[ { "path": ".github/FUNDING.yml", "content": "# These are supported funding model platforms\n\ngithub: Leguark\n" }, { "path": ".github/ISSUE_TEMPLATE/bug_report.md", "content": "---\nname: Bug report\nabout: Create a report to help us improve\ntitle: ''\nlabels: ''\nassignees: ''\n\n---\n\n**Describe the bug**\nA clear and concise description of what the bug is. Include error messages / \ntracebacks. If useful include screenshots.\n\n**To Reproduce**\nProvide detailed steps to reproduce the behavior:\n\n1.\n2.\n3.\n...\n\n**Expected behavior**\nA clear and concise description of what you expected to happen.\n\n**Screenshots**\nIf applicable, add screenshots to help explain your problem.\n\n**Desktop (please complete the following information):**\n * OS: (e.g. iOS)\n * GemPy Version\n * if installed via pip: provide GemPy version (e.g. 2.0.1)\n * if cloned from GitHub: provide Git commit id (e.g. 839bf85f)\n * Jupyter Version (if applicable)\n\n**Additional context**\nAdd any other context about the problem here.\n" }, { "path": ".github/ISSUE_TEMPLATE/feature_request.md", "content": "---\nname: Feature request\nabout: Suggest an idea for this project\ntitle: ''\nlabels: ''\nassignees: ''\n\n---\n\n**Is your feature request related to a problem? Please describe.**\nA clear and concise description of what the problem is. Ex. I'm always frustrated when [...]\n\n**Describe the solution you'd like**\nA clear and concise description of what you want to happen.\n\n**Describe alternatives you've considered**\nA clear and concise description of any alternative solutions or features you've considered.\n\n**Additional context**\nAdd any other context or screenshots about the feature request here.\n" }, { "path": ".github/ISSUE_TEMPLATE/question.md", "content": "---\nname: Question\nabout: Ask a question about GemPy\ntitle: ''\nlabels: ''\nassignees: ''\n\n---\n\n**What is your question?**\n" }, { "path": ".github/PULL_REQUEST_TEMPLATE.md", "content": "# Description\nPlease include a summary of the changes.\n\nRelates to \n\n# Checklist\n- [ ] My code uses type hinting for function and method arguments and return values.\n- [ ] I have created tests which cover my code.\n- [ ] The test code either 1. demonstrates at least one valuable use case (e.g. integration tests) \nor 2. verifies that outputs are as expected for given inputs (e.g. unit tests).\n- [ ] New tests pass locally with my changes.\n \n" }, { "path": ".gitignore", "content": ".vscode\n.idea\n.DS_Store\n.vs\n.cache/\nGeMpy/__pycache__/__init__.cpython-36.pyc\nGeMpy/__pycache__/geomodeller_xml_obj.cpython-36.pyc\nGeMpy/geomodeller_xml_obj.py.bak\nGeMpy/qgrid/__pycache__/\n../gempy_plugins/gempy_plugins/addons/RexCloud_Api_key.txt\ndocs/source/Notebooks/Erosion.rst\ndocs/source/Notebooks/Example_1_Sandstone_files/Example_1_Sandstone_39_0.png\ndocs/source/Notebooks/Example_1_Sandstone_files/Example_1_Sandstone_41_0.png\ndocs/source/Notebooks/Example_1_Sandstone_files/Example_1_Sandstone_46_1.png\ndocs/source/Notebooks/Example_2_Simple_PyMC3_files/\ndocs/source/Notebooks/Example_3_Faults_files/\ndocs/source/Notebooks/OptimizationNotebook.rst\ndocs/source/Notebooks/legacy/\ndocs/source/index_legacy.rst\ninput_data/Erosion_Foliations.csv\ninput_data/Erosion_Points.csv\ninput_data/FabLessPoints_Foliations.csv\ninput_data/FabLessPoints_Points.csv\ninput_data/Fab_Model_MorePoints_Foliations.csv\ninput_data/Fab_Model_MorePoints_Points.csv\ninput_data/Intrusion-test/\nlegacy/.ipynb_checkpoints/\ntest/.ipynb_checkpoints/\n\ntest/__pycache__/\ntest/legacy/\ntest/log\ntest/log2\n/legacy/geomodeller_xml_obj.py.bak\n!/notebooks/input_data/FabLessPoints_Foliations.csv\n!/notebooks/input_data/FabLessPoints_Points.csv\n\n### Python template\n# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nenv/\n\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\n*.egg-info/\n.installed.cfg\n*.egg\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*,cover\n.hypothesis/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# SageMath parsed files\n*.sage.py\n\n# dotenv\n.env\n\n# virtualenv\n.venv\nvenv/\nENV/\n\n# Spyder project settings\n.spyderproject\n\n# Rope project settings\n.ropeproject\n\nnotebooks/prototype notebooks/vtks\nnotebooks/prototype notebooks/vtk_res_model_adjusted_1\nnotebooks/prototype notebooks/vtk_exp_smooth_trnasitions\nnotebooks/prototype notebooks/plots\n\n# Remove the built document files - but not the static ones, which are required for the builds\ndocs/build\n# The following files are generated locally during sphinx updates - they don't have to be part of the repo\ndocs/source/examples\ndocs/source/tutorials\ndocs/source/Modeling\\ Classes\ndocs/source/GemPy\\ API\ndocs/source/Helper\\ Classes\ndocs/source/Plot\ndocs/source/external\ndocs/source/sg_execution_times.rst\ntest/figs\n\nProfiles\nexamples/data/input_data/Claudius/Results_\n\n/docs/source/gen_modules/\n\n#examples folders\nexamples/integrations/*.vs\nexamples/integrations/*.ugi\nexamples/integrations/*.h5\nexamples/integrations/*.xmf\nexamples/integrations/*.ss\nexamples/integrations/*.out\n\n# Approval tests\n*.received.txt\n*.Identifier\n/.obsidian/app.json\n/.obsidian/appearance.json\n/.obsidian/core-plugins.json\n/.obsidian/core-plugins-migration.json\n/.obsidian/hotkeys.json\n/.obsidian/workspace.json\n\n# Ignore generated tutorial files\nexamples/tutorials/z_other_tutorials/json_io/04_simple_layer_stack.py\nexamples/tutorials/z_other_tutorials/json_io/fault_scalar_field.png\nexamples/tutorials/z_other_tutorials/json_io/initial_model_y.png\nexamples/tutorials/z_other_tutorials/json_io/model_with_data.png\nexamples/tutorials/z_other_tutorials/json_io/multiple_series_faults_computed.json\n\n# Generated JSON files from examples\nexamples/tutorials/z_other_tutorials/json_io/combination_model.json\nexamples/tutorials/z_other_tutorials/json_io/combination_model_computed.json\n/test/temp/\ntest/test_modules/run_test.py\n" }, { "path": ".teamcity/Gempy/buildTypes/GempyEngine_TestingEngine.xml", "content": "\n\n TestingEngine\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n" }, { "path": ".teamcity/Gempy/buildTypes/Gempy_BuildDocs.xml", "content": "\n\n BuildDocs\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n ]]>\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n" }, { "path": ".teamcity/Gempy/buildTypes/Gempy_PublishDocs.xml", "content": "\n\n Publish Docs\n \n \n \n \n \n \n \n \n \n /dev/null || true\n\n# 4. Cleanup build artifacts before commit\nrm -rf build docs_site.zip\n\n# 3. Prevent Jekyll from ignoring files\ntouch .nojekyll\n\n# after mv build/html/* . and before git add\necho \"docs.gempy.org\" > CNAME\n\n# 4. Commit & push\ngit add -A\ngit commit -m \"Publish docs from build %build.counter%\"\ngit push origin gh-pages]]>\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n" }, { "path": ".teamcity/Gempy/buildTypes/Gempy_TestingGemPy.xml", "content": "\n\n