.0,Sandstone,Strat_Series,2,2,False,"${\bf{x}}_{\alpha \,{\bf{2}},1}$",2 24,250.0,1500.0,1100.0,Sandstone,Strat_Series,2,2,False,"${\bf{x}}_{\alpha \,{\bf{2}},2}$",2 25,1250.0,1000.0,1000.0,Sandstone,Strat_Series,2,2,False,"${\bf{x}}_{\alpha \,{\bf{2}},3}$",2 18,755.0,460.0,900.0,Siltstone,Strat_Series,3,2,False,"${\bf{x}}_{\alpha \,{\bf{3}},0}$",3 19,250.0,1500.0,900.0,Siltstone,Strat_Series,3,2,False,"${\bf{x}}_{\alpha \,{\bf{3}},1}$",3 20,1250.0,1000.0,800.0,Siltstone,Strat_Series,3,2,False,"${\bf{x}}_{\alpha \,{\bf{3}},2}$",3 21,250.0,500.0,900.0,Siltstone,Strat_Series,3,2,False,"${\bf{x}}_{\alpha \,{\bf{3}},3}$",3 16,250.0,500.0,800.0,Shale,Strat_Series,4,2,False,"${\bf{x}}_{\alpha \,{\bf{4}},0}$",4 15,1250.0,1000.0,700.0,Shale,Strat_Series,4,2,False,"${\bf{x}}_{\alpha \,{\bf{4}},1}$",4 26,715.0,505.0,800.0,Shale,Strat_Series,4,2,False,"${\bf{x}}_{\alpha \,{\bf{4}},2}$",4 13,250.0,1500.0,800.0,Shale,Strat_Series,4,2,False,"${\bf{x}}_{\alpha \,{\bf{4}},3}$",4 14,640.0,490.0,500.0,Sandstone_2,Strat_Series,5,2,False,"${\bf{x}}_{\alpha \,{\bf{5}},0}$",5 11,1250.0,1000.0,400.0,Sandstone_2,Strat_Series,5,2,False,"${\bf{x}}_{\alpha \,{\bf{5}},1}$",5 12,250.0,500.0,500.0,Sandstone_2,Strat_Series,5,2,False,"${\bf{x}}_{\alpha \,{\bf{5}},2}$",5 9,250.0,1500.0,500.0,Sandstone_2,Strat_Series,5,2,False,"${\bf{x}}_{\alpha \,{\bf{5}},3}$",5 10,250.0,1500.0,300.0,Schist,Strat_Series,6,2,False,"${\bf{x}}_{\alpha \,{\bf{6}},0}$",6 8,1250.0,1000.0,200.0,Schist,Strat_Series,6,2,False,"${\bf{x}}_{\alpha \,{\bf{6}},1}$",6 7,250.0,500.0,300.0,Schist,Strat_Series,6,2,False,"${\bf{x}}_{\alpha \,{\bf{6}},2}$",6 17,1250.0,1000.0,100.0,Gneiss,Strat_Series,7,2,False,"${\bf{x}}_{\alpha \,{\bf{7}},0}$",7 6,250.0,500.0,200.0,Gneiss,Strat_Series,7,2,False,"${\bf{x}}_{\alpha \,{\bf{7}},1}$",7 5,250.0,1500.0,200.0,Gneiss,Strat_Series,7,2,False,"${\bf{x}}_{\alpha \,{\bf{7}},2}$",7 ================================================ FILE: examples/data/input_data/lisa_models/interfaces9.csv ================================================ ,Unnamed: 0,Unnamed: 0.1,X,Y,Z,annotations,formation,formation_number,formation_value,isFault,order_series,series 10,10.0,7.0,2000.0,2000.0,10.0,"${\bf{x}}_{\alpha \,{\bf{1}},0}$",Gneiss,1,1,False,1,Default series 11,11.0,8.0,0.0,500.0,10.0,"${\bf{x}}_{\alpha \,{\bf{1}},1}$",Gneiss,1,1,False,1,Default series 0,0.0,11.0,2000.0,2000.0,1500.0,"${\bf{x}}_{\alpha \,{\bf{2}},0}$",Sandstone,2,2,False,1,Default series 1,1.0,12.0,0.0,500.0,1500.0,"${\bf{x}}_{\alpha \,{\bf{2}},1}$",Sandstone,2,2,False,1,Default series 6,6.0,3.0,2000.0,2000.0,1000.0,"${\bf{x}}_{\alpha \,{\bf{3}},0}$",Sandstone_2,3,3,False,1,Default series 7,7.0,4.0,0.0,500.0,1000.0,"${\bf{x}}_{\alpha \,{\bf{3}},1}$",Sandstone_2,3,3,False,1,Default series 8,8.0,5.0,2000.0,2000.0,700.0,"${\bf{x}}_{\alpha \,{\bf{4}},0}$",Schist,4,4,False,1,Default series 9,9.0,6.0,0.0,500.0,700.0,"${\bf{x}}_{\alpha \,{\bf{4}},1}$",Schist,4,4,False,1,Default series 4,4.0,1.0,2000.0,2000.0,1150.0,"${\bf{x}}_{\alpha \,{\bf{5}},0}$",Shale,5,5,False,1,Default series 5,5.0,2.0,0.0,500.0,1150.0,"${\bf{x}}_{\alpha \,{\bf{5}},1}$",Shale,5,5,False,1,Default series 2,2.0,0.0,0.0,500.0,1400.0,"${\bf{x}}_{\alpha \,{\bf{6}},0}$",Siltstone,6,6,False,1,Default series 3,3.0,10.0,2000.0,2000.0,1400.0,"${\bf{x}}_{\alpha \,{\bf{6}},1}$",Siltstone,6,6,False,1,Default series ================================================ FILE: examples/data/input_data/perth_basin/Paper_GU2F_sc_faults_topo_Foliations.csv ================================================ X,Y,Z,azimuth,dip,polarity,formation 393936.142320,6693632.776940,957.702303,263.276747,1.193492,1,Permian 396918.892567,6693984.396839,977.204803,83.276747,0.349367,1,Permian 351487.758871,6688628.772226,-7385.646697,83.277000,6.546000,1,Permian 338518.168564,6650708.570891,-4542.306605,256.590252,1.789915,1,Permian 397464.032464,6663077.533819,959.378895,268.100891,1.432093,1,Permian 388231.346475,6662771.397837,-5909.347105,88.100891,1.548158,1,Permian 390067.013711,6693176.666808,-2027.085197,258.305884,56.113829,1,Darling 387480.312066,6692871.734884,-5468.217697,258.453840,50.172150,1,Darling 380685.471752,6692070.728810,-2475.219197,258.181256,59.136216,1,Urella_North 378225.542286,6691780.741356,-5604.015697,258.491356,45.884855,1,Urella_North 349524.174513,6688397.296146,-1722.315697,258.469633,48.978506,1,Eneabba_South 346943.915258,6688093.123680,-4278.195197,258.462867,40.562225,1,Eneabba_South 340944.288072,6687385.860875,-2356.005197,77.995123,62.486979,-1,Coomallo 342643.483167,6687586.169570,-5228.729697,78.277337,56.597000,-1,Coomallo 348362.594755,6688260.363945,-4800.701697,83.277000,7.496000,1,Lesueur 347339.390828,6688139.744103,-5803.145697,83.277000,12.095000,1,Woodada_Kockatea 352054.428028,6688695.573713,-3672.951697,83.277000,7.472000,1,Cattamarra 377146.267897,6662140.215356,-3767.430605,252.583876,73.779822,1,Hypo_fault_E 375633.492464,6661665.672702,-8051.546105,252.583876,66.705447,1,Hypo_fault_E 358317.030388,6656233.670235,-3868.630105,72.583876,74.666723,-1,Hypo_fault_W 359797.619749,6656698.116438,-8017.813105,72.583876,64.573130,-1,Hypo_fault_W 386644.134759,6662718.769315,-2733.176605,262.967799,59.896750,1,Urella_South 384322.099881,6662641.775640,-6850.435605,262.886991,61.379678,1,Urella_South 349566.870409,6653488.831804,-1894.695784,72.584000,14.036000,1,Cretaceous 393713.418971,6662953.171554,-1357.485252,88.101000,0.000000,1,Yarragadee 362273.437500,6691196.250000,265.802829,37.647360,89.720212,-1,Abrolhos_Transfer 382171.344000,6676036.000000,273.424125,216.987277,89.473746,1,Abrolhos_Transfer ================================================ FILE: examples/data/input_data/perth_basin/Paper_GU2F_sc_faults_topo_Points.csv ================================================ X,Y,Z,formation 383267.125849,6692375.065706,42.347303,Lesueur 385656.999410,6692656.794658,42.347303,Lesueur 388158.029281,6692951.627211,42.347303,Lesueur 390510.850409,6693228.988256,-88.233697,Lesueur 352976.841866,6688804.311969,-5833.794697,Lesueur 349411.679351,6653440.149908,-4324.030105,Lesueur 351841.686031,6654202.418911,-4392.337105,Lesueur 380356.701646,6662510.291576,-6567.699105,Lesueur 382545.931510,6662582.881723,-6806.655105,Lesueur 392541.609621,6693468.383207,928.448303,Permian 395330.675019,6693797.170673,986.956303,Permian 398507.110115,6694171.623005,967.453303,Permian 338267.783260,6687070.342554,-4782.514697,Permian 340255.613728,6687304.676873,-5079.046697,Permian 348501.431395,6688276.730627,-7154.775697,Permian 352571.944537,6688756.580866,-7836.879697,Permian 357602.562530,6689349.612546,-8273.556697,Permian 372576.271591,6691114.780137,-7525.440697,Permian 385092.242016,6692590.218538,-1668.921697,Permian 388037.178103,6692937.380735,-1743.054697,Permian 364190.685794,6658076.177701,-10068.796105,Permian 370581.834459,6660081.017671,-10125.561105,Permian 381550.826391,6662549.886180,-9355.525105,Permian 337765.192857,6650305.529894,-4569.541105,Permian 339365.910313,6650910.683888,-4515.072105,Permian 348604.615926,6653186.982113,-6537.306105,Permian 350975.088988,6653930.576010,-6662.921105,Permian 396667.948468,6663051.137388,939.465895,Permian 398260.116460,6663103.930249,979.291895,Permian 387143.811652,6662735.337525,-5879.938105,Permian 389318.881298,6662807.458150,-5938.756105,Permian 344027.770807,6687749.355569,-6023.503697,Woodada_Kockatea 350933.945695,6688563.486259,-7003.554697,Woodada_Kockatea 343600.199481,6687698.951555,-2875.825697,Yarragadee 344814.486275,6687842.097096,-2788.489697,Yarragadee 347069.591029,6688107.938900,-2846.713697,Yarragadee 353590.199732,6688876.617329,-3666.615697,Yarragadee 380367.581667,6662510.652334,-3808.317105,Yarragadee 383506.431648,6662614.729839,-4126.926105,Yarragadee 364082.361402,6658042.197413,-4505.867105,Yarragadee 371404.678673,6660339.135743,-4673.112105,Yarragadee 350920.928223,6653913.586316,-1610.873105,Yarragadee 353520.717446,6654729.114419,-1894.696105,Yarragadee 342049.687219,6651550.532280,-2478.249105,Yarragadee 343851.059886,6651980.003672,-2446.320105,Yarragadee 345590.316523,6652394.665770,-2605.963105,Yarragadee 388868.827634,6662792.535339,-830.809105,Yarragadee 391549.358494,6662881.415969,-958.524105,Yarragadee 394612.827902,6662982.994017,-990.453105,Yarragadee 353033.649508,6688811.008707,-5424.239697,Eneabba 341942.065605,6651524.873845,-4152.902105,Eneabba 342823.407616,6651734.997595,-4152.902105,Eneabba 337375.397545,6650010.823057,-1199.239105,Eneabba 338177.661298,6650617.378871,-1227.176105,Eneabba 353514.305255,6688867.670550,-4863.837697,Cattamarra 353994.961003,6688924.332392,-3972.289697,Cadda 358422.044112,6689446.216689,-737.241697,Cretaceous 386659.170996,6662719.267885,-349.138105,Cretaceous 362632.034534,6657587.244254,-303.620105,Cretaceous 367192.073609,6659017.683301,-940.838105,Cretaceous 373532.695890,6661006.673645,-713.260105,Cretaceous 346487.344000,6707224.000000,229.783830,Cretaceous 346724.219000,6709119.000000,236.946081,Cretaceous 350389.250000,6683986.000000,253.713420,Cretaceous 349437.156000,6693507.000000,238.379141,Cretaceous 345866.844000,6701996.500000,209.750695,Cretaceous 363848.313000,6642899.000000,248.562248,Cretaceous 362850.906000,6651604.000000,222.729354,Cretaceous 358589.188000,6662303.500000,280.211703,Cretaceous 353762.938000,6672419.500000,254.338465,Cretaceous 391199.876318,6693310.213703,-335.036697,Darling 388934.151104,6693043.119912,-3719.133697,Darling 386026.473028,6692700.349855,-7217.301697,Darling 393786.593975,6662955.597879,-2703.767105,Darling 391487.125000,6704623.000000,246.230082,Darling 381591.762732,6692177.566432,-954.276697,Urella_North 379779.180773,6691963.891188,-3996.161697,Urella_North 376671.903799,6691597.591524,-7211.869697,Urella_North 350677.949316,6688533.308264,-391.568697,Eneabba_South 348370.399711,6688261.284028,-3053.062697,Eneabba_South 345517.430806,6687924.963332,-5503.327697,Eneabba_South 347559.222614,6652864.079376,-2586.131105,Eneabba_South 354951.313000,6642333.000000,219.196929,Eneabba_South 353707.750000,6648758.000000,196.407131,Eneabba_South 340231.045846,6687301.780702,-983.011697,Coomallo 341657.530298,6687469.941049,-3728.998697,Coomallo 343629.436036,6687702.398090,-6728.460697,Coomallo 338862.633991,6650790.696066,-1174.500105,Coomallo 340922.351837,6651281.760412,-4585.943105,Coomallo 343668.643595,6651936.513183,-7762.114105,Coomallo 338001.531000,6703286.500000,151.117327,Coomallo 377661.255065,6662301.761725,-1912.105105,Hypo_fault_E 376631.280728,6661978.668988,-5622.756105,Hypo_fault_E 374635.704200,6661352.676416,-10480.336105,Hypo_fault_E 357769.856194,6656062.027115,-1777.172105,Hypo_fault_W 358864.204582,6656405.313355,-5960.088105,Hypo_fault_W 360731.034915,6656990.919521,-10075.538105,Hypo_fault_W 387790.456275,6662756.778868,-762.774105,Urella_South 385497.813244,6662680.759763,-4703.579105,Urella_South 383146.386518,6662602.791518,-8997.292105,Urella_South 352798.250000,6698505.500000,254.579992,Abrolhos_Transfer 371748.625000,6683887.000000,282.857478,Abrolhos_Transfer 392594.063000,6668185.000000,228.405954,Abrolhos_Transfer ================================================ FILE: examples/data/input_data/striplog_integration/alpha_strip.tops ================================================ top, comp lith 0, overburden 50, miguel 75, evan 100, brian 120, dieter ================================================ FILE: examples/data/input_data/striplog_integration/beta_strip.tops ================================================ top, comp lith 0, overburden 50, miguel 80, evan 100, brian 130, dieter ================================================ FILE: examples/data/input_data/striplog_integration/epsilon_strip.tops ================================================ top, comp lith 0, overburden 50, miguel 75, brian 110, dieter ================================================ FILE: examples/data/input_data/striplog_integration/gamma_strip.tops ================================================ top, comp lith 0, overburden 50, miguel 75, evan 100, brian 130, dieter ================================================ FILE: examples/data/input_data/tests/fault_relations_test_ori.csv ================================================ X,Y,Z,G_x,G_y,G_z,id,nugget,formation 350.0,500.0,-700.0,0.8949343616020251,5.4798925069145e-17,0.4461978131098087,26244822,0.01,fault1 550.0,500.0,-600.0,-0.8949343616015264,-1.6439677520734337e-16,0.44619781311080886,124053343,0.01,fault2 400.0,500.0,-625.0,0.0,0.0,1.0,563652887,0.01,rock4 150.0,500.0,-550.0,0.0,0.0,1.0,563652887,0.01,rock4 850.0,500.0,-525.0,0.0,0.0,1.0,563652887,0.01,rock4 400.0,500.0,-675.0,0.0,0.0,1.0,438217026,0.01,rock3 150.0,500.0,-600.0,0.0,0.0,1.0,438217026,0.01,rock3 850.0,500.0,-575.0,0.0,0.0,1.0,438217026,0.01,rock3 850.0,500.0,-625.0,0.0,0.0,1.0,317776925,0.01,rock2 150.0,500.0,-650.0,0.0,0.0,1.0,317776925,0.01,rock2 150.0,500.0,-700.0,0.0,0.0,1.0,267239155,0.01,rock1 850.0,500.0,-675.0,0.0,0.0,1.0,267239155,0.01,rock1 ================================================ FILE: examples/data/input_data/tests/fault_relations_test_surf.csv ================================================ X,Y,Z,id,nugget,formation 300.0,200.0,-600.0,26244822,2e-05,fault1 300.0,500.0,-600.0,26244822,2e-05,fault1 300.0,800.0,-600.0,26244822,2e-05,fault1 350.0,200.0,-700.0,26244822,2e-05,fault1 350.0,500.0,-700.0,26244822,2e-05,fault1 350.0,800.0,-700.0,26244822,2e-05,fault1 400.0,800.0,-800.0,26244822,2e-05,fault1 400.0,500.0,-800.0,26244822,2e-05,fault1 400.0,200.0,-800.0,26244822,2e-05,fault1 500.0,500.0,-700.0,124053343,2e-05,fault2 500.0,200.0,-700.0,124053343,2e-05,fault2 500.0,800.0,-700.0,124053343,2e-05,fault2 550.0,200.0,-600.0,124053343,2e-05,fault2 550.0,500.0,-600.0,124053343,2e-05,fault2 550.0,800.0,-600.0,124053343,2e-05,fault2 600.0,200.0,-500.0,124053343,2e-05,fault2 600.0,500.0,-500.0,124053343,2e-05,fault2 600.0,800.0,-500.0,124053343,2e-05,fault2 400.0,200.0,-625.0,563652887,2e-05,rock4 100.0,500.0,-550.0,563652887,2e-05,rock4 100.0,800.0,-550.0,563652887,2e-05,rock4 100.0,200.0,-550.0,563652887,2e-05,rock4 200.0,200.0,-550.0,563652887,2e-05,rock4 200.0,500.0,-550.0,563652887,2e-05,rock4 200.0,800.0,-550.0,563652887,2e-05,rock4 800.0,200.0,-525.0,563652887,2e-05,rock4 800.0,500.0,-525.0,563652887,2e-05,rock4 800.0,800.0,-525.0,563652887,2e-05,rock4 900.0,200.0,-525.0,563652887,2e-05,rock4 900.0,500.0,-525.0,563652887,2e-05,rock4 900.0,800.0,-525.0,563652887,2e-05,rock4 400.0,500.0,-625.0,563652887,2e-05,rock4 400.0,800.0,-625.0,563652887,2e-05,rock4 400.0,800.0,-675.0,438217026,2e-05,rock3 100.0,200.0,-600.0,438217026,2e-05,rock3 100.0,500.0,-600.0,438217026,2e-05,rock3 100.0,800.0,-600.0,438217026,2e-05,rock3 200.0,200.0,-600.0,438217026,2e-05,rock3 200.0,500.0,-600.0,438217026,2e-05,rock3 200.0,800.0,-600.0,438217026,2e-05,rock3 800.0,200.0,-575.0,438217026,2e-05,rock3 800.0,500.0,-575.0,438217026,2e-05,rock3 800.0,800.0,-575.0,438217026,2e-05,rock3 900.0,500.0,-575.0,438217026,2e-05,rock3 900.0,200.0,-575.0,438217026,2e-05,rock3 900.0,800.0,-575.0,438217026,2e-05,rock3 400.0,200.0,-675.0,438217026,2e-05,rock3 400.0,500.0,-675.0,438217026,2e-05,rock3 200.0,800.0,-650.0,317776925,2e-05,rock2 200.0,500.0,-650.0,317776925,2e-05,rock2 200.0,200.0,-650.0,317776925,2e-05,rock2 100.0,500.0,-650.0,317776925,2e-05,rock2 100.0,800.0,-650.0,317776925,2e-05,rock2 100.0,200.0,-650.0,317776925,2e-05,rock2 900.0,200.0,-625.0,317776925,2e-05,rock2 900.0,500.0,-625.0,317776925,2e-05,rock2 900.0,800.0,-625.0,317776925,2e-05,rock2 800.0,200.0,-625.0,317776925,2e-05,rock2 800.0,500.0,-625.0,317776925,2e-05,rock2 800.0,800.0,-625.0,317776925,2e-05,rock2 100.0,200.0,-700.0,267239155,2e-05,rock1 100.0,500.0,-700.0,267239155,2e-05,rock1 100.0,800.0,-700.0,267239155,2e-05,rock1 200.0,200.0,-700.0,267239155,2e-05,rock1 200.0,500.0,-700.0,267239155,2e-05,rock1 200.0,800.0,-700.0,267239155,2e-05,rock1 900.0,200.0,-675.0,267239155,2e-05,rock1 900.0,500.0,-675.0,267239155,2e-05,rock1 900.0,800.0,-675.0,267239155,2e-05,rock1 800.0,200.0,-675.0,267239155,2e-05,rock1 800.0,500.0,-675.0,267239155,2e-05,rock1 800.0,800.0,-675.0,267239155,2e-05,rock1 ================================================ FILE: examples/data/input_data/tut-ch1-4/tut_ch1-4_orientations.csv ================================================ ,X,Y,Z,X_r,Y_r,Z_r,G_x,G_y,G_z,dip,azimuth,polarity,surface,series,id,order_series 0,200.0,50.0,-200.0,0.3126,0.5001,0.7501,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,seafloor,seafloor_series,1,1 1,500.0,50.0,-200.0,0.5001,0.5001,0.7501,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,seafloor,seafloor_series,1,1 2,800.0,50.0,-200.0,0.6876,0.5001,0.7501,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,seafloor,seafloor_series,1,1 4,700.0,50.0,-450.0,0.6251,0.5001,0.59385,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock1,right_series,2,2 3,900.0,50.0,-450.0,0.7501,0.5001,0.59385,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock1,right_series,2,2 6,700.0,50.0,-700.0,0.6251,0.5001,0.4376,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock2,right_series,3,2 5,900.0,50.0,-700.0,0.7501,0.5001,0.4376,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock2,right_series,3,2 7,300.0,50.0,-200.0,0.3751,0.5001,0.7501,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,onlap_surface,onlap_series,4,3 8,700.0,50.0,-1000.0,0.6251,0.5001,0.2501,1.000000000001,1.0000612323399569e-12,1.0000612323399569e-12,90.0,90.0,1.0,onlap_surface,onlap_series,4,3 9,550.0,50.0,-500.0,0.53135,0.5001,0.5626,0.766044443119978,1.0000469066937634e-12,0.6427876096875393,50.0,90.0,1.0,onlap_surface,onlap_series,4,3 10,100.0,50.0,-400.0,0.2501,0.5001,0.6251,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock3,left_series,5,4 11,450.0,50.0,-1000.0,0.46885,0.5001,0.2501,1.000000000001,2.3054614646942163e-12,1.0000612323399569e-12,90.0,89.9999999999252,1.0,rock3,left_series,5,4 12,300.0,50.0,-500.0,0.3751,0.5001,0.5626,0.766044443119978,1.0000469066937634e-12,0.6427876096875393,49.99999999992522,89.9999999999252,1.0,rock3,left_series,5,4 ================================================ FILE: examples/data/input_data/tut-ch1-4/tut_ch1-4_points.csv ================================================ ,X,Y,Z,X_r,Y_r,Z_r,surface,series,id,order_series 0,200.0,0.0,-200.0,0.3126,0.46885,0.7501,seafloor,seafloor_series,6,1 1,200.0,100.0,-200.0,0.3126,0.53135,0.7501,seafloor,seafloor_series,6,1 2,500.0,0.0,-200.0,0.5001,0.46885,0.7501,seafloor,seafloor_series,6,1 3,500.0,100.0,-200.0,0.5001,0.53135,0.7501,seafloor,seafloor_series,6,1 4,800.0,0.0,-200.0,0.6876,0.46885,0.7501,seafloor,seafloor_series,6,1 5,800.0,100.0,-200.0,0.6876,0.53135,0.7501,seafloor,seafloor_series,6,1 7,700.0,0.0,-450.0,0.6251,0.46885,0.59385,rock1,right_series,4,2 6,700.0,100.0,-450.0,0.6251,0.53135,0.59385,rock1,right_series,4,2 8,900.0,0.0,-450.0,0.7501,0.46885,0.59385,rock1,right_series,4,2 9,900.0,100.0,-450.0,0.7501,0.53135,0.59385,rock1,right_series,4,2 13,700.0,0.0,-700.0,0.6251,0.46885,0.4376,rock2,right_series,5,2 12,700.0,100.0,-700.0,0.6251,0.53135,0.4376,rock2,right_series,5,2 11,900.0,0.0,-700.0,0.7501,0.46885,0.4376,rock2,right_series,5,2 10,900.0,100.0,-700.0,0.7501,0.53135,0.4376,rock2,right_series,5,2 14,300.0,0.0,-200.0,0.3751,0.46885,0.7501,onlap_surface,onlap_series,3,3 15,300.0,100.0,-200.0,0.3751,0.53135,0.7501,onlap_surface,onlap_series,3,3 16,700.0,0.0,-1000.0,0.6251,0.46885,0.2501,onlap_surface,onlap_series,3,3 17,700.0,100.0,-1000.0,0.6251,0.53135,0.2501,onlap_surface,onlap_series,3,3 18,550.0,0.0,-500.0,0.53135,0.46885,0.5626,onlap_surface,onlap_series,3,3 19,550.0,100.0,-500.0,0.53135,0.53135,0.5626,onlap_surface,onlap_series,3,3 20,100.0,0.0,-400.0,0.2501,0.46885,0.6251,rock3,left_series,2,4 21,100.0,100.0,-400.0,0.2501,0.53135,0.6251,rock3,left_series,2,4 22,450.0,0.0,-1000.0,0.46885,0.46885,0.2501,rock3,left_series,2,4 23,450.0,100.0,-1000.0,0.46885,0.53135,0.2501,rock3,left_series,2,4 24,300.0,0.0,-500.0,0.3751,0.46885,0.5626,rock3,left_series,2,4 25,300.0,100.0,-500.0,0.3751,0.53135,0.5626,rock3,left_series,2,4 ================================================ FILE: examples/data/input_data/tut-ch1-5/tut_ch1-5_orientations.csv ================================================ ,X,Y,Z,X_r,Y_r,Z_r,G_x,G_y,G_z,dip,azimuth,polarity,surface,series,id,order_series 13,550.0,500.0,-600.0,0.53135,0.5001,0.53135,-0.8949343616010249,9.576199963080072e-18,0.4461978131108091,63.49999999993597,270.0,1.0,fault2,fault_series2,1,1 10,350.0,500.0,-700.0,0.40635,0.5001,0.46885,0.8949343616030251,1.0000547989250692e-12,0.4461978131108087,63.5,90.0,1.0,fault1,fault_series1,2,2 11,400.0,500.0,-625.0,0.4376,0.5001,0.515725,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock4,series1,3,3 7,150.0,500.0,-550.0,0.28135,0.5001,0.5626,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock4,series1,3,3 6,850.0,500.0,-525.0,0.71885,0.5001,0.578225,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock4,series1,3,3 12,400.0,500.0,-675.0,0.4376,0.5001,0.484475,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock3,series1,4,3 5,150.0,500.0,-600.0,0.28135,0.5001,0.53135,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock3,series1,4,3 4,850.0,500.0,-575.0,0.71885,0.5001,0.546975,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock3,series1,4,3 3,850.0,500.0,-625.0,0.71885,0.5001,0.515725,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock2,series1,5,3 2,150.0,500.0,-650.0,0.28135,0.5001,0.5001,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock2,series1,5,3 0,150.0,500.0,-700.0,0.28135,0.5001,0.46885,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock1,series1,6,3 1,850.0,500.0,-675.0,0.71885,0.5001,0.484475,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock1,series1,6,3 ================================================ FILE: examples/data/input_data/tut-ch1-5/tut_ch1-5_points.csv ================================================ ,X,Y,Z,X_r,Y_r,Z_r,surface,series,id,order_series 71,500.0,500.0,-700.0,0.5001,0.5001,0.46885,fault2,fault_series2,3,1 66,500.0,200.0,-700.0,0.5001,0.3126,0.46885,fault2,fault_series2,3,1 72,500.0,800.0,-700.0,0.5001,0.6876,0.46885,fault2,fault_series2,3,1 73,550.0,200.0,-600.0,0.53135,0.3126,0.53135,fault2,fault_series2,3,1 74,550.0,500.0,-600.0,0.53135,0.5001,0.53135,fault2,fault_series2,3,1 75,550.0,800.0,-600.0,0.53135,0.6876,0.53135,fault2,fault_series2,3,1 76,600.0,200.0,-500.0,0.5626,0.3126,0.59385,fault2,fault_series2,3,1 77,600.0,500.0,-500.0,0.5626,0.5001,0.59385,fault2,fault_series2,3,1 78,600.0,800.0,-500.0,0.5626,0.6876,0.59385,fault2,fault_series2,3,1 57,300.0,200.0,-600.0,0.3751,0.3126,0.53135,fault1,fault_series1,2,2 58,300.0,500.0,-600.0,0.3751,0.5001,0.53135,fault1,fault_series1,2,2 59,300.0,800.0,-600.0,0.3751,0.6876,0.53135,fault1,fault_series1,2,2 62,350.0,200.0,-700.0,0.40635,0.3126,0.46885,fault1,fault_series1,2,2 61,350.0,500.0,-700.0,0.40635,0.5001,0.46885,fault1,fault_series1,2,2 63,350.0,800.0,-700.0,0.40635,0.6876,0.46885,fault1,fault_series1,2,2 64,400.0,800.0,-800.0,0.4376,0.6876,0.40635,fault1,fault_series1,2,2 65,400.0,500.0,-800.0,0.4376,0.5001,0.40635,fault1,fault_series1,2,2 60,400.0,200.0,-800.0,0.4376,0.3126,0.40635,fault1,fault_series1,2,2 79,400.0,200.0,-625.0,0.4376,0.3126,0.515725,rock4,series1,4,3 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734500 6925000 60 -697.0076 735000 6925000 61 -701.6655 735500 6925000 62 -704.4901 736000 6925000 63 -705.2039 736500 6925000 64 -704.374 737000 6925000 65 -702.6452 737500 6925000 66 -700.4656 738000 6925000 67 -698.3013 738500 6925000 68 -696.3626 739000 6925000 69 -694.7473 739500 6925000 70 -693.5701 740000 6925000 71 -692.9161 740500 6925000 72 -692.7798 741000 6925000 73 -693.1425 741500 6925000 74 -693.9347 742000 6925000 75 -695.1215 742500 6925000 76 -696.8679 743000 6925000 77 -699.3538 743500 6925000 78 -702.2449 744000 6925000 79 -704.8959 744500 6925000 80 -706.9296 745000 6925000 81 -708.43 745500 6925000 82 -709.8936 746000 6925000 83 -711.7782 746500 6925000 84 -714.4371 747000 6925000 85 -718.046 ================================================ FILE: examples/data/input_data/tut_chapter1/.~lock.fault_unconformity_model_orientations.csv# ================================================ ,bl9,bl9,24.01.2018 18:51,file:///home/bl9/.config/libreoffice/4; ================================================ FILE: examples/data/input_data/tut_chapter1/.~lock.fault_unconformity_model_points.csv# ================================================ ,bl9,bl9,24.01.2018 18:39,file:///home/bl9/.config/libreoffice/4; ================================================ FILE: examples/data/input_data/tut_chapter1/fault_unconformity_model_orientations.csv ================================================ X,Y,Z,azimuth,dip,polarity,formation 1000,1000,950,90,18.435,1,Shale 400,1000,1400,90,18.435,1,Sandstone_2 1000,1500,1600,270,20,1,Carbonate 500,1000,864.602,270,71.565,1,Main_Fault ================================================ FILE: examples/data/input_data/tut_chapter1/fault_unconformity_model_points.csv ================================================ X,Y,Z,formation 800,50,750,Shale 800,150,700,Shale 800,300,700,Shale 800,500,800,Shale 800,1000,1000,Shale 800,1500,700,Shale 800,1700,600,Shale 800,1950,650,Shale 0,1000,1100,Shale 300,1000,1000,Shale 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1500,1500,1150,Sandstone_2 300,300,1500,Carbonate 1700,1700,1600,Carbonate 1000,1000,1300,Carbonate 300,1700,1400,Carbonate 1700,300,1500,Carbonate 700,1000,900,Main_Fault 600,1000,600,Main_Fault 500,1000,300,Main_Fault 800,1000,1200,Main_Fault 900,1000,1500,Main_Fault ================================================ FILE: examples/data/input_data/tut_chapter1/simple_fault_model_orientations.csv ================================================ X,Y,Z,azimuth,dip,polarity,formation 1000,1000,950,90,18.435,1,Shale 400,1000,1400,90,18.435,1,Sandstone_2 500,1000,864.602,270,71.565,1,Main_Fault ================================================ FILE: examples/data/input_data/tut_chapter1/simple_fault_model_orientations_geophy.csv ================================================ X,Y,Z,azimuth,dip,polarity,formation 1000,1000,-1050,90,18.435,1,target 400,1000,-600,90,18.435,1,seawater 500,1000,-1135.398,270,71.565,1,fault ================================================ FILE: examples/data/input_data/tut_chapter1/simple_fault_model_points.csv ================================================ X,Y,Z,formation 800,50,750,Shale 800,150,700,Shale 800,300,700,Shale 800,500,800,Shale 800,1000,1000,Shale 800,1500,700,Shale 800,1700,600,Shale 800,1950,650,Shale 0,1000,1100,Shale 300,1000,1000,Shale 450,1000,950,Shale 1100,1000,900,Shale 1400,1000,850,Shale 1700,1000,900,Shale 1500,500,800,Shale 1500,1500,750,Shale 800,50,450,Sandstone_1 800,150,400,Sandstone_1 800,300,400,Sandstone_1 800,500,500,Sandstone_1 800,1000,700,Sandstone_1 800,1500,400,Sandstone_1 800,1700,300,Sandstone_1 800,1950,350,Sandstone_1 0,1000,800,Sandstone_1 300,1000,700,Sandstone_1 1100,1000,600,Sandstone_1 1400,1000,550,Sandstone_1 1700,1000,600,Sandstone_1 1500,500,500,Sandstone_1 1500,1500,450,Sandstone_1 900,150,920,Siltstone 900,300,920,Siltstone 900,1500,920,Siltstone 900,1700,820,Siltstone 900,1950,870,Siltstone 0,1000,1300,Siltstone 300,1000,1200,Siltstone 600,1000,1100,Siltstone 1100,1000,1100,Siltstone 1400,1000,1050,Siltstone 1700,1000,1100,Siltstone 1500,500,1000,Siltstone 1500,1500,950,Siltstone 0,1000,1500,Sandstone_2 300,1000,1400,Sandstone_2 600,1000,1300,Sandstone_2 1100,1000,1300,Sandstone_2 1400,1000,1250,Sandstone_2 1700,1000,1300,Sandstone_2 1500,500,1200,Sandstone_2 1500,1500,1150,Sandstone_2 700,1000,900,Main_Fault 600,1000,600,Main_Fault 500,1000,300,Main_Fault 800,1000,1200,Main_Fault 900,1000,1500,Main_Fault ================================================ FILE: examples/data/input_data/tut_chapter1/simple_fault_model_points_geophy.csv ================================================ X,Y,Z,formation 800,50,-1250,target 800,150,-1300,target 800,300,-1300,target 800,500,-1200,target 800,1000,-1000,target 800,1500,-1300,target 800,1700,-1400,target 800,1950,-1350,target 0,1000,-900,target 300,1000,-1000,target 450,1000,-1050,target 1100,1000,-1100,target 1400,1000,-1150,target 1700,1000,-1100,target 1500,500,-1200,target 1500,1500,-1250,target 800,50,-1550,underburden 800,150,-1600,underburden 800,300,-1600,underburden 800,500,-1500,underburden 800,1000,-1300,underburden 800,1500,-1600,underburden 800,1700,-1700,underburden 800,1950,-1650,underburden 0,1000,-1200,underburden 300,1000,-1300,underburden 1100,1000,-1400,underburden 1400,1000,-1450,underburden 1700,1000,-1400,underburden 1500,500,-1500,underburden 1500,1500,-1550,underburden 900,150,-1080,overburden 900,300,-1080,overburden 900,1500,-1080,overburden 900,1700,-1180,overburden 900,1950,-1130,overburden 0,1000,-700,overburden 300,1000,-800,overburden 600,1000,-900,overburden 1100,1000,-900,overburden 1400,1000,-950,overburden 1700,1000,-900,overburden 1500,500,-1000,overburden 1500,1500,-1050,overburden 0,1000,-500,seawater 300,1000,-600,seawater 600,1000,-700,seawater 1100,1000,-700,seawater 1400,1000,-750,seawater 1700,1000,-700,seawater 1500,500,-800,seawater 1500,1500,-850,seawater 700,1000,-1100,fault 600,1000,-1400,fault 500,1000,-1700,fault 800,1000,-800,fault 900,1000,-500,fault ================================================ FILE: examples/data/input_data/tut_chapter6/ch6_data_fol.csv ================================================ G_x,G_y,G_z,X,X_std,Y,Y_std,Z,Z_std,annotations,azimuth,azimuth_std,dip,dip_std,formation,formation_number,group_id,index,isFault,order_series,polarity,series 0.8682431421244591,-5.537707899440688e-15,0.4961389383568339,1500.0,,6.66666666667,,990.0,,"${\bf{x}}_{\beta \,{\bf{1}},0}$",90.0,,60.2551187031,,Fault,1,fault,,True,1,1,fault 0.25881904510252074,1.5848095757158825e-17,0.9659258262890683,506.333333333,,9.66666666667,,1679.33333333,,"${\bf{x}}_{\beta \,{\bf{2}},0}$",90,,15,,Layer 2,2,l2_a,1.0,False,2,1,Rest 0.25881904510252074,1.5848095757158825e-17,0.9659258262890683,2500,,9.666666666666666,,911,,"${\bf{x}}_{\beta \,{\bf{2}},1}$",90,,15,,Layer 2,2,l2_a,1.0,False,2,1,Rest ================================================ FILE: examples/data/input_data/tut_chapter6/ch6_data_interf.csv ================================================ X,X_std,Y,Y_std,Z,Z_std,annotations,formation,formation_number,group_id,isFault,order_series,series 1700,0.0,0,0.0,640,0.0,"${\bf{x}}_{\alpha \,{\bf{1}},0}$",Fault,1,fault,True,1,fault 1500,0.0,10,0.0,990,0.0,"${\bf{x}}_{\alpha \,{\bf{1}},1}$",Fault,1,fault,True,1,fault 1300,0.0,10,0.0,1340,0.0,"${\bf{x}}_{\alpha \,{\bf{1}},2}$",Fault,1,fault,True,1,fault 2500,0.0,10,0.0,911,0.0,"${\bf{x}}_{\alpha \,{\bf{2}},0}$",Layer 2,2,l2_b,False,2,Rest 500,0.0,10,0.0,1681,0.0,"${\bf{x}}_{\alpha \,{\bf{2}},1}$",Layer 2,2,l2_a,False,2,Rest 2500,0.0,10,0.0,704,0.0,"${\bf{x}}_{\alpha \,{\bf{3}},0}$",Layer 3,3,l3_b,False,2,Rest 500,0.0,10,0.0,1474,0.0,"${\bf{x}}_{\alpha \,{\bf{3}},1}$",Layer 3,3,l3_a,False,2,Rest 2500,0.0,10,0.0,497,0.0,"${\bf{x}}_{\alpha \,{\bf{4}},0}$",Layer 4,4,l4_b,False,2,Rest 500,0.0,10,0.0,1267,0.0,"${\bf{x}}_{\alpha \,{\bf{4}},1}$",Layer 4,4,l4_a,False,2,Rest 2500,0.0,10,0.0,290,0.0,"${\bf{x}}_{\alpha \,{\bf{5}},0}$",Layer 5,5,l5_b,False,2,Rest 500,0.0,10,0.0,1060,0.0,"${\bf{x}}_{\alpha \,{\bf{5}},1}$",Layer 5,5,l5_a,False,2,Rest ================================================ FILE: examples/examples/README.rst ================================================ Examples ======== This examples intend to be a gallery of what other people has done with GemPy. Hopefully with the help of the community we can create a complete library of structures that can serve as template for new users. ================================================ FILE: examples/examples/__init__.py ================================================ ================================================ FILE: examples/examples/geometries/README.rst ================================================ Simple geometries ----------------- Some simple geometries. ================================================ FILE: examples/examples/geometries/__init__.py ================================================ ================================================ FILE: examples/examples/geometries/a01_horizontal_stratigraphic.py ================================================ """ Model 1 - Horizontal Stratigraphy =================================== This example demonstrates how to build a basic geological model with horizontally stacked layers using GemPy, an open-source Python library for implicit geological modeling. In this script, we will: - Set up and compute a simple geological model using input data (orientations and surface points). - Visualize the model in 2D and 3D. - Export model components (formations, orientations, surface points, and the regular grid) to VTK files. - Extract mesh vertices and normals, save them to Excel files, and convert the vertex data to an XYZ file. Each section includes detailed comments to explain its purpose and functionality. """ # %% # Import necessary libraries import gempy as gp import gempy_viewer as gpv import pyvista as pv import vtk import pandas as pd # sphinx_gallery_thumbnail_number = 2 # %% # Generate the Geological Model # ----------------------------- # In this section, we define the data source and create a GeoModel instance. # The model is defined with a specific spatial extent and resolution (refinement). # Input data (orientations and surface points) are loaded from a remote repository. # Define the path to the dataset hosted online data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/' # Create a GeoModel instance with the given project name, extent, and refinement level. data = gp.create_geomodel( project_name='horizontal', extent=[0, 1000, 0, 1000, 0, 1000], refinement=6, importer_helper=gp.data.ImporterHelper( path_to_orientations=data_path + "/data/input_data/jan_models/model1_orientations.csv", path_to_surface_points=data_path + "/data/input_data/jan_models/model1_surface_points.csv" ) ) # Map a geological series to the corresponding surfaces. # Here, the "Strat_Series" is associated with two formations ('rock2' and 'rock1'), # which defines the stacking order of horizontal layers. gp.map_stack_to_surfaces( gempy_model=data, mapping_object={"Strat_Series": ('rock2', 'rock1')} ) # Compute the geological model using the provided data and mappings. gp.compute_model(data) geo_data = data # %% # 2D Visualization of the Geological Model # ----------------------------------------- # The following plots provide 2D views of the model. # - The first plot shows the initial model without computed results. # - The subsequent plots display the computed geological data in the x and y directions. # Note: The boundaries are hidden in the latter plots for a cleaner visualization. # Plot the initial model in the y direction (without displaying computed results) gpv.plot_2d(geo_data, direction=['y'], show_results=False) # Plot the computed model results in the x and y directions, including data but excluding boundaries gpv.plot_2d(geo_data, direction=['x'], show_data=True, show_boundaries=False) gpv.plot_2d(geo_data, direction=['y'], show_data=True, show_boundaries=False) # %% # 3D Visualization and Export to VTK # ---------------------------------- # Visualize the model in 3D with data, results, and boundaries. # The generated 3D plot is also used to export various components of the model to VTK files. p = gpv.plot_3d(geo_data, show_data=True, show_results=True, show_boundaries=True, image=True) # Export different components of the model to VTK files: p.surface_poly['rock1'].save('rock1.vtk') # Save formation 'rock1' p.surface_poly['rock2'].save('rock2.vtk') # Save formation 'rock2' p.orientations_mesh.save('orientations.vtk') # Save the orientations mesh p.surface_points_mesh.save('surface_points.vtk') # Save the surface points mesh # Retrieve and export the regular grid (volume representation) of the model box = p.regular_grid_actor.GetMapper().GetInput() box.save('box.vtk') # %% # Display the Exported VTK Files with PyVista # ------------------------------------------- # Load and plot each exported VTK file to verify the export. pv.read('rock1.vtk').plot(show_edges=False) pv.read('rock2.vtk').plot(show_edges=False) pv.read('orientations.vtk').plot(show_edges=False) pv.read('surface_points.vtk').plot(show_edges=False) pv.read('box.vtk').plot(show_edges=False) # %% # Extract and Save Mesh Vertices and Normals # ------------------------------------------ # The following functions extract vertex coordinates and corresponding normal vectors # from a mesh (vtkPolyData), and then save this data into Excel files for further analysis. def generate_normals(polydata): """ Generate normals for the given polydata if they are not already computed. Parameters: polydata (vtk.vtkPolyData): Input polydata for which normals are to be computed. Returns: vtk.vtkPolyData: The polydata with computed point normals. """ normal_generator = vtk.vtkPolyDataNormals() normal_generator.SetInputData(polydata) normal_generator.ComputePointNormalsOn() normal_generator.ComputeCellNormalsOff() normal_generator.Update() return normal_generator.GetOutput() def get_vertices_and_normals(mesh): """ Extract vertices and normals from a mesh. Parameters: mesh: The mesh object (typically from a formation) to extract surface vertices and normals. Returns: tuple: Two lists containing vertices and normals respectively. """ # Extract the surface of the mesh surface_mesh = mesh.extract_surface() polydata = surface_mesh # Ensure normals are computed for the polydata polydata_with_normals = generate_normals(polydata) # Extract points (vertices) points = polydata_with_normals.GetPoints() vertices = [points.GetPoint(i) for i in range(points.GetNumberOfPoints())] # Extract normals associated with the points normals_array = polydata_with_normals.GetPointData().GetNormals() normals = [normals_array.GetTuple(i) for i in range(normals_array.GetNumberOfTuples())] return vertices, normals def save_to_excel(vertices, normals, vertices_file, normals_file): """ Save the vertices and normals to separate Excel files. Parameters: vertices (list): List of vertex coordinates. normals (list): List of normal vectors. vertices_file (str): File name for saving vertices. normals_file (str): File name for saving normals. """ # Create DataFrames from the lists vertices_df = pd.DataFrame(vertices, columns=['X', 'Y', 'Z']) normals_df = pd.DataFrame(normals, columns=['x', 'y', 'z']) # Write the DataFrames to Excel files vertices_df.to_excel(vertices_file, index=False) normals_df.to_excel(normals_file, index=False) # Extract vertices and normals from the mesh of formation 'rock1' mesh = p.surface_poly['rock1'] vertices, normals = get_vertices_and_normals(mesh) # Create DataFrames for later processing or verification vertices_df = pd.DataFrame(vertices, columns=['X', 'Y', 'Z']) normals_df = pd.DataFrame(normals, columns=['x', 'y', 'z']) # Define file names for the Excel outputs vertices_file = "rock1_vertices.xlsx" normals_file = "rock1_norms.xlsx" # Save the extracted data to Excel files save_to_excel(vertices, normals, vertices_file, normals_file) # Optionally, read back the Excel files to verify their content pd.read_excel(vertices_file) pd.read_excel(normals_file) # %% # Convert Vertices DataFrame to an XYZ File # ----------------------------------------- # This function converts a DataFrame containing vertex coordinates to an XYZ format file, # which is a simple text file with one point per line. def dataframe_to_xyz(df, filename): """ Write vertex coordinates from a DataFrame to an XYZ file. Parameters: df (pandas.DataFrame): DataFrame containing 'X', 'Y', and 'Z' columns. filename (str): Output filename for the XYZ file. """ with open(filename, 'w') as f: for index, row in df.iterrows(): f.write(f"{row['X']} {row['Y']} {row['Z']}\n") # Specify the output file name for the XYZ file filename = "output.xyz" # Convert the vertices DataFrame to an XYZ file dataframe_to_xyz(vertices_df, filename) ================================================ FILE: examples/examples/geometries/b02_fold.py ================================================ """ Model 2 - Anticline =================== Simple model of an anticline This script demonstrates how to create a geological model of an anticline structure using GemPy, a Python-based, open-source library for implicit geological modeling. """ # Import necessary libraries import gempy as gp import gempy_viewer as gpv # sphinx_gallery_thumbnail_number = 2 # %% # Generate the model # Define the path to data data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/' path_to_data = data_path + "/data/input_data/jan_models/" # Create a GeoModel instance data: gp.data.GeoModel = gp.create_geomodel( project_name='fold', extent=[0, 1000, 0, 1000, 0, 1000], refinement=6, importer_helper=gp.data.ImporterHelper( path_to_orientations=path_to_data + "model2_orientations.csv", path_to_surface_points=path_to_data + "model2_surface_points.csv" ) ) # Map geological series to surfaces gp.map_stack_to_surfaces( gempy_model=data, mapping_object={"Strat_Series": ('rock2', 'rock1')} ) # Compute the geological model gp.compute_model(data) geo_data = data # %% # Plot the initial geological model in the y direction without results gpv.plot_2d(geo_data, direction=['y'], show_results=False) # Plot the result of the model in the y and x direction with data and scalar gpv.plot_2d(geo_data, direction='y', show_data=True, show_scalar=False) gpv.plot_2d(geo_data, direction='x', show_data=True, show_scalar=True) ================================================ FILE: examples/examples/geometries/c03_recumbent_fold.py ================================================ """ Model 3 - Recumbent Fold ======================== Modeling a recumbent fold This script demonstrates how to create a geological model of a recumbent fold using GemPy, a Python-based, open-source library for implicit geological modeling. """ # Import necessary libraries import gempy as gp import gempy_viewer as gpv # sphinx_gallery_thumbnail_number = 2 # %% # Generate the model # Define the path to data data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/' path_to_data = data_path + "/data/input_data/jan_models/" # Create a GeoModel instance data = gp.create_geomodel( project_name='recumbent', extent=[0, 1000, 0, 1000, 0, 1000], resolution=[50, 50, 50], refinement=6, importer_helper=gp.data.ImporterHelper( path_to_orientations=path_to_data + "model3_orientations.csv", path_to_surface_points=path_to_data + "model3_surface_points.csv" ) ) # Map geological series to surfaces gp.map_stack_to_surfaces( gempy_model=data, mapping_object={"Strat_Series": ('rock2', 'rock1')} ) # Compute the geological model gp.compute_model(data) geo_data = data # %% # Plot the initial geological model in the y direction gpv.plot_2d(geo_data, direction=['y'], show_results=False) # Plot the result of the model in the y direction with data gpv.plot_2d(geo_data, direction=['y'], show_data=True) # %% gpv.plot_3d(geo_data, show_surfaces=True) ================================================ FILE: examples/examples/geometries/d04_pinchout.py ================================================ """ Model 4 - Pinchout ================== Modeling of a pinchout: combining scalar fields This script demonstrates how to create a geological model with a layer of varying thickness (pinchout) using GemPy, a Python-based, open-source library for implicit geological modeling. """ # Import necessary libraries import gempy as gp import gempy_viewer as gpv # sphinx_gallery_thumbnail_number = 3 # %% # Generate the model # Define the path to data data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/' path_to_data = data_path + "/data/input_data/jan_models/" # Create a GeoModel instance data = gp.create_geomodel( project_name='pinchout', extent=[0, 1000, 0, 1000, 0, 1000], resolution=[50, 50, 50], refinement=6, importer_helper=gp.data.ImporterHelper( path_to_orientations=path_to_data + "model4_orientations.csv", path_to_surface_points=path_to_data + "model4_surface_points.csv" ) ) # Map geological series to surfaces gp.map_stack_to_surfaces( gempy_model=data, mapping_object={"Strat_Series": ('rock2', 'rock1')} ) # Compute the geological model gp.compute_model(data) geo_data = data # %% # Plot the initial geological model in the y direction gpv.plot_2d(geo_data, direction=['y'], show_results=False) # Plot the result of the model in the x and y direction with data gpv.plot_2d(geo_data, direction=['x'], show_data=True) gpv.plot_2d(geo_data, direction=['y'], show_data=True) # %% gpv.plot_3d(geo_data, show_surfaces=True) ================================================ FILE: examples/examples/geometries/e05_fault.py ================================================ """ Model 5 - Fault =============== Modeling a fault This script demonstrates how to create a simple fault model with constant offset using GemPy, a Python-based, open-source library for implicit geological modeling. """ # Import necessary libraries import gempy as gp import gempy_viewer as gpv import numpy as np from gempy_engine.core.data.stack_relation_type import StackRelationType # sphinx_gallery_thumbnail_number = 2 # %% # Generate the model # Define the path to data data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/' path_to_data = data_path + "/data/input_data/jan_models/" # Create a GeoModel instance data = gp.create_geomodel( project_name='fault', extent=[0, 1000, 0, 1000, 0, 1000], refinement=6, importer_helper=gp.data.ImporterHelper( path_to_orientations=path_to_data + "model5_orientations.csv", path_to_surface_points=path_to_data + "model5_surface_points.csv" ) ) # Map geological series to surfaces gp.map_stack_to_surfaces( gempy_model=data, mapping_object={ "Fault_Series": 'fault', "Strat_Series": ('rock2', 'rock1') } ) # Define fault groups data.structural_frame.structural_groups[0].structural_relation = StackRelationType.FAULT data.structural_frame.fault_relations = np.array([[0, 1], [0, 0]]) # Compute the geological model gp.compute_model(data) geo_data = data # %% # Plot the initial geological model in the y direction gpv.plot_2d(geo_data, direction=['y'], show_results=False) # %% # Plot the result of the model in the x and y direction with data gpv.plot_2d(geo_data, direction='y', show_data=True) gpv.plot_2d(geo_data, direction='x', show_data=True) gpv.plot_3d(geo_data, show_data=True, show_boundaries=True, show_lith=True) ================================================ FILE: examples/examples/geometries/f06_unconformity.py ================================================ """ Model 6 - Unconformity ====================== Modeling unconformities through the combination of scalar fields This script creates an unconformity cutting an anticline structure using GemPy, an open-source, Python-based library for building implicit geological models. """ # Importing necessary libraries import gempy as gp import gempy_viewer as gpv from gempy_engine.core.data.stack_relation_type import StackRelationType # sphinx_gallery_thumbnail_number = 2 # %% # Generate the model # Define the path to data data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/' path_to_data = data_path + "/data/input_data/jan_models/" # Create a GeoModel instance data = gp.create_geomodel( project_name='unconformity', extent=[0, 1000, 0, 1000, 0, 1000], refinement=6, importer_helper=gp.data.ImporterHelper( path_to_orientations=path_to_data + "model6_orientations.csv", path_to_surface_points=path_to_data + "model6_surface_points.csv" ) ) # Map geological series to surfaces gp.map_stack_to_surfaces( gempy_model=data, mapping_object={ "Strat_Series1": 'rock3', "Strat_Series2": ('rock2', 'rock1') } ) # Define the structural relation data.structural_frame.structural_groups[0].structural_relation = StackRelationType.ERODE # Compute the geological model gp.compute_model(data) geo_data = data # %% # Plot the initial geological model in the y direction gpv.plot_2d(geo_data, direction=['y'], show_results=False) # %% # Plot the result of the model in the y and x directions with data gpv.plot_2d(geo_data, direction='y', show_data=True) gpv.plot_2d(geo_data, direction='x', show_data=True) ================================================ FILE: examples/examples/geometries/g07_combination.py ================================================ """ Model 7 - Combination ====================== Combining faults and unconformities This script creates a folded domain featuring an unconformity and a fault using GemPy, an open-source, Python-based library for building implicit geological models. """ # Importing necessary libraries import numpy as np import gempy as gp import gempy_viewer as gpv from gempy_engine.core.data.stack_relation_type import StackRelationType # sphinx_gallery_thumbnail_number = 2 # Generate the model # Define the path to data data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/' path_to_data = data_path + "/data/input_data/jan_models/" # Create a GeoModel instance data = gp.create_geomodel( project_name='combination', extent=[0, 2500, 0, 1000, 0, 1000], refinement=6, resolution=[20, 20, 20], importer_helper=gp.data.ImporterHelper( path_to_orientations=path_to_data + "model7_orientations.csv", path_to_surface_points=path_to_data + "model7_surface_points.csv" ) ) # Map geological series to surfaces gp.map_stack_to_surfaces( gempy_model=data, mapping_object={ "Fault_Series" : ('fault'), "Strat_Series1": ('rock3'), "Strat_Series2": ('rock2', 'rock1'), } ) # Define the structural relation data.structural_frame.structural_groups[0].structural_relation = StackRelationType.FAULT data.structural_frame.fault_relations = np.array( [[0, 1, 1], [0, 0, 0], [0, 0, 0]] ) # Compute the geological model data.interpolation_options.number_octree_levels_surface = 5 gp.compute_model(data) data.structural_frame # %% # Plot the initial geological model in the y direction gpv.plot_2d(data, direction=['y'], show_results=False) # %% # Plot the result of the model in the y and x directions with data and boundaries gpv.plot_2d(data, direction='y', show_data=True, show_boundaries=True) gpv.plot_2d(data, direction='x', show_data=True) # Plot the blocks accounting for fault blocks gpv.plot_2d( model=data, override_regular_grid=data.solutions.raw_arrays.litho_faults_block, show_data=True, kwargs_lithology={'cmap': 'Set1', 'norm': None} ) # %% # The 3D plot is commented out due to a bug. gpv.plot_3d(data) ================================================ FILE: examples/examples/geometries/h08_more_examples.py ================================================ """ GemPy Models - Some More Complex Examples ----------------------------------------- A set of more complex examples """ # %% # Importing gempy import gempy as gp import gempy_viewer as gpv # Aux imports import numpy as np import os # %% # Choose a model and load the corresponding data set in the line below # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # Model 1: Discordant layering # # Model 2: Anticlinal layering # # Model 3: Parallel steep dipping layers # # Model 5: Normal faulting # # Model 6: Synclinal layering # # Model 7: Graben structure with two faults # # Model 8: Similar to Model 5 # # Model 9: Parallel flat dipping layers # # %% data_path = os.path.abspath('../..') # %% def create_example(name_model, plot_section=True): """ Create an inter_data from one of the examples data_set Attr: name_model (str): name of the model that you want to generate. It has to be in ['Model 1' ,'Model 2', 'Model 3', 'Model 4','Model 5' 'Model 6','Model 7', 'Model 8', 'Model 9'] save_pickle (bool, str): Save to a pickle the interp_data object. You can pass the path as a string otherwse the default name will be given plot_section (bool) """ name_list = np.array(['Model 1', 'Model 2', 'Model 3', 'Model 4', 'Model 5', 'Model 6', 'Model 7', 'Model 8', 'Model 9']) assert name_model in name_list, 'Name model must be in the following list: ' + str(name_list) # Extract number of the model n_model = name_model[-1] # # Load right gempy geodata geo_data: gp.data.GeoModel = gp.create_geomodel( project_name=name_model, extent=[0, 2000, 0, 2000, 0, 1600], resolution=[50, 50, 50], refinement=6, # * For this model is better not to use octrees because we want to see what is happening in the scalar fields importer_helper=gp.data.ImporterHelper( path_to_orientations=data_path + "/data/input_data/lisa_models/foliations" + n_model + ".csv", path_to_surface_points=data_path + "/data/input_data/lisa_models/interfaces" + n_model + ".csv", ) ) # Set the right sequential pile subset_list_1 = np.array(['Model 1']) subset_list_2 = np.array(['Model 5', 'Model 8']) subset_list_3 = np.array(['Model 2', 'Model 3', 'Model 9', 'Model 6']) subset_list_4 = np.array(['Model 7']) ### Model 1 - Discordant layering ### if name_model in subset_list_1: gp.map_stack_to_surfaces( gempy_model=geo_data, mapping_object={ "Strat_Series_1": ('Sandstone', 'Siltstone', 'Shale'), "Strat_Series_2": ('Sandstone2', 'Siltstone2', 'Shale2') }, ) ### Model 5 - One normal Fault ### ### Model 8 - ### elif name_model in subset_list_2: gp.map_stack_to_surfaces( gempy_model=geo_data, mapping_object={ "Fault_Series": 'Main_Fault', "Strat_Series": ('Sandstone', 'Siltstone', 'Shale', 'Sandstone_2', 'Schist', 'Gneiss') }, ) gp.set_is_fault(geo_data, ['Fault_Series']) elif name_model in subset_list_3: ### Model 2 - Aufwölbung (durch Salzstock?) ### ### Model 3+9 - Parallele NNE Schichtung ohne Verwerfung ### ### Model 6 - Mulde ### gp.map_stack_to_surfaces( gempy_model=geo_data, mapping_object={ "Strat_Series": ('Sandstone', 'Siltstone', 'Shale', 'Sandstone_2', 'Schist', 'Gneiss') }, ) elif name_model in subset_list_4: ### Model 7 - Graben ### gp.map_stack_to_surfaces( gempy_model=geo_data, mapping_object={ "Fault_1": 'Fault_1', "Fault_2": 'Fault_2', "Strat_Series": ('Sandstone', 'Siltstone', 'Shale', 'Sandstone_2', 'Schist', 'Gneiss') }, ) gp.set_is_fault(geo_data, ['Fault_1', 'Fault_2']) else: print('You would never reach this point. Look for the bug') # Interpolation and Computation sol = gp.compute_model( gempy_model=geo_data, engine_config=gp.data.GemPyEngineConfig(backend=gp.data.AvailableBackends.PYTORCH) ) if plot_section is True: # 2D Plot gpv.plot_2d(geo_data, cell_number=['mid'], direction='y', show_data=True) gpv.plot_3d(geo_data, image=False) return geo_data.structural_frame # %% # Discordant layering 1 # ===================== create_example('Model 1') # %% # Discordant layering 2 # ===================== create_example('Model 2') # %% # Discordant layering 3 # ===================== create_example('Model 3') # %% # One normal Fault 1 # ================== create_example('Model 5') # %% # Fold # ==== create_example('Model 6') # %% # Graben # ====== create_example('Model 7') # %% # One normal Fault 2 # =================== create_example('Model 8') # %% # Horizontal tilted # ================= create_example('Model 9') # %% # generate_all_models(['Model 1', 'Model 2', 'Model 3', # 'Model 5', 'Model 6', 'Model 7', # 'Model 8', 'Model 9']) ================================================ FILE: examples/examples/real/Alesmodel.py ================================================ """ Model of Ales, France: Plotting Sections and Maps ====================================== """ # %% # .. admonition:: Explanation # # This model is generally quite unstable and requires float64 precision to determine a solution. # The lack of data in one corner for the TRIAS and LIAS series causes the model to bend unrealistically, # eroding the CARBO layer, which disappears in that section. # import gempy as gp import gempy_viewer as gpv import os # %% # Setting up paths cwd = os.getcwd() data_path = os.path.join(cwd, 'examples') if 'examples' not in cwd else os.path.join(cwd, '../..') path_interf = os.path.join(data_path, "data/input_data/AlesModel/2018_interf.csv") path_orient = os.path.join(data_path, "data/input_data/AlesModel/2018_orient_clust_n_init5_0.csv") path_dem = os.path.join(data_path, "data/input_data/AlesModel/_cropped_DEM_coarse.tif") # %% # Creating the geological model geo_model: gp.data.GeoModel = gp.create_geomodel( project_name='Claudius', extent=[729550.0, 751500.0, 1913500.0, 1923650.0, -1800.0, 800.0], resolution=None, refinement=6, importer_helper=gp.data.ImporterHelper( path_to_orientations=path_orient, path_to_surface_points=path_interf, ) ) # %% # Setting up the section grid gp.set_section_grid( grid=geo_model.grid, section_dict={ 'section1': ([732000, 1916000], [745000, 1916000], [200, 150]) } ) # %% # Sorting lithologies gp.map_stack_to_surfaces( gempy_model=geo_model, mapping_object={ 'fault_left': 'fault_left', 'fault_right': 'fault_right', 'fault_lr': 'fault_lr', 'Trias_Series': ('TRIAS', 'LIAS'), 'Carbon_Series': 'CARBO', 'Basement_Series': 'basement' }, remove_unused_series=True ) # %% # Changing colors geo_model.structural_frame.get_element_by_name("LIAS").color = "#015482" geo_model.structural_frame.get_element_by_name("TRIAS").color = "#9f0052" geo_model.structural_frame.get_element_by_name("CARBO").color = "#ffbe00" # %% # Plotting the 2D model gpv.plot_2d(geo_model, direction='y') # %% # Plotting section traces gpv.plot_section_traces(geo_model) # %% # Setting faults gp.set_is_fault( frame=geo_model.structural_frame, fault_groups=[ geo_model.structural_frame.get_group_by_name('fault_left'), geo_model.structural_frame.get_group_by_name('fault_right'), geo_model.structural_frame.get_group_by_name('fault_lr') ], change_color=True ) # %% # Setting topography from file gp.set_topography_from_file( grid=geo_model.grid, filepath=path_dem, crop_to_extent=[729550.0, 751500.0, 1913500.0, 1923650.0] ) # Plotting 3D model with topography gpv.plot_3d(geo_model, show_topography=True, ve=1, image=True) # %% # Getting the Carbon Series carbo = geo_model.structural_frame.get_group_by_name("Carbon_Series") # %% # Modifying interpolation options for better model fitting geo_model.interpolation_options.number_octree_levels_surface = 4 geo_model.interpolation_options.kernel_options.range = 0.8 gp.modify_surface_points( geo_model=geo_model, elements_names=["CARBO", "LIAS", "TRIAS"], nugget=0.005 ) # %% # Displaying the structural frame print(geo_model.structural_frame) geo_model.structural_frame # %% # Explanation of model characteristics and adjustments # This model has characteristics that make it difficult to get the right default values: # - It is large, and we want high resolution # - Some series have a large conditional number (i.e., the model input is not very stable) # To address these issues: # - Reduce the chunk size during evaluation to trade speed for memory # - Reduce the std of the error parameter in octree refinement, which evaluates fewer voxels but may leave some without refinement # Enable debugging options to help tune these parameters. # %% # Setting verbose and condition number options for debugging geo_model.interpolation_options.evaluation_options.verbose = True geo_model.interpolation_options.kernel_options.compute_condition_number = True # %% # Observations and parameter adjustments # The octree refinement is making the octree grid almost dense, and smaller chunks are needed to avoid running out of memory. # Adjusting parameters accordingly: geo_model.interpolation_options.evaluation_options.octree_error_threshold = 0.5 geo_model.interpolation_options.evaluation_options.evaluation_chunk_size = 50_000 # %% # Computing the model with the adjusted settings geo_model.interpolation_options.mesh_extraction = False _ = gp.compute_model( geo_model, engine_config=gp.data.GemPyEngineConfig( backend=gp.data.AvailableBackends.PYTORCH, use_gpu=True, dtype="float64" ) ) # %% # Plotting the 2D model with and without topography gpv.plot_2d(geo_model, show_topography=False, section_names=['topography'], show_lith=True) gpv.plot_2d(geo_model, cell_number=[4], direction=['y'], show_topography=True, show_data=True) gpv.plot_2d(geo_model, cell_number=[-4], direction=['y'], show_topography=True, show_data=True) # %% # Setting thumbnail number for Sphinx-Gallery # sphinx_gallery_thumbnail_number = -1 gpv.plot_3d(geo_model, show_lith=True, show_topography=True, kwargs_plot_structured_grid={'opacity': 0.8}) ================================================ FILE: examples/examples/real/Claudius.py ================================================ """ Geomodeling benchmark: the "Claudius"-Model ======== This model is part of a geomodeling benchmaring effort. More information (and, hopefully, publication) coming. """ # %% import sys, os # Importing gempy import gempy as gp import gempy_viewer as gpv # Aux imports import numpy as np import pandas as pn # %% # Loading data from repository: # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # With pandas we can do it directly from the web and with the right args # we can directly tidy the data in gempy style: # # %% data_path = os.path.abspath('../../data/input_data/Claudius') reduce_data_by = 30 dfs = [] for letter in 'ABCD': dfs.append( pn.read_csv( filepath_or_buffer=f"{data_path}/{letter}Points.csv", sep=';', names=['X', 'Y', 'Z', 'surface', 'cutoff'], header=0 )[::reduce_data_by] ) # Add fault: dfs.append( pn.read_csv( filepath_or_buffer=f"{data_path}/Fault.csv", names=['X', 'Y', 'Z', 'surface'], header=0, sep=',' ) ) surface_points = pn.concat(dfs, sort=True) surface_points['surface'] = surface_points['surface'].astype('str') surface_points.reset_index(inplace=True, drop=False) surface_points.tail() # %% surface_points.dtypes # %% # How many points are per surface # # %% surface_points.groupby('surface').count() # %% # Now we do the same with the orientations: # # %% dfs = [] for surf in ['0', '330']: o = pn.read_csv( filepath_or_buffer=f"{data_path}/Dips.csv", sep=';', names=['X', 'Y', 'Z', 'G_x', 'G_y', 'G_z', '-'], header=1 ) # Orientation needs to belong to a surface. This is mainly to categorize to which series belong and to # use the same color o['surface'] = surf dfs.append(o) orientations = pn.concat(dfs, sort=True) orientations.reset_index(inplace=True, drop=False) orientations.tail() # %% orientations.dtypes # %% # Data initialization: # ~~~~~~~~~~~~~~~~~~~~ # # Suggested size of the axis-aligned modeling box: Origin: 548800 7816600 # -8400 Maximum: 552500 7822000 -11010 # # Suggested resolution: 100m x 100m x -90m (grid size 38 x 55 x 30) # # %% # Number of voxels: np.array([38, 55, 30]).prod() surface_points_table: gp.data.SurfacePointsTable = gp.data.SurfacePointsTable.from_arrays( x=surface_points['X'].values, y=surface_points['Y'].values, z=surface_points['Z'].values, names=surface_points['surface'].values ) orientations_table: gp.data.OrientationsTable = gp.data.OrientationsTable.from_arrays( x=orientations['X'].values, y=orientations['Y'].values, z=orientations['Z'].values, G_x=orientations['G_x'].values, G_y=orientations['G_y'].values, G_z=orientations['G_z'].values, names=orientations['surface'].values, name_id_map=surface_points_table.name_id_map # ! Make sure that ids and names are shared ) structural_frame: gp.data.StructuralFrame = gp.data.StructuralFrame.from_data_tables( surface_points=surface_points_table, orientations=orientations_table ) geo_model: gp.data.GeoModel = gp.create_geomodel( project_name='Claudius', extent=[548800, 552500, 7816600, 7822000, -11010, -8400], resolution=[38, 55, 30], refinement=5, structural_frame=structural_frame ) group_fault = gp.data.StructuralGroup( name='Fault1', elements=[geo_model.structural_frame.structural_elements.pop(-2)], structural_relation=gp.data.StackRelationType.FAULT, fault_relations=gp.data.FaultsRelationSpecialCase.OFFSET_ALL ) geo_model.structural_frame.get_group_by_name("default_formation").elements.pop(-1) # Insert the fault group into the structural frame: geo_model.structural_frame.insert_group(0, group_fault) gp.set_is_fault( frame=geo_model.structural_frame, fault_groups=[geo_model.structural_frame.get_group_by_name('Fault1')] ) print(geo_model) # %% # We are going to increase the smoothness (nugget) of the data to increase # the conditional number of the matrix: # # %% gp.modify_surface_points(geo_model, nugget=0.01) # %% # Also the original poles are pointing downwards. We can change the # direction by calling the following: # # %% gp.modify_orientations(geo_model, polarity=-1) # %% # We need an orientation per series/fault. The faults does not have # orientation so the easiest is to create an orientation from the surface # points availablle: # element = geo_model.structural_frame.get_element_by_name("Claudius_fault") new_orientations: gp.data.OrientationsTable = gp.create_orientations_from_surface_points_coords( xyz_coords=element.surface_points.xyz ) gp.add_orientations( geo_model=geo_model, x=new_orientations.data['X'], y=new_orientations.data['Y'], z=new_orientations.data['Z'], pole_vector=new_orientations.grads, elements_names="Claudius_fault" ) # %% gpv.plot_2d(geo_model, direction='y') # %% # We will need to separate with surface belong to each series: # gp.map_stack_to_surfaces( gempy_model=geo_model, mapping_object={ 'Default series': ('0', '60', '250'), 'Fault': 'Claudius_fault', 'Uncomformity': '330', } ) # %% # So far we did not specify which series/faults are actula faults: # # %% gp.set_is_fault( frame=geo_model.structural_frame, fault_groups=[geo_model.structural_frame.get_group_by_name('Fault')] ) geo_model.structural_frame # %% geo_model.interpolation_options.kernel_options.range = 1 gp.compute_model( geo_model, gp.data.GemPyEngineConfig( backend=gp.data.AvailableBackends.numpy, use_gpu=False, dtype='float64' ) ) # %% sect = ['mid'] gpv.plot_2d(geo_model, cell_number=sect, series_n=1, show_scalar=True, direction='x') # %% gpv.plot_2d(geo_model, cell_number=sect, show_data=True, direction='x') # %% gpv.plot_2d(geo_model, cell_number=[28], series_n=0, direction='y', show_scalar=True) gpv.plot_2d(geo_model, cell_number=[28], series_n=1, direction='y', show_scalar=True) gpv.plot_2d(geo_model, cell_number=[28], series_n=2, direction='y', show_scalar=True) # %% gpv.plot_2d(geo_model, cell_number=[28], show_data=True, direction='y') # %% # sphinx_gallery_thumbnail_number = 8 gpv.plot_3d(geo_model, show_lith=True, show_data=True, show_boundaries=True) ================================================ FILE: examples/examples/real/Greenstone.py ================================================ """ Greenstone. =========== """ import os # Importing gempy import gempy as gp import gempy_viewer as gpv print(gp.__version__) # %% data_path = os.path.abspath('../../data/input_data/tut_SandStone') # Importing the data from csv geo_model: gp.data.GeoModel = gp.create_geomodel( project_name='Greenstone', extent=[696000, 747000, 6863000, 6930000, -20000, 200], # * Here we define the extent of the model refinement=6, # * Here we define the number of octree levels. If octree levels are defined, the resolution is ignored. importer_helper=gp.data.ImporterHelper( path_to_orientations=data_path + "/SandStone_Foliations.csv", path_to_surface_points=data_path + "/SandStone_Points.csv", hash_surface_points=None, hash_orientations=None ) ) # %% gpv.plot_2d(geo_model, direction=['z']) # %% gp.map_stack_to_surfaces( gempy_model=geo_model, mapping_object={ "EarlyGranite_Series": 'EarlyGranite', "BIF_Series": ('SimpleMafic2', 'SimpleBIF'), "SimpleMafic_Series": 'SimpleMafic1', 'Basement': 'basement' } ) # %% gp.compute_model(geo_model) # %% gpv.plot_2d(geo_model, cell_number=[-1], direction=['z'], show_data=False) # %% gpv.plot_2d(geo_model, cell_number=['mid'], direction='x') # %% # sphinx_gallery_thumbnail_number = -1 gpv.plot_3d(geo_model) ================================================ FILE: examples/examples/real/Hecho.py ================================================ """ Geomodeling benchmark: the "Hecho"-Model ======== This model is part of a geomodeling benchmaring effort. More information (and, hopefully, publication) coming. """ import os import numpy as np # %% # Aux imports import pandas as pn from gempy.modules.serialization.save_load import _validate_serialization # Importing gempy import gempy as gp import gempy_viewer as gpv # %% # Loading surface points from repository: # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # With pandas we can do it directly from the web and with the right args # we can directly tidy the data in gempy style: # # %% data_path = os.path.abspath('../../data/input_data/Hecho') dfs = [] # First stratigraphic data for letter in range(1, 10): dfs.append(pn.read_csv( filepath_or_buffer=data_path + '/H' + str(letter) + '.csv', sep=';', names=['X', 'Y', 'Z', 'surface', '_'], header=0 )) # Also faults for f in range(1, 4): fault_df = pn.read_csv( filepath_or_buffer=data_path + '/F' + str(f) + 'Line.csv', sep=';', names=['X', 'Y', 'Z'], header=0 ) fault_df['surface'] = 'f' + str(f) dfs.append(fault_df) # We put all the surfaces points together because is how gempy likes it: surface_points = pn.concat(dfs, sort=True) surface_points.reset_index(inplace=True, drop=False) surface_points.tail() # %% # Now we do the same with the orientations: # # %% orientations = pn.read_csv( filepath_or_buffer=data_path + '/Dips.csv', sep=';', names=['X', 'Y', 'Z', 'G_x', 'G_z', '_'], header=0 ) # Orientation needs to belong to a surface. This is mainly to categorize to which series belong and to # use the same color orientations['surface'] = 0 # We fill the laking direction with a dummy value: orientations['G_y'] = 0 # Replace -99999.00000 with NaN orientations.replace(-99999.00000, np.nan, inplace=True) # Drop irrelevant columns orientations.drop(columns=['_'], inplace=True) # Remove rows containing NaN orientations.dropna(inplace=True) # %% # Data initialization: # ~~~~~~~~~~~~~~~~~~~~ # # Suggested size of the axis-aligned modeling box: Origin: 0 -0.5 0 # Maximum: 16 0.5 4.5 # # Suggested resolution: 0.05m (grid size 321 x 21 x 91) # # %% surface_points_table: gp.data.SurfacePointsTable = gp.data.SurfacePointsTable.from_arrays( x=surface_points['X'].values, y=surface_points['Y'].values, z=surface_points['Z'].values, names=surface_points['surface'].values.astype(str) ) orientations_table: gp.data.OrientationsTable = gp.data.OrientationsTable.from_arrays( x=orientations['X'].values, y=orientations['Y'].values, z=orientations['Z'].values, G_x=orientations['G_x'].values, G_y=orientations['G_y'].values, G_z=orientations['G_z'].values, names=orientations['surface'].values.astype(str), name_id_map=surface_points_table.name_id_map # ! Make sure that ids and names are shared ) structural_frame: gp.data.StructuralFrame = gp.data.StructuralFrame.from_data_tables( surface_points=surface_points_table, orientations=orientations_table ) geo_model: gp.data.GeoModel = gp.create_geomodel( project_name='Moureze', extent=[0, 16, -0.5, 0.5, 0, 4.5], resolution=[321, 21, 91], structural_frame=structural_frame ) gp.set_section_grid( grid=geo_model.grid, section_dict={ 'section': ((0., 0.), (16., 0.), (321, 91)) }, ) # %% # We need an orientation per series/fault. The faults does not have # orientation so the easiest is to create an orientation from the surface # points availablle: # # %% f_names = ['f1', 'f2', 'f3'] for fn in f_names: element = geo_model.structural_frame.get_element_by_name(fn) new_orientations = gp.create_orientations_from_surface_points_coords( xyz_coords=element.surface_points.xyz ) gp.add_orientations( geo_model=geo_model, x=new_orientations.data['X'], y=new_orientations.data['Y'], z=new_orientations.data['Z'], pole_vector=new_orientations.grads, elements_names=fn, name_id_map=element.surface_points.name_id_map ) # %% # Now we can see how the data looks so far: # # %% gpv.plot_2d(geo_model) # %% # By default all surfaces belong to one unique series. # # %% geo_model.structural_frame # %% # We will need to separate with surface belong to each series: # # %% gp.map_stack_to_surfaces( gempy_model=geo_model, mapping_object={'Fault1': 'f1', 'Fault2': 'f2', 'Fault3': 'f3'} ) # %% # However if we want the faults to offset the “Default series”, they will # need to be more recent (higher on the pile). We can modify the order by: # # %% # %% # Lastly, so far we did not specify which series/faults are actula faults: # # %% gp.set_is_fault( frame=geo_model, fault_groups=['Fault1', 'Fault2', 'Fault3'] ) # %% # The default range is always the diagonal of the extent. Since in this # model data is very close we will need to reduce the range to 5-10% of # that value: # # %% geo_model.interpolation_options.kernel_options.range *= 0.2 # %% # Explanation of model characteristics and adjustments # This model has characteristics that make it difficult to get the right default values: # - It is large, and we want high resolution # - Some series have a large conditional number (i.e., the model input is not very stable) # To address these issues: # - Reduce the chunk size during evaluation to trade speed for memory # - Reduce the std of the error parameter in octree refinement, which evaluates fewer voxels but may leave some without refinement # Enable debugging options to help tune these parameters. # %% # Setting verbose and condition number options for debugging geo_model.interpolation_options.kernel_options.compute_condition_number = True # %% gp.compute_model( gempy_model=geo_model, engine_config=gp.data.GemPyEngineConfig( backend=gp.data.AvailableBackends.PYTORCH, dtype='float64' ), validate_serialization=True ) # %% gpv.plot_2d(geo_model, cell_number=[10], series_n=3, show_scalar=True) # %% gpv.plot_2d(geo_model, cell_number=[10], show_data=True) # %% gpv.plot_2d(geo_model, section_names=['section'], show_data=True) # %% # sphinx_gallery_thumbnail_number = 3 gpv.plot_3d(geo_model, kwargs_plot_structured_grid={'opacity': .2}) ================================================ FILE: examples/examples/real/Moureze.py ================================================ """ Geomodeling benchmark: the "Moureze"-Model ========================================== This model is part of a geomodeling benchmaring effort. More information (and, hopefully, publication) coming. """ import os # %% # These two lines are necessary only if gempy is not installed # Importing gempy import gempy as gp import gempy_viewer as gpv # Aux imports import numpy as np import pandas as pd from gempy_engine.config import AvailableBackends # %% # Loading surface points from repository: # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # With pandas we can do it directly from the web and with the right args # we can directly tidy the data in gempy style: # # %% data_path = os.path.abspath('../../data/input_data/Moureze') Moureze_points = pd.read_csv( filepath_or_buffer=data_path + '/Moureze_Points.csv', sep=';', names=['X', 'Y', 'Z', 'G_x', 'G_y', 'G_z', '_'], header=0, ) Sections_EW = pd.read_csv( filepath_or_buffer=data_path + '/Sections_EW.csv', sep=';', names=['X', 'Y', 'Z', 'ID', '_'], header=1 ).dropna() Sections_NS = pd.read_csv( filepath_or_buffer=data_path + '/Sections_NS.csv', sep=';', names=['X', 'Y', 'Z', 'ID', '_'], header=1 ).dropna() # %% # Extracting the orientatins: # # %% mask_surfpoints = Moureze_points['G_x'] < -9999 surface_points = Moureze_points[mask_surfpoints][::10] orientations = Moureze_points[~mask_surfpoints][::10] # %% # Giving an arbitrary value name to the surface # # %% surface_points['surface'] = '0' orientations['surface'] = '0' # %% surface_points.tail() # %% orientations.tail() # %% # Data initialization: # ~~~~~~~~~~~~~~~~~~~~ # # Suggested size of the axis-aligned modeling box: # # Origin: -5 -5 -200 # # Maximum: 305 405 -50 # # Suggested resolution: 2m (grid size 156 x 206 x 76) # # %% # Only using one orientation because otherwhise it gets a mess # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # %% # Number voxels np.array([156, 206, 76]).prod() # %% resolution_requ = [156, 206, 76] resolution = [77, 103, 38] resolution_low = [45, 51, 38] surface_points_table: gp.data.SurfacePointsTable = gp.data.SurfacePointsTable.from_arrays( x=surface_points['X'].values, y=surface_points['Y'].values, z=surface_points['Z'].values, names=surface_points['surface'].values.astype(str) ) orientations_table: gp.data.OrientationsTable = gp.data.OrientationsTable.from_arrays( x=orientations['X'].values, y=orientations['Y'].values, z=orientations['Z'].values, G_x=orientations['G_x'].values, G_y=orientations['G_y'].values, G_z=orientations['G_z'].values, names=orientations['surface'].values.astype(str), name_id_map=surface_points_table.name_id_map # ! Make sure that ids and names are shared ) structural_frame: gp.data.StructuralFrame = gp.data.StructuralFrame.from_data_tables( surface_points=surface_points_table, orientations=orientations_table ) geo_model: gp.data.GeoModel = gp.create_geomodel( project_name='Moureze', extent=[-5, 305, -5, 405, -200, -50], # resolution=resolution_low, refinement=5, structural_frame=structural_frame ) # %% # Now we can see how the data looks so far: # # %% gpv.plot_2d(geo_model, direction='y') # %% # The default range is always the diagonal of the extent. Since in this # model data is very close we will need to reduce the range to 5-10% of # that value: # # %% geo_model.interpolation_options.kernel_options.range *= 0.2 geo_model.interpolation_options.evaluation_options.verbose = True geo_model.interpolation_options.evaluation_options.octree_error_threshold = 1.5 geo_model.interpolation_options.evaluation_options.number_octree_levels_surface = 5 # %% gp.compute_model( gempy_model=geo_model, engine_config=gp.data.GemPyEngineConfig( use_gpu=False, dtype='float32', backend=AvailableBackends.PYTORCH ) ) # %% # Time # ~~~~ # # 300k voxels 3.5k points # ^^^^^^^^^^^^^^^^^^^^^^^ # # - Nvidia 2080: 500 ms ± 1.3 ms per loop (mean ± std. dev. of 7 runs, 1 # loop each), Memory 1 Gb # - CPU 14.2 s ± 82.