TestingGemPy\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n" }, { "path": ".teamcity/Gempy/buildTypes/Gempy_TestingViewer.xml", "content": "\n\n TestingViewer\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n" }, { "path": ".teamcity/Gempy/buildTypes/ReleaseGemPy.xml", "content": "\n\n Release GemPy\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n" }, { "path": ".teamcity/Gempy/buildTypes/ReleaseGemPyEngine.xml", "content": "\n\n Release GemPy Engine\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n" }, { "path": ".teamcity/Gempy/buildTypes/ReleaseGemPyViewer.xml", "content": "\n\n Release GemPy Viewer\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n" }, { "path": ".teamcity/Gempy/project-config.xml", "content": "\n\n Gempy\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n" }, { "path": ".teamcity/Gempy/vcsRoots/GempyEngine_HttpsGithubComGempyProjectGempyEngineRefsHeadsMain.xml", "content": "\n\n https://github.com/gempy-project/gempy_engine#refs/heads/main\n \n \n \n \n \n \n \n \n \n \n \n\n\n" }, { "path": ".teamcity/Gempy/vcsRoots/GempyEngine_HttpsGithubComGempyProjectGempyViewerRefsHeadsMain.xml", "content": "\n\n https://github.com/gempy-project/gempy_viewer#refs/heads/main\n \n \n \n \n \n \n \n \n \n \n \n\n\n" }, { "path": ".teamcity/Gempy/vcsRoots/Gempy_HttpsGithubComGempyProjectGempyRefsHeadsMain.xml", "content": "\n\n GemPy Main\n \n \n \n \n \n \n \n \n \n \n \n\n\n" }, { "path": ".teamcity/Gempy/vcsRoots/Gempy_HttpsGithubComGempyProjectGempyRefsHeadsMain1.xml", "content": "\n\n Caching docs vcs\n \n \n \n \n \n \n \n \n \n \n \n\n\n" }, { "path": ".teamcity/Gempy/vcsRoots/Gempy_HttpsGithubComGempyProjectGempyRefsHeadsMain2.xml", "content": "\n\n GemPy - GH Pages\n \n \n \n \n \n \n \n \n \n \n \n\n\n" }, { "path": ".travis.yml", "content": "language: python\nos: linux\ndist: xenial\n\nbranches:\n only:\n - master\n - release\n\npython:\n - '3.8'\n\nbefore_install:\n # configure a headless display\n - git clone --depth 1 git://github.com/pyvista/gl-ci-helpers.git\n - source ./gl-ci-helpers/travis/setup_headless_display.sh\n\ninstall:\n - sudo apt-get update\n # We do this conditionally because it saves us some downloading if the\n # version is the same.\n - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;\n - bash miniconda.sh -b -p $HOME/miniconda\n - export PATH=\"$HOME/miniconda/bin:$PATH\"\n - hash -r\n - conda config --set always_yes yes --set changeps1 no\n - conda update --all\n # Useful for debugging any issues with conda\n - conda info -a\n\n # Install and activate environment, install packages\n - conda create -q -n tenv python=$TRAVIS_PYTHON_VERSION\n - source activate tenv\n\n # Installs from conda-forge\n - conda install -c conda-forge python-graphviz\n - conda install -c conda-forge pip matplotlib ipython six pytest pytest-cov ipywidgets\n - conda install -c conda-forge recommonmark networkx panel setuptools mkl-service\n\n # Installs from anaconda\n - conda install -c anaconda libffi\n\n - conda install aesara\n\n # Installs from pip\n - pip install welly pymc3\n\n - pip install scikit-image>=0.17 sklearn\n\n - pip install pandas seaborn>=0.9 sphinx-gallery\n # Install\n - pip install -e .\n\n # Pyvista should have been fixed- pip install pyvista\n # Arviz broke with xarray update\n - pip install git+https://github.com/arviz-devs/arviz --force-reinstall\n\n # Install subsurface\n - pip install subsurface\n\n - conda install -c conda-forge gdal\n\n - python -c \"import pyvista as pv;print(pv.Report())\"\n\n - pip install pooch\n\nstages:\n - test\n\njobs:\n include:\n - stage: test\n script:\n - pytest -v\ncache: pip\n" }, { "path": "AUTHORS.rst", "content": ".. _authors_ref:\n\nAuthors\n-------\n\n.. image:: https://img.shields.io/github/contributors/gempy-project/gempy_engine.svg?logo=github&logoColor=white\n :target: https://github.com/gempy-projects/gempy_engine/graphs/contributors/\n\n\nThe following is a list of authors who have made substantial contributions to\nthe conception or design of this software; or the creation of new code used in\nthis software; or have drafted the work or substantively revised it:\n\n- Miguel de la Varga, (`@leguark `_)\n- Alexander Zimmerman\n- Elisa Heim\n- Alexander Schaaf\n- Fabian Stamm\n- Florian Wellmann\n" }, { "path": "CITATION.cff", "content": "cff-version: 1.2.0\nmessage: \"If you use GemPy, please cite it as below.\"\nauthors:\n - family-names: \"de la Varga\"\n given-names: \"Miguel\"\n orcid: https://orcid.org/0000-0001-6941-2685\n - family-names: \"Schaaf\"\n given-names: \"Alexander\"\n - family-names: \"Wellmann\"\n given-names: \"Florian\"\n orcid: https://orcid.org/0000-0003-2552-1876\ntitle: \"GemPy 1.0: open-source stochastic geological modeling and inversion\"\nversion: 1.0\ndoi: https://doi.org/10.5194/gmd-12-1-2019\ndate-released: 2019-01-02\nurl: \"https://github.com/cgre-aachen/gempy\"\n" }, { "path": "CODE_OF_CONDUCT.md", "content": "# Contributor Covenant Code of Conduct\n\n## Our Pledge\n\nIn the interest of fostering an open and welcoming environment, we as\ncontributors and maintainers pledge to making participation in our project and\nour community a harassment-free experience for everyone, regardless of age, body\nsize, disability, ethnicity, sex characteristics, gender identity and expression,\nlevel of experience, education, socio-economic status, nationality, personal\nappearance, race, religion, or sexual identity and orientation.\n\n## Our Standards\n\nExamples of behavior that contributes to creating a positive environment\ninclude:\n\n* Using welcoming and inclusive language\n* Being respectful of differing viewpoints and experiences\n* Gracefully accepting constructive criticism\n* Focusing on what is best for the community\n* Showing empathy towards other community members\n\nExamples of unacceptable behavior by participants include:\n\n* The use of sexualized language or imagery and unwelcome sexual attention or\n advances\n* Trolling, insulting/derogatory comments, and personal or political attacks\n* Public or private harassment\n* Publishing others' private information, such as a physical or electronic\n address, without explicit permission\n* Other conduct which could reasonably be considered inappropriate in a\n professional setting\n\n## Our Responsibilities\n\nProject maintainers are responsible for clarifying the standards of acceptable\nbehavior and are expected to take appropriate and fair corrective action in\nresponse to any instances of unacceptable behavior.\n\nProject maintainers have the right and responsibility to remove, edit, or\nreject comments, commits, code, wiki edits, issues, and other contributions\nthat are not aligned to this Code of Conduct, or to ban temporarily or\npermanently any contributor for other behaviors that they deem inappropriate,\nthreatening, offensive, or harmful.\n\n## Scope\n\nThis Code of Conduct applies both within project spaces and in public spaces\nwhen an individual is representing the project or its community. Examples of\nrepresenting a project or community include using an official project e-mail\naddress, posting via an official social media account, or acting as an appointed\nrepresentative at an online or offline event. Representation of a project may be\nfurther defined and clarified by project maintainers.\n\n## Enforcement\n\nInstances of abusive, harassing, or otherwise unacceptable behavior may be\nreported by contacting the project team at florian.wellmann@cgre.rwth-aachen.de. All\ncomplaints will be reviewed and investigated and will result in a response that\nis deemed necessary and appropriate to the circumstances. The project team is\nobligated to maintain confidentiality with regard to the reporter of an incident.\nFurther details of specific enforcement policies may be posted separately.\n\nProject maintainers who do not follow or enforce the Code of Conduct in good\nfaith may face temporary or permanent repercussions as determined by other\nmembers of the project's leadership.\n\n## Attribution\n\nThis Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,\navailable at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html\n\n[homepage]: https://www.contributor-covenant.org\n\nFor answers to common questions about this code of conduct, see\nhttps://www.contributor-covenant.org/faq\n" }, { "path": "CONTRIBUTING.md", "content": "# Contributing\n\nWe welcome and encoruage everyone to contribute to GemPy! \nContributions can be questions, bug reports, feature requests and new code. \nHere is how to get started.\n\n## Issues \n\n### Questions\n\nFor questions about GemPy (e.g. its applications, functionality, and usage), \nplease [search the existing issues for related questions](https://github.com/cgre-aachen/gempy/issues).\nIf your question has not already been asked, then [make a new issue](https://github.com/cgre-aachen/gempy/issues/new/choose).\n\n### Reporting Bugs\n\nPlease 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.\nThe template asks essential questions for you to answer so that we can to understand, reproduce, and fix the bug. \nBe verbose!\nWhenever possible, provide tracebacks and/or error messages, screenshots, and sample code or other files.\n\n### Feature Requests\n\nWe encourage users to submit ideas for improvements to the GemPy project. For\nthis please create an issue on the\n [issue page](https://github.com/cgre-aachen/gempy/issues) with the *Feature \n Request* template and label. Please make sure to use a descriptive title and to\n provide ample background information to help us implement that functionality\n in the future.\n\n## Contributing New Code\n\nAny code contributions are welcome, \nwhether fixing a typo or bug,\nadding new post-processing/plotting functionality,\nimprove core functionality,\nor anything that you think should be in the repository. \n\nContributions should address an open issue (either a bug or a feature request).\nIf you have found a new bug \nor have an idea or a new feature, \nthen please [open the issue](https://github.com/cgre-aachen/gempy/issues/new/choose) \nfor discussion and link to that issue in your pull request.\n\n### Python code guidelines\nWe aim to follow particular Python coding guidelines to improve the sustainability and positive impact of this community project:\n\n- Follow [The Zen of Python](https://www.python.org/dev/peps/pep-0020/), most importantly \"readability counts\" when writing Python code.\n- Adhere to the [Style Guide for Python Code (PEP8)](https://www.python.org/dev/peps/pep-0008/).\n- Write thorough and effective documentation: \nMake a docstring for each module, function, class, and method, \nall following [PEP 257](https://www.python.org/dev/peps/pep-0257/) for high-level guidelines\nand [Google Python Style Guidelines](http://google.github.io/styleguide/pyguide.html) for Syntax. \n**Example function documentation:**\n```python\ndef func(arg1: int, arg2: float) -> int:\n \"\"\"A concise one line summary of the function.\n \n Additional information and description of the function, if necessary. This\n can be as long and verbose as you think is necessary for other users and \n developers to understand your functionality.\n \n Args:\n arg1 (int): Description of the first argument.\n arg2 (float): Description of the second argument. Please use hanging \n indentation for multi-line argument descriptions.\n \n Returns:\n (int) Description of the return value(s)\n \"\"\"\n return 42\n```\n- The code should explain the *what* and *how*. Add inline comments to explain the *why*.\nIf an inline comment seems to be needed, consider first making the code more readable.\nFor all comments, follow [PEP 8](https://www.python.org/dev/peps/pep-0008/).\n- Test every line of code. Untested code is dead code.\n\n### Licensing\n\nAll contributed code will be licensed under \n[a EUPL-1.2 license](https://github.com/cgre-aachen/gempy/blob/master/LICENSE).\nIf you did not write the code yourself, \nit is your responsibility to ensure that the existing license is compatible \nand included in the contributed files. \nIn general we discourage contributing third party code.\n\n### Testing\n\nOur test suite uses [`pytest`](https://docs.pytest.org/). \nYou should be familiar with `pytest` before contributing.\nPlease run all tests locally before creating a pull request. \nYou can do this by running `pytest` via your terminal in your GemPy folder:\n```bash\ncd ./path/to/gempy\npytest\n```\nAll tests are located in the `test` folder and its subfolders. \nAll contributed code must include test code in the pull request.\n\n### Pull Requests\n\nAll contributions are made via pull requests (PR's) to the master branch. \nYou 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.\n" }, { "path": "DevelopersGuide.md", "content": "Before a release.\n----------------\n# set version number in setup.py, also in the config file of the documentation and init of the package\n- [ ] setup.py\n- [ ] gempy.__init__\n> Note: in the config for sphinx~ this is taken from gempy.__init__\n\nRequirements version\n--------------------\n- requirements.txt, optional_requirements.txt, dev_requirements.txt should either set minimum version and reject versions\n\nGithub release\n--------------\n # add new tag\n $ git tag X.X -m \"Add X.X tag for PyPI\"\n # push git tag\n $ git push --tags origin master\n\nPyPi release:\n------------\nNew \n```\n # First create the dist\n python -m build\n\n # Second upload the distributions\n twine upload dist/*\n```\n\n### Type of commits:\n\n- ENH: Enhancement, new functionality\n- BUG: Bug fix\n- DOC: Additions/updates to documentation\n- TST: Additions/updates to tests\n- BLD: Updates to the build process/scripts\n- PERF: Performance improvement\n- CLN: Code cleanup\n\n## Documentation update\n\nThe 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. \n\nAlso, 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.\n\nCreating an update of the documentation can therefore be quite involved (depending on the level of changes). Please follow the steps below carefully.