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop # each), Memory: 1.3 Gb # # 2.4 M voxels, 3.5k points # ^^^^^^^^^^^^^^^^^^^^^^^^^ # # - CPU 2min 33s ± 216 ms per loop (mean ± std. dev. of 7 runs, 1 loop # each) Memory: 1.3 GB # - Nvidia 2080: 1.92 s ± 6.74 ms per loop (mean ± std. dev. of 7 runs, 1 # loop each) 1 Gb # # 2.4 M voxels, 3.5k points 3.5 k orientations # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # # - Nvidia 2080: 2.53 s ± 1.31 ms per loop (mean ± std. dev. of 7 runs, 1 # loop each) # # %% gpv.plot_2d(geo_model, cell_number='mid', series_n=0, show_scalar=True) # %% gpv.plot_2d(geo_model, cell_number='mid', show_data=True, direction='y') # %% # sphinx_gallery_thumbnail_number = 4 gpv.plot_3d(geo_model) ================================================ FILE: examples/examples/real/Perth_basin.py ================================================ """ A geological model of the Perth basin, Australia ============ """ import os # Importing GemPy import gempy as gp import gempy_viewer as gpv # Importing auxiliary libraries import matplotlib matplotlib.rcParams['figure.figsize'] = (20.0, 10.0) os.environ["aesara_FLAGS"] = "mode=FAST_RUN,device=cuda" # %% cwd = os.getcwd() if 'examples' not in cwd: data_path = os.getcwd() + '/examples' else: data_path = cwd + '/../..' # %% geo_model: gp.data.GeoModel = gp.create_geomodel( project_name='Perth_Basin', extent=[337000, 400000, 6640000, 6710000, -18000, 1000], refinement=6, importer_helper=gp.data.ImporterHelper( path_to_orientations=data_path + "/data/input_data/perth_basin/Paper_GU2F_sc_faults_topo_Foliations.csv", path_to_surface_points=data_path + "/data/input_data/perth_basin/Paper_GU2F_sc_faults_topo_Points.csv", ) ) # %% geo_model.structural_frame # %% del_surfaces = ['Cadda', 'Woodada_Kockatea', 'Cattamarra'] for s in del_surfaces: gp.remove_element_by_name(geo_model, s) geo_model.structural_frame # %% gp.map_stack_to_surfaces( gempy_model=geo_model, mapping_object={ "fault_Abrolhos_Transfer": ["Abrolhos_Transfer"], "fault_Coomallo": ["Coomallo"], "fault_Eneabba_South": ["Eneabba_South"], "fault_Hypo_fault_W": ["Hypo_fault_W"], "fault_Hypo_fault_E": ["Hypo_fault_E"], "fault_Urella_North": ["Urella_North"], "fault_Darling": ["Darling"], "fault_Urella_South": ["Urella_South"], "Sedimentary_Series": ['Cretaceous', 'Yarragadee', 'Eneabba', 'Lesueur', 'Permian'] } ) # %% # Select which series are faults # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # gp.set_is_fault( geo_model, fault_groups=[ "fault_Abrolhos_Transfer", "fault_Coomallo", "fault_Eneabba_South", "fault_Hypo_fault_W", "fault_Hypo_fault_E", "fault_Urella_North", "fault_Darling", "fault_Urella_South" ], ) # %% # gp.set_fault_relation(geo_model, fr) print(geo_model.structural_frame.fault_relations) # %% # %matplotlib inline gpv.plot_2d(geo_model, direction=['z']) # %% gp.set_topography_from_random(geo_model.grid) # %% gpv.plot_3d(geo_model) # %% gp.compute_model( gempy_model=geo_model, engine_config= gp.data.GemPyEngineConfig( backend=gp.data.AvailableBackends.PYTORCH, dtype="float64", ) ) # %% gpv.plot_2d(geo_model, cell_number="mid") # %% gpv.plot_2d(geo_model, cell_number="mid", series_n=-1, show_scalar=True) # %% gpv.plot_2d(geo_model, cell_number=[12], direction=["y"], show_data=True, show_topography=True) # %% # sphinx_gallery_thumbnail_number = 6 gpv.plot_3d(geo_model, show_topography=True) ================================================ FILE: examples/examples/real/README.rst ================================================ Real case studies ----------------- These models are built with real data ================================================ FILE: examples/examples/real/__init__.py ================================================ ================================================ FILE: examples/examples/real/mik.py ================================================ """ Unknown Model: Importing Borehole Data and Building a 3D Geological Model with GemPy ==================================================================================== """ # %% # In this section, we will explore how to take borehole or drillhole data and convert it into a format compatible with GemPy, # creating a 3D geological model. Borehole data is commonly collected in the mining, oil, and gas industries and contains # information about subsurface geological formations, including lithologies, stratigraphy, and the geometry of layers or faults. # # For this, we will rely on several helper functions from the `subsurface` package to extract borehole data and translate it # into a 3D structure that GemPy can use for modeling. # We will cover: # # - Downloading the borehole data # - Processing the data using `subsurface` # - Visualizing borehole locations and lithology data in 3D # # Downloading Borehole Data # """"""""""""""""""""""""" # The borehole data is hosted online, and we can use the `pooch` library to download it directly. `pooch` is a library for # fetching datasets from the internet. We will download a CSV file that contains the borehole information, including collar # positions, survey data, and lithology logs. # # Let's start by downloading the dataset and inspecting its content. # %% # sphinx_gallery_thumbnail_number = -1 # List of relative paths used during the workshop import numpy as np import pandas as pd import pooch import pyvista from subsurface.core.geological_formats import Collars, Survey, BoreholeSet from subsurface.core.geological_formats.boreholes._combine_trajectories import MergeOptions from subsurface.core.reader_helpers.readers_data import GenericReaderFilesHelper from subsurface.core.structs.base_structures.base_structures_enum import SpecialCellCase from subsurface.modules.reader.wells.read_borehole_interface import read_collar, read_survey, read_lith import subsurface as ss from subsurface.modules.visualization import to_pyvista_points, pv_plot, to_pyvista_line, init_plotter # Importing GemPy import gempy as gp # %% md # We use `pooch` to download the dataset into a temp file: # %% url = "https://raw.githubusercontent.com/softwareunderground/subsurface/main/tests/data/borehole/kim_ready.csv" known_hash = "a91445cb960526398e25d8c1d2ab3b3a32f7d35feaf33e18887629b242256ab6" # Your code here: raw_borehole_data_csv = pooch.retrieve(url, known_hash) # %% md # Now we can use `subsurface` function to help us reading csv files into pandas dataframes that the package can understand. # Since the combination of styles data is provided can highly vary from project to project, `subsurface` provides some *helpers* # functions to parse different combination of .csv # %% # Read the collar data from the CSV collar_df: pd.DataFrame = read_collar( GenericReaderFilesHelper( file_or_buffer=raw_borehole_data_csv, index_col="name", usecols=['x', 'y', 'altitude', "name"], columns_map={ "name": "id", # ? Index name is not mapped "X": "x", "Y": "y", "altitude": "z" } ) ) # Convert to UnstructuredData unstruc: ss.UnstructuredData = ss.UnstructuredData.from_array( vertex=collar_df[["x", "y", "z"]].values, cells=SpecialCellCase.POINTS ) points = ss.PointSet(data=unstruc) collars: Collars = Collars( ids=collar_df.index.to_list(), collar_loc=points ) # %% # Visualizing the Borehole Collars # """""""""""""""""""""""""""""""" # Once we have the borehole collars data, we can visualize them in 3D using the `pyvista` package. This gives us a good # overview of where the boreholes are located in space. In this visualization, each borehole will be represented by a point, # and we will label the boreholes using their IDs. well_mesh = to_pyvista_points(collars.collar_loc) # Plot the collar points pv_plot([well_mesh], image_2d=False) # %% # Reading Borehole Survey Data # """""""""""""""""""""""""""" # Borehole surveys give us information about the trajectory of each borehole, including its depth (measured depth). # The survey data allows us to compute the full 3D path of each wellbore. We will use the `read_survey` function from # `subsurface` to read this data. # %% # Create the Survey object survey_df: pd.DataFrame = read_survey( GenericReaderFilesHelper( file_or_buffer=raw_borehole_data_csv, index_col="name", usecols=["name", "md"] ) ) survey: Survey = Survey.from_df(survey_df) survey # %% # Reading Lithology Data # """""""""""""""""""""" # Next, we will read the lithology data. Lithology logs describe the rock type or geological unit encountered at different # depths within each borehole. Using `read_lith`, we will extract the lithology data, which includes the top and base depths # of each geological formation within the borehole, as well as the formation name. # %% # Your code here: lith = read_lith( GenericReaderFilesHelper( file_or_buffer=raw_borehole_data_csv, usecols=['name', 'top', 'base', 'formation'], columns_map={ 'top': 'top', 'base': 'base', 'formation': 'component lith', } ) ) # Update survey data with lithology information survey.update_survey_with_lith(lith) lith # %% # Creating a Borehole Set and Visualizing in 3D # """"""""""""""""""""""""""""""""""""""""""""" # Now that we have both the collar data and the lithology logs, we can combine them into a `BoreholeSet` object. This object # combines the collar, survey, and lithology data and allows us to create a 3D visualization of the borehole trajectories and # their associated lithologies. # # We will use `pyvista` to plot the borehole trajectories as lines, and we will color them according to their lithologies. # Additionally, we will label the collars for easy identification. # Combine collar and survey into a BoreholeSet borehole_set = BoreholeSet( collars=collars, survey=survey, merge_option=MergeOptions.INTERSECT ) # %% # Visualize boreholes with pyvista import matplotlib.pyplot as plt well_mesh = to_pyvista_line( line_set=borehole_set.combined_trajectory, active_scalar="lith_ids", radius=40 ) p = init_plotter() # Set colormap for lithologies boring_cmap = plt.get_cmap(name="viridis", lut=14) p.add_mesh(well_mesh, cmap=boring_cmap) collar_mesh = to_pyvista_points(collars.collar_loc) p.add_mesh(collar_mesh, render_points_as_spheres=True) p.add_point_labels( points=collars.collar_loc.points, labels=collars.ids, point_size=10, shape_opacity=0.5, font_size=12, bold=True ) p.show() # %% md # Structural Elements from Borehole Set # """"""""""""""""""""""""""""""""""""" # # Now that we have successfully imported and visualized the borehole data, we can move on to creating the geological # formations (or structural elements) based on the borehole data. Each lithological unit will be associated with a unique # identifier and a color, allowing us to distinguish between different formations when we visualize the model. # # GemPy offers the function `gempy.structural_elements_from_borehole_set`, which extracts these structural elements from # the borehole data and associates each one with a name, ID, and color. # %% # Initialize the color generator for formations colors_generator = gp.data.ColorsGenerator() # Define formations and colors elements = gp.structural_elements_from_borehole_set( borehole_set=borehole_set, elements_dict={ "Basement": { "id": -1, "color": next(colors_generator) }, "etchgoin": { "id": 1, "color": next(colors_generator) }, "macoma": { "id": 2, "color": next(colors_generator) }, "chanac": { "id": 3, "color": next(colors_generator) }, "mclure": { "id": 4, "color": next(colors_generator) }, "santa_margarita": { "id": 5, "color": next(colors_generator) }, "fruitvale": { "id": 6, "color": next(colors_generator) }, "round_mountain": { "id": 7, "color": next(colors_generator) }, "olcese": { "id": 8, "color": next(colors_generator) }, "freeman_jewett": { "id": 9, "color": next(colors_generator) }, "vedder": { "id": 10, "color": next(colors_generator) }, "eocene": { "id": 11, "color": next(colors_generator) }, "cretaceous": { "id": 12, "color": next(colors_generator) }, } ) # %% md # Initializing the GemPy Model # """""""""""""""""""""""""""" # After defining the geological formations, we need to initialize the GemPy model. The first step in this process is to # create a `GeoModel` object, which serves as the core container for all data related to the geological model. # # We will also define a **regular grid** to interpolate the geological layers. GemPy uses a meshless interpolator to # create geological models in 3D space, but grids are convenient for visualization and computation. # # GemPy supports various grid types, such as regular grids for visualization, custom grids, topographic grids, and more. # For this example, we will use a regular grid with a medium resolution. # %% import gempy_viewer as gpv # Create a structural group with the elements group = gp.data.StructuralGroup( name="Stratigraphic Pile", elements=elements, structural_relation=gp.data.StackRelationType.ERODE ) # Define the structural frame structural_frame = gp.data.StructuralFrame( structural_groups=[group], color_gen=colors_generator ) # %% md # Defining Model Extent and Grid Resolution # """"""""""""""""""""""""""""""""""""""""" # We now determine the extent of our model based on the surface points provided. This ensures that the grid covers the # entire area where the geological data points are located. Additionally, we set a grid resolution of 50x50x50 for a # balance between performance and model detail. all_surface_points_coords: gp.data.SurfacePointsTable = structural_frame.surface_points_copy extent_from_data = all_surface_points_coords.xyz.min(axis=0), all_surface_points_coords.xyz.max(axis=0) # Initialize GeoModel geo_model = gp.data.GeoModel.from_args( name="Stratigraphic Pile", structural_frame=structural_frame, grid=gp.data.Grid( extent=[extent_from_data[0][0], extent_from_data[1][0], extent_from_data[0][1], extent_from_data[1][1], extent_from_data[0][2], extent_from_data[1][2]], resolution=(50, 50, 50) ), interpolation_options=gp.data.InterpolationOptions.from_args( range=5, c_o=10, mesh_extraction=True, number_octree_levels=3, ), ) # %% md # 3D Visualization of the Model # """"""""""""""""""""""""""""" # After initializing the GeoModel, we can proceed to visualize it in 3D using GemPy's `plot_3d` function. This function # allows us to see the full 3D geological model with all the defined formations. gempy_plot = gpv.plot_3d( model=geo_model, kwargs_pyvista_bounds={ 'show_xlabels': False, 'show_ylabels': False, }, show=True, image=False ) # %% md # Adding Boreholes and Collars to the Visualization # """"""""""""""""""""""""""""""""""""""""""""""""" # To enhance the 3D model, we can combine the geological formations with the borehole trajectories and collar points that we # visualized earlier. This will give us a complete picture of the subsurface, showing both the lithological units and the borehole paths. sp_mesh: pyvista.PolyData = gempy_plot.surface_points_mesh pyvista_plotter = init_plotter() pyvista_plotter.show_bounds(all_edges=True) # Set limits for the units to visualize units_limit = [0, 13] pyvista_plotter.add_mesh( well_mesh.threshold(units_limit), cmap="tab20c", clim=units_limit ) # Add collar points pyvista_plotter.add_mesh( collar_mesh, point_size=10, render_points_as_spheres=True ) # Label the collars with their names pyvista_plotter.add_point_labels( points=collars.collar_loc.points, labels=collars.ids, point_size=10, shape_opacity=0.5, font_size=12, bold=True ) # Add surface points from the geological model pyvista_plotter.add_actor(gempy_plot.surface_points_actor) # Show the final 3D plot pyvista_plotter.show() # %% md # Step-by-Step Model Building # """"""""""""""""""""""""""" # When building a geological model, it's often better to proceed step by step, adding one surface at a time, rather # than trying to interpolate all formations at once. This allows for better control over the model and helps avoid # potential issues from noisy or irregular data. # # %% md # Adding Surfaces and Formations # """""""""""""""""""""""""""""" # In GemPy, surfaces mark the bottom of each geological unit. For our model, we will add the first two formations # along with the basement, which always needs to be defined. After this, we can visualize the surfaces in 2D. # %% group = gp.data.StructuralGroup( name="Stratigraphic Pile Top", elements=elements[:3], structural_relation=gp.data.StackRelationType.ERODE ) geo_model.structural_frame.structural_groups[0] = group # Visualize the surfaces in 2D g2d = gpv.plot_2d(geo_model) # %% g2d.fig # %% md # Minimum Input Data for Interpolation # """""""""""""""""""""""""""""""""""" # To interpolate the geological layers, GemPy requires at least: # # - Two surface points per geological unit # - One orientation measurement per series # # Let's add an orientation for one of the units. gp.add_orientations( x=[300000], y=[3930000], z=[0], elements_names=elements[0].name, pole_vector=np.array([0, 0, 1]), geo_model=geo_model ) # %% md # Model Computation # """"""""""""""""" # Now that we have the necessary surface points and orientations, we can compute the final geological model. The # `compute_model` function will take all the input data and perform the interpolation to generate the 3D subsurface structure. geo_model.interpolation_options # %% gp.compute_model(geo_model) # %% md # Final 3D Visualization # """""""""""""""""""""" # Let's take a look at the final model, combining the borehole data and geological formations in 3D. g3d = gpv.plot_3d(geo_model, show_lith=False, show=False) g3d.p.add_mesh(well_mesh) g3d.p.show() # %% md # Conclusion # """""""""" # # In this tutorial, we have demonstrated how to take borehole data and create a 3D geological model in GemPy. We explored # how to extract structural elements from borehole data, set up a regular grid for interpolation, and visualize the # resulting model in both 2D and 3D. # # GemPy's flexibility allows you to iteratively build models and refine your inputs for more accurate results, and it # integrates seamlessly with borehole data for subsurface geological modeling. # # For further reading and resources, check out: # # # Extra Resources # """"""""""""""" # Page: # https://www.gempy.org/ # # Paper: # https://www.gempy.org/theory # # Gitub: # https://github.com/cgre-aachen/gempy # # Further training and collaborations # """"""""""""""""""""""""""""""""""" # https://www.terranigma-solutions.com/ # # # ================================================ FILE: examples/integrations/README.rst ================================================ Integrations ============ Here there are examples showing GemPy working together with other libraries of geoscientific stack. This needs to grow! ================================================ FILE: examples/integrations/__init__.py ================================================ ================================================ FILE: examples/integrations/gempy_export_MOOSE.py ================================================ """ Export a geological model from GemPy to use in MOOSE ==================================================== """ # %% import gempy as gp # %% # Creating a geological model # --------------------------- # # The procedure of generating a geological model is presented in detail in # `Chapter # 1-1 `__ # of the GemPy tutorials, so it will only be briefly presented here # # %% # Initiate a model geo_model = gp.create_model('tutorial_moose_exp') data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/' # %% # Import data from CSV-files with setting the resolution and model extent gp.init_data(geo_model, [0, 2000., 0, 2000., 0, 2000.], [50, 50, 80], path_o=data_path + "/data/input_data/tut_chapter1/simple_fault_model_orientations.csv", path_i=data_path + "/data/input_data/tut_chapter1/simple_fault_model_points.csv", default_values=True) # %% # present the units and series geo_model.surfaces # %% # combine units in series and make two series, as the fault needs its own gp.map_stack_to_surfaces(geo_model, {"Fault_Series": 'Main_Fault', "Strat_Series": ('Sandstone_2', 'Siltstone', 'Shale', 'Sandstone_1', 'basement')}, remove_unused_series=True) # set the fault series to be fault object geo_model.set_is_fault(['Fault_Series'], change_color=False) # %% # check whether series were assigned correctly geo_model.surfaces # %% # Model generation # ---------------- # # After loading in the data, we set it up for interpolation and compute # the model. # # %% # set up interpolator gp.set_interpolator(geo_model, compile_aesara=True, aesara_optimizer='fast_compile', verbose=[]) # %% # compute the model gp.compute_model(geo_model, compute_mesh=False); # %% # have a look at the data and computed model gp.plot_3d(geo_model) # %% # Exporting the Model to MOOSE # ---------------------------- # # The voxel-model above already is the same as a model discretized in a # hexahedral grid, so my immediately be used as input in a simulation # tool, e.g. `MOOSE `__. For this, we need to # access to the unit IDs assigned to each voxel in GemPy. The array # containing these IDs is called ``lith_block``. # # %% ids = geo_model.solutions.lith_block print(ids) # %% # This array has the shape of ``(x,)`` and would be immediately useful, if # GemPy and the chosen simulation code would *populate* a grid in the same # way. Of course, however, that is not the case. This is why we have to # restructure the ``lith_block`` array, so it can be read correctly by # MOOSE. # # %% # model resolution nx, ny, nz = geo_model.grid.octree_grid.resolution # model extent xmin, xmax, ymin, ymax, zmin, zmax = geo_model.grid.octree_grid.extent # %% # These two parameters are important to, a) restructure ``lith_block``, # and b) write the input file for MOOSE correctly. For a), we need to # reshape ``lith_block`` again to its three dimensions and *re-flatten* it # in a *MOOSE-conform* way. # # %% # reshape to 3D array units = ids.reshape((nx, ny, nz)) # flatten MOOSE conform units = units.flatten('F') # %% # | The importance of ``nx, ny, nz`` is apparent from the cell above. But # what about ``xmin``, …, ``zmax``? # | A MOOSE input-file for mesh generation has the following syntax: # # .. code:: python # # [MeshGenerators] # [./gmg] # type = GeneratedMeshGenerator # dim = 3 # nx = 50 # ny = 50 # nz = 80 # xmin = 0.0 # xmax = 2000.0 # yim = 0.0 # ymax = 2000.0 # zmin = 0.0 # zmax = 2000.0 # block_id = '1 2 3 4 5 6' # block_name = 'Main_Fault Sandstone_2 Siltstone Shale Sandstone_1 basement' # [../] # # [./subdomains] # type = ElementSubdomainIDGenerator # input = gmg # subdomain_ids = ' ' # here you paste the transformed lith_block vector # [../] # [] # # [Mesh] # type = MeshGeneratorMesh # [] # # So these parameters are required inputs in the ``[MeshGenerators]`` # object in the MOOSE input file. ``GemPy`` has a method to directly # create such an input file, stored in ``gempy.utils.export.py``. # # The following cell shows how to call the method: # # %% # sphinx_gallery_thumbnail_path = '_static/GemPy_model_combined.png' import gempy_plugins.utils.export as export export.export_moose_input(geo_model, path='') # %% # This method automatically stores a file # ``geo_model_units_moose_input.i`` at the specified path. Either this # input file could be extended with parameters to directly run a # simulation, or it is used just for creating a mesh. In the latter case, # the next step would be, to run the compiled MOOSE executable witch the # optional flag ``--mesh-only``. # # E.g. with using the `PorousFlow # module `__: # # .. code:: bash # # $path_to_moose/moose/modules/porous_flow/porous_flow-opt -i pct_voxel_mesh.i --mesh-only # # How to compile MOOSE is described in their # `documentation `__. # # The now generated mesh with the name # ``geo_model_units_moose_input_in.e`` can be used as input for another # MOOSE input file, which contains the main simulation parameters. To call # the file with the grid, the following part has to be added in the MOOSE # simulation input file: # # .. code:: python # # [Mesh] # file = geo_model_units_moose_input_in.e # [] # # .. raw:: html # #
# # The final output of the simulation may also be such an ``.e``, which # can, for instance, be opened with # `paraview `__. A simulated temperature field # (purely conductive) of the created model would look like this: # # .. figure:: https://raw.githubusercontent.com/Japhiolite/a-Moose-and-you/master/imgs/GemPy_model_combined.png # :alt: gempy_temperature # # ================================================ FILE: examples/integrations/gempy_subsurface.py ================================================ """ GemPy - Subsurface Link ======================= """ import pooch import numpy as np import pandas as pd import subsurface as sb from subsurface.reader import read_netcdf data_url = "https://raw.githubusercontent.com/softwareunderground/subsurface/main" \ "/examples/tutorials/wells_unstructured.nc" data_hash = "206290db4e563e379361725349ebf4a02628f4700d361599aedff37fab9cf5b9" borehole_unstructured_data_file = pooch.retrieve(url=data_url, known_hash=data_hash) unstruct = read_netcdf.read_unstruct(borehole_unstructured_data_file) unstruct # %% element = sb.LineSet(unstruct) lines_mesh = sb.visualization.to_pyvista_line(element, radius=50) # Plot default LITH sb.visualization.pv_plot([lines_mesh]) # %% md # Findig the boreholes bases # -------------------------- # GemPy interpolates the bottom of a unit, therefore we need to be able to extract those points to be able tointerpolate them. # xarray, pandas and numpy are using the same type of memory representation what makes possible to use the same or at least similar methods to manipulate the data to our will. # Lets find the base points of each well: # %% # Creating references to the xarray.DataArray cells_attr = unstruct.data.cell_attrs cells = unstruct.data.cells vertex = unstruct.data.vertex # Find vertex points at the boundary of two units # Marking each vertex bool_prop_change = cells_attr.values[1:] != cells_attr.values[:-1] # Getting the index of the vertex args_prop_change = np.where(bool_prop_change)[0] # Getting the attr values at those points vals_prop_change = cells_attr[args_prop_change] vals_prop_change.to_pandas() # %% # Getting the vertex values at those points vertex_args_prop_change = cells[args_prop_change, 1] interface_points = vertex[vertex_args_prop_change] interface_points # %% # Creating a new UnstructuredData interf_us= sb.UnstructuredData.from_array(vertex=interface_points.values, cells="points", cells_attr=vals_prop_change.to_pandas()) interf_us # %% md # This new UnstructuredData object instead containing data that represent lines, contain point data at the bottom of each # unit. We can plot it very similar as before: element = sb.PointSet(interf_us) point_mesh = sb.visualization.to_pyvista_points(element) sb.visualization.pv_plot([lines_mesh, point_mesh]) # %% md # GemPy: Initialize model # ----------------------- # The first step to create a GemPy model is create a gempy. # %% import gempy as gp geo_model = gp.create_model("getting started") geo_model.set_regular_grid(extent=[275619, 323824, 3914125, 3961793, -3972.6, 313.922], resolution=[50,50,50]) gp.set_interpolator(geo_model, aesara_optimizer='fast_compile', verbose=[]) # %% md # Making a model step by step. # ---------------------------- # The temptation at this point is to bring all the points into gempy and just interpolate. However, often that strategy # results in ill posed problems due to noise or irregularities in the data. gempy has been design to being able to # iterate rapidly and therefore a much better workflow use to be creating the model step by step. # # To do that, lets define a function that we can pass the name of the formation and get the assotiated vertex. Grab from # the interf_us the XYZ coordinates of the first layer: # %% def get_interface_coord_from_surfaces(surface_names: list, verbose=False): df = pd.DataFrame(columns=["X", "Y", "Z", "surface"]) for e, surface_name in enumerate(surface_names): # The properties in subsurface start at 1 val_property = formations.index(surface_name) + 1 # Find the cells with the surface id args_from_first_surface = np.where(vals_prop_change == val_property)[0] if verbose: print(args_from_first_surface) # Find the vertex points_from_first_surface = interface_points[args_from_first_surface] if verbose: print(points_from_first_surface) # xarray.DataArray to pandas.DataFrame surface_pandas = points_from_first_surface.to_pandas() # Add formation column surface_pandas["surface"] = surface_name df = df.append(surface_pandas) return df.reset_index() # %% md # Surfaces # ++++++++ formations = ["topo", "etchegoin", "macoma", "chanac", "mclure", "santa_margarita", "fruitvale", "round_mountain", "olcese", "freeman_jewett", "vedder", "eocene", "cretaceous", "basement", "null"] # %% geo_model.add_features("Formations") one_formation_every = 3 geo_model.add_surfaces(formations[0:4*one_formation_every:one_formation_every]) geo_model.map_stack_to_surfaces({"Formations": ["etchegoin", "macoma", "chanac", "mclure"], "Default series": ["topo"]}, set_series=False) # %% gempy_surface_points = get_interface_coord_from_surfaces(formations[0:3*one_formation_every:one_formation_every]) # %% geo_model.set_surface_points(gempy_surface_points, update_surfaces=False) geo_model.update_to_interpolator() # %% md # Adding orientations # ------------------- # %% # find neighbours neighbours = gp.select_nearest_surfaces_points(geo_model, geo_model._surface_points.df, 2) # calculate all fault orientations gp.set_orientation_from_neighbours_all(geo_model, neighbours) # %% md # Using the flag to subsurface, the result of the interpolation will get stored in `subsurface` data objects. In the # future exporting to subsurface will be the default behaviour. # %% gp.compute_model(geo_model, to_subsurface=True) # %% p3d = gp.plot_3d(geo_model) # %% geo_model.solutions.s_regular_grid # %% geo_model.solutions.meshes ================================================ FILE: examples/integrations/geo_model_units_moose_input.i ================================================ [MeshGenerators] [./gmg] type = GeneratedMeshGenerator dim = 3 nx = 50 ny = 50 nz = 80 xmin = 0.0 xmax = 2000.0 yim = 0.0 ymax = 2000.0 zmin = 0.0 zmax = 2000.0 block_id = '1 2 3 4 5 6' block_name = 'Main_Fault Sandstone_2 Siltstone Shale Sandstone_1 basement' [../] [./subdomains] type = ElementSubdomainIDGenerator input = gmg subdomain_ids = '6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 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2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2' [../] [] [Mesh] type = MeshGeneratorMesh [] ================================================ FILE: examples/integrations/pflotran_perth_bassin.in ================================================ #Sample simulation of the flow in the Perth bassin modeled with gempy #Illustrate how to set up properly a PFLOTRAN simulation with the # meshes outputted by gempy #Note: formation properties are not realistic SIMULATION SIMULATION_TYPE SUBSURFACE PROCESS_MODELS SUBSURFACE_FLOW flow MODE RICHARDS / / END SUBSURFACE #=========================== discretization =================================== ################################################ #Import mesh from GemPy in ASCII format GRID TYPE UNSTRUCTURED ./perth_basin_mesh.ugi #path to the grid outputted by gempy END #for each stratigraphic formation, create a PFLOTRAN region #and assign the right gempy file REGION Permian FILE ./Permian.vs END REGION Lesueur FILE ./Lesueur.vs END REGION Eneabba FILE ./Eneabba.vs END REGION Cretaceous FILE ./Cretaceous.vs END REGION basement FILE ./basement.vs END REGION Yarragadee FILE ./Yarragadee.vs END #create a region corresponding to the topographic surface (z normal faces) REGION topo_surface FILE topography_surface.ss END ################################################ #Import mesh from GemPy in HDF5 format SKIP GRID TYPE UNSTRUCTURED ./perth_basin_mesh.h5 END REGION Permian FILE ./perth_basin_mesh.h5 END REGION Lesueur FILE ./perth_basin_mesh.h5 END REGION Eneabba FILE ./perth_basin_mesh.h5 END REGION Cretaceous FILE ./perth_basin_mesh.h5 END REGION basement FILE ./perth_basin_mesh.h5 END REGION Yarragadee FILE ./perth_basin_mesh.h5 END REGION Topography_surface FILE ./perth_basin_mesh.h5 END NOSKIP ################################################ #create region on the model boundary to further apply a boundary condition REGION xmin COORDINATES 337000. -1e20 -1e20 337000. 1e20 1e20 / END REGION xmax COORDINATES 400000. -1e20 -1e20 400000. 1e20 1e20 / END REGION all COORDINATES -1e20 -1e20 -1e20 1e20 1e20 1e20 / END #=========================== material properties ============================== #Define material property for each formation MATERIAL_PROPERTY Permian ID 1 SATURATION_FUNCTION default POROSITY 0.3 PERMEABILITY PERM_ISO 1e-12 / END MATERIAL_PROPERTY Lesueur ID 2 SATURATION_FUNCTION default POROSITY 0.3 PERMEABILITY PERM_ISO 5e-12 / END MATERIAL_PROPERTY Eneabba ID 3 SATURATION_FUNCTION default POROSITY 0.3 PERMEABILITY PERM_ISO 5e-14 / END MATERIAL_PROPERTY Cretaceous ID 4 SATURATION_FUNCTION default POROSITY 0.3 PERMEABILITY PERM_ISO 1e-13 / END MATERIAL_PROPERTY Yarragadee ID 5 SATURATION_FUNCTION default POROSITY 0.3 PERMEABILITY PERM_ISO 1e-12 / END MATERIAL_PROPERTY basement ID 6 SATURATION_FUNCTION default POROSITY 0.3 PERMEABILITY PERM_ISO 5e-13 / END #=========================== characteristic curves ============================ CHARACTERISTIC_CURVES default DEFAULT END #=========================== output options =================================== OUTPUT FORMAT HDF5 VARIABLES PERMEABILITY_X / NO_PRINT_INITIAL END #=========================== times ============================================ TIME FINAL_TIME 50 y INITIAL_TIMESTEP_SIZE 50 y MAXIMUM_TIMESTEP_SIZE 1e3 y END #=========================== flow conditions ================================== FLOW_CONDITION initial TYPE PRESSURE hydrostatic / DATUM 0 0 20000. GRADIENT PRESSURE -0.01 0. 0. / PRESSURE 101325. END FLOW_CONDITION rain TYPE FLUX NEUMANN / FLUX 1.757d-9 ! [m/s] END #=========================== condition couplers =============================== # initial condition INITIAL_CONDITION FLOW_CONDITION initial REGION all END BOUNDARY_CONDITION west FLOW_CONDITION initial REGION xmin END BOUNDARY_CONDITION east FLOW_CONDITION initial REGION xmax END #BOUNDARY_CONDITION topo #apply rain on the topography # FLOW_CONDITION rain # REGION topo_surface #END #=========================== stratigraphy couplers ============================ #Couple the PFLOTRAN region and material STRATA REGION Permian MATERIAL Permian END STRATA REGION Lesueur MATERIAL Lesueur END STRATA REGION Eneabba MATERIAL Eneabba END STRATA REGION Cretaceous MATERIAL Cretaceous END STRATA REGION Yarragadee MATERIAL Yarragadee END STRATA REGION basement MATERIAL basement END END_SUBSURFACE ================================================ FILE: examples/tutorials/README.rst ================================================ GemPy Tutorials =============== Here you will find a selection of examples showing what can you do and how you can do it in GemPy ================================================ FILE: examples/tutorials/__init__.py ================================================ ================================================ FILE: examples/tutorials/a_getting_started/README.rst ================================================ Getting Started --------------- Here you can see how to run a simple GemPy example. ================================================ FILE: examples/tutorials/a_getting_started/__init__.py ================================================ ================================================ FILE: examples/tutorials/a_getting_started/get_started.py ================================================ """ Getting Started =============== """ # %% # Welcome to our introductory notebook on GemPy! Here, we will cover the essentials of GemPy, introducing you to the core concepts of # geomodeling and demonstrating how you can leverage these to create your own geological models. We will guide you through building a # model from scratch, based on a conceptual 2D cross-section with boreholes. This simple example will highlight key workflow steps # and structural features that GemPy can model. # # Installation # """""""""""" # # https://docs.gempy.org/installation.html # # Setting Up Our Environment: Importing Libraries # """"""""""""""""""""""""""""""""""""""""""""""" # # To work with Python packages in our notebook, we need to import them first. Let's start with that. In the following cell, we will # import GemPy and its GemPy viewer module, which we will be using extensively. Additionally, we will use NumPy for various functions, # as well as Matplotlib and some of its specific modules/functions for visualizing our data and results in 2D and 3D. # %% # Set environmental variable DEFAULT_BACKEND = PYTORCH import os os.environ["DEFAULT_BACKEND"] = "PYTORCH" # Importing GemPy and viewer import gempy as gp import gempy_viewer as gpv # Auxiliary libraries import numpy as np import matplotlib.pyplot as plt import matplotlib.image as mpimg # sphinx_gallery_thumbnail_number = 11 # %% md # Main Classes and Objects in GemPy # """"""""""""""""""""""""""""""""" # GemPy uses Python classes and objects to store and manipulate the data used for modeling. This object-oriented approach helps make # the code more modular, reusable, and easier to understand. Each class represents a different aspect of the geological modeling process, # and objects are instances of these classes that contain specific data and methods to operate on that data. # # Here are the main data classes: # # - :obj:`gempy.core.data.GeoModel` # - :obj:`gempy.core.data.StructuralFrame` # - :obj:`gempy.core.data.StructuralGroup` # - :obj:`gempy.core.data.StructuralElement` # - :obj:`gempy.core.data.SurfacePointsTable` # - :obj:`gempy.core.data.OrientationsTable` # - :obj:`gempy.core.data.Grid` # # Setting Up a Model: Initializing Objects and Input Data # """"""""""""""""""""""""""""""""""""""""""""""""""""""" # Before computing a geological model, we need to set up the relevant objects and input data on which the model will be based. Data can # be input in various ways. One way is to start from scratch and manually input surface points and orientations. Another way is to import # existing data from a file, such as a CSV. Here, we will start with the first method to showcase the essential elements and rules needed # to build a model in GemPy. # # Starting a Model from Scratch Based on a Cross-Section # """""""""""""""""""""""""""""""""""""""""""""""""""""" # This first example will be based on a conceptual cross-section that includes data from three boreholes in a line. Let's start by loading # the image of this cross-section and plotting it using Matplotlib. For this example, we assume that the size of the image corresponds to # the real extent of the data. We will see that extent in the plot and also return it by looking at the shape of the image file: # %% img = mpimg.imread('boreholes_concept.png') plt.imshow(img, origin='upper', alpha=.8) img.shape[:2] # %% md # OK, so we will base our model creation on this cross-section. Let's get started. To initialize our model, we create a `gempy.Model` object. # This object will contain all other data structures and necessary functionality. Here’s what we will do: # # 1. **Name the Model**: Assign a name to our model. # # 2. **Define Extent**: Specify the extent in x, y, and z. The extent should make sense depending on your use case and should enclose all # relevant data in a representative space. For this example, we align the extent with the cross-section we imported: # # - **X** is parallel to the section. # - **Y** is perpendicular. Since we have no data along y, a narrow extent makes sense. We choose an extent of 400, defining it as -200 to 200, # placing the cross-section at y=0 (in the middle). # - **Z** representing depth, takes a negative value since we are modeling the subsurface. # # 3. **Initialize Structural Framework**: Set up a default structural framework. # # 4. **Define either resolution or refinement**: In GemPy 3, you can use either regular grids or octrees. # - **Regular grids**: Define a resolution (and refinement=None). A medium resolution of 50x50x50, for example, results in 125,000 voxels. # Model voxels are prisms, not cubes, so resolution can differ from extent. Avoid exceeding 100 cells in each direction (1,000,000 voxels) # to prevent high computational costs. # - **Octrees**: Define a level of refinement (and resolution=None). Higher refinement levels increase computational costs. # # .. admonition:: Note on choice of modeling grids # # Which type of grid is used depends on the use case. Note that as of the current version of GemPy 3, # the rendering of surfaces uses dual-contouring, which is based on octrees. So even if you choose regular grids, octree-based computing will # be executed additionally in order to render the surfaces in 3D. # %% geo_model = gp.create_geomodel( project_name='Model1', extent=[0, 780, -200, 200, -582, 0], resolution=(50, 50, 50), # refinement=4, # We will use octrees structural_frame=gp.data.StructuralFrame.initialize_default_structure() ) # %% md # The `geo_model` Object # """""""""""""""""""""" # The :obj:`gempy.core.data.GeoModel` object we just initialized contains all the essential information for constructing our model, # such as the parameters defined above, # and the input data we will introduce further below. You can access different types of information by accessing the attributes. For instance, you # can retrieve the coordinates of our modeling grid as follows: # %% geo_model.grid # %% md # The `geo_model` object also contains the structural frame of our model, i.e., information about our main structural groups (also referred to as # series or stacks in our model), and their sequential and geological relationships with one another. Each group can contain several elements, # which can be surfaces representing the **bottom** interfaces of lithological units or fault planes. Each structural group also has a relation # type that defines the relation of the structural elements to others. The relation type is 'erode' by default but can also be 'onlap' or 'fault' # (more about this later). The structural frame also contains information about fault relationships, i.e., which elements are affected by which # faults. Let's look at our default structural frame: # %% geo_model.structural_frame # %% md # As you can see, by default, there is one element called 'surface 1' and no faults. However, by default, GemPy actually not only starts off # with this 'surface 1' but also a 'basement' unit which is always present. We can see this using the following function: # %% geo_model.structural_frame.structural_elements # %% md # We can also rename our structural elements and assign colors as needed. This will later become relevant for the legend in our plots of the # data and generated model. Let's assume we already know our uppermost surface, 'surface 1,' is a limestone unit. Let's also ensure it is # represented by the same color as displayed in the cross-section. For this, we input hex color codes. # %% geo_model.structural_frame.structural_elements[0].color = '#33ABFF' # Set 'surface 1' color to blue. geo_model.structural_frame.structural_elements[1].color = '#570987' # Set basement color to purple. geo_model.structural_frame.structural_elements[0].name = 'Limestone' # Renaming 'surface 1' to 'Limestone'. geo_model.structural_frame.structural_elements[0] # %% geo_model.structural_frame.structural_groups[0].name = 'Deposit. Series' # %% md # Manually Inputting Data # """"""""""""""""""""""" # Now that the `geo_model` object has been created and our first structural element renamed, we can start inputting data by reading it from # the cross-section. We start with location points that represent the boundaries between two lithological units. Let's look at the cross-section # again. This time we use a `gempy_viewer` function to create a plot that can include the cross-section image, as well as data in the object # `geo_model` - which is for now empty but we will start adding data next. Let's also add a grid to better read the location of points. # # The image cross-section indicates that there are three lithological boundaries, with one dot for each boundary shown in the first two boreholes. # The third borehole doesn't go as deep and only provides one dot, i.e., information about the first boundary. These dots represent the location # of a boundary surface in depth. Remember also that in GemPy, each surface represents the bottom of the unit it is assigned to. # %% p2d = gpv.plot_2d(geo_model, show=False) p2d.axes[0].imshow(img, origin='upper', alpha=.8, extent=(0, 780, -582, 0)) # Enable grid with minor ticks p2d.axes[0].grid(which='both') # Enable both major and minor grids p2d.axes[0].minorticks_on() # Enable minor ticks # Customize the appearance of the grid if needed p2d.axes[0].grid(which='major', linestyle='--', linewidth='0.8', color='gray') p2d.axes[0].grid(which='minor', linestyle=':', linewidth='0.4', color='gray') plt.show() # %% md # Looking at the plot, we can read the location of the surface points and start adding them to our `geo_model` object. For now, let's focus on # only the uppermost layer, the limestone, with its bottom boundary represented by the blue dots. Using the function `add_surface_points`, # we can start adding the positional points to our `geo_model` object. Let's start with only one. From looking at the plot, the first blue # point seems to be located at approximately 250 in the x direction and around 95 meters in depth. Since we assume the section to be at y=0, # we can leave y as 0. We can input that as follows: # %% gp.add_surface_points( geo_model=geo_model, x=[225], y=[0], z=[-95], elements_names=['Limestone'] ) # %% md # Now let's plot the data with the section and see how we did: # %% p2d = gpv.plot_2d(geo_model, show=False) p2d.axes[0].imshow(img, origin='upper', alpha=.8, extent=(0, 780, -582, 0)) # Enable grid with minor ticks p2d.axes[0].grid(which='both') # Enable both major and minor grids p2d.axes[0].minorticks_on() # Enable minor ticks # Customize the appearance of the grid if needed p2d.axes[0].grid(which='major', linestyle='--', linewidth='0.8', color='gray') p2d.axes[0].grid(which='minor', linestyle=':', linewidth='0.4', color='gray') plt.show() # %% md # Great! As you can see, the data point we entered sits right on top of the blue dot on the borehole. Now let's do the same for the other # borehole dots. Conveniently, we can use the same function to add several points at a time: # %% gp.add_surface_points( geo_model=geo_model, x=[460, 617], y=[0, 0], z=[-100, -10], elements_names=['Limestone', 'Limestone'] ) p2d = gpv.plot_2d(geo_model, show=False) p2d.axes[0].imshow(img, origin='upper', alpha=.8, extent=(0, 780, -582, 0)) # Enable grid with minor ticks p2d.axes[0].grid(which='both') # Enable both major and minor grids p2d.axes[0].minorticks_on() # Enable minor ticks # Customize the appearance of the grid if needed p2d.axes[0].grid(which='major', linestyle='--', linewidth='0.8', color='gray') p2d.axes[0].grid(which='minor', linestyle=':', linewidth='0.4', color='gray') plt.show() # %% md # Minimum Data for Model Computation # """""""""""""""""""""""""""""""""" # Ok, good! Now we have added the position of the bottom of this top layer for each borehole. But is this enough to compute our first layer? # Well, no. GemPy's approach is based on an implicit interpolation method that requires the following minimum data: # # - **Two surface points** for at least one surface in a structural group/series # - **One orientation** per structural group/series # # Thanks to GemPy's global interpolation approach, once you have one surface defined by two surface points and an orientation in a structural # group, you can add more surfaces (in the same group) with the minimum of one surface point, as it will now take its orientation information # from the other surface. # # So, we are missing an orientation. Let's go ahead and add one. It seems that we can assume a horizontal orientation between the first two # surface points (apparent, as we have no information about the y-direction). Let's add a corresponding orientation using the function # `add_orientations`: # %% gp.add_orientations( geo_model=geo_model, x=[350], y=[0], z=[-120], elements_names=['Limestone'], pole_vector=[np.array([0, 0, 1])] ) p2d = gpv.plot_2d(geo_model, show=False) p2d.axes[0].imshow(img, origin='upper', alpha=.8, extent=(0, 780, -582, 0)) # Enable grid with minor ticks p2d.axes[0].grid(which='both') # Enable both major and minor grids p2d.axes[0].minorticks_on() # Enable minor ticks # Customize the appearance of the grid if needed p2d.axes[0].grid(which='major', linestyle='--', linewidth='0.8', color='gray') p2d.axes[0].grid(which='minor', linestyle=':', linewidth='0.4', color='gray') plt.show() # %% md # Alright, now we actually have sufficient input data to compute the first version of our model. Let's go ahead and do that using `compute_model`: # %% geo_model.update_transform(gp.data.GlobalAnisotropy.NONE) # %% gp.compute_model(geo_model, engine_config=gp.data.GemPyEngineConfig()) # %% md # Now, the model has been computed and is ready to be visualized. Let's see what it looks like in a 2D section: # %% # 2D visualization: gpv.plot_2d(geo_model, cell_number='mid') # %% md # We can also plot it together with our cross-section image. By using transparency on the cross-section image, we can overlay it over the 2D model # visualization. # %% p2d = gpv.plot_2d(geo_model, show=False) p2d.axes[0].imshow(img, origin='upper', alpha=.8, extent=(0, 780, -582, 0)) # Enable grid with minor ticks p2d.axes[0].grid(which='both') # Enable both major and minor grids p2d.axes[0].minorticks_on() # Enable minor ticks # Customize the appearance of the grid if needed p2d.axes[0].grid(which='major', linestyle='--', linewidth='0.8', color='gray') p2d.axes[0].grid(which='minor', linestyle=':', linewidth='0.4', color='gray') plt.show() # %% md # You can see how the computed interface runs through the points we defined and is furthermore determined by the one orientation we placed. # Now, let's look at our 3D model: # %% # 3D visualization: gpv.plot_3d(geo_model, show_surfaces=True, image=False) # %% md # Very good, we have successfully computed the first iteration of our model including one lithological interface. You could now go ahead and # fine-tune the model by adding further points or orientations. For now, let's continue. # # About Scalar Fields # """"""""""""""""""" # GemPy's global interpolation approach uses scalar fields to compute models. Each structural group or series has its own scalar field, which # is defined by the data input to all of its elements and at least the minimum data mentioned above. Given that alone, a scalar field will # already be created "globally", i.e., for the full extent of the modeling space. The defined surface follows one value along that field as # an isosurface. A separate structural element will follow a different isosurface, and once you input additional points and orientations, # the scalar field will be altered accordingly. # # As mentioned, each structural group has its own scalar field. These can be combined to achieve more complex structures and relationships, # as we will see later. # # Let's take a look at the current scalar field for our current group by plotting it in 2D. Keep it in mind as we go ahead and add additional # elements and data. # %% p2d = gpv.plot_2d( model=geo_model, series_n=0, show_data=True, show_scalar=True, show_lith=False ) plt.show() # %% md # Adding a Second Lithological Unit # """"""""""""""""""""""""""""""""" # To add another unit to our model, we can define it as another structural element and then append it to our `geo_model` object. We do this # for the second unit in the following steps. See how we can already give it a name (let's assume this is a siltstone now), a color corresponding # to the dot colors in the cross-section, as well as define surface points and orientations. By appending it to `structural_groups[0]`, we are # adding it to our first (and currently only) structural group/series, i.e., it will be in the same stack as our limestone. # %% element2 = gp.data.StructuralElement( name='Siltstone', color='#FFA833', # color=next(geo_model.structural_frame.color_generator), surface_points=gp.data.SurfacePointsTable.from_arrays( x=np.array([460]), y=np.array([0]), z=np.array([-280]), names='Siltstone' ), orientations=gp.data.OrientationsTable.initialize_empty() ) geo_model.structural_frame.structural_groups[0].append_element(element2) # %% md # Now, we can see that this siltstone unit is part of our structural frame. Note below, that it has by default been added below the limestone. # The order of structural elements within one group only affects the default color assigned. We recommend being consistent in the way you # choose to order them, and to order them in accordance with geological age. The order of structural groups actually represents geological # time relationships, with groups at the top being the youngest and lower ones being older. This, together with their `StackRelationType`, # decides how they affect each other via their individual scalar fields, as we will see later. # %% geo_model.structural_frame # %% gp.compute_model(geo_model, engine_config=gp.data.GemPyEngineConfig()) # %% p2d = gpv.plot_2d(geo_model, cell='mid', show=False) p2d.axes[0].imshow(img, origin='upper', alpha=.8, extent=(0, 780, -582, 0)) # Enable grid with minor ticks p2d.axes[0].grid(which='both') # Enable both major and minor grids p2d.axes[0].minorticks_on() # Enable minor ticks # Customize the appearance of the grid if needed p2d.axes[0].grid(which='major', linestyle='--', linewidth='0.8', color='gray') p2d.axes[0].grid(which='minor', linestyle=':', linewidth='0.4', color='gray') plt.show() # %% md # The 2D visualization of our updated model now shows that the next surface follows the respective point we added. Based on the implicit # modeling approach GemPy uses, given this sparse information we have put in, the siltstone bottom interface otherwise follows the course # and orientation of the limestone we defined earlier. This is due to the scalar field which was defined by the data input for the structural # group as a whole. In our 2D plot, we can see that this fits quite well with the first borehole, too. Still, let's add the remaining point, # and while we are at it, let's also add the third lithological unit marked in green. # %% # First, we add the two missing points to the Siltstone gp.add_surface_points( geo_model=geo_model, x=[225], y=[0], z=[-270], elements_names=['Siltstone'] ) element3 = gp.data.StructuralElement( name='Sandstone', color='#72A533', # next(geo_model.structural_frame.color_generator), surface_points=gp.data.SurfacePointsTable.from_arrays( x=np.array([225, 460]), y=np.array([0, 0]), z=np.array([-436, -441]), names='Sandstone' ), orientations=gp.data.OrientationsTable.initialize_empty() ) geo_model.structural_frame.structural_groups[0].append_element(element3) # %% gp.compute_model(geo_model) # %% p2d = gpv.plot_2d(geo_model, show=False) p2d.axes[0].imshow(img, origin='upper', alpha=.8, extent=(0, 780, -582, 0)) # Enable grid with minor ticks p2d.axes[0].grid(which='both') # Enable both major and minor grids p2d.axes[0].minorticks_on() # Enable minor ticks # Customize the appearance of the grid if needed p2d.axes[0].grid(which='major', linestyle='--', linewidth='0.8', color='gray') p2d.axes[0].grid(which='minor', linestyle=':', linewidth='0.4', color='gray') plt.show() # %% # 3D visualization: gpv.plot_3d(geo_model, show_surfaces=True, image=False) # %% md # Discontinuities: Combining Scalar Fields # """""""""""""""""""""""""""""""""""""""" # # Now that we have created all the lithological units and added all the surface points we got from the boreholes, we have created a very # simple geological model. However, geological scenarios are usually more complex. In GemPy, you can not only combine numerous surface # points and orientations to create elaborate structures but also create various structural groups that affect each other through combinations # of their scalar fields. In the following part, we will look at the right side of our cross-section, where we only have limited data, and # see how we can add a new structural group to create various types of discontinuities, and with that, possibly even meaningful alternative # model hypotheses. # # So, let's define another structural element that will serve to showcase the different types of discontinuities we can implement in GemPy: # %% element_discont = gp.data.StructuralElement( name='Discont_Surface', color='#990000', # next(geo_model.structural_frame.color_generator), surface_points=gp.data.SurfacePointsTable.from_arrays( x=np.array([550, 650]), y=np.array([0, 0]), z=np.array([-30, -200]), names='Discont_Surface' ), orientations=gp.data.OrientationsTable.from_arrays( x=np.array([600]), y=np.array([0]), z=np.array([-100]), G_x=np.array([.3]), G_y=np.array([0]), G_z=np.array([.3]), names='Discont_Surface' ) ) # %% md # To place the discontinuity element in a separate structural group, we need to create one. This is what we do next. Note that we directly # add the element to the group as we create it, and we define the group's structural relation type as the default 'erode'. We can then insert # the group into the structural frame. # %% group_discont = gp.data.StructuralGroup( name='Discontinuity', elements=[element_discont], structural_relation=gp.data.StackRelationType.ERODE, ) # Insert the fault group into the structural frame: geo_model.structural_frame.insert_group(0, group_discont) # %% md # Let's take a quick look at the state of our structural frame. # %% geo_model.structural_frame # %% md # We can now see the two different structural groups: the default one containing the deposition series, and the group containing the # discontinuity. Let's go ahead and compute the model. Once the model has been computed, we can plot the scalar fields of both structural # groups independently. # %% gp.compute_model(geo_model, engine_config=gp.data.GemPyEngineConfig()) # %% p2d = gpv.plot_2d( model=geo_model, series_n=1, # This setting will now plot the scalar field used for the fault show_data=True, show_scalar=True, show_lith=False ) plt.show() # %% p2d = gpv.plot_2d( model=geo_model, series_n=0, # This setting will plot the scalar field used for the discontinuity. show_data=True, show_scalar=True, show_lith=False ) plt.show() # %% md # We now have two very different scalar fields. Note how they are each defined by the input data assigned to their respective structural # elements. Multiple scalar fields in GemPy influence each other depending on (1) their order and (2) their `StackRelationType`. The latter # defines how a younger (upper) structural group will relate to the older (lower) structural groups and possibly affect their scalar field. # # The parameter `StackRelationType` can take the following values: # # - `BASEMENT`: Treats all lower groups as the basement. # - `ERODE`: Defines erosive contact/unconformity. # - `ONLAP`: Defines the younger group to be onlapping onto the older groups. # - `FAULT`: Defines the group to be a fault. # # We will now take a look at each of these relation types except for the basement type. # %% md # Erosive Contact # """"""""""""""" # For this, we don't have to change anything now, as we already set the `StackRelationType` to be `ERODE`. If we now plot it, we will see # how this younger structural group erodes all older elements and basically "cuts them out" in our model. # %% p2d = gpv.plot_2d(geo_model, show=False) p2d.axes[0].imshow(img, origin='upper', alpha=.8, extent=(0, 780, -582, 0)) # Enable grid with minor ticks p2d.axes[0].grid(which='both') # Enable both major and minor grids p2d.axes[0].minorticks_on() # Enable minor ticks # Customize the appearance of the grid if needed p2d.axes[0].grid(which='major', linestyle='--', linewidth='0.8', color='gray') p2d.axes[0].grid(which='minor', linestyle=':', linewidth='0.4', color='gray') plt.show() # %% # 3D visualization: gpv.plot_3d(geo_model, show_surfaces=True, image=False, show_lith=False) # %% md # We can see how all units of the depositional series stop at the contact with the new discontinuity group. However, this doesn't look # quite right, and it in particular doesn't fit the surface point that was observed in the third borehole. So let's try another relation type. # # Onlapping # """"""""" # Let's change the relation type from `ERODE` to `ONLAP` to achieve a different type of discontinuity and then plot it. # %% geo_model.structural_frame.structural_groups[0].structural_relation = gp.data.StackRelationType.ONLAP # %% gp.compute_model(geo_model, engine_config=gp.data.GemPyEngineConfig()) # %% p2d = gpv.plot_2d(geo_model, show=False) p2d.axes[0].imshow(img, origin='upper', alpha=.8, extent=(0, 780, -582, 0)) # Enable grid with minor ticks p2d.axes[0].grid(which='both') # Enable both major and minor grids p2d.axes[0].minorticks_on() # Enable minor ticks # Customize the appearance of the grid if needed p2d.axes[0].grid(which='major', linestyle='--', linewidth='0.8', color='gray') p2d.axes[0].grid(which='minor', linestyle=':', linewidth='0.4', color='gray') plt.show() # %% # 3D visualization: gpv.plot_3d(geo_model, show_surfaces=True, image=False, show_lith=False) # %% md # Now the unit defined as part of the discontinuity group is onlapping onto the uppermost surface of the default group and ends there. # This also doesn't really make sense considering the data given, so let's try the last relation type. # # Faults # """""" # Let's change the relation type to `FAULT` and plot the results. For a fault, we also need to make use of the function `set_is_fault`. # %% geo_model.structural_frame.structural_groups[0].structural_relation = gp.data.StackRelationType.FAULT # %% gp.set_is_fault( frame=geo_model.structural_frame, fault_groups=['Discontinuity'] ) # %% md # See that the fault relations field in the structural frame now indicates that the fault affects the default formation, i.e., offsets it. # Let's compute our model including the fault and see what that looks like. # %% gp.compute_model(geo_model, engine_config=gp.data.GemPyEngineConfig()) # %% p2d = gpv.plot_2d(geo_model, show=False) p2d.axes[0].imshow(img, origin='upper', alpha=.8, extent=(0, 780, -582, 0)) # Enable grid with minor ticks p2d.axes[0].grid(which='both') # Enable both major and minor grids p2d.axes[0].minorticks_on() # Enable minor ticks # Customize the appearance of the grid if needed p2d.axes[0].grid(which='major', linestyle='--', linewidth='0.8', color='gray') p2d.axes[0].grid(which='minor', linestyle=':', linewidth='0.4', color='gray') plt.show() # %% # 3D visualization: gpv.plot_3d(geo_model, show_surfaces=True, image=False, show_lith=False) # %% md # In the 2D and 3D visualizations, we can now see how the insertion of a fault has created a viable alternative hypothesis that fits the # original data in the cross-section. Instead of a larger syncline structure and upward-bending of the whole depositional series, we can # now explain the shallower depth of the limestone in the third borehole by the effects of a reverse fault. In this model realization, # all lithological units are oriented near-horizontal but are offset by the fault. Note that in GemPy, the degree of offset is defined # by the location of the surface points on each side. If there is no data on one side of a fault, a very large offset will be assumed. # # Note also that by implementing a fault, the scalar field of the depositional series has been affected by the offset. # %% p2d = gpv.plot_2d( model=geo_model, series_n=1, # This will plot the scalar field used for the fault show_data=True, show_scalar=True, show_lith=False ) plt.show() # %% md # Topography and Geological Maps # """""""""""""""""""""""""""""" # In GemPy, we can add more grid types for different purposes, such as to add topography to our model. In this following section, # we will exemplify this by creating a random topography grid which allows us to intersect the surfaces as well as compute a high-resolution # geological map. GemPy has a built-in function to generate random topography. After executing it, a topography grid will be added to the # `geo_model`. It can be directly visualized in 2D and 3D. # %% # GemPy offers built-in tools to manage topographic data through gdal. # For demonstration, we'll create a random topography: gp.set_topography_from_random( grid=geo_model.grid, fractal_dimension=1.9, d_z=np.array([-150, 0]), topography_resolution=np.array([200, 200]) ) # %% gpv.plot_2d(geo_model, show_topography=True) # %% gpv.plot_3d(geo_model, show_surfaces=True, image=False, show_topography=True, show_lith=False) # %% md # If we now also re-compute our geological model, the generated topography grid will display the lithological units that intersect it, # i.e., which outcrop at the surface. We can therefore display a geological map based on our topography and the underlying 3D geological model. # To plot a top-down view of this map, you can pass the arguments `section_names=['topography']` and `show_topography=True` in the plotting # function as shown below. # %% gp.compute_model(geo_model) # %% gpv.plot_2d(geo_model, section_names=['topography'], show_topography=True) # %% gpv.plot_3d(geo_model, show_surfaces=True, image=False, show_topography=True) # %% md # We can now see how the topography displays the color of the lithologies outcropping at the surface, together with topographical contour lines. # # While this topography is random, GemPy also has the capability to include real topography files and arrays via the functions # `set_topography_from_file` and `set_topography_from_arrays`. # %% md # Extracting Model Solutions # """""""""""""""""""""""""" # Once you have built a model, you might not only want to visualize it, but also further analyze it or export it for further utilization. # For this, it is good to know that the solutions from modeling are stored in `geo_model.solutions` and can be returned from there. This # includes the following outputs in particular: # - `geo_model.solutions.dc_meshes`: A list of the surface meshes in the model with the location of vertices and edges for each. # - `geo_model.solutions.raw_arrays`: An object containing numerous arrays that define various parts of the model. Of particular importance # are the lithology block (`lith_block`), the fault block (`fault_block`), and the scalar field matrix (`scalar_field_matrix`). # # Mesh Solutions # """""""""""""" # Let's take a quick look at how we can return some key information from `geo_model.solutions`. Starting with meshes, we can see that the list # `dc_meshes` can be indexed to return specific meshes and their respective vertices or edges. Please note that the order will be the same as # in our `structural_frame`, i.e., the index `[0]` will return the first and top surface, in our case, the discontinuity surface. # %% vertices_0 = geo_model.solutions.dc_meshes[0].vertices edges_0 = geo_model.solutions.dc_meshes[0].edges print(type(vertices_0), vertices_0, edges_0) # %% md # We can see that the vertices for this mesh were returned as a Numpy array with values for *x*, *y*, and *z* positions for each vertex. # However, the values don't correspond with our model extent. That is because they have been transformed in GemPy. To return the values # corresponding to the original coordinate system, we can invert this transformation as follows: # %% geo_model.input_transform.apply_inverse(vertices_0) # %% md # Lithology Block # """"""""""""""" # The lithology block is an array that, for a given model realization/solution, returns the ID of the lithology for each voxel. Note below # that the `lith_block` first returns all values in the shape of one row. You might need to reshape it as shown below. For a regular grid, # you can reshape it using the resolution used in `geo_model`. # # %% lith_block = geo_model.solutions.raw_arrays.lith_block print(lith_block.shape, lith_block) # %% lith_block = lith_block.reshape(50, 50, 50) print(lith_block.shape, lith_block) # %% md # Grid Values # """"""""""" # Apart from these solutions, you might also need to return grid values. You can access the values for each grid in your `geo_model` # object via `geo_model.grid` as shown below. # %% geo_model.grid.regular_grid.values # %% geo_model.grid.topography.values ================================================ FILE: examples/tutorials/ch1_fundamentals/README.rst ================================================ Chapter 1: GemPy fundamentals ----------------------------- These examples show the basic functionality to create geometries. ================================================ FILE: examples/tutorials/ch1_fundamentals/__init__.py ================================================ ================================================ FILE: examples/tutorials/ch1_fundamentals/ch1_1_basics.py ================================================ """ 1.1 -Basics of geological modeling with GemPy ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ """ import numpy as np # %% # Importing Necessary Libraries # """""""""""""""""""""""""""""" import gempy as gp import gempy_viewer as gpv # %% # Importing and Defining Input Data # """"""""""""""""""""""""""""""""" # :obj:`gempy.core.data.GeoModel` # GemPy uses Python objects to store the data that builds the geological model. The main data classes include: # # - :obj:`gempy.core.data.GeoModel` # - :obj:`gempy.core.data.StructuralFrame` # - :obj:`gempy.core.data.StructuralGroup` # - :obj:`gempy.core.data.StructuralElement` # - :obj:`gempy.core.data.SurfacePointsTable` # - :obj:`gempy.core.data.OrientationsTable` # - :obj:`gempy.core.data.Grid` # # You can also create data from raw CSV files (comma-separated values). This could be useful if you are exporting model data # from a different program or creating it in a spreadsheet software like Microsoft Excel or LibreOffice Calc. # # In this tutorial, we'll use CSV files to generate input data. You can find these example files in the `gempy data` # repository on GitHub. The data consists of x, y, and z positional values for all surface points and orientation # measurements. Additional data includes poles, azimuth and polarity (or the gradient components). Surface points are # assigned a formation, which can be a lithological unit (like "Sandstone") or a structural feature (like "Main Fault"). # # It's important to note that, in GemPy, interface position points mark the **bottom** of a layer. If you need points # to represent the top of a formation (for example, when modeling an intrusion), you can define an inverted orientation measurement. # # While generating data from CSV files, we also need to define the model's real extent in x, y, and z. This extent # defines the area used for interpolation and many of the plotting functions. We also set a resolution to establish a # regular grid right away. This resolution will dictate the number of voxels used during modeling. We're using a medium # resolution of 50x50x50 here, which results in 125,000 voxels. The model extent should enclose all relevant data in a # representative space. As our model voxels are prisms rather than cubes, the resolution can differ from the extent. # However, it is recommended to avoid going beyond 100 cells in each direction (1,000,000 voxels) to prevent excessive # computational costs. # # # %% data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/' geo_model: gp.data.GeoModel = gp.create_geomodel( project_name='Tutorial_ch1_1_Basics', extent=[0, 2000, 0, 2000, 0, 750], refinement=6, # * Here we define the number of octree levels. If octree levels are defined, the resolution is ignored. importer_helper=gp.data.ImporterHelper( path_to_orientations=data_path + "/data/input_data/getting_started/simple_fault_model_orientations.csv", path_to_surface_points=data_path + "/data/input_data/getting_started/simple_fault_model_points.csv", hash_surface_points="4cdd54cd510cf345a583610585f2206a2936a05faaae05595b61febfc0191563", hash_orientations="7ba1de060fc8df668d411d0207a326bc94a6cdca9f5fe2ed511fd4db6b3f3526" ) ) # %% # .. admonition:: New in GemPy 3! # # GemPy 3 has introduced the ``ImporterHelper`` class to streamline importing data from various sources. This class # simplifies the process of passing multiple arguments needed for importing data and will likely see further # extensions in the future. Currently, one of its uses is to handle `pooch` arguments for downloading data from the internet. # # # # Reviewing the Imported Data # """"""""""""""""""""""""""" # Now that the `geo_model` object is set up and the data imported from the CSV files, we review the data imported using the properties `surface_points_copy` and `orientations_copy`. # # Using `structural_frame.element_id_name_map`, we can see which ID corresponds to which structural element name in the data. # %% geo_model.surface_points_copy # %% geo_model.orientations_copy # %% # Declaring the Sequential Order of Structural Elements (Geological Formations) # """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" # In our model, we want the geological units to appear in the correct chronological order. This order could be determined by a sequence # of stratigraphic deposition, unconformities due to erosion, or other lithological genesis events like igneous intrusions. A similar # age-related order is declared for faults in our model. In GemPy, we use the function `gempy.map_stack_to_surfaces` to assign formations # or faults to different sequential series by declaring them in a Python dictionary. # # # The correct ordering of series is crucial for model construction! It's possible to assign several surfaces to one series. The order of # units within a series only affects the color code, so we recommend maintaining consistency. The order can be defined by simply changing # the order of the lists within the `gempy.core.data.StructuralFrame.structural_groups` and `gempy.core.data.StructuralGroups.elements` attributes. # # Faults are treated as independent groups and must be younger than the groups they affect. The relative order between different faults # defines their tectonic relationship (the first entry is the youngest). # # For a model with simple sequential stratigraphy, all layer formations can be assigned to one series without an issue. All unit # boundaries and their order would then be determined by interface points. However, to model more complex lithostratigraphical # relations and interactions, separate series definitions become important. For example, modeling an unconformity or an intrusion # that disrupts older stratigraphy would require declaring a younger series. # # By default, a simple sequence/group is created inferred from the data as shown above. # # Our example model comprises four main layers (plus an underlying basement that is automatically generated by GemPy) and one main # normal fault displacing those layers. Assuming a simple stratigraphy where each younger unit was deposited onto the underlying # older one, we can assign these layer formations to one structural group called "Strat_Series". For the fault, we declare a # respective "Fault_Series" as the first key entry in the mapping dictionary. We could give any other names to these series; # the formations, however, have to be referred to as named in the input data. # # In the following, we map the "Main Fault" to the "Fault Series" and the individual formations to the "Strat Series". # %% gp.map_stack_to_surfaces( gempy_model=geo_model, mapping_object= # TODO: This mapping I do not like it too much. We should be able to do it passing the data objects directly { "Fault_Series": 'Main_Fault', "Strat_Series": ('Sandstone_2', 'Siltstone', 'Shale', 'Sandstone_1') } ) # %% # Note how the structural frame still indicates the "Fault Series" group to have a relation type "erode". # We still need to tell GemPy that we want this group to be a fault. We do this using the function `set_is_fault`. # %% gp.set_is_fault( frame=geo_model.structural_frame, fault_groups=['Fault_Series'] ) # %% # Now, all surfaces have been assigned to a series and are displayed in the correct order # (from young to old). # # Returning Information from Our Input Data # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Our model input data, named "geo_model", contains all essential information for constructing # our model. You can access different types of information by accessing the attributes. # For instance, you can retrieve the coordinates of our modeling grid as follows: # %% geo_model.grid # %% # # Visualizing input data # ~~~~~~~~~~~~~~~~~~~~~~ # # Since we have already imported our input data, we can go ahead and visualize it in 2D and 3D. This can be useful to check if all points # and orientations are defined the way we want them to be. Using the function `gempy_viewer.plot2d`, we obtain a 2D projection of our # data points onto a plane of the chosen *direction* (we can choose this attribute to be either x, y or z, with the default being y). # Below, we can freely switch the direction and check out the projection from different sides to get a good idea. # %% plot = gpv.plot_2d(geo_model, show_lith=False, show_boundaries=False) # %% # Beyond 2D, however, we can also visualize our input data in full 3D using `gempy_viewer.plot_3d`. Note that direct 3D visualization # in GemPy requires [the Visualization Toolkit](https://www.vtk.org/) (VTK) to be installed. # %% gpv.plot_3d(geo_model, image=False, plotter_type='basic') # %% # Model Generation # ~~~~~~~~~~~~~~~~ # Once we've correctly defined all our primary information in our `gempy.core.data.GeoModel` object (referred to as `geo_model` # in these tutorials), we can proceed with the model computation step. We can go ahead and save the solution of a specific computation # as we do below, but solutions are also stored within the `gempy.core.data.GeoModel` object for future reference. # # # .. admonition:: New in GemPy 3! Numpy and TensorFlow backend # # Unlike previous versions, GemPy 3 doesn't rely on `theano` or `asera`. # Instead, it utilizes `numpy` or `tensorflow`. Consequently, we no longer need # to recompile all theano functions (TensorFlow uses eager execution; we found no # notable speed difference after profiling the XLA compiler). # %% # The parameters used for the interpolation are stored in # `gempy.core.data.GeoModel.interpolation_options`. These parameters have sensible default values # that you can modify if necessary. However, we advise caution when changing these parameters # unless you fully understand their implications. # %% # Display the current interpolation options geo_model.interpolation_options # %% # With all our prerequisites in place, we can now compute our complete geological model # using :func:`gempy.compute_model`. This function returns a :obj:`gempy.core.data.Solutions` object. # # The following sections illustrate these different model solutions and how to utilize them. # %% # Compute the geological model and get the solutions sol = gp.compute_model(geo_model) sol # %% # Solutions are also stored within the :obj:`gempy.core.data.GeoModel` object, for future reference. # %% geo_model.solutions # %% # Direct model visualization in GemPy # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # Model solutions can be easily visualized in 2D sections in GemPy # directly. Let's take a look at our lithology block: # # %% gpv.plot_2d(geo_model, show_data=True, cell_number="mid", direction='y') # %% # With ``cell_number=mid``, we have chosen a section going through # the middle of our block. We have moved in ``direction='y'``, # the plot thus depicts a plane parallel to the :math:`x`- and # :math:`y`-axes. Setting ``show_data=True``, we could plot original data # together with the results. Changing the values for ``cell_number`` and # ``direction``, we can move through our 3D block model and explore it by # looking at different 2D planes. # # We can do the same with the underlying scalar-field solution: # # %% gpv.plot_2d( model=geo_model, series_n=0, # This will plot the scalar field used for the fault show_data=False, show_scalar=True, show_lith=False ) # %% gpv.plot_2d( model=geo_model, series_n=1, # This will plot the scalar field used for the stratigraphy show_data=False, show_scalar=True, show_lith=False ) # %% # Dual Contouring and vtk visualization # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # In addition to 2D sections we can extract surfaces to visualize in 3D # renderers. Surfaces can be visualized as 3D triangle complexes in VTK # (see function plot\_surfaces\_3D below). To create these triangles, we # need to extract respective vertices and simplices from the potential # fields of lithologies and faults. This process is automatized in GemPy # using dual contouring in the :obj:`gempy_engine`. # # .. admonition:: New in GemPy 3! Dual Contouring # # GemPy 3 uses dual contouring to extract surfaces from the scalar fields. The method is completely coded in :obj:`gempy_engine` what also # enables further improvements in the midterm. This method is more efficient to use # together with octrees and suited better the new capabilities of gempy3. # %% gpv.plot_3d(geo_model, show_data=False, image=False, plotter_type='basic') # %% # Adding topography # ~~~~~~~~~~~~~~~~~~~~~~~~~~~ # In gempy we can add more grid types for different purposes. We will explore this concept in more detail in the # next tutorials (:doc:`ch1_3a_grids`). For now, we will just add a topography grid to our model. This grid allows us to intersect the # surfaces as well as compute a high resolution geological mal. # %% gp.set_topography_from_random( grid=geo_model.grid, fractal_dimension=1.2, d_z=np.array([350, 750]), topography_resolution=np.array([50, 50]), ) gp.compute_model(geo_model) gpv.plot_2d(geo_model, show_topography=True) gpv.plot_3d( model=geo_model, plotter_type='basic', show_topography=True, show_surfaces=True, show_lith=True, image=False ) # %% # Compute at a given location # ~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # This is done by modifying the grid to a custom grid and recomputing. # # %% x_i = np.array([[1000, 1000, 1000]]) lith_values_at_coords: np.ndarray = gp.compute_model_at( gempy_model=geo_model, at=x_i ) lith_values_at_coords # %% # Therefore if we just want the value at **x\_i**: # %% geo_model.solutions.raw_arrays.custom # %% # .. admonition:: Work in progress # # GemPy3 model serialization is currently being redisigned. Therefore, at the current version, there is not a build in # method to save the model. However, since now the data model should be completely robust, you should be able to save the # :obj:`gempy.core.data.GeoModel` and all its attributes using the standard python library [pickle](https://docs.python.org/3/library/pickle.html) # # sphinx_gallery_thumbnail_number = -2 ================================================ FILE: examples/tutorials/ch1_fundamentals/ch1_3a_grids.py ================================================ """ 1.3a: Grids. ============ """ import numpy as np import matplotlib.pyplot as plt import gempy as gp from gempy.core.data import Grid from gempy.core.data.grid_modules import RegularGrid np.random.seed(55500) # %% # The Grid Class # -------------- # # The Grid class interacts with the rest of the data classes and grid # subclasses. Its main purpose is to feed coordinates XYZ to the # interpolator. # # %% grid = Grid() # %% # The most important attribute of Grid is ``values`` (and ``values_r`` # which are the values rescaled) which are the 3D points in space that # kriging will be evaluated on. This array will be fed by "grid types" on # a **composition** relation with Grid: # # %% # .. image:: /_static/grids.jpg # # %% print(grid.values) # %% # At the moment of writing this tutorial, there are 5 grid types. The # number of grid types is scalable, and down the road we aim to connect # other grid packages (like `Discretize `_) as an extra Grid type. # # %% # This is an enum now print(grid.GridTypes) # %% # Each grid contains its own ``values`` attribute as well as other # methods to manipulate them depending on the type of grid. # # %% print(grid.values) # %% # We can see which grids are activated (i.e. they are going to be # interpolated and therefore will live on ``Grid().values``) by: # # %% print(grid.active_grids) # %% # By default, only the *regular grid* (``grid.regular_grid``) is active. However, since the regular # grid is still empty, ``Grid().values`` is empty too. # # %% print(grid.values) # %% # The last important attribute of Grid is the length: # # %% print(grid.length) # %% # Length gives back the interface indices between grids on the # ``Grid().values`` attribute. This can be used after interpolation to # know which interpolated values and coordinates correspond to each grid # type. You can use the method ``get_grid_args`` to return the indices by # name: # # %% print(grid.topography) # %% # By now all is a bit confusing because we have no values. Let's start # adding values to the different grids: # # %% # Regular Grid # ~~~~~~~~~~~~ # # The ``Grid`` class has a bunch of methods to set each grid type and # activate them. # # %% help(RegularGrid) # %% extent = np.array([0, 100, 0, 100, -100, 0]) resolution = np.array([20, 20, 20]) grid.dense_grid = RegularGrid(extent, resolution) print(grid.regular_grid) # RegularGrid will return either dense grid or octree grid depending on what is set # %% # Now the regular grid object composed in ``Grid`` has been filled: # # %% print(grid.regular_grid.values) # %% # And the regular grid has been set active (it was already active in any # case): # # %% print(grid.active_grids) # %% # Therefore, the grid values will be equal to the regular grid: # # %% print(grid.values) # %% # And the indices to extract the different arrays: # # %% print(grid.length) # %% # Custom Grid # ~~~~~~~~~~~ # # Completely free XYZ values. # # %% gp.set_custom_grid(grid, np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])) # %% # Again, ``set_any_grid`` will create a grid and activate it. So now the # composed object will contain values: # # %% print(grid.custom_grid.values) # %% # And since it is active, it will be added to the grid.values stack: # # %% print(grid.active_grids) # %% print(grid.values.shape) # %% # We can still recover those values with ``get_grid`` or by getting the # slicing args: # # %% print(grid.custom_grid) # %% print(grid.custom_grid.values) # %% # Topography # ~~~~~~~~~~ # # Now we can set the topography. :class:`Topography ` # contains methods to create manual topographies as well as using gdal for # dealing with raster data. By default, we will create a random topography: # # %% gp.set_topography_from_random(grid) # %% print(grid.active_grids) # %% # Now the grid values will contain both the regular grid and topography: # # %% print(grid.values, grid.length) # %% print(grid.topography.values) # %% # We can compare it to the topography.values: # # %% print(grid.topography.values) # %% # Now that we have more than one grid, we can activate and deactivate any # of them in real time: # # %% grid.active_grids ^= grid.GridTypes.TOPOGRAPHY grid.active_grids ^= grid.GridTypes.DENSE # %% # Since now all grids are deactivated, the values will be empty: # # %% print(grid.values) # %% grid.active_grids |= grid.GridTypes.TOPOGRAPHY # %% print(grid.values, grid.values.shape) # %% grid.active_grids |= grid.GridTypes.DENSE # %% print(grid.values) # %% # Centered Grid # ~~~~~~~~~~~~~ # # This grid contains an irregular grid where the majority of voxels are # centered around a value (or values). This type of grid is usually used # to compute certain types of forward physics where the influence # decreases with distance (e.g. gravity: Check `tutorial 2.2-Cell-selection `_ # ) # # %% gp.set_centered_grid( grid, centers=np.array([[300, 0, 0], [0, 0, 0]]), resolution=[10, 10, 20], radius=np.array([100, 100, 100]) ) # %% # Resolution and radius create a geometrically spaced kernel (blue dots) which # will be used to create a grid around each of the center points (red # dots): # # %% fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.scatter( grid.centered_grid.values[:, 0], grid.centered_grid.values[:, 1], grid.centered_grid.values[:, 2], '.', alpha=.2 ) ax.scatter( np.array([[300, 0, 0], [0, 0, 0]])[:, 0], np.array([[300, 0, 0], [0, 0, 0]])[:, 1], np.array([[300, 0, 0], [0, 0, 0]])[:, 2], c='r', alpha=1, s=30 ) ax.set_xlim(-100, 400) ax.set_ylim(-100, 100) ax.set_zlim(-120, 0) ax.set_xlabel('X Label') ax.set_ylabel('Y Label') ax.set_zlabel('Z Label') plt.show() # %% # Section Grid # ~~~~~~~~~~~~ # # This grid type has its own tutorial. See :doc:`ch1_3b_cross_sections` # ================================================ FILE: examples/tutorials/ch1_fundamentals/ch1_3b_cross_sections.py ================================================ """ 1.3b: 2-D sections ================== """ # %% # Importing import gempy as gp import gempy_viewer as gpv import numpy as np np.random.seed(1234) # %% # Setup the model # --------------- # # %% # Importing the data from CSV-files and setting extent and resolution data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/' geo_model: gp.data.GeoModel = gp.create_geomodel( project_name='Tutorial_ch1_1_Basics', extent=[0, 2000, 0, 2000, 0, 750], resolution=[20, 20, 20], # * Here we define the resolution of the voxels refinement=4, # * Here we define the number of octree levels. If octree levels are defined, the resolution is ignored. importer_helper=gp.data.ImporterHelper( path_to_orientations=data_path + "/data/input_data/getting_started/simple_fault_model_orientations.csv", path_to_surface_points=data_path + "/data/input_data/getting_started/simple_fault_model_points.csv", hash_surface_points="4cdd54cd510cf345a583610585f2206a2936a05faaae05595b61febfc0191563", hash_orientations="7ba1de060fc8df668d411d0207a326bc94a6cdca9f5fe2ed511fd4db6b3f3526" ) ) gp.map_stack_to_surfaces( gempy_model=geo_model, mapping_object= # TODO: This mapping I do not like it too much. We should be able to do it passing the data objects directly { "Fault_Series": 'Main_Fault', "Strat_Series": ('Sandstone_2', 'Siltstone', 'Shale', 'Sandstone_1') } ) gp.set_is_fault( frame=geo_model.structural_frame, fault_groups=['Fault_Series'] ) # %% # Add sections # ~~~~~~~~~~~~ # # %% # pass section dictionary with startpoint, endpoint and resolution for # every section: # # %% gp.set_section_grid( grid=geo_model.grid, section_dict={ 'section1': ([0, 0], [2000, 2000], [100, 80]), 'section2': ([800, 0], [800, 2000], [150, 100]), 'section3': ([0, 200], [1500, 500], [200, 150]) } # p1,p2,resolution ) # %% # Add topography # ~~~~~~~~~~~~~~ # # %% gp.set_topography_from_random( grid=geo_model.grid, fractal_dimension=1.2, d_z=np.array([300, 750]), topography_resolution=np.array([50, 50]) ) # %% # Active grids: # # %% geo_model.grid.active_grids # %% gpv.plot_section_traces(geo_model) # %% # %% geo_model.interpolation_options.mesh_extraction = False sol = gp.compute_model(geo_model) # %% gpv.plot_2d(geo_model, section_names=['topography']) # %% gpv.plot_2d(geo_model, section_names=['section1']) # %% gpv.plot_2d( model=geo_model, section_names=['section1', 'section2', 'section3', 'topography'], show_topography=True ) # sphinx_gallery_thumbnail_number = 4 ================================================ FILE: examples/tutorials/ch1_fundamentals/ch1_3c_topography.py ================================================ """ 1.3c: Adding topography to geological models ============================================ """ # %% import gempy as gp import gempy_viewer as gpv import numpy as np import os # %% # 1. The common procedure to set up a model: # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # %% data_path = os.path.abspath('../../') geo_model: gp.data.GeoModel = gp.create_geomodel( project_name='Single_layer_topo', extent=[450000, 460000, 70000, 80000, -1000, 500], resolution=[50, 50, 50], refinement=4, importer_helper=gp.data.ImporterHelper( path_to_orientations=data_path + "/data/input_data/tut-ch1-7/onelayer_orient.csv", path_to_surface_points=data_path + "/data/input_data/tut-ch1-7/onelayer_interfaces.csv", ) ) # %% # %% gp.set_section_grid( grid=geo_model.grid, section_dict={ 'section1': ([450000, 75000], [460000, 75500], [100, 100]), } ) # %% # 2. Adding topography # ~~~~~~~~~~~~~~~~~~~~ # # %% # 2 a. Load from raster file # ^^^^^^^^^^^^^^^^^^^^^^^^^^ # # # .. admonition:: Coming soon: Importing raster data # # This feature is not yet available in the current version of GemPy. Probably will be moved to `subsurface` since # coupling it with the geological model does not add much value. # # %% # This is to make it work in sphinx gallery # cwd = os.getcwd() # if not 'examples' in cwd: # path_dir = os.getcwd() + '/examples/tutorials/ch5_probabilistic_modeling' # else: # path_dir = cwd # # fp = path_dir + "/../../data/input_data/tut-ch1-7/bogota.tif" # # # %% # geo_model.set_topography(source='gdal', filepath=fp) # gp.plot_2d(geo_model, show_topography=True, section_names=['topography'], show_lith=False, # show_boundaries=False, # kwargs_topography={'cmap': 'gray', 'norm': None} # ) # plt.show() # %% # 2 b. create fun topography # ^^^^^^^^^^^^^^^^^^^^^^^^^^ # # %% # If there is no topography file, but you think that your model with # topography would look significantly cooler, you can