\n\n### General case: using a fork\n\n#### Create fork and perform changes\n\n- 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).\n- Clone the repository to your local computer.\n- Create a new branch of the `main` repository for the subsequent pull request.\n- Perform the changes, e.g.:\n - Update text in the documentation\n - Update examples (and/ or add new ones)\n - Perform changes in code and/ or function docstrings\n - etc.\n- 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).\n\n#### Update documentation using Sphinx\n\n- Update the documentation using sphinx in the branch where the changes were made (`main` branch or new branch based on `main`):\n - Check that you have `sphinx` and `sphinx-gallery` installed;\n - Change to folder with documentation from main repo with `cd docs`;\n - Run update with `make html`;\n - Wait... (can take some hours when run for the first time)\n- The updated documentation is now in the subfolder `build/html`. Preview the generated documentation locally: open `build/html/index.html` in a web browser.\n\n#### Add updated documentation to branch `gh-pages`\n\n\n- If everything is fine, first commit all changes that were made in the main repo:\n - `git add .`\n - `git commit -m \"update message\"`\n- Copy updated documentation to a temporary directory,for example `/tmp/docs-html` with:\n - `mkdir /tmp/docs-html`\n - `cp -R docs/build/html/* /tmp/docs-html/`\n- checkout the documentation branch: `git checkout gh-pages`\n- Copy the updated files into the main folder of the `gh-pages` branch: `cp -R /tmp/docs-html/* .`\n- To be sure: check updated documentation, open `index.html`, now in the main folder of the repository.\n- Commit all changes:\n - `git add .`\n - `git commit -m \"Updated documentation in gh-pages branch`\n\n#### Contribute updates back to main repository\n\n- Push _both_ branches `main` and `gh-pages` back to your own remote GitHub profile\n- 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)\n- Please include meaningful descriptions about your changes (both, in the main repo, as well as in the documnetation).\n\n\n\n### Special case: documentation update for maintainers with repository write rights\n\n- `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.\n- `cp -r --force ./docs/build/html/* ../gempy_docs/` This will copy the contents of the `docs/build/html` folder to the `VisualBayesicDocs` folder.\n- `cd ../gempy_docs` This will change the current directory to the `gempy_docs` folder.\n- `git add .` This will add all the files in the current directory to the staging area.\n- `git commit -m \"Update docs\"` This will commit the changes to the `gh-pages` branch.\n- `git push origin gh-pages` This will push the changes to the `gh-pages` branch of the repo.\n- `cd ../gempy` This will change the current directory back to the `gempy` folder.\n" }, { "path": "LICENSE", "content": "Copyright (c) 2020-2023 The GemPy Engine Developers\n\n EUROPEAN UNION PUBLIC LICENCE v. 1.2\n EUPL © the European Union 2007, 2016\n\nThis European Union Public Licence (the ‘EUPL’) applies to the Work (as defined\nbelow) which is provided under the terms of this Licence. 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\n \n[![GitHub Stars](https://img.shields.io/github/stars/cgre-aachen/gempy.svg)](https://github.com/cgre-aachen/gempy/stargazers)\n[![GitHub Forks](https://img.shields.io/github/forks/cgre-aachen/gempy.svg)](https://github.com/cgre-aachen/gempy/network)\n[![Documentation Status](https://assets.readthedocs.org/static/projects/badges/passing-flat.svg)](http://docs.gempy.org)\n[![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)\n[![PyPI](https://img.shields.io/badge/python-3.10%20%7C%203.11%20%7C%203.12-blue)](https://www.python.org/downloads/)\n[![PyPI](https://img.shields.io/badge/pypi-1.0-blue.svg)](https://pypi.org/project/gempy/)\n[![license: EUPL v1.2](https://img.shields.io/badge/license-EUPL%20v1.2-blue.svg)](https://github.com/cgre-aachen/gempy/blob/master/LICENSE)\n[![DOI](https://zenodo.org/badge/96211155.svg)](https://zenodo.org/badge/latestdoi/96211155)\n\n\n## What's New: GemPy 2024.1 (a.k.a GemPy v3) Release!\n\n\nWelcome 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.\n\n Delve into the exciting new features in the [What's New in GemPy v3](WhatsNewGemPy3.md). \n\nThe 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.\n\n## Overview\n\n[GemPy](https://www.gempy.org/) is a Python-based, **open-source geomodeling library**. It is\ncapable of constructing complex **3D geological models** of folded\nstructures, fault networks and unconformities, based on the underlying\npowerful **implicit representation** approach. \n\n## Installation\n\nWe provide the latest release version of GemPy via PyPi package services. We highly recommend using PyPi,\n\n`$ pip install gempy[base]`\n \n## Resources\n\nAfter installation, you can either check the [notebook tutorials](https://docs.gempy.org/getting_started/get_started.html#sphx-glr-getting-started-get-started-py) \nor the [video introduction](https://www.youtube.com/watch?v=n0btC5Zilyc) to get started.\n\nGo to the [documentation site](http://docs.gempy.org/) for further information and enjoy the [tutorials and examples](https://www.gempy.org/tutorials).\n\nFor questions and support, please use [discussions](https://github.com/cgre-aachen/gempy/discussions).\n\nIf you find a bug or have a feature request, create an [issue](https://github.com/cgre-aachen/gempy/issues).\n\nFollow these [guidelines](https://github.com/cgre-aachen/gempy/blob/WIP_readme-update-march21/CONTRIBUTING.md) to contribute to GemPy.\n\n\n\n## Gallery\n### Geometries\n\n

\n\n\n\n \n \n \n \n \n\n\n\n\n \n \n \n \n \n\n\n
\n \n \"colormapped\n \n \n \n \"colormapped\n \n \n \n \"colormapped\n \n
\n \n \"colormapped\n \n \n \n \"colormapped\n \n \n \n \"colormapped\n \n
\n

\n\n### Features\n\n

\n\n\n\n \n \n \n \n \n\n\n\n \n \n \n \n \n\n\n
\n \n \"colormapped\n \n \n \n \"colormapped\n \n \n \n \"colormapped\n \n
\n \n \"colormapped\n \n \n \n \"colormapped\n \n \n \n \"colormapped\n \n
\n\n\n\n### Case studies\n\n

\n\n\n\n \n \n \n \n \n\n
\n \n \"colormapped\n \n \n \n \"colormapped\n \n \n \n \"colormapped\n \n
\n\n\n\n## Publications using GemPy\n\n* 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. \n\n\n* 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.\n\n* 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.\n\n* 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.\n\n* 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.\n\n* 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. \n\n* 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.\n\n* 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. \n\n* 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.\n\n* 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.\n\n* 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\n \n* 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.\n\n* 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.\n\n* 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.\n \n* 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](\nhttps://www.sciencedirect.com/science/article/pii/B9780128140482000156).\nIn Developments in Structural Geology and Tectonics (Vol. 5, pp. 189-204). Elsevier.\n\nPlease let us know if your publication is missing!\n\nA continuously growing list of gempy-applications (e.g. listing real-world models) can be found [here](https://hackmd.io/@Japhiolite/B1juPvCxc).\n\n## References \n\n* 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.\n* 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.\n* 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.\n* 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.\n" }, { "path": "WhatsNewGemPy3.md", "content": "## **What's New: GemPy v3 Release!**\n\n**Introducing GemPy Version 3: Future-Proofing GemPy and its Role in the Open-Source Geoscience Ecosystem**\n\nWe 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.\n\n**1. Transition from GemPy v2 to v3: Significant Changes**\n\nWhile 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).\n\nWe 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.\n\nAlthough the core GemPy team will not develop new features for the legacy version, we are committed to maintaining it based on community requests.\n\n**2. Refined API**\n\nThe 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.\n\n**3. Transitioning from Theano/Aesara to NumPy & PyTorch**\n\nDue 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.\n\n**4. General Refactoring for Greater Robustness**\n\nWe've undertaken a refactoring of GemPy to enhance its robustness and reliability. Key improvements include:\n\n- **Enhanced State Management:** We’ve made improvements to ensure consistently valid states in GemPy. Before, invalid states could arise during data adjustments.\n- **Optimized Dependency Management:** We've streamlined the handling of dependencies. Most dependencies are now optional, which simplifies setup and integration for users.\n- **Modular Design:** We've divided GemPy into several distinct libraries. This modular approach allows users to leverage specific functionalities according to their needs:\n - **`gempy_engine`** [here](https://github.com/gempy-project/gempy_engine): Handles core computational algorithms.\n - **`gempy_viewer`** [here](https://github.com/gempy-project/gempy_viewer): Provides visualization capabilities.\n - **`gempy_plugins`** [here](https://github.com/gempy-project/gempy_plugins): Supports additional functionalities through plugins.\n - **`gempy_probability`**: Focuses on probabilistic modeling and uncertainty analysis (Coming soon).\n - **`gempy`**: Now leaner and primarily focused on documentation and managing the API.\n\n**5. Octree Implementation for Efficient Model Computation**\n\nWe have introduced an octree-based approach in GemPy v3 to optimize model computation and iteration times.\n\n**6. Dual Contouring for High-Quality Meshing**\n\nTo 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.\n\n**7. Initial Steps in LiquidEarth Integration**\n\nWe 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.\n\nThis 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.\n\n1. **Other Changes, Prototypes, and Future Developments**\n\nGemPy 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.\n\n---\n\n**In Conclusion**\n\nWith 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!\n" }, { "path": "docs/.buildinfo", "content": "# Sphinx build info version 1\n# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.\nconfig: 6b4aef288104d0a5b66327c5a7278fec\ntags: 645f666f9bcd5a90fca523b33c5a78b7\n" }, { "path": "docs/.nojekyll", "content": "" }, { "path": "docs/CNAME", "content": "gempy.rocks" }, { "path": "docs/Makefile", "content": "# Minimal makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line.\nSPHINXOPTS =\nSPHINXBUILD = sphinx-build\nSPHINXPROJ = gempy\nSOURCEDIR = source\nBUILDDIR = build\n\n# Put it first so that \"make\" without argument is like \"make help\".\nhelp:\n\t@$(SPHINXBUILD) -M help \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\n.PHONY: help Makefile\n\n# Catch-all target: route all unknown targets to Sphinx using the new\n# \"make mode\" option. $(O) is meant as a shortcut for $(SPHINXOPTS).\n%: Makefile\n\t@$(SPHINXBUILD) -M $@ \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\nhtml-noplot: export SPHX_GLR_THEME = $(theme)\nhtml-noplot:\n\t$(SPHINXBUILD) -D plot_gallery=0 -b html $(ALLSPHINXOPTS) $(SOURCEDIR) $(BUILDDIR)/html\n\t@echo\n\t@echo \"Build finished. The HTML pages are in $(BUILDDIR)/html.\"\n\n" }, { "path": "docs/__init__.py", "content": "" }, { "path": "docs/developers_notes/dev_log/2022-2023.md", "content": "## Refactoring for v3 notes:\n\n- I want to make proper subpackages of the library\n- Once the subpackages are done we can start moving them out to different repos\n- First proposition:\n - integration tests\n - notebooks\n - scripts\n - examples\n - GemPy\n - Plugins: \n - Addons\n - Assets\n - Bayesian\n - API\n - API Modules\n - gempy_api\n - Plotting\n - Core\n - Data\n - Interpolation\n - Theano \n - GemPy Engine\n\n> I think Pluggins and addons have to be split depending on how couple are they to the interpolator?\n\n\n## Refactoring notes:\n\nDoing:\n - [-] Refactoring interpolation (Aug-Sep 2023)\n\n- TODO:\n - [ ] Saving and loading models (Sep-Oct 2023)\n - [ ] Refactoring Geophysics (Oct-Nov 2023)\n - [ ] Refactoring topology\n - [ ] Orientations from surface points - Move to gempy_plugins" }, { "path": "docs/developers_notes/dev_log/2025_05.md", "content": "### From gempy_prob notes:\n**Saving models**\n- ? 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?\n- A way to save the model already with the correct **nugget effects** instead of having to \"load nuggets\"\n\n# TODO: \n-[ ] Saving and loading models \n - [x] Make tests passing for InterpOptions serializable\n - [x] Dealing with large numpy arrays\n - [x] Trying to have a better implementation for deserializing complex fields\n - [ ] Make save and load function\n-[ ] Better api for nugget effect optimization\n\n## Saving models\n- From gempy\n - StructuralFrame\n - OrientationsTable\n - SurfacePointsTable\n- From Engine\n - Input\n - EngineGrid\n - GeophysicsInput\n - InterpolationOptions\n - InputDataDescriptor\n - InterpolatorInput\n - Solutions (Not sure if I want to save them)\n - RawArraysSolutions\n - Solutions\n- Testing \n - [ ] 10 Test to go in modules\n - [ ] test api\n\n## What do I have in the engine server logic?\n" }, { "path": "docs/developers_notes/dev_log/log_2024-05.md", "content": "## Optimizing solver\n\n### Debugging Vector model 1\n\n#### Questions\n\n- [x] How many times do the system of equations gets solved?\n - Without caching is 1 per scalar field x 2 (centers and corners) x n_octree levels\n\n- [x] How many iterations we need to reach the solution?