use gempys # :meth:`set_topography ` function # to generate a random topography based on a fractal grid: # # %% # sphinx_gallery_thumbnail_number = 2 gp.set_topography_from_random(grid=geo_model.grid) gpv.plot_2d(geo_model, show_topography=True, section_names=['topography']) # %% # It has additional keywords to play around with: # # * fd: fractal dimension: # defaults to 2.0. The higher (try 2.9), the rougher the landscape will # be. # # * d\_z: height difference: # If none, last 20% of the model in z # direction. # # * extent: # extent in xy direction. If none, # ``geo_model.grid.extent`` is used. # # * resolution: # resolution of the topography array. # If none, ``geo_model.grid.resoution`` is used. Increasing the resolution leads to # much nicer geological maps! # # # %% gp.set_topography_from_random( grid=geo_model.grid, fractal_dimension=1.9, d_z=np.array([0, 250]), topography_resolution=np.array([200, 200]) ) # %% # Compute model # ~~~~~~~~~~~~~ # # %% gp.compute_model(geo_model) # %% # Visualize: # ^^^^^^^^^^ # # Now, the solutions object does also contain the computed geological map. # It can be visualized using the 2D and 3D plotting functionality: # # %% gpv.plot_2d(geo_model, show_topography=True, section_names=['topography'], show_boundaries=False, show_data=True) # %% gpv.plot_2d(geo_model, show_topography=True, section_names=['section1']) # %% g3d = gpv.plot_3d( model=geo_model, show_topography=True, show_lith=False, show_surfaces=False, show_results=False, ve=5 ) # %% # sphinx_gallery_thumbnail_number = 3 g3d = gpv.plot_3d( model=geo_model, show_topography=True, show_lith=True, show_surfaces=True, ve=5 ) ================================================ FILE: examples/tutorials/ch1_fundamentals/ch1_4_onlap_relations.py ================================================ """ 1.4: Unconformity relationships =============================== """ # sphinx_gallery_thumbnail_number = 2 # %% # Importing gempy import gempy as gp import gempy_viewer as gpv # Aux imports import numpy as np import os from gempy_engine.config import AvailableBackends np.random.seed(1515) # %% # We import a model from an existing folder, representing a subduction # zone with onlap relationships. data_path = os.path.abspath('../../') geo_model: gp.data.GeoModel = gp.create_geomodel( project_name='Onlap_relations', extent=[-200, 1000, -500, 500, -1000, 0], resolution=[50, 50, 50], refinement=6, importer_helper=gp.data.ImporterHelper( path_to_orientations=data_path + "/data/input_data/tut-ch1-4/tut_ch1-4_orientations.csv", path_to_surface_points=data_path + "/data/input_data/tut-ch1-4/tut_ch1-4_points.csv", ) ) gp.set_topography_from_random(grid=geo_model.grid, d_z=np.array([-600, -100])) # %% gpv.plot_2d(geo_model) # %% # %% # Raw structural frame geo_model.structural_frame # %% geo_model.input_transform.apply_anisotropy(gp.data.GlobalAnisotropy.NONE) gp.add_structural_group( model=geo_model, group_index=0, structural_group_name="seafloor_series", elements=[geo_model.structural_frame.get_element_by_name("seafloor")], structural_relation=gp.data.StackRelationType.ERODE, ) gp.add_structural_group( model=geo_model, group_index=1, structural_group_name="right_series", elements=[ geo_model.structural_frame.get_element_by_name("rock1"), geo_model.structural_frame.get_element_by_name("rock2"), ], structural_relation=gp.data.StackRelationType.ONLAP ) gp.add_structural_group( model=geo_model, group_index=2, structural_group_name="onlap_series", elements=[geo_model.structural_frame.get_element_by_name("onlap_surface")], structural_relation=gp.data.StackRelationType.ERODE ) gp.add_structural_group( model=geo_model, group_index=3, structural_group_name="left_series", elements=[geo_model.structural_frame.get_element_by_name("rock3")], structural_relation=gp.data.StackRelationType.BASEMENT ) gp.remove_structural_group_by_name(model=geo_model, group_name="default_formation") # Final structural frame geo_model.structural_frame # %% s = gp.compute_model( gempy_model=geo_model, engine_config=gp.data.GemPyEngineConfig( backend=AvailableBackends.PYTORCH ) ) # %% gpv.plot_2d(geo_model, show_data=True) gpv.plot_3d( model=geo_model, show_surfaces=True, show_data=True, image=True, show_topography=True, kwargs_plot_structured_grid={'opacity': .2} ) # %% gpv.plot_2d( model=geo_model, cell_number=2, override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix[0], show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None} ) gpv.plot_2d( model=geo_model, cell_number=2, override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix[1], show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None} ) gpv.plot_2d( model=geo_model, cell_number=2, override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix[2], show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None} ) gpv.plot_2d( model=geo_model, cell_number=2, override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix[3], show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None} ) # %% gpv.plot_2d( model=geo_model, cell_number=2, override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix_squeezed[0], show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None} ) gpv.plot_2d( model=geo_model, cell_number=2, override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix_squeezed[1], show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None} ) gpv.plot_2d( model=geo_model, cell_number=2, override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix_squeezed[2], show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None} ) gpv.plot_2d( model=geo_model, cell_number=2, override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix_squeezed[3], show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None} ) ================================================ FILE: examples/tutorials/ch1_fundamentals/ch1_5_fault_relations.py ================================================ """ 1.5: Fault relations ==================== """ # %% # Importing gempy import gempy as gp import gempy_viewer as gpv # Aux imports import numpy as np import os np.random.seed(1515) # %% # We import a model from an existing folder. # # %% data_path = os.path.abspath('../../') geo_model: gp.data.GeoModel = gp.create_geomodel( project_name='Faults_relations', extent=[0, 1000, 0, 1000, -1000, -400], resolution=[20, 20, 20], refinement=6, # * For this model is better not to use octrees because we want to see what is happening in the scalar fields importer_helper=gp.data.ImporterHelper( path_to_orientations=data_path + "/data/input_data/tut-ch1-5/tut_ch1-5_orientations.csv", path_to_surface_points=data_path + "/data/input_data/tut-ch1-5/tut_ch1-5_points.csv", ) ) print(geo_model) # %% # One fault model # --------------- # %% # Setting the structural frame fault1: gp.data.StructuralElement = geo_model.structural_frame.get_element_by_name("fault1") fault2: gp.data.StructuralElement = geo_model.structural_frame.get_element_by_name("fault2") # Remove the faults from the default group default_group: gp.data.StructuralGroup = geo_model.structural_frame.get_group_by_name("default_formation") default_group.elements.remove(fault1) default_group.elements.remove(fault2) # Add a new group for the fault gp.add_structural_group( model=geo_model, group_index=0, structural_group_name="fault_series_1", elements=[fault1], structural_relation=gp.data.StackRelationType.FAULT, fault_relations=gp.data.FaultsRelationSpecialCase.OFFSET_ALL ) print(geo_model.structural_frame) # %% geo_model.input_transform.apply_anisotropy(gp.data.GlobalAnisotropy.NONE) gp.compute_model(geo_model) # %% print(geo_model.solutions.raw_arrays.block_matrix[0]) # This contains the block values for the fault1 print(geo_model.solutions.raw_arrays.block_matrix[1]) # This contains the block values for the formations # %% gpv.plot_2d(geo_model, show_data=True) gpv.plot_3d(geo_model, show_data=True, kwargs_plot_structured_grid={'opacity': .2}) # %5 # Graben example # -------------- # %% gp.add_structural_group( model=geo_model, group_index=1, structural_group_name="fault_series_2", elements=[fault2], structural_relation=gp.data.StackRelationType.FAULT, fault_relations=gp.data.FaultsRelationSpecialCase.OFFSET_ALL ) print(geo_model.structural_frame) gp.compute_model(geo_model) # %% gpv.plot_2d(geo_model, show_data=True) gpv.plot_3d(geo_model, show_data=True, image=True, kwargs_plot_structured_grid={'opacity': .2}) # %% gpv.plot_2d(geo_model, show_scalar=True, show_lith=False, series_n=0) gpv.plot_2d(geo_model, show_scalar=True, show_lith=False, series_n=1) gpv.plot_2d(geo_model, show_scalar=True, show_lith=False, series_n=2) # %% # Finite Faults # ------------- # %% # Faults relations # ---------------- # %% # Let's split the formations in two groups gp.add_structural_group( model=geo_model, group_index=2, structural_group_name="series_1", elements=[ geo_model.structural_frame.get_element_by_name("rock4"), geo_model.structural_frame.get_element_by_name("rock3") ], structural_relation=gp.data.StackRelationType.ERODE ) default_group.elements.remove(geo_model.structural_frame.get_element_by_name("rock4")) default_group.elements.remove(geo_model.structural_frame.get_element_by_name("rock3")) gp.set_fault_relation( frame=geo_model.structural_frame, rel_matrix=np.array([ [0, 1, 1, 1], [0, 0, 0, 1], [0, 0, 0, 0], [0, 0, 0, 0] ] ) ) print(geo_model.structural_frame) # %% gp.compute_model(geo_model) # %% gpv.plot_2d(geo_model, show_data=True) gpv.plot_3d(geo_model, show_data=True, image=False, kwargs_plot_structured_grid={'opacity': .2}) ================================================ FILE: examples/tutorials/ch1_fundamentals/ch1_6_2d_visualization.py ================================================ """ 1.6: 2D Visualization. ====================== """ # %% import os # Importing auxiliary libraries import numpy as np # Importing GemPy import gempy as gp import gempy_viewer as gpv # sphinx_gallery_thumbnail_number = -1 np.random.seed(1515) # %% # Model interpolation # ~~~~~~~~~~~~~~~~~~~ # # %% # Data Preparation data_path = os.path.abspath('../../') geo_data: gp.data.GeoModel = gp.create_geomodel( project_name='viz_2d', extent=[0, 1000, 0, 1000, 0, 1000], resolution=[10, 10, 10], refinement=4, importer_helper=gp.data.ImporterHelper( path_to_orientations=data_path + "/data/input_data/jan_models/model5_orientations.csv", path_to_surface_points=data_path + "/data/input_data/jan_models/model5_surface_points.csv", ) ) gp.set_topography_from_random(grid=geo_data.grid, d_z=np.array([500, 1000])) # %% gpv.plot_2d(geo_data) # %% section_dict = {'section1': ([0, 0], [1000, 1000], [100, 80]), 'section2': ([800, 0], [800, 1000], [150, 100]), 'section3': ([50, 200], [100, 500], [200, 150])} # %% gp.set_section_grid(geo_data.grid, section_dict) gpv.plot_section_traces(geo_data) # %% geo_data.grid.sections # %% # %% gp.map_stack_to_surfaces( gempy_model=geo_data, mapping_object={ "Fault_Series": 'fault', "Strat_Series": ('rock2', 'rock1') } ) gp.set_is_fault( frame=geo_data.structural_frame, fault_groups=['Fault_Series'] ) # %% geo_data.grid.active_grids # %% gp.compute_model(geo_data) # %% # new plotting api gpv.plot_2d(geo_data, section_names=['section1']) # %% # Plot API # -------- # # %% # If nothing is passed, a Plot2D object is created and therefore you are # in the same situation as above: # # %% p3 = gpv.plot_2d(geo_data) # %% # Alternatively you can pass section\_names, cell\_numbers + direction or # any combination of the above: # # %% gpv.plot_2d(geo_data, section_names=['topography']) # %% gpv.plot_2d(geo_data, section_names=['section1']) # %% gpv.plot_2d(geo_data, section_names=['section1', 'section2']) # %% gpv.plot_2d(geo_data, figsize=(15, 15), section_names=['section1', 'section2', 'topography'], cell_number='mid') ================================================ FILE: examples/tutorials/ch1_fundamentals/ch1_7_3d_visualization.py ================================================ """ 1.7: 3-D Visualization ====================== """ # %% # Importing GemPy import gempy as gp import gempy_viewer as gpv from gempy import generate_example_model from gempy.core.data.enumerators import ExampleModel import dotenv dotenv.load_dotenv() # sphinx_gallery_thumbnail_number = -1 # %% # Loading an example geomodel # ~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # %% geo_model = generate_example_model(ExampleModel.GRABEN) gp.compute_model(geo_model) # %% # Basic plotting API # ------------------ # # %% # Data plot # ~~~~~~~~~ # # %% gpv.plot_3d( model=geo_model, show_surfaces=False, show_data=True, show_lith=False, image=False ) # %% # Geomodel plot # ~~~~~~~~~~~~~ # # %% gpv.plot_3d(geo_model, image=False) # %% # LiquidEarth Integration # ~~~~~~~~~~~~~~~~~~~~~~~ # Beyond the classical plotting capabilities introduced in GemPy v3, users can now also upload models to LiquidEarth. # `LiquidEarth `_ is a collaborative platform designed for 3D visualization, # developed by many of the main `gempy` maintainers, with a strong focus on collaboration and sharing. # This makes it an excellent tool for sharing your models with others and viewing them across different platforms. # To upload a model to LiquidEarth, you must have an account and a user token. Once your model is uploaded, # you can easily share the link with anyone. # %% link = gpv.plot_to_liquid_earth( geo_model=geo_model, space_name="[PUBLIC] GemPy Tutorial 1.7: 3-D Visualization", file_name="gempy_model", user_token=None, # If None, it will try to grab it from the environment grab_link=True, make_new_space=False ) print(f"Generated Link: {link}") # %% # Now we can use `this link `_ to visualize the model in Liquid Earth. # %% # .. image:: /_static/gp_model_in_le.png ================================================ FILE: examples/tutorials/ch2-Geophysics/README.rst ================================================ Chapter 2: Forward Geophysics ----------------------------- These examples show how to use some of the in built functionality for forward geophysics. At this date GemPy is not meant to be a package for classical geophysical inversion rather to allow probabilistic inversion. This means that we need algorithms that can be fast, differentiable but still capable to estimate the right answer! ================================================ FILE: examples/tutorials/ch2-Geophysics/ch2_1_gravity.py ================================================ """ 2.1 Forward Gravity: Simple example ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ """ # %% # Importing gempy import gempy as gp import gempy_viewer as gpv # Aux imports import numpy as np import pandas as pd import os import matplotlib.pyplot as plt np.random.seed(1515) pd.set_option('display.precision', 2) # %% data_path = os.path.abspath('../../data/input_data/tut_SandStone') # Importing the data from csv geo_model: gp.data.GeoModel = gp.create_geomodel( project_name='Greenstone', extent=[696000, 747000, 6863000, 6930000, -20000, 200], # * Here we define the extent of the model refinement=5, # * Here we define the number of octree levels. If octree levels are defined, the resolution is ignored. importer_helper=gp.data.ImporterHelper( path_to_orientations=data_path + "/SandStone_Foliations.csv", path_to_surface_points=data_path + "/SandStone_Points.csv", hash_surface_points=None, hash_orientations=None ) ) # %% gp.map_stack_to_surfaces( gempy_model=geo_model, mapping_object={ "EarlyGranite_Series": 'EarlyGranite', "BIF_Series": ('SimpleMafic2', 'SimpleBIF'), "SimpleMafic_Series": 'SimpleMafic1', 'Basement': 'basement' } ) # %% gpv.plot_2d(geo_model) # %% # %% # Creating grid # ~~~~~~~~~~~~~ # # %% # First we need to define the location of the devices. For this example we # can make a map: # # %% grav_res = 20 X = np.linspace(7.050000e+05, 747000, grav_res) Y = np.linspace(6863000, 6925000, grav_res) Z = 300 xyz = np.meshgrid(X, Y, Z) xy_ravel = np.vstack(list(map(np.ravel, xyz))).T xy_ravel # %% # We can see the location of the devices relative to the model data: # # %% gpv.plot_2d(geo_model, direction='z', show=False) plt.scatter(xy_ravel[:, 0], xy_ravel[:, 1], s=1) plt.show() # %% # Now we need to create the grid centered on the devices (see: # https://github.com/cgre-aachen/gempy/blob/master/notebooks/tutorials/ch1-3-Grids.ipynb) # # %% # geo_model.set_centered_grid(xy_ravel, resolution=[10, 10, 15], radius=5000) gp.set_centered_grid( grid=geo_model.grid, centers=xy_ravel, resolution=np.array([10, 10, 15]), radius=np.array([5000, 5000, 5000]) ) # %% geo_model.grid.centered_grid.kernel_grid_centers # %% # Now we need to compute the component tz (see # https://github.com/cgre-achen/gempy/blob/master/notebooks/tutorials/ch2-2-Cell_selection.ipynb) # # %% gravity_gradient = gp.calculate_gravity_gradient(geo_model.grid.centered_grid) gravity_gradient # %% geo_model.geophysics_input = gp.data.GeophysicsInput( tz=gravity_gradient, densities=np.array([2.61, 2.92, 3.1, 2.92, 2.61]), ) # %% # Once we have created a gravity interpolator we can call it from compute # model as follows: # # %% geo_model.interpolation_options.mesh_extraction = False sol = gp.compute_model( gempy_model=geo_model, engine_config=gp.data.GemPyEngineConfig( backend=gp.data.AvailableBackends.numpy, dtype='float32' ) ) grav = sol.gravity # %% gpv.plot_2d(geo_model, cell_number=[-1], direction=['z'], show_data=False) # %% gpv.plot_2d(geo_model, cell_number=['mid'], direction='x') # %% gpv.plot_2d(geo_model, direction=['z'], height=7, show_results=False, show_data=True, show=False) plt.scatter(xy_ravel[:, 0], xy_ravel[:, 1], s=1) plt.imshow(sol.gravity.reshape(grav_res, grav_res), extent=(xy_ravel[:, 0].min() + (xy_ravel[0, 0] - xy_ravel[1, 0]) / 2, xy_ravel[:, 0].max() - (xy_ravel[0, 0] - xy_ravel[1, 0]) / 2, xy_ravel[:, 1].min() + (xy_ravel[0, 1] - xy_ravel[30, 1]) / 2, xy_ravel[:, 1].max() - (xy_ravel[0, 1] - xy_ravel[30, 1]) / 2), cmap='viridis_r', origin='lower') plt.show() # %% # Plotting lithologies # ^^^^^^^^^^^^^^^^^^^^ # # If we want to compute the lithologies we will need to create a normal # interpolator object as seen in the Chapter 1 of the tutorials # # %% # Now we can plot all together (change the alpha parameter to see the # gravity overlying): # # %% gpv.plot_2d(geo_model, cell_number=[-1], direction=['z'], show=False, kwargs_regular_grid={'alpha': .5}) plt.scatter(xy_ravel[:, 0], xy_ravel[:, 1], s=1) plt.imshow(grav.reshape(grav_res, grav_res), extent=(xy_ravel[:, 0].min() + (xy_ravel[0, 0] - xy_ravel[1, 0]) / 2, xy_ravel[:, 0].max() - (xy_ravel[0, 0] - xy_ravel[1, 0]) / 2, xy_ravel[:, 1].min() + (xy_ravel[0, 1] - xy_ravel[30, 1]) / 2, xy_ravel[:, 1].max() - (xy_ravel[0, 1] - xy_ravel[30, 1]) / 2), cmap='viridis_r', origin='lower', alpha=.8) cbar = plt.colorbar() cbar.set_label(r'$\mu$gal') plt.show() # sphinx_gallery_thumbnail_number = -2 ================================================ FILE: examples/tutorials/ch2-Geophysics/ch2_2_cell_selection.py ================================================ """ 2.2: Centered Grid. ^^^^^^^^^^^^^^^^^^^ """ # %% # # Concept of a measurement-centered grid # """""""""""""""""""""""""""""""""""""" # # Geophysics Preprocessing builds on the centered grid # (https://github.com/cgre-aachen/gempy/blob/master/notebooks/tutorials/ch1-3-Grids.ipynb) # to precompute the constant part of forward physical computations as for # example gravity: # .. math:: # F_z = G_{\\rho} ||| x \\ln(y+r) + y \\ln (x+r) - z \\arctan (\\frac{x y}{z r}) |^{x_2}_{x_1}|^{y_2}_{y_1}|^{ # z_2}_{z_1} # where we can compress the grid dependent terms as # .. math:: # t_z = ||| x \ln (y+r) + y \ln (x+r)-z \\arctan ( \\frac{x y}{z r} ) |^{x_2}_{x_1}|^{y_2}_{y_1}|^{z_2}_{z_1} # By doing this decomposition an keeping the grid constant we can compute # the forward gravity by simply operate: # .. math:: # F_z = G_{\\rho} \cdot t_z # %% # Importing gempy import gempy as gp # Aux imports import numpy as np import pandas as pd import matplotlib.pyplot as plt np.random.seed(1515) pd.set_option('display.precision', 2) # %% from gempy_engine.core.data.centered_grid import CenteredGrid centered_grid = CenteredGrid( centers=np.array([0,0,0]), resolution=[10, 10, 20], radius=100 ) # %% # ``create_irregular_grid_kernel`` will create a constant kernel around # the point 0,0,0. This kernel will be what we use for each device. # # %% centered_grid.kernel_grid_centers # %% # :math:`t_z` is only dependent on distance and therefore we can use the # kerenel created on the previous cell # # %% gravity_gradient = gp.calculate_gravity_gradient(centered_grid) gravity_gradient # %% # To compute tz we also need the edges of each voxel. The distance to the # edges are stored on ``kernel_dxyz_left`` and ``kernel_dxyz_right``. We # can plot all the data as follows: # # %% a, b, c = centered_grid.kernel_grid_centers, centered_grid.left_voxel_edges, centered_grid.right_voxel_edges tz = gravity_gradient # %% fig = plt.figure(figsize=(13, 7)) plt.quiver(a[:, 0].reshape(11, 11, 21)[5, :, :].ravel(), a[:, 2].reshape(11, 11, 21)[:, 5, :].ravel(), np.zeros(231), tz.reshape(11, 11, 21)[5, :, :].ravel(), label='$t_z$', alpha=.3 ) plt.plot(a[:, 0].reshape(11, 11, 21)[5, :, :].ravel(), a[:, 2].reshape(11, 11, 21)[:, 5, :].ravel(), 'o', alpha=.3, label='Centers') plt.plot(a[:, 0].reshape(11, 11, 21)[5, :, :].ravel() - b[:, 0].reshape(11, 11, 21)[5, :, :].ravel(), a[:, 2].reshape(11, 11, 21)[:, 5, :].ravel(), '.', alpha=.3, label='Lefts') plt.plot(a[:, 0].reshape(11, 11, 21)[5, :, :].ravel(), a[:, 2].reshape(11, 11, 21)[:, 5, :].ravel() - b[:, 2].reshape(11, 11, 21)[:, 5, :].ravel(), '.', alpha=.6, label='Ups') plt.plot(a[:, 0].reshape(11, 11, 21)[5, :, :].ravel() + c[:, 0].reshape(11, 11, 21)[5, :, :].ravel(), a[:, 2].reshape(11, 11, 21)[:, 5, :].ravel(), '.', alpha=.3, label='Rights') plt.plot(a[:, 0].reshape(11, 11, 21)[5, :, :].ravel(), a[:, 2].reshape(11, 11, 21)[:, 5, :].ravel() + c[:, 2].reshape(11, 11, 21)[5, :, :].ravel(), '.', alpha=.3, label='Downs') plt.xlim(-200, 200) plt.ylim(-200, 0) plt.legend() plt.show() # %% # Just the quiver: # # %% fig = plt.figure(figsize=(13, 7)) plt.quiver(a[:, 0].reshape(11, 11, 21)[5, :, :].ravel(), a[:, 2].reshape(11, 11, 21)[:, 5, :].ravel(), np.zeros(231), tz.reshape(11, 11, 21)[5, :, :].ravel() ) plt.show() # %% # Remember this is happening always in 3D: # # %% fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.scatter(a[:, 0], a[:, 1], a[:, 2], c=tz) ax.set_xlabel('X Label') ax.set_ylabel('Y Label') ax.set_zlabel('Z Label') plt.show() ================================================ FILE: examples/tutorials/ch3-Interpolations/README.rst ================================================ Chapter 3: Domain Kriging ------------------------- GemPy uses Kriging to interpolate the scalar field to construct the structural model. However to populate petrophysical data within each domain we also provide functionality to interpolate data in 3D. ================================================ FILE: examples/tutorials/ch3-Interpolations/ch3_1_kriging_interpolation_and_simulation.py ================================================ """ 3.1: Simple example of kriging in gempy ======================================= """ # %% # In this notebook it will be shown how to create a kriged or simulated # field in a simple geological model in gempy. We start by creating a # simple model with three horizontally layered units, as shown in the # gempy examples. # # %% # Importing GemPy import gempy as gp import gempy_viewer as gpv # Importing auxiliary libraries import numpy as np import matplotlib.pyplot as plt import os # new for this from gempy_plugins.kriging import kriging np.random.seed(5555) # %% # Creating the model by importing the input data and displaying it: # # %% data_path = os.path.abspath('../../') geo_data: gp.data.GeoModel = gp.create_geomodel( project_name='kriging', extent=[0, 1000, 0, 50, 0, 1000], resolution=[50, 10, 50], refinement=1, importer_helper=gp.data.ImporterHelper( path_to_orientations=data_path + "/data/input_data/jan_models/model1_orientations.csv", path_to_surface_points=data_path + "/data/input_data/jan_models/model1_surface_points.csv", ) ) # %% # Setting and ordering the units and series: # # %% gp.map_stack_to_surfaces( gempy_model=geo_data, mapping_object={ "Strat_Series": ('rock2', 'rock1'), "Basement_Series": ('basement') } ) # %% # Calculating the model: # # %% # no mesh computed as basically 2D model sol = gp.compute_model(geo_data) # %% # So here is the very simple, basically 2D model that we created: # # %% gpv.plot_2d(geo_data, cell_number=0, show_data=False) # %% # 1) Creating domain # ------------------ # # Let us assume we have a couple of measurements in a domain of interest # within our model. In our case the unit of interest is the central rock # layer (rock1). In the kriging module we can define the domain by # handing over a number of surfaces by id - in this case the id of rock1 # is 2. In addition we define four input data points in cond_data, each # defined by x,y,z coordinate and a measurement value. # # %% # conditioning data (data measured at locations) cond_data = np.array([[100, .5, 500, 2], [900, .5, 500, 1], [500, .5, 550, 1], [300, .5, 400, 5]]) # %% # creating a domain object from the gempy solution, a defined domain conditioning data domain = kriging.Domain( model_solutions=sol, transform=geo_data.input_transform, domain=[2], data=cond_data ) # %% # 2) Creating a variogram model # ----------------------------- # # %% variogram_model = kriging.VariogramModel( theoretical_model='exponential', range_=200, sill=np.var(cond_data[:, 3]) ) # %% variogram_model.plot(type_='both', show_parameters=True) plt.show() # %% # 3) Kriging interpolation # ------------------------ # # %% # In the following we define an object called kriging_model and set all # input parameters. Finally we generate the kriged field. # # %% kriging_solution = kriging.create_kriged_field(domain, variogram_model) # %% # The result of our calculation is saved in the following dataframe, # containing an estimated value and the kriging variance for each point in # the grid: # # %% kriging_solution.results_df.head() # %% # It is also possible to plot the results in cross section similar to the # way gempy models are plotted. # # %% from gempy_viewer.modules.plot_2d.visualization_2d import Plot2D plot_2d: Plot2D = gpv.plot_2d( model=geo_data, cell_number=0, show_data=False, show=False, kwargs_lithology={ 'alpha': 0.5 } ) kriging.plot_kriging_results( geo_data=geo_data, kriging_solution=kriging_solution, plot_2d=plot_2d, title='Kriging interpolation: Estimated values', result_column=['estimated value'] ) # %% plot_2d_both = gpv.plot_2d( model=geo_data, cell_number=[0, 0], show_data=False, show=False, kwargs_lithology={ 'alpha': 0.5 } ) kriging.plot_kriging_results( geo_data=geo_data, kriging_solution=kriging_solution, plot_2d=plot_2d_both, title='Kriging interpolation: Estimated values', result_column=['estimated value', 'estimation variance'] ) # %% # 4) Simulated field # ------------------ # # Based on the same objects (domain and varigoram model) also a simulated # field (stationary Gaussian Field) can be generated. A Sequential # Gaussian Simulation approach is applied in this module: # # %% solution_sim = kriging.create_gaussian_field(domain, variogram_model) # %% solution_sim.results_df.head() # %% solution_sim.results_df['estimated value'] # %% plot_2d: Plot2D = gpv.plot_2d( model=geo_data, cell_number=0, show_data=False, show=False, kwargs_lithology={ 'alpha': 0.5 } ) kriging.plot_kriging_results( geo_data=geo_data, kriging_solution=solution_sim, plot_2d=plot_2d, title='Kriging interpolation: Estimated values', result_column=['estimated value'] ) # %% plot_2d_both = gpv.plot_2d( model=geo_data, cell_number=[0, 0], show_data=False, show=False, kwargs_lithology={ 'alpha': 0.5 } ) kriging.plot_kriging_results( geo_data=geo_data, kriging_solution=solution_sim, plot_2d=plot_2d_both, title='Kriging interpolation: Estimated values', result_column=['estimated value', 'estimation variance'] ) # sphinx_gallery_thumbnail_number = 3 ================================================ FILE: examples/tutorials/ch4-Topology/README.rst ================================================ Chapter 4: Topological analysis ------------------------------- One of the reasons to perform structural modeling is to analyse the topology of the system, i.e. the connectivity between layers. GemPy provide built-in functionality to calculate and analyse this aspect of each geological modeling ================================================ FILE: examples/tutorials/ch4-Topology/__init__.py ================================================ ================================================ FILE: examples/tutorials/ch4-Topology/ch4-1-Topology.py ================================================ """ Chapter 4: Analyzing Geomodel Topology ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """ import gempy as gp import gempy_viewer as gpv from gempy_viewer.modules.plot_2d.visualization_2d import Plot2D from gempy_plugins.topology_analysis import topology as tp import os import warnings warnings.filterwarnings("ignore") # %% # Load example Model # ^^^^^^^^^^^^^^^^^^ # # First let's set up a very simple example model. For that we initialize # the geo_data object with the correct model extent and the resolution we # like. Then we load our data points from csv files and set the series and # order the formations (stratigraphic pile). # # %% data_path = os.path.abspath('../../') geo_model: gp.data.GeoModel = gp.create_geomodel( project_name='Model_Tutorial6', extent= [0, 3000, 0, 20, 0, 2000], resolution=[50, 10, 67], refinement=1, # * For this model is better not to use octrees because we want to see what is happening in the scalar fields importer_helper=gp.data.ImporterHelper( path_to_orientations=data_path + "/data/input_data/tut_chapter6/ch6_data_fol.csv", path_to_surface_points=data_path + "/data/input_data/tut_chapter6/ch6_data_interf.csv", ) ) gp.map_stack_to_surfaces( gempy_model=geo_model, mapping_object= { "fault": "Fault", "Rest": ('Layer 2', 'Layer 3', 'Layer 4', 'Layer 5') } ) gp.set_is_fault(geo_model, ['fault']) geo_model.interpolation_options.mesh_extraction = False sol = gp.compute_model(geo_model) # %% gpv.plot_2d(geo_model, cell_number=[5]) # %% # Analyzing Topology # ^^^^^^^^^^^^^^^^^^ # # GemPy sports in-built functionality to analyze the topology of its # models. All we need for this is our geo_data object, lithology block and # the fault block. We input those into *gp.topology_compute* and get # several useful outputs: # # - an adjacency graph **G**, representing the topological relationships # of the model # - the **centroids** of the all the unique topological regions in the # model (x,y,z coordinates of their center) # - a list of all the unique labels (labels_unique) # - two look-up-tables from the lithology id's to the node labels, and # vice versa # # %% edges, centroids = tp.compute_topology(geo_model) # %% # The first output of the topology function is the ``set`` of edges # representing topology relationships between unique geobodies of the # block model. An edge is represented by a ``tuple`` of two ``int`` # geobody (or node) labels: # # %% edges # %% # The second output is the centroids ``dict``, mapping the unique geobody # id's (graph node id's) to the geobody centroid position in grid # coordinates: # # %% centroids # %% # After computing the model topology, we can overlay the topology graph # over a model section: # # %% # Visualizing topology # ~~~~~~~~~~~~~~~~~~~~ # # 2-D Visualization of the Topology Graph # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # %% gpv.plot_topology( regular_grid=geo_model.grid.regular_grid, edges=edges, centroids=centroids ) # %% plot_2d: Plot2D = gpv.plot_2d(geo_model, cell_number=[5], show=False) gpv.plot_topology( regular_grid=geo_model.grid.regular_grid, edges=edges, centroids=centroids, ax=plot_2d.axes[0] ) # %% # Adjacency Matrix # ~~~~~~~~~~~~~~~~ # # Another way to encode and visualize the geomodel topology is using an # adjacency graph: # # %% M = tp.get_adjacency_matrix(geo_model, edges, centroids) print(M) # %% tp.plot_adjacency_matrix(geo_model, M) # %% # Look-up tables # ~~~~~~~~~~~~~~ # # %% # The ``topology`` asset provides several look-up tables to work with the # unique geobody topology id's. # # Mapping node id's back to lithology / surface id's: # # %% lith_lot = tp.get_lot_node_to_lith_id(geo_model, centroids) lith_lot # %% # Figuring out which nodes are in which fault block: # # %% fault_lot = tp.get_lot_node_to_fault_block(geo_model, centroids) fault_lot # %% # We can also easily map the lithology id to the corresponding topology # id's: # # %% tp.get_lot_lith_to_node_id(lith_lot) # %% # Detailed node labeling # ~~~~~~~~~~~~~~~~~~~~~~ # # %% # sphinx_gallery_thumbnail_number = 4 dedges, dcentroids = tp.get_detailed_labels(geo_model, edges, centroids) # %% plot_2d: Plot2D = gpv.plot_2d(geo_model, cell_number=[5], show=False) gpv.plot_topology( regular_grid=geo_model.grid.regular_grid, edges=dedges, centroids=dcentroids, ax=plot_2d.axes[0] ) # %% dedges # %% dcentroids # %% # Checking adjacency # ~~~~~~~~~~~~~~~~~~ # # %% # So lets say we want to check if the purple layer (id 5) is connected # across the fault to the yellow layer (id 3). For this we can make easy # use of the detailed labeling and the ``check_adjacency`` function: # # %% tp.check_adjacency(dedges, "5_1", "3_0") # %% # We can also check all geobodies that are adjacent to the purple layer # (id 5) on the left side of the fault (fault id 1): # # %% tp.get_adjacencies(dedges, "5_1") # %% ================================================ FILE: examples/tutorials/z_other_tutorials/README.rst ================================================ Video tutorials --------------- Compilation of tutorials for videos on the `gempy` YouTube channel https://www.youtube.com/@GemPy3D. ================================================ FILE: examples/tutorials/z_other_tutorials/__init__.py ================================================ ================================================ FILE: examples/tutorials/z_other_tutorials/json_io/01_surface_points_io.py ================================================ """ Tutorial for JSON I/O operations in GemPy - Surface Points ======================================================= This tutorial demonstrates how to save and load surface points data using JSON files in GemPy. """ import json import numpy as np import gempy as gp from gempy.modules.json_io import JsonIO # Create a sample surface points dataset # ------------------------------------ # Create some sample surface points x = np.array([0, 1, 2, 3, 4]) y = np.array([0, 1, 2, 3, 4]) z = np.array([0, 1, 2, 3, 4]) ids = np.array([0, 0, 1, 1, 2]) # Three different surfaces nugget = np.array([0.00002, 0.00002, 0.00002, 0.00002, 0.00002]) # Create name to id mapping name_id_map = {f"surface_{id}": id for id in np.unique(ids)} # Create a SurfacePointsTable surface_points = gp.data.SurfacePointsTable.from_arrays( x=x, y=y, z=z, names=[f"surface_{id}" for id in ids], nugget=nugget, name_id_map=name_id_map ) # Create a JSON file with the surface points data # --------------------------------------------- # Create the JSON structure json_data = { "metadata": { "name": "sample_model", "creation_date": "2024-03-19", "last_modification_date": "2024-03-19", "owner": "tutorial" }, "surface_points": [ { "x": float(x[i]), "y": float(y[i]), "z": float(z[i]), "id": int(ids[i]), "nugget": float(nugget[i]) } for i in range(len(x)) ], "orientations": [], "faults": [], "series": [], "grid_settings": { "regular_grid_resolution": [10, 10, 10], "regular_grid_extent": [0, 4, 0, 4, 0, 4], "octree_levels": None }, "interpolation_options": {} } # Save the JSON file with open("sample_surface_points.json", "w") as f: json.dump(json_data, f, indent=4) # Load the surface points from JSON # ------------------------------- # Load the model from JSON loaded_surface_points = JsonIO._load_surface_points(json_data["surface_points"]) # Verify the loaded data print("\nOriginal surface points:") print(surface_points) print("\nLoaded surface points:") print(loaded_surface_points) # Verify the data matches print("\nVerifying data matches:") print(f"X coordinates match: {np.allclose(surface_points.xyz[:, 0], loaded_surface_points.xyz[:, 0])}") print(f"Y coordinates match: {np.allclose(surface_points.xyz[:, 1], loaded_surface_points.xyz[:, 1])}") print(f"Z coordinates match: {np.allclose(surface_points.xyz[:, 2], loaded_surface_points.xyz[:, 2])}") print(f"IDs match: {np.array_equal(surface_points.ids, loaded_surface_points.ids)}") print(f"Nugget values match: {np.allclose(surface_points.nugget, loaded_surface_points.nugget)}") # Print the name_id_maps to compare print("\nName to ID mappings:") print(f"Original: {surface_points.name_id_map}") print(f"Loaded: {loaded_surface_points.name_id_map}") ================================================ FILE: examples/tutorials/z_other_tutorials/json_io/02_horizontal_stratigraphic.py ================================================ """ Tutorial: Loading a Horizontal Stratigraphic Model using JSON I/O =============================================================== This tutorial demonstrates how to load a horizontal stratigraphic model using GemPy's JSON I/O functionality. The model consists of two horizontal layers (rock1 and rock2) with surface points and orientations. """ # %% # Import necessary libraries import matplotlib matplotlib.use('Agg') # Use non-interactive backend import gempy as gp import gempy_viewer as gpv import numpy as np import json from pathlib import Path import matplotlib.pyplot as plt from datetime import datetime # %% # Define the model data model_data = { "metadata": { "name": "Horizontal Stratigraphic Model", "creation_date": "2024-03-24", "last_modification_date": datetime.now().strftime("%Y-%m-%d"), "owner": "GemPy Team" }, "surface_points": [ {"x": 0.0, "y": 0.0, "z": 0.0, "id": 1, "nugget": 0.0}, {"x": 1.0, "y": 0.0, "z": 0.0, "id": 1, "nugget": 0.0}, {"x": 0.0, "y": 1.0, "z": 0.0, "id": 1, "nugget": 0.0}, {"x": 1.0, "y": 1.0, "z": 0.0, "id": 1, "nugget": 0.0}, {"x": 0.0, "y": 0.0, "z": 1.0, "id": 2, "nugget": 0.0}, {"x": 1.0, "y": 0.0, "z": 1.0, "id": 2, "nugget": 0.0}, {"x": 0.0, "y": 1.0, "z": 1.0, "id": 2, "nugget": 0.0}, {"x": 1.0, "y": 1.0, "z": 1.0, "id": 2, "nugget": 0.0} ], "orientations": [ {"x": 0.5, "y": 0.5, "z": 0.0, "G_x": 0.0, "G_y": 0.0, "G_z": 1.0, "id": 1, "nugget": 0.0, "polarity": 1}, {"x": 0.5, "y": 0.5, "z": 1.0, "G_x": 0.0, "G_y": 0.0, "G_z": 1.0, "id": 2, "nugget": 0.0, "polarity": 1} ], "series": [ { "name": "series1", "surfaces": ["layer1", "layer2"], "structural_relation": "ERODE", "colors": ["#ff0000", "#00ff00"] } ], "grid_settings": { "regular_grid_resolution": [10, 10, 10], "regular_grid_extent": [0.0, 1.0, 0.0, 1.0, 0.0, 1.0], "octree_levels": None }, "interpolation_options": { "kernel_options": { "range": 1.7, "c_o": 10 }, "mesh_extraction": True, "number_octree_levels": 1 }, "fault_relations": None, "id_name_mapping": { "name_to_id": { "layer1": 1, "layer2": 2 } } } # %% # Save the model data to a JSON file tutorial_dir = Path(__file__).parent json_file = tutorial_dir / "horizontal_stratigraphic.json" with open(json_file, "w") as f: json.dump(model_data, f, indent=4) # %% # Load the model from JSON model = gp.modules.json_io.JsonIO.load_model_from_json(str(json_file)) # %% # Compute the geological model gp.compute_model(model) # %% # Plot the model # Plot the initial geological model in the y direction without results fig, ax = plt.subplots(figsize=(10, 6)) gpv.plot_2d(model, direction=['y'], show_results=False, ax=ax) plt.title("Initial Geological Model (y direction)") plt.savefig('initial_model_y.png') plt.close() # Plot the result of the model in the x and y direction with data and without boundaries fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6)) gpv.plot_2d(model, direction=['x'], show_data=True, show_boundaries=False, ax=ax1) ax1.set_title("Model with Data (x direction)") gpv.plot_2d(model, direction=['y'], show_data=True, show_boundaries=False, ax=ax2) ax2.set_title("Model with Data (y direction)") plt.tight_layout() plt.savefig('model_with_data.png') plt.close() ================================================ FILE: examples/tutorials/z_other_tutorials/json_io/03_multiple_series_faults.py ================================================ """ Tutorial: Loading a Model with Multiple Series and Faults using JSON I/O ===================================================================== This tutorial demonstrates how to load a geological model with multiple series and faults using GemPy's JSON I/O functionality. The model consists of two layers (rock1, rock2) and a fault that offsets them. """ # %% # Import necessary libraries import matplotlib # matplotlib.use('Agg') # Use non-interactive backend import gempy as gp import gempy_viewer as gpv import numpy as np import json from pathlib import Path import matplotlib.pyplot as plt from gempy_engine.core.data.stack_relation_type import StackRelationType from gempy.modules.json_io.json_operations import JsonIO # Updated import path from datetime import datetime # %% # Define the model data model_data = { "metadata": { "name": "Multiple Series and Faults Model", "creation_date": "2024-03-24", # Fixed creation date "last_modification_date": datetime.now().strftime("%Y-%m-%d"), # Current date only "owner": "GemPy Team" }, "surface_points": [ # rock1 points {"x": 0, "y": 200, "z": 600, "id": 0, "nugget": 0.00002}, {"x": 0, "y": 500, "z": 600, "id": 0, "nugget": 0.00002}, {"x": 0, "y": 800, "z": 600, "id": 0, "nugget": 0.00002}, {"x": 200, "y": 200, "z": 600, "id": 0, "nugget": 0.00002}, {"x": 200, "y": 500, "z": 600, "id": 0, "nugget": 0.00002}, {"x": 200, "y": 800, "z": 600, "id": 0, "nugget": 0.00002}, {"x": 800, "y": 200, "z": 200, "id": 0, "nugget": 0.00002}, {"x": 800, "y": 500, "z": 200, "id": 0, "nugget": 0.00002}, {"x": 800, "y": 800, "z": 200, "id": 0, "nugget": 0.00002}, {"x": 1000, "y": 200, "z": 200, "id": 0, "nugget": 0.00002}, {"x": 1000, "y": 500, "z": 200, "id": 0, "nugget": 0.00002}, {"x": 1000, "y": 800, "z": 200, "id": 0, "nugget": 0.00002}, # rock2 points {"x": 0, "y": 200, "z": 800, "id": 1, "nugget": 0.00002}, {"x": 0, "y": 800, "z": 800, "id": 1, "nugget": 0.00002}, {"x": 200, "y": 200, "z": 800, "id": 1, "nugget": 0.00002}, {"x": 200, "y": 800, "z": 800, "id": 1, "nugget": 0.00002}, {"x": 800, "y": 200, "z": 400, "id": 1, "nugget": 0.00002}, {"x": 800, "y": 800, "z": 400, "id": 1, "nugget": 0.00002}, {"x": 1000, "y": 200, "z": 400, "id": 1, "nugget": 0.00002}, {"x": 1000, "y": 800, "z": 400, "id": 1, "nugget": 0.00002}, # fault points {"x": 500, "y": 500, "z": 500, "id": 2, "nugget": 0.00002}, {"x": 450, "y": 500, "z": 600, "id": 2, "nugget": 0.00002}, {"x": 500, "y": 200, "z": 500, "id": 2, "nugget": 0.00002}, {"x": 450, "y": 200, "z": 600, "id": 2, "nugget": 0.00002}, {"x": 500, "y": 800, "z": 500, "id": 2, "nugget": 0.00002}, {"x": 450, "y": 800, "z": 600, "id": 2, "nugget": 0.00002} ], "orientations": [ # rock2 orientations {"x": 100, "y": 500, "z": 800, "G_x": 0, "G_y": 0, "G_z": 1, "id": 1, "nugget": 0.00002, "polarity": 1}, {"x": 900, "y": 500, "z": 400, "G_x": 0, "G_y": 0, "G_z": 1, "id": 1, "nugget": 0.00002, "polarity": 1}, # rock1 orientations {"x": 100, "y": 500, "z": 600, "G_x": 0, "G_y": 0, "G_z": 1, "id": 0, "nugget": 0.00002, "polarity": 1}, {"x": 900, "y": 500, "z": 200, "G_x": 0, "G_y": 0, "G_z": 1, "id": 0, "nugget": 0.00002, "polarity": 1}, # fault orientation (60-degree dip) {"x": 500, "y": 500, "z": 500, "G_x": 0.866, "G_y": 0, "G_z": 0.5, "id": 2, "nugget": 0.00002, "polarity": 1} ], "series": [ { "name": "Fault_Series", "surfaces": ["fault"], "structural_relation": "FAULT", "colors": ["#ffbe00"] }, { "name": "Strat_Series", "surfaces": ["rock2", "rock1"], "structural_relation": "ERODE", "colors": ["#015482", "#9f0052"] } ], "grid_settings": { "regular_grid_resolution": [50, 50, 50], "regular_grid_extent": [0, 1000, 0, 1000, 0, 1000], "octree_levels": None }, "interpolation_options": { "kernel_options": { "range": 1.7, "c_o": 10 }, "mesh_extraction": True, "number_octree_levels": 1 }, "fault_relations": [[0, 1], [0, 0]], # Fault series affects Strat_Series "id_name_mapping": { "name_to_id": { "rock1": 0, "rock2": 1, "fault": 2 } } } # %% # Save the model data to a JSON file tutorial_dir = Path(__file__).parent json_file = tutorial_dir / "multiple_series_faults.json" with open(json_file, "w") as f: json.dump(model_data, f, indent=4) # %% # Load the model from JSON model = JsonIO.load_model_from_json(str(json_file)) # Print metadata to verify it's properly loaded print("\nModel Metadata:") print(f"Name: {model.meta.name}") print(f"Creation Date: {model.meta.creation_date}") print(f"Last Modification Date: {model.meta.last_modification_date}") print(f"Owner: {model.meta.owner}") # Print structural groups print("\nStructural Groups:") print(model.structural_frame.structural_groups) # %% # Compute the geological model gp.compute_model(model) # Print scalar field values to verify they are calculated print("\nScalar Field Values (Fault Series):") print(f"Shape: {model.solutions.raw_arrays.scalar_field_matrix.shape}") print(f"Min value: {model.solutions.raw_arrays.scalar_field_matrix[0].min()}") print(f"Max value: {model.solutions.raw_arrays.scalar_field_matrix[0].max()}") print(f"Mean value: {model.solutions.raw_arrays.scalar_field_matrix[0].mean()}") # %% # Save the computed model to a new JSON file computed_json_file = tutorial_dir / "multiple_series_faults_computed.json" JsonIO.save_model_to_json(model, str(computed_json_file)) print(f"\nSaved computed model to: {computed_json_file}") # %% # Load the computed model back to verify metadata is preserved reloaded_model = JsonIO.load_model_from_json(str(computed_json_file)) print("\nReloaded Model Metadata:") print(f"Name: {reloaded_model.meta.name}") print(f"Creation Date: {reloaded_model.meta.creation_date}") print(f"Last Modification Date: {reloaded_model.meta.last_modification_date}") print(f"Owner: {reloaded_model.meta.owner}") # %% # Create plots with proper configuration # Plot 1: Cross-section in Y direction (XZ plane) fig = plt.figure(figsize=(10, 8)) ax = fig.add_subplot(111) gpv.plot_2d( model, cell_number=25, # Middle of the model direction='y', show_data=True, show_boundaries=True, show_results=True, ax=ax ) plt.title("Geological Model - Y Direction (XZ plane)") plt.savefig('model_y_direction.png', dpi=300, bbox_inches='tight') plt.close() # Plot 2: Cross-section in X direction (YZ plane) fig = plt.figure(figsize=(10, 8)) ax = fig.add_subplot(111) gpv.plot_2d( model, cell_number=25, # Middle of the model direction='x', show_data=True, show_boundaries=True, show_results=True, ax=ax ) plt.title("Geological Model - X Direction (YZ plane)") plt.savefig('model_x_direction.png', dpi=300, bbox_inches='tight') plt.close() # Plot 3: Scalar field of the fault plt.figure(figsize=(10, 8)) ax = plt.gca() # Get scalar field values and reshape to grid scalar_field = model.solutions.raw_arrays.scalar_field_matrix[0].reshape(50, 50, 50) # Plot middle slice in Y direction middle_slice = scalar_field[:, 25, :] im = ax.imshow(middle_slice.T, extent=[0, 1000, 0, 1000], origin='lower', cmap='RdBu', aspect='equal') # Add colorbar plt.colorbar(im, ax=ax, label='Scalar Field Value') # Plot surface points fault_element = model.structural_frame.get_element_by_name("fault") if fault_element and fault_element.surface_points is not None: fault_points_coords = fault_element.surface_points.xyz # Filter points near the slice (Y around 500) mask = np.abs(fault_points_coords[:, 1] - 500) < 100 filtered_points = fault_points_coords[mask] if len(filtered_points) > 0: ax.scatter(filtered_points[:, 0], filtered_points[:, 2], c='red', s=50, label='Surface Points') ax.legend() ax.set_xlabel('X') ax.set_ylabel('Z') ax.set_title('Fault Scalar Field - Y Direction (Middle Slice)') # Save plot plt.savefig('fault_scalar_field.png', dpi=300, bbox_inches='tight') plt.close() print("\nPlot saved as fault_scalar_field.png") # Plot 4: 3D visualization (optional) PLOT_3D = False # Set to True to enable 3D plotting if PLOT_3D: try: import pyvista as pv p = pv.Plotter(notebook=False, off_screen=True) gpv.plot_3d( model, show_data=True, show_surfaces=True, show_boundaries=True, plotter=p ) p.screenshot('model_3d.png', transparent_background=False) p.close() except Exception as e: print(f"Could not create 3D plot: {e}") # %% ================================================ FILE: examples/tutorials/z_other_tutorials/json_io/04_combination_model.py ================================================ # %% """ Combination Model with JSON I/O ============================== This example demonstrates how to create a model combining faults and unconformities using GemPy's JSON I/O functionality. The model is based on the g07_combination.py example, featuring a folded domain with an unconformity and a fault. Part 1: Create and save the initial model structure Part 2: Load the model, compute it, and visualize results """ import gempy as gp import gempy_viewer as gpv import numpy as np import os from gempy_engine.core.data.stack_relation_type import StackRelationType from gempy_engine.config import AvailableBackends from gempy.modules.json_io.json_operations import JsonIO # %% # Part 1: Create and save the initial model structure # ------------------------------------------------- # Define paths data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/' path_to_data = data_path + "/data/input_data/jan_models/" json_file = "combination_model.json" computed_json_file = "combination_model_computed.json" # Create the model with data import model = gp.create_geomodel( project_name='Combination Model', extent=[0, 2500, 0, 1000, 0, 1000], resolution=[20, 20, 20], refinement=6, importer_helper=gp.data.ImporterHelper( path_to_orientations=path_to_data + "model7_orientations.csv", path_to_surface_points=path_to_data + "model7_surface_points.csv" ) ) # Set metadata model.meta.creation_date = "2024-03-24" model.meta.last_modification_date = "2024-03-24" # Map geological series to surfaces gp.map_stack_to_surfaces( gempy_model=model, mapping_object={ "Fault_Series": ('fault',), "Strat_Series1": ('rock3',), "Strat_Series2": ('rock2', 'rock1'), } ) # Set structural relations model.structural_frame.structural_groups[0].structural_relation = StackRelationType.FAULT model.structural_frame.fault_relations = np.array([ [0, 1, 1], [0, 0, 0], [0, 0, 0] ]) # Set colors for visualization model.structural_frame.get_element_by_name("fault").color = "#015482" model.structural_frame.get_element_by_name("rock3").color = "#9f0052" model.structural_frame.get_element_by_name("rock2").color = "#ffbe00" model.structural_frame.get_element_by_name("rock1").color = "#728f02" # Set interpolation options model.interpolation_options.number_octree_levels_surface = 5 # Save the model data to a JSON file JsonIO.save_model_to_json(model, json_file) print(f"\nSaved initial model to: {os.path.abspath(json_file)}") # %% # Part 2: Load the model and compute # --------------------------------- print("\nLoading model from JSON...") model = JsonIO.load_model_from_json(json_file) print("\nModel Metadata:") print(f"Name: {model.meta.name}") print(f"Creation Date: {model.meta.creation_date}") # TODO: This does not update here when running. In 03 you have a current date time setter print(f"Last Modified: {model.meta.last_modification_date}") print("\nStructural Groups:") print(model.structural_frame) # Compute the model print("\nComputing the model...") s = gp.compute_model( gempy_model=model, engine_config=gp.data.GemPyEngineConfig( backend=AvailableBackends.numpy ) ) # Save the computed model # TODO: This is identical to the file saved before computation (no results stored) JsonIO.save_model_to_json(model, computed_json_file) print(f"\nSaved computed model to: {os.path.abspath(computed_json_file)}") #%% # Plot the results print("\nGenerating plots...") # 2D plots gpv.plot_2d(model, direction='y', show_results=False) gpv.plot_2d(model, direction='y', show_data=True, show_boundaries=True) gpv.plot_2d(model, direction='x', show_data=True) # Plot the blocks accounting for fault blocks gpv.plot_2d( model=model, override_regular_grid=model.solutions.raw_arrays.litho_faults_block, show_data=True, kwargs_lithology={'cmap': 'Set1', 'norm': None} ) # 3D plot gpv.plot_3d(model) print("\nDone! The model has been:") print("1. Created and saved to:", json_file) print("2. Loaded from JSON") print("3. Computed") print("4. Saved with computation results to:", computed_json_file) print("5. Visualized with various plots") ================================================ FILE: examples/tutorials/z_other_tutorials/json_io/05_minimal_json.py ================================================ """ Tutorial: Minimal JSON I/O with default metadata, interpolation options, and grid settings This tutorial demonstrates how to use the JSON I/O functionality with minimal input, relying on default metadata values, interpolation options, and grid settings. """ # %% import numpy as np from datetime import datetime import gempy as gp import gempy_viewer as gpv import json import pyvista as pv from gempy.modules.json_io.json_operations import JsonIO # Updated import path # %% # Create the corresponding minimal JSON model model_data = { "surface_points": [ { "x": 50.0, "y": 0.0, "z": -20.0, "id": 0, } ], "orientations": [ { "x": 50.0, "y": 0.0, "z": -20.0, "G_x": 1.0, "G_y": 0.0, "G_z": 1.0, "id": 0, "polarity": 1 } ], "grid_settings": { "regular_grid_resolution": [100, 2, 100], "regular_grid_extent": [0, 150, -10, 10, -100, 0] } } # %% # Save the minimal model to JSON with open("minimal_model.json", "w") as f: json.dump(model_data, f, indent=4) # Load the model from JSON geo_model = JsonIO.load_model_from_json("minimal_model.json") # Compute the geological model gp.compute_model(geo_model) p2d = gpv.plot_2d(geo_model) # %% # Print the model metadata (should use default values) print("\nModel Metadata:") print(f"Name: {geo_model.meta.name}") print(f"Creation Date: {geo_model.meta.creation_date}") print(f"Last Modification Date: {geo_model.meta.last_modification_date}") print(f"Owner: {geo_model.meta.owner}") # Print the interpolation options (should use default values) print("\nInterpolation Options:") print(f"Range: {geo_model.interpolation_options.kernel_options.range}") print(f"Mesh Extraction: {geo_model.interpolation_options.mesh_extraction}") # Print the grid settings (should use default values) print("\nGrid Settings:") print(f"Resolution: {geo_model.grid._dense_grid.resolution}") print(f"Extent: {geo_model.grid._dense_grid.extent}") # Print the structural groups print("\nStructural Groups:") for group in geo_model.structural_frame.structural_groups: print(group) # Save the loaded model to verify the metadata, interpolation options, and grid settings are preserved JsonIO.save_model_to_json(geo_model, "minimal_model_loaded.json") # %% ================================================ FILE: examples/tutorials/z_other_tutorials/json_io/05c_minimal_comparison.py ================================================ """ Tutorial: Compare minimal GemPy model with JSON representation This tutorial demonstrates how to create a minimal GemPy model and compare it with its JSON representation. """ # %% import gempy as gp import gempy_viewer as gpv import json import numpy as np from gempy.modules.json_io.json_operations import JsonIO # %% # Set up the basic model geo_model: gp.data.GeoModel = gp.create_geomodel( project_name='Model1', extent=[0, 150, -10, 10, -100, 0], resolution=[100, 2, 100], structural_frame=gp.data.StructuralFrame.initialize_default_structure() ) # Add a surface point gp.add_surface_points( geo_model=geo_model, x=[50], y=[0], z=[-20], elements_names=['surface1'] ) # Add an orientation gp.add_orientations( geo_model=geo_model, x=[50], y=[0], z=[-68], elements_names=['surface1'], pole_vector=[[1, 0, 1]] ) # Set interpolation options geo_model.interpolation_options.kernel_options.range = 10. geo_model.interpolation_options.kernel_options.c_o = 5. geo_model.interpolation_options.mesh_extraction = True geo_model.interpolation_options.number_octree_levels = 2 geo_model.update_transform(gp.data.GlobalAnisotropy.NONE) gp.compute_model(geo_model, engine_config=gp.data.GemPyEngineConfig()) # %% # Plot the model p2d = gpv.plot_2d(geo_model) # %% # Create the corresponding minimal JSON model json_model_data = { "surface_points": [ { "x": 50.0, "y": 0.0, "z": -20.0, "id": 0 } ], "orientations": [ { "x": 50.0, "y": 0.0, "z": -68.0, "G_x": 1.0, "G_y": 0.0, "G_z": 1.0, "id": 0 } ], "grid_settings": { "regular_grid_resolution": [100, 2, 100], "regular_grid_extent": [0, 150, -10, 10, -100, 0] }, "interpolation_options": { "kernel_options": { "range": 10.0, "c_o": 5.0 }, "mesh_extraction": True, "number_octree_levels": 2 }, "series": [ { "name": "default_formations", "surfaces": ["surface1"], "structural_relation": "ERODE" } ] } # Save the JSON model with open("minimal_model.json", "w") as f: json.dump(json_model_data, f, indent=4) # Load the model from JSON json_geo_model = JsonIO.load_model_from_json("minimal_model.json") # Compute the JSON model gp.compute_model(json_geo_model, engine_config=gp.data.GemPyEngineConfig()) # %% # Compare the models print("\nComparing GemPy and JSON models:") print("\n1. Surface Points:") print("GemPy model:") print(geo_model.surface_points_copy) print("\nJSON model:") print(json_geo_model.surface_points_copy) print("\n2. Orientations:") print("GemPy model:") print(geo_model.orientations_copy) print("\nJSON model:") print(json_geo_model.orientations_copy) print("\n3. Grid Settings:") print("GemPy model:") print(f"Resolution: {geo_model.grid._dense_grid.resolution}") print(f"Extent: {geo_model.grid._dense_grid.extent}") print("\nJSON model:") print(f"Resolution: {json_geo_model.grid._dense_grid.resolution}") print(f"Extent: {json_geo_model.grid._dense_grid.extent}") print("\n4. Interpolation Options:") print("GemPy model:") print(f"Range: {geo_model.interpolation_options.kernel_options.range}") print(f"C_o: {geo_model.interpolation_options.kernel_options.c_o}") print(f"Mesh Extraction: {geo_model.interpolation_options.mesh_extraction}") print(f"Number Octree Levels: {geo_model.interpolation_options.number_octree_levels}") print("\nJSON model:") print(f"Range: {json_geo_model.interpolation_options.kernel_options.range}") print(f"C_o: {json_geo_model.interpolation_options.kernel_options.c_o}") print(f"Mesh Extraction: {json_geo_model.interpolation_options.mesh_extraction}") print(f"Number Octree Levels: {json_geo_model.interpolation_options.number_octree_levels}") print("\n5. Structural Groups:") print("GemPy model:") for group in geo_model.structural_frame.structural_groups: print(group) print("\nJSON model:") for group in json_geo_model.structural_frame.structural_groups: print(group) # %% # Plot both models side by side p2d_gempy = gpv.plot_2d(geo_model, title="GemPy Model") p2d_json = gpv.plot_2d(json_geo_model, title="JSON Model") # %% ================================================ FILE: examples/tutorials/z_other_tutorials/json_io/horizontal_stratigraphic.json ================================================ { "metadata": { "name": "horizontal_stratigraphic", "creation_date": "2024-03-19", "last_modification_date": "2024-03-19", "owner": "tutorial" }, "surface_points": [ { "x": 100.0, "y": 200.0, "z": 600.0, "id": 1, "nugget": 2e-05 }, { "x": 500.0, "y": 200.0, "z": 600.0, "id": 1, "nugget": 2e-05 }, { "x": 900.0, "y": 200.0, "z": 600.0, "id": 1, "nugget": 2e-05 }, { "x": 100.0, "y": 800.0, "z": 600.0, "id": 1, "nugget": 2e-05 }, { "x": 500.0, "y": 800.0, "z": 600.0, "id": 1, "nugget": 2e-05 }, { "x": 900.0, "y": 800.0, "z": 600.0, "id": 1, "nugget": 2e-05 }, { "x": 100.0, "y": 200.0, "z": 400.0, "id": 0, "nugget": 2e-05 }, { "x": 500.0, "y": 200.0, "z": 400.0, "id": 0, "nugget": 2e-05 }, { "x": 900.0, "y": 200.0, "z": 400.0, "id": 0, "nugget": 2e-05 }, { "x": 100.0, "y": 800.0, "z": 400.0, "id": 0, "nugget": 2e-05 }, { "x": 500.0, "y": 800.0, "z": 400.0, "id": 0, "nugget": 2e-05 }, { "x": 900.0, "y": 800.0, "z": 400.0, "id": 0, "nugget": 2e-05 } ], "orientations": [ { "x": 500.0, "y": 500.0, "z": 600.0, "G_x": 0.0, "G_y": 0.0, "G_z": 1.0, "id": 1, "nugget": 0.01, "polarity": 1 }, { "x": 500.0, "y": 500.0, "z": 400.0, "G_x": 0.0, "G_y": 0.0, "G_z": 1.0, "id": 0, "nugget": 0.01, "polarity": 1 } ], "series": [ { "name": "Strat_Series", "surfaces": [ "rock2", "rock1" ] } ], "grid_settings": { "regular_grid_resolution": [ 10, 10, 10 ], "regular_grid_extent": [ 0, 1000, 0, 1000, 0, 1000 ], "octree_levels": null }, "interpolation_options": {} } ================================================ FILE: examples/tutorials/z_other_tutorials/json_io/minimal_model.json ================================================ { "surface_points": [ { "x": 50.0, "y": 0.0, "z": -20.0, "id": 0 } ], "orientations": [ { "x": 50.0, "y": 0.0, "z": -68.0, "G_x": 1.0, "G_y": 0.0, "G_z": 1.0, "id": 0 } ], "grid_settings": { "regular_grid_resolution": [ 100, 2, 100 ], "regular_grid_extent": [ 0, 150, -10, 10, -100, 0 ] }, "interpolation_options": { "kernel_options": { "range": 10.0, "c_o": 5.0 }, "mesh_extraction": true, "number_octree_levels": 2 }, "series": [ { "name": "default_formations", "surfaces": [ "surface1" ], "structural_relation": "ERODE" } ] } ================================================ FILE: examples/tutorials/z_other_tutorials/json_io/minimal_model_loaded.json ================================================ { "metadata": { "name": "GemPy Model", "creation_date": "2025-03-28", "last_modification_date": "2025-03-28", "owner": "GemPy Modeller" }, "surface_points": [ { "x": 50.0, "y": 0.0, "z": -20.0, "id": 34966260, "nugget": 0.0 } ], "orientations": [ { "x": 50.0, "y": 0.0, "z": -20.0, "G_x": 1.0, "G_y": 0.0, "G_z": 1.0, "id": 34966260, "nugget": 0.01, "polarity": 1 } ], "series": [ { "name": "Strat_Series", "surfaces": [ "surface_0" ], "structural_relation": "ERODE", "colors": [ "#015482" ] } ], "grid_settings": { "regular_grid_resolution": [ 100, 2, 100 ], "regular_grid_extent": [ 0.0, 150.0, -10.0, 10.0, -100.0, 0.0 ], "octree_levels": null }, "interpolation_options": { "kernel_options": { "range": 1.7, "c_o": 10.0 }, "mesh_extraction": true, "number_octree_levels": 4 }, "fault_relations": [ [ 0 ] ], "id_name_mapping": { "name_to_id": { "surface_0": 34966260, "basement": 91927817 } } } ================================================ FILE: examples/tutorials/z_other_tutorials/json_io/multiple_series_faults.json ================================================ { "metadata": { "name": "multiple_series_faults", "creation_date": "2024-03-19", "last_modification_date": "2024-03-19", "owner": "tutorial" }, "surface_points": [ { "x": 0.0, "y": 200.0, "z": 600.0, "id": 2, "nugget": 2e-05 }, { "x": 0.0, "y": 500.0, "z": 600.0, "id": 2, "nugget": 2e-05 }, { "x": 0.0, "y": 800.0, "z": 600.0, "id": 2, "nugget": 2e-05 }, { "x": 200.0, "y": 200.0, "z": 600.0, "id": 2, "nugget": 2e-05 }, { "x": 200.0, "y": 500.0, "z": 600.0, "id": 2, "nugget": 2e-05 }, { "x": 200.0, "y": 800.0, "z": 600.0, "id": 2, "nugget": 2e-05 }, { "x": 800.0, "y": 200.0, "z": 200.0, "id": 2, "nugget": 2e-05 }, { "x": 800.0, "y": 500.0, "z": 200.0, "id": 2, "nugget": 2e-05 }, { "x": 800.0, "y": 800.0, "z": 200.0, "id": 2, "nugget": 2e-05 }, { "x": 1000.0, "y": 200.0, "z": 200.0, "id": 2, "nugget": 2e-05 }, { "x": 1000.0, "y": 500.0, "z": 200.0, "id": 2, "nugget": 2e-05 }, { "x": 1000.0, "y": 800.0, "z": 200.0, "id": 2, "nugget": 2e-05 }, { "x": 0.0, "y": 200.0, "z": 800.0, "id": 1, "nugget": 2e-05 }, { "x": 0.0, "y": 800.0, "z": 800.0, "id": 1, "nugget": 2e-05 }, { "x": 200.0, "y": 200.0, "z": 800.0, "id": 1, "nugget": 2e-05 }, { "x": 200.0, "y": 800.0, "z": 800.0, "id": 1, "nugget": 2e-05 }, { "x": 800.0, "y": 200.0, "z": 400.0, "id": 1, "nugget": 2e-05 }, { "x": 800.0, "y": 800.0, "z": 400.0, "id": 1, "nugget": 2e-05 }, { "x": 1000.0, "y": 200.0, "z": 400.0, "id": 1, "nugget": 2e-05 }, { "x": 1000.0, "y": 800.0, "z": 400.0, "id": 1, "nugget": 2e-05 }, { "x": 500.0, "y": 500.0, "z": 500.0, "id": 0, "nugget": 2e-05 }, { "x": 450.0, "y": 500.0, "z": 600.0, "id": 0, "nugget": 2e-05 }, { "x": 500.0, "y": 200.0, "z": 500.0, "id": 0, "nugget": 2e-05 }, { "x": 450.0, "y": 200.0, "z": 600.0, "id": 0, "nugget": 2e-05 }, { "x": 500.0, "y": 800.0, "z": 500.0, "id": 0, "nugget": 2e-05 }, { "x": 450.0, "y": 800.0, "z": 600.0, "id": 0, "nugget": 2e-05 } ], "orientations": [ { "x": 100.0, "y": 500.0, "z": 800.0, "G_x": 0.0, "G_y": 0.0, "G_z": 1.0, "id": 1, "nugget": 0.01, "polarity": 1 }, { "x": 100.0, "y": 500.0, "z": 600.0, "G_x": 0.0, "G_y": 0.0, "G_z": 1.0, "id": 2, "nugget": 0.01, "polarity": 1 }, { "x": 500.0, "y": 500.0, "z": 500.0, "G_x": 0.8, "G_y": 0.0, "G_z": 0.6, "id": 0, "nugget": 0.01, "polarity": 1 }, { "x": 900.0, "y": 500.0, "z": 400.0, "G_x": 0.0, "G_y": 0.0, "G_z": 1.0, "id": 1, "nugget": 0.01, "polarity": 1 }, { "x": 900.0, "y": 500.0, "z": 200.0, "G_x": 0.0, "G_y": 0.0, "G_z": 1.0, "id": 2, "nugget": 0.01, "polarity": 1 } ], "series": [ { "name": "Fault_Series", "surfaces": [ "fault" ], "structural_relation": "FAULT", "color": "#015482" }, { "name": "Strat_Series", "surfaces": [ "rock2", "rock1" ], "structural_relation": "ERODE", "colors": [ "#ffbe00", "#9f0052" ] } ], "grid_settings": { "regular_grid_resolution": [ 90, 30, 30 ], "regular_grid_extent": [ 0, 1000, 0, 1000, 0, 1000 ], "octree_levels": null }, "interpolation_options": {} } ================================================ FILE: examples/tutorials/z_other_tutorials/json_io/sample_surface_points.json ================================================ { "metadata": { "name": "sample_model", "creation_date": "2024-03-19", "last_modification_date": "2024-03-19", "owner": "tutorial" }, "surface_points": [ { "x": 0.0, "y": 0.0, "z": 0.0, "id": 0, "nugget": 2e-05 }, { "x": 1.0, "y": 1.0, "z": 1.0, "id": 0, "nugget": 2e-05 }, { "x": 2.0, "y": 2.0, "z": 2.0, "id": 1, "nugget": 2e-05 }, { "x": 3.0, "y": 3.0, "z": 3.0, "id": 1, "nugget": 2e-05 }, { "x": 4.0, "y": 4.0, "z": 4.0, "id": 2, "nugget": 2e-05 } ], "orientations": [], "faults": [], "series": [], "grid_settings": { "regular_grid_resolution": [ 10, 10, 10 ], "regular_grid_extent": [ 0, 4, 0, 4, 0, 4 ], "octree_levels": null }, "interpolation_options": {} } ================================================ FILE: examples/tutorials/z_other_tutorials/video_tutorial_model_1.py ================================================ """ Video Tutorial "code-along": Modeling step by step ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ """ # %% # This tutorial demonstrates step-by-step geological modeling using the `gempy` and `gempy_viewer` libraries. # It follows the Video tutorial series available on the `gempy YouTube channel `_. # %% # Video tutorial 1: Introduction # """""""""""""""""""""""""""""" # # The first video is an introduction to GemPy - please view online before starting the tutorial. # # # %% #.. raw:: html # # # %% # Video tutorial 2: Input data # """""""""""""""""""""""""""" # %% #.. raw:: html # # # Required imports import gempy as gp import gempy_viewer as gpv # %% # Path to input data data_path = "https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/" path_to_data = data_path + "/data/input_data/video_tutorials_v3/" # %% # Create instance of geomodel geo_model = gp.create_geomodel( project_name = 'tutorial_model', extent=[0,2500,0,1000,0,1000], resolution=[100,40,40], importer_helper=gp.data.ImporterHelper( path_to_orientations=path_to_data+"tutorial_model_orientations.csv", path_to_surface_points=path_to_data+"tutorial_model_surface_points.csv" ) ) # %% # Display a basic cross section of input data gpv.plot_2d(geo_model) # %% # Manually add a surface point gp.add_surface_points( geo_model=geo_model, x=[2250], y=[500], z=[750], elements_names=['rock1'] ) # %% # Show added point in cross section gpv.plot_2d(geo_model) # %% # Video tutorial 3: Structural frame # """""""""""""""""""""""""""""""""" # %% #.. raw:: html # # # View structural frame geo_model.structural_frame # %% # View structural elements geo_model.structural_frame.structural_elements # %% # Define structural groups and age/stratigraphic relationship gp.map_stack_to_surfaces( gempy_model=geo_model, mapping_object={ "Strat_Series2": ("rock3"), "Strat_Series1": ("rock2", "rock1") } ) # %% # Video tutorial 4: Computation and results # """"""""""""""""""""""""""""""""""""""""" # %% #.. raw:: html # # # View interpolation options geo_model.interpolation_options # %% # Compute a solution for the model gp.compute_model(geo_model) # %% # Display the result in 2d section gpv.plot_2d(geo_model, cell_number=20) # %% # Some examples of how to access results print(geo_model.solutions.raw_arrays.lith_block) print(geo_model.grid.dense_grid.values) # %% # Video tutorial 5: 2D visualization # """""""""""""""""""""""""""""""""" # %% #.. raw:: html # # # 2d plotting options gpv.plot_2d(geo_model, show_value=True, show_lith=False, show_scalar=True, series_n=1, cell_number=25) # %% # Create custom section lines gp.set_section_grid( grid=geo_model.grid, section_dict={ 'section1': ([0, 0], [2500, 1000], [100, 50]), 'section2': ([1000, 1000], [1500, 0], [100, 100]), } ) # %% # Show custom cross-section traces gpv.plot_section_traces(geo_model) # %% # Recompute model as a new grid was added gp.compute_model(geo_model) # %% # Display custom cross-sections gpv.plot_2d(geo_model, section_names=['section1', 'section2'], show_data=False) # %% # Video tutorial 6: 3D visualization # """""""""""""""""""""""""""""""""" # %% #.. raw:: html # # # Display the result in 3d gpv.plot_3d(geo_model, show_lith=True, show_boundaries=True, ve=None) # %% # How to access DC meshes geo_model.solutions.dc_meshes[0].dc_data # transform mesh vertices to original coordinate system back_transformed_vertices = geo_model.input_transform.apply_inverse(geo_model.solutions.dc_meshes[0].vertices) # %% # Video tutorial 7: Topography # """"""""""""""""""""""""""""""" # %% #.. raw:: html # # # Setting a randomly generated topography import numpy as np gp.set_topography_from_random( grid=geo_model.grid, fractal_dimension=1.2, d_z=np.array([700, 900]), topography_resolution=np.array([250, 100]) ) # %% # Recompute model as a new grid was added gp.compute_model(geo_model) # %% # Display a cross-section with topography gpv.plot_2d(geo_model, show_topography=True) # %% # Displaying a geological map gpv.plot_2d(geo_model, show_topography=True, section_names=['topography'], show_boundaries=False, show_data=False) # %% # Display the 3d model with topography gpv.plot_3d(geo_model, show_lith=True, show_topography=True) # sphinx_gallery_thumbnail_number = -1 ================================================ FILE: examples/tutorials/z_other_tutorials/video_tutorial_model_2_faults.py ================================================ """ Video Tutorial "code-along": Faults ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ """ # %% # This tutorial demonstrates step-by-step how to add faults to our geological models created with `gempy`. # It follows the Video tutorial series available on the [gempy YouTube channel](https://www.youtube.com/@GemPy3D). # Please follow the first part of the tutorial to learn the basics of modeling with gempy before diving into this tutorial. # %% # Video tutorial faults: Introduction # """""""""""""""""""""""""""""""""""" # # The first video introduces the concept of modeling faults with GemPy - please view online before starting the tutorial. # # # %% # .. raw:: html # # # # # %% # Required imports import gempy as gp import gempy_viewer as gpv import numpy as np # %% # Path to input data data_path = "https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/" path_to_data = data_path + "/data/input_data/video_tutorials_v3/" # %% # Create instance of geomodel geo_model = gp.create_geomodel( project_name = 'tutorial_model_faults', extent=[0,3000,0,1000,0,1000], resolution=[90,30,30], importer_helper=gp.data.ImporterHelper( path_to_orientations=path_to_data + "tutorial_model_faults_1_orientations.csv", path_to_surface_points=path_to_data + "tutorial_model_faults_1_surface_points.csv" ) ) # %% # Display a basic cross section of input data gpv.plot_2d(geo_model); # %% # Map geological series to surfaces gp.map_stack_to_surfaces( gempy_model=geo_model, mapping_object={ "Fault_Series1" : ('fault'), "Strat_Series1": ('rock3'), "Strat_Series2": ('rock2', 'rock1'), } ) # %% # Define youngest structural group as fault gp.set_is_fault(geo_model, ["Fault_Series1"]) # %% # Compute a solution for the model gp.compute_model(geo_model) # %% # Display the result in 2d section gpv.plot_2d(geo_model, cell_number=20) # gpv.plot_3d(geo_model) # %% # Display the scalar field of the fault in 2d section gpv.plot_2d(geo_model, show_scalar=True, show_lith=False, series_n=0) gpv.plot_2d(geo_model, show_scalar=True, show_lith=False, series_n=1) gpv.plot_2d(geo_model, show_scalar=True, show_lith=False, series_n=2) # %% # Video tutorial 9: Fault relations # """""""""""""""""""""""""""" # %% # .. raw:: html # # # # # ***Warning***: In the following section we will make several changes to the existing model. This includes adding new elements, groups, surface points and orientation and reordering the structural frame. Executing these cells more than once can lead to errors as things will be added twice. # %% # Creating a new strucutral element with surface point and orientation data new_element = gp.data.StructuralElement( name='fault0', color=next(geo_model.structural_frame.color_generator), surface_points=gp.data.SurfacePointsTable.from_arrays( x=np.array([2750, 2750, 2750]), y=np.array([0, 500, 1000]), z=np.array([400, 400, 400]), names=['fault0']*3 ), orientations=gp.data.OrientationsTable.from_arrays( x=np.array([2750]), y=np.array([500]), z=np.array([400]), G_x=np.array([0.8]), G_y=np.array([0]), G_z=np.array([0.6]), names=['fault0'] ) ) # Creating a new structural group that contains the new element group_fault0 = gp.data.StructuralGroup( name='Fault_Series0', elements=[new_element], structural_relation=gp.data.StackRelationType.ERODE, ) # Insert the fault group into the structural frame at first position (index 0) geo_model.structural_frame.insert_group(index=0, group=group_fault0); # %% # Define youngest structural group as fault gp.set_is_fault(geo_model, ["Fault_Series0"]) # %% # Add additional information for exisitng elements on other side of fault gp.add_surface_points( geo_model=geo_model, x=[2950, 2950, 2950], y=[0, 500, 1000], z=[500, 500, 500], elements_names=['rock2']*3 ); gp.add_surface_points( geo_model=geo_model, x=[2950, 2950, 2950], y=[0, 500, 1000], z=[350, 350, 350], elements_names=['rock1']*3 ); gp.add_surface_points( geo_model=geo_model, x=[2950, 2950, 2950], y=[0, 500, 1000], z=[550, 500, 500], elements_names=['rock3']*3 ); # %% # Display input data with the new fault and the additional input data for the other elements gpv.plot_2d(geo_model, show_data=True, show_boundaries=False, show_lith=False) # %% # Recompute the model with the new information gp.compute_model(geo_model) # %% # Display the new result gpv.plot_2d(geo_model) # %% # Switching the order by adding a new group containing rock3 on top gp.add_structural_group( model=geo_model, group_index=0, structural_group_name="Strat_Series0", elements=[ geo_model.structural_frame.get_element_by_name("rock3") ], structural_relation=gp.data.StackRelationType.ERODE ) # Removing the old group that contained rock3 gp.remove_structural_group_by_name(geo_model, group_name="Strat_Series1") # %% # Recompute the model with new order gp.compute_model(geo_model) # %% # Display the new result gpv.plot_2d(geo_model) # %% # Set fault relations manually gp.set_fault_relation( frame=geo_model.structural_frame, rel_matrix=np.array([ [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1], [0, 0, 0, 0] ] ) ) # %% # Recompute model gp.compute_model(geo_model) # %% # Display result gpv.plot_2d(geo_model) # %% # Video tutorial 10: Fault groups and cross-cutting faults # """""""""""""""""""""""""""""""""""" # %% # .. raw:: html # # # # # %% # Create instance of new geomodel geo_model_cross = gp.create_geomodel( project_name = 'tutorial_model_faults_2', extent=[0,1000,0,500,0,1000], resolution=[50,25,50], importer_helper=gp.data.ImporterHelper( path_to_orientations=path_to_data + "tutorial_model_faults_2_orientations.csv", path_to_surface_points=path_to_data + "tutorial_model_faults_2_surface_points.csv" ) ) # %% # Map geological series to surfaces gp.map_stack_to_surfaces( gempy_model=geo_model_cross, mapping_object={ "Fault_Series2" : ('fault3', 'fault2'), "Fault_Series1" : ('fault1'), "Strat_Series": ('rock3', 'rock2', 'rock1'), } ) # Define youngest structural group as fault gp.set_is_fault(geo_model_cross, ["Fault_Series1", "Fault_Series2"]) # Change color of basement for better visibility geo_model_cross.structural_frame.basement_color="#F7B529" geo_model_cross.structural_frame.structural_elements[0].color = '#000000' geo_model_cross.structural_frame.structural_elements[1].color = '#36454F' geo_model_cross.structural_frame.structural_elements[2].color = '#D3D3D3' # %% geo_model_cross.structural_frame # %% # Set fault relations manually gp.set_fault_relation( frame=geo_model_cross.structural_frame, rel_matrix=np.array([ [0, 1, 1], [0, 0, 1], [0, 0, 0], ] ) ) # %% # Display input data on cross section gpv.plot_2d(geo_model_cross) # %% # Compute model gp.compute_model(geo_model_cross) # %% # Display reusult on cross section gpv.plot_2d(geo_model_cross) # sphinx_gallery_thumbnail_number = -1 ================================================ FILE: examples/tutorials/z_other_tutorials/video_tutorial_model_3_onlap.py ================================================ """ Video Tutorial "code-along": Onlap relations ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ """ # %% # This tutorial demonstrates step-by-step how to incorporate onlap relations to our geological models created with gempy. # It follows the Video tutorial series available on the [gempy YouTube channel](https://www.youtube.com/@GemPy3D). # Please follow the first and second part of the tutorials to learn the basics of modeling with gempy before diving into this tutorial. # %% # Video tutorial 11: Basic onlap scenario # """""""""""""""""""""""""""""""""""" # %% # .. raw:: html # # # # # %% # Required imports import gempy as gp import gempy_viewer as gpv import numpy as np # %% # Path to input data data_path = "https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/" path_to_data = data_path + "/data/input_data/video_tutorials_v3/" # %% # Create instance of geomodel geo_model_onlap = gp.create_geomodel( project_name = 'tutorial_model_onlap_1', extent=[0,2000,0,1000,0,1000], resolution=[100,50,50], importer_helper=gp.data.ImporterHelper( path_to_orientations=path_to_data+"tutorial_model_onlap_1_orientations.csv", path_to_surface_points=path_to_data+"tutorial_model_onlap_1_surface_points.csv" ) ) # %% # Map geological series to surfaces gp.map_stack_to_surfaces( gempy_model=geo_model_onlap, mapping_object={ "Young_Series": ("basin_fill_2", "basin_fill_1"), "Old_Series": ("basin_top", "basin_bottom") } ) # Alternative way of mapping geological series to surfaces # gp.map_stack_to_surfaces( # gempy_model=geo_model_onlap, # mapping_object={ # "Young_Series": ("basin_fill_2", "basin_fill_1"), # "Onlap_Series": ("basin_top"), # "Old_Series": ("basin_bottom") # } # ) # %% # Display a basic cross section of input data gpv.plot_2d(geo_model_onlap, show_data=True) # %% # Compute a solution for the model gp.compute_model(geo_model_onlap) # %% # Display the result in 2d section gpv.plot_2d(geo_model_onlap, show_boundaries=False) # %% # Set the relation of the youngest group to Onlap from gempy_engine.core.data.stack_relation_type import StackRelationType geo_model_onlap.structural_frame.structural_groups[0].structural_relation = StackRelationType.ONLAP # %% # Display updated strucutral frame geo_model_onlap.structural_frame # %% # Compute a solution for the model gp.compute_model(geo_model_onlap) # %% # Display the result in 2d section gpv.plot_2d(geo_model_onlap, show_boundaries=False) # %% # Video tutorial 12: Advanced onlap - Subduction zone # """""""""""""""""""""""""""" # %% # .. raw:: html # # # # # %% # Create instance of geomodel geo_model_subduction = gp.create_geomodel( project_name = 'tutorial_model_onlap_2', extent=[0,2000,0,1000,0,1000], resolution=[100,50,50], importer_helper=gp.data.ImporterHelper( path_to_orientations=path_to_data+"tutorial_model_onlap_2_orientations.csv?", path_to_surface_points=path_to_data+"tutorial_model_onlap_2_surface_points.csv?" ) ) # %% # Display a basic cross section of input data gpv.plot_2d(geo_model_subduction) # %% # Map geological series to surfaces gp.map_stack_to_surfaces( gempy_model=geo_model_subduction, mapping_object={ "Top": ("continental_top"), "Continental_Series": ("continental_shallow", "continental_deep"), "Oceanic_Series": ("oceanic_top", "oceanic_bottom") } ) # %% # Set the realtion of the youngest and second youngest group to Onlap geo_model_subduction.structural_frame.structural_groups[0].structural_relation = StackRelationType.ONLAP geo_model_subduction.structural_frame.structural_groups[1].structural_relation = StackRelationType.ONLAP # %% # Display updated structural frame geo_model_subduction.structural_frame # %% # Create a simple topography using numpy # Define grid spacing spacing = 20 # Generate grid x = np.arange(geo_model_subduction.grid.regular_grid.extent[0], geo_model_subduction.grid.regular_grid.extent[1] + spacing, spacing) y = np.arange(geo_model_subduction.grid.regular_grid.extent[2], geo_model_subduction.grid.regular_grid.extent[3] + spacing, spacing) X, Y = np.meshgrid(x, y) # Define elevation (z) based on x, creating a simple mountain range Z = np.ones_like(X) * 590 # Default elevation Z[(X >= 570) & (X < 1000)] = 590 + (200 * (X[(X >= 570) & (X < 1000)] - 600) / 400) Z[(X >= 1000) & (X < 1300)] = 810 - (250 * (X[(X >= 1000) & (X < 1300)] - 1000) / 300) Z[X >= 1300] = 540 # Flatten the data into (N,3) shape topography_points = np.vstack((X.ravel(), Y.ravel(), Z.ravel())).T # %% # Set topography from numpy array gp.set_topography_from_arrays(grid=geo_model_subduction.grid, xyz_vertices=topography_points) # %% # Compute a solution for the model gp.compute_model(geo_model_subduction) # %% # Display the result in 2d section gpv.plot_2d(geo_model_subduction, show_topography=True, show_boundaries=False) # %% # Display 3d plot of final model gpv.plot_3d(geo_model_subduction, show_topography=True, image=True) # sphinx_gallery_thumbnail_number = -1 ================================================ FILE: gempy/API/__init__.py ================================================ # Initialization API from .initialization_API import ( create_data_legacy, create_geomodel, structural_elements_from_borehole_set ) # Compute API from .compute_API import ( compute_model, compute_model_at ) # Map stack to surfaces API from .map_stack_to_surfaces_API import map_stack_to_surfaces # Grid API from .grid_API import ( set_active_grid, set_custom_grid, set_section_grid, set_centered_grid, set_topography_from_random, set_topography_from_file, set_topography_from_subsurface_structured_grid, set_topography_from_arrays ) # Examples generator from .examples_generator import generate_example_model # Faults API from .faults_API import ( set_fault_relation, set_is_fault, set_is_finite_fault ) # Data manipulation: points from ..modules.data_manipulation.manipulate_points import ( add_orientations, add_surface_points, delete_orientations, delete_surface_points, modify_orientations, modify_surface_points ) # Data manipulation: orientations from surface points from ..modules.data_manipulation.orientations_from_surface_points import ( create_orientations_from_surface_points_coords ) # Data manipulation: structural frame from ..modules.data_manipulation.manipulate_structural_frame import ( add_structural_group, remove_structural_group_by_index, remove_structural_group_by_name, remove_element_by_name ) from ..modules.serialization.save_load import save_model, load_model # Geophysics from gempy_engine.modules.geophysics.gravity_gradient import calculate_gravity_gradient __all__ = [ 'create_data_legacy', 'create_geomodel', 'structural_elements_from_borehole_set', 'compute_model', 'compute_model_at', 'map_stack_to_surfaces', 'set_section_grid', 'set_active_grid', 'set_topography_from_random', 'set_topography_from_file', 'set_topography_from_subsurface_structured_grid', 'set_topography_from_arrays', 'set_custom_grid', 'set_centered_grid', 'generate_example_model', 'set_fault_relation', 'set_is_fault', 'set_is_finite_fault', 'add_surface_points', 'add_orientations', 'delete_surface_points', 'delete_orientations', 'create_orientations_from_surface_points_coords', 'modify_surface_points', 'modify_orientations', 'add_structural_group', 'remove_structural_group_by_index', 'remove_structural_group_by_name', 'remove_element_by_name', 'calculate_gravity_gradient', 'save_model', 'load_model' ] ================================================ FILE: gempy/API/_version.py ================================================ ================================================ FILE: gempy/API/compute_API.py ================================================ import dotenv import numpy as np import os from typing import Optional import gempy_engine from gempy_engine.config import AvailableBackends from gempy_engine.core.backend_tensor import BackendTensor from gempy_engine.core.data import Solutions from .grid_API import set_custom_grid from ..core.data import StructuralGroup from ..core.data.gempy_engine_config import GemPyEngineConfig from ..core.data.geo_model import GeoModel from ..modules.data_manipulation import interpolation_input_from_structural_frame from ..modules.optimize_nuggets import nugget_optimizer dotenv.load_dotenv() def compute_model(gempy_model: GeoModel, engine_config: Optional[GemPyEngineConfig] = None, **kwargs) -> Solutions: """ Compute the geological model given the provided GemPy model. Args: gempy_model (GeoModel): The GemPy model to compute. engine_config (Optional[GemPyEngineConfig]): Configuration for the computational engine. Defaults to None, in which case a default configuration will be used. Raises: ValueError: If the provided backend in the engine_config is not supported. Returns: Solutions: The computed geological model. """ engine_config = engine_config or GemPyEngineConfig(use_gpu=False) match engine_config.backend: case AvailableBackends.numpy | AvailableBackends.PYTORCH: BackendTensor.change_backend_gempy( engine_backend=engine_config.backend, use_gpu=engine_config.use_gpu, dtype=engine_config.dtype ) # TODO: To decide what to do with this. interpolation_input = interpolation_input_from_structural_frame(gempy_model) gempy_model.taped_interpolation_input = interpolation_input # * This is used for gradient tape gempy_model.solutions = gempy_engine.compute_model( interpolation_input=interpolation_input, options=gempy_model.interpolation_options, data_descriptor=gempy_model.input_data_descriptor, geophysics_input=gempy_model.geophysics_input, ) case _: raise ValueError(f'Backend {engine_config} not supported') if os.getenv("VALIDATE_SERIALIZATION", "False") == "True" and kwargs.get("validate_serialization", True): from ..modules.serialization.save_load import save_model import tempfile with tempfile.NamedTemporaryFile(mode='w+', delete=True) as tmp: save_model(model=gempy_model, path=tmp.name, validate_serialization=True) return gempy_model.solutions def compute_model_at(gempy_model: GeoModel, at: np.ndarray, engine_config: Optional[GemPyEngineConfig] = None) -> np.ndarray: """ Compute the geological model at specific coordinates. Note: This function sets a custom grid and computes the model so be wary of side effects. Args: gempy_model (GeoModel): The GemPy model to compute. at (np.ndarray): The coordinates at which to compute the model. engine_config (Optional[GemPyEngineConfig], optional): Configuration for the computational engine. Defaults to None, in which case a default configuration will be used. Returns: np.ndarray: The computed geological model at the specified coordinates. """ print("WARNING: This function sets a custom grid and computes the model so be wary of side effects.") set_custom_grid( grid=gempy_model.grid, xyz_coord=at, reset=True ) sol = compute_model(gempy_model, engine_config, validate_serialization=True) return sol.raw_arrays.custom def optimize_nuggets(geo_model: GeoModel, engine_config: GemPyEngineConfig, max_epochs: int = 10, convergence_criteria: float = 1e5, only_groups:list[StructuralGroup] | None = None) -> GeoModel: """ Optimize the nuggets of the interpolation input of the provided model. """ if engine_config.backend != AvailableBackends.PYTORCH: raise ValueError(f'Only PyTorch backend is supported for optimization. Received {engine_config.backend}') geo_model = nugget_optimizer( target_cond_num=convergence_criteria, engine_cfg=engine_config, model=geo_model, max_epochs=max_epochs, only_groups=only_groups ) return geo_model def optimize_and_compute(geo_model: GeoModel, engine_config: GemPyEngineConfig, max_epochs: int = 10, convergence_criteria: float = 1e5): optimize_nuggets(geo_model, engine_config, max_epochs, convergence_criteria) geo_model.solutions = gempy_engine.compute_model( interpolation_input=geo_model.taped_interpolation_input, options=geo_model.interpolation_options, data_descriptor=geo_model.input_data_descriptor, geophysics_input=geo_model.geophysics_input, ) return geo_model.solutions ================================================ FILE: gempy/API/examples_generator.py ================================================ import os import numpy as np import gempy as gp from gempy_engine.core.data.stack_relation_type import StackRelationType from gempy.core.data.enumerators import ExampleModel def generate_example_model(example_model: ExampleModel, compute_model: bool = True) -> gp.data.GeoModel: match example_model: case ExampleModel.HORIZONTAL_STRAT: return _generate_horizontal_stratigraphic_model(compute_model) case ExampleModel.ANTICLINE: return _generate_anticline_model(compute_model) case ExampleModel.ONE_FAULT: return _generate_one_fault_model(compute_model) case ExampleModel.TWO_AND_A_HALF_D: return _generate_2_5d_model(compute_model) case ExampleModel.COMBINATION: return _generate_combination_model(compute_model) case ExampleModel.ONE_FAULT_GRAVITY: return _generate_one_fault_model_gravity(compute_model) case ExampleModel.GRABEN: return _generate_graben_model(compute_model) case ExampleModel.GREENSTONE: return _generate_greenstone_model(compute_model) case ExampleModel.FAULT_RELATION: return _generate_fault_relation_model(compute_model) case _: raise NotImplementedError(f"Example model {example_model} not implemented.") def _generate_2_5d_model(compute_model: bool) -> gp.data.GeoModel: geo_model: gp.data.GeoModel = gp.create_geomodel( project_name='Model1', extent=[0, 791, -200, 200, -582, 0], resolution=[50, 50, 50], refinement=1, structural_frame=gp.data.StructuralFrame.initialize_default_structure() ) gp.add_surface_points( geo_model=geo_model, x=[223, 458, 612], y=[0.01, 0, 0], z=[-94, -197, -14], elements_names='surface1' ) gp.add_orientations( geo_model=geo_model, x=[350], y=[1], z=[-300], elements_names=['surface1'], pole_vector=[[0, 0, 1]] ) geo_model.update_transform(gp.data.GlobalAnisotropy.NONE) # * Remove the auto anisotropy for this 2.5D model element2 = gp.data.StructuralElement( name='surface2', color=next(geo_model.structural_frame.color_generator), surface_points=gp.data.SurfacePointsTable.from_arrays( x=np.array([225, 459]), y=np.array([0, 0]), z=np.array([-269, -279]), names='surface2' ), orientations=gp.data.OrientationsTable.initialize_empty() ) geo_model.structural_frame.structural_groups[0].append_element(element2) element3 = gp.data.StructuralElement( name='surface3', color=next(geo_model.structural_frame.color_generator), surface_points=gp.data.SurfacePointsTable.from_arrays( x=np.array([225, 464, 619]), y=np.array([0, 0, 0]), z=np.array([-439, -456, -433]), names='surface3' ), orientations=gp.data.OrientationsTable.initialize_empty() ) geo_model.structural_frame.structural_groups[0].append_element(element3) element_fault = gp.data.StructuralElement( name='fault1', color=next(geo_model.structural_frame.color_generator), surface_points=gp.data.SurfacePointsTable.from_arrays( x=np.array([550, 650]), y=np.array([0, 0]), z=np.array([-30, -200]), names='fault1' ), orientations=gp.data.OrientationsTable.from_arrays( x=np.array([600]), y=np.array([0]), z=np.array([-100]), G_x=np.array([.3]), G_y=np.array([0]), G_z=np.array([.3]), names='fault1' ) ) group_fault = gp.data.StructuralGroup( name='Fault1', elements=[element_fault], structural_relation=gp.data.StackRelationType.FAULT, fault_relations=gp.data.FaultsRelationSpecialCase.OFFSET_ALL ) geo_model.structural_frame.insert_group(0, group_fault) # * We are placing it already in the right place so we do not need to map anything gp.set_topography_from_random( grid=geo_model.grid, fractal_dimension=1.9, d_z=np.array([-150, 0]), topography_resolution=np.array([50, 40]) ) if compute_model: gp.compute_model(geo_model) return geo_model def _generate_horizontal_stratigraphic_model(compute_model: bool) -> gp.data.GeoModel: """ Function to create a geological model of horizontally stacked layers, map the geological series to surfaces, and compute the geological model. """ # Define the path to data data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/' # Create a GeoModel instance geo_data = gp.create_geomodel( project_name='horizontal', extent=[0, 1000, 0, 1000, 0, 1000], resolution=[50, 5, 50], refinement=3, importer_helper=gp.data.ImporterHelper( path_to_orientations=data_path + "/data/input_data/jan_models/model1_orientations.csv", path_to_surface_points=data_path + "/data/input_data/jan_models/model1_surface_points.csv" ) ) # Map geological series to surfaces gp.map_stack_to_surfaces( gempy_model=geo_data, mapping_object={"Strat_Series": ('rock2', 'rock1')} ) if compute_model: # Compute the geological model gp.compute_model(geo_data) return geo_data def _generate_anticline_model(compute_model: bool) -> gp.data.GeoModel: """ Function to create a geological model of an anticline structure, map the geological series to surfaces, and compute the geological model. """ # Define the path to data data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/' path_to_data = data_path + "/data/input_data/jan_models/" # Create a GeoModel instance geo_data: gp.data.GeoModel = gp.create_geomodel( project_name='fold', extent=[0, 1000, 0, 1000, 0, 1000], refinement=5, importer_helper=gp.data.ImporterHelper( path_to_orientations=path_to_data + "model2_orientations.csv", path_to_surface_points=path_to_data + "model2_surface_points.csv" ) ) # Map geological series to surfaces gp.map_stack_to_surfaces( gempy_model=geo_data, mapping_object={"Strat_Series": ('rock2', 'rock1')} ) if compute_model: # Compute the geological model gp.compute_model(geo_data) return geo_data def _generate_one_fault_model(compute_model: bool) -> gp.data.GeoModel: """ Function to create a simple fault model, map the geological series to surfaces, and compute the geological model. """ # Define the path to data data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/' path_to_data = data_path + "/data/input_data/jan_models/" # Create a GeoModel instance geo_data = gp.create_geomodel( project_name='fault', extent=[0, 1000, 0, 1000, 0, 1000], refinement=6, # resolution=[20, 20, 20], importer_helper=gp.data.ImporterHelper( path_to_orientations=path_to_data + "model5_orientations.csv", path_to_surface_points=path_to_data + "model5_surface_points.csv" ) ) # Map geological series to surfaces gp.map_stack_to_surfaces( gempy_model=geo_data, mapping_object={ "Fault_Series": 'fault', "Strat_Series": ('rock2', 'rock1') } ) # Define fault groups geo_data.structural_frame.structural_groups[0].structural_relation = StackRelationType.FAULT geo_data.structural_frame.fault_relations = np.array([[0, 1], [0, 0]]) gp.set_is_fault( frame=geo_data, fault_groups=['Fault_Series'] ) if compute_model: # Compute the geological model gp.compute_model(geo_data) return geo_data def _generate_combination_model(compute_model: bool) -> gp.data.GeoModel: """ Function to create a model with a folded domain featuring an unconformity and a fault, map the geological series to surfaces, and compute the geological model. """ # Define the path to data data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/' path_to_data = data_path + "/data/input_data/jan_models/" # Create a GeoModel instance geo_data = gp.create_geomodel( project_name='combination', extent=[0, 2500, 0, 1000, 0, 1000], refinement=4, importer_helper=gp.data.ImporterHelper( path_to_orientations=path_to_data + "model7_orientations.csv", path_to_surface_points=path_to_data + "model7_surface_points.csv" ), legacy_octree_init=False ) geo_data.interpolation_options.evaluation_options.number_octree_levels_surface = 4 # Map geological series to surfaces gp.map_stack_to_surfaces( gempy_model=geo_data, mapping_object={ "Fault_Series" : ('fault'), "Strat_Series1": ('rock3'), "Strat_Series2": ('rock2', 'rock1'), } ) # Define the structural relation geo_data.structural_frame.structural_groups[0].structural_relation = StackRelationType.FAULT geo_data.structural_frame.fault_relations = np.array( [[0, 1, 1], [0, 0, 0], [0, 0, 0]] ) # Compute the geological model if compute_model: gp.compute_model( gempy_model=geo_data, engine_config=gp.data.GemPyEngineConfig( backend=gp.data.AvailableBackends.numpy ) ) return geo_data def _generate_one_fault_model_gravity(compute_model): from gempy_engine.core.backend_tensor import BackendTensor from gempy.optional_dependencies import require_pandas pd = require_pandas() resolution = [150, 10, 150] extent = [0, 200, -100, 100, -100, 0] # %% # Configure GemPy for geological modeling with PyTorch backend BackendTensor.change_backend_gempy(engine_backend=gp.data.AvailableBackends.PYTORCH, dtype="float64") geo_model: gp.data.GeoModel = gp.create_geomodel( project_name='Fault model', extent=extent, resolution=resolution, structural_frame=gp.data.StructuralFrame.initialize_default_structure() ) interpolation_options = geo_model.interpolation_options interpolation_options.mesh_extraction = False interpolation_options.kernel_options.range = .7 interpolation_options.kernel_options.c_o = 3 interpolation_options.kernel_options.compute_condition_number = True gp.add_surface_points( geo_model=geo_model, x=[40, 60, 120, 140], y=[0, 0, 0, 0], z=[-50, -50, -60, -60], elements_names=['surface1', 'surface1', 'surface1', 'surface1'] ) gp.add_orientations( geo_model=geo_model, x=[130], y=[0], z=[-50], elements_names=['surface1'], pole_vector=[[0, 0, 1.]] ) # Define second element element2 = gp.data.StructuralElement( name='surface2', color=next(geo_model.structural_frame.color_generator), surface_points=gp.data.SurfacePointsTable.from_arrays( x=np.array([120]), y=np.array([0]), z=np.array([-40]), names='surface2' ), orientations=gp.data.OrientationsTable.initialize_empty() ) # Add second element to structural frame geo_model.structural_frame.structural_groups[0].append_element(element2) # add fault # Calculate orientation from point values fault_point_1 = (80, -20) fault_point_2 = (110, -80) # calculate angle angle = np.arctan((fault_point_2[0] - fault_point_1[0]) / (fault_point_2[1] - fault_point_1[1])) x = np.cos(angle) z = - np.sin(angle) element_fault = gp.data.StructuralElement( name='fault1', color=next(geo_model.structural_frame.color_generator), surface_points=gp.data.SurfacePointsTable.from_arrays( x=np.array([fault_point_1[0], fault_point_2[0]]), y=np.array([0, 0]), z=np.array([fault_point_1[1], fault_point_2[1]]), names='fault1' ), orientations=gp.data.OrientationsTable.from_arrays( x=np.array([fault_point_1[0]]), y=np.array([0]), z=np.array([fault_point_1[1]]), G_x=np.array([x]), G_y=np.array([0]), G_z=np.array([z]), names='fault1' ) ) group_fault = gp.data.StructuralGroup( name='Fault1', elements=[element_fault], structural_relation=gp.data.StackRelationType.FAULT, fault_relations=gp.data.FaultsRelationSpecialCase.OFFSET_ALL ) # Insert the fault group into the structural frame: geo_model.structural_frame.insert_group(0, group_fault) # %% md ## Compute model # %% geo_model.update_transform(gp.data.GlobalAnisotropy.NONE) interesting_columns = pd.DataFrame() # x_vals = np.arange(20, 191, 10) x_vals = np.linspace(20, 191, 6) interesting_columns['X'] = x_vals interesting_columns['Y'] = np.zeros_like(x_vals) # Configuring the data correctly is key for accurate gravity calculations. device_location = interesting_columns[['X', 'Y']] device_location['Z'] = 0 # Add a Z-coordinate # Set up a centered grid for geophysical calculations # This grid will be used for gravity gradient calculations. gp.set_centered_grid( grid=geo_model.grid, centers=device_location, resolution=np.array([75 / 3, 5, 150 / 3]), radius=np.array([150, 10, 300]) ) # Calculate the gravity gradient using GemPy # Gravity gradient data is critical for geophysical modeling and inversion. gravity_gradient = gp.calculate_gravity_gradient(geo_model.grid.centered_grid) densities_tensor = BackendTensor.t.array([2., 2., 3., 2.]) densities_tensor.requires_grad = True # Set geophysics input for the GemPy model # Configuring this input is crucial for the forward gravity calculation. geo_model.geophysics_input = gp.data.GeophysicsInput( tz=BackendTensor.t.array(gravity_gradient), densities=densities_tensor ) # %% # Compute the geological model with geophysical data # This computation integrates the geological model with gravity data. if compute_model: sol = gp.compute_model( gempy_model=geo_model, engine_config=gp.data.GemPyEngineConfig( backend=gp.data.AvailableBackends.PYTORCH, dtype='float32', use_gpu=True ) ) grav = - sol.gravity grav[0].backward() return geo_model def _generate_graben_model(compute_model: bool) -> gp.data.GeoModel: # Data path is in root/examples/data # script_dir = os.path.dirname(os.path.abspath(__file__)) # data_path = os.path.join(script_dir, '.../examples/') # n_model = 7 # https: // github.com / gempy - project / gempy / blob / 279 # bbe904283e16320c54d868fe74be873177cca / examples / data / input_data / lisa_models / interfaces7.csv # csv_ = data_path + "/data/input_data/lisa_models/foliations" + n_model + ".csv" data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/' path_to_data = data_path + "/data/input_data/lisa_models/" geo_data: gp.data.GeoModel = gp.create_geomodel( project_name="Graben", extent=[0, 2000, 0, 2000, 0, 1600], resolution=[50, 50, 50], refinement=6, # * For this model is better not to use octrees because we want to see what is happening in the scalar fields importer_helper=gp.data.ImporterHelper( path_to_orientations=path_to_data + "foliations7.csv", path_to_surface_points=path_to_data + "interfaces7.csv" ) ) gp.map_stack_to_surfaces( gempy_model=geo_data, mapping_object={ "Fault_1" : 'Fault_1', "Fault_2": 'Fault_2', "Strat_Series": ('Sandstone', 'Siltstone', 'Shale', 'Sandstone_2', 'Schist', 'Gneiss') }, ) gp.set_is_fault(geo_data, ['Fault_1', 'Fault_2']) if compute_model: sol = gp.compute_model(gempy_model=geo_data) return geo_data def _generate_greenstone_model(compute_model: bool) -> gp.data.GeoModel: test_dir = os.path.dirname(os.path.abspath(__file__)) # Build the path relative to the test file location path = os.path.join(test_dir, '..', '..', 'examples', 'data', 'gempy_models', 'Greenstone.gempy') with open(path, 'rb') as f: binary_file = f.read() from gempy.modules.serialization.save_load import _load_model_from_bytes geo_model: gp.data.GeoModel = _load_model_from_bytes(binary_file) if compute_model: sol = gp.compute_model( gempy_model=geo_model, engine_config=gp.data.GemPyEngineConfig( backend=gp.data.AvailableBackends.numpy, dtype='float32' ) ) return geo_model def _generate_fault_relation_model(compute_model: bool) -> gp.data.GeoModel: # Path to input data test_dir = os.path.dirname(os.path.abspath(__file__)) data_path = os.path.join(test_dir, '..', '..', 'examples', 'data', 'input_data', 'tests') # data_path = os.path.join(test_dir, "..\\..\\examples\\data\\input_data\\tests\\") # Create instance of new geomodel geo_model = gp.create_geomodel( project_name='fault_reations_test', extent=[0, 1000, 0, 1000, -1000, -400], resolution=[20, 20, 20], refinement=4, importer_helper=gp.data.ImporterHelper( path_to_orientations=data_path + "/fault_relations_test_ori.csv", path_to_surface_points=data_path + "/fault_relations_test_surf.csv" ) ) # Map geological series to surfaces gp.map_stack_to_surfaces( gempy_model=geo_model, mapping_object={ "fault_series_1": ('fault1'), "fault_series_2": ('fault2'), "series_1" : ('rock4', 'rock3'), "default_series": ('rock2', 'rock1') } ) gp.set_is_fault(geo_model, ["fault_series_1", "fault_series_2"]) gp.set_fault_relation( frame=geo_model.structural_frame, rel_matrix=np.array([ [0, 1, 1, 1], [0, 0, 0, 1], [0, 0, 0, 0], [0, 0, 0, 0] ] ) ) geo_model.input_transform.apply_anisotropy(gp.data.GlobalAnisotropy.NONE) if compute_model: sol = gp.compute_model( gempy_model=geo_model, engine_config=gp.data.GemPyEngineConfig( backend=gp.data.AvailableBackends.numpy, dtype='float32' ), validate_serialization=False ) return geo_model ================================================ FILE: gempy/API/faults_API.py ================================================ from typing import Union import numpy as np from gempy_engine.core.data.stack_relation_type import StackRelationType from gempy.core.data import GeoModel, StructuralFrame from gempy.core.data.structural_group import StructuralGroup, FaultsRelationSpecialCase def set_is_fault(frame: Union[GeoModel, StructuralFrame], fault_groups: Union[list[str], list[StructuralGroup]], faults_relation_type: FaultsRelationSpecialCase = FaultsRelationSpecialCase.OFFSET_FORMATIONS, change_color: bool = True) -> StructuralFrame: """ Sets given groups as fault in the structural frame of the GeoModel. It can optionally change the color of these groups. Args: frame (Union[GeoModel, StructuralFrame]): GeoModel or its StructuralFrame to be modified. fault_groups (Union[list[str], list[StructuralGroup]]): Groups to be set as faults. faults_relation_type (FaultsRelationSpecialCase, optional): Faults relation type to be set. Defaults to FaultsRelationSpecialCase.OFFSET_FORMATIONS. change_color (bool, optional): If True, changes the color of the fault groups. Defaults to True. Returns: StructuralFrame: The updated StructuralFrame object. """ if isinstance(frame, GeoModel): frame = frame.structural_frame frame = _find_and_set_fields( frame=frame, fault_groups=fault_groups, faults_relation_type=faults_relation_type, stack_relation_type=StackRelationType.FAULT, change_color=change_color ) # * TODO: Set the fault colors return frame def unset_is_fault(frame: Union[GeoModel, StructuralFrame], fault_groups: Union[list[str], list[StructuralGroup]]) -> StructuralFrame: """ Unsets given groups as fault in the structural frame of the GeoModel. Args: frame (Union[GeoModel, StructuralFrame]): GeoModel or its StructuralFrame to be modified. fault_groups (Union[list[str], list[StructuralGroup]]): Groups to be unset as faults. Returns: StructuralFrame: The updated StructuralFrame object. """ if isinstance(frame, GeoModel): frame = frame.structural_frame frame = _find_and_set_fields( frame=frame, fault_groups=fault_groups, faults_relation_type=FaultsRelationSpecialCase.OFFSET_NONE, stack_relation_type=StackRelationType.ERODE, change_color=False ) return frame def set_fault_relation(frame: Union[GeoModel, StructuralFrame], rel_matrix: np.ndarray) -> StructuralFrame: """ Sets the fault relations in the structural frame of the GeoModel. Args: frame (Union[GeoModel, StructuralFrame]): GeoModel or its StructuralFrame to be modified. rel_matrix (np.ndarray): Fault relation matrix to be set. Returns: StructuralFrame: The updated StructuralFrame object. """ if isinstance(frame, GeoModel): frame = frame.structural_frame frame.fault_relations = rel_matrix return frame def set_is_finite_fault(self, series_fault=None, toggle: bool = True): """""" raise NotImplementedError s = self._faults.set_is_finite_fault(series_fault, toggle) # change df in Fault obj # change shared aesara variable for infinite factor self._interpolator.set_aesara_shared_is_finite() return s # TODO: Move to a faults module def _find_and_set_fields(frame: StructuralFrame, fault_groups: list[StructuralGroup], faults_relation_type: FaultsRelationSpecialCase, stack_relation_type: StackRelationType, change_color: bool) -> StructuralFrame: for index, group in enumerate(fault_groups): if isinstance(group, str): group = next((g for g in frame.structural_groups if g.name == group), None) if isinstance(group, StructuralGroup): group.structural_relation = stack_relation_type group.fault_relations = faults_relation_type # * Set the default fault relations if change_color: for element in group.elements: element.color = '#527682' else: raise ValueError(f"Could not find group '{group}' in structural frame.") return frame ================================================ FILE: gempy/API/gp2_gp3_compatibility/__init__.py ================================================ ================================================ FILE: gempy/API/gp2_gp3_compatibility/gp3_to_gp2_input.py ================================================ from gempy.core.data import GeoModel from gempy.core.data.orientations import OrientationsTable from gempy.core.data.surface_points import SurfacePointsTable from gempy.optional_dependencies import require_gempy_legacy def gempy3_to_gempy2(geo_model: GeoModel) -> "gempy_legacy.Project": gl = require_gempy_legacy() legacy_model: "gempy_legacy.Project" = gl.create_model(project_name=geo_model.meta.name) surface_points: SurfacePointsTable = geo_model.structural_frame.surface_points surface_points_df = surface_points.df # This is a property surface_points_df['surfaces'] = surface_points_df['id'].map(geo_model.structural_frame.element_id_name_map) orientations: OrientationsTable = geo_model.structural_frame.orientations orientations_df = orientations.df orientations_df['surfaces'] = orientations_df['id'].map(geo_model.structural_frame.element_id_name_map) # * Set data gl.init_data( geo_model=legacy_model, extent=geo_model.grid.regular_grid.extent, resolution=geo_model.grid.regular_grid.resolution, surface_points_df=surface_points_df, orientations_df=orientations_df ) # # * Map StructuralFrame mapper: dict[str, list[str]] = geo_model.structural_frame.groups_to_mapper gl.map_stack_to_surfaces( geo_model=legacy_model, mapping_object=mapper ) legacy_model.add_surfaces("basement") return legacy_model ================================================ FILE: gempy/API/gp2_gp3_compatibility/gp3_to_gp2_output.py ================================================ import numpy as np from gempy import Project, Solution from gempy_engine.core.data.dual_contouring_mesh import DualContouringMesh from gempy_engine.core.data.octree_level import OctreeLevel from gempy_engine.core.data.solutions import Solutions from gempy_engine.modules.octrees_topology.octrees_topology_interface import get_regular_grid_value_for_level def set_gp3_solutions_to_gp2_solution(gp3_solution: Solutions, geo_model: Project) -> Solution: octree_lvl = -1 octree_output: OctreeLevel = gp3_solution.octrees_output[octree_lvl] regular_grid_scalar = get_regular_grid_value_for_level(gp3_solution.octrees_output).astype("int8") _set_block_matrix(geo_model, octree_output) _set_lith_block(geo_model, octree_output) _set_scalar_field(geo_model, octree_output) _set_scalar_field_at_surface_points(geo_model, octree_output) meshes: list[DualContouringMesh] = gp3_solution.dc_meshes geo_model.set_surface_order_from_solution() if meshes: _set_surfaces_meshes(geo_model, meshes) return geo_model.solutions def _set_surfaces_meshes(geo_model: Project, meshes: list[DualContouringMesh]) -> Project: geo_model.solutions.vertices = [mesh.vertices for mesh in meshes] geo_model.solutions.edges = [mesh.edges for mesh in meshes] rescaling_factor: float = geo_model._additional_data.rescaling_data.df.loc['values', 'rescaling factor'] shift: np.array = geo_model._additional_data.rescaling_data.df.loc['values', 'centers'] surfaces_df = geo_model.solutions.surfaces.df idx_of_vertices = surfaces_df.columns.get_loc('vertices') idx_of_edges = surfaces_df.columns.get_loc('edges') # flip mesh 4 and 5 # meshed_flipped = [ meshes[0], meshes[1], meshes[2], meshes[4], meshes[3]] bug: meshes are not in the right order yet in_ = meshes for i in range(0, len(meshes)): surfaces_df.iloc[i, idx_of_vertices] = [(in_[i].vertices - 0.5001) * rescaling_factor + shift] # ! remember the 0.5001 surfaces_df.iloc[i, idx_of_edges] = [in_[i].edges] return geo_model def _set_block_matrix(geo_model: Project, octree_output: OctreeLevel) -> Project: temp_list = [] for i in range(octree_output.number_of_outputs): temp_list.append(octree_output.outputs[i].values_block) block_matrix_stacked = np.vstack(temp_list) geo_model.solutions.block_matrix = block_matrix_stacked return geo_model def _set_scalar_field(geo_model: Project, octree_output: OctreeLevel) -> Project: temp_list = [] for i in range(octree_output.number_of_outputs): temp_list.append(octree_output.outputs[i].scalar_fields.exported_fields.scalar_field) scalar_field_stacked = np.vstack(temp_list) geo_model.solutions.scalar_field_matrix = scalar_field_stacked return geo_model def _set_scalar_field_at_surface_points(geo_model: Project, octree_output: OctreeLevel) -> Project: temp_list = [] for i in range(octree_output.number_of_outputs): temp_list.append(octree_output.outputs[i].scalar_fields.exported_fields.scalar_field_at_surface_points) geo_model.solutions.scalar_field_at_surface_points = temp_list return geo_model def _set_lith_block(geo_model: Project, octree_output: OctreeLevel) -> Project: block = octree_output.last_output_center.ids_block block[block == 0] = block.max() + 1 geo_model.solutions.lith_block = block return geo_model ================================================ FILE: gempy/API/grid_API.py ================================================ from typing import Union, Sequence import numpy as np from ..core.data import Grid from ..core.data.grid_modules import CustomGrid, Sections from ..core.data.grid_modules.topography import Topography from ..modules.grids.create_topography import create_random_topography from ..optional_dependencies import require_subsurface def set_section_grid(grid: Grid, section_dict: dict): if grid.sections is None: grid.sections = Sections( z_ext=grid.regular_grid.extent[4:], section_dict=section_dict ) else: grid.sections.set_sections(section_dict, regular_grid=grid.regular_grid) set_active_grid(grid, [Grid.GridTypes.SECTIONS]) return grid.sections def set_topography_from_random(grid: Grid, fractal_dimension: float = 2.0, d_z: Union[Sequence, None] = None, topography_resolution: Union[Sequence, None] = None): """ Sets the topography of the grid using a randomly generated topography. Args: grid (Grid): The grid object on which to set the topography. fractal_dimension (float, optional): The fractal dimension of the random topography. Defaults to 2.0. d_z (Union[Sequence, None], optional): The sequence of elevation increments for the random topography. If None, a default sequence will be used. Defaults to None. topography_resolution (Union[Sequence, None], optional): The resolution of the random topography. If None, the resolution of the grid's regular grid will be used. Defaults to None. Returns: The topography object that was set on the grid. Example: >>> grid = Grid() >>> set_topography_from_random(grid, fractal_dimension=1.5, d_z=[0.1, 0.2, 0.3], topography_resolution=[10, 10]) Note: If topography_resolution is None, the resolution of the grid's regular grid will be used. If d_z is None, a default sequence of elevation increments will be used. """ if topography_resolution is None: topography_resolution = grid.regular_grid.resolution random_topography: np.ndarray = create_random_topography( extent=grid.regular_grid.extent, resolution=topography_resolution, dz=d_z, fractal_dimension=fractal_dimension ) grid.topography = Topography( _regular_grid=grid.regular_grid, values_2d=random_topography ) set_active_grid(grid, [Grid.GridTypes.TOPOGRAPHY]) return grid.topography def set_topography_from_subsurface_structured_grid(grid: Grid, struct: "subsurface.StructuredData"): grid.topography = Topography.from_subsurface_structured_data(struct, grid.regular_grid) set_active_grid(grid, [Grid.GridTypes.TOPOGRAPHY]) return grid.topography def set_topography_from_arrays(grid: Grid, xyz_vertices: np.ndarray): grid.topography = Topography.from_unstructured_mesh(grid.regular_grid, xyz_vertices) set_active_grid(grid, [Grid.GridTypes.TOPOGRAPHY]) return grid.topography def set_topography_from_file(grid: Grid, filepath: str, crop_to_extent: Union[Sequence, None] = None): ss = require_subsurface() struct: ss.StructuredData = ss.modules.reader.read_structured_topography( path=filepath, crop_to_extent=crop_to_extent ) return set_topography_from_subsurface_structured_grid(grid, struct) def set_custom_grid(grid: Grid, xyz_coord: np.ndarray, reset: bool = False): custom_grid = CustomGrid(values=xyz_coord) grid.custom_grid = custom_grid set_active_grid(grid, grid_type=[Grid.GridTypes.CUSTOM], reset=reset) return grid.custom_grid def set_centered_grid(grid: Grid, centers: np.ndarray, resolution: Sequence[float], radius: Union[float, Sequence[float]]): from gempy_engine.core.data.centered_grid import CenteredGrid centered_grid = CenteredGrid( centers=centers, resolution=resolution, radius=radius ) grid.centered_grid = centered_grid set_active_grid(grid, [Grid.GridTypes.CENTERED]) return grid.centered_grid def set_topography_from_gdal(): raise NotImplementedError("This is not implemented yet") def set_topography_from_array(): raise NotImplementedError("This is not implemented yet") def set_active_grid(grid: Grid, grid_type: list[Grid.GridTypes], reset: bool = False): if reset is True: grid.active_grids = Grid.GridTypes.NONE for grid_type in grid_type: grid.active_grids |= grid_type grid._update_values() print(f'Active grids: {grid.active_grids}') return grid ================================================ FILE: gempy/API/implicit_functions.py ================================================ from ..modules.custom_implicit_functions.ellipsoid_implicit_function import ellipsoid_3d_factory __all__ = ['ellipsoid_3d_factory'] ================================================ FILE: gempy/API/initialization_API.py ================================================ import warnings from typing import Union, Hashable, Literal import numpy as np from numpy import ndarray from gempy.API.io_API import read_surface_points, read_orientations from gempy_engine.core.data import InterpolationOptions from ..core.data import StructuralElement from ..core.data.geo_model import GeoModel from ..core.data.grid import Grid from ..core.data.importer_helper import ImporterHelper from ..core.data.options import InterpolationOptionsType from ..core.data.orientations import OrientationsTable from ..core.data.structural_frame import StructuralFrame from ..core.data.surface_points import SurfacePointsTable from ..optional_dependencies import require_pooch from ..optional_dependencies import require_subsurface def create_geomodel( *, project_name: str = 'default_project', extent: Union[list, ndarray] = None, resolution: Union[list, ndarray] = None, refinement: int = 1, structural_frame: StructuralFrame = None, importer_helper: ImporterHelper = None, intpolation_options_tye: InterpolationOptionsType = InterpolationOptionsType.OCTREE, **kwargs ) -> GeoModel: # ? Do I need to pass pandas read kwargs? """ Creates a geological model based on input parameters, spatial configuration, and interpolation options. This function initializes a geological model by defining the grid (dense grid or octree-based), selecting interpolation options based on the desired type, and configuring the structural frame using either a provided structural frame or an importer helper instance. The geological model is tailored for specific project-based requirements and can be either dense or with variable resolution depending on the refinement level. Args: project_name (str): Name of the geological model project. Defaults to 'default_project'. extent (list, ndarray): Spatial extent of the geological model in the form of [min_x, max_x, min_y, max_y, min_z, max_z]. resolution (list, ndarray): Resolution of the model grid in the form [x_res, y_res, z_res]. If not provided, the function will default to octree initialization. refinement (int): Refinement level for the octree grid. Ignored if resolution is provided. Defaults to 1. structural_frame (StructuralFrame): Pre-configured instance of StructuralFrame for the geological model. If not provided, an importer_helper must be supplied. importer_helper (ImporterHelper): Helper object for initializing a structural frame if none is explicitly provided. intpolation_options_tye (InterpolationOptionsType): Enum representing the desired type of interpolation options. Defaults to InterpolationOptionsType.OCTREE. Returns: GeoModel: An initialized geological model with specified spatial configuration and interpolation properties. Raises: ValueError: If neither structural_frame nor importer_helper is provided. ValueError: If the interpolation options type is unrecognized. """ # init resolutions well if resolution is None: grid: Grid = Grid.init_octree_grid( extent=extent, octree_levels=refinement, legacy=kwargs.get('legacy_octree_init', False) ) else: grid: Grid = Grid.init_dense_grid( extent=extent, resolution=resolution ) match intpolation_options_tye: case InterpolationOptionsType.DENSE_GRID: interpolation_options: InterpolationOptions = InterpolationOptions.init_dense_grid_options() case InterpolationOptionsType.OCTREE: interpolation_options: InterpolationOptions = InterpolationOptions.init_octree_options(refinement=refinement) case _: raise ValueError(f"Interpolation options type {intpolation_options_tye} not recognized. Use InterpolationOptionsType.DENSE_GRID or InterpolationOptionsType.OCTREE.") match (structural_frame, importer_helper): case (None, None): # ? For now my gut feeling is that is better to pass the structural frame explicitly raise ValueError("Either structural_frame or importer_helper must be provided. You can use StructuralFrame.initialize_default_structure() to create a default structural frame.") structural_frame = StructuralFrame.initialize_default_structure() case (None, _): structural_frame = _initialize_structural_frame(importer_helper) case _: pass geo_model: GeoModel = GeoModel.from_args( name=project_name, structural_frame=structural_frame, grid=grid, interpolation_options=interpolation_options ) return geo_model def structural_elements_from_borehole_set( borehole_set: "subsurface.core.geological_formats.BoreholeSet", elements_dict: dict, group_by: Literal['component lith', 'lith_ids'] = 'lith_ids' ) -> list[StructuralElement]: """Creates a list of StructuralElements from a BoreholeSet. Args: borehole_set (subsurface.core.geological_formats.BoreholeSet): The BoreholeSet object containing the boreholes. elements_dict (dict): A dictionary containing the properties of the structural elements to be created. Returns: list[StructuralElement]: A list of StructuralElement objects created from the borehole set. Raises: ValueError: If a top lithology ID specified in `elements_dict` is not found in the borehole set. """ ss = require_subsurface() borehole_set: ss.core.geological_formats.BoreholeSet elements = [] component_lith: dict[Hashable, np.ndarray] = borehole_set.get_bottom_coords_for_each_lith(group_by=group_by) for name, properties in elements_dict.items(): top_coordinates = component_lith.get(properties['id']) if top_coordinates is None: raise ValueError(f"Top lithology {properties['id']} not found in borehole set.") element = StructuralElement( name=name, id=properties['id'], color=properties['color'], surface_points=SurfacePointsTable.from_arrays( x=top_coordinates[:, 0], y=top_coordinates[:, 1], z=top_coordinates[:, 2], names=[name], name_id_map={name: properties['id']} ), orientations=OrientationsTable(np.zeros(0, dtype=OrientationsTable.dt)) ) elements.append(element) # Reverse the list to have the oldest rocks at the bottom return elements def create_data_legacy( *, project_name: str = 'default_project', extent: Union[list, ndarray] = None, resolution: Union[list, ndarray] = None, path_i: str = None, path_o: str = None) -> GeoModel: # ? Do I need to pass pandas read kwargs? warnings.warn("This method is deprecated. Use create_geomodel instead.", DeprecationWarning) return create_geomodel( project_name=project_name, extent=extent, resolution=resolution, importer_helper=ImporterHelper( path_to_surface_points=path_i, path_to_orientations=path_o ) ) def _initialize_structural_frame(importer_helper: ImporterHelper) -> StructuralFrame: surface_points, orientations = _read_input_points(importer_helper) return StructuralFrame.from_data_tables(surface_points, orientations) def _read_input_points(importer_helper: ImporterHelper) -> (SurfacePointsTable, OrientationsTable): orientations_file, surface_points_file = _fetch_data_with_pooch( orientations_hash=importer_helper.hash_orientations, orientations_path=importer_helper.path_to_orientations, surface_points_hash=importer_helper.hash_surface_points, surface_points_path=importer_helper.path_to_surface_points ) surface_points: SurfacePointsTable = read_surface_points( path=surface_points_file, coord_x_name=importer_helper.coord_x_name, coord_y_name=importer_helper.coord_y_name, coord_z_name=importer_helper.coord_z_name, surface_name=importer_helper.surface_name, pandas_kwargs=importer_helper.pandas_reader_kwargs ) orientations: OrientationsTable = read_orientations( path=orientations_file, coord_x_name=importer_helper.coord_x_name, coord_y_name=importer_helper.coord_y_name, coord_z_name=importer_helper.coord_z_name, surface_name=importer_helper.surface_name, gx_name=importer_helper.gx_name, gy_name=importer_helper.gy_name, gz_name=importer_helper.gz_name, pandas_kwargs=importer_helper.pandas_reader_kwargs, name_id_map=surface_points.name_id_map ) return surface_points, orientations def _fetch_data_with_pooch(orientations_hash, orientations_path, surface_points_hash, surface_points_path): def is_url(url): from urllib.parse import urlparse try: result = urlparse(url) return all([result.scheme, result.netloc]) except ValueError: return False pooch = require_pooch() if is_url(surface_points_path) or is_url(orientations_path) else None # * Fetch or define path for surface points if is_url(surface_points_path): surface_points_file = pooch.retrieve( url=surface_points_path, known_hash=surface_points_hash ) print("Surface points hash: ", pooch.file_hash(surface_points_file)) else: surface_points_file = surface_points_path # * Fetch or define path for orientations if is_url(orientations_path): orientations_file = pooch.retrieve( url=orientations_path, known_hash=orientations_hash ) print("Orientations hash: ", pooch.file_hash(orientations_file)) else: orientations_file = orientations_path return orientations_file, surface_points_file ================================================ FILE: gempy/API/io_API.py ================================================ from typing import Optional import numpy as np from gempy.core.data.orientations import OrientationsTable from gempy.core.data.surface_points import SurfacePointsTable from gempy.optional_dependencies import require_pandas def read_surface_points(path: str, coord_x_name="X", coord_y_name="Y", coord_z_name="Z", surface_name="formation", name_id_map: Optional[dict[str, int]] = None, pandas_kwargs: dict = None) -> SurfacePointsTable: pandas_kwargs = pandas_kwargs or {} if 'sep' not in pandas_kwargs: pandas_kwargs['sep'] = ',' pd = require_pandas() csv = pd.read_csv(path, **pandas_kwargs) csv = _standardize(csv) surface_points: SurfacePointsTable = SurfacePointsTable.from_arrays( x=csv[coord_x_name].values, y=csv[coord_y_name].values, z=csv[coord_z_name].values, names=csv[surface_name].values, # TODO: This we will have to map it with StructuralFrame name_id_map=name_id_map ) return surface_points def read_orientations( path: str, coord_x_name="X", coord_y_name="Y", coord_z_name="Z", gx_name="G_x", gy_name="G_y", gz_name="G_z", surface_name="formation", name_id_map: Optional[dict[str, int]] = None, pandas_kwargs: dict = None ) -> OrientationsTable: pandas_kwargs = pandas_kwargs or {} if 'sep' not in pandas_kwargs: pandas_kwargs['sep'] = ',' pd = require_pandas() csv = pd.read_csv(path, **pandas_kwargs) csv_standardized = _standardize(csv) csv_with_gradient = _add_gradient_columns(csv_standardized) orientations: OrientationsTable = OrientationsTable.from_arrays( x=csv_with_gradient[coord_x_name].values, y=csv_with_gradient[coord_y_name].values, z=csv_with_gradient[coord_z_name].values, G_x=csv_with_gradient[gx_name].values, G_y=csv_with_gradient[gy_name].values, G_z=csv_with_gradient[gz_name].values, names=csv_with_gradient[surface_name].values, # TODO: This we will have to map it with StructuralFrame name_id_map=name_id_map ) return orientations COLUMN_NAME_MAPPING = { "X" : ["X", "x"], "Y" : ["Y", "y"], "Z" : ["Z", "z"], "azimuth" : ["azimuth", "Azimuth"], "dip" : ["dip", "Dip"], "polarity" : ["polarity", "Polarity"], "formation": ["formation", "Formation", "surface"], "G_x" : ["G_x", "gradient_x"], "G_y" : ["G_y", "gradient_y"], "G_z" : ["G_z", "gradient_z"], } def _standardize(df: 'pandas.DataFrame'): for column in df.columns: for standard_name, possible_names in COLUMN_NAME_MAPPING.items(): if column in possible_names: df.rename(columns={column: standard_name}, inplace=True) return df def _add_gradient_columns(df): if "azimuth" in df.columns and "dip" in df.columns and "polarity" in df.columns: # Convert azimuth, dip, polarity to gradient df['G_x'] = np.sin(np.deg2rad(df['dip'])) * np.sin(np.deg2rad(df['azimuth'])) * df['polarity'] df['G_y'] = np.sin(np.deg2rad(df['dip'])) * np.cos(np.deg2rad(df['azimuth'])) * df['polarity'] df['G_z'] = np.cos(np.deg2rad(df['dip'])) * df['polarity'] return df ================================================ FILE: gempy/API/map_stack_to_surfaces_API.py ================================================ from typing import Union from gempy_engine.core.data.stack_relation_type import StackRelationType from ..core.data.geo_model import GeoModel from ..core.data.structural_frame import StructuralFrame from ..core.data.structural_group import StructuralGroup def map_stack_to_surfaces(gempy_model: GeoModel, mapping_object: Union[dict[str, list[str]] | dict[str, tuple]], set_series: bool = True, remove_unused_series=True, series_data: list = None) -> StructuralFrame: """ Map stack (series) to surfaces by reorganizing elements between groups in a GeoModel's structural frame. This function reorganizes structural elements (surfaces) based on a mapping object and updates the structural frame of the GeoModel. It can also create new series and remove unused ones. Args: gempy_model (GeoModel): The GeoModel object whose structural frame is to be modified. mapping_object (Union[dict[str, list[str]] | dict[str, tuple]]): Dictionary mapping group names to element names. set_series (bool, optional): If True, creates new series for groups not present in the GeoModel. Defaults to True. remove_unused_series (bool, optional): If True, removes groups without any elements. Defaults to True. series_data (list, optional): List of series data from JSON containing structural relations. Defaults to None. Returns: StructuralFrame: The updated StructuralFrame object. """ structural_groups: list[StructuralGroup] = gempy_model.structural_frame.structural_groups for index, (group_name, elements) in enumerate(mapping_object.items()): # region Create new series if needed group_already_exists = any(group.name == group_name for group in structural_groups) if set_series and not group_already_exists: # Get structural relation from series_data if available structural_relation = StackRelationType.ERODE # Default value if series_data: for series in series_data: if series['name'] == group_name: structural_relation = StackRelationType[series['structural_relation']] break new_group = StructuralGroup( name=group_name, elements=[], structural_relation=structural_relation ) structural_groups.insert(index, new_group) # endregion # Make sure elements is a list or tuple if isinstance(elements, str): elements = [elements] for element_name in elements: # Here we need to find out the current group of the element. # This can be done by looking up the element in each group. from_group_name = None for group in structural_groups: if any(element.name == element_name for element in group.elements): from_group_name = group.name break # If we've found the group, we can proceed with moving the element. if from_group_name: _move_element( structural_groups=structural_groups, element_name=element_name, from_group_name=from_group_name, to_group_name=group_name ) else: print(f"Could not find element '{element_name}' in any group.") # Remove empty groups if remove_unused_series: structural_groups = [group for group in structural_groups if group.elements] gempy_model.structural_frame.structural_groups = structural_groups return gempy_model.structural_frame def _move_element(structural_groups: list[StructuralGroup], element_name: str, from_group_name: str, to_group_name: str): # Find the source and destination groups from_group = next((group for group in structural_groups if group.name == from_group_name), None) to_group = next((group for group in structural_groups if group.name == to_group_name), None) # Check if both groups exist if from_group is None or to_group is None: raise ValueError("One or both group names are not found.") # Find the element in the source group element = next((element for element in from_group.elements if element.name == element_name), None) # Check if element exists if element is None: raise ValueError(f"Element '{element_name}' not found in group '{from_group_name}'.") # Remove the element from the source group and add it to the destination group from_group.elements.remove(element) to_group.elements.append(element) ================================================ FILE: gempy/__init__.py ================================================ """ Module initialisation for GemPy Created on 06.19.2023 @author: Miguel de la Varga """ import sys from . import config # =================== CORE =================== from .core import data from .core.color_generator import ColorsGenerator # =================== API =================== from .API import * from .API import implicit_functions # * Assert at least pyton 3.10 assert sys.version_info[0] >= 3 and sys.version_info[1] >= 10, "GemPy requires Python 3.10 or higher" from ._version import __version__ if __name__ == '__main__': pass ================================================ FILE: gempy/_version.py ================================================ # file generated by setuptools_scm # don't change, don't track in version control TYPE_CHECKING = False if TYPE_CHECKING: from typing import Tuple, Union VERSION_TUPLE = Tuple[Union[int, str], ...] else: VERSION_TUPLE = object version: str __version__: str __version_tuple__: VERSION_TUPLE version_tuple: VERSION_TUPLE __version__ = version = '2024.2.0.3.dev0+gf344a731.d20240626' __version_tuple__ = version_tuple = (2024, 2, 0, 3, 'dev0', 'gf344a731.d20240626') ================================================ FILE: gempy/config.py ================================================ import os from dotenv import load_dotenv # Define the paths for the .env files script_dir = os.path.dirname(os.path.abspath(__file__)) dotenv_path = os.path.join(script_dir, '../.env') dotenv_gempy_engine_path = os.path.expanduser('~/.env') # Check if the .env files exist and prioritize the local .env file if os.path.exists(dotenv_path): load_dotenv(dotenv_path) elif os.path.exists(dotenv_gempy_engine_path): load_dotenv(dotenv_gempy_engine_path) else: load_dotenv() ================================================ FILE: gempy/core/__init__.py ================================================ if __name__ == '__main__': pass ================================================ FILE: gempy/core/color_generator.py ================================================ from dataclasses import dataclass from functools import cached_property from typing import Optional import numpy as np from pydantic import Field @dataclass class ColorsGenerator: """ Object that handles the color management. """ _gempy_default_colors = [ '#015482', '#9f0052', '#ffbe00', '#728f02', '#443988', '#ff3f20', '#5DA629', '#b271d0', '#72e54a', '#583bd1', '#d0e63d', '#b949e2', '#95ce4b', '#6d2b9f', '#60eb91', '#d746be', '#52a22e', '#5e63d8', '#e5c339', '#371970', '#d3dc76', '#4d478e', '#43b665', '#d14897', '#59e5b8', '#e5421d', '#62dedb', '#df344e', '#9ce4a9', '#d94077', '#99c573', '#842f74', '#578131', '#708de7', '#df872f', '#5a73b1', '#ab912b', '#321f4d', '#e4bd7c', '#142932', '#cd4f30', '#69aedd', '#892a23', '#aad6de', '#5c1a34', '#cfddb4', '#381d29', '#5da37c', '#d8676e', '#52a2a3', '#9b405c', '#346542', '#de91c9', '#555719', '#bbaed6', '#945624', '#517c91', '#de8a68', '#3c4b64', '#9d8a4d', '#825f7e', '#2c3821', '#ddadaa', '#5e3524', '#a3a68e', '#a2706b', '#686d56' ] # source: https://medialab.github.io/iwanthue/ hex_colors: list[str] = Field( default=_gempy_default_colors, exclude=True ) _index: int = 0 def __post_init__(self): self.regenerate_color_palette() @staticmethod def _random_hexcolor() -> str: """Generates a random hex color string.""" return f"#{np.random.randint(0, 0xffffff):06x}" def regenerate_color_palette(self, seaborn_palettes: Optional[list[str]] = None): try: import seaborn as sns seaborn_installed = True except ImportError: seaborn_installed = False if seaborn_palettes and seaborn_installed: hex_colors = [] for palette in seaborn_palettes: # for each palette hex_colors += sns.color_palette(palette).as_hex() # get all colors in palette and add to list elif seaborn_palettes and not seaborn_installed: raise ImportError("Seaborn is not installed. Please install it to use color palettes.") else: hex_colors = list(self._gempy_default_colors) self.hex_colors = hex_colors def __iter__(self) -> 'ColorsGenerator': """Returns the object itself as an iterator.""" return self def __next__(self) -> str: """Generator that yields the next color.""" color = self.up_next() self._index += 1 del self._next_color return color def up_next(self) -> str: """Returns the next color without incrementing the index.""" return self._next_color @cached_property def _next_color(self) -> str: if self._index < len(self.hex_colors): return self.hex_colors[self._index] return self._random_hexcolor() ================================================ FILE: gempy/core/data/DEP_xsolution.py ================================================ from typing import Union from gempy.core.data_modules.stack import Stack from gempy import Surfaces, Grid try: import xarray as xr import subsurface from subsurface.structs.base_structures.common_data_utils import to_netcdf except: print("Not subsurface compatibility available") import numpy as np import pandas as pd class XSolution(object): """This class stores the output of the interpolation and the necessary objects to visualize and manipulate this data using xarray as backend. Depending on the activated grid (see :class:`Grid`) a different number of properties are returned returned: Args: grid (Grid): [s0] surfaces (Surfaces): [s1] series (Series): [s2] Attributes: grid (Grid) surfaces (Surfaces) series (Series) weights_vector (numpy.array): [s3] scalar_field_at_surface_points (numpy.array): [s4] block_at_surface_points (numpy.array): [s5] mask_at_surface_points (numpy.array): [s6] values_at_surface_points (numpy.array): [s7] lith_block (numpy.array): [s8] scalar_field_matrix (numpy.array): [s9] block_matrix (numpy.array): [s10] mask_matrix (numpy.array): [s11] mask_matrix_pad (numpy.array): mask matrix padded 2 block in order to guarantee that the layers intersect each other after marching cubes values_matrix (numpy.array): [s12] vertices (list[numpy.array]): [s13] edges (list[numpy.array]): [s14] geological_map (numpy.array): [s15] """ def __init__(self, grid: Grid, surfaces: Surfaces = None, stack: Stack = None, ): self.grid = grid self.stack = stack self.surfaces = surfaces # Used to store ver/sim there # Define xarrays self.weights_vector = None self.at_surface_points = None self.s_regular_grid = None # xr.Dataset() self.s_custom_grid = None # xr.Dataset() self.s_topography = None # xr.Dataset() self.s_at_surface_points = None # xr.Dataset() self.s_sections = dict() self.meshes = None # xr.Dataset() def set_meshes(self, surfaces: Surfaces = None): """Create xarray from the Surfaces object. In GemPy Engine we will set them directly. """ surf_ver_sim = surfaces.df[['id', 'vertices', 'edges']] vertex = [] simplex = [] ids = [] last_idx = 0 self.extract_each_surface_representations(ids, last_idx, simplex, surf_ver_sim, vertex) vertex_array = np.concatenate(vertex) simplex_array = np.concatenate(simplex) ids_array = np.concatenate(ids) self.meshes = subsurface.UnstructuredData.from_array( vertex=vertex_array, cells=simplex_array, attributes=pd.DataFrame(ids_array, columns=['id']) ) return self.meshes @staticmethod def extract_each_surface_representations(ids, last_idx, simplex, surf_ver_sim, vertex): for index, row in surf_ver_sim.iterrows(): v_ = row['vertices'] e_ = row['edges'] + last_idx if v_ is not np.nan and e_ is not np.nan: i_ = np.ones(e_.shape[0]) * row['id'] vertex.append(v_) simplex.append(e_) ids.append(i_) last_idx = e_[-20:].max() + 1 def set_values(self, values: list, active_features=None, surf_properties=None, ): """ At this stage we should split values into the different grids Args: values: Returns: """ # Get an array with all the indices for each grid l = self.grid.length coords_base, xyz = self.prepare_common_args(active_features, surf_properties) self.weights_vector = values[3] if self.grid.active_grids_bool[0]: self.set_values_to_regular_grid_(values, l[0], l[1], coords_base.copy()) if self.grid.active_grids_bool[1]: self.set_values_to_custom_grid(values, l[1], l[2], coords_base.copy(), xyz=xyz) if self.grid.active_grids_bool[2]: self.set_values_to_topography(values, l[2], l[3], coords_base.copy()) if self.grid.active_grids_bool[3]: self.set_values_to_sections(values, l[3], l[4], coords_base.copy()) if self.grid.active_grids_bool[4]: self.set_values_to_centered() # TODO: Add xyz from surface points self.set_values_to_surface_points_(values, l[-1], coords_base, xyz=None) def prepare_common_args(self, active_features, surf_properties): if active_features is None and self.stack is not None: active_features = self.stack.df.groupby('isActive').get_group(True).index if surf_properties is None and self.surfaces is not None: surf_properties = self.surfaces.properties_val coords_base = dict() if active_features is not None: coords_base['Features'] = active_features.to_list() if surf_properties is not None: coords_base['Properties'] = surf_properties.to_list() if self.grid.custom_grid is not None: xyz = self.grid.custom_grid.values else: xyz = None return coords_base, xyz def set_values_to_centered(self): return def set_values_to_surface_points_(self, values, l0, coords_base, xyz=None): coords = coords_base l1 = values[0].shape[-1] arrays = self.create_unstruct_xarray(values, l0, l1, xyz) self.s_at_surface_points = xr.Dataset( data_vars=arrays, coords=coords ) return self.s_at_surface_points @staticmethod def create_struc_xarrays(values, l0, l1, res: Union[list, np.ndarray]): arrays = dict() n_dim = len(res) xyz = ['X', 'Y', 'Z'][:n_dim] if values[0] is not None: # This encompass lith_block and values matrix property_matrix = xr.DataArray( data=values[0][:, l0:l1].reshape(-1, *res), dims=['Properties', *xyz], ) arrays['property_matrix'] = property_matrix if values[1] is not None: # This is the block matrix i, j, _ = values[1].shape block_matrix = xr.DataArray( data=values[1][:, :, l0:l1].reshape(i, j, *res), dims=['Features', 'Properties', *xyz], ) arrays['block_matrix'] = block_matrix # Fault block? if values[4] is not None: # Scalar field matrix scalar_matrix = xr.DataArray( data=values[4][:, l0:l1].reshape(-1, *res), dims=['Features', *xyz], ) arrays['scalar_field_matrix'] = scalar_matrix if values[6] is not None: # Mask matrix mask_matrix = xr.DataArray( data=values[6][:, l0:l1].reshape(-1, *res), dims=['Features', *xyz], ) arrays['mask_matrix'] = mask_matrix if values[7] is not None: # Fault mask matrix fault_mask = xr.DataArray( data=values[7][:, l0:l1].reshape(-1, *res), dims=['Features', *xyz], ) arrays['fault_mask'] = fault_mask return arrays @staticmethod def create_unstruct_xarray(values, l0, l1, xyz): arrays = dict() if xyz is not None: cartesian_matrix = xr.DataArray( data=xyz, dims=['Point', 'XYZ'], coords={'XYZ': ['X', 'Y', 'Z']} ) arrays['cartesian_matrix'] = cartesian_matrix if values[0] is not None: # Values and lith block property_v3 = xr.DataArray( data=values[0][:, l0:l1], dims=['cell_attr', 'cell'], ) arrays['property'] = property_v3 if values[1] is not None: # block block_v3 = xr.DataArray( data=values[1][:, :, l0:l1], dims=['Features', 'cell_attr', 'cell'], ) arrays['block'] = block_v3 if values[4] is not None: # Scalar field scalar_field_v3 = xr.DataArray( data=values[4][:, l0:l1], dims=['Features', 'cell'], ) arrays['scalar_field'] = scalar_field_v3 if values[6] is not None: # Scalar field mask_v3 = xr.DataArray( data=values[6][:, l0:l1], dims=['Features', 'cell'], ) arrays['mask'] = mask_v3 return arrays def set_values_to_custom_grid(self, values: list, l0, l1, coords_base: dict, xyz=None): coords = coords_base arrays = self.create_unstruct_xarray(values, l0, l1, xyz=None) self.s_custom_grid = subsurface.UnstructuredData.from_array( vertex=xyz, cells="points", cells_attr=arrays, coords=coords, default_cells_attr_name="block" ) return self.s_custom_grid def set_values_to_regular_grid_(self, values: list, l0, l1, coords_base: dict): coords = self.add_cartesian_coords(coords_base) arrays = self.create_struc_xarrays(values, l0, l1, self.grid.regular_grid.resolution) self.s_regular_grid = subsurface.StructuredData.from_dict(data_dict=arrays, coords=coords) def add_cartesian_coords(self, coords_base): coords = coords_base coords['X'] = self.grid.regular_grid.x coords['Y'] = self.grid.regular_grid.y coords['Z'] = self.grid.regular_grid.z return coords def set_values_to_topography(self, values: list, l0, l1, coords_base): coords = coords_base coords['X'] = self.grid.topography.x coords['Y'] = self.grid.topography.y resolution = self.grid.topography.resolution arrays = self.create_struc_xarrays(values, l0, l1, resolution) self.s_topography = subsurface.StructuredData.from_dict(data_dict=arrays, coords=coords) return self.s_topography def set_values_to_sections(self, values: list, l0, l1, coords_base): coords = coords_base sections = self.grid.sections for e, axis_coord in enumerate(sections.generate_axis_coord()): resolution = sections.resolution[e] l0_s = sections.length[e] l1_s = sections.length[e + 1] name, xy = axis_coord coords['X'] = xy[:, 0] coords['Y'] = xy[:, 1] arrays = self.create_struc_xarrays(values, l0 + l0_s, l0 + l1_s, resolution) self.s_sections[name] = subsurface.StructuredData.from_dict(data_dict=arrays, coords=coords) return self.s_sections @property def data_structures(self): # TODO: Add sections args = [self.s_regular_grid, self.s_custom_grid, self.s_topography, self.meshes] names = ['regular_grid', 'custom_grid', 'topography', 'meshes'] return zip(args, names) def to_netcdf(self, path, name, **kwargs): for a, n in self.data_structures: if a is not None: to_netcdf(a, f'{path}/{name}_{n}.nc', **kwargs) ================================================ FILE: gempy/core/data/__init__.py ================================================ from .geo_model import GeoModel from .structural_frame import StructuralFrame from .structural_group import StructuralGroup from .structural_element import StructuralElement from .orientations import OrientationsTable from .surface_points import SurfacePointsTable from .grid import Grid, Topography from .importer_helper import ImporterHelper from .gempy_engine_config import GemPyEngineConfig from .structural_group import FaultsRelationSpecialCase from ..color_generator import ColorsGenerator from gempy_engine.core.data.interpolation_input import InterpolationInput from gempy_engine.core.data.stack_relation_type import StackRelationType from gempy_engine.core.data.options import InterpolationOptions from gempy_engine.core.data.solutions import Solutions from gempy_engine.core.data.raw_arrays_solution import RawArraysSolution from gempy_engine.core.data.transforms import GlobalAnisotropy, Transform from gempy_engine.core.data.kernel_classes.faults import FaultsData, FiniteFaultData from gempy_engine.config import AvailableBackends from gempy_engine.core.data.geophysics_input import GeophysicsInput __all__ = [ # From gempy 'GeoModel', 'StructuralFrame', 'StructuralGroup', 'StructuralElement', 'OrientationsTable', 'SurfacePointsTable', 'Grid', 'Topography', 'ImporterHelper', 'GemPyEngineConfig', 'FaultsRelationSpecialCase', 'ColorsGenerator', # From gempy engine 'InterpolationInput','StackRelationType', 'InterpolationOptions', 'Solutions', 'RawArraysSolution', 'GlobalAnisotropy', 'Transform', 'FaultsData', 'FiniteFaultData', 'AvailableBackends', 'GeophysicsInput' ] ================================================ FILE: gempy/core/data/_data_points_helpers.py ================================================ import hashlib import sys from typing import Sequence import numpy as np def structural_element_hasher(i: int, name: str, hash_length: int = 8) -> int: # Get the last 'hash_length' digits from the hash name_hash = int(hashlib.md5(name.encode('utf-8')).hexdigest(), 16) % (10 ** hash_length) return i * (10 ** hash_length) + name_hash def generate_ids_from_names(name_id_map, names, x): name_id_map = name_id_map or {name: structural_element_hasher(i, name) for i, name in enumerate(np.unique(names))} if isinstance(names, str): ids = np.array([name_id_map[names]] * len(x)) elif isinstance(names, Sequence) or isinstance(names, np.ndarray): ids = np.array([name_id_map[name] for name in names]) elif type(names).__module__.startswith('pandas') and type(names).__name__ == 'StringArray': ids = np.array([name_id_map[name] for name in names]) else: raise TypeError(f"Names should be a string or a NumPy array, not {type(names)}") return ids, name_id_map ================================================ FILE: gempy/core/data/core_utils.py ================================================ import numpy as np from gempy.optional_dependencies import require_scipy def calculate_line_coordinates_2points(p1, p2, res): if isinstance(p1, list) or isinstance(p1, tuple): p1 = np.array(p1) if isinstance(p2, list) or isinstance(p2, tuple): p2 = np.array(p2) v = p2 - p1 # vector pointing from p1 to p2 u = v / np.linalg.norm(v) # normalize it distance = distance_2_points(p1, p2) steps = np.linspace(0, distance, res) values = p1.reshape(2, 1) + u.reshape(2, 1) * steps.ravel() return values.T def distance_2_points(p1, p2): return np.sqrt(np.diff((p1[0], p2[0])) ** 2 + np.diff((p1[1], p2[1])) ** 2) def interpolate_zvals_at_xy(xy, topography, method='DEP'): """ Interpolates DEM values on a defined section. Args: xy (np.ndarray): Array of shape (n, 2) containing x (EW) and y (NS) coordinates of the profile. topography (Topography): An instance of Topography containing the DEM data. Returns: np.ndarray: z values, i.e., topography along the profile. """ scipy = require_scipy() xj = topography.values_2d[:, 0, 0] yj = topography.values_2d[0, :, 1] zj = topography.values_2d[:, :, 2] spline = scipy.interpolate.RectBivariateSpline(xj, yj, zj) zi = spline.ev(xy[:, 0], xy[:, 1]) return zi ================================================ FILE: gempy/core/data/encoders/__init__.py ================================================ ================================================ FILE: gempy/core/data/encoders/binary_encoder.py ================================================ import numpy as np from ..surface_points import SurfacePointsTable from ..orientations import OrientationsTable def deserialize_input_data_tables(binary_array: bytes, name_id_map: dict, sp_binary_length_: int, ori_binary_length_: int) -> tuple[OrientationsTable, SurfacePointsTable]: """ Deserializes binary data into two tables: OrientationsTable and SurfacePointsTable. This function takes a binary array, a mapping of names to IDs, and lengths for specific parts of the binary data to extract and deserialize two distinct data tables: OrientationsTable and SurfacePointsTable. It uses the provided lengths to split the binary data accordingly and reconstructs the table contents from their respective binary representations. Args: binary_array (bytes): A bytes array containing the serialized data for both the OrientationsTable and SurfacePointsTable. name_id_map (dict): A dictionary mapping names to IDs which is used to help reconstruct the table objects. sp_binary_length_ (int): The length of the binary segment corresponding to the SurfacePointsTable data. ori_binary_length_ (int): The length of the binary segment corresponding to the OrientationsTable data. Returns: tuple[OrientationsTable, SurfacePointsTable]: A tuple containing two table objects: first the OrientationsTable, and second the SurfacePointsTable. """ sp_binary = binary_array[:sp_binary_length_] ori_binary = binary_array[sp_binary_length_:sp_binary_length_+ori_binary_length_] # Reconstruct arrays sp_data: np.ndarray = np.frombuffer(sp_binary, dtype=SurfacePointsTable.dt) ori_data: np.ndarray = np.frombuffer(ori_binary, dtype=OrientationsTable.dt) surface_points_table = SurfacePointsTable(data=sp_data, name_id_map=name_id_map) orientations_table = OrientationsTable(data=ori_data, name_id_map=name_id_map) return orientations_table, surface_points_table def deserialize_grid(binary_array:bytes, custom_grid_length: int, topography_length: int) -> tuple[np.ndarray, np.ndarray]: """ Deserialize binary grid data into two numpy arrays. This function takes a binary array representing a grid and splits it into two separate numpy arrays: one for the custom grid and one for the topography. The binary array is segmented based on the provided lengths for the custom grid and topography. Args: binary_array: The binary data representing the combined custom grid and topography data. custom_grid_length: The length of the custom grid data segment in bytes. topography_length: The length of the topography data segment in bytes. Returns: A tuple where the first element is a numpy array representing the custom grid, and the second element is a numpy array representing the topography data. Raises: ValueError: If input lengths do not match the specified boundaries or binary data. """ custom_grid_binary = binary_array[:custom_grid_length] topography_binary = binary_array[custom_grid_length:custom_grid_length + topography_length] custom_grid = np.frombuffer(custom_grid_binary, dtype=np.float64) topography = np.frombuffer(topography_binary) return custom_grid, topography ================================================ FILE: gempy/core/data/encoders/converters.py ================================================ from typing import Annotated from contextlib import contextmanager from contextvars import ContextVar import numpy as np from pydantic import BeforeValidator def convert_to_arrays(values, keys): for key in keys: if key in values and not isinstance(values[key], np.ndarray): values[key] = np.array(values[key]) return values def validate_numpy_array(v): return np.array(v) if v is not None else None short_array_type = Annotated[np.ndarray, (BeforeValidator(lambda v: np.array(v) if v is not None else None))] def instantiate_if_necessary(data: dict, key: str, type: type) -> None: """ Creates instances of the specified type for a dictionary key if the key exists and its current type does not match the specified type. This function modifies the dictionary in place by converting the value associated with the key into an instance of the given type. This is typically used when a dictionary contains data that needs to be represented as objects of a specific class type. Args: data (dict): The dictionary containing the key-value pair to inspect and possibly convert. key (str): The key in the dictionary whose value should be inspected and converted if necessary. type (type): The type to which the value of `key` should be converted, if it is not already an instance of the type. Returns: None """ if key in data and not isinstance(data[key], type): data[key] = type(**data[key]) numpy_array_short_validator = BeforeValidator(validate_numpy_array) # First, create a context variable loading_model_context = ContextVar('loading_model_context', default={}) @contextmanager def loading_model_from_binary(input_binary: bytes, grid_binary: bytes): token = loading_model_context.set({ 'input_binary': input_binary, 'grid_binary': grid_binary }) try: yield finally: loading_model_context.reset(token) ================================================ FILE: gempy/core/data/encoders/json_geomodel_encoder.py ================================================ import numpy as np def encode_numpy_array(array: np.ndarray): # Check length if array.size > 10: return [] return array.tolist() ================================================ FILE: gempy/core/data/enumerators.py ================================================ from enum import Enum, auto class ExampleModel(Enum): TWO_AND_A_HALF_D = auto() HORIZONTAL_STRAT = auto() ANTICLINE = auto() ONE_FAULT = auto() COMBINATION = auto() ONE_FAULT_GRAVITY = auto() GRABEN = auto() GREENSTONE = auto() FAULT_RELATION = auto() ================================================ FILE: gempy/core/data/gempy_engine_config.py ================================================ from dataclasses import dataclass from typing import Optional from gempy_engine import config from gempy_engine.config import AvailableBackends @dataclass class GemPyEngineConfig: backend: AvailableBackends = config.DEFAULT_BACKEND # ? This can be grabbed from gempy.config file? use_gpu: bool = False dtype: Optional[str] = None #: The data type used in the engine. If None, the default data type of the backend is used. ================================================ FILE: gempy/core/data/geo_model.py ================================================ import datetime import numpy as np import pprint import warnings from dataclasses import dataclass from pydantic import BaseModel, ConfigDict, Field, PrivateAttr, computed_field, model_validator, ValidationError from pydantic.functional_validators import ModelWrapValidatorHandler from typing import Sequence, Optional, Union from gempy_engine.core.data import InterpolationOptions from gempy_engine.core.data import Solutions from gempy_engine.core.data.engine_grid import EngineGrid from gempy_engine.core.data.geophysics_input import GeophysicsInput from gempy_engine.core.data.input_data_descriptor import InputDataDescriptor from gempy_engine.core.data.interpolation_input import InterpolationInput from gempy_engine.core.data.raw_arrays_solution import RawArraysSolution from gempy_engine.core.data.transforms import Transform, GlobalAnisotropy from .encoders.converters import instantiate_if_necessary from .encoders.json_geomodel_encoder import encode_numpy_array from .grid import Grid from .orientations import OrientationsTable from .structural_frame import StructuralFrame from .surface_points import SurfacePointsTable from ...modules.data_manipulation import interpolation_input_from_structural_frame """ TODO: - [ ] StructuralFrame will all input points chunked on Elements. Here I will need a property to put all together to feed to InterpolationInput """ @dataclass class GeoModelMeta: """ Container for metadata associated with a GeoModel. Attributes: name (str): Name of the geological model. creation_date (str): Date of creation of the model. last_modification_date (str): Last modification date of the model. owner (str): Owner of the geological model. """ name: str creation_date: str last_modification_date: str | None owner: str | None class GeoModel(BaseModel): """ Class representing a geological model. """ meta: GeoModelMeta = Field(exclude=False) #: Meta-information about the geological model, like its name, creation and modification dates, and owner. structural_frame: StructuralFrame = Field(exclude=False) #: The structural information of the geological model. grid: Grid = Field(exclude=False, default=None) #: The general grid used in the geological model. # region GemPy engine data types _interpolation_options: InterpolationOptions #: The interpolation options provided by the user. geophysics_input: GeophysicsInput | None = Field(default=None, exclude=False) #: The geophysics input of the geological model. input_transform: Transform = Field(default=None, exclude=False) #: The transformation used in the geological model for input points. interpolation_grid: EngineGrid = Field(default=None, exclude=True) #: ptional grid used for interpolation. Can be seen as a cache field. _interpolationInput: InterpolationInput = PrivateAttr(default=None) #: Input data for interpolation. Fed by the structural frame and can be seen as a cache field. _input_data_descriptor: InputDataDescriptor = PrivateAttr(default=None) #: Descriptor of the input data. Fed by the structural frame and can be seen as a cache field. # endregion _solutions: Solutions = PrivateAttr(init=False, default=None) #: The computed solutions of the geological model. _taped_interpolation_input: InterpolationInput | None = PrivateAttr(default=None) def __repr__(self): # TODO: Improve this return pprint.pformat(self.__dict__) # region Properties @computed_field(alias="_interpolation_options") @property def interpolation_options(self) -> InterpolationOptions: self._infer_dense_grid_solution() self._interpolation_options.cache_model_name = self.meta.name return self._interpolation_options @interpolation_options.setter def interpolation_options(self, value): self._interpolation_options = value @property def solutions(self) -> Solutions: return self._solutions @solutions.setter def solutions(self, value: Solutions): # * This is set from the gempy engine self._solutions = value # * Set solutions per element for e, element in enumerate(self.structural_frame.structural_elements[:-1]): # * Ignore basement element.scalar_field_at_interface = value.scalar_field_at_surface_points[e] if self._solutions.dc_meshes is None: continue dc_mesh = self._solutions.dc_meshes[e] if dc_mesh is None: continue # TODO: These meshes are in the order of the scalar field world_coord_vertices = self.input_transform.apply_inverse(dc_mesh.vertices) world_coord_vertices = self.grid.transform.apply_inverse_with_cached_pivot(world_coord_vertices) element.vertices = world_coord_vertices element.edges = (dc_mesh.edges if dc_mesh is not None else None) # * Reordering the elements according to the scalar field for e, order_per_structural_group in enumerate(self._solutions._ordered_elements): elements = self.structural_frame.structural_groups[e].elements reordered_elements = [elements[i] for i in order_per_structural_group] self.structural_frame.structural_groups[e].elements = reordered_elements @property def surface_points_copy(self): """This is a copy! Returns a SurfacePointsTable for all surface points across the structural elements""" surface_points_table = self.structural_frame.surface_points_copy return surface_points_table @property def surface_points_copy_transformed(self) -> SurfacePointsTable: og_sp = self.surface_points_copy og_sp.xyz_view = self.grid.transform.apply_with_cached_pivot(og_sp.xyz) og_sp.xyz_view = self.input_transform.apply(og_sp.xyz) return og_sp @property def surface_points(self): raise AttributeError("This property can only be set, not read. You can access the copy with `surface_points_copy` or" "the original on the individual structural elements.") @surface_points.setter def surface_points(self, value): self.structural_frame.surface_points = value @property def orientations_copy(self) -> OrientationsTable: """This is a copy! Returns a OrientationsTable for all orientations across the structural elements""" orientations_table = self.structural_frame.orientations_copy return orientations_table @property def orientations_copy_transformed(self) -> OrientationsTable: # ! This is not done og_or = self.orientations_copy total_transform: Transform = self.input_transform + self.grid.transform og_or.xyz_view = self.grid.transform.apply_with_cached_pivot(og_or.xyz) og_or.xyz_view = self.input_transform.apply(og_or.xyz) og_or.grads_view = total_transform.transform_gradient(og_or.grads) return og_or @property def regular_grid_coordinates(self) -> np.ndarray: return self.grid.regular_grid.get_values_vtk_format(orthogonal=False) @property def regular_grid_coordinates_transformed(self) -> np.ndarray: values_transformed = self.grid.regular_grid.get_values_vtk_format(orthogonal=True) values_transformed = self.input_transform.apply(values_transformed) return values_transformed @property def orientations(self) -> OrientationsTable: raise AttributeError("This property can only be set, not read. You can access the copy with `orientations_copy` or" "the original on the individual structural elements.") @orientations.setter def orientations(self, value): self.structural_frame.orientations = value @property def project_bounds(self) -> np.ndarray: return self.grid.bounding_box @property def extent_transformed_transformed_by_input(self) -> np.ndarray: transformed = self.input_transform.apply(self.project_bounds) # ! grid already has the grid transform applied new_extents = np.array([transformed[:, 0].min(), transformed[:, 0].max(), transformed[:, 1].min(), transformed[:, 1].max(), transformed[:, 2].min(), transformed[:, 2].max()]) return new_extents @property def interpolation_input_copy(self): warnings.warn("This property is deprecated. Use directly " "`interpolation_input_from_structural_frame` instead.", DeprecationWarning) if self.structural_frame.is_dirty is False: return self._interpolationInput self._interpolationInput = interpolation_input_from_structural_frame( geo_model=self ) return self._interpolationInput @property def input_data_descriptor(self) -> InputDataDescriptor: # TODO: This should have the exact same dirty logic as interpolation_input return self.structural_frame.input_data_descriptor @property def taped_interpolation_input(self) -> InterpolationInput: return self._taped_interpolation_input @taped_interpolation_input.setter def taped_interpolation_input(self, value: InterpolationInput): self._taped_interpolation_input = value # endregion # region Constructors @classmethod def from_args(cls, name: str, structural_frame: StructuralFrame, grid: Grid, interpolation_options: InterpolationOptions): # TODO: Fill the arguments properly meta = GeoModelMeta( name=name, creation_date=datetime.datetime.now().isoformat(), last_modification_date=None, owner=None ) structural_frame = structural_frame # ? This could be Optional grid = grid _interpolation_options = interpolation_options input_transform = Transform.from_input_points( surface_points=structural_frame.surface_points_copy, orientations=structural_frame.orientations_copy ) model = GeoModel( meta=meta, structural_frame=structural_frame, grid=grid, input_transform=input_transform, _interpolation_options=_interpolation_options ) return model # endregion # region Methods def update_transform(self, auto_anisotropy: GlobalAnisotropy = GlobalAnisotropy.NONE, anisotropy_limit: Optional[np.ndarray] = None): """Update the transformation of the geological model. This function updates the transformation of the geological model using the provided surface points and orientations. It also applies anisotropy based on the specified type and limit. Args: auto_anisotropy (GlobalAnisotropy): The type of anisotropy to apply. Defaults to GlobalAnisotropy.NONE. anisotropy_limit (Optional[np.ndarray]): Anisotropy limit values. If None, no limit is applied. """ self.input_transform = Transform.from_input_points( surface_points=self.surface_points_copy, orientations=self.orientations_copy ) self.input_transform.apply_anisotropy(anisotropy_type=auto_anisotropy, anisotropy_limit=anisotropy_limit) def add_surface_points(self, X: Sequence[float], Y: Sequence[float], Z: Sequence[float], surface: Sequence[str], nugget: Optional[Sequence[float]] = None) -> None: raise NotImplementedError("This method is deprecated. Use `gp.add_surface_points` instead") # endregion # region Pydantic serialization model_config = ConfigDict( arbitrary_types_allowed=True, use_enum_values=False, json_encoders={ np.ndarray: encode_numpy_array, } ) @model_validator(mode='wrap') @classmethod def deserialize_properties(cls, data: Union["GeoModel", dict], constructor: ModelWrapValidatorHandler["GeoModel"]) -> "GeoModel": match data: case GeoModel(): return data case dict(): # instance: GeoModel = constructor(data) instantiate_if_necessary( data=data, key="_interpolation_options", type=InterpolationOptions ) instance._interpolation_options = data.get("_interpolation_options") # * Reset geophysics if necessary centered_grid = instance.grid.centered_grid if centered_grid is not None and instance.geophysics_input is not None: from gempy_engine.modules.geophysics.gravity_gradient import calculate_gravity_gradient instance.geophysics_input.tz = calculate_gravity_gradient(centered_grid) return instance case _: raise ValidationError # endregion def _infer_dense_grid_solution(self): octrees_set: bool = Grid.GridTypes.OCTREE in self.grid.active_grids dense_set: bool = Grid.GridTypes.DENSE in self.grid.active_grids # Create a tuple representing the conditions match (octrees_set, dense_set): case (True, False): self._interpolation_options.block_solutions_type = RawArraysSolution.BlockSolutionType.OCTREE case (True, True): warnings.warn("Both octree levels and resolution are set. The default grid for the `raw_array_solution`" "and plots will be the dense regular grid. To use octrees instead, set resolution to None in the " "regular grid.") self._interpolation_options.block_solutions_type = RawArraysSolution.BlockSolutionType.DENSE_GRID case (False, True): self._interpolation_options.block_solutions_type = RawArraysSolution.BlockSolutionType.DENSE_GRID case (False, False): self._interpolation_options.block_solutions_type = RawArraysSolution.BlockSolutionType.NONE ================================================ FILE: gempy/core/data/grid.py ================================================ import dataclasses import enum import numpy as np from pydantic import Field, model_validator, computed_field, ValidationError from pydantic.functional_validators import ModelWrapValidatorHandler from typing import Optional, Annotated, Union from gempy_engine.core.data.centered_grid import CenteredGrid from gempy_engine.core.data.options import EvaluationOptions from gempy_engine.core.data.transforms import Transform from .encoders.binary_encoder import deserialize_grid from .encoders.converters import loading_model_context from .grid_modules import RegularGrid, CustomGrid, Sections from .grid_modules.topography import Topography @dataclasses.dataclass(init=True) class Grid: class GridTypes(enum.Flag): OCTREE = 2 ** 0 DENSE = 2 ** 1 CUSTOM = 2 ** 2 TOPOGRAPHY = 2 ** 3 SECTIONS = 2 ** 4 CENTERED = 2 ** 5 NONE = 2 ** 10 # ? What should we do with the extent? values: Annotated[np.ndarray, Field(exclude=True)] = dataclasses.field(default_factory=lambda: np.empty((0, 3))) length: Annotated[np.ndarray, Field(exclude=True)] = dataclasses.field(default_factory=lambda: np.empty(0)) _octree_grid: Optional[RegularGrid] = None _dense_grid: Optional[RegularGrid] = None _custom_grid: Optional[CustomGrid] = None _topography: Optional[Topography] = None _sections: Optional[Sections] = None _centered_grid: Optional[CenteredGrid] = None _active_grids = GridTypes.NONE _transform: Optional[Transform] = None _octree_levels: int = -1 def __init__(self, extent=None, resolution=None): self.values = np.empty((0, 3)) self.length = np.empty(0) # Init basic grid empty if extent is not None and resolution is not None: self.dense_grid = RegularGrid(extent, resolution) def __setattr__(self, name, value): if name == 'active_grids': self._active_grids = value self._update_values() else: super().__setattr__(name, value) @model_validator(mode='wrap') @classmethod def deserialize_properties(cls, data: Union["Grid", dict], constructor: ModelWrapValidatorHandler["Grid"]) -> "Grid": try: match data: case Grid(): return data case dict(): grid: Grid = constructor(data) grid._active_grids = Grid.GridTypes(data["active_grids"]) # TODO: Digest binary data metadata = data.get('binary_meta_data', {}) context = loading_model_context.get() if 'grid_binary' in context: custom_grid_vals, topography_vals = deserialize_grid( binary_array=context['grid_binary'], custom_grid_length=metadata["custom_grid_binary_length"], topography_length=metadata["topography_binary_length"] ) if grid.custom_grid is not None: grid.custom_grid.values = custom_grid_vals.reshape(-1, 3) if grid.topography is not None: grid.topography.set_values2d(values=topography_vals) if grid.octree_grid is not None: # * Update the octree grid values. In the future we should also serialize them. grid.octree_grid.set_regular_grid( extent=grid.octree_grid.extent, resolution=grid.octree_grid.resolution, transform=grid.octree_grid.transform ) grid._update_values() return grid case _: raise ValidationError except ValidationError: raise @property def grid_binary(self): custom_grid_bytes = self._custom_grid.values.astype("float64").tobytes() if self._custom_grid else b'' topography_bytes = self._topography.values.astype("float64").tobytes() if self._topography else b'' return custom_grid_bytes + topography_bytes @computed_field def binary_meta_data(self) -> dict: return { 'custom_grid_binary_length': len(self._custom_grid.values.astype("float64").tobytes()) if self._custom_grid else 0, 'topography_binary_length' : len(self._topography.values.astype("float64").tobytes()) if self._topography else 0, } @computed_field(alias="active_grids") @property def active_grids(self) -> GridTypes: return self._active_grids @active_grids.setter def active_grids(self, value: GridTypes): self._active_grids = value self._update_values() @classmethod def init_octree_grid(cls, extent, octree_levels, legacy: bool = False): grid = cls() if legacy: base_resolution = (np.array([2, 2, 2])) else: lengths = np.array([ extent[1] - extent[0], # x extent[3] - extent[2], # y extent[5] - extent[4] # z ]) min_length = np.min(lengths) base_resolution = np.round(lengths / min_length).astype(int) * 2 grid._octree_grid = RegularGrid.octree_init( extent=extent, octree_levels=octree_levels, base_resolution=base_resolution ) grid.active_grids |= grid.GridTypes.OCTREE grid._update_values() return grid @classmethod def init_dense_grid(cls, extent, resolution): return cls(extent, resolution) def __str__(self): active_grid_types_str = [g_type for g_type in self.GridTypes if self.active_grids & g_type] grid_summary = [f"{g_type} (active: {getattr(self, g_type + '_grid_active')}): {len(getattr(self, g_type + '_grid').values)} points" for g_type in active_grid_types_str] grid_summary_str = "\n".join(grid_summary) return f"Grid Object:\n{grid_summary_str}" @property def transform(self) -> Transform: if self.dense_grid is not None: return self.dense_grid.transform elif self.octree_grid is not None: return self.octree_grid.transform else: return Transform.init_neutral() @transform.setter def transform(self, value: Transform): self._transform = value @property def extent(self): if self.dense_grid is not None: return self.dense_grid.extent elif self.octree_grid is not None: return self.octree_grid.extent else: raise AttributeError('Extent is not defined') @property def corner_min(self): return self.extent[::2] @property def corner_max(self): return self.extent[1::2] @property def bounding_box(self): extents = self.extent # Define 3D points of the bounding box corners based on extents bounding_box_points = np.array([[extents[0], extents[2], extents[4]], # min x, min y, min z [extents[0], extents[2], extents[5]], # min x, min y, max z [extents[0], extents[3], extents[4]], # min x, max y, min z [extents[0], extents[3], extents[5]], # min x, max y, max z [extents[1], extents[2], extents[4]], # max x, min y, min z [extents[1], extents[2], extents[5]], # max x, min y, max z [extents[1], extents[3], extents[4]], # max x, max y, min z [extents[1], extents[3], extents[5]]]) # max x, max y, max z return bounding_box_points @property def dense_grid(self) -> RegularGrid: return self._dense_grid @dense_grid.setter def dense_grid(self, value): self._dense_grid = value self.active_grids |= self.GridTypes.DENSE self._update_values() @property def octree_grid(self): return self._octree_grid @octree_grid.setter def octree_grid(self, value): raise AttributeError('Octree grid is not allowed to be set directly. Use init_octree_grid instead') def set_octree_grid(self, regular_grid: RegularGrid): self._octree_grid = regular_grid self.active_grids |= self.GridTypes.OCTREE self._update_values() def set_octree_grid_by_levels(self, octree_levels: int, evaluation_options: EvaluationOptions, extent: Optional[np.ndarray] = None): if extent is None: extent = self.extent self._octree_grid = RegularGrid( extent=extent, resolution=np.array([2 ** octree_levels] * 3), ) evaluation_options.number_octree_levels = octree_levels self.active_grids |= self.GridTypes.OCTREE self._update_values() @property def octree_levels(self): return self._octree_levels @octree_levels.setter def octree_levels(self, value): raise AttributeError('Octree levels are not allowed to be set directly. Use set_octree_grid instead') @property def custom_grid(self): return self._custom_grid @custom_grid.setter def custom_grid(self, value): self._custom_grid = value self.active_grids |= self.GridTypes.CUSTOM self._update_values() @property def topography(self): return self._topography @topography.setter def topography(self, value): self._topography = value self.active_grids |= self.GridTypes.TOPOGRAPHY self._update_values() @property def sections(self): return self._sections @sections.setter def sections(self, value): self._sections = value self.active_grids |= self.GridTypes.SECTIONS self._update_values() @property def centered_grid(self): return self._centered_grid @centered_grid.setter def centered_grid(self, value): self._centered_grid = value self.active_grids |= self.GridTypes.CENTERED self._update_values() @property def regular_grid(self): dense_grid_exists_and_active = self.dense_grid is not None and self.GridTypes.DENSE in self.active_grids octree_grid_exists_and_active = self.octree_grid is not None and self.GridTypes.OCTREE in self.active_grids if dense_grid_exists_and_active and octree_grid_exists_and_active: raise AttributeError('Both dense_grid and octree_grid are active. This is not possible.') elif dense_grid_exists_and_active: return self.dense_grid elif octree_grid_exists_and_active: return self.octree_grid else: return None # noinspection t def _update_values(self): values = [] if self.GridTypes.OCTREE in self.active_grids: if self.octree_grid is None: raise AttributeError('Octree grid is active but not defined') values.append(self.octree_grid.values) if self.GridTypes.DENSE in self.active_grids: if self.dense_grid is None: raise AttributeError('Dense grid is active but not defined') values.append(self.dense_grid.values) if self.GridTypes.CUSTOM in self.active_grids: if self.custom_grid is None: raise AttributeError('Custom grid is active but not defined') values.append(self.custom_grid.values) if self.GridTypes.TOPOGRAPHY in self.active_grids: if self.topography is None: raise AttributeError('Topography grid is active but not defined') values.append(self.topography.values) if self.GridTypes.SECTIONS in self.active_grids: if self.sections is None: raise AttributeError('Sections grid is active but not defined') values.append(self.sections.values) if self.GridTypes.CENTERED in self.active_grids: if self.centered_grid is None: raise AttributeError('Centered grid is active but not defined') values.append(self.centered_grid.values) # make sure values is not empty if len(values) == 0: return self.values self.values = np.concatenate(values) return self.values def get_section_args(self, section_name: str): # TODO: This method should be part of the sections # assert type(section_name) is str, 'Only one section type can be retrieved' l0, l1 = self.get_grid_args('sections') where = np.where(self.sections.names == section_name)[0][0] return l0 + self.sections.length[where], l0 + self.sections.length[where + 1] ================================================ FILE: gempy/core/data/grid_modules/__init__.py ================================================ from .regular_grid import RegularGrid from .custom_grid import CustomGrid from .sections_grid import Sections from .topography import Topography ================================================ FILE: gempy/core/data/grid_modules/custom_grid.py ================================================ import dataclasses import numpy as np from pydantic import Field @dataclasses.dataclass class CustomGrid: """Object that contains arbitrary XYZ coordinates. Args: xyx_coords (numpy.ndarray like): XYZ (in columns) of the desired coordinates Attributes: values (np.ndarray): XYZ coordinates """ values: np.ndarray = Field( exclude=True, default_factory=lambda: np.zeros((0, 3)), repr=False ) def __post_init__(self): custom_grid = np.atleast_2d(self.values) assert type(custom_grid) is np.ndarray and custom_grid.shape[1] == 3, \ 'The shape of new grid must be (n,3) where n is the number of' \ ' points of the grid' @property def length(self): return self.values.shape[0] ================================================ FILE: gempy/core/data/grid_modules/diamond_square.py ================================================ """Implementation of Diamond-Square algorithm This algorithm is often used for random topography generation, following Fournier et al., 1982 see https://en.wikipedia.org/wiki/Diamond-square_algorithm. Fournier, Alain; Fussell, Don; Carpenter, Loren (June 1982). "Computer rendering of stochastic models". Communications of the ACM. 25 (6): 371–384. Here the description from the wikipedia page: +++ begin Wikipedia +++ The diamond-square algorithm begins with a 2D square array of width and height 2n + 1. The four corner points of the array must first be set to initial values. The diamond and square steps are then performed alternately until all array values have been set. The diamond step: For each square in the array, set the midpoint of that square to be the average of the four corner points plus a random value. The square step: For each diamond in the array, set the midpoint of that diamond to be the average of the four corner points plus a random value. At each iteration, the magnitude of the random value should be reduced. During the square steps, points located on the edges of the array will have only three adjacent values set rather than four. There are a number of ways to handle this complication - the simplest being to take the average of just the three adjacent values. Another option is to 'wrap around', taking the fourth value from the other side of the array. When used with consistent initial corner values this method also allows generated fractals to be stitched together without discontinuities. +++ end Wikipedia +++ The implementations (in Python) I could find online were either difficult to understand or had many case selections, especially at edges. Here is a fully vectorized implementation, using padding at edges, to avoid all these case selections for a more straight-forward implementation (I hope). This implementation is also adjusted to work on non-square start matrices, and on reduced hierarchies with more initial (internal) points. Created on 10.04.2020 @author: Florian Wellmann """ import numpy as np import matplotlib.pyplot as plt class DiaomondSquare(object): def __init__(self, size: tuple = (16, 16), roughness: float = 0.5, z_min: float = 0, z_max: float = 1, r_type: chr = 'default', **kwds): """Implementation of vectorized Diaomnd-Square algorithm for random topography generation Args: size (int, int): shape of grid to interpolate; note: the standard diamond-square algorithm operates on a square grid with side length 2**n+1. This implementation is adjusted to non-square grids with (2**n+1, 2**m+1). If the input size (int, int) is not matching to the ideal dimension, the next bigger size is taken and the grid finally cut (lower left corner is kept); roughness: roughness parameter, [0,1]: 0: deterministic interpolation, 1: very rough and bumpy (in standard randomisation method - other options available in self.random_func()) z_min: minimum height of surface z_max: maximum height of surface seed: seed for random function to enable reproducibility and testing r_type: 'basic', 'level_scale' (See below for details) Options for the randomization function (self.r_type) are: - 'default': standard reduction - 'level_scale' : uniform distribution between z_min, z_max, scaled by (level + 1) """ self.size = size # Create mesh with optimal size (2**n+1, 2**m+1) # If the input size `self.size` does not match to these dimensions, then the next larger suitable # size is chosen. self.n = np.ceil(np.log2(self.size[0] - 1)).astype('int8') self.m = np.ceil(np.log2(self.size[1] - 1)).astype('int8') self.grid = np.zeros((2 ** self.n + 1, 2 ** self.m + 1)) self.roughness = roughness self.z_min = z_min self.z_max = z_max if 'seed' in kwds: np.random.seed(kwds['seed']) self.base_rand_range = 1 self.r_type = r_type def random_func(self, i, level_shape): """Define random function to adjust range for subsequent steps The random function type is defined on class level (self.r_type) """ if self.r_type == 'default': if self.roughness > 0: rand_range = self.base_rand_range * 2 ** (- (1 - self.roughness) * i) return np.random.uniform(-rand_range, rand_range, level_shape) else: return 0. if self.r_type == 'long_range': if self.roughness > 0: rand_range = self.base_rand_range * 16 ** (- (1 - self.roughness) * i) return np.random.uniform(-rand_range, rand_range, level_shape) else: return 0. elif self.r_type == 'level_scale': if self.roughness > 0: return np.random.uniform(self.z_min, self.z_max, level_shape) / (1 + i) else: return 0. elif self.r_type == 'deterministic': # No randomization at all - deterministic interpolation result return 0. else: raise NotImplementedError("Random function type %s not implemented" % self.r_type) def random_initialization(self, level='highest', verbose: bool=False): """Initialize cells on speicifc hierarchy with random values Args: level = 'hightest', int : hierarchy level for interpolation (default: highest) verbose: verbose output (default: False) With highest hierarchy, we refer here to the largest diamond-square step, i.e. the corner points for a square grid; Or, more formally: the diamond points for `min(self.n, self.m)` """ if level == 'highest': m_pow_max = min(self.n, self.m) else: m_pow_max = level step_size = int(2 ** m_pow_max) if verbose: print("Initialize on step size %d" % step_size) level_shape = self.grid[::step_size, ::step_size].shape self.grid[::step_size, ::step_size] = np.random.uniform(0, 1, level_shape) # self.random_func(0, level_shape) def interpolate(self, level='highest'): """Perform diamond-square interpolation Args: level = 'hightest', int : hierarchy level for interpolation (default: highest) This step follows the conventional procedure: Iterate over hierarchies and repeat: 2) Perform Diamond interpolation step 3) Perform Square interpolation step 4) Reduce roughness factor """ if level == 'highest': # determine highest hierarchy level (determined by shorter rectangle side) m_pow_max = min(self.n, self.m) else: m_pow_max = level for i, m_pow in enumerate(np.arange(m_pow_max)[::-1]): self.perform_diamond_step(i, m_pow) self.perform_square_step(i, m_pow) def reset_grid(self): """Reset grid back to zero values""" self.grid[:, :] = 0 def perform_diamond_step(self, i: int, m_pow: int): """Perform one diamond interpolation step on hierarchy m_pow Note: for more details on the vectorized selection, see self.get_selection_diamond() """ step_size = int(2 ** m_pow) # Diamond step # ---------------- # get shape of this step level_shape = self.grid[step_size::2 * step_size, step_size::2 * step_size].shape self.grid[step_size::2 * step_size, step_size::2 * step_size] = \ (self.grid[:-2 * step_size:2 * step_size, :-2 * step_size:2 * step_size] + self.grid[:-2 * step_size:2 * step_size, 2 * step_size::2 * step_size] + self.grid[2 * step_size::2 * step_size, :-2 * step_size:2 * step_size] + self.grid[2 * step_size::2 * step_size, 2 * step_size::2 * step_size]) / \ 4. + \ self.random_func(i, level_shape) # np.random.uniform(-rand_range, rand_range, level_shape) # self.d * 2**() # self.roughness ** i * (np.random.random(step_shape) - 0.5) # (np.random.random(step_shape) - 0.5) * i * self.roughness def perform_square_step(self, i: int, m_pow: int): """Perform one square interpolation step on hierarchy m_pow Note: for more details on the vectorized selection, see self.get_selection_square() """ step_size = int(2 ** m_pow) # pad cells with zero value z_pad = np.pad(self.grid, step_size, mode='constant') # also create a grid for division to divide only by 3 on borders grid_div = np.ones_like(self.grid[1:-1, 1:-1]) * 4. grid_div = np.pad(grid_div, step_size + 1, mode='constant', constant_values=3.) # Checkerboard odd # ---------------- # get shape of this step level_shape = grid_div[step_size::2 * step_size, 2 * step_size:-2 * step_size:2 * step_size].shape z_pad[step_size::2 * step_size, 2 * step_size:-2 * step_size:2 * step_size] = \ (z_pad[step_size::2 * step_size, step_size:-2 * step_size:2 * step_size] + z_pad[step_size::2 * step_size, 3 * step_size:-step_size:2 * step_size] + z_pad[:-step_size:2 * step_size, 2 * step_size:-2 * step_size:2 * step_size] + z_pad[2 * step_size::2 * step_size, 2 * step_size:-2 * step_size:2 * step_size]) / \ grid_div[step_size::2 * step_size, 2 * step_size:-2 * step_size:2 * step_size] + \ self.random_func(i, level_shape) # np.random.uniform(-rand_range, rand_range, level_shape) # self.roughness ** i * (np.random.random(step_shape) - 0.5) # (np.random.random(step_shape) - 0.5) * i * self.roughness # Checkerboard even # ----------------- # get shape of this step level_shape = z_pad[2 * step_size:-2 * step_size:2 * step_size, step_size:-step_size:2 * step_size].shape # check-even, values to interpolate: z_pad[2 * step_size:-2 * step_size:2 * step_size, step_size:-step_size:2 * step_size] = \ (z_pad[2 * step_size:-2 * step_size:2 * step_size, :-2 * step_size:2 * step_size] + z_pad[2 * step_size:-2 * step_size:2 * step_size, 2 * step_size::2 * step_size] + z_pad[step_size:-2 * step_size:2 * step_size, step_size:-step_size:2 * step_size] + z_pad[3 * step_size::2 * step_size, step_size:-step_size:2 * step_size]) / \ grid_div[2 * step_size:-2 * step_size:2 * step_size, step_size:-step_size:2 * step_size] + \ self.random_func(i, level_shape) # np.random.uniform(-rand_range, rand_range, level_shape) # self.roughness ** i * (np.random.random(step_shape) - 0.5) # (np.random.random(step_shape) - 0.5) * i * self.roughness # assign results back to self.grid self.grid = z_pad[step_size:-step_size, step_size:-step_size] def get_selection_diamond(self, m_pow: int): """get selected points for diamond step on grid z on hierarchy m This method is mostly implemented for testing and visualization purposes. """ step_size = int(2 ** m_pow) z = np.zeros_like(self.grid, dtype='int8') # points to interpolate z[step_size::2 * step_size, step_size::2 * step_size] = 1 # top left z[:-2 * step_size:2 * step_size, :-2 * step_size:2 * step_size] = 2 # top right z[:-2 * step_size:2 * step_size, 2 * step_size::2 * step_size] = 2 # bottom left z[2 * step_size::2 * step_size, :-2 * step_size:2 * step_size] = 2 # bottom right z[2 * step_size::2 * step_size, 2 * step_size::2 * step_size] = 2 return z def get_selection_square(self, m_pow: int): """Plot selected points for square step on grid z on hierarchy m This method is mostly implemented for testing and visualization purposes. """ z = np.zeros_like(self.grid, dtype='int8') step_size = int(2 ** m_pow) # pad cells with zero value z_pad = np.pad(z, step_size, mode='constant') # Checkerboard odd # ---------------- # check-odd, values to interpolate: z_pad[step_size::2 * step_size, 2 * step_size:-2 * step_size:2 * step_size] = 1 # check-odd, left z_pad[step_size::2 * step_size, step_size:-2 * step_size:2 * step_size] = 2 # check-odd, right z_pad[step_size::2 * step_size, 3 * step_size:-step_size:2 * step_size] = 2 # check-odd, top z_pad[:-step_size:2 * step_size, 2 * step_size:-2 * step_size:2 * step_size] = 2 # check-odd, bottom z_pad[2 * step_size::2 * step_size, 2 * step_size:-2 * step_size:2 * step_size] = 2 # Checkerboard even # ----------------- # check-even, values to interpolate: z_pad[2 * step_size:-2 * step_size:2 * step_size, step_size:-step_size:2 * step_size] = 1 # check-even, left: z_pad[2 * step_size:-2 * step_size:2 * step_size, :-2 * step_size:2 * step_size] = 2 # check-even, right: z_pad[2 * step_size:-2 * step_size:2 * step_size, 2 * step_size::2 * step_size] = 2 # check-even, top: z_pad[step_size:-2 * step_size:2 * step_size, step_size:-step_size:2 * step_size] = 2 # check-even, bottom: z_pad[3 * step_size::2 * step_size, step_size:-step_size:2 * step_size] = 2 return z_pad def plot_diamond_and_square(self, pad=False): """Plot selected points for diamond and square step for all hierarchies side by side""" m_pow_max = min(self.n, self.m) shape_ratio = self.n / self.m f, axes = plt.subplots(2, m_pow_max, figsize=(12, 12 * shape_ratio / m_pow_max * 2)) for i, m_pow in enumerate(np.arange(m_pow_max)[::-1]): m = 2 ** m_pow # z_zero = np.zeros_like(self.grid) z_diamond = self.get_selection_diamond(m_pow) z_square = self.get_selection_square(m_pow) if pad: z_pad = np.pad(z_diamond, m, mode='constant') axes[0, i].imshow(z_pad, cmap='viridis', vmin=0, vmax=2) axes[1, i].imshow(z_square, cmap='viridis', vmin=0, vmax=2) else: axes[0, i].imshow(z_diamond, cmap='viridis', vmin=0, vmax=2) axes[1, i].imshow(z_square[m:-m, m:-m], cmap='viridis', vmin=0, vmax=2) ================================================ FILE: gempy/core/data/grid_modules/regular_grid.py ================================================ import dataclasses from pydantic import model_validator, Field from typing import Optional, Sequence, Annotated import numpy as np from ..encoders.converters import numpy_array_short_validator from .... import optional_dependencies from gempy_engine.core.data.transforms import Transform @dataclasses.dataclass class RegularGrid: """ Class with the methods and properties to manage 3D regular grids where the model will be interpolated. """ resolution: Annotated[np.ndarray, numpy_array_short_validator] = dataclasses.field(default_factory=lambda: np.ones((0, 3), dtype='int64')) extent: Annotated[np.ndarray, numpy_array_short_validator] = dataclasses.field(default_factory=lambda: np.zeros(6, dtype='float64')) #: this is the ORTHOGONAL extent. If the grid is rotated, the extent will be different values: Annotated[np.ndarray, Field(exclude=True)] = dataclasses.field(default_factory=lambda: np.zeros((0, 3))) mask_topo: Annotated[np.ndarray, Field(exclude=True)] = dataclasses.field(default_factory=lambda: np.zeros((0, 3), dtype=bool)) _transform: Transform | None = None #: If a transform exists, it will be applied to the grid _base_resolution: Annotated[np.ndarray, numpy_array_short_validator] = dataclasses.field(default_factory=lambda: np.array([2,2,2], dtype='int64')) def __init__(self, extent: np.ndarray, resolution: np.ndarray, transform: Optional[Transform] = None): self.resolution = np.ones((0, 3), dtype='int64') self.extent = np.zeros(6, dtype='float64') self.values = np.zeros((0, 3)) self.mask_topo = np.zeros((0, 3), dtype=bool) self._base_resolution = np.array([2,2,2]) self.set_regular_grid(extent, resolution, transform) @classmethod def octree_init(cls, extent: np.ndarray, octree_levels: int, base_resolution: np.ndarray, transform: Optional[Transform] = None): grid = cls( extent=extent, resolution=base_resolution * 2 ** (octree_levels - 1), transform=transform ) grid._base_resolution = base_resolution return grid @property def base_resolution(self): return self._base_resolution @base_resolution.setter def base_resolution(self, value): self._base_resolution = value @model_validator(mode="after") def _validate_regular_grid(self): self._create_regular_grid_3d() return self def _create_regular_grid_3d(self): coords = self.x_coord, self.y_coord, self.z_coord g = np.meshgrid(*coords, indexing="ij") values = np.vstack(tuple(map(np.ravel, g))).T.astype("float64") # Transform the values if self.transform is not None: self.values = self.transform.apply_inverse_with_pivot( points=values, pivot=np.array([self.extent[0], self.extent[2], self.extent[4]]) ) else: self.values = values def set_regular_grid(self, extent: Sequence[float], resolution: Sequence[int], transform: Optional[Transform] = None): """ Set a regular grid into the values parameters for further computations Args: extent (list, np.ndarry): [x_min, x_max, y_min, y_max, z_min, z_max] resolution (list, np.ndarray): [nx, ny, nz] """ # * Check extent and resolution are not the same extent_equal = np.array_equal(extent, self.extent) resolution_equal = np.array_equal(resolution, self.resolution) if extent_equal and resolution_equal: return self.values self.extent = np.asarray(extent, dtype='float64') self.resolution = np.asarray(resolution) self.transform = transform self._create_regular_grid_3d() @property def transform(self) -> Transform: if self._transform is None: return Transform.init_neutral() return self._transform @transform.setter def transform(self, value: Transform): self._transform = value @classmethod def from_corners_box(cls, pivot: tuple, point_x_axis: tuple, distance_point3: float, zmin: float, zmax: float, resolution: np.ndarray, plot: bool = True): """Always rotate around the z axis towards the positive x axis. The distance_point3 is the distance from the pivot to the point3.""" def _calculate_rotated_box_val(v1, v2): # Check if the points are collinear # noinspection PyUnreachableCode cross = np.cross(v1[:2], v2[:2]) if np.isclose(cross, 0): raise ValueError("The points are collinear") # Check if v1 and v2 are perpendicular if not np.isclose(np.dot(v1, v2), 0, atol=1e-4): # Compute the closest valid p3 v1_norm = v1 / np.linalg.norm(v1) v2_projected = v2 - np.dot(v2, v1_norm) * v1_norm p3_corrected = np.array([x1, y1, 0]) + v2_projected x3, y3 = p3_corrected[:2] raise ValueError(f"The provided points are not perpendicular. Suggested corrected point3: ({x3}, {y3})") # Calculate the orthonormal basis v1_norm = v1 / np.linalg.norm(v1) v2_norm = v2 / np.linalg.norm(v2) # noinspection PyUnreachableCode # v3_norm = np.cross(v1_norm, v2_norm) v3_norm = np.array([0, 0, 1]) # ! We assume rotation is always around z axis return v1_norm, v2_norm, v3_norm # Define the coordinates of the three points x1, y1 = pivot x2, y2 = point_x_axis dx, dy = point_x_axis[0] - pivot[0], point_x_axis[1] - pivot[1] x3, y3 = (pivot[0] - dy * distance_point3 / np.hypot(dx, dy), pivot[1] + dx * distance_point3 / np.hypot(dx, dy)) # Calculate the vectors along the sides of the rectangle v1 = np.array([x2 - x1, y2 - y1, 0]) v2 = np.array([x3 - x1, y3 - y1, 0]) v1_norm, v2_norm, v3_norm = _calculate_rotated_box_val(v1, v2) # Create the rotation matrix rotation_matrix = np.array([ [v1_norm[0], v2_norm[0], v3_norm[0], 0], [v1_norm[1], v2_norm[1], v3_norm[1], 0], [v1_norm[2], v2_norm[2], v3_norm[2], 0], [0, 0, 0, 1] ]) inverted_rotation_matrix = np.linalg.inv(rotation_matrix) # Calculate the extents in the new coordinate system extent_x = np.linalg.norm(v1) extent_y = np.linalg.norm(v2) # We transform the origin point1 to the new coordinates origin_ = [x1, y1, zmin] xmin, ymin, zmin = origin_ xmax, ymax, zmax = xmin + extent_x, ymin + extent_y, zmin + zmax - zmin extent = np.array([xmin, xmax, ymin, ymax, zmin, zmax], dtype='float64') transform = Transform.from_matrix(inverted_rotation_matrix) transform.cached_pivot = origin_ grid = cls(extent=extent, resolution=resolution, transform=transform) if plot: cls.plot_rotation( regular_grid=grid, pivot=pivot, point_x_axis=point_x_axis, point_y_axis=(x3, y3) ) return grid @property def bounding_box(self) -> np.ndarray: extents = self.extent # Define 3D points of the bounding box corners based on extents bounding_box_points = np.array([[extents[0], extents[2], extents[4]], # min x, min y, min z [extents[0], extents[2], extents[5]], # min x, min y, max z [extents[0], extents[3], extents[4]], # min x, max y, min z [extents[0], extents[3], extents[5]], # min x, max y, max z [extents[1], extents[2], extents[4]], # max x, min y, min z [extents[1], extents[2], extents[5]], # max x, min y, max z [extents[1], extents[3], extents[4]], # max x, max y, min z [extents[1], extents[3], extents[5]]]) # max x, max y, max z return bounding_box_points @property def x_coord(self): return np.linspace(self.extent[0] + self.dx / 2, self.extent[1] - self.dx / 2, self.resolution[0], dtype="float64") @property def y_coord(self): return np.linspace(self.extent[2] + self.dy / 2, self.extent[3] - self.dy / 2, self.resolution[1], dtype="float64") @property def z_coord(self): return np.linspace(self.extent[4] + self.dz / 2, self.extent[5] - self.dz / 2, self.resolution[2], dtype="float64") @property def dx_dy_dz(self): dx = (self.extent[1] - self.extent[0]) / self.resolution[0] dy = (self.extent[3] - self.extent[2]) / self.resolution[1] dz = (self.extent[5] - self.extent[4]) / self.resolution[2] return dx, dy, dz @property def dx(self): return (self.extent[1] - self.extent[0]) / self.resolution[0] @property def dy(self): return (self.extent[3] - self.extent[2]) / self.resolution[1] @property def dz(self): return (self.extent[5] - self.extent[4]) / self.resolution[2] @property def values_vtk_format(self) -> np.ndarray: return self.get_values_vtk_format() def get_values_vtk_format(self, orthogonal: bool = False) -> np.ndarray: extent = self.extent resolution = self.resolution + 1 x = np.linspace(extent[0], extent[1], resolution[0], dtype="float64") y = np.linspace(extent[2], extent[3], resolution[1], dtype="float64") z = np.linspace(extent[4], extent[5], resolution[2], dtype="float64") xv, yv, zv = np.meshgrid(x, y, z, indexing="ij") g = np.vstack((xv.ravel(), yv.ravel(), zv.ravel())).T # Transform the values if self.transform is not None and orthogonal is False: g = self.transform.apply_inverse_with_pivot( points=g, pivot=np.array([self.extent[0], self.extent[2], self.extent[4]]) ) return g @staticmethod def plot_rotation(regular_grid, pivot, point_x_axis, point_y_axis): plt = optional_dependencies.require_matplotlib() x1, y1 = pivot x2, y2 = point_x_axis x3, y3 = point_y_axis # Plot the original corners plt.scatter([x1, x2, x3], [y1, y2, y3], c='r') # Plot lines with distance labels plt.plot([x1, x2], [y1, y2], 'r') plt.text((x1 + x2) / 2, (y1 + y2) / 2, f'{np.linalg.norm([x1 - x2, y1 - y2]):.2f}', color='r') plt.plot([x1, x3], [y1, y3], 'r') plt.text((x1 + x3) / 2, (y1 + y3) / 2, f'{np.linalg.norm([x1 - x3, y1 - y3]):.2f}', color='r') # Plot the transformed corners transformed_extent = regular_grid.extent bounding_box = regular_grid.bounding_box plt.scatter(bounding_box[:, 0], bounding_box[:, 1], c='b') # Plot lines with distance labels plt.plot([bounding_box[2, 0], bounding_box[0, 0]], [bounding_box[2, 1], bounding_box[0, 1]], 'b') plt.text((bounding_box[2, 0] + bounding_box[0, 0]) / 2, (bounding_box[2, 1] + bounding_box[0, 1]) / 2, f'{np.linalg.norm(bounding_box[2] - bounding_box[0]):.2f}', color='b') plt.plot([bounding_box[0, 0], bounding_box[4, 0]], [bounding_box[0, 1], bounding_box[4, 1]], 'b') plt.text((bounding_box[0, 0] + bounding_box[4, 0]) / 2, (bounding_box[0, 1] + bounding_box[4, 1]) / 2, f'{np.linalg.norm(bounding_box[0] - bounding_box[4]):.2f}', color='b') # Plot the values of the grid values = regular_grid.values plt.scatter(values[:, 0], values[:, 1], c='g') plt.show() ================================================ FILE: gempy/core/data/grid_modules/sections_grid.py ================================================ from __future__ import annotations # Python 3.7+ only import dataclasses import numpy as np from pydantic import Field from typing import Tuple, Dict, List, Optional, Any from ..core_utils import calculate_line_coordinates_2points from ..encoders.converters import short_array_type from ....optional_dependencies import require_pandas @dataclasses.dataclass class Sections: """ Object that creates a grid of cross sections between two points. Args: regular_grid: Model.grid.regular_grid section_dict: {'section name': ([p1_x, p1_y], [p2_x, p2_y], [xyres, zres])} """ """ Pydantic v2 model of your original Sections class. All computed fields are initialized with model_validator. """ # user‐provided inputs z_ext: Tuple[float, float] | short_array_type section_dict: Dict[ str, Tuple[ Tuple[float, float], # start Tuple[float, float], # stop Tuple[int, int] # resolution ] ] # computed/internal (will be serialized too unless excluded) names: short_array_type = Field(default=np.array([]), exclude=True) points: List[List[Tuple[float, float]]] = Field(default_factory=list, exclude=True) resolution: List[Tuple[int, int]] = Field(default_factory=list, exclude=True) length: np.ndarray = Field(default_factory=lambda: np.array([0]), exclude=True) dist: np.ndarray = Field(default_factory=lambda: np.array([]), exclude=True) df: Optional[Any] = Field(default=None, exclude=True) values: np.ndarray = Field(default_factory=lambda: np.empty((0, 3)), exclude=True) extent: Optional[np.ndarray] = Field(default=None, exclude=True) def __post_init__(self): self.initialize_computations() pass def initialize_computations(self): # copy names self.names = np.array(list(self.section_dict.keys())) # build points/resolution/length self._get_section_params() # compute distances self._calculate_all_distances() # re-build DataFrame pd = require_pandas() df = pd.DataFrame.from_dict( data=self.section_dict, orient="index", columns=["start", "stop", "resolution"], ) df["dist"] = self.dist self.df = df # compute the XYZ grid self._compute_section_coordinates() def _repr_html_(self): return self.df.to_html() def __repr__(self): return self.df.to_string() def show(self): pass def set_sections(self, section_dict, regular_grid=None, z_ext=None): pd = require_pandas() self.section_dict = section_dict if regular_grid is not None: self.z_ext = regular_grid.extent[4:] self.initialize_computations() def _get_section_params(self): self.points = [] self.resolution = [] self.length = [0] for i, section in enumerate(self.names): points = [self.section_dict[section][0], self.section_dict[section][1]] assert points[0] != points[ 1], 'The start and end points of the section must not be identical.' self.points.append(points) self.resolution.append(self.section_dict[section][2]) self.length = np.append(self.length, self.section_dict[section][2][0] * self.section_dict[section][2][1]) self.length = np.array(self.length).cumsum() def _calculate_all_distances(self): self.coordinates = np.array(self.points).ravel().reshape(-1, 4) # axis are x1,y1,x2,y2 self.dist = np.sqrt(np.diff(self.coordinates[:, [0, 2]]) ** 2 + np.diff( self.coordinates[:, [1, 3]]) ** 2) def _compute_section_coordinates(self): for i in range(len(self.names)): xy = calculate_line_coordinates_2points(self.coordinates[i, :2], self.coordinates[i, 2:], self.resolution[i][0]) zaxis = np.linspace(self.z_ext[0], self.z_ext[1], self.resolution[i][1], dtype="float64") X, Z = np.meshgrid(xy[:, 0], zaxis, indexing='ij') Y, _ = np.meshgrid(xy[:, 1], zaxis, indexing='ij') xyz = np.vstack((X.flatten(), Y.flatten(), Z.flatten())).T if i == 0: self.values = xyz else: self.values = np.vstack((self.values, xyz)) def generate_axis_coord(self): for i, name in enumerate(self.names): xy = calculate_line_coordinates_2points( self.coordinates[i, :2], self.coordinates[i, 2:], self.resolution[i][0] ) yield name, xy def get_section_args(self, section_name: str): where = np.where(self.names == section_name)[0][0] return self.length[where], self.length[where + 1] def get_section_grid(self, section_name: str): l0, l1 = self.get_section_args(section_name) return self.values[l0:l1] ================================================ FILE: gempy/core/data/grid_modules/topography.py ================================================ import dataclasses import warnings from pydantic import Field from typing import Optional, Tuple, Annotated import numpy as np from .regular_grid import RegularGrid from ....optional_dependencies import require_skimage, require_scipy from dataclasses import field, dataclass from ..encoders.converters import short_array_type @dataclass class Topography: """ Object to include topography in the model. Notes: This always assumes that the topography we pass fits perfectly the extent. """ _regular_grid: RegularGrid values_2d: Annotated[np.ndarray, Field(exclude=True)] = field(default_factory=lambda: np.zeros((0, 0, 3))) source: Optional[str] = None # Fields managed internally values: short_array_type = field(init=False, default_factory=lambda: np.zeros((0, 3))) resolution: Tuple[int, int] = Field(init=True, default=(0, 0)) raster_shape: Tuple[int, ...] = field(init=False, default_factory=tuple) _mask_topo: Optional[np.ndarray] = field(init=False, default=None, repr=False) _x: Optional[np.ndarray] = field(init=False, default=None, repr=False) _y: Optional[np.ndarray] = field(init=False, default=None, repr=False) def __post_init__(self): # if a non-empty array was provided, initialize the flattened values if self.values_2d.size: self.set_values(self.values_2d) @classmethod def from_subsurface_structured_data(cls, structured_data: 'subsurface.StructuredData', regular_grid: RegularGrid): """Creates a topography object from a subsurface structured data object Args: structured_data (subsurface.StructuredData): Structured data object Returns: :class:`gempy.core.grid_modules.topography.Topography` """ # Generate meshgrid for x and y coordinates ds = structured_data.data x_coordinates = ds['x'] y_coordinates = ds['y'] height_values = ds['topography'] return cls.from_arrays(regular_grid, x_coordinates, y_coordinates, height_values) @classmethod def from_unstructured_mesh(cls, regular_grid, xyz_vertices): """Creates a topography object from an unstructured mesh of XYZ vertices. Args: regular_grid (RegularGrid): The regular grid object. xyz_vertices (numpy.ndarray): Array of XYZ vertices of the unstructured mesh. Returns: :class:`gempy.core.grid_modules.topography.Topography` """ # Perform Delaunay triangulation on the vertices # Generate the regular grid points from scipy.interpolate import griddata x_regular, y_regular = np.meshgrid( np.linspace(regular_grid.extent[0], regular_grid.extent[1], regular_grid.resolution[0]), np.linspace(regular_grid.extent[2], regular_grid.extent[3], regular_grid.resolution[1]), indexing='ij' ) # Interpolate the z-values onto the regular grid z_regular = griddata( points=xyz_vertices[:, :2], values=xyz_vertices[:, 2], xi=(x_regular, y_regular), method='nearest', fill_value=np.nan # You can choose a different fill value or method ) # Reshape the grid for compatibility with existing structure values_2d = np.stack((x_regular, y_regular, z_regular), axis=-1) return cls(_regular_grid=regular_grid, values_2d=values_2d) @classmethod def from_arrays(cls, regular_grid, x_coordinates, y_coordinates, height_values,): x_vals, y_vals = np.meshgrid(x_coordinates, y_coordinates, indexing='ij') # Reshape arrays for stacking x_vals = x_vals[:, :, np.newaxis] # shape (73, 34, 1) y_vals = y_vals[:, :, np.newaxis] # shape (73, 34, 1) topography_vals = height_values.values[:, :, np.newaxis] # shape (73, 34, 1) # Stack along the last dimension result = np.concatenate([x_vals, y_vals, topography_vals], axis=2) # shape (73, 34, 3) return cls(_regular_grid=regular_grid, values_2d=result) @property def extent(self): return self._regular_grid.extent @property def regular_grid_resolution(self): return self._regular_grid.resolution @property def x(self): if self._x is not None: return self._x else: val = self.values[:, 0].copy() val.sort() return np.unique(val) @property def y(self): if self._y is not None: return self._y else: val = self.values[:, 1].copy() val.sort() return np.unique(val) def set_values(self, values_2d: np.ndarray): """General method to set topography Args: values_2d (numpy.ndarray[float,float, 3]): array with the XYZ values in 2D Returns: :class:`gempy.core.grid_modules.topography.Topography` """ # Original topography data self.values_2d = values_2d self.resolution = values_2d.shape[:2] # n,3 array self.values = values_2d.reshape((-1, 3), order='C') return self def set_values2d(self, values: np.ndarray) -> "Topography": """ Reconstruct the 2D topography (shape = resolution + [3]) from a flat Nx3 array (or from self.values if none is provided). """ # default to the already-flattened XYZ array # compute expected size nx, ny = self.resolution expected = nx * ny * 3 if values.size != expected: raise ValueError( f"Cannot reshape array of size {values.size} into shape {(nx, ny, 3)}." ) # reshape in C-order to (nx, ny, 3) self.set_values( values_2d=values.reshape(nx, ny, 3, order="C") ) # invalidate any cached mask self._mask_topo = None return self def set_values2d_(self, values: np.ndarray): resolution = (60, 60) self.values_2d = values.reshape(*resolution, 3) @property def topography_mask(self): """This method takes a topography grid of the same extent as the regular grid and creates a mask of voxels """ # * Check if the topography is the same as the cached one and if so, return the cached mask if self._mask_topo is not None: return self._mask_topo # interpolate topography values to the regular grid skimage = require_skimage() regular_grid_topo = skimage.transform.resize( image=self.values_2d, output_shape=(self.regular_grid_resolution[0], self.regular_grid_resolution[1]), mode='constant', anti_aliasing=False, preserve_range=True ) # Adjust the topography to be lower by half a voxel height # Assumes your voxel heights are uniform and can be calculated as the total height divided by resolution voxel_height = self._regular_grid.dz * 2 regular_grid_topo = regular_grid_topo - voxel_height # Reshape the Z values of the regular grid to 3d values_3d = self._regular_grid.values[:, 2].reshape(self.regular_grid_resolution) if regular_grid_topo.ndim == 3: regular_grid_topo_z = regular_grid_topo[:, :, [2]] elif regular_grid_topo.ndim == 2: regular_grid_topo_z = regular_grid_topo else: raise ValueError() mask = np.greater(values_3d[:, :, :], regular_grid_topo_z) self._mask_topo = mask return self._mask_topo def resize_topo(self): skimage = require_skimage() regular_grid_topo = skimage.transform.resize( self.values_2d, (self.regular_grid_resolution[0], self.regular_grid_resolution[1]), mode='constant', anti_aliasing=False, preserve_range=True) return regular_grid_topo def load_random_hills(self, **kwargs): warnings.warn('This function is deprecated. Use load_from_random instead', DeprecationWarning) if 'extent' in kwargs: self.extent = kwargs.pop('extent') if 'resolution' in kwargs: self.regular_grid_resolution = kwargs.pop('resolution') dem = _LoadDEMArtificial(extent=self.extent, resolution=self.regular_grid_resolution, **kwargs) self._x, self._y = dem.x, dem.y self.set_values(dem.get_values()) def save(self, path): np.save(path, self.values_2d) def load(self, path): self.set_values(np.load(path)) self._x, self._y = None, None return self.values def load_from_saved(self, *args, **kwargs): self.load(*args, **kwargs) class _LoadDEMArtificial: # * Cannot think of a good reason to be a class def __init__(self, grid=None, fd=2.0, extent=None, resolution=None, d_z=None): """Class to create a random topography based on a fractal grid algorithm. Args: fd: fractal dimension, defaults to 2.0 d_z: maximum height difference. If none, last 20% of the model in z direction extent: extent in xy direction. If none, geo_model.grid.extent resolution: desired resolution of the topography array. If none, geo_model.grid.resolution """ self.values_2d = np.array([]) self.resolution = grid.resolution[:2] if resolution is None else resolution assert all(np.asarray(self.resolution) >= 2), 'The regular grid needs to be at least of size 2 on all directions.' self.extent = grid.extent if extent is None else extent if d_z is None: self.d_z = np.array([self.extent[5] - (self.extent[5] - self.extent[4]) * 1 / 5, self.extent[5]]) print(self.d_z) else: self.d_z = d_z topo = self.fractalGrid(fd, n=self.resolution.max()) topo = np.interp(topo, (topo.min(), topo.max()), self.d_z) self.dem_zval = topo[:self.resolution[0], :self.resolution[1]] # crop fractal grid with resolution self.create_topo_array() @staticmethod def fractalGrid(fd, n=256): """ Modified after https://github.com/samthiele/pycompass/blob/master/examples/3_Synthetic%20Examples.ipynb Generate isotropic fractal surface image using spectral synthesis method [1, p.] References: 1. Yuval Fisher, Michael McGuire, The Science of Fractal Images, 1988 (cf. http://shortrecipes.blogspot.com.au/2008/11/python-isotropic-fractal-surface.html) **Arguments**: -fd = the fractal dimension -N = the size of the fractal surface/image """ h = 1 - (fd - 2) # X = np.zeros((N, N), complex) a = np.zeros((n, n), complex) powerr = -(h + 1.0) / 2.0 for i in range(int(n / 2) + 1): for j in range(int(n / 2) + 1): phase = 2 * np.pi * np.random.rand() if i != 0 or j != 0: rad = (i * i + j * j) ** powerr * np.random.normal() else: rad = 0.0 a[i, j] = complex(rad * np.cos(phase), rad * np.sin(phase)) if i == 0: i0 = 0 else: i0 = n - i if j == 0: j0 = 0 else: j0 = n - j a[i0, j0] = complex(rad * np.cos(phase), -rad * np.sin(phase)) a.imag[int(n / 2)][0] = 0.0 a.imag[0, int(n / 2)] = 0.0 a.imag[int(n / 2)][int(n / 2)] = 0.0 for i in range(1, int(n / 2)): for j in range(1, int(n / 2)): phase = 2 * np.pi * np.random.rand() rad = (i * i + j * j) ** powerr * np.random.normal() a[i, n - j] = complex(rad * np.cos(phase), rad * np.sin(phase)) a[n - i, j] = complex(rad * np.cos(phase), -rad * np.sin(phase)) scipy = require_scipy() itemp = scipy.fftpack.ifft2(a) itemp = itemp - itemp.min() return itemp.real / itemp.real.max() def create_topo_array(self): """for masking the lith block""" x = np.linspace(self.extent[0], self.extent[1], self.resolution[0]) y = np.linspace(self.extent[2], self.extent[3], self.resolution[1]) self.x = x self.y = y xx, yy = np.meshgrid(x, y, indexing='ij') self.values_2d = np.dstack([xx, yy, self.dem_zval]) def get_values(self): return self.values_2d ================================================ FILE: gempy/core/data/importer_helper.py ================================================ from dataclasses import dataclass from typing import Optional @dataclass class ImporterHelper: path_to_surface_points: Optional[str] path_to_orientations: Optional[str] hash_surface_points: Optional[str] = None hash_orientations: Optional[str] = None coord_x_name: Optional[str] = "X" coord_y_name: Optional[str] = "Y" coord_z_name: Optional[str] = "Z" surface_name: Optional[str] = "formation" gx_name: Optional[str] = "G_x" gy_name: Optional[str] = "G_y" gz_name: Optional[str] = "G_z" pandas_reader_kwargs: Optional[dict] = None # ? Add here pandas reader options ================================================ FILE: gempy/core/data/options.py ================================================ import enum class InterpolationOptionsType(enum.Enum): DENSE_GRID = enum.auto() OCTREE = enum.auto() ================================================ FILE: gempy/core/data/orientations.py ================================================ from dataclasses import dataclass from typing import Optional, Sequence, Union import numpy as np from gempy_engine.core.data.transforms import Transform from pydantic import Field from ...optional_dependencies import require_pandas from ._data_points_helpers import generate_ids_from_names DEFAULT_ORI_NUGGET = 0.01 # ? Maybe we should merge this with the SurfacePoints class from gempy_engine @dataclass(init=True) class OrientationsTable: """ A dataclass to represent a table of orientations in a geological model. """ dt = np.dtype([('X', 'f8'), ('Y', 'f8'), ('Z', 'f8'), ('G_x', 'f8'), ('G_y', 'f8'), ('G_z', 'f8'), ('id', 'i4'), ('nugget', 'f8')]) #: The custom data type for the data array. data: np.ndarray = Field( default=np.zeros(0, dtype=dt), exclude=True, description="A structured NumPy array holding the X, Y, Z coordinates, gradients G_x, G_y, G_z, id, and nugget of each orientation.", ) #: A structured NumPy array holding the X, Y, Z coordinates, id, and nugget of each surface point. name_id_map: Optional[dict[str, int]] = None #: A mapping between orientation names and ids. _model_transform: Optional[Transform] = None def __post_init__(self): # Check if the data array has the correct data type if self.data.dtype != OrientationsTable.dt: raise ValueError(f"Data array must have the following data type: {OrientationsTable.dt}") @classmethod def from_arrays(cls, x: np.ndarray, y: np.ndarray, z: np.ndarray, G_x: np.ndarray, G_y: np.ndarray, G_z: np.ndarray, names: Union[Sequence | str], nugget: Optional[np.ndarray] = None, name_id_map: Optional[dict[str, int]] = None) -> 'OrientationsTable': """Create an OrientationsTable from arrays. Args: x (np.ndarray): Array of x-coordinates. y (np.ndarray): Array of y-coordinates. z (np.ndarray): Array of z-coordinates. G_x (np.ndarray): Array of x-components of the gradients. G_y (np.ndarray): Array of y-components of the gradients. G_z (np.ndarray): Array of z-components of the gradients. names (Union[Sequence[str], str]): Sequence of names corresponding to each orientation. nugget (Optional[np.ndarray]): Array of nugget values. If None, defaults are used. name_id_map (Optional[dict[str, int]]): Mapping between names and ids. Returns: OrientationsTable: The created OrientationsTable. """ data, name_id_map = cls._data_from_arrays(x, y, z, G_x, G_y, G_z, names, nugget, name_id_map) return cls(data, name_id_map) @classmethod def _data_from_arrays(cls, x, y, z, G_x, G_y, G_z, names, nugget, name_id_map=None) -> tuple[np.ndarray, dict[str, int]]: if nugget is None: nugget = np.zeros_like(x) + DEFAULT_ORI_NUGGET if name_id_map is None: ids, name_id_map = generate_ids_from_names(name_id_map, names, x) else: ids = np.array([name_id_map[name] for name in names]) data = np.zeros(len(x), dtype=OrientationsTable.dt) data['X'], data['Y'], data['Z'], data['G_x'], data['G_y'], data['G_z'], data['id'], data['nugget'] = x, y, z, G_x, G_y, G_z, ids, nugget return data, name_id_map @classmethod def initialize_empty(cls) -> 'OrientationsTable': return cls(np.zeros(0, dtype=OrientationsTable.dt)) @property def xyz(self) -> np.ndarray: """Get the XYZ coordinates. Returns: np.ndarray: The XYZ coordinates. """ return np.array([self.data['X'], self.data['Y'], self.data['Z']]).T @property def xyz_view(self) -> np.ndarray: """Get a view of the XYZ coordinates. Returns: np.ndarray: A view of the XYZ coordinates. """ return self.data[['X', 'Y', 'Z']] @xyz_view.setter def xyz_view(self, value: np.ndarray): """Set the XYZ coordinates. Args: value (np.ndarray): The new XYZ coordinates. """ self.data['X'], self.data['Y'], self.data['Z'] = value.T @property def grads(self) -> np.ndarray: """Get the gradient components. Returns: np.ndarray: The gradient components. """ return np.array([self.data['G_x'], self.data['G_y'], self.data['G_z']]).T @property def grads_view(self) -> np.ndarray: """Get a view of the gradient components. Returns: np.ndarray: A view of the gradient components. """ return self.data[['G_x', 'G_y', 'G_z']] @grads_view.setter def grads_view(self, value: np.ndarray): """Set the gradient components. Args: value (np.ndarray): The new gradient components. """ self.data['G_x'], self.data['G_y'], self.data['G_z'] = value.T @property def nugget(self) -> np.ndarray: """Get the nugget values. Returns: np.ndarray: The nugget values. """ return self.data['nugget'] @property def ids(self) -> np.ndarray: """Get the IDs. Returns: np.ndarray: The IDs. """ return self.data['id'] def get_orientations_by_name(self, name: str) -> 'OrientationsTable': """Get orientations by name. Args: name (str): The name of the orientations. Returns: OrientationsTable: The orientations corresponding to the given name. """ return self.get_orientations_by_id(self.name_id_map[name]) def get_orientations_by_id(self, id: int) -> 'OrientationsTable': """Get orientations by ID. Args: id (int): The ID of the orientations. Returns: OrientationsTable: The orientations corresponding to the given ID. """ return OrientationsTable(self.data[self.data['id'] == id], self.name_id_map) def get_orientations_by_id_groups(self) -> list['OrientationsTable']: """Get orientations grouped by ID. Returns: list[OrientationsTable]: A list of OrientationsTable objects, each corresponding to a unique ID. """ ids = np.unique(self.data['id']) return [self.get_orientations_by_id(id) for id in ids] @classmethod def fill_missing_orientations_groups(cls, orientations_groups: list['OrientationsTable'], surface_points_groups: list['SurfacePointsTable']) -> list['OrientationsTable']: """ Fill Missing Orientations Groups Fills in missing orientations in a list of orientations groups based on a list of surface points. Args: """ # region Deal with elements without orientations if len(surface_points_groups) > len(orientations_groups): # Check the ids of the surface points and find the missing ones surface_points_ids = [surface_points_group.id for surface_points_group in surface_points_groups] orientations_ids = [orientations_group.id for orientations_group in orientations_groups] missing_ids = list(set(surface_points_ids) - set(orientations_ids)) empty_orientations = [cls(data=np.zeros(0, dtype=cls.dt)) for id in missing_ids] # Create empty orientations for empty_orientation, id in zip(empty_orientations, missing_ids): # Insert the empty orientations in the right position orientations_groups.insert(id, empty_orientation) # endregion return orientations_groups @classmethod def empty_orientation(cls, id: int) -> 'OrientationsTable': zeros = np.zeros(0, dtype=cls.dt) zeros['id'] = id return cls(data=zeros, name_id_map={}) @property def id(self) -> int: # Check id is the same in the whole column and return it or throw an error ids = np.unique(self.data['id']) if len(ids) > 1: raise ValueError(f"OrientationsTable contains more than one id: {ids}") if len(ids) == 0: raise ValueError(f"OrientationsTable contains no ids") return ids[0] @property def model_transform(self) -> Transform: if self._model_transform is None: raise ValueError("Model transform is not set. If you want to use this property use GeoModel.surface_points to get the SurfaceTable with transform attached.") return self._model_transform @model_transform.setter def model_transform(self, value: Transform): self._model_transform = value @property def df(self) -> 'pd.DataFrame': pd = require_pandas() return pd.DataFrame(self.data) def __str__(self): return "\n" + np.array2string(self.data, precision=2, separator=',', suppress_small=True) def __repr__(self): return f"OrientationsTable(data=\n{np.array2string(self.data, precision=2, separator=',', suppress_small=True)},\nname_id_map={self.name_id_map})" def _repr_html_(self): rows_to_display = 10 # Define the number of rows to display from beginning and end html = "" html += "" if len(self.data) > 2 * rows_to_display: for point in self.data[:rows_to_display]: html += "".format(*point) html += "" for point in self.data[-rows_to_display:]: html += "".format(*point) else: for point in self.data: html += "".format(*point) html += "
XYZG_xG_yG_zidnugget
{:.2f}{:.2f}{:.2f}{:.2f}{:.2f}{:.2f}{}{:.2f}
........................
{:.2f}{:.2f}{:.2f}{:.2f}{:.2f}{:.2f}{}{:.2f}
{:.2f}{:.2f}{:.2f}{:.2f}{:.2f}{:.2f}{}{:.2f}
" return html def __len__(self): return len(self.data) ================================================ FILE: gempy/core/data/structural_element.py ================================================ import re from dataclasses import dataclass, field from pydantic import Field from typing import Optional import numpy as np from .orientations import OrientationsTable from .surface_points import SurfacePointsTable """ TODO: - [ ] Add repr and _repr_html_ methods. Legacy representations depended on pandas, which is optional now """ @dataclass class StructuralElement: """ Class that represents a structural element in a geological model. """ name: str #: The name of the structural element. is_active: bool #: The active state of the structural element. _color: str #: The color of the structural element in hexadecimal format. surface_points: SurfacePointsTable #: The points on the surface of the structural element. orientations: OrientationsTable #: The orientations of the structural element. # Output # ? Should we extract this to a separate class? vertices: np.ndarray | None = Field(default=None, exclude=True) #: The vertices of the element in 3D space. edges: np.ndarray | None = Field(default=None, exclude=True) #: The edges of the element in 3D space. scalar_field_at_interface: float | None = None #: The scalar field value for the element. _id: int = -1 def __init__(self, name: str, surface_points: SurfacePointsTable, orientations: OrientationsTable, id: Optional[int] = -1, is_active: Optional[bool] = True, color: Optional[str] = None): self.name = name self.surface_points = surface_points self.orientations = orientations self.is_active = is_active self.color = color self._id = id @property def id(self): if self._id == -1: from gempy.core.data._data_points_helpers import structural_element_hasher return structural_element_hasher(0, self.name) return self._id def __repr__(self): r, g, b = int(self._color[1:3], 16), int(self._color[3:5], 16), int(self._color[5:7], 16) colored_color = f'\033[38;2;{r};{g};{b}m' + self._color + '\033[0m' return f"Element(\n\tname={self.name},\n\tcolor={colored_color},\n\tis_active={self.is_active}\n)" def _repr_html_(self): html = f"""
StructuralElement:
Name:{self.name}
""" return html def _repr_html_2(self): html = f"""
    StructuralElement:
      Name: {self.name}
      Color: 

      Is Active: {'Yes' if self.is_active else 'No'}
""" return html @property def number_of_points(self) -> int: return len(self.surface_points) @property def number_of_orientations(self) -> int: return len(self.orientations) @property def color(self): return self._color @color.setter def color(self, value): if not isinstance(value, str) or not re.match("^#([A-Fa-f0-9]{6}|[A-Fa-f0-9]{3})$", value): raise ValueError(f"Invalid color: {value}") self._color = value @property def is_basement(self): # ? Not sure if this will be necessary raise NotImplementedError @property def has_data(self): raise NotImplementedError @property def index(self): raise NotImplementedError @property def structural_group(self): raise NotImplementedError ================================================ FILE: gempy/core/data/structural_frame.py ================================================ import hashlib import numpy as np import warnings from dataclasses import dataclass from pydantic import model_validator, computed_field, ValidationError, Field from pydantic.functional_validators import ModelWrapValidatorHandler from typing import Generator, Union from gempy_engine.core.data.input_data_descriptor import InputDataDescriptor from gempy_engine.core.data.kernel_classes.faults import FaultsData from gempy_engine.core.data.stack_relation_type import StackRelationType from .encoders.binary_encoder import deserialize_input_data_tables from .encoders.converters import loading_model_context from .orientations import OrientationsTable from .structural_element import StructuralElement from .structural_group import StructuralGroup, FaultsRelationSpecialCase from .surface_points import SurfacePointsTable from ..color_generator import ColorsGenerator @dataclass class StructuralFrame: """ Represents a structural frame, which is a collection of structural groups that constitute a geological model. Attributes: structural_groups (list[StructuralGroup]): List of structural groups that constitute the geological model. color_generator (ColorsGenerator): Instance of ColorsGenerator used for assigning distinct colors to different structural elements. is_dirty (bool): Boolean flag indicating if the structural frame has been modified. """ structural_groups: list[StructuralGroup] color_generator: ColorsGenerator = Field(default_factory=ColorsGenerator) basement_color: str = None # ? Should I create some sort of structural options class? For example, the masking descriptor and faults relations pointer is_dirty: bool = True # region Constructor # def __init__(self, structural_groups: list[StructuralGroup], color_gen: ColorsGenerator): self.structural_groups = structural_groups # ? This maybe could be optional self.color_generator = color_gen def __post_init__(self): pass @classmethod def from_data_tables(cls, surface_points: SurfacePointsTable, orientations: OrientationsTable): surface_points_groups: list[SurfacePointsTable] = surface_points.get_surface_points_by_id_groups() colors_generator = ColorsGenerator() structural_elements = [] for i in range(len(surface_points_groups)): id_ = surface_points_groups[i].id orientation_i = orientations.get_orientations_by_id(id_) if len(orientation_i) == 0: orientation_i = OrientationsTable.empty_orientation(id_) structural_element: StructuralElement = StructuralElement( name=surface_points.id_to_name(i), id=id_, surface_points=surface_points_groups[i], orientations=orientation_i, color=next(colors_generator) ) structural_elements.append(structural_element) # * Structural groups definitions default_formation: StructuralGroup = StructuralGroup( name="default_formation", elements=structural_elements, structural_relation=StackRelationType.ERODE ) # ? Should I move this to the constructor? structural_frame: StructuralFrame = cls( structural_groups=[default_formation], color_gen=colors_generator ) return structural_frame @classmethod def initialize_default_structure(cls) -> 'StructuralFrame': """ Initialize the default structure. This method is used to initialize the default structure for a `StructuralFrame` object. Args: None Returns: 'StructuralFrame': A `StructuralFrame` object representing the default structure. Example: structural_frame = initialize_default_structure() """ color_gen = ColorsGenerator() structural_group = StructuralGroup( name="default_formations", elements=[ StructuralElement( name="surface1", surface_points=SurfacePointsTable.initialize_empty(), orientations=OrientationsTable.initialize_empty(), color=next(color_gen) ) ], structural_relation=StackRelationType.ERODE ) structural_frame = cls( structural_groups=[structural_group], color_gen=color_gen ) return structural_frame # endregion # region Methods def get_element_by_name(self, element_name: str) -> StructuralElement: elements: Generator = (group.get_element_by_name(element_name) for group in self.structural_groups) valid_elements: Generator = (element for element in elements if element is not None) element = next(valid_elements, None) if element is None: raise ValueError(f"Element with name {element_name} not found in the structural frame.") return element def get_group_by_name(self, group_name: str) -> StructuralGroup: groups: Generator = (group for group in self.structural_groups if group.name == group_name) group = next(groups, None) if group is None: raise ValueError(f"Group with name {group_name} not found in the structural frame.") return group def get_group_by_element(self, element: StructuralElement) -> StructuralGroup: groups: Generator = (group for group in self.structural_groups if element in group.elements) group = next(groups, None) if group is None: raise ValueError(f"Element {element.name} not found in any group in the structural frame.") return group def append_group(self, group: StructuralGroup): self.structural_groups.append(group) def insert_group(self, index: int, group: StructuralGroup): self.structural_groups.insert(index, group) def __repr__(self): structural_groups_repr = ',\n'.join([repr(g) for g in self.structural_groups]) fault_relations_str = np.array2string(self.fault_relations, precision=2, separator=', ', suppress_small=True) if self.fault_relations is not None else 'None' return (f"StructuralFrame(\n" f"\tstructural_groups=[\n{structural_groups_repr}\n],\n" f"\tfault_relations=\n{fault_relations_str},\n" ) def _repr_html_(self): structural_groups_html = '
'.join([g._repr_html_() for g in self.structural_groups]) if self.fault_relations is not None: # Define the colors for True and False values true_color = '#527682' false_color = '#FFB6C1' table_headers = '' + ''.join('{}'.format((g.name[:10] + '...') if len(g.name) > 10 else g.name) for g in self.structural_groups) table_rows = ''.join('{}{}'.format(self.structural_groups[i].name, ''.join(''.format(true_color if cell else false_color) for cell in row)) for i, row in enumerate(self.fault_relations)) fault_relations_str = '{}{}
'.format(table_headers, table_rows) else: fault_relations_str = 'None' # Define the legend legend = f"""
True
False
""" html = f"""
Structural Groups:{structural_groups_html}
Fault Relations:{fault_relations_str}
{legend}
""" return html # endregion # region Properties @property def structural_elements(self) -> list[StructuralElement]: """Returns a list of all structural elements across the structural groups.""" elements = [] for group in self.structural_groups: elements.extend(group.elements) elements.append(self._basement_element) return elements @property def n_elements(self) -> int: """Returns the total number of elements in the structural frame.""" return len(self.structural_elements) @property def _basement_element(self) -> StructuralElement: """Returns the basement structural element with a unique color.""" def _get_unique_basement_color(color_generator: ColorsGenerator, used_colors: list[str]) -> str: color = next(color_generator) if color in used_colors: return _get_unique_basement_color(color_generator, used_colors) return color elements = [] for group in self.structural_groups: elements.extend(group.elements) used_colors = [element.color for element in elements] if self.basement_color is None or self.basement_color in used_colors: self.basement_color = _get_unique_basement_color( color_generator=self.color_generator, used_colors=used_colors ) basement = StructuralElement( name="basement", surface_points=SurfacePointsTable(data=np.zeros(0, dtype=SurfacePointsTable.dt)), orientations=OrientationsTable(data=np.zeros(0, dtype=OrientationsTable.dt)), color=self.basement_color ) return basement # ? Should I move this property to StructuralGroup? @property def fault_relations(self) -> np.ndarray: """Returns a array describing the fault relations between the structural groups.""" # Initialize an empty boolean array with dimensions len(structural_groups) x len(structural_groups) fault_relations = np.zeros((len(self.structural_groups), len(self.structural_groups)), dtype=bool) # We assume that the list is ordered from older to younger # Iterate over the list of structural_groups for i, group in enumerate(self.structural_groups): match (group.structural_relation, group.fault_relations): case (StackRelationType.FAULT, FaultsRelationSpecialCase.OFFSET_ALL): # It affects all younger groups fault_relations[i, i + 1:] = True case (StackRelationType.FAULT, FaultsRelationSpecialCase.OFFSET_NONE): # It affects no groups pass case (StackRelationType.FAULT, FaultsRelationSpecialCase.OFFSET_FORMATIONS): # It affects all younger groups that are formations do_offset = [] for group_internal in self.structural_groups[i + 1:]: do_offset.append(group_internal.structural_relation != StackRelationType.FAULT) fault_relations[i, i + 1:] = do_offset case (StackRelationType.FAULT, list(fault_groups)) if fault_groups: # It affects only the specified groups for fault_group in fault_groups: j = self.structural_groups.index(fault_group) if j <= i: # Only consider groups that are raise ValueError(f"Fault {group.name} cannot affect older fault {fault_group.name}") fault_relations[i, j] = True case (StackRelationType.FAULT, _): raise ValueError(f"Fault {group.name} has an invalid fault relation") case _: pass # If not a fault or fault relation is not specified, do nothing return fault_relations @fault_relations.setter def fault_relations(self, matrix: np.ndarray): """Sets the fault relations between structural groups using the provided matrix.""" assert matrix.shape == (len(self.structural_groups), len(self.structural_groups)) # Iterate over each StructuralGroup for i, group in enumerate(self.structural_groups): affected_groups = matrix[i, :] # * If the group is a fault # If all younger groups are affected all_younger_groups_affected = np.all(affected_groups[i + 1:]) any_younger_groups_affected = np.any(affected_groups[i + 1:]) if all_younger_groups_affected: group.fault_relations = FaultsRelationSpecialCase.OFFSET_ALL elif not any_younger_groups_affected: group.fault_relations = FaultsRelationSpecialCase.OFFSET_NONE else: # * A specific set of groups are affected group.fault_relations = [g for j, g in enumerate(self.structural_groups) if affected_groups[j]] @property def group_is_fault(self) -> list[bool]: """Returns a list of booleans indicating if each structural element is a fault.""" return [group.is_fault for group in self.structural_groups] @property def group_is_lithology(self) -> list[bool]: """Returns a list of booleans indicating if each structural element is a lithology.""" return [group.is_lithology for group in self.structural_groups] @property def input_data_descriptor(self): """Returns a descriptor for the input data, detailing the relations and faults between groups.""" # TODO: This should have the exact same dirty logic as interpolation_input self._validate_faults_relations() return InputDataDescriptor.from_structural_frame( structural_frame=self, making_descriptor=self.groups_structural_relation, faults_relations=self.fault_relations, faults_input_data=self.faults_input_data ) @property def faults_input_data(self): """Returns a descriptor for the input data, detailing the relations and faults between groups.""" faults_input_data: list[FaultsData] = [group.faults_input_data for group in self.structural_groups] return faults_input_data @property def groups_structural_relation(self) -> list[StackRelationType]: """Returns a list of the structural relations for each group.""" groups_ = [group.structural_relation for group in self.structural_groups] groups_[-1] = StackRelationType.BASEMENT return groups_ @property def number_of_points_per_element(self) -> np.ndarray: """Returns an array with the number of points for each structural element.""" return np.array([element.number_of_points for element in self.structural_elements]) @property def number_of_points_per_group(self) -> np.ndarray: """Returns an array with the number of points for each structural group.""" return np.array([group.number_of_points for group in self.structural_groups]) @property def number_of_orientations_per_group(self) -> np.ndarray: """Returns an array with the number of orientations for each structural group.""" return np.array([group.number_of_orientations for group in self.structural_groups]) @property def number_of_elements_per_group(self) -> np.ndarray: """Returns an array with the number of elements for each structural group.""" return np.array([group.number_of_elements for group in self.structural_groups]) @property def surfaces(self) -> list[StructuralElement]: """Returns a list of all surfaces in the structural elements.""" return self.structural_elements @property def number_of_elements(self) -> int: """Returns the total number of elements in the structural frame.""" return len(self.structural_elements) @property def elements_names(self) -> list[str]: """Returns a list of names of all structural elements.""" return [element.name for element in self.structural_elements] @property def elements_ids(self) -> np.ndarray: """Returns an array of IDs for all structural elements.""" return np.arange(len(self.structural_elements)) + 1 @property def surface_points_copy(self) -> SurfacePointsTable: """Returns a SurfacePointsTable for all surface points across the structural elements. This is a copy!""" all_data: np.ndarray = np.concatenate([element.surface_points.data for element in self.structural_elements]) return SurfacePointsTable(data=all_data, name_id_map=self.element_name_id_map) @property def surface_points(self): raise AttributeError("This property can only be set, not read. You can access the copy with `surface_points_copy` or" "the original on the individual structural elements.") @surface_points.setter def surface_points(self, modified_surface_points: SurfacePointsTable) -> None: """Distributes the modified surface points back to the structural elements.""" for element in self.structural_elements: element.surface_points.data = modified_surface_points.get_surface_points_by_id(element.id).data @property def orientations_copy(self) -> OrientationsTable: """Returns an OrientationsTable for all orientations across the structural elements.""" all_data: np.ndarray = np.concatenate([element.orientations.data for element in self.structural_elements]) return OrientationsTable(data=all_data) @property def orientations(self) -> OrientationsTable: raise AttributeError("This property can only be set, not read. You can access the copy with `orientations_copy` or" "the original on the individual structural elements.") @orientations.setter def orientations(self, modified_orientations: OrientationsTable) -> None: """Distributes the modified orientations back to the structural elements.""" for element in self.structural_elements: element.orientations.data = modified_orientations.get_orientations_by_id(element.id).data @property def input_tables_binary(self): return self.surface_points_copy.data.tobytes() + self.orientations_copy.data.tobytes() @property def element_id_name_map(self) -> dict[int, str]: """Returns a dictionary mapping element IDs to names.""" return {element.id: element.name for i, element in enumerate(self.structural_elements)} @property def element_name_id_map(self) -> dict[str, int]: """Returns a dictionary mapping element names to IDs.""" return {element.name: element.id for i, element in enumerate(self.structural_elements)} @property def elements_colors(self) -> list[str]: """Returns a list of colors assigned to each structural element. Used in matplotlib""" # reversed return [element.color for element in self.structural_elements][::-1] @property def elements_colors_volumes(self) -> list[str]: """Returns a list of colors assigned to each structural element for volume representation. Used in pyvista""" return self.elements_colors @property def elements_colors_contacts(self) -> list[str]: """Returns a list of colors assigned to each structural element for contact representation. Used in many places""" points_ = [element.color for element in self.structural_elements if len(element.surface_points) > 0] return points_ @property def elements_colors_orientations(self) -> list[str]: """Returns a list of colors assigned to each structural element for orientation representation. Used to paint orientations in pyvista """ orientations_ = [element.color for element in self.structural_elements if len(element.orientations) > 0] return orientations_ @property def surface_points_colors_per_item(self) -> list[str]: """Returns a list of colors assigned to each surface point across structural elements. Used in matplotlib""" surface_points_colors = [element.color for element in self.structural_elements for _ in range(element.number_of_points)] return surface_points_colors @property def orientations_colors_per_item(self) -> list[str]: """Returns a list of colors assigned to each orientation across structural elements. Used in matplotlib""" orientations_colors = [element.color for element in self.structural_elements for _ in range(element.number_of_orientations)] return orientations_colors @property def groups_to_mapper(self) -> dict[str, list[str]]: """Returns a dictionary mapping each structural group to its corresponding elements.""" result_dict = {} for group in self.structural_groups: element_names = [element.name for element in group.elements] result_dict[group.name] = element_names return result_dict # region Depends on Pandas @property def surfaces_df(self) -> 'pd.DataFrame': """Returns a DataFrame representation of all surfaces across structural elements.""" # TODO: Loop every structural element. Each element should be a row in the dataframe # TODO: The columns have to be ['element, 'group', 'color'] raise NotImplementedError # endregion # endregion # region Pydantic @model_validator(mode="wrap") @classmethod def deserialize_binary(cls, data: Union["StructuralFrame", dict], constructor: ModelWrapValidatorHandler["StructuralFrame"]) -> "StructuralFrame": match data: case StructuralFrame(): return data case dict(): instance: StructuralFrame = constructor(data) metadata = data.get('binary_meta_data', {}) context = loading_model_context.get() if 'input_binary' not in context: return instance instance.orientations, instance.surface_points = deserialize_input_data_tables( binary_array=context['input_binary'], name_id_map=instance.surface_points_copy.name_id_map, sp_binary_length_=metadata["sp_binary_length"], ori_binary_length_=metadata["ori_binary_length"] ) return instance case _: raise ValidationError(f"Invalid data type for StructuralFrame: {type(data)}") # Access the context variable to get injected data @computed_field def binary_meta_data(self) -> dict: return { 'sp_binary_length': len(self.surface_points_copy.data.tobytes()), 'ori_binary_length': len(self.orientations_copy.data.tobytes()) , } # endregion def _validate_faults_relations(self): """Check that if there are any StackRelationType.FAULT in the structural groups the fault relation matrix is given and shape is the right one, i.e. a square matrix of size equals to len(groups)""" if any([group.structural_relation == StackRelationType.FAULT for group in self.structural_groups]): if self.fault_relations is None: raise ValueError("The fault relations matrix is not given") if self.fault_relations.shape != (len(self.structural_groups), len(self.structural_groups)): raise ValueError("The fault relations matrix is not the right shape") ================================================ FILE: gempy/core/data/structural_group.py ================================================ import pprint from abc import ABC from dataclasses import dataclass, field from enum import Enum, auto from typing import Optional, Union, Generator from gempy_engine.core.data.kernel_classes.faults import FaultsData from gempy_engine.core.data.raw_arrays_solution import RawArraysSolution from gempy_engine.core.data.stack_relation_type import StackRelationType from gempy.core.data.structural_element import StructuralElement class FaultsRelationSpecialCase(Enum): OFFSET_FORMATIONS = auto() OFFSET_NONE = auto() OFFSET_ALL = auto() @dataclass class StructuralGroup(ABC): """ An abstract base class that represents a structural group within a geological model. """ name: str #: The name of the structural group. elements: list[StructuralElement] = field(repr=False) #: A list of structural elements within the group. structural_relation: StackRelationType #: The type of relation between the structural elements in the group. #: Relations with other groups in terms of faults. fault_relations: Optional[Union[list["StructuralGroup"], FaultsRelationSpecialCase]] = field(default=None, repr=False) faults_input_data: Optional[FaultsData] = field(default=None, repr=False) solution: Optional[RawArraysSolution] = field(init=False, default=None, repr=False) #: Solution related to this group from geological computations. def __post_init__(self): if not isinstance(self.elements, list): raise TypeError("elements must be a list of StructuralElement objects.") for e in self.elements: if not isinstance(e, StructuralElement): raise TypeError("elements must be a list of StructuralElement objects.") def __repr__(self): elements_repr = ',\n'.join([repr(e) for e in self.elements]) return f"StructuralGroup(\n" \ f"\tname={self.name},\n" \ f"\tstructural_relation={self.structural_relation},\n" \ f"\telements=[\n{elements_repr}\n]\n)" def _repr_html_(self): elements_html = '
'.join([e._repr_html_() for e in self.elements]) html = f""" StructuralGroup:
Name:{self.name}
Structural Relation:{self.structural_relation}
Elements:{elements_html}
""" return html def append_element(self, element: StructuralElement): self.elements.append(element) def remove_element(self, element: StructuralElement): self.elements.remove(element) @property def id(self): raise NotImplementedError @property def is_fault(self)-> bool: return self.structural_relation == StackRelationType.FAULT @property def is_lithology(self)-> bool: return self.structural_relation == StackRelationType.ERODE or self.structural_relation == StackRelationType.ONLAP @property def number_of_points(self) -> int: return sum([element.number_of_points for element in self.elements]) @property def number_of_orientations(self) -> int: return sum([element.number_of_orientations for element in self.elements]) @property def number_of_elements(self) -> int: return len(self.elements) def get_element_by_name(self, element_name: str) -> StructuralElement | None: matched_elements: Generator = (element for element in self.elements if element.name == element_name) return next(matched_elements, None) # ? I think these two subclasses are not necessary @dataclass class Stack(StructuralGroup): def __int__(self, name: str, elements: list[StructuralElement]): super().__init__(name, elements) def __repr__(self): return pprint.pformat(self.__dict__) @dataclass class Fault(StructuralGroup): pass ================================================ FILE: gempy/core/data/surface_points.py ================================================ from dataclasses import dataclass from typing import Optional, Union, Sequence import numpy as np from gempy_engine.core.data.transforms import Transform from pydantic import Field from ._data_points_helpers import generate_ids_from_names from ...optional_dependencies import require_pandas DEFAULT_SP_NUGGET = 0.00002 # ? Maybe we should merge this with the SurfacePoints class from gempy_engine # ? It does not seem a good a idea because gempy_engine.SurfacePoints is too terse # ! ids are not used apparently @dataclass(init=True) class SurfacePointsTable: """ A dataclass to represent a table of surface points in a geological model. """ dt = np.dtype([('X', 'f8'), ('Y', 'f8'), ('Z', 'f8'), ('id', 'i4'), ('nugget', 'f8')]) #: The custom data type for the data array. data: np.ndarray = Field( default=np.zeros(0, dtype=dt), exclude=True, description="A structured NumPy array holding the X, Y, Z coordinates, id, and nugget of each surface point." ) #: A structured NumPy array holding the X, Y, Z coordinates, id, and nugget of each surface point. name_id_map: Optional[dict[str, int]] = None #: A mapping between surface point names and ids. _model_transform: Optional[Transform] = None def __post_init__(self): # Check if the data array has the correct data type if self.data.dtype != SurfacePointsTable.dt: raise ValueError(f"Data array must have the following data type: {SurfacePointsTable.dt}") def __str__(self): return "\n" + np.array2string(self.data, precision=2, separator=',', suppress_small=True) def __repr__(self): return f"SurfacePointsTable(data=\n{np.array2string(self.data, precision=2, separator=',', suppress_small=True)},\nname_id_map={self.name_id_map})" def _repr_html_(self): rows_to_display = 10 # Define the number of rows to display from beginning and end html = "" html += "" if len(self.data) > 2 * rows_to_display: for point in self.data[:rows_to_display]: html += "".format(*point) html += "" for point in self.data[-rows_to_display:]: html += "".format(*point) else: for point in self.data: html += "".format(*point) html += "
XYZidnugget
{:.2f}{:.2f}{:.2f}{}{:.2f}
...............