\n - Model 1 takes 5000 iterations (actually starts to diverge after 4k)\n \n- [ ] Has Pykeops become slower with the latest additions to the gradient?\n\n- [x] Is the Jacobi pre-conditioner bad for all cases?\n - Model 1:\n - Numpy Jacobi: Converge in\n - 1602 iterations and reach 5000\n - time 150 sec\n - More or less good results: Mesh closed\n - Numpy No jacobi\n - 1018 iterations and reach 5000\n - time 150 sec too\n - Results similar? I would say maybe a bit worse\n - Pykeosp No jacobi\n - 92 iterations and reach 5000\n - time 324 (all on running the solver)\n- [ ] Can be that pykeops is faster than invert the matrix but still slower that any other CG\n - Since the matrix is already constructed, it makes sense\n - [ ] Then which cases is worth using pykeops? The number of iterations will need to be rather small\n Will it make sense on edits reusing the previous weights? This is going to need a hell of orchestrator\n- [ ] How much memory takes numpy direct, numpy cg and pykeops cg\n - numpy direct: 1.5 or so\n - numpy cg: 1.25\n - pykeops cg: 1.1\n - **Note** Now the exporting takes quite a lot of memory\n\n\n\n### Experiments conclusions\n- Using pykeops CG to solve a ill posed problem is not realistic \n - 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\n - 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\n\n- I didn't find any improvement using the Jacobi pre-conditioner. Mostly the opposite\n- With ichol we get different results with numpy or pykeops\n\t- Pykeops\n- ichol does not give super good results either at least before the convergence happens\n - Model 1 converge **without** ichol on 1290 and 8518\n - Model 1 converge **With** ichol 496 (506 in pykeops) 5080 (4995 in pykeops)\n\t\n\n### TODO:\n\n- [x] Clean solver interface\n- [ ] Add citation for pymatting\n\n### Things to try:\n- [x] Pykeops as linear operator to use exact solver\n - Does not look good. Probably is not even possible mathematically\n- [x] Falkon\n\n### Optimal solver\n\n- Tools we have:\n - keops\n - CG\n - Incomplete cholesky decomposition\n - passing the weights as initial point\n- Variables to choose what to use:\n - Size of the covariance matrix\n - Number of CG iterations we are going to need. This depends on:\n - Conditional number\n - Do we have weights\n\n- **Strategy**:\n - If the Number of CG iterations is a handful -> **use keops**\n - If the number of CG iterations is large:\n - Use **direct solver** if the matrix fits in memory\n - Then for changes hopefully we can use the weights as initial point and use **keops**\n - If the matrix does not fit in memory:\n - Construct the sparse version of the matrix\n - Use **incomplete cholesky decomposition**\n - 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) \n - Optimizing nugget effects still seems the best way to do this\n - If memory is not an issue. **Direct solver with torch and keops for exporting seems to be the best option**\n\n\n#### Notes\n- [ ] I need to make similar default env file for gempy itself and using the default when we call the engine or whatever\n\n\n\n" }, { "path": "docs/developers_notes/dev_log/log_2024-06-25.md", "content": "## Goals\n\n- [ ] 1. Fixing 2024.2.0 for whatever version of python that is not working\n\n\n## TODO:\n\n- [x] Analyze error:\n - [x] Set up environment 3.11\n- [x] Fixing the lines of code\n- [x] Test the code \n- [ ] Release hot fix\n\n## Extra points:\n- [ ] Setting up github actions\n - [ ] Categories for the tests like I did in subsurface\n - [ ] Run tests in python 3.10, 3.11 and 3.12" }, { "path": "docs/developers_notes/dev_log/log_2024-06.md", "content": "# Refactoring grid to add transform\n\n## TODO:\n- [x] Rotation of pyvista grid\n- [x] Rotation of pyvista meshes\n- [ ] Run something with octrees again\n- [ ] **We do need settings presets**\n\n\n## Release Notes:\n- (Beta) Pivot grids \n- Analysis tools: \n - Eigenvalues and condition numbers\n - Transformed data plots\n - Octree refinement evaluation\n- Improved octree testing\n- LiquidEarth integration\n- Chunking for large grids\n \n## Notes:\n### Optimizing interpolation June 2024\n\n- Create_grad_kernel is call 27 times\n\n### Wholes issue\n- Spremberg with 1.5 std of extra refinement:\n Number of voxels marked by stats: 424 of torch.Size([512]).\n Number of voxels marked total by corners: 281\n Dense Grid would be 512 voxels\n Number of voxels marked by stats: 2535 of torch.Size([3568]).\n Number of voxels marked total by corners: 1492\n Dense Grid would be 4096 voxels\n Number of voxels marked by stats: 12148 of torch.Size([21152]).\n Number of voxels marked total by corners: 7614\n Dense Grid would be 32768 voxels\n Number of voxels marked by stats: 69619 of torch.Size([101432]).\n Number of voxels marked total by corners: 37423\n Dense Grid would be 262144 voxels\n Chunking done: 12 chunks\n- Spremberg with 1\n Number of voxels marked by stats: 408 of torch.Size([512]).\n Number of voxels marked total by corners: 281\n Dense Grid would be 512 voxels\n Number of voxels marked by stats: 1953 of torch.Size([3504]).\n Number of voxels marked total by corners: 1492\n Dense Grid would be 4096 voxels\n Number of voxels marked by stats: 8632 of torch.Size([16856]).\n Number of voxels marked total by corners: 7612\n Dense Grid would be 32768 voxels\n Chunking done: 13 chunks\n Number of voxels marked by stats: 47653 of torch.Size([79080]).\n Number of voxels marked total by corners: 37408\n Dense Grid would be 262144 voxels\n Chunking done: 10 chunks\n\n### Other:\n{'_cached_pivot': [200, 200, 0],\n'_is_default_transform': False,\n'position': array([0., 0., 0.]),\n'rotation': array([ 0., -0., -45.]),\n'scale': array([1., 1., 1.])}\n\n## Questions:\n" }, { "path": "docs/index.html", "content": "\n\n\n\n \n\n Title\n\n\n\n\n" }, { "path": "docs/main_docstrings", "content": ":class:`gempy.interpolator.InterpolatorData`\n:class:`gempy.data_management.InputData`\n:class:`gempy.data_management.GridClass`\n:class:`_pn.core.frame.DataFrame`\n:class:`_np.ndarray`\n\n\n:class:`matplotlib.pyplot.Figure`\n:class:`DataFrame`\n:class:`ndarray`\n\nitype(str {'all', 'interfaces', 'orientaions', 'formations', 'series', 'faults', 'fautls_relations'}): input\n data type to be retrieved." }, { "path": "docs/source/_static/nothing.txt", "content": "" }, { "path": "docs/source/_templates/base.rst", "content": "{{ fullname | escape | underline}}\n\n.. currentmodule:: {{ module }}\n\n.. auto{{ objtype }}:: {{ objname }}\n\n.. include:: ../gen_modules/backreferences/{{fullname}}.{{item}}examples\n.. raw:: html\n\n

" }, { "path": "docs/source/_templates/class.rst", "content": "{{ fullname | escape | underline}}\n\n.. currentmodule:: {{ module }}\n\n\n.. autoclass:: {{ objname }}\n :no-undoc-members:\n\n {% block examples%}\n .. include:: ../gen_modules/backreferences/{{fullname}}.examples\n .. raw:: html\n\n