{:.2f}{:.2f}{:.2f}{}{:.2f}
{:.2f}{:.2f}{:.2f}{}{:.2f}
" return html @classmethod def from_arrays(cls, x: np.ndarray, y: np.ndarray, z: np.ndarray, names: Union[Sequence | str], nugget: Optional[np.ndarray] = None, name_id_map: Optional[dict[str, int]] = None ) -> 'SurfacePointsTable': """Create a SurfacePointsTable from arrays of coordinates and names. Args: x (np.ndarray): Array of x-coordinates. y (np.ndarray): Array of y-coordinates. z (np.ndarray): Array of z-coordinates. names (Union[Sequence, str]): Names of the surface points. nugget (Optional[np.ndarray]): Nugget values for the surface points. name_id_map (Optional[dict[str, int]]): Mapping between surface point names and ids. Returns: SurfacePointsTable: A new instance of SurfacePointsTable. """ data, name_id_map = cls._data_from_arrays(x, y, z, names, nugget, name_id_map) return cls(data, name_id_map) @classmethod def _data_from_arrays(cls, x: np.ndarray, y: np.ndarray, z: np.ndarray, names: Union[Sequence | str], nugget: Optional[np.ndarray] = None, name_id_map: dict[str, int] = None) -> tuple[np.ndarray, dict[str, int]]: if nugget is None: nugget = np.zeros_like(x) + DEFAULT_SP_NUGGET if name_id_map is None: ids, name_id_map = generate_ids_from_names(name_id_map, names, x) else: ids = np.array([name_id_map[name] for name in names]) data = np.zeros(len(x), dtype=SurfacePointsTable.dt) data['X'], data['Y'], data['Z'], data['id'], data['nugget'] = x, y, z, ids, nugget return data, name_id_map @classmethod def initialize_empty(cls) -> 'SurfacePointsTable': """Initialize an empty SurfacePointsTable. Returns: SurfacePointsTable: An empty instance of SurfacePointsTable. """ return cls(np.zeros(0, dtype=SurfacePointsTable.dt), {}) def id_to_name(self, id: int) -> str: """Get the name corresponding to a given id. Args: id (int): The id of the surface point. Returns: str: The name of the surface point. """ return list(self.name_id_map.keys())[id] @property def xyz(self) -> np.ndarray: return np.array([self.data['X'], self.data['Y'], self.data['Z']]).T @property def xyz_view(self) -> np.ndarray: return self.data[['X', 'Y', 'Z']] @xyz_view.setter def xyz_view(self, value: np.ndarray): self.data['X'], self.data['Y'], self.data['Z'] = value.T @property def nugget(self) -> np.ndarray: return self.data['nugget'] @nugget.setter def nugget(self, value: np.ndarray): self.data['nugget'] = value @property def model_transform(self) -> Transform: if self._model_transform is None: raise ValueError("Model transform is not set. If you want to use this property use GeoModel.surface_points to get the SurfaceTable with transform attached.") return self._model_transform @model_transform.setter def model_transform(self, value: Transform): self._model_transform = value def __len__(self): return len(self.data) def get_surface_points_by_name(self, name: str) -> 'SurfacePointsTable': """Get a SurfacePointsTable containing points with the specified name. Args: name (str): The name of the surface points. Returns: SurfacePointsTable: A new instance containing the specified surface points. """ return self.get_surface_points_by_id(self.name_id_map[name]) def get_surface_points_by_id(self, id: int) -> 'SurfacePointsTable': """Get a SurfacePointsTable containing points with the specified id. Args: id (int): The id of the surface points. Returns: SurfacePointsTable: A new instance containing the specified surface points. """ return SurfacePointsTable(self.data[self.data['id'] == id], self.name_id_map) def get_surface_points_by_id_groups(self) -> list['SurfacePointsTable']: """Get a list of SurfacePointsTable objects, each containing points with a unique id. Returns: list[SurfacePointsTable]: A list of SurfacePointsTable objects. """ ids = np.unique(self.data['id']) return [self.get_surface_points_by_id(id) for id in ids] @property def ids(self) -> np.ndarray: return self.data['id'] @property def id(self) -> int: # Check id is the same in the whole column and return it or throw an error ids = np.unique(self.data['id']) if len(ids) > 1: raise ValueError(f"OrientationsTable contains more than one id: {ids}") if len(ids) == 0: raise ValueError(f"OrientationsTable contains no ids") return ids[0] @property def df(self) -> 'pd.DataFrame': pd = require_pandas() return pd.DataFrame(self.data) ================================================ FILE: gempy/core/engine.rst ================================================ Design document for GemPy Engine 3 ================================== Early thoughts about how to redesign the interpolator classes. Some of the main specifications are: - ?Independent interpolator from the rest so we can use it as each own **microservice** via some sort of **sync** function that set up all the constant values - Compatibility with **TensorFlow** and **Numpy** - Outputs of the graph scalable - Independence of each scalar field - **No global constants**, e.g. each field will have its own range - **Scalar field injection**, any recursive value of one field into the next one can have an access point. - **Octtress** are Native. To deactive it, we just need to set the number of levels to 1 - Multiple solver (such as **sparse solver** easy to use) + cuda solver + gradient solver + sparse solver - Interpolator data stored in df and always updated. - Having several sets of coordinates systems to choose from when we call compute for **anisotropies** Graph Logic ~~~~~~~~~~~ Tensor super class ^^^^^^^^^^^^^^^^^^ aesara, numpy and tensorflow have a very similar syntax but not 100% exact. We could create a tensor wrapper that makes sure that it returns the right value. For example sin is the same in all 3: .. code-block:: python class GemPyTensor: def __init__(package='numpy') if package=='numpy': _tt = np elif package=='aesara': _tt = aesara.tensor # Same numpy-aesara easy def sin(x): return _tt.sin(x) # Different def set_subtensor(x: slice, new_slice) if package=='numpy': x = new_slice return x if package == 'aesara': return _tt.set_subtensor(x , new_slice) Advantages ---------- - Maintainability: Not having to write 3 different graphs - If we use aesara syntax as base for `GemPyTensor` we do not need to rewrite even the current graph - If a function exist in one package, we can mock it on the others using the correspondent wrappers. Disadvantages ------------- - How do we deal with control flow on run time that is done vastly different Data Flow ~~~~~~~~~ Scalable Output ^^^^^^^^^^^^^^^ In GemPy 2.1 we moved from having different interpolator objects with different graphs to having always one interpolator capable to select how much of the whole graph is going to be constructed at compile time. As I see it there some possible paths here: .. note:: GemPy-server design allows to have several engines up so we could have pretty much anything 1. We construct always the full graph. This will enable to choose in runtime what do we want to compute e.g. some gravity, without having to recompile. The cost is memory and as the graph gains in complexity with features that may not be used too often it will be a waste. 2. Caching, every time ... Independent Interpolator ^^^^^^^^^^^^^^^^^^^^^^^^ To obtain this we would need to split the interpolator in two classes. 1) One buffer class containing all the data 2) A graph class A sync function would feed the graph class *Which consequences has this for the* ``Model`` *methods?* The graph class could be in a different process or could be in the same process. ================================================ FILE: gempy/modules/__init__.py ================================================ """ GemPy modules package. """ from . import json_io from . import custom_implicit_functions from . import data_manipulation from . import grids from . import advance_pile __all__ = [ 'json_io', 'custom_implicit_functions', 'data_manipulation', 'grids', 'advance_pile' ] ================================================ FILE: gempy/modules/advance_pile/__init__.py ================================================ ================================================ FILE: gempy/modules/advance_pile/strat_definition.py ================================================ ================================================ FILE: gempy/modules/custom_implicit_functions/__init__.py ================================================ ================================================ FILE: gempy/modules/custom_implicit_functions/ellipsoid_implicit_function.py ================================================ import functools import numpy as np def ellipsoid_3d_factory(center: np.ndarray, radius: np.ndarray, max_slope: float) -> callable: """ Implicit 3D ellipsoid. """ implicit_ellipsoid = functools.partial( ellipsoid_scalar_field, center=center, radii=radius, k_factors=max_slope, ) return implicit_ellipsoid def ellipsoid_scalar_field(xyz, center, radii, k_factors): """Calculate scalar field value for given coordinates. Parameters: - xyz: numpy array of shape (N, 3), where N is the number of points - center: numpy array of shape (3,) representing the center of the ellipsoid - radii: numpy array of shape (3,) representing the semiaxes a, b, and c of the ellipsoid - k_factors: numpy array of shape (3,) representing the slope factors for x, y, and z directions. Returns: - A numpy array of shape (N,) containing the scalar field values. """ displacements = xyz - center values = ((displacements[:, 0] / (radii[0] * k_factors[0])) ** 2 + (displacements[:, 1] / (radii[1] * k_factors[1])) ** 2 + (displacements[:, 2] / (radii[2] * k_factors[2])) ** 2 - 1) return - sigmoid(values * np.prod(k_factors)) + 1 # multiplying by the product of k_factors to keep the transition sharper def sigmoid(x): """Standard sigmoid function.""" return 1 / (1 + np.exp(-x)) def _implicit_3d_ellipsoid_to_slope_(xyz: np.ndarray, center: np.ndarray, radius: np.ndarray, max_slope: float = 1000): """ Implicit 3D ellipsoid. """ # ! Finite faults needs quite a bit of fine tunning once we can compute models in real time # ! This function only works for elipses perpendicular to the cartesian axis scalar = - np.sum((xyz - center) ** 2.00 / (radius ** 2), axis=1) scalar_shifted = scalar - scalar.min() sigmoid_slope = 10 # ? This probably should be also public Z_x = scalar_shifted drift_0 = 4 # ? Making it a %. It depends on the radius scale_0 = max_slope scalar_final = scale_0 / (1 + np.exp(-sigmoid_slope * (Z_x - drift_0))) return scalar_final ================================================ FILE: gempy/modules/data_manipulation/__init__.py ================================================ from ._engine_factory import interpolation_input_from_structural_frame ================================================ FILE: gempy/modules/data_manipulation/_engine_factory.py ================================================ from typing import Optional import numpy as np from ...core.data.grid import Grid from ...core.data.structural_frame import StructuralFrame from gempy_engine.core.data import SurfacePoints, Orientations from gempy_engine.core.data import engine_grid from gempy_engine.core.data.interpolation_input import InterpolationInput from gempy_engine.core.data.transforms import Transform def interpolation_input_from_structural_frame(geo_model: "gempy.data.GeoModel") -> InterpolationInput: import gempy # ! This is important for type safety geo_model: gempy.data.GeoModel = geo_model _legacy_factor = 0 structural_frame: StructuralFrame = geo_model.structural_frame input_transform: Transform = geo_model.input_transform grid: Grid = geo_model.grid total_transform: Transform = input_transform + grid.transform surface_points_copy_transformed = geo_model.surface_points_copy_transformed surface_points: SurfacePoints = SurfacePoints( sp_coords=geo_model.surface_points_copy_transformed.xyz, nugget_effect_scalar=surface_points_copy_transformed.nugget ) orientations_copy_transformed = geo_model.orientations_copy_transformed orientations: Orientations = Orientations( dip_positions=orientations_copy_transformed.xyz, dip_gradients=orientations_copy_transformed.grads, nugget_effect_grad=orientations_copy_transformed.nugget ) grid: engine_grid.EngineGrid = _apply_input_transform_to_grids( grid=grid, input_transform=input_transform, extent_transformed=geo_model.extent_transformed_transformed_by_input ) weights = [] if geo_model.solutions is not None: for stack_sol in geo_model.solutions.root_output.outputs: weights.append(stack_sol.weights) interpolation_input: InterpolationInput = InterpolationInput( surface_points=surface_points, orientations=orientations, grid=grid, unit_values=structural_frame.elements_ids, # TODO: Here we will need to pass densities etc. weights=weights ) return interpolation_input def _apply_input_transform_to_grids(grid: Grid, input_transform: Transform, extent_transformed: np.ndarray) -> engine_grid.EngineGrid: new_extents = extent_transformed # Initialize all variables to None octree_grid: Optional[engine_grid.RegularGrid] = None regular_grid: Optional[engine_grid.RegularGrid] = None custom_values: Optional[engine_grid.GenericGrid] = None topography_values: Optional[engine_grid.GenericGrid] = None section_values: Optional[engine_grid.GenericGrid] = None centered_grid: Optional[engine_grid.CenteredGrid] = None if grid.GridTypes.DENSE in grid.active_grids: regular_grid = engine_grid.RegularGrid( orthogonal_extent=new_extents, regular_grid_shape=grid.dense_grid.resolution, ) if grid.GridTypes.CUSTOM in grid.active_grids and grid.custom_grid is not None: custom_values = engine_grid.GenericGrid(values=input_transform.apply(grid.custom_grid.values)) if grid.GridTypes.TOPOGRAPHY in grid.active_grids and grid.topography is not None: topography_values = engine_grid.GenericGrid(values=input_transform.apply(grid.topography.values)) if grid.GridTypes.SECTIONS in grid.active_grids and grid.sections is not None: section_values = engine_grid.GenericGrid(values=input_transform.apply(grid.sections.values)) if grid.GridTypes.CENTERED in grid.active_grids and grid.centered_grid is not None: centered_grid = engine_grid.CenteredGrid( centers=input_transform.apply(grid.centered_grid.centers), radius=input_transform.scale_points(np.atleast_2d(grid.centered_grid.radius))[0], resolution=grid.centered_grid.resolution ) if grid.octree_grid is not None: base_grid_resolution = grid.octree_grid.base_resolution else: base_grid_resolution = np.array([2, 2, 2]) octree_grid = engine_grid.RegularGrid( orthogonal_extent=new_extents, regular_grid_shape=base_grid_resolution ) grid: engine_grid.EngineGrid = engine_grid.EngineGrid( # * Here we convert the GemPy grid to the octree_grid=octree_grid, # BUG: Adapt the engine to deal with this dense_grid=regular_grid, topography=topography_values, sections=section_values, custom_grid=custom_values, geophysics_grid=centered_grid ) return grid ================================================ FILE: gempy/modules/data_manipulation/manipulate_points.py ================================================ from typing import Sequence, Optional, Union import numpy as np from ...core.data import GeoModel, StructuralFrame, SurfacePointsTable, StructuralElement, OrientationsTable from ...core.data.orientations import DEFAULT_ORI_NUGGET from ...core.data.surface_points import DEFAULT_SP_NUGGET def add_surface_points( geo_model: GeoModel, x: Sequence[float], y: Sequence[float], z: Sequence[float], elements_names: Sequence[str], nugget: Optional[Sequence[float]] = None ) -> StructuralFrame: """Add surface points to the geological model. This function adds surface points to the specified geological elements in the model. The points are grouped by element names, and optional nugget values can be specified for each point. Args: geo_model (GeoModel): The geological model to which the surface points will be added. x (Sequence[float]): Sequence of x-coordinates for the surface points. y (Sequence[float]): Sequence of y-coordinates for the surface points. z (Sequence[float]): Sequence of z-coordinates for the surface points. elements_names (Sequence[str]): Sequence of element names corresponding to each surface point. nugget (Optional[Sequence[float]]): Sequence of nugget values for each surface point. If not provided, a default value will be used for all points. Returns: StructuralFrame: The updated structural frame of the geological model. Raises: ValueError: If the length of the nugget sequence does not match the lengths of the other input sequences. """ elements_names = _validate_args(elements_names, x, y, z) # If nugget is not provided, create a Sequence filled with the default value if nugget is None: nugget = [DEFAULT_SP_NUGGET] * len(x) # Ensure nugget also has the same length as the other Sequences if len(nugget) != len(x): raise ValueError("The length of the nugget Sequence does not match the lengths of other input Sequences.") # * Split the sequences according to the elements_names x = np.array(x) y = np.array(y) z = np.array(z) elements_names = np.array(elements_names) nugget = np.array(nugget) unique_names = np.unique(elements_names) grouped_data = {} for name in unique_names: mask = (elements_names == name) grouped_data[name] = { 'x' : x[mask], 'y' : y[mask], 'z' : z[mask], 'nugget': nugget[mask] } # * Loop per element_name for element_name, data in grouped_data.items(): formatted_data, _ = SurfacePointsTable._data_from_arrays( x=data['x'], y=data['y'], z=data['z'], names=[element_name] * len(data['x']), nugget=data['nugget'], name_id_map=None ) element: StructuralElement = geo_model.structural_frame.get_element_by_name(element_name) element.surface_points.data = np.concatenate([ element.surface_points.data, formatted_data ]) return geo_model.structural_frame def delete_surface_points(): raise NotImplementedError def add_orientations(geo_model: GeoModel, x: Sequence[float], y: Sequence[float], z: Sequence[float], elements_names: Sequence[str], pole_vector: Optional[Union[Sequence[np.ndarray], np.ndarray]] = None, orientation: Optional[Union[Sequence[np.ndarray], np.ndarray]] = None, nugget: Optional[Sequence[float]] = None, name_id_map: Optional[dict[str, int]] = None #: A mapping between orientation names and ids. ) -> StructuralFrame: """Add orientation data to the geological model. This function adds orientation data to the specified geological elements in the model. The orientation can be provided directly as pole vectors or as orientation angles (azimuth, dip, polarity). Optional nugget values can also be specified for each orientation point. Args: geo_model (GeoModel): The geological model to which the orientations will be added. x (Sequence[float]): Sequence of x-coordinates for the orientation points. y (Sequence[float]): Sequence of y-coordinates for the orientation points. z (Sequence[float]): Sequence of z-coordinates for the orientation points. elements_names (Sequence[str]): Sequence of element names corresponding to each orientation point. pole_vector (Optional[Union[Sequence[np.ndarray], np.ndarray]]): Sequence of pole vectors for each orientation point. If is np.ndarray, it should have shape (n, 3). orientation (Optional[Union[Sequence[np.ndarray], np.ndarray]]): Sequence of orientation angles for each orientation point. If is np.ndarray, it should have shape (n, 3). nugget (Optional[Sequence[float]]): Sequence of nugget values for each orientation point. If not provided, a default value will be used for all points. Returns: StructuralFrame: The updated structural frame of the geological model. Raises: ValueError: If neither pole_vector nor orientation is provided, or if the length of the nugget sequence does not match the lengths of the other input sequences. """ if pole_vector is None and orientation is None: raise ValueError("Either pole_vector or orientation must be provided.") if orientation is not None: orientation = np.array(orientation, ndmin=2) pole_vector = convert_orientation_to_pole_vector( azimuth=orientation[:, 0], dip=orientation[:, 1], polarity=orientation[:, 2] ) else: pole_vector = np.array(pole_vector, ndmin=2) elements_names = _validate_args(elements_names, x, y, z, pole_vector) if nugget is None: # If nugget is not provided, create a Sequence filled with the default value nugget = [DEFAULT_ORI_NUGGET] * len(x) # Ensure nugget also has the same length as the other Sequences if len(nugget) != len(x): raise ValueError("The length of the nugget Sequence does not match the lengths of other input Sequences.") # * Split the sequences according to the elements_names x = np.array(x) y = np.array(y) z = np.array(z) elements_names = np.array(elements_names) pole_vector = np.array(pole_vector) nugget = np.array(nugget) unique_names = np.unique(elements_names) grouped_data = {} for name in unique_names: mask = (elements_names == name) grouped_data[name] = { 'x' : x[mask], 'y' : y[mask], 'z' : z[mask], 'pole_vector': pole_vector[mask], 'nugget' : nugget[mask] } # * Loop per element_name for element_name, data in grouped_data.items(): formatted_data, _ = OrientationsTable._data_from_arrays( x=data['x'], y=data['y'], z=data['z'], G_x=data['pole_vector'][..., 0], G_y=data['pole_vector'][..., 1], G_z=data['pole_vector'][..., 2], names=[element_name] * len(data['x']), nugget=data['nugget'], name_id_map=name_id_map ) element: StructuralElement = geo_model.structural_frame.get_element_by_name(element_name) element.orientations.data = np.concatenate([ element.orientations.data, formatted_data ]) return geo_model.structural_frame def modify_orientations(geo_model: GeoModel, slice: Optional[Union[int, slice]] = None, **orientation_field: Union[float, np.ndarray]) -> StructuralFrame: """ Modifies specified fields of all orientations in the structural frame. The keys of the orientation_field dictionary should match the field names in the orientations (e.g., "X", "Y", "Z", "G_x", "G_y", "G_z", "nugget"). Args: geo_model (GeoModel): The GeoModel instance to modify. slice (Optional[Union[int, slice]]): The slice of orientations to modify. If None, all orientations will be modified. Keyword Args: X (Union[float, np.ndarray]): X coordinates of the orientations. Y (Union[float, np.ndarray]): Y coordinates of the orientations. Z (Union[float, np.ndarray]): Z coordinates of the orientations. azimuth (Union[float, np.ndarray]): Azimuth angles of the orientations. dip (Union[float, np.ndarray]): Dip angles of the orientations. polarity (Union[float, np.ndarray]): Polarity values of the orientations. G_x (Union[float, np.ndarray]): X component of the gradient vector. G_y (Union[float, np.ndarray]): Y component of the gradient vector. G_z (Union[float, np.ndarray]): Z component of the gradient vector. nugget (Union[float, np.ndarray]): Nugget value of the orientations. Returns: StructuralFrame: The modified structural frame. """ orientations = geo_model.structural_frame.orientations_copy # If no slice is provided, target all rows; else, target specified slice target_rows = slice if slice is not None else np.s_[:] # Extract provided orientation fields without modifying the dictionary azimuth = orientation_field.pop('azimuth', None) dip = orientation_field.pop('dip', None) polarity = orientation_field.pop('polarity', None) # Update all the other fields for key, value in orientation_field.items(): if isinstance(value, np.ndarray) and len(value) != len(orientations.data[target_rows]): raise ValueError(f"Length mismatch: Expected size {len(orientations.data[target_rows])} for field {key}, but got {len(value)}.") orientations.data[key][target_rows] = value # Check if azimuth, dip, or polarity are provided any_polar_fields = azimuth is not None or dip is not None or polarity is not None all_polar_fields = azimuth is not None and dip is not None and polarity is not None match (any_polar_fields, all_polar_fields): case (True, True): # All polar fields provided, convert to gradients gx, gy, gz = convert_orientation_to_pole_vector(np.asarray(azimuth), np.asarray(dip), np.asarray(polarity)) orientations.data['G_x'][target_rows] = gx orientations.data['G_y'][target_rows] = gy orientations.data['G_z'][target_rows] = gz case (True, False): # Some polar fields missing, compute missing fields from gradients prev_azimuth, prev_dip, prev_polarity = compute_adp_from_gradients( orientations.data['G_x'], orientations.data['G_y'], orientations.data['G_z'] ) azimuth = np.asarray(azimuth) if azimuth is not None else prev_azimuth dip = np.asarray(dip) if dip is not None else prev_dip polarity = np.asarray(polarity) if polarity is not None else prev_polarity gradients = convert_orientation_to_pole_vector(azimuth, dip, polarity) orientations.data['G_x'][target_rows] = gradients[:, 0] orientations.data['G_y'][target_rows] = gradients[:, 1] orientations.data['G_z'][target_rows] = gradients[:, 2] case (_, _): pass geo_model.orientations = orientations return geo_model.structural_frame def modify_surface_points(geo_model: GeoModel, slice: Optional[Union[int, slice]] = None, elements_names: Optional[Sequence[str]] = None, **surface_points_field: Union[float, np.ndarray]) -> StructuralFrame: """ Modifies specified fields of all surface points in the structural frame. The keys of the surface_points_field dictionary should match the field names in the surface points (e.g., "X", "Y", "Z", "nugget"). Args: geo_model (GeoModel): The GeoModel instance to modify. slice (Optional[Union[int, slice]]): The slice of surface points to modify. If None, all surface points will be modified. Keyword Args: X (Union[float, np.ndarray]): X coordinates of the surface points. Y (Union[float, np.ndarray]): Y coordinates of the surface points. Z (Union[float, np.ndarray]): Z coordinates of the surface points. nugget (Union[float, np.ndarray]): Nugget value of the surface points. Returns: StructuralFrame: The modified structural frame. """ if elements_names is not None and slice is not None: raise ValueError("Cannot provide both elements_names and slice.") surface_points = geo_model.structural_frame.surface_points_copy if elements_names is not None: ids = [surface_points.name_id_map[element] for element in elements_names] slice = np.s_[np.isin(surface_points.data['id'], ids)] # If no slice is provided, target all rows; else, target specified slice target_rows = slice if slice is not None else np.s_[:] for key, value in surface_points_field.items(): if isinstance(value, np.ndarray) and len(value) != len(surface_points.data[target_rows]): raise ValueError(f"Length mismatch: Expected size {len(surface_points.data[target_rows])} for field {key}, but got {len(value)}.") surface_points.data[key][target_rows] = value geo_model.surface_points = surface_points return geo_model.structural_frame def delete_orientations(): raise NotImplementedError def convert_orientation_to_pole_vector(azimuth: Sequence[float], dip: Sequence[float], polarity: Sequence[float]) -> Sequence[np.ndarray]: # Convert sequences to numpy arrays for vectorized operations azimuth = np.array(azimuth) dip = np.array(dip) polarity = np.array(polarity) # Calculate gradient components G_x = np.sin(np.deg2rad(dip)) * np.sin(np.deg2rad(azimuth)) * polarity G_y = np.sin(np.deg2rad(dip)) * np.cos(np.deg2rad(azimuth)) * polarity G_z = np.cos(np.deg2rad(dip)) * polarity # Combine gradient components into an array of vectors gradients = np.vstack([G_x, G_y, G_z]).T return gradients def compute_adp_from_gradients(G_x: np.ndarray, G_y: np.ndarray, G_z: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]: # Calculate polarity (here assumed to be 1 for all, but you can adapt if needed) polarity = np.ones_like(G_x) # Calculate dip dip = np.rad2deg(np.nan_to_num(np.arccos(G_z / polarity))) # Calculate azimuth azimuth = np.rad2deg(np.nan_to_num(np.arctan2(G_x / polarity, G_y / polarity))) # Shift values from [-pi, 0] to [pi,2*pi] azimuth[azimuth < 0] += 360 # Adjust azimuth where dip is nearly zero, because if dip is zero, azimuth is undefined azimuth[dip < 0.001] = 0 return azimuth, dip, polarity def _validate_args(elements_names, *args): if isinstance(elements_names, str): elements_names = np.array([elements_names] * len(args[0])) elif isinstance(elements_names, Sequence) or isinstance(elements_names, np.ndarray): pass else: raise TypeError(f"Names should be a string or a NumPy array, not {type(elements_names)}") # Ensure all provided Sequences have the same length lengths = {len(arg) for arg in args} if len(lengths) != 1: raise ValueError("All input Sequences must have the same length.") return elements_names ================================================ FILE: gempy/modules/data_manipulation/manipulate_structural_frame.py ================================================ from gempy_engine.core.data.stack_relation_type import StackRelationType from gempy.core.data import StructuralGroup, GeoModel, FaultsRelationSpecialCase, StructuralElement, StructuralFrame def add_structural_group( model: GeoModel, group_index: int, structural_group_name: str, elements: list[StructuralElement], structural_relation: StackRelationType, fault_relations: FaultsRelationSpecialCase = FaultsRelationSpecialCase.OFFSET_ALL) -> StructuralFrame: # Check elements are a Sequence if not isinstance(elements, list): raise TypeError("elements must be a list of StructuralElement objects.") new_group = StructuralGroup( name=structural_group_name, elements=elements, structural_relation=structural_relation, fault_relations=fault_relations ) # Insert the fault group into the structural frame: model.structural_frame.insert_group(group_index, new_group) return model.structural_frame def remove_structural_group_by_index(model: GeoModel, group_index: int) -> StructuralFrame: model.structural_frame.structural_groups.pop(group_index) return model.structural_frame def remove_structural_group_by_name(model: GeoModel, group_name: str) -> StructuralFrame: group = model.structural_frame.get_group_by_name(group_name) group_index = model.structural_frame.structural_groups.index(group) model.structural_frame.structural_groups.pop(group_index) return model.structural_frame def remove_element_by_name(model: GeoModel, element_name: str) -> StructuralFrame: element = model.structural_frame.get_element_by_name(element_name) element_group: StructuralGroup = model.structural_frame.get_group_by_element(element) element_group.remove_element(element) return model.structural_frame ================================================ FILE: gempy/modules/data_manipulation/orientations_from_surface_points.py ================================================ from typing import Optional import numpy as np from numpy.linalg import svd from gempy.core.data import SurfacePointsTable, OrientationsTable def create_orientations_from_surface_points_coords(xyz_coords: np.ndarray, subset: Optional[np.ndarray] = None, element_name: Optional[str] = "Generated") -> OrientationsTable: # Initialize the arrays center = np.empty((len(subset) if subset is not None else 1, 3)) normal = np.empty((len(subset) if subset is not None else 1, 3)) if subset is None: center[0], normal[0] = _plane_fit(xyz_coords) else: for idx, i in enumerate(subset): center[idx], normal[idx] = _plane_fit(xyz_coords[i]) orientations = OrientationsTable.from_arrays( x=center[:, 0], y=center[:, 1], z=center[:, 2], G_x=normal[:, 0], G_y=normal[:, 1], G_z=normal[:, 2], names=[element_name] ) return orientations def _plane_fit(point_list): """ Fit plane to points in PointSet Fit an d-dimensional plane to the points in a point set. adjusted from: http://stackoverflow.com/questions/12299540/plane-fitting-to-4-or-more-xyz-points Args: point_list (array_like): array of points XYZ Returns: Return a point, p, on the plane (the point-cloud centroid), and the normal, n. """ points = point_list.T points = np.reshape(points, (np.shape(points)[0], -1)) # Collapse trialing dimensions assert points.shape[0] <= points.shape[1], "There are only {} points in {} dimensions.".format(points.shape[1], points.shape[0]) ctr = points.mean(axis=1) x = points - ctr[:, np.newaxis] M = np.dot(x, x.T) # Could also use np.cov(x) here. # ctr = Point(x=ctr[0], y=ctr[1], z=ctr[2], type='utm', zone=self.points[0].zone) normal = svd(M)[0][:, -1] # return ctr, svd(M)[0][:, -1] if normal[2] < 0: normal = - normal return ctr, normal ================================================ FILE: gempy/modules/grids/__init__.py ================================================ ================================================ FILE: gempy/modules/grids/create_topography.py ================================================ """ This file is part of gempy. Created on 16.04.2019 @author: Elisa Heim """ from typing import Optional import numpy as np from ...core.data.grid_modules.topography import _LoadDEMArtificial def create_random_topography(extent: np.array, resolution: np.array, dz: Optional[np.array] = None, fractal_dimension: Optional[float] = 2.0) -> np.array: dem = _LoadDEMArtificial( extent=extent, resolution=resolution, d_z=dz, fd=fractal_dimension ) return dem.get_values() ================================================ FILE: gempy/modules/json_io/__init__.py ================================================ """ JSON I/O module for GemPy. This module provides functionality to load and save GemPy models to/from JSON files. """ from .json_operations import JsonIO __all__ = ['JsonIO'] ================================================ FILE: gempy/modules/json_io/json_operations.py ================================================ """ Module for JSON I/O operations in GemPy. This module provides functionality to load and save GemPy models to/from JSON files. """ import json from typing import Dict, Any, Optional, List import numpy as np from datetime import datetime from .schema import SurfacePoint, Orientation, GemPyModelJson, IdNameMapping from gempy_engine.core.data.stack_relation_type import StackRelationType class JsonIO: """Class for handling JSON I/O operations for GemPy models.""" @staticmethod def _numpy_to_list(obj): """Convert numpy arrays to lists for JSON serialization.""" if isinstance(obj, np.ndarray): return obj.tolist() elif isinstance(obj, (np.integer, np.int32, np.int64)): return int(obj) elif isinstance(obj, (np.floating, np.float32, np.float64)): return float(obj) return obj @staticmethod def _create_id_to_name(name_to_id: Dict[str, int]) -> Dict[int, str]: """Create an id_to_name mapping from a name_to_id mapping.""" return {id: name for name, id in name_to_id.items()} @staticmethod def _calculate_default_range(grid_extent: List[float]) -> float: """Calculate the default range based on the model extent (room diagonal).""" # Extract min and max coordinates x_min, x_max = grid_extent[0], grid_extent[1] y_min, y_max = grid_extent[2], grid_extent[3] z_min, z_max = grid_extent[4], grid_extent[5] # Calculate the room diagonal (Euclidean distance) return np.sqrt((x_max - x_min)**2 + (y_max - y_min)**2 + (z_max - z_min)**2) @staticmethod def _calculate_default_grid_settings(surface_points: List[SurfacePoint], orientations: List[Orientation]) -> Dict[str, Any]: """Calculate default grid settings based on data points. Args: surface_points: List of surface points orientations: List of orientations Returns: Dict containing grid settings with default values """ # Collect all x, y, z coordinates all_x = [sp['x'] for sp in surface_points] + [ori['x'] for ori in orientations] all_y = [sp['y'] for sp in surface_points] + [ori['y'] for ori in orientations] all_z = [sp['z'] for sp in surface_points] + [ori['z'] for ori in orientations] # Calculate extents x_min, x_max = min(all_x), max(all_x) y_min, y_max = min(all_y), max(all_y) z_min, z_max = min(all_z), max(all_z) # Calculate ranges x_range = x_max - x_min y_range = y_max - y_min z_range = z_max - z_min # Add 10% padding to each dimension x_padding = x_range * 0.1 y_padding = y_range * 0.1 z_padding = z_range * 0.1 return { "regular_grid_resolution": [20, 20, 20], "regular_grid_extent": [ x_min - x_padding, x_max + x_padding, y_min - y_padding, y_max + y_padding, z_min - z_padding, z_max + z_padding ] } @staticmethod def load_model_from_json(file_path: str): """ Load a GemPy model from a JSON file. Args: file_path (str): Path to the JSON file Returns: GeoModel: A new GemPy model instance """ # Import here to avoid circular imports from gempy.core.data.geo_model import GeoModel, GeoModelMeta from gempy.core.data.grid import Grid from gempy.core.data.structural_frame import StructuralFrame from gempy_engine.core.data import InterpolationOptions from gempy.API.map_stack_to_surfaces_API import map_stack_to_surfaces with open(file_path, 'r') as f: data = json.load(f) # Validate the JSON data against our schema if not JsonIO._validate_json_schema(data): raise ValueError("Invalid JSON schema") # Get unique surface IDs from surface points and orientations surface_ids = set(sp['id'] for sp in data['surface_points']) surface_ids.update(ori['id'] for ori in data['orientations']) # Create default series if not provided if 'series' not in data: data['series'] = [{ 'name': 'Strat_Series', 'surfaces': [f'surface_{id}' for id in sorted(surface_ids)], 'structural_relation': 'ERODE' }] # Get surface names from series data surface_names = [] for series in data['series']: surface_names.extend(series['surfaces']) # Create ID to name mapping if not provided if 'id_name_mapping' in data: id_to_name = JsonIO._create_id_to_name(data['id_name_mapping']['name_to_id']) else: # Create mapping from series data id_to_name = {i: name for i, name in enumerate(surface_names)} # Create surface points table surface_points_table = JsonIO._load_surface_points(data['surface_points'], id_to_name) # Create orientations table orientations_table = JsonIO._load_orientations(data['orientations'], id_to_name) # Create structural frame structural_frame = StructuralFrame.from_data_tables(surface_points_table, orientations_table) # Get grid settings with defaults if not provided grid_settings = data.get('grid_settings', JsonIO._calculate_default_grid_settings(data['surface_points'], data['orientations'])) # Create grid grid = Grid( resolution=grid_settings['regular_grid_resolution'], extent=grid_settings['regular_grid_extent'] ) # Calculate default range based on model extent # default_range = JsonIO._calculate_default_range(grid_settings['regular_grid_extent']) # set as fixed value default_range = 1.7 # Match standard GemPy default # Create interpolation options with defaults if not provided interpolation_options = InterpolationOptions.from_args( range=data.get('interpolation_options', {}).get('kernel_options', {}).get('range', default_range), c_o=data.get('interpolation_options', {}).get('kernel_options', {}).get('c_o', 10), mesh_extraction=data.get('interpolation_options', {}).get('mesh_extraction', True), number_octree_levels=data.get('interpolation_options', {}).get('number_octree_levels', 1) ) # Create GeoModel with default metadata if not provided current_date = datetime.now().strftime("%Y-%m-%d") model_name = data.get('metadata', {}).get('name', "GemPy Model") model = GeoModel.from_args( name=model_name, structural_frame=structural_frame, grid=grid, interpolation_options=interpolation_options ) # Set the metadata with proper dates and defaults metadata = data.get('metadata', {}) model_meta = GeoModelMeta( name=metadata.get('name', model.meta.name), # Use model's name if available creation_date=metadata.get('creation_date', current_date), # Set current date as default last_modification_date=metadata.get('last_modification_date', current_date), # Set current date as default owner=metadata.get('owner', "GemPy Modeller") # Set default owner ) model.meta = model_meta # Map series to surfaces with structural relations mapping_object = {series['name']: series['surfaces'] for series in data['series']} # Ensure each series has structural_relation set to ERODE by default series_data = [] for series in data['series']: series_copy = series.copy() if 'structural_relation' not in series_copy: series_copy['structural_relation'] = 'ERODE' series_data.append(series_copy) map_stack_to_surfaces(model, mapping_object, series_data=series_data) # Set fault relations after structural groups are set up if 'fault_relations' in data and data['fault_relations'] is not None: fault_relations = np.array(data['fault_relations']) if fault_relations.shape == (len(model.structural_frame.structural_groups), len(model.structural_frame.structural_groups)): model.structural_frame.fault_relations = fault_relations # Set colors for each element for series in data['series']: if 'colors' in series: for surface_name, color in zip(series['surfaces'], series['colors']): element = model.structural_frame.get_element_by_name(surface_name) if element is not None: element.color = color return model @staticmethod def _load_surface_points(surface_points_data: List[SurfacePoint], id_to_name: Optional[Dict[int, str]] = None): """ Load surface points from JSON data. Args: surface_points_data (List[SurfacePoint]): List of surface point dictionaries id_to_name (Optional[Dict[int, str]]): Optional mapping from surface IDs to names Returns: SurfacePointsTable: A new SurfacePointsTable instance Raises: ValueError: If the data is invalid or missing required fields """ # Import here to avoid circular imports from gempy.core.data.surface_points import SurfacePointsTable # Validate data structure required_fields = {'x', 'y', 'z', 'nugget', 'id'} for i, sp in enumerate(surface_points_data): missing_fields = required_fields - set(sp.keys()) if missing_fields: raise ValueError(f"Missing required fields in surface point {i}: {missing_fields}") # Validate data types if not all(isinstance(sp[field], (int, float)) for field in ['x', 'y', 'z', 'nugget']): raise ValueError(f"Invalid data type in surface point {i}. All coordinates and nugget must be numeric.") if not isinstance(sp['id'], int): raise ValueError(f"Invalid data type in surface point {i}. ID must be an integer.") # Extract coordinates and other data x = np.array([sp['x'] for sp in surface_points_data]) y = np.array([sp['y'] for sp in surface_points_data]) z = np.array([sp['z'] for sp in surface_points_data]) nugget = np.array([sp['nugget'] for sp in surface_points_data]) # Handle names based on whether id_to_name mapping is provided if id_to_name is not None: names = [id_to_name.get(sp['id'], f"surface_{sp['id']}") for sp in surface_points_data] else: # If no mapping provided, use surface IDs as names names = [f"surface_{sp['id']}" for sp in surface_points_data] # Create SurfacePointsTable return SurfacePointsTable.from_arrays( x=x, y=y, z=z, names=names, nugget=nugget ) @staticmethod def _load_orientations(orientations_data: List[Orientation], id_to_name: Optional[Dict[int, str]] = None): """ Load orientations from JSON data. Args: orientations_data (List[Orientation]): List of orientation dictionaries id_to_name (Optional[Dict[int, str]]): Optional mapping from surface IDs to names Returns: OrientationsTable: A new OrientationsTable instance Raises: ValueError: If the data is invalid or missing required fields """ # Import here to avoid circular imports from gempy.core.data.orientations import OrientationsTable # Validate data structure required_fields = {'x', 'y', 'z', 'G_x', 'G_y', 'G_z', 'nugget', 'polarity', 'id'} for i, ori in enumerate(orientations_data): missing_fields = required_fields - set(ori.keys()) if missing_fields: raise ValueError(f"Missing required fields in orientation {i}: {missing_fields}") # Validate data types if not all(isinstance(ori[field], (int, float)) for field in ['x', 'y', 'z', 'G_x', 'G_y', 'G_z', 'nugget']): raise ValueError(f"Invalid data type in orientation {i}. All coordinates, gradients, and nugget must be numeric.") if not isinstance(ori.get('polarity', 1), int) or ori.get('polarity', 1) not in {-1, 1}: raise ValueError(f"Invalid polarity in orientation {i}. Must be 1 (normal) or -1 (reverse).") if not isinstance(ori['id'], int): raise ValueError(f"Invalid data type in orientation {i}. ID must be an integer.") # Extract coordinates and other data x = np.array([ori['x'] for ori in orientations_data]) y = np.array([ori['y'] for ori in orientations_data]) z = np.array([ori['z'] for ori in orientations_data]) G_x = np.array([ori['G_x'] for ori in orientations_data]) G_y = np.array([ori['G_y'] for ori in orientations_data]) G_z = np.array([ori['G_z'] for ori in orientations_data]) nugget = np.array([ori['nugget'] for ori in orientations_data]) # Handle names based on whether id_to_name mapping is provided if id_to_name is not None: names = [id_to_name.get(ori['id'], f"surface_{ori['id']}") for ori in orientations_data] else: # If no mapping provided, use surface IDs as names names = [f"surface_{ori['id']}" for ori in orientations_data] # Apply polarity to gradients for i, ori in enumerate(orientations_data): if ori.get('polarity', 1) == -1: G_x[i] *= -1 G_y[i] *= -1 G_z[i] *= -1 # Create OrientationsTable return OrientationsTable.from_arrays( x=x, y=y, z=z, G_x=G_x, G_y=G_y, G_z=G_z, names=names, nugget=nugget ) @staticmethod def save_model_to_json(model, file_path: str) -> None: """ Save a GemPy model to a JSON file. Args: model: The GemPy model to save file_path (str): Path where to save the JSON file """ # Get current date for default values current_date = datetime.now().strftime("%Y-%m-%d") # Create JSON structure with metadata handling json_data = { "metadata": { "name": model.meta.name if model.meta.name is not None else "GemPy Model", "creation_date": model.meta.creation_date if model.meta.creation_date is not None else current_date, "last_modification_date": model.meta.last_modification_date if model.meta.last_modification_date is not None else current_date, "owner": model.meta.owner if model.meta.owner is not None else "GemPy Modeller" }, "surface_points": [], "orientations": [], "series": [], "grid_settings": { "regular_grid_resolution": [int(x) for x in model.grid._dense_grid.resolution], "regular_grid_extent": [float(x) for x in model.grid._dense_grid.extent], "octree_levels": None # TODO: Add octree levels if needed }, "interpolation_options": { "kernel_options": { "range": float(model.interpolation_options.kernel_options.range), "c_o": float(model.interpolation_options.kernel_options.c_o) }, "mesh_extraction": bool(model.interpolation_options.mesh_extraction), "number_octree_levels": int(model.interpolation_options.number_octree_levels) }, "fault_relations": [[int(x) for x in row] for row in model.structural_frame.fault_relations] if hasattr(model.structural_frame, 'fault_relations') else None, "id_name_mapping": { "name_to_id": {k: int(v) for k, v in model.structural_frame.element_name_id_map.items()} } } # Get series and surface information for group in model.structural_frame.structural_groups: series_entry = { "name": str(group.name), "surfaces": [str(element.name) for element in group.elements], "structural_relation": str(group.structural_relation.name), "colors": [str(element.color) for element in group.elements] } json_data["series"].append(series_entry) # Get surface points surface_points_table = model.surface_points_copy xyz_values = surface_points_table.xyz ids = surface_points_table.ids nugget_values = surface_points_table.nugget for i in range(len(xyz_values)): point = { "x": float(xyz_values[i, 0]), "y": float(xyz_values[i, 1]), "z": float(xyz_values[i, 2]), "id": int(ids[i]), "nugget": float(nugget_values[i]) } json_data["surface_points"].append(point) # Get orientations orientations_table = model.orientations_copy ori_xyz_values = orientations_table.xyz ori_grads_values = orientations_table.grads ori_ids = orientations_table.ids ori_nugget_values = orientations_table.nugget for i in range(len(ori_xyz_values)): orientation = { "x": float(ori_xyz_values[i, 0]), "y": float(ori_xyz_values[i, 1]), "z": float(ori_xyz_values[i, 2]), "G_x": float(ori_grads_values[i, 0]), "G_y": float(ori_grads_values[i, 1]), "G_z": float(ori_grads_values[i, 2]), "id": int(ori_ids[i]), "nugget": float(ori_nugget_values[i]), "polarity": 1 # Default value, update if available } json_data["orientations"].append(orientation) # Save to file with open(file_path, 'w') as f: json.dump(json_data, f, indent=4) @staticmethod def _validate_json_schema(data: Dict) -> None: """Validate the JSON schema and set default values.""" # Required top-level keys required_keys = ['surface_points', 'orientations', 'grid_settings'] for key in required_keys: if key not in data: raise ValueError(f"Missing required key: {key}") # Validate surface points for point in data['surface_points']: required_point_keys = ['x', 'y', 'z'] for key in required_point_keys: if key not in point: raise ValueError(f"Missing required key in surface point: {key}") # Set default nugget if not provided if 'nugget' not in point: point['nugget'] = 0.0 # Default nugget for surface points # Set default id if not provided if 'id' not in point: point['id'] = 0 # Default id # Validate orientations for orientation in data['orientations']: required_orientation_keys = ['x', 'y', 'z', 'G_x', 'G_y', 'G_z'] for key in required_orientation_keys: if key not in orientation: raise ValueError(f"Missing required key in orientation: {key}") # Set default nugget if not provided if 'nugget' not in orientation: orientation['nugget'] = 0.01 # Default nugget for orientations # Set default polarity if not provided if 'polarity' not in orientation: orientation['polarity'] = 1 # Set default id if not provided if 'id' not in orientation: orientation['id'] = 0 # Default id # Validate grid settings grid_settings = data['grid_settings'] required_grid_keys = ['regular_grid_resolution', 'regular_grid_extent'] for key in required_grid_keys: if key not in grid_settings: raise ValueError(f"Missing required key in grid_settings: {key}") # Validate series if provided if 'series' in data: for series in data['series']: required_series_keys = ['name', 'surfaces'] for key in required_series_keys: if key not in series: raise ValueError(f"Missing required key in series: {key}") # Set default structural relation if not provided if 'structural_relation' not in series: series['structural_relation'] = "ERODE" # Validate colors if provided if 'colors' in series: if not isinstance(series['colors'], list): raise ValueError("Colors must be a list") if not all(isinstance(color, str) and color.startswith('#') for color in series['colors']): raise ValueError("Colors must be hex color codes starting with #") # Validate interpolation options if present if 'interpolation_options' in data: if not isinstance(data['interpolation_options'], dict): raise ValueError("Interpolation options must be a dictionary") if 'kernel_options' in data['interpolation_options']: kernel_options = data['interpolation_options']['kernel_options'] if not isinstance(kernel_options, dict): raise ValueError("Kernel options must be a dictionary") if 'range' in kernel_options and not isinstance(kernel_options['range'], (int, float)): raise ValueError("Kernel range must be a number") if 'c_o' in kernel_options and not isinstance(kernel_options['c_o'], (int, float)): raise ValueError("Kernel c_o must be a number") if 'mesh_extraction' in data['interpolation_options'] and not isinstance(data['interpolation_options']['mesh_extraction'], bool): raise ValueError("Mesh extraction must be a boolean") if 'number_octree_levels' in data['interpolation_options'] and not isinstance(data['interpolation_options']['number_octree_levels'], int): raise ValueError("Number of octree levels must be an integer") # Validate and set default metadata if 'metadata' not in data: data['metadata'] = {} metadata = data['metadata'] if not isinstance(metadata, dict): raise ValueError("Metadata must be a dictionary") # Set default values for metadata current_date = datetime.now().strftime("%Y-%m-%d") if 'name' not in metadata or metadata['name'] is None: metadata['name'] = "GemPy Model" elif not isinstance(metadata['name'], str): raise ValueError("Metadata name must be a string") if 'creation_date' not in metadata or metadata['creation_date'] is None: metadata['creation_date'] = current_date elif not isinstance(metadata['creation_date'], str): raise ValueError("Metadata creation_date must be a string") if 'last_modification_date' not in metadata or metadata['last_modification_date'] is None: metadata['last_modification_date'] = current_date elif not isinstance(metadata['last_modification_date'], str): raise ValueError("Metadata last_modification_date must be a string") if 'owner' not in metadata or metadata['owner'] is None: metadata['owner'] = "GemPy Modeller" elif not isinstance(metadata['owner'], str): raise ValueError("Metadata owner must be a string") return True ================================================ FILE: gempy/modules/json_io/schema.py ================================================ """ Schema definitions for JSON I/O operations in GemPy. This module defines the expected structure of JSON files for loading and saving GemPy models. """ from typing import TypedDict, List, Dict, Any, Optional, Union, Sequence try: from typing import NotRequired except ImportError: from typing_extensions import NotRequired # Fallback for older Python versions class SurfacePoint(TypedDict): x: float y: float z: float id: int nugget: float class Orientation(TypedDict): x: float y: float z: float G_x: float # X component of the gradient G_y: float # Y component of the gradient G_z: float # Z component of the gradient id: int nugget: float polarity: int # 1 for normal, -1 for reverse class Surface(TypedDict, total=False): name: str # Required id: NotRequired[int] # Optional, will be auto-generated if not provided color: NotRequired[Optional[str]] # Optional hex color code vertices: NotRequired[Optional[List[List[float]]]] # Optional list of [x, y, z] coordinates class Fault(TypedDict, total=False): name: str # Required id: NotRequired[int] # Optional, will be auto-generated is_active: NotRequired[bool] # Optional, defaults to True surface: Surface class Series(TypedDict, total=False): name: str # Required surfaces: Union[List[str], List[Surface]] # Required, can be list of names or Surface objects id: NotRequired[int] # Optional, will be auto-generated is_active: NotRequired[bool] # Optional, defaults to True is_fault: NotRequired[bool] # Optional, defaults to False order_series: NotRequired[int] # Optional, will be auto-generated faults: NotRequired[List[Fault]] # Optional, defaults to empty list structural_relation: NotRequired[str] # Optional, defaults to "ONLAP" colors: NotRequired[Optional[List[str]]] # Optional class GridSettings(TypedDict, total=False): regular_grid_resolution: NotRequired[List[int]] # Optional, defaults to [10, 10, 10] regular_grid_extent: NotRequired[List[float]] # Optional, auto-calculated from data octree_levels: NotRequired[Optional[int]] # Optional class ModelMetadata(TypedDict, total=False): name: NotRequired[str] # Optional, defaults to "GemPy Model" creation_date: NotRequired[str] # Optional, defaults to current date last_modification_date: NotRequired[str] # Optional, defaults to current date owner: NotRequired[Optional[str]] # Optional, defaults to "GemPy Team" class IdNameMapping(TypedDict, total=False): name_to_id: Dict[str, int] # Required if id_name_mapping is provided class GemPyModelJson(TypedDict): surface_points: List[SurfacePoint] # Required orientations: List[Orientation] # Required series: List[Series] # Required but with minimal required fields metadata: NotRequired[ModelMetadata] # Optional grid_settings: NotRequired[Optional[GridSettings]] # Optional interpolation_options: NotRequired[Optional[Dict[str, Any]]] # Optional id_name_mapping: NotRequired[IdNameMapping] # Optional ================================================ FILE: gempy/modules/mesh_extranction/__init__.py ================================================ ================================================ FILE: gempy/modules/mesh_extranction/marching_cubes.py ================================================ import os import numpy as np from typing import Optional from skimage import measure from gempy_engine.core.data.interp_output import InterpOutput from gempy_engine.core.data.raw_arrays_solution import RawArraysSolution from gempy.core.data import GeoModel, StructuralElement, StructuralGroup from gempy.core.data.grid_modules import RegularGrid def set_meshes_with_marching_cubes(model: GeoModel) -> None: """Extract meshes for all structural elements using the marching cubes algorithm. Parameters ---------- model : GeoModel The geological model containing solutions and structural elements. Raises ------ ValueError If the model solutions do not contain dense grid data. """ # Verify that solutions contain dense grid data solution_not_having_dense: bool = model.solutions.block_solution_type != RawArraysSolution.BlockSolutionType.DENSE_GRID if model.solutions is None or solution_not_having_dense: raise ValueError("Model solutions must contain dense grid data for mesh extraction.") regular_grid: RegularGrid = model.grid.regular_grid structural_groups: list[StructuralGroup] = model.structural_frame.structural_groups if not model.solutions.octrees_output or not model.solutions.octrees_output[0].outputs: raise ValueError("No interpolation outputs available for mesh extraction.") output_lvl0: list[InterpOutput] = model.solutions.octrees_output[0].outputs for e, structural_group in enumerate(structural_groups): if e >= len(output_lvl0): continue output_group: InterpOutput = output_lvl0[e] scalar_field_matrix = output_group.exported_fields_dense_grid.scalar_field if structural_group.is_fault is False: slice_: slice = output_group.grid.dense_grid_slice mask = output_group.combined_scalar_field.squeezed_mask_array[slice_] else: mask = np.ones_like(scalar_field_matrix, dtype=bool) for element in structural_group.elements: extract_mesh_for_element( structural_element=element, regular_grid=regular_grid, scalar_field=scalar_field_matrix, mask=mask ) def extract_mesh_for_element(structural_element: StructuralElement, regular_grid: RegularGrid, scalar_field: np.ndarray, mask: Optional[np.ndarray] = None) -> None: """Extract a mesh for a single structural element using marching cubes. Parameters ---------- structural_element : StructuralElement The structural element for which to extract a mesh. regular_grid : RegularGrid The regular grid defining the spatial discretization. scalar_field : np.ndarray The scalar field used for isosurface extraction. mask : np.ndarray, optional Optional mask to restrict the mesh extraction to specific regions. """ if type(scalar_field).__module__ == 'torch': import torch scalar_field = scalar_field.detach().numpy() if type(mask).__module__ == "torch": import torch mask = torch.to_numpy(mask) # Extract mesh using marching cubes verts, faces, _, _ = measure.marching_cubes( volume=scalar_field.reshape(regular_grid.resolution), level=structural_element.scalar_field_at_interface, spacing=(regular_grid.dx, regular_grid.dy, regular_grid.dz), mask=mask.reshape(regular_grid.resolution) if mask is not None else None, allow_degenerate=False, method="lewiner" ) # Adjust vertices to correct coordinates in the model's extent verts = (verts + [regular_grid.extent[0], regular_grid.extent[2], regular_grid.extent[4]]) # Store mesh in the structural element structural_element.vertices = verts structural_element.edges = faces ================================================ FILE: gempy/modules/optimize_nuggets/__init__.py ================================================ from ._optimizer import nugget_optimizer ================================================ FILE: gempy/modules/optimize_nuggets/_ops.py ================================================ import gempy_engine from gempy.optional_dependencies import require_torch from gempy_engine.core.data.continue_epoch import ContinueEpoch def run_optimization(lr, max_epochs, min_impr, model, nugget, patience, target_cond_num): torch = require_torch() opt = torch.optim.Adam( params=[ nugget, ], lr=lr ) prev_cond = float('inf') for epoch in range(max_epochs): opt.zero_grad() try: gempy_engine.compute_model( interpolation_input=model.taped_interpolation_input, options=model.interpolation_options, data_descriptor=model.input_data_descriptor, geophysics_input=model.geophysics_input, ) except ContinueEpoch: if True: # Keep only top 10% gradients _gradient_masking(nugget, focus=0.01) else: if epoch % 1 == 0: mask_sp = _mask_iqr(nugget.grad.abs().view(-1), multiplier=3) print(f"Outliers sp: {torch.nonzero(mask_sp)}") _apply_outlier_gradients(tensor=nugget, mask=mask_sp) # Step & clamp safely opt.step() with torch.no_grad(): nugget.clamp_(min=1e-7) # Evaluate condition number cur_cond = model.interpolation_options.kernel_options.condition_number print(f"[Epoch {epoch}] cond. num. = {cur_cond:.2e}") if _has_converged(cur_cond, prev_cond, target_cond_num, epoch, min_impr, patience): break prev_cond = cur_cond # Condition number to numpy model.interpolation_options.kernel_options.condition_number = model.interpolation_options.kernel_options.condition_number.detach().numpy() return nugget def _mask_iqr(grads, multiplier: float = 1.5) -> "torch.BoolTensor": q1, q3 = grads.quantile(0.25), grads.quantile(0.75) thresh = q3 + multiplier * (q3 - q1) return grads > thresh def _apply_outlier_gradients( tensor: "torch.Tensor", mask: "torch.BoolTensor", amplification: float = 1.0, ): # wrap in no_grad if you prefer, but .grad modifications are fine tensor.grad.view(-1)[mask] *= amplification tensor.grad.view(-1)[~mask] = 0 def _gradient_masking(nugget, focus=0.01): """Old way of avoiding exploding gradients.""" torch = require_torch() grads = nugget.grad.abs() k = int(grads.numel() * focus) top_vals, top_idx = torch.topk(grads, k, largest=True) mask = torch.zeros_like(grads) mask[top_idx] = 1 nugget.grad.mul_(mask) def _has_converged( current: float, previous: float, target: float = 1e5, epoch: int = 0, min_improvement: float = 0.01, patience: int = 10, ) -> bool: if current < target: return True if epoch > patience: # relative improvement rel_impr = abs(current - previous) / max(previous, 1e-8) return rel_impr < min_improvement return False ================================================ FILE: gempy/modules/optimize_nuggets/_optimizer.py ================================================ from gempy_engine.core.backend_tensor import BackendTensor from gempy_engine.core.data.interpolation_input import InterpolationInput from ._ops import run_optimization from ..data_manipulation.manipulate_points import modify_surface_points from ...core.data import GeoModel, GemPyEngineConfig, StructuralGroup from ...modules.data_manipulation import interpolation_input_from_structural_frame def nugget_optimizer( target_cond_num: float, engine_cfg: GemPyEngineConfig, model: GeoModel, max_epochs: int, only_groups: list[StructuralGroup] | None = None, lr: float = .01, patience: int = 10, min_impr: float = 0.01, ) -> GeoModel: """ Optimize the nugget effect scalar to achieve a target condition number. Returns the final nugget effect value. """ # 1) Backend setup (ideally done once, not every call) BackendTensor.change_backend_gempy( engine_backend=engine_cfg.backend, use_gpu=engine_cfg.use_gpu, dtype=engine_cfg.dtype, grads=True ) # 2) Prepare data all_groups: list[StructuralGroup] = model.structural_frame.structural_groups if only_groups is not None: groups_to_optimize = only_groups else: groups_to_optimize: list[StructuralGroup] = all_groups for group in groups_to_optimize: model.structural_frame.structural_groups = [group] interp_in: InterpolationInput = interpolation_input_from_structural_frame(model) model.taped_interpolation_input = interp_in nugget: "torch.Tensor" = interp_in.surface_points.nugget_effect_scalar nugget.requires_grad_(True) model.interpolation_options.kernel_options.optimizing_condition_number = True nuggets = run_optimization(lr, max_epochs, min_impr, model, nugget, patience, target_cond_num) modify_surface_points( geo_model=model, nugget=nuggets.detach().numpy() ) model.interpolation_options.kernel_options.optimizing_condition_number = False model.structural_frame.structural_groups = all_groups return model ================================================ FILE: gempy/modules/serialization/__init__.py ================================================ ================================================ FILE: gempy/modules/serialization/save_load.py ================================================ import re import warnings from ...core.data import GeoModel from ...core.data.encoders.converters import loading_model_from_binary from ...optional_dependencies import require_zlib import pathlib import os import io import zipfile def save_model(model: GeoModel, path: str | None = None, validate_serialization: bool = True): """ Save a GeoModel to a file with proper extension validation. Parameters: ----------- model : GeoModel The geological model to save path : str The file path where to save the model Raises: ------- ValueError If the file has an extension other than .gempy """ # Warning about preview warnings.warn("This function is still in development. It may not work as expected.") # Define the valid extension for gempy models VALID_EXTENSION = ".gempy" if path is None: path = model.meta.name + VALID_EXTENSION # Check if path has an extension path_obj = pathlib.Path(path) if path_obj.suffix: # If extension exists but is not valid, raise error if path_obj.suffix.lower() != VALID_EXTENSION: raise ValueError(f"Invalid file extension: {path_obj.suffix}. Expected: {VALID_EXTENSION}") else: # If no extension, add the valid extension path = str(path_obj) + VALID_EXTENSION binary_file = model_to_bytes(model) if validate_serialization: model_deserialized = _load_model_from_bytes(binary_file) _validate_serialization(model, model_deserialized) # Create directory if it doesn't exist directory = os.path.dirname(path) if directory and not os.path.exists(directory): os.makedirs(directory) with open(path, 'wb') as f: f.write(binary_file) return path # Return the actual path used (helpful if extension was added) def model_to_binary(model: GeoModel) -> bytes: # Compress the binary data zlib = require_zlib() compressed_binary_input = zlib.compress(model.structural_frame.input_tables_binary) compressed_binary_grid = zlib.compress(model.grid.grid_binary) compressed_binary_grid = zlib.compress(model.grid.grid_binary, level=6) import hashlib print("len raw bytes:", len(model.grid.grid_binary)) print("raw bytes hash:", hashlib.sha256(model.grid.grid_binary).hexdigest()) print("compressed length:", len(compressed_binary_grid)) print("zlib version:", zlib.ZLIB_VERSION) # * Add here the serialization meta parameters like: len_bytes model.structural_frame._input_binary_size = len(compressed_binary_input) model.grid._grid_binary_size = len(compressed_binary_grid) model_json = model.model_dump_json(by_alias=True, indent=4) binary_file = _to_binary( header_json=model_json, body_input=compressed_binary_input, body_grid=compressed_binary_grid ) return binary_file def load_model(path: str) -> GeoModel: """ Load a GeoModel from a file with extension validation. Parameters: ----------- path : str Path to the gempy model file Returns: -------- GeoModel The loaded geological model Raises: ------- ValueError If the file doesn't have the proper .gempy extension FileNotFoundError If the file doesn't exist """ # Warning about preview warnings.warn("This function is still in development. It may not work as expected.") VALID_EXTENSION = ".gempy" # Check if path has the valid extension path_obj = pathlib.Path(path) if not path_obj.suffix or path_obj.suffix.lower() != VALID_EXTENSION: raise ValueError(f"Invalid file extension: {path_obj.suffix}. Expected: {VALID_EXTENSION}") # Check if file exists if not os.path.exists(path): raise FileNotFoundError(f"File not found: {path}") with open(path, 'rb') as f: binary_file = f.read() return _load_model_from_bytes(binary_file) def model_to_bytes(model: GeoModel) -> bytes: # 1) Make a fully deterministic JSON header # header_dict = model.model_dump(by_alias=True) # header_json = json.dumps( # header_dict, # sort_keys=True, # always sort object keys # separators=(",", ":"), # no extra whitespace # ).encode("utf-8") header_json = model.model_dump_json(by_alias=True, indent=4) # 2) Raw binary chunks (no additional zlib.compress here) input_raw = model.structural_frame.input_tables_binary grid_raw = model.grid.grid_binary # 3) Pack into a ZIP archive in a fixed order: buf = io.BytesIO() with zipfile.ZipFile( buf, mode="w", compression=zipfile.ZIP_DEFLATED, compresslevel=6 ) as zf: # Force a fixed timestamp (1980-01-01) so the file headers don't vary def make_info(name): zi = zipfile.ZipInfo(name, date_time=(1980,1,1,0,0,0)) zi.external_attr = 0 # clear OS-specific file permissions return zi zf.writestr(make_info("header.json"), header_json) zf.writestr(make_info("input.bin"), input_raw) zf.writestr(make_info("grid.bin"), grid_raw) return buf.getvalue() def _load_model_from_bytes(data: bytes) -> GeoModel: from ...core.data.encoders.converters import loading_model_from_binary buf = io.BytesIO(data) with zipfile.ZipFile(buf, "r") as zf: header_json = zf.read("header.json").decode("utf-8") # header = json.loads(header_json) input_raw = zf.read("input.bin") grid_raw = zf.read("grid.bin") # If you want to validate or decompress further, do it here… with loading_model_from_binary( input_binary=input_raw, grid_binary= grid_raw ): model = GeoModel.model_validate_json(header_json) return model def _deserialize_binary_file(binary_file): import json # Get header length from first 4 bytes header_length = int.from_bytes(binary_file[:4], byteorder='little') # Split header and body header_json= binary_file[4:4 + header_length].decode('utf-8') header = json.loads(header_json) input_metadata = header["structural_frame"]["binary_meta_data"] input_size = input_metadata["input_binary_size"] grid_metadata = header["grid"]["binary_meta_data"] grid_size = grid_metadata["grid_binary_size"] input_binary = binary_file[4 + header_length: 4 + header_length + input_size] all_sections_length = 4 + header_length + input_size + grid_size if all_sections_length != len(binary_file): raise ValueError("Binary file is corrupted") grid_binary = binary_file[4 + header_length + input_size: all_sections_length] zlib = require_zlib() with loading_model_from_binary( input_binary=(zlib.decompress(input_binary)), grid_binary=(zlib.decompress(grid_binary)) ): model = GeoModel.model_validate_json(header_json) return model def _to_binary(header_json, body_input, body_grid) -> bytes: header_json_bytes = header_json.encode('utf-8') header_json_length = len(header_json_bytes) header_json_length_bytes = header_json_length.to_bytes(4, byteorder='little') file = header_json_length_bytes + header_json_bytes + body_input + body_grid return file def _validate_serialization(original_model, model_deserialized): a = hash(original_model.structural_frame.surface_points_copy.data.tobytes()) b = hash(model_deserialized.structural_frame.surface_points_copy.data.tobytes()) o_a = hash(original_model.structural_frame.orientations_copy.data.tobytes()) o_b = hash(model_deserialized.structural_frame.orientations_copy.data.tobytes()) assert a == b, "Hashes for surface points are not equal" assert o_a == o_b, "Hashes for orientations are not equal" original_model___str__ = re.sub(r'\s+', ' ', original_model.__str__()) deserialized___str__ = re.sub(r'\s+', ' ', model_deserialized.__str__()) if original_model___str__ != deserialized___str__: # Find first char that is not the same for i in range(min(len(original_model___str__), len(deserialized___str__))): if original_model___str__[i] != deserialized___str__[i]: break print(f"First difference at index {i}:") i1 = 10 print(f"Original: {original_model___str__[i - i1:i + i1]}") print(f"Deserialized: {deserialized___str__[i - i1:i + i1]}") assert deserialized___str__ == original_model___str__ ================================================ FILE: gempy/optional_dependencies.py ================================================ def require_pandas(): try: import pandas as pd except ImportError: raise ImportError("The pandas library is required to use this function.") return pd def require_pooch(): try: import pooch except ImportError: raise ImportError("The pooch library is required to use this function.") return pooch def require_gempy_legacy(): try: import gempy_legacy except ImportError: raise ImportError("The gempy_legacy library is required to use this function.") return gempy_legacy def require_gempy_viewer(): try: import gempy_viewer except ImportError: raise ImportError("The gempy_viewer package is required to run this function.") return gempy_viewer def require_matplotlib(): try: import matplotlib.pyplot as plt except ImportError: raise ImportError("The matplotlib package is required to run this function.") return plt def require_skimage(): try: import skimage except ImportError: raise ImportError("The skimage package is required to run this function.") return skimage def require_scipy(): try: import scipy except ImportError: raise ImportError("The scipy package is required to run this function.") return scipy def require_subsurface(): try: import subsurface except ImportError: raise ImportError("The subsurface package is required to run this function.") return subsurface def require_zlib(): try: import zlib except ImportError: raise ImportError("The zlib package is required to run this function.") return zlib def require_torch(): try: import torch except ImportError: raise ImportError("The torch package is required to run this function.") return torch ================================================ FILE: requirements/base-requirements.txt ================================================ -r requirements.txt # This install matplotlib gempy_viewer~=2025.1.4 pandas>=2.2.0,<3.0.0 ================================================ FILE: requirements/dev-requirements.txt ================================================ -r optional-requirements.txt pytest pyvista subsurface-terra wellpathpy torch # Testing pytest-approvaltests ================================================ FILE: requirements/docs_requirements.txt ================================================ -r dev-requirements.txt torch rasterio sphinx sphinx-gallery sphinx_design sphinx_automodapi liquid-earth-sdk wellpathpy openpyxl ================================================ FILE: requirements/optional-requirements.txt ================================================ -r base-requirements.txt gempy_plugins # for data download pooch scipy scikit-image ================================================ FILE: requirements/requirements.txt ================================================ # This install also numpy gempy_engine>=2026.0.1dev0,<2026.1.0 ================================================ FILE: scripts/build_docs.sh ================================================ #!/bin/bash source ~/.virtualenvs/gempy_dependencies/bin/activate cd ../docs || exit #make clean make html cd - || exit ================================================ FILE: scripts/deploy.sh ================================================ #!/usr/bin/env bash set -o errexit #abort if any command fails me=$(basename "$0") help_message="\ Usage: $me [-c FILE] [] Deploy generated files to a git branch. Options: -h, --help Show this help information. -v, --verbose Increase verbosity. Useful for debugging. -e, --allow-empty Allow deployment of an empty directory. -m, --message MESSAGE Specify the message used when committing on the deploy branch. -n, --no-hash Don't append the source commit's hash to the deploy commit's message. -c, --config-file PATH Override default & environment variables' values with those in set in the file at 'PATH'. Must be the first option specified. Variables: GIT_DEPLOY_DIR Folder path containing the files to deploy. GIT_DEPLOY_BRANCH Commit deployable files to this branch. GIT_DEPLOY_REPO Push the deploy branch to this repository. These variables have default values defined in the script. The defaults can be overridden by environment variables. Any environment variables are overridden by values set in a '.env' file (if it exists), and in turn by those set in a file specified by the '--config-file' option." echo "docs.gempy.org" > docs/build/html/CNAME parse_args() { # Set args from a local environment file. if [ -e ".env" ]; then source .env fi # Set args from file specified on the command-line. if [[ $1 = "-c" || $1 = "--config-file" ]]; then source "$2" shift 2 fi # Parse arg flags # If something is exposed as an environment variable, set/overwrite it # here. Otherwise, set/overwrite the internal variable instead. while : ; do if [[ $1 = "-h" || $1 = "--help" ]]; then echo "$help_message" return 0 elif [[ $1 = "-v" || $1 = "--verbose" ]]; then verbose=true shift elif [[ $1 = "-e" || $1 = "--allow-empty" ]]; then allow_empty=true shift elif [[ ( $1 = "-m" || $1 = "--message" ) && -n $2 ]]; then commit_message=$2 shift 2 elif [[ $1 = "-n" || $1 = "--no-hash" ]]; then GIT_DEPLOY_APPEND_HASH=false shift else break fi done # Set internal option vars from the environment and arg flags. All internal # vars should be declared here, with sane defaults if applicable. # Source directory & target branch. deploy_directory=${GIT_DEPLOY_DIR:-docs/build/html} deploy_branch=${GIT_DEPLOY_BRANCH:-gh-pages} #if no user identity is already set in the current git environment, use this: default_username=${GIT_DEPLOY_USERNAME:-leguark} default_email=${GIT_DEPLOY_EMAIL:-miguel@terranigma-solutions.com} #repository to deploy to. must be readable and writable. repo=${GIT_DEPLOY_REPO:-origin} #append commit hash to the end of message by default append_hash=${GIT_DEPLOY_APPEND_HASH:-true} } main() { parse_args "$@" enable_expanded_output if ! git diff --exit-code --quiet --cached; then echo Aborting due to uncommitted changes in the index >&2 return 1 fi commit_title=`git log -n 1 --format="%s" HEAD` commit_hash=` git log -n 1 --format="%H" HEAD` #default commit message uses last title if a custom one is not supplied if [[ -z $commit_message ]]; then commit_message="publish: $commit_title" fi #append hash to commit message unless no hash flag was found if [ $append_hash = true ]; then commit_message="$commit_message"$'\n\n'"generated from commit $commit_hash" fi previous_branch=`git rev-parse --abbrev-ref HEAD` if [ ! -d "$deploy_directory" ]; then echo "Deploy directory '$deploy_directory' does not exist. Aborting." >&2 return 1 fi # must use short form of flag in ls for compatibility with OS X and BSD if [[ -z `ls -A "$deploy_directory" 2> /dev/null` && -z $allow_empty ]]; then echo "Deploy directory '$deploy_directory' is empty. Aborting. If you're sure you want to deploy an empty tree, use the --allow-empty / -e flag." >&2 return 1 fi if git ls-remote --exit-code $repo "refs/heads/$deploy_branch" ; then # deploy_branch exists in $repo; make sure we have the latest version disable_expanded_output git fetch --force $repo $deploy_branch:$deploy_branch enable_expanded_output fi # check if deploy_branch exists locally if git show-ref --verify --quiet "refs/heads/$deploy_branch" then incremental_deploy else initial_deploy fi restore_head } initial_deploy() { git --work-tree "$deploy_directory" checkout --orphan $deploy_branch git --work-tree "$deploy_directory" add --all commit+push } incremental_deploy() { #make deploy_branch the current branch git symbolic-ref HEAD refs/heads/$deploy_branch #put the previously committed contents of deploy_branch into the index git --work-tree "$deploy_directory" reset --mixed --quiet git --work-tree "$deploy_directory" add --all set +o errexit diff=$(git --work-tree "$deploy_directory" diff --exit-code --quiet HEAD --)$? set -o errexit case $diff in 0) echo No changes to files in $deploy_directory. Skipping commit.;; 1) commit+push;; *) echo git diff exited with code $diff. Aborting. Staying on branch $deploy_branch so you can debug. To switch back to master, use: git symbolic-ref HEAD refs/heads/master && git reset --mixed >&2 return $diff ;; esac } commit+push() { set_user_id git --work-tree "$deploy_directory" commit -m "$commit_message" disable_expanded_output #--quiet is important here to avoid outputting the repo URL, which may contain a secret token git push --quiet $repo $deploy_branch enable_expanded_output } #echo expanded commands as they are executed (for debugging) enable_expanded_output() { if [ $verbose ]; then set -o xtrace set +o verbose fi } #this is used to avoid outputting the repo URL, which may contain a secret token disable_expanded_output() { if [ $verbose ]; then set +o xtrace set -o verbose fi } set_user_id() { if [[ -z `git config user.name` ]]; then git config user.name "$default_username" fi if [[ -z `git config user.email` ]]; then git config user.email "$default_email" fi } restore_head() { if [[ $previous_branch = "HEAD" ]]; then #we weren't on any branch before, so just set HEAD back to the commit it was on git update-ref --no-deref HEAD $commit_hash $deploy_branch else git symbolic-ref HEAD refs/heads/$previous_branch fi git reset --mixed } filter() { sed -e "s|$repo|\$repo|g" } sanitize() { "$@" 2> >(filter 1>&2) | filter } [[ $1 = --source-only ]] || main "$@" ================================================ FILE: scripts/update_doc_elements.sh ================================================ #!/bin/bash # #!/bin/bash # sets the working directory to the current directory cd './notebooks/tutorials' # converts all Jupyter Notebook files to basic html for f in *.ipynb; do jupyter nbconvert --to rst --output-dir ../../docs/source/_notebooks $f; done cd ../../docs make html ================================================ FILE: setup.cfg ================================================ [metadata] description-file = README.md ================================================ FILE: setup.py ================================================ import os from os import path from setuptools import setup, find_packages def read_requirements(file_name, base_path=""): # Construct the full path to the requirements file full_path = os.path.join(base_path, file_name) requirements = [] with open(full_path, "r", encoding="utf-8") as f: for line in f: # Strip whitespace and ignore comments line = line.strip() if line.startswith("#") or not line: continue # Handle -r directive if line.startswith("-r "): referenced_file = line.split()[1] # Extract the file name # Recursively read the referenced file, making sure to include the base path requirements.extend(read_requirements(referenced_file, base_path=base_path)) else: requirements.append(line) return requirements with open("README.md", "r") as fh: long_description = fh.read() setup( name='gempy', packages=find_packages(exclude=('test', 'docs', 'examples')), install_requires=read_requirements("requirements.txt", "requirements"), extras_require={ "opt": read_requirements("optional-requirements.txt", "requirements"), "base": read_requirements("base-requirements.txt", "requirements"), }, url='https://github.com/cgre-aachen/gempy', license='EUPL-1.2', author='Miguel de la Varga, Alexander Zimmerman, Elisa Heim, Alexander Schaaf, Fabian Stamm, Florian Wellmann, Jan Niederau, Andrew Annex', author_email='gempy@terranigma-solutions.com', description='An Open-source, Python-based 3-D structural geological modeling software.', long_description=long_description, long_description_content_type='text/markdown', keywords=['geology', '3-D modeling', 'structural geology', 'uncertainty'], classifiers=[ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "License :: OSI Approved :: European Union Public Licence 1.2 (EUPL 1.2)", "Operating System :: OS Independent", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", ], setup_requires=['setuptools_scm'], use_scm_version={ "root" : ".", "relative_to" : __file__, "write_to" : path.join("gempy", "_version.py"), "fallback_version": "3.0.0" }, ) ================================================ FILE: test/Test Results - pytest_in_test.xml ================================================ Setting kriging parameters to their default values. Compiling theano function... Level of Optimization: fast_run Device: cpu Precision: float64 Number of faults: 0 Compilation Done! Kriging values: values range 0.0 $C_o$ 0.0 drift equations [3] Active grids: ['regular'] Active grids: ['regular' 'topography'] Setting kriging parameters to their default values. PASSED [ 0%] surface ... color 2 rock3 ... #ffbe00 1 rock2 ... #9f0052 0 rock1 ... #015482 [3 rows x 4 columns] PASSED [ 1%] PASSED [ 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Downloading data from 'https://github.com/cgre-aachen/gempy_data/raw/master/data/gempy_models/combination.zip' to file '/home/miguel/.cache/pooch/b3eaa3b88746691a91bb91f922f3b338-combination.zip'. SHA256 hash of downloaded file: 8f2e02fa778ce50c54bb7afa18ebd9d7063b0deb893062fca81f4827feee68c1 Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future. Active grids: ['regular'] Fault colors changed. If you do not like this behavior, set change_color to False. PASSED [ 3%] PASSED [ 4%]Active grids: ['regular'] PASSED [ 5%]Active grids: ['regular'] Fault colors changed. If you do not like this behavior, set change_color to False. PASSED [ 5%]Active grids: ['regular'] Fault colors changed. If you do not like this behavior, set change_color to False. PASSED [ 6%]Active grids: ['regular'] Setting kriging parameters to their default values. Compiling theano function... Level of Optimization: fast_run Device: cpu Precision: float64 Number of faults: 0 Compilation Done! Kriging values: values range 0.0 $C_o$ 0.0 drift equations [3] PASSED [ 7%]Active grids: ['regular'] Active grids: ['regular' 'centered'] Setting kriging parameters to their default values. values Structure isLith True isFault False number faults 0 number surfaces 2 number series 1 number surfaces per series [2] len surfaces surface_points [2, 2] len series surface_points [4] len series orientations [1] Options dtype float64 output geology theano_optimizer fast_compile device cpu verbosity None Kriging range 13.266499 $C_o$ 4.190476 drift equations [3] Rescaling rescaling factor 12.0 centers [6.0, 0.0, 2.51] [-1624.17138237] PASSED [ 7%] Setting kriging parameters to their default values. Compiling theano function... Level of Optimization: fast_run Device: cpu Precision: float64 Number of faults: 0 Compilation Done! Kriging values: values range 0.0 $C_o$ 0.0 drift equations [3] PASSED [ 8%]Active grids: ['regular'] Active grids: ['regular' 'centered'] Setting kriging parameters to their default values. 