\n {% endblock %}\n {% block methods %}\n {% if methods %}\n .. rubric:: Methods\n .. autosummary::\n {% for item in methods %}\n ~{{ name }}.{{ item }}\n {%- endfor %}\n {% endif %}\n {% endblock %}\n\n {% block attributes %}\n {% if attributes %}\n .. rubric:: Attributes\n\n .. autosummary::\n {% for item in attributes %}\n ~{{ name }}.{{ item }}\n {%- endfor %}\n {% endif %}\n {% endblock %}\n\n\n\n" }, { "path": "docs/source/_templates/module.rst", "content": "{{ fullname | escape | underline}}\n\n.. automodule:: {{ fullname }}\n\n {% block functions %}\n {% if functions %}\n .. rubric:: Functions\n\n .. autosummary::\n {% for item in functions %}\n {{ item }}\n {%- endfor %}\n {% endif %}\n {% endblock %}\n\n {% block classes %}\n {% if classes %}\n .. rubric:: Classes\n\n .. autosummary::\n {% for item in classes %}\n {{ item }}\n {%- endfor %}\n {% endif %}\n {% endblock %}\n\n {% block exceptions %}\n {% if exceptions %}\n .. rubric:: Exceptions\n\n .. autosummary::\n {% for item in exceptions %}\n {{ item }}\n {%- endfor %}\n {% endif %}\n {% endblock %}\n" }, { "path": "docs/source/api_reference.rst", "content": "Code\n====\n\n.. toctree::\n :maxdepth: 3\n\n\nGemPy API\n---------\n.. currentmodule:: gempy\n.. autosummary::\n :toctree: GemPy API\n :template: base.rst\n\n compute_model\n create_geomodel\n map_stack_to_surfaces\n structural_elements_from_borehole_set\n set_section_grid\n set_active_grid\n set_topography_from_random\n set_topography_from_file\n set_topography_from_subsurface_structured_grid\n set_topography_from_arrays\n set_custom_grid\n set_centered_grid\n generate_example_model\n set_fault_relation\n set_is_fault\n set_is_finite_fault\n add_surface_points\n add_orientations\n delete_surface_points\n delete_orientations\n create_orientations_from_surface_points_coords\n modify_surface_points\n modify_orientations\n add_structural_group\n remove_structural_group_by_index\n remove_structural_group_by_name\n remove_element_by_name\n calculate_gravity_gradient\n\n\nPlot\n----\n.. currentmodule:: gempy_viewer\n.. autosummary::\n :toctree: Plot\n :template: base.rst\n\n plot_2d\n plot_3d\n\nData Classes\n============\n\n.. toctree::\n :maxdepth: 3\n\nModeling Classes\n----------------\n.. module:: gempy.core.data\n.. autosummary::\n :toctree: Modeling Classes\n :template: class.rst\n\n GeoModel\n StructuralFrame\n StructuralGroup\n StructuralElement\n SurfacePointsTable\n OrientationsTable\n InterpolationOptions\n Grid\n Topography\n Solutions\n RawArraysSolution\n FaultsData\n GeophysicsInput\n Transform\n\nHelper Classes\n--------------\n.. currentmodule:: gempy.core.data\n.. autosummary::\n :toctree: Helper Classes\n :template: class.rst\n\n ImporterHelper\n GemPyEngineConfig\n ColorsGenerator\n\nEnumerators\n-----------\n.. currentmodule:: gempy.core.data\n.. autosummary::\n :toctree: Modeling Classes\n :template: class.rst\n \n StackRelationType\n GlobalAnisotropy\n AvailableBackends\n FaultsRelationSpecialCase\n" }, { "path": "docs/source/conf.py", "content": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n#\n# GemPy documentation build configuration file, created by\n# sphinx-quickstart on Tue Jul 4 10:28:52 2017.\n#\n# This file is execfile()d with the current directory set to its\n# containing dir.\n#\n# Note that not all possible configuration values are present in this\n# autogenerated file.\n#\n# All configuration values have a default; values that are commented out\n# serve to show the default.\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport datetime\nimport os\nimport sys\nimport warnings\n\nimport pyvista\nfrom sphinx_gallery.sorting import FileNameSortKey\n\nimport gempy\n\n# External examples:\nsys.path.insert(0, os.path.abspath('.'))\nimport make_external_gallery\n\nmake_external_gallery.make_example_gallery()\n\n# region PyVista Configuration\npyvista.set_error_output_file('errors.txt')\npyvista.OFF_SCREEN = True # Not necessary - simply an insurance policy. Ensure that offscreen rendering is used for docs generation\npyvista.set_plot_theme('document') # Preferred plotting style for documentation\npyvista.FIGURE_PATH = os.path.join(os.path.abspath('_images/'), 'auto-generated/') # Save figures in specified directory\npyvista.BUILDING_GALLERY = True\nif not os.path.exists(pyvista.FIGURE_PATH):\n os.makedirs(pyvista.FIGURE_PATH)\n\nsys.path.insert(0, os.path.abspath('.'))\n\n# -- General configuration ------------------------------------------------\n\n# If your documentation needs a minimal Sphinx version, state it here.\n#\n# needs_sphinx = '1.0'\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n 'sphinx.ext.autodoc',\n 'sphinx.ext.autosummary',\n 'sphinx.ext.doctest',\n 'sphinx.ext.intersphinx',\n 'sphinx.ext.todo',\n 'sphinx.ext.coverage',\n 'sphinx.ext.mathjax',\n 'sphinx.ext.viewcode',\n 'sphinx.ext.githubpages',\n 'sphinx.ext.napoleon',\n 'sphinx_gallery.gen_gallery',\n \"pyvista.ext.plot_directive\",\n \"sphinx_design\",\n 'sphinx_automodapi.automodapi',\n 'sphinx_automodapi.smart_resolver',\n]\n\nif run_intersphinx := False: # Example configuration for intersphinx: refer to the Python standard library.\n intersphinx_mapping = {\n 'numpy' : ('https://numpy.org/doc/stable/', None),\n 'python' : ('https://docs.python.org/{.major}'.format(sys.version_info), None),\n 'matplotlib': ('https://matplotlib.org/stable/', None),\n 'mayavi' : ('http://docs.enthought.com/mayavi/mayavi', None),\n 'sklearn' : ('https://scikit-learn.org/stable', None),\n 'skimage' : ('https://scikit-image.org/docs/dev/', None),\n 'pyvista' : ('https://docs.pyvista.org/', None),\n 'sphinx' : ('https://www.sphinx-doc.org/en/master/', None),\n 'pandas' : ('https://pandas.pydata.org/pandas-docs/stable/', None),\n }\n\nnapoleon_google_docstring = True\n\nautodoc_default_options = {\n 'autodoc_default_flags': ['members'],\n 'members' : None,\n 'member-order' : 'bysource',\n 'special-members' : '__init__',\n 'undoc-members' : True,\n 'exclude-members' : '__weakref__'\n}\nautosummary_generate = True\nautosummary_imported_members = True\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# The suffix(es) of source filenames.\n# You can specify multiple suffix as a list of string:\n#\n\nsource_suffix = ['.rst']\n\n# The master toctree document.\nmaster_doc = 'index'\n\n# region General information about the project.\nproject = 'GemPy'\nyear = datetime.date.today().year\ncopyright = u'2017-{}, Gempy Developers'.format(year)\n\nwith open(os.path.join(os.path.dirname(__file__), '../../AUTHORS.rst'), 'r') as f:\n author = f.read()\n\nversion = gempy.__version__\nrelease = gempy.__version__ # The full version, including alpha/beta/rc tags.\ntoday_fmt = '%d %B %Y'\nlanguage = 'en'\n\n# endregion\n\n# region building the documentation configuration\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This patterns also effect to html_static_path and html_extra_path\nexclude_patterns = ['_build', '**.ipynb_checkpoints', 'Thumbs.db', '.DS_Store', 'errors.txt', '../test']\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = 'sphinx'\nhighlight_language = 'python3'\n\ntodo_include_todos = True # If true, `todo` and `todoList` produce output, else they produce nothing.