125 [473.78364798] Active grids: ['centered'] Setting kriging parameters to their default values. PASSED [ 8%]Active grids: ['centered'] 0 Active grids: ['regular'] Fault colors changed. If you do not like this behavior, set change_color to False. Setting kriging parameters to their default values. PASSED [ 9%] PASSED [ 10%] Active grids: ['regular'] Setting kriging parameters to their default values. PASSED [ 10%] PASSED [ 11%] PASSED [ 12%]Active grids: ['regular'] SKIPPED (Expensive test. It should be ran manually.) [ 12%] SKIPPED (Private data missing from the repo) [ 13%] PASSED [ 14%] order_series BottomRelation isActive foo3 1 Erosion False boo 2 Erosion False foo7 3 Erosion False foo5 4 Erosion False foo20 5 Erosion False surface series order_surfaces color id 0 foo foo20 1 #015482 1 1 foo2 foo20 2 #9f0052 2 2 foo5 foo20 3 #ffbe00 3 3 feeeee foo20 4 #728f02 4 0 foo 1 foo2 2 foo5 3 feeeee Name: surface, dtype: object surface series order_surfaces color id value_0 0 foo foo20 1 #015482 1 2 1 foo2 foo20 2 #9f0052 2 2 2 foo5 foo20 3 #ffbe00 3 2 3 feeeee foo20 4 #728f02 4 5 surface series order_surfaces color id value_0 val_foo val2_foo 0 foo foo20 1 #015482 1 2 2 2 1 foo2 foo20 2 #9f0052 2 2 2 2 2 foo5 foo20 3 #ffbe00 3 2 2 1 3 feeeee foo20 4 #728f02 4 5 6 8 surface series order_surfaces color id val2_foo 0 foo foo20 1 #015482 1 2 1 foo2 foo20 2 #9f0052 2 2 2 foo5 foo20 3 #ffbe00 3 1 3 feeeee foo20 4 #728f02 4 8 surface series order_surfaces color id val_foo val2_foo 0 foo foo20 1 #015482 1 2 2 1 foo2 foo20 2 #9f0052 2 2 2 2 foo5 foo20 3 #ffbe00 3 2 1 3 feeeee foo20 4 #728f02 4 6 8 0 foo20 1 foo20 2 foo20 3 foo20 Name: series, dtype: category Categories (5, object): ['foo3', 'boo', 'foo7', 'foo5', 'foo20'] 0 foo 1 foo2 2 foo5 3 feeeee Name: surface, dtype: object surface series order_surfaces color id val_foo val2_foo 1 foo2 boo 1 #9f0052 1 2 2 2 foo5 boo 2 #ffbe00 2 2 1 0 foo foo7 1 #015482 3 2 2 3 feeeee foo20 1 #728f02 4 6 8 surface series order_surfaces color id val_foo val2_foo 1 foo2 boo 1 #9f0052 1 2 2 2 foo5 boo 2 #ffbe00 2 2 1 0 foo foo7 1 #015482 3 2 2 3 feeeee foo20 1 #728f02 4 6 8 surface series order_surfaces color id val_foo val2_foo 1 lala boo 1 #9f0052 1 2 2 2 foo5 boo 2 #ffbe00 2 2 1 0 foo foo7 1 #015482 3 2 2 3 feeeee foo20 1 #728f02 4 6 8 surface series order_surfaces color id val_foo val2_foo 1 lala boo 1 #9f0052 1 2 2 2 foo5 boo 2 #ffbe00 2 22 1 0 foo foo7 1 #015482 3 2 2 3 feeeee foo20 1 #728f02 4 6 8 PASSED [ 14%] X Y Z G_x G_y G_z smooth surface 0 1.0 1.0 1.0 1.000000e+00 0.000000e+00 1.0 0.01 foo 1 2.0 2.0 2.0 1.000000e-12 1.000000e-12 1.0 0.01 foo 2 1.0 1.0 1.0 1.000000e+00 0.000000e+00 1.0 0.01 foo PASSED [ 15%] PASSED [ 16%] Empty DataFrame Columns: [X, Y, Z, smooth, surface] Index: [] X Y Z smooth surface 0 0.907111 0.505988 0.391950 0.000002 foo 1 0.477907 0.556531 0.713103 0.000002 foo5 2 0.567881 0.533721 0.085224 0.000002 lala 3 0.474279 0.213689 0.775950 0.000002 foo5 4 0.135712 0.226703 0.228706 0.000002 lala 5 0.865928 0.366988 0.052788 0.000002 feeeee X Y Z smooth surface 0 0.907111 0.505988 0.391950 0.000002 foo 1 0.477907 0.556531 0.713103 0.000002 foo5 2 0.567881 0.533721 0.085224 0.000002 lala 3 0.474279 0.213689 0.775950 0.000002 foo5 4 0.135712 0.226703 0.228706 0.000002 lala 5 0.865928 0.366988 0.052788 0.000002 feeeee X Y Z smooth surface 0 0.907111 0.505988 0.391950 0.000002 foo 1 0.477907 0.556531 0.713103 0.000002 foo5 2 0.567881 0.533721 0.085224 0.000002 lala 3 0.474279 0.213689 0.775950 0.000002 foo5 4 0.135712 0.226703 0.228706 0.000002 lala 5 0.865928 0.366988 0.052788 0.000002 feeeee X Y Z smooth surface 0 0.907111 0.505988 0.391950 0.000002 foo 1 0.477907 0.556531 0.713103 0.000002 foo5 2 0.567881 0.533721 0.085224 0.000002 lala 3 0.474279 0.213689 0.775950 0.000002 foo5 4 0.135712 0.226703 0.228706 0.000002 lala 5 0.865928 0.366988 0.052788 0.000002 feeeee X Y Z smooth surface 2 0.567881 0.533721 0.085224 0.000002 lala 4 0.135712 0.226703 0.228706 0.000002 lala 1 0.477907 0.556531 0.713103 0.000002 foo5 3 0.474279 0.213689 0.775950 0.000002 foo5 0 0.907111 0.505988 0.391950 0.000002 foo 5 0.865928 0.366988 0.052788 0.000002 feeeee PASSED [ 16%] Empty DataFrame Columns: [X, Y, Z, G_x, G_y, G_z, smooth, surface] Index: [] X Y Z G_x G_y G_z smooth surface 0 6.158143 1.074522 7.457793 0.006508 0.860719 0.639610 0.01 foo 1 1.339547 9.933769 6.031268 0.689821 0.693746 0.314390 0.01 foo5 2 3.655285 9.722756 3.908049 0.638101 0.231377 0.471284 0.01 lala 3 5.149455 7.101185 1.878485 0.973958 0.934199 0.797727 0.01 foo5 4 5.946195 3.441145 3.070265 0.264553 0.578896 0.218209 0.01 lala 5 0.819608 0.011488 0.608167 0.896632 0.560491 0.377451 0.01 feeeee X Y Z G_x G_y G_z smooth surface 0 4.554007 7.983589 5.890162 0.321616 1.619556 7.214686 0.01 foo 1 9.349686 5.224257 7.963614 0.572737 2.630836 17.388708 0.01 foo5 2 3.900684 6.100325 8.972885 0.732125 2.229721 7.274096 0.01 lala 3 7.580344 5.776509 7.915510 0.001390 0.971052 4.771013 0.01 foo5 4 9.062750 3.596733 8.679631 0.226881 1.050554 15.519829 0.01 lala 5 4.979871 4.711573 4.951103 0.968313 3.949245 14.006075 0.01 feeeee X Y Z G_x G_y G_z smooth surface 0 4.554007 7.983589 5.890162 0.321616 1.619556 7.214686 0.01 foo 1 9.349686 5.224257 7.963614 0.572737 2.630836 17.388708 0.01 foo5 2 3.900684 6.100325 8.972885 0.732125 2.229721 7.274096 0.01 lala 3 7.580344 5.776509 7.915510 0.001390 0.971052 4.771013 0.01 foo5 4 9.062750 3.596733 8.679631 0.226881 1.050554 15.519829 0.01 lala 5 4.979871 4.711573 4.951103 0.968313 3.949245 14.006075 0.01 feeeee X Y Z G_x G_y G_z smooth surface 0 4.554007 7.983589 5.890162 0.321616 1.619556 7.214686 0.01 foo 1 9.349686 5.224257 7.963614 0.572737 2.630836 17.388708 0.01 foo5 2 3.900684 6.100325 8.972885 0.732125 2.229721 7.274096 0.01 lala 3 7.580344 5.776509 7.915510 0.001390 0.971052 4.771013 0.01 foo5 4 9.062750 3.596733 8.679631 0.226881 1.050554 15.519829 0.01 lala 5 4.979871 4.711573 4.951103 0.968313 3.949245 14.006075 0.01 feeeee X Y Z G_x G_y G_z smooth surface 0 4.554007 7.983589 5.890162 0.321616 1.619556 7.214686 0.01 foo 1 9.349686 5.224257 7.963614 0.572737 2.630836 17.388708 0.01 foo5 2 3.900684 6.100325 8.972885 0.732125 2.229721 7.274096 0.01 lala 3 7.580344 5.776509 7.915510 0.001390 0.971052 4.771013 0.01 foo5 4 9.062750 3.596733 8.679631 0.226881 1.050554 15.519829 0.01 lala 5 4.979871 4.711573 4.951103 0.968313 3.949245 14.006075 0.01 feeeee PASSED [ 17%] PASSED [ 17%] PASSED [ 18%] PASSED [ 19%] PASSED [ 19%]The surface passed does not exist in the pandas categories. This may imply thatdoes not exist in the surface object either. PASSED [ 20%] PASSED [ 21%] PASSED [ 21%] PASSED [ 22%] PASSED [ 23%] PASSED [ 23%] PASSED [ 24%] X Y Z smooth surface 0 400.0 300.0 -500.0 0.000001 foo2 X Y Z smooth surface 0 400.0 800 -500.0 0.000001 foo2 PASSED [ 25%] PASSED [ 25%] PASSED [ 26%] PASSED [ 26%]Fault colors changed. If you do not like this behavior, set change_color to False. Fault colors changed. If you do not like this behavior, set change_color to False. ['foo2'] PASSED [ 27%] PASSED [ 28%]Active grids: ['regular'] PASSED [ 28%]Empty DataFrame Columns: [surface, series, order_surfaces, color, id] Index: [] PASSED [ 29%]Active grids: ['regular'] PASSED [ 30%] Setting kriging parameters to their default values. Active grids: ['regular' 'topography'] PASSED [ 30%]foo PASSED [ 31%]Active grids: ['regular'] Active grids: ['regular' 'topography'] PASSED [ 32%]is path /home/miguel/.cache/pooch/0744c4cf13b4426d3eb20cca3d23e6f6-viz_3d Active grids: ['regular'] Active grids: ['regular' 'topography'] Downloading data from 'https://github.com/cgre-aachen/gempy_data/raw/master/data/gempy_models/viz_3d.zip' to file '/home/miguel/.cache/pooch/0744c4cf13b4426d3eb20cca3d23e6f6-viz_3d.zip'. SHA256 hash of downloaded file: 37e8a5c5fff03cb38ed12344a585942f91324031f2bfed6f746ac10dbd9ab8be Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future. PASSED [ 32%] Downloading data from 'https://nowhere.zip' to file '/home/miguel/.cache/pooch/0f0b20cbf7607d1a32c3b8d06ce93942-'. PASSED [ 33%]is path /home/miguel/.cache/pooch/bdc57aae09be8b8517ff7162a3f12498-Onlap_relations Active grids: ['regular'] Active grids: ['regular' 'topography'] Setting kriging parameters to their default values. Compiling theano function... Level of Optimization: fast_compile Device: cpu Precision: float64 Number of faults: 0 Compilation Done! Kriging values: values range 1854.723699 $C_o$ 81904.761905 drift equations [3, 3, 3, 3, 3] nugget grad 0.01 nugget scalar 0.000001 Downloading data from 'https://github.com/cgre-aachen/gempy_data/raw/master/data/gempy_models/Onlap_relations.zip' to file '/home/miguel/.cache/pooch/bdc57aae09be8b8517ff7162a3f12498-Onlap_relations.zip'. SHA256 hash of downloaded file: 927968b50ae6ec8d9ff742bca736875bf2c143d993ee7fbc28d514088cd8ba6d Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future. PASSED [ 33%]<pooch.core.Pooch object at 0x7f682196d150> PASSED [ 34%]Active grids: ['regular'] X Y Z smooth surface 0 0.231023 1.721407 0.356143 0.000002 surface_1 1 0.020010 0.692064 -0.649647 0.000002 surface_1 2 0.443810 -1.231450 -0.688408 0.000002 surface_1 3 0.865262 -0.583194 0.918076 0.000002 surface_2 4 -0.774628 -0.969160 1.526413 0.000002 surface_2 X Y Z ... G_z smooth surface 0 0.812639 -0.845421 0.099480 ... 2.052763 0.01 surface_1 1 -1.190455 0.212550 -0.682445 ... 0.425689 0.01 surface_1 2 -0.806670 -1.136213 2.332153 ... -0.725706 0.01 surface_1 3 -1.933519 -0.346317 0.296644 ... 0.232094 0.01 surface_2 4 0.679137 -0.091081 0.053065 ... -0.661261 0.01 surface_2 [5 rows x 8 columns] Active grids: ['regular'] X Y Z smooth surface 0 0.231023 1.721407 0.356143 0.000002 surface_1 1 0.020010 0.692064 -0.649647 0.000002 surface_1 2 0.443810 -1.231450 -0.688408 0.000002 surface_1 3 0.865262 -0.583194 0.918076 0.000002 surface_2 4 -0.774628 -0.969160 1.526413 0.000002 surface_2 X Y Z ... G_z smooth surface 0 0.812639 -0.845421 0.099480 ... 2.052763 0.01 surface_1 1 -1.190455 0.212550 -0.682445 ... 0.425689 0.01 surface_1 2 -0.806670 -1.136213 2.332153 ... -0.725706 0.01 surface_1 3 -1.933519 -0.346317 0.296644 ... 0.232094 0.01 surface_2 4 0.679137 -0.091081 0.053065 ... -0.661261 0.01 surface_2 [5 rows x 8 columns] Active grids: ['regular'] PASSED [ 35%] Fault colors changed. If you do not like this behavior, set change_color to False. PASSED [ 35%] surface series order_surfaces color id 4 Main_Fault Fault_Series 1 #527682 1 0 Shale Strat_Series 1 #015482 2 1 Sandstone_1 Strat_Series 2 #9f0052 3 2 Siltstone Strat_Series 3 #ffbe00 4 3 Sandstone_2 Strat_Series 4 #728f02 5 5 basement Strat_Series 5 #ff3f20 6 PASSED [ 36%]Fault colors changed. If you do not like this behavior, set change_color to False. ['Fault_Series'] Fault colors changed. If you do not like this behavior, set change_color to False. [] PASSED [ 37%]Setting kriging parameters to their default values. PASSED [ 37%]Setting kriging parameters to their default values. Directory already exists, files will be overwritten PASSED [ 38%]Setting kriging parameters to their default values. [6. 6. 6. ... 2. 2. 2.] values Structure isLith True isFault True number faults 1 number surfaces 5 number series 2 number surfaces per series [1, 4] len surfaces surface_points [5, 8, 13, 16, 15] len series surface_points [5, 52] len series orientations [1, 2] Options dtype float64 output geology theano_optimizer fast_compile device cpu verbosity None Kriging range 3464.101615 $C_o$ 285714.285714 drift equations [3, 3] Rescaling rescaling factor 3800.0 centers [850.0, 1000.0, 900.0] [0 3] [0 3] drift equations range $C_o$ drift equations values 1 285714.285714 [0, 3] [6. 6. 6. ... 2. 2. 2.] values Structure isLith True isFault True number faults 1 number surfaces 5 number series 2 number surfaces per series [1, 4] len surfaces surface_points [5, 8, 13, 16, 15] len series surface_points [5, 52] len series orientations [1, 2] Options dtype float64 output geology theano_optimizer fast_compile device cpu verbosity None Kriging range 1 $C_o$ 285714.285714 drift equations [0, 3] Rescaling rescaling factor 3800.0 centers [850.0, 1000.0, 900.0] Compiling theano function... Level of Optimization: fast_compile Device: cpu Precision: float64 Number of faults: 1 Compilation Done! Kriging values: values range 1 $C_o$ 285714.285714 drift equations [0, 3] [4.00645492 4.00623068 4.00600644 ... 3.9931591 3.99293487 3.99271063] values Structure isLith True isFault True number faults 1 number surfaces 5 number series 2 number surfaces per series [1, 4] len surfaces surface_points [5, 8, 13, 16, 15] len series surface_points [5, 52] len series orientations [1, 2] Options dtype float64 output geology theano_optimizer fast_compile device cpu verbosity None Kriging range 1 $C_o$ 285714.285714 drift equations [0, 3] Rescaling rescaling factor 3800.0 centers [850.0, 1000.0, 900.0] PASSED [ 39%] PASSED [ 39%]Active grids: ['regular'] Fault colors changed. If you do not like this behavior, set change_color to False. Fault colors changed. If you do not like this behavior, set change_color to False. Setting kriging parameters to their default values. PASSED [ 40%]Active grids: ['regular'] Fault colors changed. If you do not like this behavior, set change_color to False. Fault colors changed. If you do not like this behavior, set change_color to False. Setting kriging parameters to their default values. PASSED [ 41%]Active grids: ['regular'] Fault colors changed. If you do not like this behavior, set change_color to False. order_series BottomRelation isActive isFault isFinite Fault1 1 Fault False True False Cycle1 2 Erosion False False False PASSED [ 41%]Active grids: ['regular'] Setting kriging parameters to their default values. Fault colors changed. If you do not like this behavior, set change_color to False. Fault colors changed. If you do not like this behavior, set change_color to False. PASSED [ 42%]Active grids: ['regular'] Setting kriging parameters to their default values. [array([[0.087789 , 0.09072717, 0.7501 ], [0.087789 , 0.10739383, 0.7501 ], [0.10445567, 0.09072717, 0.7501 ], ..., [0.90445567, 0.8740605 , 0.7501 ], [0.90445567, 0.89072717, 0.7501 ], [0.90445567, 0.90739383, 0.7501 ]]), array([[0.087789 , 0.09072717, 0.2501 ], [0.087789 , 0.10739383, 0.2501 ], [0.10445567, 0.09072717, 0.2501 ], ..., [0.90445567, 0.8740605 , 0.2501 ], [0.90445567, 0.89072717, 0.2501 ], [0.90445567, 0.90739383, 0.2501 ]])] Active grids: ['regular'] Setting kriging parameters to their default values. PASSED [ 42%]Active grids: ['regular' 'custom'] PASSED [ 43%][[[False False False ... False False False] [False False False ... False False False] [False False False ... False False False] ... [False False False ... False False False] [False False False ... False False False] [False False False ... False False False]] [[False False False ... False False False] [False False False ... False False False] [False False False ... False False False] ... [False False False ... False False False] [False False False ... False False False] [False False False ... False False False]] [[False False False ... False False False] [False False False ... False False False] [False False False ... False False False] ... [False False False ... False False False] [False False False ... False False False] [False False False ... False False False]] ... [[False False False ... False False False] [False False False ... False False False] [False False False ... False False False] ... [False False False ... False False False] [False False False ... False False False] [False False False ... False False False]] [[False False False ... False False False] [False False False ... False False False] [False False False ... False False False] ... [False False False ... False False False] [False False False ... False False False] [False False False ... False False False]] [[False False False ... False False False] [False False False ... False False False] [False False False ... False False False] ... [False False False ... False False False] [False False False ... False False False] [False False False ... False False False]]] [[[ True True True ... False False False] [ True True True ... False False False] [ True True True ... False False False] ... [ True True True ... False False False] [ True True True ... False False False] [ True True True ... False False False]] [[ True True True ... False False False] [ True True True ... False False False] [ True True True ... False False False] ... [ True True True ... False False False] [ True True True ... False False False] [ True True True ... False False False]] [[ True True True ... False False False] [ True True True ... False False False] [ True True True ... False False False] ... [ True True True ... False False False] [ True True True ... False False False] [ True True True ... False False False]] ... [[ True True True ... False False False] [ True True True ... False False False] [ True True True ... False False False] ... [ True True True ... False False False] [ True True True ... False False False] [ True True True ... False False False]] [[ True True True ... False False False] [ True True True ... False False False] [ True True True ... False False False] ... [ True True True ... False False False] [ True True True ... False False False] [ True True True ... False False False]] [[ True True True ... False False False] [ True True True ... False False False] [ True True True ... False False False] ... [ True True True ... False False False] [ True True True ... False False False] [ True True True ... False False False]]] [False False False ... True True True] PASSED [ 44%]Active grids: ['regular' 'topography' 'sections'] Active grids: ['sections'] Setting kriging parameters to their default values. [-400. 0.] FAILED [ 44%] test/test_core/test_xsol.py:94 (test_xsol)sol_values = [array([[9, 6, 6, ..., 3, 1, 3], [9, 8, 0, ..., 2, 2, 8], [4, 3, 7, ..., 7, 7, 6]]), array([[[8, 1, 4, ....[27, 23, 22, ..., 22, 20, 23], [28, 22, 22, ..., 20, 29, 21], [26, 29, 20, ..., 22, 29, 26]]), None, ...] a_grid = Grid Object. Values: array([[ 20. , 20. , -1980. ], [ 20. , 20. , -194....4040404], [ 1750. , 250. , -20.2020202], [ 1750. , 250. , 0. ]]) stack_eg = order_series BottomRelation isActive isFault isFinite foo3 1 Erosion True False ... 4 Erosion True False False foo20 5 Erosion True False False surface_eg = surface series order_surfaces color id val_foo val2_foo 0 foo foo20 1 #015482 1 ... foo20 3 #ffbe00 3 2 1 3 fee foo20 4 #728f02 4 6 8 def test_xsol(sol_values, a_grid, stack_eg, surface_eg): sol = XSolution(a_grid, stack=stack_eg, surfaces=surface_eg) > sol.set_values(sol_values) test_core/test_xsol.py:97: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../gempy/core/xsolution.py:131: in set_values self.set_values_to_regular_grid_(values, l[0], l[1], coords_base.copy()) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <gempy.core.xsolution.XSolution object at 0x7f68266ceb30> values = [array([[9, 6, 6, ..., 3, 1, 3], [9, 8, 0, ..., 2, 2, 8], [4, 3, 7, ..., 7, 7, 6]]), array([[[8, 1, 4, ....[27, 23, 22, ..., 22, 20, 23], [28, 22, 22, ..., 20, 29, 21], [26, 29, 20, ..., 22, 29, 26]]), None, ...] l0 = 0, l1 = 125000 coords_base = {'Features': ['foo3', 'boo', 'foo7', 'foo5', 'foo20'], 'Properties': ['id', 'val_foo', 'val2_foo'], 'X': array([ 20.,... 1460., 1500., 1540., 1580., 1620., 1660., 1700., 1740., 1780., 1820., 1860., 1900., 1940., 1980.]), ...} def set_values_to_regular_grid_(self, values: list, l0, l1, coords_base: dict): coords = self.add_cartesian_coords(coords_base) arrays = self.create_struc_xarrays(values, l0, l1, self.grid.regular_grid.resolution) > self.s_regular_grid = subsurface.StructuredData.from_dict(data_dict=arrays, coords=coords) E NameError: name 'subsurface' is not defined ../gempy/core/xsolution.py:297: NameError FAILED [ 45%] test/test_core/test_xsol.py:107 (test_xsol_unstructured)sol_values = [array([[9, 6, 6, ..., 3, 1, 3], [9, 8, 0, ..., 2, 2, 8], [4, 3, 7, ..., 7, 7, 6]]), array([[[8, 1, 4, ....[27, 23, 22, ..., 22, 20, 23], [28, 22, 22, ..., 20, 29, 21], [26, 29, 20, ..., 22, 29, 26]]), None, ...] a_grid = Grid Object. Values: array([[ 20. , 20. , -1980. ], [ 20. , 20. , -194....4040404], [ 1750. , 250. , -20.2020202], [ 1750. , 250. , 0. ]]) stack_eg = order_series BottomRelation isActive isFault isFinite foo3 1 Erosion True False ... 4 Erosion True False False foo20 5 Erosion True False False surface_eg = surface series order_surfaces color id val_foo val2_foo 0 foo foo20 1 #015482 1 ... foo20 3 #ffbe00 3 2 1 3 fee foo20 4 #728f02 4 6 8 def test_xsol_unstructured(sol_values, a_grid, stack_eg, surface_eg): sol = XSolution(a_grid, stack=stack_eg, surfaces=surface_eg) > sol.set_values(sol_values) test_core/test_xsol.py:110: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../gempy/core/xsolution.py:131: in set_values self.set_values_to_regular_grid_(values, l[0], l[1], coords_base.copy()) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <gempy.core.xsolution.XSolution object at 0x7f68264e6680> values = [array([[9, 6, 6, ..., 3, 1, 3], [9, 8, 0, ..., 2, 2, 8], [4, 3, 7, ..., 7, 7, 6]]), array([[[8, 1, 4, ....[27, 23, 22, ..., 22, 20, 23], [28, 22, 22, ..., 20, 29, 21], [26, 29, 20, ..., 22, 29, 26]]), None, ...] l0 = 0, l1 = 125000 coords_base = {'Features': ['foo3', 'boo', 'foo7', 'foo5', 'foo20'], 'Properties': ['id', 'val_foo', 'val2_foo'], 'X': array([ 20.,... 1460., 1500., 1540., 1580., 1620., 1660., 1700., 1740., 1780., 1820., 1860., 1900., 1940., 1980.]), ...} def set_values_to_regular_grid_(self, values: list, l0, l1, coords_base: dict): coords = self.add_cartesian_coords(coords_base) arrays = self.create_struc_xarrays(values, l0, l1, self.grid.regular_grid.resolution) > self.s_regular_grid = subsurface.StructuredData.from_dict(data_dict=arrays, coords=coords) E NameError: name 'subsurface' is not defined ../gempy/core/xsolution.py:297: NameError FAILED [ 46%] test/test_core/test_xsol.py:113 (test_xsol_structured)sol_values = [array([[9, 6, 6, ..., 3, 1, 3], [9, 8, 0, ..., 2, 2, 8], [4, 3, 7, ..., 7, 7, 6]]), array([[[8, 1, 4, ....[27, 23, 22, ..., 22, 20, 23], [28, 22, 22, ..., 20, 29, 21], [26, 29, 20, ..., 22, 29, 26]]), None, ...] a_grid = Grid Object. Values: array([[ 20. , 20. , -1980. ], [ 20. , 20. , -194....4040404], [ 1750. , 250. , -20.2020202], [ 1750. , 250. , 0. ]]) stack_eg = order_series BottomRelation isActive isFault isFinite foo3 1 Erosion True False ... 4 Erosion True False False foo20 5 Erosion True False False surface_eg = surface series order_surfaces color id val_foo val2_foo 0 foo foo20 1 #015482 1 ... foo20 3 #ffbe00 3 2 1 3 fee foo20 4 #728f02 4 6 8 def test_xsol_structured(sol_values, a_grid, stack_eg, surface_eg): sol = XSolution(a_grid, stack=stack_eg, surfaces=surface_eg) > sol.set_values(sol_values) test_core/test_xsol.py:116: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../gempy/core/xsolution.py:131: in set_values self.set_values_to_regular_grid_(values, l[0], l[1], coords_base.copy()) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <gempy.core.xsolution.XSolution object at 0x7f68262c4670> values = [array([[9, 6, 6, ..., 3, 1, 3], [9, 8, 0, ..., 2, 2, 8], [4, 3, 7, ..., 7, 7, 6]]), array([[[8, 1, 4, ....[27, 23, 22, ..., 22, 20, 23], [28, 22, 22, ..., 20, 29, 21], [26, 29, 20, ..., 22, 29, 26]]), None, ...] l0 = 0, l1 = 125000 coords_base = {'Features': ['foo3', 'boo', 'foo7', 'foo5', 'foo20'], 'Properties': ['id', 'val_foo', 'val2_foo'], 'X': array([ 20.,... 1460., 1500., 1540., 1580., 1620., 1660., 1700., 1740., 1780., 1820., 1860., 1900., 1940., 1980.]), ...} def set_values_to_regular_grid_(self, values: list, l0, l1, coords_base: dict): coords = self.add_cartesian_coords(coords_base) arrays = self.create_struc_xarrays(values, l0, l1, self.grid.regular_grid.resolution) > self.s_regular_grid = subsurface.StructuredData.from_dict(data_dict=arrays, coords=coords) E NameError: name 'subsurface' is not defined ../gempy/core/xsolution.py:297: NameError FAILED [ 46%] test/test_core/test_xsol.py:119 (test_xsol_to_disk)sol_values = [array([[9, 6, 6, ..., 3, 1, 3], [9, 8, 0, ..., 2, 2, 8], [4, 3, 7, ..., 7, 7, 6]]), array([[[8, 1, 4, ....[27, 23, 22, ..., 22, 20, 23], [28, 22, 22, ..., 20, 29, 21], [26, 29, 20, ..., 22, 29, 26]]), None, ...] a_grid = Grid Object. Values: array([[ 20. , 20. , -1980. ], [ 20. , 20. , -194....4040404], [ 1750. , 250. , -20.2020202], [ 1750. , 250. , 0. ]]) stack_eg = order_series BottomRelation isActive isFault isFinite foo3 1 Erosion True False ... 4 Erosion True False False foo20 5 Erosion True False False surface_eg = surface series order_surfaces color id val_foo val2_foo 0 foo foo20 1 #015482 1 ... foo20 3 #ffbe00 3 2 1 3 fee foo20 4 #728f02 4 6 8 tmpdir = local('/tmp/pytest-of-miguel/pytest-0/test_xsol_to_disk0') def test_xsol_to_disk(sol_values, a_grid, stack_eg, surface_eg, tmpdir): sol = XSolution(a_grid, stack=stack_eg, surfaces=surface_eg) > sol.set_values(sol_values) test_core/test_xsol.py:122: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../gempy/core/xsolution.py:131: in set_values self.set_values_to_regular_grid_(values, l[0], l[1], coords_base.copy()) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <gempy.core.xsolution.XSolution object at 0x7f682641c2e0> values = [array([[9, 6, 6, ..., 3, 1, 3], [9, 8, 0, ..., 2, 2, 8], [4, 3, 7, ..., 7, 7, 6]]), array([[[8, 1, 4, ....[27, 23, 22, ..., 22, 20, 23], [28, 22, 22, ..., 20, 29, 21], [26, 29, 20, ..., 22, 29, 26]]), None, ...] l0 = 0, l1 = 125000 coords_base = {'Features': ['foo3', 'boo', 'foo7', 'foo5', 'foo20'], 'Properties': ['id', 'val_foo', 'val2_foo'], 'X': array([ 20.,... 1460., 1500., 1540., 1580., 1620., 1660., 1700., 1740., 1780., 1820., 1860., 1900., 1940., 1980.]), ...} def set_values_to_regular_grid_(self, values: list, l0, l1, coords_base: dict): coords = self.add_cartesian_coords(coords_base) arrays = self.create_struc_xarrays(values, l0, l1, self.grid.regular_grid.resolution) > self.s_regular_grid = subsurface.StructuredData.from_dict(data_dict=arrays, coords=coords) E NameError: name 'subsurface' is not defined ../gempy/core/xsolution.py:297: NameError SKIPPED (Test for gempy_lite) [ 47%] SKIPPED (Test for gempy_lite) [ 48%] FAILED [ 48%] test/test_core/test_xsol.py:157 (test_xsol_full)model_horizontal_two_layers = interpolator 2023-03-07 07:25 def test_xsol_full(model_horizontal_two_layers): model_horizontal_two_layers.update_to_interpolator() vals = gp.compute_model(model_horizontal_two_layers, set_solutions=False) sol = XSolution( model_horizontal_two_layers._grid, stack=model_horizontal_two_layers._stack, surfaces=model_horizontal_two_layers._surfaces) > sol.set_values(vals) test_core/test_xsol.py:165: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../gempy/core/xsolution.py:131: in set_values self.set_values_to_regular_grid_(values, l[0], l[1], coords_base.copy()) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <gempy.core.xsolution.XSolution object at 0x7f6828d65de0> values = [array([[3. , 3. , 3. , ..., 2.5, 1.5, 2.5]]), array([[[3. , 3. , 3. , ..., 2.5, 1.5, 2.5]]]), array([[0., 0., 0., ......0.18353333, 0.21686667, 0.2502 , ..., 0.5002 , 1.5002 , 0.5002 ]]), array([[1.5002, 0.5002]]), ...] l0 = 0, l1 = 125000 coords_base = {'Features': ['Default series'], 'Properties': ['id'], 'X': array([0.1, 0.3, 0.5, 0.7, 0.9, 1.1, 1.3, 1.5, 1.7, 1.9, 2... 5.9, 6.1, 6.3, 6.5, 6.7, 6.9, 7.1, 7.3, 7.5, 7.7, 7.9, 8.1, 8.3, 8.5, 8.7, 8.9, 9.1, 9.3, 9.5, 9.7, 9.9]), ...} def set_values_to_regular_grid_(self, values: list, l0, l1, coords_base: dict): coords = self.add_cartesian_coords(coords_base) arrays = self.create_struc_xarrays(values, l0, l1, self.grid.regular_grid.resolution) > self.s_regular_grid = subsurface.StructuredData.from_dict(data_dict=arrays, coords=coords) E NameError: name 'subsurface' is not defined ../gempy/core/xsolution.py:297: NameError PASSED [ 49%] FAILED [ 50%] test/test_core/test_xsol.py:174 (test_set_meshes)model_horizontal_two_layers = interpolator 2023-03-07 07:25 def test_set_meshes(model_horizontal_two_layers): > import subsurface E ModuleNotFoundError: No module named 'subsurface' test_core/test_xsol.py:176: ModuleNotFoundError PASSED [ 50%] PASSED [ 51%] PASSED [ 51%] PASSED [ 52%] PASSED [ 53%] PASSED [ 53%] PASSED [ 54%] PASSED [ 55%] PASSED [ 55%] PASSED [ 56%] PASSED [ 57%] PASSED [ 57%] PASSED [ 58%] PASSED [ 58%]Active grids: ['regular'] [ 800. 1000.] Active grids: ['regular' 'topography'] Active grids: ['regular' 'topography' 'sections'] start stop resolution dist section1 [0, 0] [1000, 1000] [100, 80] 1414.213562 section2 [800, 0] [800, 1000] [150, 100] 1000.000000 section3 [50, 200] [100, 500] [200, 150] 304.138127 PASSED [ 59%]Active grids: ['regular'] Active grids: ['regular' 'sections'] Active grids: ['regular' 'sections'] start stop resolution dist section1 [0, 0] [1000, 1000] [100, 80] 1414.213562 section2 [800, 0] [800, 1000] [150, 100] 1000.000000 section3 [50, 200] [100, 500] [200, 150] 304.138127 PASSED [ 60%] FAILED [ 60%]Active grids: ['regular'] test/test_core/test_grids/test_topography.py:31 (test_real_grid_ales)@pytest.mark.skipif(("TRAVIS" in os.environ and os.environ["TRAVIS"] == "true") or ("GITHUBACTION" in os.environ and os.environ["GITHUBACTION"] == "true"), reason="Skipping this test on Travis CI because gdal.") def test_real_grid_ales(): resolution = [30, 20, 50] extent = np.array([729550.0, 751500.0, 1913500.0, 1923650.0, -50, 800.0]) path_interf = data_path + "/2018_interf.csv" path_orient = data_path + "/2018_orient_clust_n_init5_0.csv" path_dem = data_path + "/_cropped_DEM_coarse.tif" geo_model = gp.create_model('Alesmodel') gp.init_data(geo_model, extent=extent, resolution=resolution, path_i=path_interf, path_o=path_orient) > geo_model.set_topography(source='gdal', filepath=path_dem) test_core/test_grids/test_topography.py:47: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../gempy/plot/decorators.py:108: in pst topography = func(*args, **kwargs) ../gempy/core/model.py:428: in set_topography self._grid.create_topography(source, **kwargs) ../gempy/core/data.py:183: in create_topography self.topography.load_from_gdal(filepath) ../gempy/core/grid_modules/topography.py:122: in load_from_gdal dem = LoadDEMGDAL(filepath, extent=self.extent) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <gempy.core.grid_modules.create_topography.LoadDEMGDAL object at 0x7f68264392d0> path_dem = '/home/miguel/PycharmProjects/gempy/test/test_core/test_grids/../../input_data/_cropped_DEM_coarse.tif' grid = None extent = array([ 7.29550e+05, 7.51500e+05, 1.91350e+06, 1.92365e+06, -5.00000e+01, 8.00000e+02]) delete_temp = True def __init__(self, path_dem, grid=None, extent=None, delete_temp=True): """ Args: path_dem: path where dem is stored. file format: GDAL raster formats if grid: cropped to geomodel extent """ if GDAL_IMPORT == False: > raise ImportError('Gdal package is not installed. No support for raster formats.') E ImportError: Gdal package is not installed. No support for raster formats. ../gempy/core/grid_modules/create_topography.py:35: ImportError [1600. 2000.] [[ 0. 0. 1965.35297791] [ 0. 40.81632653 1951.7566901 ] [ 0. 81.63265306 1938.28261911] ... [2000. 1918.36734694 1994.00395337] [2000. 1959.18367347 1999.46369539] [2000. 2000. 1991.12328024]] [[[ 0. 0. 1965.35297791] [ 0. 40.81632653 1951.7566901 ] [ 0. 81.63265306 1938.28261911] ... [ 0. 1918.36734694 1981.16072145] [ 0. 1959.18367347 1985.82487834] [ 0. 2000. 1985.588668 ]] [[ 40.81632653 0. 1977.20208871] [ 40.81632653 40.81632653 1973.08605219] [ 40.81632653 81.63265306 1933.43176595] ... [ 40.81632653 1918.36734694 1957.60528886] [ 40.81632653 1959.18367347 1966.79428539] [ 40.81632653 2000. 1976.11353164]] [[ 81.63265306 0. 1967.71153881] [ 81.63265306 40.81632653 1960.09034566] [ 81.63265306 81.63265306 1936.30847248] ... [ 81.63265306 1918.36734694 1942.74336416] [ 81.63265306 1959.18367347 1946.83944313] [ 81.63265306 2000. 1963.58112269]] ... [[1918.36734694 0. 1938.41053067] [1918.36734694 40.81632653 1933.36534759] [1918.36734694 81.63265306 1933.98495031] ... [1918.36734694 1918.36734694 1964.2780338 ] [1918.36734694 1959.18367347 1966.82838829] [1918.36734694 2000. 1958.19672717]] [[1959.18367347 0. 1954.76901087] [1959.18367347 40.81632653 1936.43380299] [1959.18367347 81.63265306 1929.18684525] ... [1959.18367347 1918.36734694 1993.02967815] [1959.18367347 1959.18367347 2000. ] [1959.18367347 2000. 1986.34117143]] [[2000. 0. 1959.80102413] [2000. 40.81632653 1940.01437642] [2000. 81.63265306 1927.47976069] ... [2000. 1918.36734694 1994.00395337] [2000. 1959.18367347 1999.46369539] [2000. 2000. 1991.12328024]]] FAILED [ 61%][1600. 2000.] Active grids: ['regular' 'topography'] test/test_core/test_grids/test_topography.py:97 (test_plot_2d_topography)one_fault_model_no_interp = one_fault_model 2023-03-07 07:24 artificial_grid = <gempy.core.grid_modules.topography.Topography object at 0x7f68209ec640> def test_plot_2d_topography(one_fault_model_no_interp, artificial_grid): geo_model = one_fault_model_no_interp #geo_model._grid.topography = artificial_grid geo_model.set_topography() > p2d = gp.plot_2d(geo_model, section_names=['topography'], show_topography=True, kwargs_topography={'hillshade': False}) test_core/test_grids/test_topography.py:102: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../gempy/plot/plot_api.py:216: in plot_2d p.plot_section_traces(temp_ax) ../gempy/plot/visualization_2d.py:695: in plot_section_traces self.plot_data(ax, section_name='topography', **kwargs) ../gempy/plot/visualization_2d.py:458: in plot_data points_df.plot.scatter(x=x, y=y, ax=ax, c=points_df['surface'].map(self._color_lot), ../venv/lib/python3.10/site-packages/pandas/plotting/_core.py:1636: in scatter return self(kind="scatter", x=x, y=y, s=s, c=c, **kwargs) ../venv/lib/python3.10/site-packages/pandas/plotting/_core.py:917: in __call__ return plot_backend.plot(data, x=x, y=y, kind=kind, **kwargs) ../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/__init__.py:71: in plot plot_obj.generate() ../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py:291: in generate self._adorn_subplots() ../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py:497: in _adorn_subplots handle_shared_axes( ../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/tools.py:432: in handle_shared_axes if is_first_col(ax): _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ x = <Axes: ylabel='elevation [m]'> > is_first_col = lambda x: x.get_subplotspec().is_first_col() E AttributeError: 'NoneType' object has no attribute 'is_first_col' ../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/tools.py:393: AttributeError FAILED [ 62%] test/test_core/test_grids/test_topography.py:114 (test_plot_3d_structure_topo2)unconformity_model_topo = unconformity_model 2023-03-07 07:24 artificial_grid = <gempy.core.grid_modules.topography.Topography object at 0x7f68209ec640> def test_plot_3d_structure_topo2(unconformity_model_topo, artificial_grid): geo_model = unconformity_model_topo with pytest.raises(AssertionError): geo_model._grid.regular_grid.set_topography_mask(artificial_grid) # geo_model._grid.regular_grid.set_topography_mask(geo_model._grid.topography) > p2d = gp.plot_2d(geo_model, section_names=['topography'], show_topography=True, show_lith=False, kwargs_topography={'hillshade': True}) test_core/test_grids/test_topography.py:123: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../gempy/plot/plot_api.py:216: in plot_2d p.plot_section_traces(temp_ax) ../gempy/plot/visualization_2d.py:695: in plot_section_traces self.plot_data(ax, section_name='topography', **kwargs) ../gempy/plot/visualization_2d.py:458: in plot_data points_df.plot.scatter(x=x, y=y, ax=ax, c=points_df['surface'].map(self._color_lot), ../venv/lib/python3.10/site-packages/pandas/plotting/_core.py:1636: in scatter return self(kind="scatter", x=x, y=y, s=s, c=c, **kwargs) ../venv/lib/python3.10/site-packages/pandas/plotting/_core.py:917: in __call__ return plot_backend.plot(data, x=x, y=y, kind=kind, **kwargs) ../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/__init__.py:71: in plot plot_obj.generate() ../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py:291: in generate self._adorn_subplots() ../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py:497: in _adorn_subplots handle_shared_axes( ../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/tools.py:432: in handle_shared_axes if is_first_col(ax): _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ x = <Axes: ylabel='elevation [m]'> > is_first_col = lambda x: x.get_subplotspec().is_first_col() E AttributeError: 'NoneType' object has no attribute 'is_first_col' ../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/tools.py:393: AttributeError Active grids: ['regular'] Setting kriging parameters to their default values. PASSED [ 62%] Lithology ids [5. 2. 2. ... 1. 1. 1.] PASSED [ 63%] Lithology ids [5. 5. 5. ... 1. 1. 1.] PASSED [ 64%] Lithology ids [5. 5. 5. ... 3. 3. 1.] PASSED [ 64%]Active grids: ['regular'] Setting kriging parameters to their default values. Compiling theano function... Level of Optimization: fast_compile Device: cpu Precision: float64 Number of faults: 0 Compilation Done! Kriging values: values range 1854.723699 $C_o$ 81904.761905 drift equations [3, 3, 3, 3, 3] nugget grad 0.01 nugget scalar 0.000001 Active grids: ['regular'] PASSED [ 65%]Active grids: ['regular'] Fault colors changed. If you do not like this behavior, set change_color to False. Setting kriging parameters to their default values. Compiling theano function... Level of Optimization: fast_compile Device: cpu Precision: float64 Number of faults: 16 Compilation Done! Kriging values: values range 54089.18561 $C_o$ 69658095.238095 drift equations [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3] PASSED [ 66%] Fault_2235 ... Rocklea_Inlier_metagranitic_unit Fault_2235 0.0 ... 0 Fault_2843 0.0 ... 0 Fault_2248 0.0 ... 0 Fault_16769 0.0 ... 0 Fault_2997 0.0 ... 0 Fault_5993 0.0 ... 0 Fault_3496 0.0 ... 0 Fault_7439 0.0 ... 0 Fault_3498 0.0 ... 0 Fault_12647 0.0 ... 0 Fault_12658 0.0 ... 0 Fault_7436 NaN ... 1 Fault_7099 NaN ... 0 Fault_10762 NaN ... 0 Fault_12660 NaN ... 0 Fault_14378 NaN ... 0 [16 rows x 15 columns] FAILED [ 66%]Active grids: ['regular'] test/test_integrations/test_map2loop.py:69 (test_loop2gempy2)@pytest.mark.skipif(("TRAVIS" in os.environ and os.environ["TRAVIS"] == "true") or ("GITHUBACTION" in os.environ and os.environ["GITHUBACTION"] == "true"), reason="Skipping this test on Travis CI. For some reason there is a linalg " "error.") def test_loop2gempy2(): topo = fp2 # topo = None > loop2gempy(contacts_file2, orientations_file2, extent[:4], series_file2, extent[4], extent[5], dtm_reproj_file=topo, faults_contact=faults_contact2, faults_orientations=faults_orientations2, faults_faults_rel=ff, faults_groups_rel=fg, model_name='testing_map', compute=True, vtk=True, vtk_path=None, image_2d=True) test_integrations/test_map2loop.py:78: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../gempy/addons/map2gempy.py:128: in loop2gempy geo_model.set_topography(source=source, **topo_kwarg) ../gempy/plot/decorators.py:108: in pst topography = func(*args, **kwargs) ../gempy/core/model.py:428: in set_topography self._grid.create_topography(source, **kwargs) ../gempy/core/data.py:183: in create_topography self.topography.load_from_gdal(filepath) ../gempy/core/grid_modules/topography.py:122: in load_from_gdal dem = LoadDEMGDAL(filepath, extent=self.extent) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <gempy.core.grid_modules.create_topography.LoadDEMGDAL object at 0x7f6825c44370> path_dem = '/home/miguel/PycharmProjects/gempy/test/test_integrations/../input_data/dtm_rp2.tif' grid = None extent = array([ 5.15687310e+05, 5.62666860e+05, 7.47344677e+06, 7.52127357e+06, -3.20000000e+03, 1.20000000e+03]) delete_temp = True def __init__(self, path_dem, grid=None, extent=None, delete_temp=True): """ Args: path_dem: path where dem is stored. file format: GDAL raster formats if grid: cropped to geomodel extent """ if GDAL_IMPORT == False: > raise ImportError('Gdal package is not installed. No support for raster formats.') E ImportError: Gdal package is not installed. No support for raster formats. ../gempy/core/grid_modules/create_topography.py:35: ImportError FAILED [ 67%]Active grids: ['regular'] test/test_integrations/test_map2loop.py:90 (test_map2loop_model_import_data)@pytest.mark.skipif(("TRAVIS" in os.environ and os.environ["TRAVIS"] == "true") or ("GITHUBACTION" in os.environ and os.environ["GITHUBACTION"] == "true"), reason="Skipping this test on Travis CI. For some reason there is a linalg " "error.") def test_map2loop_model_import_data(): geo_model = gp.create_model('test_map2Loop') gp.init_data( geo_model, extent=[bbox[0], bbox[2], bbox[1], bbox[3], model_base, model_top], resolution=[50, 50, 50], path_o=orientations_file, path_i=contacts_file ) # Load Topology > geo_model.set_topography(source='gdal', filepath=fp) test_integrations/test_map2loop.py:106: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../gempy/plot/decorators.py:108: in pst topography = func(*args, **kwargs) ../gempy/core/model.py:428: in set_topography self._grid.create_topography(source, **kwargs) ../gempy/core/data.py:183: in create_topography self.topography.load_from_gdal(filepath) ../gempy/core/grid_modules/topography.py:122: in load_from_gdal dem = LoadDEMGDAL(filepath, extent=self.extent) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <gempy.core.grid_modules.create_topography.LoadDEMGDAL object at 0x7f681fe811b0> path_dem = '/home/miguel/PycharmProjects/gempy/test/test_integrations/../input_data/dtm_rp.tif' grid = None extent = array([5.00e+05, 5.45e+05, 7.49e+06, 7.52e+06, 0.00e+00, 8.00e+02]) delete_temp = True def __init__(self, path_dem, grid=None, extent=None, delete_temp=True): """ Args: path_dem: path where dem is stored. file format: GDAL raster formats if grid: cropped to geomodel extent """ if GDAL_IMPORT == False: > raise ImportError('Gdal package is not installed. No support for raster formats.') E ImportError: Gdal package is not installed. No support for raster formats. ../gempy/core/grid_modules/create_topography.py:35: ImportError FAILED [ 67%]Active grids: ['regular'] Setting kriging parameters to their default values. Compiling theano function... Level of Optimization: fast_compile Device: cpu Precision: float64 Number of faults: 0 Compilation Done! Kriging values: values range 54230.987452 $C_o$ 70023809.52381 drift equations [3, 3] test/test_integrations/test_map2loop.py:117 (test_map2loop_model_no_faults)@pytest.mark.skipif(("TRAVIS" in os.environ and os.environ["TRAVIS"] == "true") or ("GITHUBACTION" in os.environ and os.environ["GITHUBACTION"] == "true"), reason="Skipping this test on Travis CI. For some reason there is a linalg " "error.") def test_map2loop_model_no_faults(): # Location box bbox = (500000, 7490000, 545000, 7520000) model_base = -3200 # Original 3200 model_top = 800 # Input files geo_model = gp.create_model('test_map2Loop') gp.init_data( geo_model, extent=[bbox[0], bbox[2], bbox[1], bbox[3], model_base, model_top], resolution=[50, 50, 80], path_o=orientations_file, path_i=contacts_file ) gp.set_interpolator(geo_model) # Load Topology > geo_model.set_topography(source='gdal', filepath=fp) test_integrations/test_map2loop.py:141: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../gempy/plot/decorators.py:108: in pst topography = func(*args, **kwargs) ../gempy/core/model.py:428: in set_topography self._grid.create_topography(source, **kwargs) ../gempy/core/data.py:183: in create_topography self.topography.load_from_gdal(filepath) ../gempy/core/grid_modules/topography.py:122: in load_from_gdal dem = LoadDEMGDAL(filepath, extent=self.extent) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <gempy.core.grid_modules.create_topography.LoadDEMGDAL object at 0x7f682024b130> path_dem = '/home/miguel/PycharmProjects/gempy/test/test_integrations/../input_data/dtm_rp.tif' grid = None extent = array([ 5.00e+05, 5.45e+05, 7.49e+06, 7.52e+06, -3.20e+03, 8.00e+02]) delete_temp = True def __init__(self, path_dem, grid=None, extent=None, delete_temp=True): """ Args: path_dem: path where dem is stored. file format: GDAL raster formats if grid: cropped to geomodel extent """ if GDAL_IMPORT == False: > raise ImportError('Gdal package is not installed. No support for raster formats.') E ImportError: Gdal package is not installed. No support for raster formats. ../gempy/core/grid_modules/create_topography.py:35: ImportError Active grids: ['regular'] Fault colors changed. If you do not like this behavior, set change_color to False. Setting kriging parameters to their default values. PASSED [ 68%]default_project 2023-03-07 07:25 2023-03-07 07:25 PASSED [ 69%] X Y Z ... G_x G_y G_z surface_points 99 2000 0 400 ... NaN NaN NaN 100 2000 500 400 ... NaN NaN NaN 101 2000 1000 400 ... NaN NaN NaN 0 0 0 800 ... NaN NaN NaN 1 0 500 800 ... NaN NaN NaN ... ... ... ... ... ... ... ... 98 1300 1000 799 ... NaN NaN NaN orientations 3 2000 500 400 ... 8.191520e-01 1.000050e-12 0.573576 2 500 500 800 ... 1.000000e-12 1.000000e-12 1.000000 0 0 500 450 ... -5.000000e-01 9.999694e-13 0.866025 1 0 500 250 ... -2.588190e-01 9.999842e-13 0.965926 [106 rows x 8 columns] values Structure isLith True isFault True number faults 1 number surfaces 4 number series 4 number surfaces per series [1, 1, 2, 0] len surfaces surface_points [3, 15, 39, 45] len series surface_points [3, 15, 84, 0] len series orientations [1, 1, 2, 0] Options dtype float64 output geology theano_optimizer fast_compile device cpu verbosity None Kriging range 2872.281323 $C_o$ 196428.571429 drift equations [3, 3, 3, 3] Rescaling rescaling factor 4600 centers [1150.0, 500.0, 525.0] PASSED [ 69%]one_fault_model 2023-03-07 07:24 PASSED [ 70%] X Y Z ... G_x G_y G_z surface_points 52 700 1000 900.000 ... NaN NaN NaN 53 600 1000 600.000 ... NaN NaN NaN 54 500 1000 300.000 ... NaN NaN NaN 55 800 1000 1200.000 ... NaN NaN NaN 56 900 1000 1500.000 ... NaN NaN NaN 44 0 1000 1500.000 ... NaN NaN NaN 45 300 1000 1400.000 ... NaN NaN NaN 46 600 1000 1300.000 ... NaN NaN NaN 47 1100 1000 1300.000 ... NaN NaN NaN 48 1400 1000 1250.000 ... NaN NaN NaN 49 1700 1000 1300.000 ... NaN NaN NaN 50 1500 500 1200.000 ... NaN NaN NaN 51 1500 1500 1150.000 ... NaN NaN NaN 31 900 150 920.000 ... NaN NaN NaN 32 900 300 920.000 ... NaN NaN NaN 33 900 1500 920.000 ... NaN NaN NaN 34 900 1700 820.000 ... NaN NaN NaN 35 900 1950 870.000 ... NaN NaN NaN 36 0 1000 1300.000 ... NaN NaN NaN 37 300 1000 1200.000 ... NaN NaN NaN 38 600 1000 1100.000 ... NaN NaN NaN 39 1100 1000 1100.000 ... NaN NaN NaN 40 1400 1000 1050.000 ... NaN NaN NaN 41 1700 1000 1100.000 ... NaN NaN NaN 42 1500 500 1000.000 ... NaN NaN NaN 43 1500 1500 950.000 ... NaN NaN NaN 0 800 50 750.000 ... NaN NaN NaN 1 800 150 700.000 ... NaN NaN NaN 2 800 300 700.000 ... NaN NaN NaN 3 800 500 800.000 ... NaN NaN NaN 4 800 1000 1000.000 ... NaN NaN NaN 5 800 1500 700.000 ... NaN NaN NaN 6 800 1700 600.000 ... NaN NaN NaN 7 800 1950 650.000 ... NaN NaN NaN 8 0 1000 1100.000 ... NaN NaN NaN 9 300 1000 1000.000 ... NaN NaN NaN 10 450 1000 950.000 ... NaN NaN NaN 11 1100 1000 900.000 ... NaN NaN NaN 12 1400 1000 850.000 ... NaN NaN NaN 13 1700 1000 900.000 ... NaN NaN NaN 14 1500 500 800.000 ... NaN NaN NaN 15 1500 1500 750.000 ... NaN NaN NaN 16 800 50 450.000 ... NaN NaN NaN 17 800 150 400.000 ... NaN NaN NaN 18 800 300 400.000 ... NaN NaN NaN 19 800 500 500.000 ... NaN NaN NaN 20 800 1000 700.000 ... NaN NaN NaN 21 800 1500 400.000 ... NaN NaN NaN 22 800 1700 300.000 ... NaN NaN NaN 23 800 1950 350.000 ... NaN NaN NaN 24 0 1000 800.000 ... NaN NaN NaN 25 300 1000 700.000 ... NaN NaN NaN 26 1100 1000 600.000 ... NaN NaN NaN 27 1400 1000 550.000 ... NaN NaN NaN 28 1700 1000 600.000 ... NaN NaN NaN 29 1500 500 500.000 ... NaN NaN NaN 30 1500 1500 450.000 ... NaN NaN NaN orientations 2 500 1000 864.602 ... -0.948683 9.998257e-13 0.316229 1 400 1000 1400.000 ... 0.316229 1.000019e-12 0.948683 0 1000 1000 950.000 ... 0.316229 1.000019e-12 0.948683 [60 rows x 8 columns] values Structure isLith True isFault True number faults 1 number surfaces 5 number series 3 number surfaces per series [1, 4, 0] len surfaces surface_points [5, 8, 13, 16, 15] len series surface_points [5, 52, 0] len series orientations [1, 2, 0] Options dtype float64 output geology theano_optimizer fast_compile device cpu verbosity None Kriging range 3464.101615 $C_o$ 285714.285714 drift equations [3, 3, 3] Rescaling rescaling factor 3800.0 centers [850.0, 1000.0, 900.0] PASSED [ 71%] PASSED [ 71%] Lithology ids [6. 6. 6. ... 2. 2. 2.] PASSED [ 72%] PASSED [ 73%]Active grids: ['regular'] Setting kriging parameters to their default values. PASSED [ 73%]Active grids: ['regular'] Setting kriging parameters to their default values. PASSED [ 74%]Active grids: ['regular'] Setting kriging parameters to their default values. PASSED [ 75%]Active grids: ['regular'] Fault colors changed. If you do not like this behavior, set change_color to False. Setting kriging parameters to their default values. PASSED [ 75%]Active grids: ['regular'] Fault colors changed. If you do not like this behavior, set change_color to False. Setting kriging parameters to their default values. PASSED [ 76%]Active grids: ['regular'] Setting kriging parameters to their default values. Fault colors changed. If you do not like this behavior, set change_color to False. [array([[ 686.66664124, 20. , -1940. ], [ 673.33328247, 20. , -1980. ], [ 686.66664124, 60. , -1940. ], ..., [ 1326.66671753, 1940. , -20. ], [ 1313.33328247, 1980. , -60. ], [ 1326.66671753, 1980. , -20. ]]), array([[ 20. , 20. , -286.66992188], [ 20. , 60. , -291.58874512], [ 60. , 20. , -289.60845947], ..., [1980. , 1924.77844238, -420. ], [1980. , 1940. , -409.91333008], [1980. , 1980. , -383.2472229 ]]), array([[ 20. , 20. , -421.09481812], [ 20. , 60. , -424.28619385], [ 60. , 20. , -424.64828491], ..., [1980. , 1940. , -549.03137207], [1980. , 1954.659729 , -540. ], [1980. , 1980. , -524.12384033]]), array([[ 24.65882838, 20. , -1060. ], [ 60. , 54.68887568, -1060. ], [ 60. , 20. , -1069.35462952], ..., [ 1980. , 1900.90560913, -1140. ], [ 1980. , 1940. , -1127.25219727], [ 1980. , 1980. , -1113.39775085]]), array([[ 20. , 20. , -1587.05173492], [ 60. , 58.98488045, -1580. ], [ 60. , 20. , -1602.81368256], ..., [ 1980. , 1900. , -1555.06591797], [ 1980. , 1940. , -1551.07387543], [ 1980. , 1980. , -1546.51187897]])] [array([[ 2, 1, 0], [ 3, 1, 2], [ 5, 0, 4], ..., [3296, 3199, 3195], [3299, 3296, 3297], [3298, 3296, 3299]], dtype=int32), array([[ 2, 1, 0], [ 3, 1, 2], [ 6, 5, 4], ..., [3912, 3991, 3992], [3992, 3913, 3912], [3993, 3913, 3992]], dtype=int32), array([[ 2, 1, 0], [ 3, 1, 2], [ 3, 4, 1], ..., [3819, 3894, 3820], [3821, 3820, 3894], [3821, 3894, 3895]], dtype=int32), array([[ 2, 1, 0], [ 3, 0, 1], [ 4, 3, 1], ..., [3850, 3913, 3914], [3914, 3851, 3850], [3915, 3851, 3914]], dtype=int32), array([[ 2, 1, 0], [ 0, 1, 3], [ 4, 3, 1], ..., [4200, 4122, 4199], [4200, 4123, 4122], [4201, 4123, 4200]], dtype=int32)] PASSED [ 76%]Active grids: ['regular'] Setting kriging parameters to their default values. Fault colors changed. If you do not like this behavior, set change_color to False. PASSED [ 77%]Active grids: ['regular'] Setting kriging parameters to their default values. Compiling theano function... Level of Optimization: fast_compile Device: cpu Precision: float64 Number of faults: 0 Compilation Done! Kriging values: values range 8.485281 $C_o$ 1.714286 drift equations [3] [0] [0] drift equations range $C_o$ drift equations values 50 1.714286 [0] scalar_field_iter __str__ = [ 0. -0.169 0. ] n_surface_op_float_sigmoid __str__ = [[0. 1. 2. 0.]] n_surface_0 __str__ = [[0.]] a __str__ = 0.0 n_surface_1 __str__ = [[1.]] b __str__ = -0.16908401533258222 drift[slice_init:slice_init+1][0] __str__ = [[1.]] middle point __str__ = [[0. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1. 1. 0. 0. 0. 1. 0.5 0.499 0.504 0.503 0.501 0.494 0.5 0.5 0.5 0.5 0.5 0.5 ]] n_surface_0 __str__ = [[2.]] a __str__ = -0.16908401533258222 n_surface_1 __str__ = [[0.]] b __str__ = 0.0 drift[slice_init:slice_init+1][0] __str__ = [[2.]] middle point __str__ = [[2. 2. 0. 0. 2. 2. 0. 0. 2. 2. 0. 0. 2. 2. 2. 0. 1. 1.003 0.991 0.994 0.998 1.011 1. 1. 1. 1. 1. 1. ]] values Structure isLith True isFault False number faults 0 number surfaces 1 number series 1 number surfaces per series [1] len surfaces surface_points [7] len series surface_points [7] len series orientations [2] Options dtype float64 output geology theano_optimizer fast_compile device cpu verbosity [n_surface_op_float_sigmoid, scalar_field_iter, compare, sigma] Kriging range 50 $C_o$ 1.714286 drift equations [0] Rescaling rescaling factor 12.0 centers [0.0, 0.0, 1.5] [[-0.287 -0.218 -0.107 0.05 -0.258 -0.204 -0.115 0.021 -0.227 -0.194 -0.147 -0.047 -0.206 -0.189 -0.177 -0.137]] [[-3. 0. -3.] [-3. 0. -1.] [-3. 0. 1.] [-3. 0. 3.] [-1. 0. -3.] [-1. 0. -1.] [-1. 0. 1.] [-1. 0. 3.] [ 1. 0. -3.] [ 1. 0. -1.] [ 1. 0. 1.] [ 1. 0. 3.] [ 3. 0. -3.] [ 3. 0. -1.] [ 3. 0. 1.] [ 3. 0. 3.]] [ 6.446e+00 -1.281e+01 2.255e-15 -2.784e-15 1.239e+01 2.838e+01 -6.678e+01 -6.108e+02 1.664e+03 1.064e+03 2.490e+02 -2.300e+03] PASSED [ 78%] PASSED [ 78%] PASSED [ 79%] FAILED [ 80%]Active grids: ['regular' 'topography' 'sections'] Active grids: ['regular' 'topography' 'sections'] test/test_plotting/test_2d.py:23 (test_plot_2d_test_labels)one_fault_model_no_interp = one_fault_model 2023-03-07 07:24 def test_plot_2d_test_labels(one_fault_model_no_interp): geo_model = one_fault_model_no_interp section_dict = {'section_SW-NE': ([250, 250], [1750, 1750], [100, 100]), 'section_NW-SE': ([250, 1750], [1750, 250], [100, 100])} geo_model.set_section_grid(section_dict) geo_model.set_topography(fd=1.2, d_z=np.array([600, 2000]), resolution=np.array([60, 60])) > gp.plot_2d(geo_model, section_names=['section_NW-SE', 'section_NW-SE', 'topography'], direction=['x'], cell_number=['mid'], show_topography=True, ) test_plotting/test_2d.py:31: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../gempy/plot/plot_api.py:216: in plot_2d p.plot_section_traces(temp_ax) ../gempy/plot/visualization_2d.py:695: in plot_section_traces self.plot_data(ax, section_name='topography', **kwargs) ../gempy/plot/visualization_2d.py:458: in plot_data points_df.plot.scatter(x=x, y=y, ax=ax, c=points_df['surface'].map(self._color_lot), ../venv/lib/python3.10/site-packages/pandas/plotting/_core.py:1636: in scatter return self(kind="scatter", x=x, y=y, s=s, c=c, **kwargs) ../venv/lib/python3.10/site-packages/pandas/plotting/_core.py:917: in __call__ return plot_backend.plot(data, x=x, y=y, kind=kind, **kwargs) ../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/__init__.py:71: in plot plot_obj.generate() ../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py:291: in generate self._adorn_subplots() ../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py:497: in _adorn_subplots handle_shared_axes( ../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/tools.py:403: in handle_shared_axes layout[row_num(ax), col_num(ax)] = ax.get_visible() _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ x = <Axes: ylabel='elevation [m]'> > row_num = lambda x: x.get_subplotspec().rowspan.start E AttributeError: 'NoneType' object has no attribute 'rowspan' ../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/tools.py:386: AttributeError Active grids: ['sections'] Active grids: ['sections'] Setting kriging parameters to their default values. PASSED [ 80%] PASSED [ 81%] PASSED [ 82%] PASSED [ 82%]Active grids: ['topography' 'sections'] PASSED [ 83%] PASSED [ 83%] PASSED [ 84%] PASSED [ 85%] PASSED [ 85%] PASSED [ 86%] PASSED [ 87%] PASSED [ 87%] SKIPPED (github actions does not like background plot) [ 88%] PASSED [ 89%]foo PASSED [ 89%]foo PASSED [ 90%]foo SKIPPED (github actions does not like background plot) [ 91%] PASSED [ 91%]foo PASSED [ 92%] PASSED [ 92%] PASSED [ 93%] PASSED [ 94%]foo PASSED [ 94%][1600. 2000.] Active grids: ['topography' 'sections'] foo PASSED [ 95%]foo PASSED [ 96%]foo PASSED [ 96%]foo PASSED [ 97%]foo PASSED [ 98%]foo PASSED [ 98%] PASSED [ 99%] PASSED [100%] ================================================ FILE: test/__init__.py ================================================ ================================================ FILE: test/conftest.py ================================================ import enum import os input_path = os.path.dirname(__file__) + '/input_data' input_path2 = os.path.dirname(__file__) + '/../examples/data/input_data/' import numpy as np np.random.seed(1234) class TestSpeed(enum.Enum): MILLISECONDS = 0 SECONDS = 1 MINUTES = 2 HOURS = 3 class Requirements(enum.Enum): CORE = enum.auto() BASE = enum.auto() OPTIONAL = enum.auto() DEV = enum.auto() TEST_SPEED = TestSpeed.MINUTES # * Use seconds for compile errors, minutes before pushing and hours before release REQUIREMENT_LEVEL = Requirements.CORE # * Use CORE for mandatory tests, OPTIONAL for optional tests and DEV for development tests ================================================ FILE: test/context.py ================================================ """ The main function of the code you provided is to add the parent directory of the tests folder to the Python path (sys.path). his is done with sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))). his line is adding the directory one level up from the directory of the context.py file to the front of the sys.path. sys.path is the list of directories that Python checks when it's trying to import a module. By inserting the parent directory of the tests directory at the front of sys.path, Python will check this directory first when importing modules. This allows test scripts in the tests directory to import modules from the parent directory (like gempy in your case) as if they were in the same directory. This is very helpful when you're running tests because it ensures the tests are running against the source code in your project, rather than an installed version of your package. """ # -*- coding: utf-8 -*- import sys import os sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) ================================================ FILE: test/pytest.ini ================================================ [pytest] addopts = -v norecursedirs = test\test_modules\_geophysics_TO_UPDATE ================================================ FILE: test/test_api/__init__.py ================================================ ================================================ FILE: test/test_api/test_backends.py ================================================ import pytest import gempy as gp import gempy_viewer as gpv from gempy.core.data.gempy_engine_config import GemPyEngineConfig from gempy_engine.config import AvailableBackends from gempy.core.data.enumerators import ExampleModel from test.conftest import TEST_SPEED, TestSpeed @pytest.mark.skipif(TEST_SPEED.value < TestSpeed.SECONDS.value, reason="Global test speed below this test value.") class TestBackends: @pytest.fixture(scope='class') def geo_model(self): geo_model: gp.data.GeoModel = gp.generate_example_model( example_model=ExampleModel.ONE_FAULT, compute_model=False ) geo_model.interpolation_options.number_octree_levels = 6 return geo_model def test_backends_numpy(self, geo_model): gp.compute_model(geo_model, engine_config=GemPyEngineConfig(backend=AvailableBackends.numpy)) gpv.plot_3d( model=geo_model, show_data=True, show_boundaries=True, show_lith=False, image=True ) def test_numpy_pykeops(self, geo_model): gp.compute_model( gempy_model=geo_model, engine_config=GemPyEngineConfig( backend=AvailableBackends.numpy, use_gpu=False, )) gpv.plot_3d( model=geo_model, show_data=True, show_boundaries=True, show_lith=False, image=True ) @pytest.mark.skip(reason="Not finished yet. Fault data is not passed to legacy properly.") @pytest.mark.skipif(TEST_SPEED.value <= 1, reason="Global test speed below this test value.") def test_backends_legacy(self, geo_model): # TODO: (Sep 2023, Miguel) Pass fault data to legacy properly gp.compute_model(geo_model, engine_config=GemPyEngineConfig(backend=AvailableBackends.legacy)) from gempy.optional_dependencies import require_gempy_legacy gpl = require_gempy_legacy() gpl.plot_3d( model=geo_model.legacy_model, show_data=True, show_boundaries=True, show_lith=False, image=True ) ================================================ FILE: test/test_api/test_fault_api.py ================================================ import numpy as np import gempy as gp from gempy.core.data.structural_group import FaultsRelationSpecialCase def test_fault_api(): frame = _create_structural_frame() gp.set_is_fault( frame=frame, fault_groups=['1', '3'], faults_relation_type=FaultsRelationSpecialCase.OFFSET_FORMATIONS, ) print(frame) print(frame.fault_relations) np.testing.assert_array_equal( frame.fault_relations, np.array( [ [False, True, False, True], [False, False, False, False], [False, False, False, True], [False, False, False, False] ] ) ) def test_fault_api_setter(): frame = _create_structural_frame() # Use the setter to set the fault_relations frame.fault_relations = np.array( [ [False, True, False, True], [False, False, False, False], [False, False, False, True], [False, False, False, False] ] ) # Now we verify if the StructuralGroup fault_relations have been updated correctly assert frame.structural_groups[0].fault_relations == [frame.structural_groups[1], frame.structural_groups[3]] assert frame.structural_groups[1].fault_relations == FaultsRelationSpecialCase.OFFSET_NONE assert frame.structural_groups[2].fault_relations == FaultsRelationSpecialCase.OFFSET_ALL # ! Last group does not matter really. assert frame.structural_groups[3].fault_relations == FaultsRelationSpecialCase.OFFSET_NONE def _create_structural_frame(): frame = gp.data.StructuralFrame( structural_groups=[ gp.data.StructuralGroup( name='1', elements=[], structural_relation=gp.data.StackRelationType.ERODE ), gp.data.StructuralGroup( name='2', elements=[], structural_relation=gp.data.StackRelationType.ERODE ), gp.data.StructuralGroup( name='3', elements=[], structural_relation=gp.data.StackRelationType.ERODE ), gp.data.StructuralGroup( name='4', elements=[], structural_relation=gp.data.StackRelationType.ERODE ), ], color_gen=gp.ColorsGenerator() # * Irrlevant for this test ) return frame ================================================ FILE: test/test_api/test_gempy_legacy_comp.py ================================================ import gempy as gp import gempy_viewer from gempy.core.data import Solutions from gempy.API.gp2_gp3_compatibility.gp3_to_gp2_input import gempy3_to_gempy2 from gempy.optional_dependencies import require_gempy_legacy from test.test_api.test_initialization_and_compute_api import _create_data # Skip pytest if legacy is not installed try: require_gempy_legacy() except ImportError: import pytest pytest.skip("Legacy is not installed.", allow_module_level=True) def test_compare_numpy_with_legacy(): geo_data = _create_data() gp.map_stack_to_surfaces( gempy_model=geo_data, mapping_object={"Strat_Series": ('rock2', 'rock1')} ) sol: Solutions = gp.compute_model(geo_data) gempy_viewer.plot_2d(geo_data, direction=['y'], show_data=True, show_boundaries=False) legacy_model = gempy3_to_gempy2(geo_data) gl = require_gempy_legacy() gl.set_interpolator(legacy_model) gl.compute_model(legacy_model) gl.plot_2d(legacy_model, direction=['y']) ================================================ FILE: test/test_api/test_initialization_and_compute_api.py ================================================ from pprint import pprint import gempy as gp import gempy_viewer from gempy.core.data import GeoModel from gempy_engine.core.data.solutions import Solutions def test_api_create_data(): geo_data = _create_data() pprint(geo_data) def _create_data(): data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/' geo_data: GeoModel = gp.create_geomodel( project_name='horizontal', extent=[0, 1000, 0, 1000, 0, 1000], resolution=[50, 5, 50], importer_helper=gp.data.ImporterHelper( path_to_surface_points=data_path + "/data/input_data/jan_models/model1_surface_points.csv", path_to_orientations=data_path + "/data/input_data/jan_models/model1_orientations.csv" ) ) return geo_data def test_map_stack_to_surfaces(): geo_data = _create_data() gp.map_stack_to_surfaces( gempy_model=geo_data, mapping_object={"Strat_Series": ('rock2', 'rock1')} ) pprint(geo_data.structural_frame) def test_api_compute_model(): geo_data = _create_data() gp.map_stack_to_surfaces( gempy_model=geo_data, mapping_object={"Strat_Series": ('rock2', 'rock1')} ) gempy_viewer.plot_2d(geo_data, direction=['y']) sol: Solutions = gp.compute_model(geo_data) gempy_viewer.plot_2d(geo_data, direction=['y'], show_data=True, show_boundaries=True, show_lith=False) gempy_viewer.plot_2d(geo_data, direction=['y'], show_data=False, show_scalar=True) ================================================ FILE: test/test_api/test_model_construction_granular.py ================================================ import pooch import gempy as gp import gempy_engine.core.data.solutions import gempy_viewer from gempy.core.color_generator import ColorsGenerator from gempy_engine.core.data import InterpolationOptions from gempy.core.data import GeoModel from gempy.API.io_API import read_orientations, read_surface_points from gempy.core.data.orientations import OrientationsTable from gempy.core.data.structural_element import StructuralElement from gempy.core.data.structural_frame import StructuralFrame from gempy.core.data.structural_group import Stack from gempy.core.data.surface_points import SurfacePointsTable from gempy.optional_dependencies import require_gempy_viewer """ - [x] create data - [x] map series - [ ] plot data - [ ] compute - [ ] plot results """ def test_read_input_points(): _, _ = _read_data() def _read_data() -> tuple[SurfacePointsTable, OrientationsTable]: data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/' surface_points_file = pooch.retrieve( url=data_path + "data/input_data/jan_models/model1_surface_points.csv", known_hash="6f1a39ed77e87a4057f03629c946b1876b87e24409cadfe0e1cf7ab1488f69e4" ) orientations_file = pooch.retrieve( url=data_path + "data/input_data/jan_models/model1_orientations.csv", known_hash="04c307ae23f70252fe54144a2fb95ca7d96584a2d497ea539ed32dfd23e7cd5d" ) print(pooch.file_hash(surface_points_file)) print(pooch.file_hash(orientations_file)) surface_points: SurfacePointsTable = read_surface_points( path=surface_points_file, ) orientations: OrientationsTable = read_orientations( path=orientations_file ) return surface_points, orientations def test_create_grid(): _generate_grid() def _generate_grid(): grid: gp.data.Grid = gp.data.Grid( extent=[0, 1000, 0, 1000, 0, 500], resolution=[50, 5, 50] ) return grid def test_create_structural_frame(): # * Structural elements _create_structural_frame() def _create_structural_frame(): surface_points, orientations = _read_data() surface_points_groups = surface_points.get_surface_points_by_id_groups() orientations_groups = orientations.get_orientations_by_id_groups() structural_elements = [] color_gen = ColorsGenerator() for i in range(len(surface_points_groups)): # TODO: Split surface points and orientations by id structural_element: StructuralElement = StructuralElement( name="layer1", surface_points=surface_points_groups[i], orientations=orientations_groups[i], color=next(color_gen) ) structural_elements.append(structural_element) # * Structural groups definitions default_formation: Stack = Stack( name="default_formation", elements=structural_elements, structural_relation=gp.data.StackRelationType.ERODE ) # ? Should I move this to the constructor? structural_frame: StructuralFrame = StructuralFrame( structural_groups=[default_formation], color_gen=color_gen ) return structural_frame def test_create_interpolation_options(): interpolation_options = _generate_interpolation_options() def _generate_interpolation_options(): range_ = 1000.0 interpolation_options: InterpolationOptions = InterpolationOptions.from_args( range=range_, c_o=(range_ ** 2) / 14 / 3, ) return interpolation_options def _create_geomodel() -> GeoModel: geo_model: GeoModel = GeoModel.from_args( name="horizontal", structural_frame=_create_structural_frame(), grid=_generate_grid(), interpolation_options=_generate_interpolation_options() ) return geo_model def test_structural_frame_surface_points(): structural_frame: StructuralFrame = _create_structural_frame() print(structural_frame.surface_points_copy) pass def _interpolate_numpy() -> GeoModel: geo_model: GeoModel = _create_geomodel() solutions: gempy_engine.core.data.solutions.Solutions = gempy_engine.compute_model( interpolation_input=geo_model.interpolation_input_copy, options=geo_model.interpolation_options, data_descriptor=geo_model.input_data_descriptor ) print(solutions) geo_model.solutions = solutions # TODO: Use gempy API if PLOT := True: from gempy_engine.core.data.output.blocks_value_type import ValueType from gempy_engine.plugins.plotting.helper_functions import plot_block_and_input_2d plot_block_and_input_2d( stack_number=0, interpolation_input=geo_model.interpolation_input_copy, outputs=geo_model.solutions.octrees_output, structure=geo_model.structural_frame.input_data_descriptor.stack_structure, value_type=ValueType.ids ) gempy_viewer.plot_3d(geo_model, show_boundaries=True, image=True) return geo_model def test_interpolate_aesara(): geo_model: GeoModel = _create_geomodel() def test_plot_input(): geo_model: GeoModel = _create_geomodel() gp_viewer: gempy_viewer = require_gempy_viewer() # TODO: Add all the plot data in a plot options class # TODO: Make options required gp_viewer.plot_2d( geo_model, direction=['y'], show_results=False ) def test_plot_results(): solved_geo_model: GeoModel = _interpolate_numpy() gp_viewer: gempy_viewer = require_gempy_viewer() gp_viewer.plot_2d( solved_geo_model, direction=['y'], show_boundaries=False, # TODO: Fix boundaries show_results=True ) ================================================ FILE: test/test_api/test_modify_input_data.py ================================================ from gempy import generate_example_model from gempy.core.data.enumerators import ExampleModel import gempy_viewer as gp_viewer import gempy as gp def test_modify_surface_point_by_name_and_index(): model = generate_example_model(ExampleModel.ONE_FAULT, compute_model=False) print(model.structural_frame) gp_viewer.plot_2d( model, direction=['y'], show_boundaries=False, # TODO: Fix boundaries ) gp.modify_surface_points( geo_model=model, elements_names=["fault"], Z=800 # This can be an array of the same length as the number of points ) gp_viewer.plot_2d( model, direction=['y'], show_boundaries=False, # TODO: Fix boundaries ) def test_modify_surface_point_by_global_index(): model = generate_example_model(ExampleModel.ONE_FAULT, compute_model=False) print(model.surface_points_copy.df) gp_viewer.plot_2d( model, direction=['y'], show_boundaries=False, # TODO: Fix boundaries ) gp.modify_surface_points( geo_model=model, slice=0, Z=800 ) print(model.surface_points_copy.df) gp_viewer.plot_2d( model, direction=['y'], show_boundaries=False, # TODO: Fix boundaries ) ================================================ FILE: test/test_api/test_show_input_data.py ================================================ from gempy import generate_example_model from gempy.core.data.enumerators import ExampleModel def test_print_structural_frame(): model = generate_example_model(ExampleModel.ONE_FAULT, compute_model=False) print(model.structural_frame) ================================================ FILE: test/test_community_bugs/__init__.py ================================================ ================================================ FILE: test/test_community_bugs/test_community_bugs.py ================================================ """ This scrip is to add test for specific issues that the community has found. Use the the following template: def test_issue_XXX(): ... """ ================================================ FILE: test/test_core/__init__.py ================================================ ================================================ FILE: test/test_core/test_color_generator.py ================================================ import gempy.core.color_generator as cg def test_color_generator(): # create an instance of ColorsGenerator color_iterator: iter = cg.ColorsGenerator() # generate color dictionary (with optional seaborn palettes if you want) # get the generator from next_color method # now you can get colors one by one print(next(color_iterator)) # prints first color print(next(color_iterator)) # prints second color left_colors = color_iterator._gempy_default_colors[2:] assert left_colors == [ color for color, _ in zip(color_iterator, left_colors) ] # get a random color assert color_iterator.up_next() == next(color_iterator) # override the random color generator cg.np.random.randint = lambda a, b: 0x00ff42 assert color_iterator._random_hexcolor() == "#00ff42" assert next(color_iterator) == "#00ff42" # trigger up_next method for the cache assert color_iterator.up_next() == "#00ff42" cg.np.random.randint = lambda a, b: 0x000000 # ensure that the up_next method still returns the same color assert color_iterator.up_next() == "#00ff42" # trigger the cache assert next(color_iterator) == "#00ff42" assert next(color_iterator) == "#000000" # etc... ================================================ FILE: test/test_core/test_installation.py ================================================ def test_installation(): import gempy import gempy_engine ================================================ FILE: test/test_core/test_transfoms.py ================================================ from gempy.core.data import GeoModel from gempy_engine.core.data.transforms import Transform from test.test_api.test_initialization_and_compute_api import _create_data import numpy as np import matplotlib.pyplot as plt def test_transform_1(): geo_data: GeoModel = _create_data() print(geo_data.input_transform) transformed_xyz = geo_data.input_transform.apply(geo_data.surface_points_copy.xyz) print(transformed_xyz) return # Assuming you have your Transform class defined above this. def plot_points(points, title): fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.scatter(points[:, 0], points[:, 1], points[:, 2], c='r', marker='o') ax.set_xlabel('X') ax.set_ylabel('Y') ax.set_zlabel('Z') ax.set_title(title) plt.show() def test_transform_operations_scale_move(): transform = Transform( position=np.array([1, 2, 3]), rotation=np.array([0, 0, 0]), scale=np.array([2, 2, 2])) original_points = np.array([ [0, 0, 0], [1, 1, 1], [-1, -1, -1] ]) transformed_points = transform.apply(original_points) inv_transformed_points = transform.apply_inverse(transformed_points) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') # Original points ax.scatter(original_points[:, 0], original_points[:, 1], original_points[:, 2], c='r', marker='o', label='Original') # Transformed points ax.scatter(transformed_points[:, 0], transformed_points[:, 1], transformed_points[:, 2], c='g', marker='^', label='Transformed') # Inverse transformed points ax.scatter(inv_transformed_points[:, 0], inv_transformed_points[:, 1], inv_transformed_points[:, 2], c='b', marker='x', label='Inv Transformed') ax.legend() plt.show() assert np.allclose(original_points, inv_transformed_points) def test_transform_operations_rotate(): transform = Transform( position=np.array([0, 0, 0]), rotation=np.array([45, 45, 45]), scale=np.array([1, 1, 1])) original_points = np.array([ [0, 0, 0], [1, 1, 1], [-1, -1, -1] ]) transformed_points = transform.apply(original_points) inv_transformed_points = transform.apply_inverse(transformed_points) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') # Original points ax.scatter(original_points[:, 0], original_points[:, 1], original_points[:, 2], c='r', marker='o', label='Original') # Transformed points ax.scatter(transformed_points[:, 0], transformed_points[:, 1], transformed_points[:, 2], c='g', marker='^', label='Transformed') # Inverse transformed points ax.scatter(inv_transformed_points[:, 0], inv_transformed_points[:, 1], inv_transformed_points[:, 2], c='b', marker='x', label='Inv Transformed') ax.legend() plt.show() assert np.allclose(original_points, inv_transformed_points) ================================================ FILE: test/test_model_types/__init__.py ================================================ ================================================ FILE: test/test_model_types/test_example_models_I.py ================================================ from typing import Any from numpy import dtype, ndarray import gempy as gp from gempy.core.data import GeoModel from gempy.core.data.enumerators import ExampleModel from gempy.optional_dependencies import require_gempy_viewer from gempy_engine.core.data.interp_output import InterpOutput from gempy_viewer import GemPyToVista from test.verify_helper import gempy_verify_array import pytest from test.conftest import TEST_SPEED, TestSpeed # ! When importing the model is computed # TODO []: Use the generator and do some approval testing PLOT = True pytestmark = pytest.mark.skipif(TEST_SPEED.value < TestSpeed.MINUTES.value, reason="Global test speed below this test value.") def _verify_scalar_field(model, name): outputs_centers_: InterpOutput = model.solutions.octrees_output[-1].outputs[0] scalar_field = outputs_centers_.exported_fields.scalar_field scalar_field = scalar_field[::int(len(scalar_field) / 50)] # Pick 50 values from the scalar field array gempy_verify_array(scalar_field, name) def test_generate_horizontal_stratigraphic_model(): model = gp.generate_example_model(ExampleModel.HORIZONTAL_STRAT, compute_model=True) print(model.structural_frame) if PLOT: gpv = require_gempy_viewer() gpv.plot_3d(model, show_data=True, image=True) if False: _verify_scalar_field( model=model, name="Horizontal Stratigraphic Scalar Field" ) def test_generate_fold_model(): model = gp.generate_example_model(ExampleModel.ANTICLINE, compute_model=True) print(model.structural_frame) if PLOT or False: gpv = require_gempy_viewer() gpv.plot_3d(model, image=True) _verify_scalar_field( model=model, name="Anticline Scalar Field" ) def test_generate_fault_model(): model = gp.generate_example_model(ExampleModel.ONE_FAULT, compute_model=True) print(model.structural_frame) if PLOT: gpv = require_gempy_viewer() gpv.plot_3d(model, image=True) _verify_scalar_field( model=model, name="Fault Scalar Field" ) def test_generate_combination_model(): model = gp.generate_example_model(ExampleModel.COMBINATION, compute_model=True) print(model.structural_frame) if PLOT: gpv = require_gempy_viewer() gpv.plot_2d(model) pd3 = gpv.plot_3d( model, image=True, show_lith=True, show_octree=True, show=True ) _verify_scalar_field( model=model, name="Combination Scalar Field" ) def test_generate_greenstone_model(): model = gp.generate_example_model(ExampleModel.GREENSTONE, compute_model=True) print(model.structural_frame) if PLOT: gpv = require_gempy_viewer() gpv.plot_3d(model, image=True) ================================================ FILE: test/test_model_types/test_example_models_I.test_generate_combination_model.Combination Scalar Field.approved.txt ================================================ [-0.48762027 -0.37851606 -0.20602752 0.18466945 0.24586016 0.34157167 -0.37574516 -0.23997105 -0.38798331 -0.30116176 -0.22103656 -0.22657835 -0.40356962 -0.26444733 -0.30116176 -0.26664095 -0.28892361 -0.23997105 -0.12532504 -0.01622083 -0.019569 0.03607991 -0.20879842 -0.06297978 -0.07856609 0.03942809 0.10950876 0.14957136 0.07279434 0.10396697 0.19967849 0.25867558 0.30208635 0.14957136 0.17739582 0.09172883 0.22750295 0.34549713 0.27761007 0.25752102 0.23858653 0.33210443 0.36327706 0.40668784 0.49016121 0.25752102 0.26975916 0.39110152 0.38221155 0.43786047 0.52133384] ================================================ FILE: test/test_model_types/test_example_models_I.test_generate_fault_model.Fault Scalar Field.approved.txt ================================================ [-1.13708400e-02 -2.52401253e-01 -1.58105889e-01 -2.53046188e-01 -1.02730053e-01 3.07857424e-02 1.65039406e-02 1.02339531e-02 -7.49849706e-03 2.03358730e-03 1.68709401e-02 1.16318104e-02 -5.46883208e-03 -3.66307173e-01 -2.16332415e-01 -3.08865566e-01 -2.15389261e-01 -1.11256902e-01 2.72083076e-02 4.20445442e-02 -7.47304561e-02 3.98513512e-02 1.63139249e-02 4.29008353e-02 -7.33691160e-03 3.78053797e-02 1.68817500e-03 1.19697379e-02 2.92719967e-02 1.01708771e-02 2.46994485e-02 2.91498040e-02 2.38183394e-02 1.43569767e-01 2.64840495e-01 3.68607253e-01 2.35431983e-01 3.12159958e-01 2.13482001e-05 5.67351744e-02 -8.42197714e-03 1.74023726e-01 1.53633321e-02 6.80918669e-03 -1.60048318e-03 1.23676916e-01 2.07197240e-01 2.84933975e-01 1.27564214e-02 2.58711537e-01 2.99561004e-02] ================================================ FILE: test/test_model_types/test_example_models_I.test_generate_fold_model.Anticline Scalar Field.approved.txt ================================================ [-0.52188333 -0.50112162 -0.4782676 -0.4049219 -0.52592991 -0.35477513 -0.32950309 -0.49867861 -0.39645404 -0.3705816 -0.53297922 -0.4170038 -0.38884941 -0.50066294 -0.52343835 -0.50147135 -0.38406676 -0.49930607 -0.34000787 -0.36981774 -0.39715035 -0.50713183 -0.38966825 -0.5074257 -0.53982527 -0.35470835 -0.49995446 -0.383908 -0.54971153 -0.47881636 -0.54893268 -0.39154008 -0.41173995 -0.50805229 -0.37732251 -0.35443146 -0.40539656 -0.38387161 -0.54517857 -0.52338116 -0.40620542 -0.47800398 -0.49943244 -0.50482445 -0.5102548 -0.35318898 -0.55119547 -0.35075437 -0.41029029 -0.32958424 -0.35520657] ================================================ FILE: test/test_model_types/test_example_models_I.test_generate_horizontal_stratigraphic_model.Horizontal Stratigraphic Scalar Field.approved.txt ================================================ [-0.53138973 -0.53401697 -0.53639193 -0.53851076 -0.54037354 -0.54198423 -0.54335025 -0.54448177 -0.54539063 -0.54608928 -0.54658948 -0.54690111 -0.54703108 -0.5469825 -0.54675437 -0.54634166 -0.54573594 -0.5449263 -0.54390055 -0.54264645 -0.54115292 -0.53941111 -0.53741528 -0.53516341 -0.5326574 -0.53138973 -0.53401697 -0.53639193 -0.53851076 -0.54037354 -0.54198423 -0.54335025 -0.54448177 -0.54539063 -0.54608928 -0.54658948 -0.54690111 -0.54703108 -0.5469825 -0.54675437 -0.54634166 -0.54573594 -0.5449263 -0.54390055 -0.54264645 -0.54115292 -0.53941111 -0.53741528 -0.53516341 -0.5326574 ] ================================================ FILE: test/test_model_types/test_subduction.py ================================================ import os import gempy as gp import gempy_viewer as gpv def test_subduction(): # Get the directory containing the test file current_dir = os.path.dirname(os.path.abspath(__file__)) # Navigate to examples directory relative to test location data_path = os.path.join(current_dir, '../..', 'examples') geo_model: gp.data.GeoModel = gp.create_geomodel( project_name='Onlap_relations', extent=[-200, 1000, -500, 500, -1000, 0], resolution=[50, 50, 50], refinement=5, importer_helper=gp.data.ImporterHelper( path_to_orientations=data_path + "/data/input_data/tut-ch1-4/tut_ch1-4_orientations.csv", path_to_surface_points=data_path + "/data/input_data/tut-ch1-4/tut_ch1-4_points.csv", ) ) gp.add_structural_group( model=geo_model, group_index=0, structural_group_name="seafloor_series", elements=[geo_model.structural_frame.get_element_by_name("seafloor")], structural_relation=gp.data.StackRelationType.ERODE, ) gp.add_structural_group( model=geo_model, group_index=1, structural_group_name="right_series", elements=[ geo_model.structural_frame.get_element_by_name("rock1"), geo_model.structural_frame.get_element_by_name("rock2"), ], structural_relation=gp.data.StackRelationType.ONLAP ) gp.add_structural_group( model=geo_model, group_index=2, structural_group_name="onlap_series", elements=[geo_model.structural_frame.get_element_by_name("onlap_surface")], structural_relation=gp.data.StackRelationType.ERODE ) gp.add_structural_group( model=geo_model, group_index=3, structural_group_name="left_series", elements=[geo_model.structural_frame.get_element_by_name("rock3")], structural_relation=gp.data.StackRelationType.BASEMENT ) gp.remove_structural_group_by_name(model=geo_model, group_name="default_formation") # %% from gempy_engine.core.data.options import MeshExtractionMaskingOptions from gempy_engine.core.data.options import EvaluationOptions evaluation_options: EvaluationOptions = geo_model.interpolation_options.evaluation_options evaluation_options.mesh_extraction_masking_options = MeshExtractionMaskingOptions.INTERSECT s = gp.compute_model(geo_model) gpv.plot_2d(geo_model) # %% gpv.plot_3d( model=geo_model, show_surfaces=True, show_data=True, image=True, show_topography=True, kwargs_plot_structured_grid={'opacity': 0.1} ) # %% # ! White are True, black are False if False: p = gpv.plot_2d( model=geo_model, cell_number=2, override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix_squeezed[0], show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None} ) gpv.plot_2d( model=geo_model, cell_number=2, override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix_squeezed[1], show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None} ) gpv.plot_2d( model=geo_model, cell_number=2, override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix_squeezed[2], show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None} ) gpv.plot_2d( model=geo_model, cell_number=2, override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix_squeezed[3], show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None} ) ================================================ FILE: test/test_modules/__init__.py ================================================ ================================================ FILE: test/test_modules/test_cg/__init__.py ================================================ ================================================ FILE: test/test_modules/test_cg/test_cg_solver.py ================================================ import pytest import gempy as gp from gempy.core.data.enumerators import ExampleModel from gempy.optional_dependencies import require_gempy_viewer import gempy_engine.core.backend_tensor as BackendTensor from gempy_engine.modules.weights_cache.weights_cache_interface import WeightCache PLOT = True pytest.mark.skip("Run explicitly") def test_solve_with_cg(): model = gp.generate_example_model(ExampleModel.GREENSTONE, compute_model=False) print(model.structural_frame) WeightCache.clear_cache() BackendTensor.PYKEOPS = False sol = gp.compute_model( gempy_model=model, engine_config=gp.data.GemPyEngineConfig( backend=gp.data.AvailableBackends.PYTORCH, use_gpu=False, dtype='float64' ) ) if PLOT: gpv = require_gempy_viewer() gpv.plot_3d(model, image=True) def test_save_weights(): model = gp.generate_example_model(ExampleModel.GREENSTONE, compute_model=False) print(model.structural_frame) sol = gp.compute_model( gempy_model=model, engine_config=gp.data.GemPyEngineConfig( backend=gp.data.AvailableBackends.PYTORCH, use_gpu=False, dtype='float32' ) ) weights1 = sol.octrees_output[0].outputs[0].weights weights2 = sol.octrees_output[0].outputs[1].weights weights3 = sol.octrees_output[0].outputs[2].weights WeightCache.clear_cache() BackendTensor.PYKEOPS = False sol = gp.compute_model( gempy_model=model, engine_config=gp.data.GemPyEngineConfig( backend=gp.data.AvailableBackends.PYTORCH, use_gpu=False, dtype='float32' ) ) if PLOT: gpv = require_gempy_viewer() gpv.plot_3d(model, image=True) def test_keops_x_torch(): model = gp.generate_example_model(ExampleModel.GREENSTONE, compute_model=False) print(model.structural_frame) WeightCache.clear_cache() BackendTensor.PYKEOPS = False sol = gp.compute_model( gempy_model=model, engine_config=gp.data.GemPyEngineConfig( backend=gp.data.AvailableBackends.PYTORCH, use_gpu=False, dtype='float32' ) ) if PLOT: gpv = require_gempy_viewer() gpv.plot_3d(model, image=True) ================================================ FILE: test/test_modules/test_compute_times_for_grids.py ================================================ import numpy as np import gempy as gp import time PLOT = True DENSE_RESOLUTION = [20, 20, 20] def test_compute_time_dense_dense(): geo_model: gp.data.GeoModel = _setup_model() geo_model.interpolation_options.evaluation_options.mesh_extraction = True geo_model.grid.active_grids = gp.data.Grid.GridTypes.DENSE start_time = time.perf_counter() gp.compute_model(geo_model) print(f"Computing model on grids: {geo_model.grid.active_grids}") end_time = time.perf_counter() computation_time_dense_I = end_time - start_time start_time = time.perf_counter() gp.compute_model(geo_model) print(f"Computing model on grids: {geo_model.grid.active_grids}") end_time = time.perf_counter() computation_time_dense_II = end_time - start_time print(f"Computation only model dense grid 125*50*50: {computation_time_dense_I:.2f} seconds") print(f"Computation only model dense grid 125*50*50: {computation_time_dense_II:.2f} seconds") # Assert that it is not too different assert abs(computation_time_dense_I - computation_time_dense_II) < 0.2 def test_compute_time_topo_dense_grid(): geo_model: gp.data.GeoModel = _setup_model() geo_model.grid.active_grids = gp.data.Grid.GridTypes.DENSE start_time = time.perf_counter() gp.compute_model(geo_model) print(f"Computing model on grids: {geo_model.grid.active_grids}") end_time = time.perf_counter() computation_time_dense = end_time - start_time # Compute a solution for the model geo_model.grid.active_grids = gp.data.Grid.GridTypes.TOPOGRAPHY start_time = time.perf_counter() gp.compute_model(geo_model) print(f"Computing model on grids: {geo_model.grid.active_grids}") end_time = time.perf_counter() computation_time_topo = end_time - start_time # Recompute model as a new grid was added geo_model.grid.active_grids = gp.data.Grid.GridTypes.TOPOGRAPHY | gp.data.Grid.GridTypes.DENSE start_time = time.perf_counter() gp.compute_model(geo_model) print(f"Computing model on grids: {geo_model.grid.active_grids}") end_time = time.perf_counter() computation_time_topo_dense = end_time - start_time print(f"Computation only model dense grid 125*50*50: {computation_time_dense:.2f} seconds") print(f"Computation time with topography 125*50: {computation_time_topo:.2f} seconds") print(f"Computation time with topography and dense grid 125*50*50: {computation_time_topo_dense:.2f} seconds") # Assert that dense takes longer than topo and that the sum of both is close assert computation_time_topo_dense > computation_time_dense def test_compute_time_custom_dense_grid(): geo_model: gp.data.GeoModel = _setup_model() # numpy array with random coordinates within the extent of the model custom_coordinates = np.random.uniform( low=geo_model.grid.extent[:3], high=geo_model.grid.extent[3:], size=(1000, 3) ) start_time = time.perf_counter() gp.compute_model_at(geo_model, custom_coordinates) end_time = time.perf_counter() computation_time_at = end_time - start_time print(f"Computation compute_at with 1000 custom points: {computation_time_at:.2f} seconds") def _setup_model(): # Define the path to data data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/' path_to_data = data_path + "/data/input_data/jan_models/" # Create a GeoModel instance geo_model = gp.create_geomodel( project_name='EGU_example', extent=[0, 2500, 0, 1000, 0, 1000], resolution=DENSE_RESOLUTION, importer_helper=gp.data.ImporterHelper( path_to_orientations=path_to_data + "model7_orientations.csv", path_to_surface_points=path_to_data + "model7_surface_points.csv" ) ) # Map geological series to surfaces gp.map_stack_to_surfaces( gempy_model=geo_model, mapping_object={ "Fault_Series" : ('fault'), "Strat_Series1": ('rock3'), "Strat_Series2": ('rock2', 'rock1'), } ) # Define youngest structural group as fault gp.set_is_fault(geo_model, ["Fault_Series"]) # Setting a randomly generated topography gp.set_topography_from_random( grid=geo_model.grid, fractal_dimension=2, d_z=np.array([700, 950]), topography_resolution=np.array([125, 50]) ) return geo_model ================================================ FILE: test/test_modules/test_faults/__init__.py ================================================ ================================================ FILE: test/test_modules/test_faults/test_fault_relations.py ================================================ import numpy as np import gempy as gp from gempy.core.data.enumerators import ExampleModel from gempy.optional_dependencies import require_gempy_viewer PLOT = True def test_fault_relations_implementation(): # TODO! (Miguel Dec25) These fault description are not serializing! model = gp.generate_example_model(ExampleModel.FAULT_RELATION, compute_model=True) correct_relations = np.array([ [0, 1, 1, 1], [0, 0, 0, 1], [0, 0, 0, 0], [0, 0, 0, 0] ], dtype=bool) # Assert assert np.array_equal(model.structural_frame.fault_relations, correct_relations) == True if PLOT: gpv = require_gempy_viewer() gtv: gpv.GemPyToVista = gpv.plot_3d( model=model, show_data=True, image=True, show=True ) ================================================ FILE: test/test_modules/test_faults/test_finite_faults.py ================================================ import numpy as np import pytest import gempy as gp import gempy_viewer as gpv from gempy.core.data.enumerators import ExampleModel from test.verify_helper import verify_model_serialization from gempy_viewer.optional_dependencies import require_pyvista from test.conftest import TEST_SPEED, TestSpeed center = np.array([500, 500, 500]) radius = np.array([500, 100, 200]) / 2 k = np.array([1, 1, 1]) * 1 PLOT = False @pytest.mark.skipif(TEST_SPEED.value < TestSpeed.SECONDS.value, reason="Global test speed below this test value.") def test_finite_fault_scalar_field_on_fault(): geo_model: gp.data.GeoModel = gp.generate_example_model( example_model=ExampleModel.ONE_FAULT, compute_model=False ) regular_grid = geo_model.grid.regular_grid # TODO: Extract grid from the model scaled_center = geo_model.input_transform.apply(center.reshape(1, -1))[0] scaled_radius = geo_model.input_transform.scale_points(radius.reshape(1, -1))[0] scalar_funtion: callable = gp.implicit_functions.ellipsoid_3d_factory( # * This paints the 3d regular grid center=scaled_center, radius=scaled_radius, max_slope=k # * This controls the speed of the transition ) transform = gp.data.Transform( position=np.array([0, 0, 0]), rotation=np.array([0, 60, 0]), scale=np.ones(3) ) faults_data = gp.data.FaultsData( fault_values_everywhere=np.zeros(0), fault_values_on_sp=np.zeros(0), thickness=None, fault_values_ref=np.zeros(0), fault_values_rest=np.zeros(0), finite_fault_data=gp.data.FiniteFaultData( implicit_function=scalar_funtion, implicit_function_transform=transform, pivot=scaled_center, ) ) geo_model.structural_frame.structural_groups[0].faults_input_data = faults_data verify_model_serialization( model=geo_model, verify_moment="after", file_name=f"verify/{geo_model.meta.name}" ) gp.compute_model(geo_model) # TODO: Try to do this afterwards if plot_pyvista := True: plot3d = gpv.plot_3d( geo_model, show_lith=False, show=False ) scalar_block_for_viz = _apply( regular_grid=regular_grid, scalar_funtion=gp.implicit_functions.ellipsoid_3d_factory( # * This paints the 3d regular grid center=center, radius=radius, max_slope=k # * This controls the speed of the transition ), transform=transform ) _plot_scalar_field(regular_grid, scalar_block_for_viz, plot3d.p, background_field=False) def test_finite_fault_scalar_field(): geo_model: gp.data.GeoModel = gp.generate_example_model( example_model=ExampleModel.ONE_FAULT, compute_model=False ) regular_grid = geo_model.grid.regular_grid # TODO: Extract grid from the model scalar_funtion: callable = gp.implicit_functions.ellipsoid_3d_factory( # * This paints the 3d regular grid center=center, radius=radius, max_slope=k # * This controls the speed of the transition ) transform = gp.data.Transform( position=np.array([0, 0, 0]), rotation=np.array([0, 0, 30]), scale=np.ones(3) ) transformed_points = transform.apply_inverse_with_pivot( points=regular_grid.values, pivot=center ) scalar_block = scalar_funtion(transformed_points) # TODO: Try to do this afterwards # scalar_fault = scalar_funtion(regular_grid.values) if plot_pyvista := True: _plot_scalar_field(regular_grid, scalar_block) @pytest.mark.skipif(TEST_SPEED.value < TestSpeed.SECONDS.value, reason="Global test speed below this test value.") def test_finite_fault_scalar_field_on_fault_ZERO(): geo_model: gp.data.GeoModel = gp.generate_example_model( example_model=ExampleModel.ONE_FAULT, compute_model=True ) regular_grid = geo_model.grid.regular_grid # TODO: Extract grid from the model scalar_funtion: callable = gp.implicit_functions.ellipsoid_3d_factory( # * This paints the 3d regular grid center=center, radius=radius, max_slope=k # * This controls the speed of the transition ) transform = gp.data.Transform( position=np.array([0, 0, 0]), rotation=np.array([0, 0, 30]), scale=np.ones(3) ) transformed_points = transform.apply_inverse_with_pivot( points=regular_grid.values, pivot=center ) scalar_block = scalar_funtion(transformed_points) # TODO: Try to do this afterwards # scalar_fault = scalar_funtion(regular_grid.values) if plot_pyvista := True: plot3d = gpv.plot_3d( geo_model, show=False ) _plot_scalar_field(regular_grid, scalar_block, plot3d.p) def _apply(regular_grid, scalar_funtion, transform): transformed_points = transform.apply_inverse_with_pivot( points=regular_grid.values, # ! This depends on the octree pivot=center ) scalar_block = scalar_funtion(transformed_points) return scalar_block def _plot_scalar_field(regular_grid, scalar_block, plotter=None, background_field=True): if not PLOT: return pv = require_pyvista() p = plotter or pv.Plotter() regular_grid_values = regular_grid.values_vtk_format grid_3d = regular_grid_values.reshape(*(regular_grid.resolution + 1), 3).T regular_grid_mesh = pv.StructuredGrid(*grid_3d) regular_grid_mesh["lith"] = scalar_block if True: area_of_effect = regular_grid_mesh.threshold([.000000001, 1.1]) p.add_mesh(area_of_effect, show_edges=True, opacity=.4) area_of_effect_2 = regular_grid_mesh.threshold([.2, 1.1]) p.add_mesh(area_of_effect_2, show_edges=True, opacity=.8) if background_field: p.add_mesh(regular_grid_mesh, show_edges=False, opacity=.2) p.show_bounds(bounds=regular_grid.extent) # * Add the fault if False: dual_mesh = pv.PolyData(fault_mesh.vertices, np.insert(fault_mesh.edges, 0, 3, axis=1).ravel()) dual_mesh["bar"] = scalar_fault p.add_mesh(dual_mesh, opacity=1, silhouette=True, show_edges=True) p.show() ================================================ FILE: test/test_modules/test_faults/test_finite_faults.test_finite_fault_scalar_field_on_fault.verify/fault.approved.txt ================================================ { "meta": { "name": "fault", "creation_date": "", "last_modification_date": null, "owner": null }, "structural_frame": { "structural_groups": [ { "name": "Fault_Series", "elements": [ { "name": "fault", "is_active": true, "_color": "#527682", "surface_points": { "name_id_map": { "fault": 65970106, "rock1": 167239155, "rock2": 217776925 }, "_model_transform": null }, "orientations": { "name_id_map": { "fault": 65970106, "rock1": 167239155, "rock2": 217776925 }, "_model_transform": null }, "scalar_field_at_interface": null, "_id": 65970106 } ], "structural_relation": 3, "fault_relations": 1, "faults_input_data": { "fault_values_everywhere": [], "fault_values_on_sp": [], "fault_values_ref": [], "fault_values_rest": [], "thickness": null, "finite_fault_data": { "implicit_function_transform": { "position": [ 0, 0, 0 ], "rotation": [ 0, 60, 0 ], "scale": [ 1.0, 1.0, 1.0 ], "_is_default_transform": false, "_cached_pivot": null }, "pivot": [ 0.0, 0.0, 0.0 ] } }, "solution": null }, { "name": "Strat_Series", "elements": [ { "name": "rock2", "is_active": true, "_color": "#ffbe00", "surface_points": { "name_id_map": { "fault": 65970106, "rock1": 167239155, "rock2": 217776925 }, "_model_transform": null }, "orientations": { "name_id_map": { "fault": 65970106, "rock1": 167239155, "rock2": 217776925 }, "_model_transform": null }, "scalar_field_at_interface": null, "_id": 217776925 }, { "name": "rock1", "is_active": true, "_color": "#9f0052", "surface_points": { "name_id_map": { "fault": 65970106, "rock1": 167239155, "rock2": 217776925 }, "_model_transform": null }, "orientations": { "name_id_map": { "fault": 65970106, "rock1": 167239155, "rock2": 217776925 }, "_model_transform": null }, "scalar_field_at_interface": null, "_id": 167239155 } ], "structural_relation": 1, "fault_relations": 3, "faults_input_data": null, "solution": null } ], "color_generator": { "_index": 4 }, "basement_color": "#728f02", "is_dirty": true, "binary_meta_data": { "sp_binary_length": 792, "ori_binary_length": 300 } }, "grid": { "_octree_grid": { "resolution": [ 64, 64, 64 ], "extent": [ 0.0, 1000.0, 0.0, 1000.0, 0.0, 1000.0 ], "_transform": null, "_base_resolution": [ 2, 2, 2 ] }, "_dense_grid": null, "_custom_grid": null, "_topography": null, "_sections": null, "_centered_grid": null, "_transform": null, "_octree_levels": -1, "binary_meta_data": { "custom_grid_binary_length": 0, "topography_binary_length": 0 }, "active_grids": 1025 }, "geophysics_input": null, "input_transform": { "position": [ -500.0, -500.0, -500.0 ], "rotation": [ 0.0, 0.0, 0.0 ], "scale": [ 0.0005, 0.0005, 0.0005 ], "_is_default_transform": false, "_cached_pivot": null }, "_interpolation_options": { "kernel_options": { "range": 1.7, "c_o": 10.0, "uni_degree": 1, "i_res": 4.0, "gi_res": 2.0, "number_dimensions": 3, "kernel_function": "cubic", "kernel_solver": 1, "compute_condition_number": false, "optimizing_condition_number": false, "condition_number": null }, "evaluation_options": { "_number_octree_levels": 6, "_number_octree_levels_surface": 4, "octree_curvature_threshold": -1.0, "octree_error_threshold": 1.0, "octree_min_level": 2, "mesh_extraction": true, "mesh_extraction_masking_options": 3, "mesh_extraction_fancy": true, "evaluation_chunk_size": 500000, "compute_scalar": true, "compute_scalar_gradient": false, "verbose": false }, "debug": false, "cache_mode": 3, "cache_model_name": "fault", "block_solutions_type": 1, "sigmoid_slope": 5000000 } } ================================================ FILE: test/test_modules/test_gempy_subsurface.py ================================================ import pytest import gempy as gp import gempy_viewer as gpv from gempy import optional_dependencies from gempy.core.data.enumerators import ExampleModel import numpy as np from ..conftest import REQUIREMENT_LEVEL, Requirements pytestmark = pytest.mark.skipif( condition=REQUIREMENT_LEVEL.value < Requirements.DEV.value and False, reason="This test needs higher requirements." ) pd = pytest.importorskip("pandas", reason="Pandas is not installed") def test_gempy_to_subsurface(): model = gp.generate_example_model(ExampleModel.ANTICLINE, compute_model=True) if False: gpv.plot_3d(model) vertex: list[np.ndarray] = model.solutions.raw_arrays.vertices simplex_list: list[np.ndarray] = model.solutions.raw_arrays.edges idx_max = 0 for simplex_array in simplex_list: simplex_array += idx_max idx_max = simplex_array.max() + 1 vertex_id_array = [np.full(v.shape[0], i + 1) for i, v in enumerate(vertex)] cell_id_array = [np.full(v.shape[0], i + 1) for i, v in enumerate(simplex_list)] concatenated_id_array = np.concatenate(vertex_id_array) concatenated_cell_id_array = np.concatenate(cell_id_array) subsurface = optional_dependencies.require_subsurface() ss = subsurface meshes: ss.UnstructuredData = ss.UnstructuredData.from_array( vertex=np.concatenate(vertex), cells=np.concatenate(simplex_list), vertex_attr=pd.DataFrame({'id': concatenated_id_array}), cells_attr=pd.DataFrame({'id': concatenated_cell_id_array}) ) trisurf = subsurface.core.structs.unstructured_elements.triangular_surface.TriSurf(meshes) pyvista_mesh = ss.visualization.to_pyvista_mesh(trisurf) ss.visualization.pv_plot([pyvista_mesh], image_2d=True) def test_gempy_to_subsurface_II(): model: gp.data.GeoModel = gp.generate_example_model(ExampleModel.ANTICLINE, compute_model=True) subsurface = optional_dependencies.require_subsurface() ss = subsurface meshes: ss.UnstructuredData = model.solutions.raw_arrays.meshes_to_subsurface() trisurf = subsurface.core.structs.unstructured_elements.triangular_surface.TriSurf(meshes) pyvista_mesh = ss.visualization.to_pyvista_mesh(trisurf) ss.visualization.pv_plot([pyvista_mesh], image_2d=True) def test_gempy_to_subsurface_III(): model = gp.generate_example_model(ExampleModel.ANTICLINE, compute_model=True) subsurface = optional_dependencies.require_subsurface() ss = subsurface meshes: ss.UnstructuredData = model.solutions.meshes_to_unstruct() trisurf = subsurface.core.structs.unstructured_elements.triangular_surface.TriSurf(meshes) pyvista_mesh = ss.visualization.to_pyvista_mesh(trisurf) ss.visualization.pv_plot([pyvista_mesh], image_2d=True) ================================================ FILE: test/test_modules/test_geophysics/__init__.py ================================================ import pytest pytestmark = pytest.mark.skip(reason="Geophysics module is not yet updated") ================================================ FILE: test/test_modules/test_geophysics/test_gravity.py ================================================ # Importing GemPy import gempy as gp # Importing auxiliary libraries import numpy as np from test.verify_helper import verify_model_serialization def test_gravity(): color_generator = gp.data.ColorsGenerator() element1 = gp.data.StructuralElement( name='surface1', color=next(color_generator), surface_points=gp.data.SurfacePointsTable.from_arrays( x=np.array([3, 9]), y=np.array([0, 0]), z=np.array([3.05, 3.05]), names='surface1' ), orientations=gp.data.OrientationsTable.from_arrays( x=np.array([6]), y=np.array([0]), z=np.array([4]), G_x=np.array([0]), G_y=np.array([0]), G_z=np.array([1]), names='surface1' ) ) element2 = gp.data.StructuralElement( name='surface2', color=next(color_generator), surface_points=gp.data.SurfacePointsTable.from_arrays( x=np.array([3, 9]), y=np.array([0, 0]), z=np.array([1.02, 1.02]), names='surface2' ), orientations=gp.data.OrientationsTable.initialize_empty() ) frame = gp.data.StructuralFrame( structural_groups=[gp.data.StructuralGroup( name='default', elements=[element1, element2], structural_relation=gp.data.StackRelationType.ERODE ) ], color_gen=color_generator ) geo_model: gp.data.GeoModel = gp.create_geomodel( project_name="2-layers", extent=[0, 12, -2, 2, 0, 4], resolution=[500, 1, 500], refinement=1, structural_frame=frame, ) gp.compute_model(geo_model, validate_serialization=True) import gempy_viewer as gpv gpv.plot_2d(geo_model, cell_number=0) gp.set_centered_grid( grid=geo_model.grid, centers=np.array([[6, 0, 4]], dtype="float"), resolution=np.array([10, 10, 100], dtype="float"), radius=np.array([16000, 16000, 16000], dtype="float") # ? This radius makes 0 sense but it is the original one in gempy v2 ) gravity_gradient = gp.calculate_gravity_gradient(geo_model.grid.centered_grid) geo_model.geophysics_input = gp.data.GeophysicsInput( tz=gravity_gradient, densities=np.array([2.6, 2.4, 3.2]), ) verify_model_serialization( model=geo_model, verify_moment="after", file_name=f"verify/{geo_model.meta.name}" ) gp.compute_model(geo_model) print(geo_model.solutions.gravity) np.testing.assert_almost_equal(geo_model.solutions.gravity, np.array([-1624.1714]), decimal=4) ================================================ FILE: test/test_modules/test_geophysics/test_gravity.test_gravity.verify/2-layers.approved.txt ================================================ { "meta": { "name": "2-layers", "creation_date": "", "last_modification_date": null, "owner": null }, "structural_frame": { "structural_groups": [ { "name": "default", "elements": [ { "name": "surface1", "is_active": true, "_color": "#015482", "surface_points": { "name_id_map": { "surface1": 57292991 }, "_model_transform": null }, "orientations": { "name_id_map": { "surface1": 57292991 }, "_model_transform": null }, "scalar_field_at_interface": 0.09000000000000002, "_id": -1 }, { "name": "surface2", "is_active": true, "_color": "#9f0052", "surface_points": { "name_id_map": { "surface2": 21816406 }, "_model_transform": null }, "orientations": { "name_id_map": null, "_model_transform": null }, "scalar_field_at_interface": -0.2483333333333333, "_id": -1 } ], "structural_relation": 1, "fault_relations": null, "faults_input_data": null, "solution": null } ], "color_generator": { "_index": 3 }, "basement_color": "#ffbe00", "is_dirty": true, "binary_meta_data": { "sp_binary_length": 144, "ori_binary_length": 60 } }, "grid": { "_octree_grid": null, "_dense_grid": { "resolution": [ 500, 1, 500 ], "extent": [ 0.0, 12.0, -2.0, 2.0, 0.0, 4.0 ], "_transform": null, "_base_resolution": [ 2, 2, 2 ] }, "_custom_grid": null, "_topography": null, "_sections": null, "_centered_grid": { "centers": [ [ 6.0, 0.0, 4.0 ] ], "resolution": [ 10.0, 10.0, 100.0 ], "radius": [ 16000.0, 16000.0, 16000.0 ] }, "_transform": null, "_octree_levels": -1, "binary_meta_data": { "custom_grid_binary_length": 0, "topography_binary_length": 0 }, "active_grids": 1058 }, "geophysics_input": { "gravity_input": { "tz": [], "densities": [ 2.6, 2.4, 3.2 ] }, "magnetics_input": null }, "input_transform": { "position": [ -6.0, -0.0, -2.51 ], "rotation": [ 0.0, 0.0, 0.0 ], "scale": [ 0.08333333333333333, 0.16778523489932887, 0.08333333333333333 ], "_is_default_transform": false, "_cached_pivot": null }, "_interpolation_options": { "kernel_options": { "range": 1.7, "c_o": 10.0, "uni_degree": 1, "i_res": 4.0, "gi_res": 2.0, "number_dimensions": 3, "kernel_function": "cubic", "kernel_solver": 1, "compute_condition_number": false, "optimizing_condition_number": false, "condition_number": null }, "evaluation_options": { "_number_octree_levels": 1, "_number_octree_levels_surface": 4, "octree_curvature_threshold": -1.0, "octree_error_threshold": 1.0, "octree_min_level": 2, "mesh_extraction": true, "mesh_extraction_masking_options": 3, "mesh_extraction_fancy": true, "evaluation_chunk_size": 500000, "compute_scalar": true, "compute_scalar_gradient": false, "verbose": false }, "debug": false, "cache_mode": 3, "cache_model_name": "2-layers", "block_solutions_type": 2, "sigmoid_slope": 5000000 } } ================================================ FILE: test/test_modules/test_grids/__init__.py ================================================ ================================================ FILE: test/test_modules/test_grids/test_custom_grid.py ================================================ import numpy as np import pytest from test.verify_helper import verify_model_serialization from test.conftest import TEST_SPEED, TestSpeed import gempy as gp from gempy.core.data.enumerators import ExampleModel pytestmark = pytest.mark.skipif(TEST_SPEED.value < TestSpeed.SECONDS.value, reason="Global test speed below this test value.") xyz_coord = np.array( [[0, 0, 0], [1000, 0, 0], [0, 1000, 0], [1000, 1000, 0], [0, 0, 1000], [1000, 0, 1000], [0, 1000, 1000], [1000, 1000, 1000]], dtype=float ) def test_custom_grid(): geo_model: gp.data.GeoModel = gp.generate_example_model( example_model=ExampleModel.ANTICLINE, compute_model=False ) geo_model.interpolation_options.number_octree_levels = 2 gp.set_custom_grid( grid=geo_model.grid, xyz_coord=xyz_coord ) verify_model_serialization( model=geo_model, verify_moment="after", file_name=f"verify/{geo_model.meta.name}" ) sol: gp.data.Solutions = gp.compute_model(geo_model, validate_serialization=True) np.testing.assert_array_equal( sol.raw_arrays.custom, np.array([3., 3., 3., 3., 1., 1., 1., 1.]) ) def test_compute_at(): geo_model: gp.data.GeoModel = gp.generate_example_model( example_model=ExampleModel.ANTICLINE, compute_model=False ) geo_model.interpolation_options.number_octree_levels = 2 sol: np.ndarray = gp.compute_model_at( gempy_model=geo_model, at=xyz_coord ) np.testing.assert_array_equal( sol, np.array([3., 3., 3., 3., 1., 1., 1., 1.]) ) ================================================ FILE: test/test_modules/test_grids/test_custom_grid.test_custom_grid.verify/fold.approved.txt ================================================ { "meta": { "name": "fold", "creation_date": "", "last_modification_date": null, "owner": null }, "structural_frame": { "structural_groups": [ { "name": "Strat_Series", "elements": [ { "name": "rock2", "is_active": true, "_color": "#9f0052", "surface_points": { "name_id_map": { "rock1": 67239155, "rock2": 117776925 }, "_model_transform": null }, "orientations": { "name_id_map": { "rock1": 67239155, "rock2": 117776925 }, "_model_transform": null }, "scalar_field_at_interface": null, "_id": 117776925 }, { "name": "rock1", "is_active": true, "_color": "#015482", "surface_points": { "name_id_map": { "rock1": 67239155, "rock2": 117776925 }, "_model_transform": null }, "orientations": { "name_id_map": { "rock1": 67239155, "rock2": 117776925 }, "_model_transform": null }, "scalar_field_at_interface": null, "_id": 67239155 } ], "structural_relation": 1, "fault_relations": null, "faults_input_data": null, "solution": null } ], "color_generator": { "_index": 3 }, "basement_color": "#ffbe00", "is_dirty": true, "binary_meta_data": { "sp_binary_length": 1296, "ori_binary_length": 120 } }, "grid": { "_octree_grid": { "resolution": [ 32, 32, 32 ], "extent": [ 0.0, 1000.0, 0.0, 1000.0, 0.0, 1000.0 ], "_transform": null, "_base_resolution": [ 2, 2, 2 ] }, "_dense_grid": null, "_custom_grid": {}, "_topography": null, "_sections": null, "_centered_grid": null, "_transform": null, "_octree_levels": -1, "binary_meta_data": { "custom_grid_binary_length": 192, "topography_binary_length": 0 }, "active_grids": 1029 }, "geophysics_input": null, "input_transform": { "position": [ -500.0, -500.0, -510.0 ], "rotation": [ 0.0, 0.0, 0.0 ], "scale": [ 0.0005, 0.0005, 0.0005 ], "_is_default_transform": false, "_cached_pivot": null }, "_interpolation_options": { "kernel_options": { "range": 1.7, "c_o": 10.0, "uni_degree": 1, "i_res": 4.0, "gi_res": 2.0, "number_dimensions": 3, "kernel_function": "cubic", "kernel_solver": 1, "compute_condition_number": false, "optimizing_condition_number": false, "condition_number": null }, "evaluation_options": { "_number_octree_levels": 2, "_number_octree_levels_surface": 4, "octree_curvature_threshold": -1.0, "octree_error_threshold": 1.0, "octree_min_level": 2, "mesh_extraction": true, "mesh_extraction_masking_options": 3, "mesh_extraction_fancy": true, "evaluation_chunk_size": 500000, "compute_scalar": true, "compute_scalar_gradient": false, "verbose": false }, "debug": false, "cache_mode": 3, "cache_model_name": "fold", "block_solutions_type": 1, "sigmoid_slope": 5000000 } } ================================================ FILE: test/test_modules/test_grids/test_diamond_square.py ================================================ import gempy.core.data.grid_modules.diamond_square import pytest # to add fixtures and to test error raises import numpy as np # as another testing environment def test_class_nocrash(): """Simply check if class can be instantiated""" gempy.core.data.grid_modules.diamond_square.DiaomondSquare(size=(5, 5)) def test_grid_generation(): """Test grid generation and extension for non-suitable grid sizes""" ds = gempy.core.data.grid_modules.diamond_square.DiaomondSquare(size=(5, 5)) assert ds.grid.shape == (5, 5) ds = gempy.core.data.grid_modules.diamond_square.DiaomondSquare(size=(8, 10)) assert ds.grid.shape == (9, 17) def test_diamond_selection(): """Test selection of diamond positions""" ds = gempy.core.data.grid_modules.diamond_square.DiaomondSquare(size=(5, 5)) z = ds.get_selection_diamond(1) assert np.all(z == np.array([[2, 0, 0, 0, 2], [0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 0, 0], [2, 0, 0, 0, 2]])) z = ds.get_selection_diamond(0) assert np.all(z == np.array([[2, 0, 2, 0, 2], [0, 1, 0, 1, 0], [2, 0, 2, 0, 2], [0, 1, 0, 1, 0], [2, 0, 2, 0, 2]])) def test_square_selection(): """Test selection of diamond positions""" ds = gempy.core.data.grid_modules.diamond_square.DiaomondSquare(size=(5, 5)) z = ds.get_selection_square(0) assert np.all(z == np.array([[0, 0, 2, 0, 2, 0, 0], [0, 2, 1, 2, 1, 2, 0], [2, 1, 2, 1, 2, 1, 2], [0, 2, 1, 2, 1, 2, 0], [2, 1, 2, 1, 2, 1, 2], [0, 2, 1, 2, 1, 2, 0], [0, 0, 2, 0, 2, 0, 0]])) z = ds.get_selection_square(1) assert np.all(z == np.array([[0, 0, 0, 0, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 1, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 1, 0, 2, 0, 1, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 1, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 0, 0]])) def test_random_initialization(): """Test random initialization of corner points""" ds = gempy.core.data.grid_modules.diamond_square.DiaomondSquare(size=(5, 6), seed=52062) ds.random_initialization() m_pow_max = min(ds.n, ds.m) step_size = int(2 ** m_pow_max) np.testing.assert_array_almost_equal(ds.grid[::step_size, ::step_size], np.array([[0.35127005, 0.55476571, 0.93745213], [0.66668382, 0.85215985, 0.53222795]])) def test_random_initialization_level(): """Test random initialization on lower level""" ds = gempy.core.data.grid_modules.diamond_square.DiaomondSquare(size=(33, 33), seed=52062) level = 3 ds.random_initialization(level=level) step_size = int(2 ** level) np.testing.assert_array_almost_equal(ds.grid[::step_size, ::step_size], np.array([[0.35127005, 0.55476571, 0.93745213, 0.66668382, 0.85215985], [0.53222795, 0.55800027, 0.20974513, 0.74837501, 0.64394326], [0.0359961, 0.22723278, 0.56347804, 0.13438884, 0.32613594], [0.20868763, 0.03116471, 0.1498014, 0.20755495, 0.86021482], [0.64707457, 0.44744272, 0.36504945, 0.52473407, 0.27948164]])) def test_reset_grid(): ds = gempy.core.data.grid_modules.diamond_square.DiaomondSquare(size=(5, 6), seed=52062) ds.random_initialization() ds.reset_grid() np.testing.assert_array_almost_equal(ds.grid, np.array([[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., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0.]])) def test_random_func(): """Test random function implementation""" ds = gempy.core.data.grid_modules.diamond_square.DiaomondSquare(size=(33, 33), seed=52062) np.testing.assert_array_almost_equal(ds.random_func(2, 2), np.array([-0.14872995, 0.05476571])) # testing for correct default implementation ds.r_type = 'default' np.testing.assert_array_almost_equal(ds.random_func(2, 2), np.array([0.43745213, 0.16668382])) # testing long-range correlation ds.r_type = 'long_range' np.testing.assert_array_almost_equal(ds.random_func(2, 2), np.array([0.04401998, 0.00402849])) # testing level-scale correlation ds.r_type = 'level_scale' np.testing.assert_array_almost_equal(ds.random_func(2, 2), np.array([0.18600009, 0.06991504])) # testing deterministic implementation (no random value) ds.r_type = 'deterministic' assert ds.random_func(2, 2) == 0.0 def test_random_func_raises_error(): """Test if random function raises NonImplementedError correctly""" ds = gempy.core.data.grid_modules.diamond_square.DiaomondSquare(size=(33, 33), seed=52062) ds.r_type = 'fail' with pytest.raises(NotImplementedError): ds.random_func(2, 2) def test_interpolate(): """Test interpolation step itself""" ds = gempy.core.data.grid_modules.diamond_square.DiaomondSquare(size=(9, 9), seed=52062) ds.interpolate() np.testing.assert_array_almost_equal(ds.grid, np.array([[0., 0.2951411, 0.21781267, 0.29361906, 0.01037812, -0.0376406, -0.59889259, 0.01136296, 0.], [-0.13102895, -0.07079394, 0.40240191, -0.24139454, -0.64535709, -0.25358984, -0.20811689, -0.38977623, -0.02280871], [-0.32311967, -0.08246826, 0.03236034, -0.72313104, -0.6863271, -0.09742037, 0.16154592, -0.41643384, -0.23968483], [-0.53344647, -0.09313507, -0.66247738, -0.42849468, -0.06519284, -0.50628043, -0.31159035, 0.53516982, 0.07387422], [0.23421434, 0.32817758, -0.45156142, -0.24627659, -0.2974599, 0.16071127, 0.36261452, 0.62070397, 0.60516641], [-0.20172896, -0.05668301, -0.26331217, -0.22196496, 0.42029741, 0.35078669, 0.77129922, 0.38999358, 0.95701668], [-0.41296032, -0.04377428, -0.23235603, 0.60954786, 0.72643437, 0.37788456, 0.62211967, 0.16198846, 0.61709021], [0.00192109, -0.28399285, 0.28596529, 0.54081866, 1.00235637, 0.25454729, 0.1248549, 0.85789169, 0.23511424], [0., -0.54507578, -0.33592062, 0.62216544, 0.77575097, 0.5338132, 0.22007596, 0.02926128, 0.]])) ================================================ FILE: test/test_modules/test_grids/test_grids_I.py ================================================ import pytest import gempy as gp import gempy_viewer as gpv from gempy.core.data.enumerators import ExampleModel from gempy_engine.core.data.output.blocks_value_type import ValueType from gempy_engine.plugins.plotting.helper_functions import plot_block_and_input_2d from test.conftest import TEST_SPEED, TestSpeed pytestmark = pytest.mark.skipif(TEST_SPEED.value < TestSpeed.MINUTES.value, reason="Global test speed below this test value.") def test_octree(): geo_model: gp.data.GeoModel = gp.generate_example_model( example_model=ExampleModel.ANTICLINE, compute_model=False ) geo_model.interpolation_options.number_octree_levels = 4 gp.compute_model(geo_model) plot_block_and_input_2d( stack_number=0, interpolation_input=geo_model.interpolation_input_copy, outputs=geo_model.solutions.octrees_output, structure=geo_model.structural_frame.input_data_descriptor.stack_structure, value_type=ValueType.ids ) gpv.plot_3d(geo_model, show_data=True, show_boundaries=True, show_lith=False, image=True) geo_model.interpolation_options.number_octree_levels = 2 gp.compute_model(geo_model) plot_block_and_input_2d( stack_number=0, interpolation_input=geo_model.interpolation_input_copy, outputs=geo_model.solutions.octrees_output, structure=geo_model.structural_frame.input_data_descriptor.stack_structure, value_type=ValueType.ids ) gpv.plot_3d(geo_model, show_data=True, show_boundaries=True, show_lith=False, image=True) ================================================ FILE: test/test_modules/test_grids/test_grids_sections.py ================================================ import numpy as np import pytest import gempy as gp import gempy_viewer as gpv from gempy.core.data.enumerators import ExampleModel from test.verify_helper import verify_model_serialization from test.conftest import TEST_SPEED, TestSpeed def test_section_grids(): geo_model: gp.data.GeoModel = gp.generate_example_model( example_model=ExampleModel.ANTICLINE, compute_model=False ) geo_model.grid.set_octree_grid_by_levels( octree_levels=2, evaluation_options=geo_model.interpolation_options.evaluation_options ) gp.set_section_grid( grid=geo_model.grid, section_dict={ 'section_SW-NE': ((250., 250.), (1750., 1750.), (100, 100)), 'section_NW-SE': ((250., 1750.), (1750., 250.), (100, 100)) } ) gp.set_topography_from_random( grid=geo_model.grid, fractal_dimension=1.2, d_z=np.array([200, 1000]), topography_resolution=np.array([60, 60]) ) verify_model_serialization( model=geo_model, verify_moment="after", file_name=f"verify/{geo_model.meta.name}" ) gp.compute_model(geo_model, validate_serialization=True) gpv.plot_2d( model=geo_model, section_names=['section_SW-NE', 'section_NW-SE', 'topography'], direction=['x'], cell_number=['mid'], show_lith=[False, True, False, True], show_boundaries=[False, False, False, True], show_topography=True, show_section_traces=True # TODO: Test this one ) gpv.plot_2d(geo_model, show_boundaries=False, section_names=['topography']) gpv.plot_3d( geo_model, show_data=True, show_boundaries=True, show_lith=False, image=True ) @pytest.mark.skipif(TEST_SPEED.value < TestSpeed.MINUTES.value, reason="Global test speed below this test value.") def test_topography_II(): geo_model: gp.data.GeoModel = gp.generate_example_model( example_model=ExampleModel.TWO_AND_A_HALF_D, compute_model=False ) gpv.plot_3d( geo_model, show_data=True, show_boundaries=True, show_lith=False, image=True ) verify_model_serialization( model=geo_model, verify_moment="after", file_name=f"verify/{geo_model.meta.name}" ) gp.compute_model(geo_model) gpv.plot_2d(geo_model, show_boundaries=False, section_names=['topography']) gpv.plot_3d( geo_model, show_data=True, show_boundaries=True, show_lith=True, image=True, kwargs_plot_structured_grid={'opacity': .1} ) ================================================ FILE: test/test_modules/test_grids/test_grids_sections.test_section_grids.verify/fold.approved.txt ================================================ { "meta": { "name": "fold", "creation_date": "", "last_modification_date": null, "owner": null }, "structural_frame": { "structural_groups": [ { "name": "Strat_Series", "elements": [ { "name": "rock2", "is_active": true, "_color": "#9f0052", "surface_points": { "name_id_map": { "rock1": 67239155, "rock2": 117776925 }, "_model_transform": null }, "orientations": { "name_id_map": { "rock1": 67239155, "rock2": 117776925 }, "_model_transform": null }, "scalar_field_at_interface": null, "_id": 117776925 }, { "name": "rock1", "is_active": true, "_color": "#015482", "surface_points": { "name_id_map": { "rock1": 67239155, "rock2": 117776925 }, "_model_transform": null }, "orientations": { "name_id_map": { "rock1": 67239155, "rock2": 117776925 }, "_model_transform": null }, "scalar_field_at_interface": null, "_id": 67239155 } ], "structural_relation": 1, "fault_relations": null, "faults_input_data": null, "solution": null } ], "color_generator": { "_index": 3 }, "basement_color": "#ffbe00", "is_dirty": true, "binary_meta_data": { "sp_binary_length": 1296, "ori_binary_length": 120 } }, "grid": { "_octree_grid": { "resolution": [ 4, 4, 4 ], "extent": [ 0.0, 1000.0, 0.0, 1000.0, 0.0, 1000.0 ], "_transform": null, "_base_resolution": [ 2, 2, 2 ] }, "_dense_grid": null, "_custom_grid": null, "_topography": { "_regular_grid": { "resolution": [ 4, 4, 4 ], "extent": [ 0.0, 1000.0, 0.0, 1000.0, 0.0, 1000.0 ], "_transform": null, "_base_resolution": [ 2, 2, 2 ] }, "source": null, "values": [], "resolution": [ 60, 60 ], "raster_shape": [], "_mask_topo": null, "_x": null, "_y": null }, "_sections": { "z_ext": [ 0.0, 1000.0 ], "section_dict": { "section_SW-NE": [ [ 250.0, 250.0 ], [ 1750.0, 1750.0 ], [ 100, 100 ] ], "section_NW-SE": [ [ 250.0, 1750.0 ], [ 1750.0, 250.0 ], [ 100, 100 ] ] } }, "_centered_grid": null, "_transform": null, "_octree_levels": -1, "binary_meta_data": { "custom_grid_binary_length": 0, "topography_binary_length": 86400 }, "active_grids": 1049 }, "geophysics_input": null, "input_transform": { "position": [ -500.0, -500.0, -510.0 ], "rotation": [ 0.0, 0.0, 0.0 ], "scale": [ 0.0005, 0.0005, 0.0005 ], "_is_default_transform": false, "_cached_pivot": null }, "_interpolation_options": { "kernel_options": { "range": 1.7, "c_o": 10.0, "uni_degree": 1, "i_res": 4.0, "gi_res": 2.0, "number_dimensions": 3, "kernel_function": "cubic", "kernel_solver": 1, "compute_condition_number": false, "optimizing_condition_number": false, "condition_number": null }, "evaluation_options": { "_number_octree_levels": 2, "_number_octree_levels_surface": 4, "octree_curvature_threshold": -1.0, "octree_error_threshold": 1.0, "octree_min_level": 2, "mesh_extraction": true, "mesh_extraction_masking_options": 3, "mesh_extraction_fancy": true, "evaluation_chunk_size": 500000, "compute_scalar": true, "compute_scalar_gradient": false, "verbose": false }, "debug": false, "cache_mode": 3, "cache_model_name": "fold", "block_solutions_type": 1, "sigmoid_slope": 5000000 } } ================================================ FILE: test/test_modules/test_grids/test_grids_sections.test_topography_II.verify/Model1.approved.txt ================================================ { "meta": { "name": "Model1", "creation_date": "", "last_modification_date": null, "owner": null }, "structural_frame": { "structural_groups": [ { "name": "Fault1", "elements": [ { "name": "fault1", "is_active": true, "_color": "#443988", "surface_points": { "name_id_map": { "fault1": 26244822 }, "_model_transform": null }, "orientations": { "name_id_map": { "fault1": 26244822 }, "_model_transform": null }, "scalar_field_at_interface": null, "_id": -1 } ], "structural_relation": 3, "fault_relations": 3, "faults_input_data": null, "solution": null }, { "name": "default_formations", "elements": [ { "name": "surface1", "is_active": true, "_color": "#015482", "surface_points": { "name_id_map": {}, "_model_transform": null }, "orientations": { "name_id_map": null, "_model_transform": null }, "scalar_field_at_interface": null, "_id": -1 }, { "name": "surface2", "is_active": true, "_color": "#ffbe00", "surface_points": { "name_id_map": { "surface2": 21816406 }, "_model_transform": null }, "orientations": { "name_id_map": null, "_model_transform": null }, "scalar_field_at_interface": null, "_id": -1 }, { "name": "surface3", "is_active": true, "_color": "#728f02", "surface_points": { "name_id_map": { "surface3": 98435767 }, "_model_transform": null }, "orientations": { "name_id_map": null, "_model_transform": null }, "scalar_field_at_interface": null, "_id": -1 } ], "structural_relation": 1, "fault_relations": null, "faults_input_data": null, "solution": null } ], "color_generator": { "_index": 5 }, "basement_color": "#9f0052", "is_dirty": true, "binary_meta_data": { "sp_binary_length": 360, "ori_binary_length": 120 } }, "grid": { "_octree_grid": null, "_dense_grid": { "resolution": [ 50, 50, 50 ], "extent": [ 0.0, 791.0, -200.0, 200.0, -582.0, 0.0 ], "_transform": null, "_base_resolution": [ 2, 2, 2 ] }, "_custom_grid": null, "_topography": { "_regular_grid": { "resolution": [ 50, 50, 50 ], "extent": [ 0.0, 791.0, -200.0, 200.0, -582.0, 0.0 ], "_transform": null, "_base_resolution": [ 2, 2, 2 ] }, "source": null, "values": [], "resolution": [ 50, 40 ], "raster_shape": [], "_mask_topo": null, "_x": null, "_y": null }, "_sections": null, "_centered_grid": null, "_transform": null, "_octree_levels": -1, "binary_meta_data": { "custom_grid_binary_length": 0, "topography_binary_length": 48000 }, "active_grids": 1034 }, "geophysics_input": null, "input_transform": { "position": [ -417.5, -0.5, 157.0 ], "rotation": [ 0.0, 0.0, 0.0 ], "scale": [ 0.0012853470437017994, 0.0012853470437017994, 0.0012853470437017994 ], "_is_default_transform": false, "_cached_pivot": null }, "_interpolation_options": { "kernel_options": { "range": 1.7, "c_o": 10.0, "uni_degree": 1, "i_res": 4.0, "gi_res": 2.0, "number_dimensions": 3, "kernel_function": "cubic", "kernel_solver": 1, "compute_condition_number": false, "optimizing_condition_number": false, "condition_number": null }, "evaluation_options": { "_number_octree_levels": 1, "_number_octree_levels_surface": 4, "octree_curvature_threshold": -1.0, "octree_error_threshold": 1.0, "octree_min_level": 2, "mesh_extraction": true, "mesh_extraction_masking_options": 3, "mesh_extraction_fancy": true, "evaluation_chunk_size": 500000, "compute_scalar": true, "compute_scalar_gradient": false, "verbose": false }, "debug": false, "cache_mode": 3, "cache_model_name": "Model1", "block_solutions_type": 2, "sigmoid_slope": 5000000 } } ================================================ FILE: test/test_modules/test_json_io.py ================================================ """ Tests for JSON I/O operations in GemPy. """ import json import numpy as np import pytest import gempy as gp from gempy.modules.json_io import JsonIO from gempy_engine.core.data.stack_relation_type import StackRelationType from gempy.core.data.importer_helper import ImporterHelper import pandas as pd from gempy.core.data.structural_group import FaultsRelationSpecialCase @pytest.fixture def sample_surface_points(): """Create sample surface points data for testing.""" x = np.array([0, 1, 2, 3, 4]) y = np.array([0, 1, 2, 3, 4]) z = np.array([0, 1, 2, 3, 4]) nugget = np.array([0.00002, 0.00002, 0.00002, 0.00002, 0.00002]) # Create a SurfacePointsTable surface_points = gp.data.SurfacePointsTable.from_arrays( x=x, y=y, z=z, names=["surface_0", "surface_0", "surface_1", "surface_1", "surface_2"], nugget=nugget ) return surface_points, x, y, z, nugget @pytest.fixture def sample_orientations(): """Create sample orientation data for testing.""" x = np.array([0.5, 1.5, 2.5, 3.5]) y = np.array([0.5, 1.5, 2.5, 3.5]) z = np.array([0.5, 1.5, 2.5, 3.5]) G_x = np.array([0, 0, 0, 0]) G_y = np.array([0, 0, 0, 0]) G_z = np.array([1, 1, -1, 1]) # One reversed orientation nugget = np.array([0.01, 0.01, 0.01, 0.01]) # Create an OrientationsTable orientations = gp.data.OrientationsTable.from_arrays( x=x, y=y, z=z, G_x=G_x, G_y=G_y, G_z=G_z, names=["surface_0", "surface_1", "surface_1", "surface_2"], nugget=nugget ) return orientations, x, y, z, G_x, G_y, G_z, nugget @pytest.fixture def sample_json_data(sample_surface_points, sample_orientations): """Create sample JSON data for testing.""" _, x_sp, y_sp, z_sp, nugget_sp = sample_surface_points _, x_ori, y_ori, z_ori, G_x, G_y, G_z, nugget_ori = sample_orientations return { "metadata": { "name": "sample_model", "creation_date": "2024-03-19", "last_modification_date": "2024-03-19", "owner": "tutorial" }, "surface_points": [ { "x": float(x_sp[i]), "y": float(y_sp[i]), "z": float(z_sp[i]), "id": 0, # Default ID, not used in tests "nugget": float(nugget_sp[i]) } for i in range(len(x_sp)) ], "orientations": [ { "x": float(x_ori[i]), "y": float(y_ori[i]), "z": float(z_ori[i]), "G_x": float(G_x[i]), "G_y": float(G_y[i]), "G_z": float(G_z[i]), "id": 0, # Default ID, not used in tests "nugget": float(nugget_ori[i]), "polarity": 1 # Always set to 1 since we're testing the raw G_z values } for i in range(len(x_ori)) ], "series": [ { "name": "series1", "surfaces": ["surface_0", "surface_1"], "structural_relation": "erode", "colors": ["#FF0000", "#00FF00"] }, { "name": "series2", "surfaces": ["surface_2"], "structural_relation": "erode", "colors": ["#0000FF"] } ], "grid_settings": { "regular_grid_resolution": [10, 10, 10], "regular_grid_extent": [0, 4, 0, 4, 0, 4], "octree_levels": None }, "interpolation_options": {} } def test_surface_points_loading(): """Test loading surface points from JSON data.""" data = { "surface_points": [ { "x": 1.0, "y": 1.0, "z": 1.0, "id": 0, "nugget": 0.0 } ] } surface_points = JsonIO._load_surface_points(data["surface_points"]) assert len(surface_points.xyz) == 1 assert surface_points.xyz[0, 0] == 1.0 # x coordinate assert surface_points.xyz[0, 1] == 1.0 # y coordinate assert surface_points.xyz[0, 2] == 1.0 # z coordinate assert surface_points.nugget[0] == 0.0 # nugget def test_orientations_loading(): """Test loading orientations from JSON data.""" data = { "orientations": [ { "x": 1.0, "y": 1.0, "z": 1.0, "G_x": 0.0, "G_y": 0.0, "G_z": 1.0, "id": 0, "nugget": 0.01, "polarity": 1 } ] } orientations = JsonIO._load_orientations(data["orientations"]) assert len(orientations.xyz) == 1 assert orientations.xyz[0, 0] == 1.0 # x coordinate assert orientations.xyz[0, 1] == 1.0 # y coordinate assert orientations.xyz[0, 2] == 1.0 # z coordinate assert orientations.grads[0, 0] == 0.0 # G_x assert orientations.grads[0, 1] == 0.0 # G_y assert orientations.grads[0, 2] == 1.0 # G_z assert orientations.nugget[0] == 0.01 # nugget def test_surface_points_saving(tmp_path): """Test saving surface points to a JSON file.""" # Create sample surface points surface_points = np.array([[1.0, 1.0, 1.0, 0, 0.0]]) # Create a complete model data structure data = { "surface_points": [ { "x": 1.0, "y": 1.0, "z": 1.0, "id": 0, "nugget": 0.0 } ], "orientations": [], "grid_settings": { "regular_grid_resolution": [10, 10, 10], "regular_grid_extent": [0, 10, 0, 10, 0, 10] } } # Save to temporary file file_path = tmp_path / "test_surface_points.json" with open(file_path, 'w') as f: json.dump(data, f) # Load and verify loaded_model = JsonIO.load_model_from_json(str(file_path)) assert len(loaded_model.surface_points_copy.xyz) == 1 assert loaded_model.surface_points_copy.xyz[0, 0] == 1.0 # x coordinate assert loaded_model.surface_points_copy.xyz[0, 1] == 1.0 # y coordinate assert loaded_model.surface_points_copy.xyz[0, 2] == 1.0 # z coordinate assert loaded_model.surface_points_copy.nugget[0] == 0.0 # nugget def test_invalid_surface_points_data(): """Test that invalid surface points data raises appropriate errors.""" data = { 'surface_points': [{'x': 0, 'y': 0}], # Missing z 'orientations': [], 'grid_settings': {'regular_grid_resolution': [50, 50, 50], 'regular_grid_extent': [0, 1, 0, 1, 0, 1]} } with pytest.raises(ValueError, match="Missing required key in surface point: z"): JsonIO._validate_json_schema(data) def test_missing_surface_points_data(): """Test handling of missing surface points data.""" data = { "orientations": [] # Missing surface_points key } with pytest.raises(ValueError, match="Missing required key: surface_points"): JsonIO._validate_json_schema(data) def test_invalid_orientations_data(): """Test that invalid orientations data raises appropriate errors.""" data = { 'surface_points': [{'x': 0, 'y': 0, 'z': 0}], 'orientations': [{'x': 0, 'y': 0, 'z': 0}], # Missing G_x, G_y, G_z 'grid_settings': {'regular_grid_resolution': [50, 50, 50], 'regular_grid_extent': [0, 1, 0, 1, 0, 1]} } with pytest.raises(ValueError, match="Missing required key in orientation: G_x"): JsonIO._validate_json_schema(data) def test_missing_orientations_data(): """Test handling of missing orientations data.""" data = { "surface_points": [] # Missing orientations key } with pytest.raises(ValueError, match="Missing required key: orientations"): JsonIO._validate_json_schema(data) def test_invalid_orientation_polarity(): """Test handling of invalid orientation polarity.""" data = { "orientations": [ { "x": 1.0, "y": 1.0, "z": 1.0, "G_x": 0.0, "G_y": 0.0, "G_z": 1.0, "id": 0, "nugget": 0.01, "polarity": 2 # Invalid polarity value } ] } with pytest.raises(ValueError, match="Invalid polarity in orientation"): JsonIO._load_orientations(data["orientations"]) def test_default_nugget_values(tmp_path): """Test that default nugget values are correctly applied.""" # Create minimal data without nugget values data = { "surface_points": [ { "x": 1.0, "y": 1.0, "z": 1.0, "id": 0 } ], "orientations": [ { "x": 1.0, "y": 1.0, "z": 1.0, "G_x": 0.0, "G_y": 0.0, "G_z": 1.0, "id": 0, "polarity": 1 } ], "grid_settings": { "regular_grid_resolution": [10, 10, 10], "regular_grid_extent": [0, 10, 0, 10, 0, 10] } } # Save data to temporary file file_path = tmp_path / "test_default_nugget.json" with open(file_path, 'w') as f: json.dump(data, f) # Load model from JSON model = JsonIO.load_model_from_json(str(file_path)) # Verify default nugget values assert model.surface_points_copy.nugget[0] == 0.0 # Default surface point nugget assert model.orientations_copy.nugget[0] == 0.01 # Default orientation nugget def test_default_series_values(tmp_path): """Test that default series values are correctly applied.""" # Create minimal data without series data = { "surface_points": [ { "x": 1.0, "y": 1.0, "z": 1.0, "id": 0, "nugget": 0.0 } ], "orientations": [ { "x": 1.0, "y": 1.0, "z": 1.0, "G_x": 0.0, "G_y": 0.0, "G_z": 1.0, "id": 0, "nugget": 0.01, "polarity": 1 } ], "grid_settings": { "regular_grid_resolution": [10, 10, 10], "regular_grid_extent": [0, 10, 0, 10, 0, 10] } } # Save data to temporary file file_path = tmp_path / "test_default_series.json" with open(file_path, 'w') as f: json.dump(data, f) # Load model from JSON (this will create default series) model = JsonIO.load_model_from_json(str(file_path)) # Verify default series assert len(model.structural_frame.structural_groups) == 1 assert model.structural_frame.structural_groups[0].name == "Strat_Series" assert model.structural_frame.structural_groups[0].structural_relation == StackRelationType.ERODE def test_default_interpolation_options(): """Test that default interpolation options are correctly applied.""" data = { 'surface_points': [{'x': 0, 'y': 0, 'z': 0}], 'orientations': [{'x': 0, 'y': 0, 'z': 0, 'G_x': 0, 'G_y': 0, 'G_z': 1}], 'grid_settings': {'regular_grid_resolution': [50, 50, 50], 'regular_grid_extent': [0, 1, 0, 1, 0, 1]}, 'interpolation_options': {} } JsonIO._validate_json_schema(data) assert data['interpolation_options'].get('number_octree_levels', 4) == 4 # Default is now 4 assert data['interpolation_options'].get('mesh_extraction', True) is True def test_default_metadata(tmp_path): """Test that default metadata is correctly applied.""" # Create minimal data without metadata data = { "surface_points": [ { "x": 1.0, "y": 1.0, "z": 1.0, "id": 0, "nugget": 0.0 } ], "orientations": [ { "x": 1.0, "y": 1.0, "z": 1.0, "G_x": 0.0, "G_y": 0.0, "G_z": 1.0, "id": 0, "nugget": 0.01, "polarity": 1 } ], "grid_settings": { "regular_grid_resolution": [10, 10, 10], "regular_grid_extent": [0, 10, 0, 10, 0, 10] } } # Save data to temporary file file_path = tmp_path / "test_default_metadata.json" with open(file_path, 'w') as f: json.dump(data, f) # Load model from JSON model = JsonIO.load_model_from_json(str(file_path)) # Verify default metadata assert model.meta.name == "GemPy Model" # Default name assert model.meta.owner == "GemPy Modeller" # Default owner assert model.meta.creation_date is not None # Should be set to current date assert model.meta.last_modification_date is not None # Should be set to current date def test_optional_series_colors(): """Test that series colors are optional and properly validated.""" data = { 'surface_points': [{'x': 0, 'y': 0, 'z': 0}], 'orientations': [{'x': 0, 'y': 0, 'z': 0, 'G_x': 0, 'G_y': 0, 'G_z': 1}], 'grid_settings': {'regular_grid_resolution': [50, 50, 50], 'regular_grid_extent': [0, 1, 0, 1, 0, 1]}, 'series': [ {'name': 'Series1', 'surfaces': ['Surface1'], 'colors': ['#015482']}, # Valid color {'name': 'Series2', 'surfaces': ['Surface2']} # No colors specified ] } JsonIO._validate_json_schema(data) assert data['series'][0]['colors'] == ['#015482'] # Color should be preserved assert 'colors' not in data['series'][1] # No colors should be added if not specified @pytest.fixture def sample_model_with_series(): """Create a sample model with series and fault relations.""" # Create a simple model with two series and a fault from gempy.core.data.structural_frame import StructuralFrame from gempy.core.data import StructuralElement, StructuralGroup from gempy_engine.core.data.stack_relation_type import StackRelationType # Create structural frame first structural_frame = StructuralFrame.initialize_default_structure() # Create model with structural frame model = gp.create_geomodel( project_name="test_model", extent=[0, 10, 0, 10, 0, 10], resolution=[10, 10, 10], structural_frame=structural_frame ) # Create structural elements fault_element = StructuralElement( name="fault", color=next(model.structural_frame.color_generator), surface_points=gp.data.SurfacePointsTable.initialize_empty(), orientations=gp.data.OrientationsTable.initialize_empty() ) rock1_element = StructuralElement( name="rock1", color=next(model.structural_frame.color_generator), surface_points=gp.data.SurfacePointsTable.initialize_empty(), orientations=gp.data.OrientationsTable.initialize_empty() ) rock2_element = StructuralElement( name="rock2", color=next(model.structural_frame.color_generator), surface_points=gp.data.SurfacePointsTable.initialize_empty(), orientations=gp.data.OrientationsTable.initialize_empty() ) # Create structural groups fault_group = StructuralGroup( name="fault_series", elements=[fault_element], structural_relation=StackRelationType.FAULT ) strat_group = StructuralGroup( name="strat_series", elements=[rock1_element, rock2_element], structural_relation=StackRelationType.ERODE ) # Set up the structural frame model.structural_frame.structural_groups = [fault_group, strat_group] # Set fault relations (2x2 matrix: fault affects strat_series) model.structural_frame.fault_relations = np.array([ [0, 1], # fault_series affects strat_series [0, 0] # strat_series doesn't affect any series ]) # Add surface points gp.add_surface_points( geo_model=model, x=[1, 2, 3, 4, 5], y=[1, 2, 3, 4, 5], z=[1, 2, 3, 4, 5], elements_names=["fault", "rock1", "rock1", "rock2", "rock2"] ) # Add orientations gp.add_orientations( geo_model=model, x=[1.5, 2.5, 3.5, 4.5], y=[1.5, 2.5, 3.5, 4.5], z=[1.5, 2.5, 3.5, 4.5], elements_names=["fault", "rock1", "rock1", "rock2"], pole_vector=[[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1]] ) return model def test_metadata_handling(tmp_path, sample_model_with_series): """Test metadata handling in JSON I/O.""" model = sample_model_with_series # Add custom metadata model.meta.owner = "test_value" # Save model file_path = tmp_path / "test_metadata.json" JsonIO.save_model_to_json(model, str(file_path)) # Load model loaded_model = JsonIO.load_model_from_json(str(file_path)) # Verify metadata assert loaded_model.meta.owner == "test_value" assert loaded_model.meta.creation_date is not None assert loaded_model.meta.last_modification_date is not None def test_grid_settings_handling(tmp_path, sample_model_with_series): """Test grid settings handling in JSON I/O.""" model = sample_model_with_series # Modify grid settings model.grid.regular_grid.resolution = [20, 20, 20] model.grid.regular_grid.extent = [0, 20, 0, 20, 0, 20] # Save model file_path = tmp_path / "test_grid_settings.json" JsonIO.save_model_to_json(model, str(file_path)) # Load model loaded_model = JsonIO.load_model_from_json(str(file_path)) # Verify grid settings np.testing.assert_array_equal( model.grid.regular_grid.resolution, loaded_model.grid.regular_grid.resolution ) np.testing.assert_array_equal( model.grid.regular_grid.extent, loaded_model.grid.regular_grid.extent ) def test_interpolation_options_handling(tmp_path, sample_model_with_series): """Test interpolation options handling in JSON I/O.""" model = sample_model_with_series # Set custom interpolation options model.interpolation_options.kernel_options.range = 10.0 model.interpolation_options.kernel_options.c_o = 15.0 model.interpolation_options.mesh_extraction = False model.interpolation_options.number_octree_levels = 2 # Save model file_path = tmp_path / "test_interpolation_options.json" JsonIO.save_model_to_json(model, str(file_path)) # Load model loaded_model = JsonIO.load_model_from_json(str(file_path)) # Verify interpolation options assert loaded_model.interpolation_options.kernel_options.range == 10.0 assert loaded_model.interpolation_options.kernel_options.c_o == 15.0 assert loaded_model.interpolation_options.mesh_extraction is False assert loaded_model.interpolation_options.number_octree_levels == 2 def test_fault_relationships(tmp_path): """Test saving and loading models with fault relationships.""" # Create temporary CSV files for surface points and orientations surface_points_data = pd.DataFrame({ 'X': [500, 500, 500], 'Y': [500, 500, 500], 'Z': [800, 500, 200], 'surface': ['fault', 'rock2', 'rock1'] }) orientations_data = pd.DataFrame({ 'X': [500, 500, 500], 'Y': [500, 500, 500], 'Z': [800, 500, 200], 'dip': [90, 0, 0], 'azimuth': [90, 90, 90], 'polarity': [1, 1, 1], 'surface': ['fault', 'rock2', 'rock1'] }) surface_points_path = tmp_path / "surface_points.csv" orientations_path = tmp_path / "orientations.csv" surface_points_data.to_csv(surface_points_path, index=False) orientations_data.to_csv(orientations_path, index=False) # Create a model with fault relationships importer_helper = ImporterHelper( path_to_surface_points=str(surface_points_path), path_to_orientations=str(orientations_path) ) model = gp.create_geomodel( project_name='fault_test', extent=[0, 1000, 0, 1000, 0, 1000], resolution=[10, 10, 10], importer_helper=importer_helper ) # Map stack to surfaces gp.map_stack_to_surfaces( gempy_model=model, mapping_object={ "Fault_Series": ['fault'], "Strat_Series1": ['rock2'], "Strat_Series2": ['rock1'] } ) # Set structural relations model.structural_frame.structural_groups[0].structural_relation = StackRelationType.FAULT model.structural_frame.structural_groups[0].fault_relations = FaultsRelationSpecialCase.OFFSET_ALL # Save the model file_path = tmp_path / "fault_model.json" JsonIO.save_model_to_json(model, str(file_path)) # Load the model and verify loaded_model = JsonIO.load_model_from_json(str(file_path)) # Check structural groups assert len(loaded_model.structural_frame.structural_groups) == 3 assert loaded_model.structural_frame.structural_groups[0].name == "Fault_Series" assert loaded_model.structural_frame.structural_groups[0].structural_relation == StackRelationType.FAULT assert loaded_model.structural_frame.structural_groups[0].fault_relations == FaultsRelationSpecialCase.OFFSET_ALL # Check fault relations matrix np.testing.assert_array_equal( loaded_model.structural_frame.fault_relations, np.array([[0, 1, 1], [0, 0, 0], [0, 0, 0]]) ) def test_multiple_series_relationships(tmp_path): """Test saving and loading models with multiple series and different structural relationships.""" # Create temporary CSV files for surface points and orientations surface_points_data = pd.DataFrame({ 'X': [500, 500, 500, 500], 'Y': [500, 500, 500, 500], 'Z': [800, 600, 400, 200], 'surface': ['fault', 'rock3', 'rock2', 'rock1'] }) orientations_data = pd.DataFrame({ 'X': [500, 500, 500, 500], 'Y': [500, 500, 500, 500], 'Z': [800, 600, 400, 200], 'dip': [90, 0, 0, 0], 'azimuth': [90, 90, 90, 90], 'polarity': [1, 1, 1, 1], 'surface': ['fault', 'rock3', 'rock2', 'rock1'] }) surface_points_path = tmp_path / "surface_points.csv" orientations_path = tmp_path / "orientations.csv" surface_points_data.to_csv(surface_points_path, index=False) orientations_data.to_csv(orientations_path, index=False) # Create a model with multiple series relationships importer_helper = ImporterHelper( path_to_surface_points=str(surface_points_path), path_to_orientations=str(orientations_path) ) model = gp.create_geomodel( project_name='multiple_series_test', extent=[0, 1000, 0, 1000, 0, 1000], resolution=[10, 10, 10], importer_helper=importer_helper ) # Map stack to surfaces gp.map_stack_to_surfaces( gempy_model=model, mapping_object={ "Fault_Series": ['fault'], "Series3": ['rock3'], "Series2": ['rock2'], "Series1": ['rock1'] } ) # Set structural relations model.structural_frame.structural_groups[0].structural_relation = StackRelationType.FAULT model.structural_frame.structural_groups[0].fault_relations = FaultsRelationSpecialCase.OFFSET_ALL model.structural_frame.structural_groups[1].structural_relation = StackRelationType.ONLAP model.structural_frame.structural_groups[2].structural_relation = StackRelationType.ERODE model.structural_frame.structural_groups[3].structural_relation = StackRelationType.ERODE # Set colors for verification model.structural_frame.get_element_by_name("fault").color = '#527682' # Fault color model.structural_frame.get_element_by_name("rock3").color = '#9f0052' # rock3 color model.structural_frame.get_element_by_name("rock2").color = '#015482' # rock2 color model.structural_frame.get_element_by_name("rock1").color = '#728f02' # rock1 color # Save the model file_path = tmp_path / "multiple_series_model.json" JsonIO.save_model_to_json(model, str(file_path)) # Load the model and verify loaded_model = JsonIO.load_model_from_json(str(file_path)) # Check structural groups assert len(loaded_model.structural_frame.structural_groups) == 4 assert loaded_model.structural_frame.structural_groups[0].structural_relation == StackRelationType.FAULT assert loaded_model.structural_frame.structural_groups[1].structural_relation == StackRelationType.ONLAP assert loaded_model.structural_frame.structural_groups[2].structural_relation == StackRelationType.ERODE # Check colors are preserved assert loaded_model.structural_frame.get_element_by_name("fault").color == '#527682' assert loaded_model.structural_frame.get_element_by_name("rock3").color == '#9f0052' assert loaded_model.structural_frame.get_element_by_name("rock2").color == '#015482' assert loaded_model.structural_frame.get_element_by_name("rock1").color == '#728f02' def test_combination_model(tmp_path): """Test saving and loading a complex model that combines faults and unconformities.""" # Create temporary CSV files for surface points and orientations surface_points_data = pd.DataFrame({ 'X': [500, 500, 500], 'Y': [500, 500, 500], 'Z': [800, 500, 200], 'surface': ['fault', 'rock2', 'rock1'] }) orientations_data = pd.DataFrame({ 'X': [500, 500, 500], 'Y': [500, 500, 500], 'Z': [800, 500, 200], 'dip': [90, 0, 0], 'azimuth': [90, 90, 90], 'polarity': [1, 1, 1], 'surface': ['fault', 'rock2', 'rock1'] }) surface_points_path = tmp_path / "surface_points.csv" orientations_path = tmp_path / "orientations.csv" surface_points_data.to_csv(surface_points_path, index=False) orientations_data.to_csv(orientations_path, index=False) # Create a combination model importer_helper = ImporterHelper( path_to_surface_points=str(surface_points_path), path_to_orientations=str(orientations_path) ) model = gp.create_geomodel( project_name='Combination Model', extent=[0, 1000, 0, 1000, 0, 1000], resolution=[10, 10, 10], importer_helper=importer_helper ) # Map stack to surfaces gp.map_stack_to_surfaces( gempy_model=model, mapping_object={ "Fault_Series": ['fault'], "Strat_Series1": ['rock2'], "Strat_Series2": ['rock1'] } ) # Set structural relations model.structural_frame.structural_groups[0].structural_relation = StackRelationType.FAULT model.structural_frame.structural_groups[0].fault_relations = FaultsRelationSpecialCase.OFFSET_ALL model.structural_frame.structural_groups[1].structural_relation = StackRelationType.ERODE model.structural_frame.structural_groups[2].structural_relation = StackRelationType.ONLAP # Set metadata model.meta.creation_date = "2024-03-24" model.meta.last_modification_date = "2024-03-24" # Save the model file_path = tmp_path / "combination_model.json" JsonIO.save_model_to_json(model, str(file_path)) # Load the model and verify loaded_model = JsonIO.load_model_from_json(str(file_path)) # Check structural setup assert len(loaded_model.structural_frame.structural_groups) == 3 assert loaded_model.structural_frame.structural_groups[0].structural_relation == StackRelationType.FAULT assert loaded_model.structural_frame.structural_groups[1].structural_relation == StackRelationType.ERODE assert loaded_model.structural_frame.structural_groups[2].structural_relation == StackRelationType.ONLAP # Check fault relations np.testing.assert_array_equal( loaded_model.structural_frame.fault_relations, np.array([[0, 1, 1], [0, 0, 0], [0, 0, 0]]) ) # Check metadata assert loaded_model.meta.creation_date == "2024-03-24" assert loaded_model.meta.last_modification_date == "2024-03-24" ================================================ FILE: test/test_modules/test_marching_cubes.py ================================================ import numpy as np from gempy_engine.core.data.raw_arrays_solution import RawArraysSolution import gempy as gp from gempy.core.data.enumerators import ExampleModel from gempy.core.data.grid_modules import RegularGrid from gempy.modules.mesh_extranction import marching_cubes from gempy.optional_dependencies import require_gempy_viewer PLOT = True def test_marching_cubes_implementation(): model = gp.generate_example_model(ExampleModel.COMBINATION, compute_model=False) # Change the grid to only be the dense grid dense_grid: RegularGrid = RegularGrid( extent=model.grid.extent, resolution=np.array([40, 20, 20]) ) model.grid.dense_grid = dense_grid gp.set_active_grid( grid=model.grid, grid_type=[model.grid.GridTypes.DENSE], reset=True ) model.interpolation_options = gp.data.InterpolationOptions.init_dense_grid_options() gp.compute_model(model) # Assert assert model.solutions.block_solution_type == RawArraysSolution.BlockSolutionType.DENSE_GRID assert model.solutions.dc_meshes is None arrays = model.solutions.raw_arrays # * arrays is equivalent to gempy v2 solutions # assert arrays.scalar_field_matrix.shape == (3, 8_000) # * 3 surfaces, 8000 points marching_cubes.set_meshes_with_marching_cubes(model) # Assert assert model.solutions.block_solution_type == RawArraysSolution.BlockSolutionType.DENSE_GRID assert model.solutions.dc_meshes is None assert model.structural_frame.structural_groups[0].elements[0].vertices.shape == (600, 3) assert model.structural_frame.structural_groups[1].elements[0].vertices.shape == (860, 3) assert model.structural_frame.structural_groups[2].elements[0].vertices.shape == (1_256, 3) assert model.structural_frame.structural_groups[2].elements[1].vertices.shape == (1_680, 3) if PLOT: gpv = require_gempy_viewer() gtv: gpv.GemPyToVista = gpv.plot_3d( model=model, show_data=True, image=True, show=True ) ================================================ FILE: test/test_modules/test_marching_cubes_pytorch.py ================================================ """ Copied from "test_marching_cubes.py" to test the pytorch implementation of marching cubes with minor adjustments """ import os os.environ["DEFAULT_BACKEND"] = "PYTORCH" import numpy as np from gempy_engine.core.data.raw_arrays_solution import RawArraysSolution import gempy as gp from gempy.core.data.enumerators import ExampleModel from gempy.core.data.grid_modules import RegularGrid from gempy.modules.mesh_extranction import marching_cubes from gempy.optional_dependencies import require_gempy_viewer PLOT = True def test_marching_cubes_implementation(): assert os.environ["DEFAULT_BACKEND"] == "PYTORCH" model = gp.generate_example_model(ExampleModel.COMBINATION, compute_model=False) # Change the grid to only be the dense grid dense_grid: RegularGrid = RegularGrid( extent=model.grid.extent, resolution=np.array([40, 20, 20]) ) model.grid.dense_grid = dense_grid gp.set_active_grid( grid=model.grid, grid_type=[model.grid.GridTypes.DENSE], reset=True ) model.interpolation_options = gp.data.InterpolationOptions.init_dense_grid_options() gp.compute_model(model) # Assert assert model.solutions.block_solution_type == RawArraysSolution.BlockSolutionType.DENSE_GRID assert model.solutions.dc_meshes is None arrays = model.solutions.raw_arrays # * arrays is equivalent to gempy v2 solutions # assert arrays.scalar_field_matrix.shape == (3, 8_000) # * 3 surfaces, 8000 points marching_cubes.set_meshes_with_marching_cubes(model) # Assert assert model.solutions.block_solution_type == RawArraysSolution.BlockSolutionType.DENSE_GRID assert model.solutions.dc_meshes is None assert model.structural_frame.structural_groups[0].elements[0].vertices.shape == (600, 3) assert model.structural_frame.structural_groups[1].elements[0].vertices.shape == (860, 3) assert model.structural_frame.structural_groups[2].elements[0].vertices.shape == (1_256, 3) assert model.structural_frame.structural_groups[2].elements[1].vertices.shape == (1_680, 3) if PLOT: gpv = require_gempy_viewer() gtv: gpv.GemPyToVista = gpv.plot_3d( model=model, show_data=True, image=True, show=True ) ================================================ FILE: test/test_modules/test_outliers.py ================================================ import gempy as gp from gempy.optional_dependencies import require_gempy_viewer from gempy.core.data.enumerators import ExampleModel from gempy.core.data.grid_modules import RegularGrid import numpy as np PLOT = True def test_outliers_model_1(): # Path to input data data_path = "https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/" path_to_data = data_path + "/data/input_data/video_tutorials_v3/" # Create instance of geomodel model = gp.create_geomodel( project_name='tutorial_model_onlap_1', extent=[0, 2000, 0, 1000, 0, 1000], resolution=[100, 50, 50], importer_helper=gp.data.ImporterHelper( path_to_orientations=path_to_data + "tutorial_model_onlap_1_orientations.csv?cache=", path_to_surface_points=path_to_data + "tutorial_model_onlap_1_surface_points.csv?cache=" ) ) # Map geological series to surfaces gp.map_stack_to_surfaces( gempy_model=model, mapping_object={ "Young_Series": ("basin_fill_2", "basin_fill_1"), "Old_Series" : ("basin_top", "basin_bottom") } ) # Set the relation of the youngest group to Onlap from gempy_engine.core.data.stack_relation_type import StackRelationType model.structural_frame.structural_groups[0].structural_relation = StackRelationType.ONLAP model.interpolation_options.sigmoid_slope = 5_000_000 # Compute a solution for the model gp.compute_model( gempy_model=model, engine_config=gp.data.GemPyEngineConfig( backend=gp.data.AvailableBackends.numpy ) ) # Assert arrays = model.solutions.raw_arrays # * arrays is equivalent to gempy v2 solutions assert arrays.scalar_field_matrix.shape == (2, 250_000) # * 2 groups, 250000 points if PLOT: gpv = require_gempy_viewer() gpv.plot_2d( model=model, show_data=True, show_boundaries=False, show=True ) gpv.plot_2d( model=model, show_data=False, show_boundaries=False, show_scalar=True, show=True ) gpv.plot_2d( model=model, show_data=False, show_boundaries=False, show_scalar=True, series_n=1, show=True ) ================================================ FILE: test/test_modules/test_pile/__init__.py ================================================ ================================================ FILE: test/test_modules/test_pile/test_stratigraphic_pile.py ================================================ import os import pandas as pd import pytest from subsurface.core.geological_formats.boreholes.boreholes import BoreholeSet, MergeOptions from subsurface.core.geological_formats.boreholes.collars import Collars from subsurface.core.geological_formats.boreholes.survey import Survey from subsurface.core.reader_helpers.readers_data import GenericReaderFilesHelper from subsurface.modules.reader.wells.read_borehole_interface import read_lith, read_survey, read_collar from subsurface.modules.visualization import to_pyvista_line, pv_plot, to_pyvista_points import gempy as gp import gempy_viewer as gpv # Check if PATH_TO_SPREMBERG_STRATIGRAPHY is set if not skip the test @pytest.mark.skipif( os.getenv("PATH_TO_SPREMBERG") is None, reason="PATH_TO_SPREMBERG_STRATIGRAPHY is not set" ) class TestStratigraphicPile: @pytest.fixture(autouse=True) def borehole_set(self): reader: GenericReaderFilesHelper = GenericReaderFilesHelper( file_or_buffer=os.getenv("PATH_TO_SPREMBERG") + "Spremberg_stratigraphy.csv", columns_map={ 'hole_id' : 'id', 'depth_from': 'top', 'depth_to' : 'base', 'lit_code' : 'component lith' } ) lith: pd.DataFrame = read_lith(reader) reader: GenericReaderFilesHelper = GenericReaderFilesHelper( file_or_buffer=os.getenv("PATH_TO_SPREMBERG") + "Spremberg_survey.csv", columns_map={ 'depth' : 'md', 'dip' : 'dip', 'azimuth': 'azi' }, encoding="latin-1" ) df = read_survey(reader) survey: Survey = Survey.from_df(df) survey.update_survey_with_lith(lith) reader_collar: GenericReaderFilesHelper = GenericReaderFilesHelper( file_or_buffer=os.getenv("PATH_TO_SPREMBERG") + "Spremberg_collar_updated.csv", header=0, usecols=[0, 1, 2, 4], columns_map={ "hole_id" : "id", # ? Index name is not mapped "X_GK5_incl_inserted": "x", "Y__incl_inserted" : "y", "Z_GK" : "z" } ) df_collar = read_collar(reader_collar) collar = Collars.from_df(df_collar) borehole_set = BoreholeSet( collars=collar, survey=survey, merge_option=MergeOptions.INTERSECT ) return borehole_set # TODO: Rename this to test structural elements from borehole set def test_structural_elements_from_borehole_set(self, borehole_set: BoreholeSet): from subsurface import LineSet borehole_trajectory: LineSet = borehole_set.combined_trajectory if PLOT := False: s = to_pyvista_line( line_set=borehole_trajectory, radius=10, active_scalar="lith_ids" ) pv_plot([s], image_2d=False, cmap="tab20c") elements = gp.structural_elements_from_borehole_set( borehole_set=borehole_set, elements_dict={ "Buntsandstein" : { "id" : 53_300, "color": "#983999" }, "Werra-Anhydrit" : { "id" : 61_730, "color": "#00923f" }, "Kupfershiefer" : { "id" : 61_760, "color": "#da251d" }, "Zechsteinkonglomerat": { "id" : 61_770, "color": "#f8c300" }, "Rotliegend" : { "id" : 62_000, "color": "#bb825b" } }, group_by="component lith" ) group = gp.data.StructuralGroup( name="Stratigraphic Pile", elements=elements, structural_relation=gp.data.StackRelationType.ERODE ) structural_frame = gp.data.StructuralFrame( structural_groups=[group], color_gen=gp.data.ColorsGenerator() ) print(group) all_surface_points_coords: gp.data.SurfacePointsTable = structural_frame.surface_points_copy extent_from_data = all_surface_points_coords.xyz.min(axis=0), all_surface_points_coords.xyz.max(axis=0) geo_model = gp.data.GeoModel.from_args( name="Stratigraphic Pile", structural_frame=structural_frame, grid=gp.data.Grid( extent=[extent_from_data[0][0], extent_from_data[1][0], extent_from_data[0][1], extent_from_data[1][1], extent_from_data[0][2], extent_from_data[1][2]], resolution=(50, 50, 50) ), interpolation_options=gp.data.InterpolationOptions.from_args( range=5, c_o=10, mesh_extraction=True, number_octree_levels=3, ), ) gempy_plot = gpv.plot_3d( model=geo_model, # ve=10, kwargs_pyvista_bounds={ 'show_xlabels': False, 'show_ylabels': False, # 'show_zlabels': True, }, show=True, image=True ) ================================================ FILE: test/test_modules/test_serialize_model.py ================================================ import tempfile import json import os import pprint from gempy_engine.core.data import InterpolationOptions import gempy as gp from gempy.core.data.encoders.converters import loading_model_from_binary from gempy.core.data.enumerators import ExampleModel from gempy.modules.serialization.save_load import save_model, load_model from test.verify_helper import verify_json def test_generate_horizontal_stratigraphic_model(): model: gp.data.GeoModel = gp.generate_example_model(ExampleModel.HORIZONTAL_STRAT, compute_model=False) model_json = model.model_dump_json(by_alias=True, indent=4) # Write the JSON to disk if False: file_path = os.path.join("temp", "horizontal_stratigraphic_model.json") with open(file_path, "w") as f: f.write(model_json) with loading_model_from_binary( input_binary=model.structural_frame.input_tables_binary, grid_binary=model.grid.grid_binary # binary_body=model.structural_frame.input_tables_binary ): model_deserialized = gp.data.GeoModel.model_validate_json(model_json) _validate_serialization(model, model_deserialized) # # Validate json against schema if True: # Ensure the 'verify/' directory exists os.makedirs("verify", exist_ok=True) verify_model = json.loads(model_json) verify_model["meta"]["creation_date"] = "" verify_json(json.dumps(verify_model, indent=4), name="verify/Horizontal Stratigraphic Model serialization") def test_save_model_to_disk(): model = gp.generate_example_model(ExampleModel.COMBINATION, compute_model=False) with tempfile.NamedTemporaryFile(mode='w+', delete=True) as tmp: tmp_name = tmp.name + ".gempy" # Store the name to use it later save_model(model, tmp_name) # Load the model from disk loaded_model = load_model(tmp_name) _validate_serialization(model, loaded_model) gp.compute_model(loaded_model) if True: import gempy_viewer as gpv gpv.plot_3d(loaded_model, image=True) # Test save after compute with tempfile.NamedTemporaryFile(mode='w+', delete=True) as tmp: tmp_name = tmp.name + ".gempy" # Store the name to use it later save_model( model=model, path=tmp_name, validate_serialization=True ) def test_interpolation_options(): options = InterpolationOptions.from_args( range=1.7, c_o=10. ) json = options.model_dump(mode="json") # Pretty print json pprint.pp(json) # Deserialize with pydantic options2 = InterpolationOptions.model_validate(json) assert options.__str__() == options2.__str__() def _validate_serialization(original_model, model_deserialized): a = hash(original_model.structural_frame.surface_points_copy.data.tobytes()) b = hash(model_deserialized.structural_frame.surface_points_copy.data.tobytes()) o_a = hash(original_model.structural_frame.orientations_copy.data.tobytes()) o_b = hash(model_deserialized.structural_frame.orientations_copy.data.tobytes()) assert a == b, "Hashes for surface points are not equal" assert o_a == o_b, "Hashes for orientations are not equal" assert model_deserialized.__str__() == original_model.__str__() ================================================ FILE: test/test_modules/test_serialize_model.test_generate_horizontal_stratigraphic_model.verify/Horizontal Stratigraphic Model serialization.approved.txt ================================================ { "meta": { "name": "horizontal", "creation_date": "", "last_modification_date": null, "owner": null }, "structural_frame": { "structural_groups": [ { "name": "Strat_Series", "elements": [ { "name": "rock2", "is_active": true, "_color": "#9f0052", "surface_points": { "name_id_map": { "rock1": 67239155, "rock2": 117776925 }, "_model_transform": null }, "orientations": { "name_id_map": { "rock1": 67239155, "rock2": 117776925 }, "_model_transform": null }, "scalar_field_at_interface": null, "_id": 117776925 }, { "name": "rock1", "is_active": true, "_color": "#015482", "surface_points": { "name_id_map": { "rock1": 67239155, "rock2": 117776925 }, "_model_transform": null }, "orientations": { "name_id_map": { "rock1": 67239155, "rock2": 117776925 }, "_model_transform": null }, "scalar_field_at_interface": null, "_id": 67239155 } ], "structural_relation": 1, "fault_relations": null, "faults_input_data": null, "solution": null } ], "color_generator": { "_index": 3 }, "basement_color": "#ffbe00", "is_dirty": true, "binary_meta_data": { "sp_binary_length": 432, "ori_binary_length": 120 } }, "grid": { "_octree_grid": null, "_dense_grid": { "resolution": [ 50, 5, 50 ], "extent": [ 0.0, 1000.0, 0.0, 1000.0, 0.0, 1000.0 ], "_transform": null, "_base_resolution": [ 2, 2, 2 ] }, "_custom_grid": null, "_topography": null, "_sections": null, "_centered_grid": null, "_transform": null, "_octree_levels": -1, "binary_meta_data": { "custom_grid_binary_length": 0, "topography_binary_length": 0 }, "active_grids": 1026 }, "geophysics_input": null, "input_transform": { "position": [ -500.0, -500.0, -500.0 ], "rotation": [ 0.0, 0.0, 0.0 ], "scale": [ 0.000625, 0.000625, 0.000625 ], "_is_default_transform": false, "_cached_pivot": null }, "_interpolation_options": { "kernel_options": { "range": 1.7, "c_o": 10.0, "uni_degree": 1, "i_res": 4.0, "gi_res": 2.0, "number_dimensions": 3, "kernel_function": "cubic", "kernel_solver": 1, "compute_condition_number": false, "optimizing_condition_number": false, "condition_number": null }, "evaluation_options": { "_number_octree_levels": 3, "_number_octree_levels_surface": 4, "octree_curvature_threshold": -1.0, "octree_error_threshold": 1.0, "octree_min_level": 2, "mesh_extraction": true, "mesh_extraction_masking_options": 3, "mesh_extraction_fancy": true, "evaluation_chunk_size": 500000, "compute_scalar": true, "compute_scalar_gradient": false, "verbose": false }, "debug": false, "cache_mode": 3, "cache_model_name": "horizontal", "block_solutions_type": 2, "sigmoid_slope": 5000000 } } ================================================ FILE: test/test_modules/test_transformed_space.py ================================================ import numpy as np import gempy as gp from gempy.core.data.enumerators import ExampleModel from gempy.optional_dependencies import require_gempy_viewer PLOT = True def test_plot_transformed_data_only_transform_input(): model = gp.generate_example_model(ExampleModel.ANTICLINE, compute_model=True) print(model.structural_frame) if PLOT: gpv = require_gempy_viewer() gpv.plot_3d( model, image=True, transformed_data=False, show_boundaries=True, show_lith=True, kwargs_plot_data={ 'arrow_size': 10 } ) gpv.plot_3d( model, image=True, transformed_data=True, show_boundaries=True, show_lith=True, kwargs_plot_data={ 'arrow_size': .01 } ) def test_plot_transformed_data_including_grid_transform(): model = gp.generate_example_model(ExampleModel.ANTICLINE, compute_model=False) # Calculate point_y_axis regular_grid = gp.data.grid.RegularGrid.from_corners_box( pivot=(200, 200), point_x_axis=(800, 800), distance_point3=1000, zmin=model.grid.extent[4], zmax=model.grid.extent[5], resolution=np.array([20, 20, 20]), plot=True ) model.grid = gp.data.grid.Grid() model.grid.dense_grid = regular_grid gp.compute_model(model) if PLOT: gpv = require_gempy_viewer() gpv.plot_3d( model, image=True, transformed_data=True, show_boundaries=True, show_lith=True, kwargs_plot_data={ 'arrow_size': .01 } ) gpv.plot_3d( model, image=True, transformed_data=False, show_boundaries=True, show_lith=True, kwargs_plot_data={ 'arrow_size': .01 } ) def test_plot_transformed_data_including_grid_transform_octree(): model = gp.generate_example_model(ExampleModel.ANTICLINE, compute_model=False) i = 4 # Calculate point_y_axis regular_grid = gp.data.grid.RegularGrid.from_corners_box( pivot=(200, 200), point_x_axis=(800, 800), distance_point3=1000, zmin=model.grid.extent[4], zmax=model.grid.extent[5], resolution=np.array([2 ** i] * 3), plot=True ) # model.grid.active_grids ^= gp.data.grid.Grid.GridTypes.DENSE options = model.interpolation_options options.number_octree_levels = 4 model.grid.set_octree_grid(regular_grid) gp.compute_model(model) if PLOT: gpv = require_gempy_viewer() gpv.plot_3d( model, image=True, transformed_data=True, show_boundaries=True, show_lith=True, kwargs_plot_data={ 'arrow_size': .01 } ) gpv.plot_3d( model, image=True, transformed_data=False, show_boundaries=True, show_lith=True, kwargs_plot_data={ 'arrow_size': .01 } ) ================================================ FILE: test/test_private/__init__.py ================================================ ================================================ FILE: test/test_private/test_terranigma/__init__.py ================================================ ================================================ FILE: test/test_private/test_terranigma/test_nuggets/__init__.py ================================================ ================================================ FILE: test/test_private/test_terranigma/test_nuggets/_aux_func.py ================================================ import os import xarray as xr import numpy as np import gempy as gp import subsurface def process_file(filename, global_extent, color_generator: gp.data.ColorsGenerator): dataset: xr.Dataset = xr.open_dataset(filename) file_extent = _calculate_extent(dataset) global_extent = _update_global_extent(global_extent, file_extent) base, ext = os.path.splitext(filename) base = os.path.basename(base) structural_element = _extract_surface_points_and_orientations(dataset, base, color_generator) return structural_element, global_extent def initialize_geo_model(structural_elements: list[gp.data.StructuralElement], extent: list[float], topography: xr.Dataset, **create_geomod_kwargs ) -> gp.data.GeoModel: structural_group_red = gp.data.StructuralGroup( name="Red", elements=[structural_elements[i] for i in [0, 4, 8]], structural_relation=gp.data.StackRelationType.ERODE ) # Any, Probably we can decimize this an extra notch structural_group_green = gp.data.StructuralGroup( name="Green", elements=[structural_elements[i] for i in [5]], structural_relation=gp.data.StackRelationType.ERODE ) # Blue range 2 cov 4 structural_group_blue = gp.data.StructuralGroup( name="Blue", elements=[structural_elements[i] for i in [2, 3]], structural_relation=gp.data.StackRelationType.ERODE ) structural_group_intrusion = gp.data.StructuralGroup( name="Intrusion", elements=[structural_elements[i] for i in [1]], structural_relation=gp.data.StackRelationType.ERODE ) structural_groups = [ # structural_group_intrusion, structural_group_green, structural_group_blue, structural_group_red ] structural_frame = gp.data.StructuralFrame( structural_groups=structural_groups, color_gen=gp.data.ColorsGenerator() ) # TODO: If elements do not have color maybe loop them on structural frame constructor? geo_model: gp.data.GeoModel = gp.create_geomodel( project_name='Tutorial_ch1_1_Basics', extent=extent, # resolution=[20, 10, 20], refinement=5, # * Here we define the number of octree levels. If octree levels are defined, the resolution is ignored. structural_frame=structural_frame, **create_geomod_kwargs ) if topography is not None: gp.set_topography_from_arrays( grid=geo_model.grid, xyz_vertices=topography.vertex.values ) return geo_model # Function to calculate the extent (ROI) for a single dataset def _calculate_extent(dataset): x_coord, y_coord, z_coord = dataset.vertex[:, 0], dataset.vertex[:, 1], dataset.vertex[:, 2] return [x_coord.min().values, x_coord.max().values, y_coord.min().values, y_coord.max().values, z_coord.min().values, z_coord.max().values] # Function to extract surface points and orientations from the dataset def _extract_surface_points_and_orientations(dataset, name, color_generator) -> gp.data.StructuralElement: # Extract surface points and orientations unstruct = subsurface.UnstructuredData(dataset) ts = subsurface.TriSurf(mesh=unstruct) triangulated_mesh = subsurface.visualization.to_pyvista_mesh(ts) # print(name) # subsurface.visualization.pv_plot([triangulated_mesh]) # Decimate the mesh and compute normals decimated_mesh = triangulated_mesh.decimate_pro(0.95) normals = decimated_mesh.compute_normals(point_normals=False, cell_normals=True, consistent_normals=True) normals_array = np.array(normals.cell_data["Normals"]) # Filter points vertex_sp = normals.cell_centers().points vertex_grads = normals_array positive_z = vertex_grads[:, 2] > 0 largest_component_z = np.all(vertex_grads[:, 2:3] >= np.abs(vertex_grads[:, :2]), axis=1) filter_mask = np.logical_and(positive_z, largest_component_z) surface_points_xyz = vertex_sp[filter_mask] orientations_gxyz = vertex_grads[filter_mask] nuggets = np.ones(len(surface_points_xyz)) * 0.000001 # Create SurfacePointsTable and OrientationsTable every = 10 surface_points = gp.data.SurfacePointsTable.from_arrays( x=surface_points_xyz[::every, 0], y=surface_points_xyz[::every, 1], z=surface_points_xyz[::every, 2], names=name, nugget=nuggets[::every] ) every = 10 orientations = gp.data.OrientationsTable.from_arrays( x=surface_points_xyz[::every, 0], y=surface_points_xyz[::every, 1], z=surface_points_xyz[::every, 2], G_x=orientations_gxyz[::every, 0], G_y=orientations_gxyz[::every, 1], G_z=orientations_gxyz[::every, 2], nugget=4, names=name ) structural_element = gp.data.StructuralElement( name=name, surface_points=surface_points, orientations=orientations, color=next(color_generator) ) return structural_element # Function to update the global extent based on each file's extent def _update_global_extent(global_extent, file_extent): if not global_extent: return file_extent else: return [min(global_extent[0], file_extent[0]), max(global_extent[1], file_extent[1]), min(global_extent[2], file_extent[2]), max(global_extent[3], file_extent[3]), min(global_extent[4], file_extent[4]), max(global_extent[5], file_extent[5])] ================================================ FILE: test/test_private/test_terranigma/test_nuggets/test_nugget_effect_optimization.py ================================================ import os import dotenv import numpy as np import time import pytest torch = pytest.importorskip("torch") import gempy as gp from ._aux_func import process_file, initialize_geo_model dotenv.load_dotenv() # %% # Config seed = 123456 torch.manual_seed(seed) # %% # Start the timer for benchmarking purposes start_time = time.time() # %% # Load necessary configuration and paths from environment variables path = os.getenv("PATH_TO_NUGGET_TEST_MODEL") # Skip if not torch def test_optimize_nugget_effect(): # Initialize lists to store structural elements for the geological model structural_elements = [] global_extent = None color_gen = gp.data.ColorsGenerator() for filename in os.listdir(path): base, ext = os.path.splitext(filename) if ext == '.nc': structural_element, global_extent = process_file(os.path.join(path, filename), global_extent, color_gen) structural_elements.append(structural_element) import xarray as xr geo_model: gp.data.GeoModel = initialize_geo_model( structural_elements=structural_elements, extent=(np.array(global_extent)), topography=(xr.open_dataset(os.path.join(path, "Topography.nc"))), legacy_octree_init=True ) geo_model.interpolation_options.cache_mode = gp.data.InterpolationOptions.CacheMode.NO_CACHE geo_model.interpolation_options.kernel_options.range = 0.5 gp.API.compute_API.optimize_nuggets( geo_model=geo_model, engine_config=gp.data.GemPyEngineConfig( backend=gp.data.AvailableBackends.PYTORCH, ), max_epochs=100, convergence_criteria=1e5, only_groups=[geo_model.structural_frame.get_group_by_name("Red")] ) gp.compute_model( gempy_model=geo_model, engine_config=gp.data.GemPyEngineConfig( backend=gp.data.AvailableBackends.PYTORCH, ), validate_serialization=True ) print(f"Final cond number: {geo_model.interpolation_options.kernel_options.condition_number}") nugget_effect = geo_model.taped_interpolation_input.surface_points.nugget_effect_scalar.detach().numpy() if plot_evaluation := True: import matplotlib.pyplot as plt plt.hist(nugget_effect, bins=50, color='black', alpha=0.7, log=True) plt.xlabel('Eigenvalue') plt.ylabel('Frequency') plt.title('Histogram of Eigenvalues (nugget-grad)') plt.show() if plot_result := True: import gempy_viewer as gpv import pyvista as pv image = True gempy_vista = gpv.plot_3d( model=geo_model, show=False, show_boundaries=True, show_topography=False, image=image, kwargs_plot_structured_grid={'opacity': 0.3} ) # Create a point cloud mesh if image == False: surface_points_xyz = geo_model.surface_points_copy.df[['X', 'Y', 'Z']].to_numpy() point_cloud = pv.PolyData(surface_points_xyz[0:]) point_cloud['values'] = nugget_effect gempy_vista.p.add_mesh( point_cloud, scalars='values', cmap='inferno', point_size=25, ) if False: ori_cloud = pv.PolyData(geo_model.orientations_copy.df[['X', 'Y', 'Z']].to_numpy()) ori_cloud['values2'] = geo_model.taped_interpolation_input.orientations.nugget_effect_grad.detach().numpy() gempy_vista.p.add_mesh( ori_cloud, scalars='values2', cmap='viridis', point_size=20, ) gempy_vista.p.show() ================================================ FILE: test/verify_helper.py ================================================ import os from typing import Literal import subprocess import numpy as np from approvaltests.core import Comparator import json from approvaltests import verify, Options from approvaltests.namer import NamerFactory from approvaltests.reporters import GenericDiffReporter, GenericDiffReporterConfig from gempy.core.data import GeoModel from gempy.modules.serialization.save_load import _load_model_from_bytes, model_to_bytes class WSLWindowsDiffReporter(GenericDiffReporter): def get_command(self, received, approved): # Convert WSL paths to Windows paths win_received = subprocess.check_output(['wslpath', '-w', received]).decode().strip() win_approved = subprocess.check_output(['wslpath', '-w', approved]).decode().strip() cmd = [self.path] + self.extra_args + [win_received, win_approved] return cmd class LinuxDiffReporter(GenericDiffReporter): pass def verify_json(item, name: str): config = GenericDiffReporterConfig( name="custom", path=r"pycharm", extra_args=["diff"] ) if os.environ.get("WSL_DISTRO_NAME"): reporter = WSLWindowsDiffReporter(config) else: reporter = LinuxDiffReporter(config) parameters: Options = NamerFactory \ .with_parameters(name) \ .with_reporter(reporter) verify(item, options=parameters) def gempy_verify_array(item, name: str, rtol: float = 1e-5, atol: float = 1e-5, ): # ! You will have to set the path to your diff tool reporter = GenericDiffReporter.create( diff_tool_path=r"pycharm" ) reporter.extra_args = ["diff"] parameters: Options = NamerFactory \ .with_parameters(name) \ .with_comparator( comparator=ArrayComparator(atol=atol, rtol=rtol) ).with_reporter( reporter=reporter ) verify(np.asarray(item), options=parameters) class ArrayComparator(Comparator): # TODO: Make tolerance a variable rtol: float = 1e-05 atol: float = 1e-05 def __init__(self, rtol: float = 1e-05, atol: float = 1e-05): self.rtol = rtol self.atol = atol def compare(self, received_path: str, approved_path: str) -> bool: from approvaltests.file_approver import exists import filecmp import pathlib if not exists(approved_path) or not exists(received_path): return False if filecmp.cmp(approved_path, received_path): return True try: approved_raw = pathlib.Path(approved_path).read_text() approved_text = approved_raw.replace("\r\n", "\n") received_raw = pathlib.Path(received_path).read_text() received_text = received_raw.replace("\r\n", "\n") # Parse 2D matrices received = np.matrix(received_text) approved = np.matrix(approved_text) allclose = np.allclose(received, approved, rtol=self.rtol, atol=self.atol) self.rtol = 1e-05 self.atol = 1e-05 return allclose except BaseException: return False class JsonSerializer: """Serializer that writes JSON with an indent and declares its own extension.""" def get_default_extension(self) -> str: return "json" def write(self, received, received_path: str) -> None: with open(received_path, "w", encoding="utf-8") as f: json.dump(received, f, indent=2, ensure_ascii=False) def verify_model_serialization(model: GeoModel, verify_moment: Literal["before", "after"], file_name: str): """ Verifies the serialization and deserialization process of a GeoModel instance by ensuring the serialized JSON and binary data match during either the initial or post-process phase, based on the specified verification moment. Args: model: The GeoModel instance to be verified. verify_moment: A literal value specifying whether to verify the model before or after the deserialization process. Accepts "before" or "after" as valid inputs. file_name: The filename to associate with the verification process for logging or output purposes. Raises: ValueError: If `verify_moment` is not set to "before" or "after". """ binary_file = model_to_bytes(model) original_model = model original_model.meta.creation_date = "" if verify_moment == "before": verify_json( item=original_model.model_dump_json(by_alias=True, indent=4), name=file_name ) elif verify_moment == "after": model_deserialized = _load_model_from_bytes(binary_file) model_deserialized.meta.creation_date = "" verify_json( item=model_deserialized.model_dump_json(by_alias=True, indent=4), name=file_name ) else: raise ValueError("Invalid model parameter")