\n\nlinkcheck_retries = 3\nlinkcheck_timeout = 500\n\n# * -- Sphinx Gallery Options\nsphinx_gallery_conf = {\n # path to your examples scripts\n \"examples_dirs\" : [\n \"../../examples/tutorials\",\n \"../../examples/examples\",\n # \"../../examples/integrations\",\n ],\n # path where to save gallery generated examples\n \"gallery_dirs\" : [\n 'tutorials',\n \"examples\",\n # 'integrations'\n ],\n \"filename_pattern\" : r\"\\.py\",\n # \"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\n \"download_all_examples\" : False, # Remove the \"Download all examples\" button from the top level gallery\n \"within_subsection_order\": FileNameSortKey, # Sort gallery example by file name instead of number of lines (default)\n \"backreferences_dir\" : 'gen_modules/backreferences', # directory where function granular galleries are stored\n \"doc_module\" : (\"gempy\", \"gempy_viewer\", 'numpy', 'pandas', 'matplotlib'), # Modules for which function level galleries are created. In\n \"image_scrapers\" : ('pyvista', 'matplotlib'),\n 'first_notebook_cell' : (\"%matplotlib inline\\n\"\n \"from pyvista import set_plot_theme\\n\"\n \"set_plot_theme('document')\"),\n 'reference_url' : {\n 'gempy': None, # The module you locally document uses None\n 'numpy': 'https://numpy.org/doc/stable/'\n\n },\n}\n\n# endregion\n\n# region -- Options for HTML output ----------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\n\nhtml_theme = 'alabaster'\nhtml_theme_options = {\n 'github_user' : 'gempy-project',\n 'github_repo' : 'gempy',\n 'github_type' : 'star',\n 'logo' : 'logos/gempy.png',\n 'logo_name' : False,\n 'travis_button' : False,\n 'page_width' : '1200px',\n 'fixed_sidebar' : False,\n 'show_related' : True,\n 'sidebar_collapse': True,\n}\n\n# Custom sidebar templates, maps document names to template names.\n# html_sidebars = { '**': ['globaltoc.html', 'relations.html', 'sourcelink.html', 'searchbox.html']}\n\nhtml_sidebars = {'**': ['about.html', 'navigation.html',\n 'relations.html',\n 'searchbox.html',\n 'donate.html', ]}\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\nhtml_favicon = '_static/logos/favicon.ico'\n\n# endregion\n\n# -- Options for HTMLHelp output ------------------------------------------\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = 'GemPydoc'\n\n# -- Options for manual page output ---------------------------------------\n\n# One entry per manual page. List of tuples\n# (source start file, name, description, authors, manual section).\nman_pages = [(master_doc, 'gempy', 'GemPy Documentation', [author], 1)]\n\n# region other options\n# Remove matplotlib agg warnings from generated doc when using plt.show\nwarnings.filterwarnings(\"ignore\", category=UserWarning,\n message='Matplotlib is currently using agg, which is a'\n ' non-GUI backend, so cannot show the figure.')\n# endregion\n" }, { "path": "docs/source/index.rst", "content": ".. GeMpy documentation master file, created by\r\n sphinx-quickstart on Wed Dec 14 12:44:40 2016.\r\n You can adapt this file completely to your liking, but it should at least\r\n contain the root `toctree` directive.\r\n\r\n.. ../logos/gempy1.png\r\n :width: 30%\r\n\r\nAbout\r\n=====\r\nOpen-source software for implicit 3D structural geological modeling in Python.\r\n******************************************************************************\r\n\r\nOverview\r\n--------\r\n\r\n``GemPy`` is a Python-based, community-driven, **open-source geomodeling library**. It is\r\ncapable of constructing complex **3D geological models** including various features such as\r\nfold structures, fault networks and unconformities, based on an underlying\r\npowerful **implicit** approach. From the ground up, ``GemPy`` was designed to be easily embedded in probabilistic frameworks for conducting\r\nuncertainty analysis regarding subsurface structures.\r\n\r\n.. Check out the documentation either in `gempy.org `_\r\n (better option), or `read the docs `_.\r\n\r\n3D models created with GemPy may look like this:\r\n\r\n.. image:: ./_images/AlesModel_example.png\r\n\r\nContents:\r\n\r\n.. toctree::\r\n :maxdepth: 1\r\n\r\n self\r\n installation\r\n\r\n.. toctree::\r\n :maxdepth: 1\r\n\r\n tutorials/index\r\n examples/index\r\n\r\n.. toctree::\r\n :maxdepth: 1\r\n \r\n external/external_examples\r\n\r\n.. toctree::\r\n :maxdepth: 1\r\n\r\n api_reference\r\n\r\n\r\nFeatures\r\n--------\r\n\r\nGeological features\r\n^^^^^^^^^^^^^^^^^^^\r\n\r\n\r\n``GemPy`` is capable of modeling complex 3D geological scenarios, including:\r\n\r\n* Multiple conformal layers (e.g. sequences of sedimentary layers)\r\n* Several sequences of layers, with conformal continuation or unconformities\r\n* Magmatic bodies of (almost) arbitrary shapes\r\n* Faults (offset calculated automatically from affected geological objects)\r\n* Full fault networks (faults affecting faults)\r\n* Folds (affecting single layers or entire layer stacks, including overturned and recumbent folds)\r\n\r\nCombining these elements in GemPy allows for the generation of realistic\r\n3D geological models, on a par with most commercial geomodeling software.\r\n\r\n\r\nInterpolation approach\r\n^^^^^^^^^^^^^^^^^^^^^^\r\n\r\nThe generation of complex structural settings is based on the powerful\r\ninterpolation algorithm underlying ``GemPy``\\ , a unviersal cokriging method\r\ndevised by `Lajaunie et al. (1997)` and extended by `Calcagno et al. (2008)`\\ .\r\nThis method is used to interpolate a 3D scalar field, such that geologically\r\nsignificant interfaces are isosurfces in this field.\r\n\r\nThe algorithm allows for a direct integration of two of the most relevant\r\ngeological input data types:\r\n\r\n\r\n* **Surface contact points**\\ : 3D coordinates of points marking the boundaries\r\n between different features (e.g. layer interfaces, fault planes, unconformities).\r\n* **Orientation measurements**\\ : Orientation of the poles perpendicular to\r\n the dipping of surfaces at any point in 3D space.\r\n\r\n``GemPy`` also allows for the definition of topological elements such as\r\ncombining multiple stratigraphic sequences and\r\ncomplex fault networks to be considered in the modeling process.\r\n\r\n.. image:: ./_images/data_to_model.png\r\n\r\n\r\n\r\nIntegrated visualization\r\n^^^^^^^^^^^^^^^^^^^^^^^^\r\n\r\nModels generated with ``GemPy`` can be visualized in several ways:\r\n\r\n\r\n* direct visualization of 2D model sections (or geological maps) using\r\n `matplotlib `_, including hillshading and other options for intuitive\r\n representation of results;\r\n* 3D visualization using `Pyvista `_, including interactive plots\r\n\r\n.. image:: _images/3D_view_pyvista_example.png\r\n :target: https://cgre-aachen.github.io/gempy/_images/sphx_glr_ch1_1_basics_009.png\r\n :width: 70%\r\n\r\nFor a more detailed elaboration of the theory behind ``GemPy``\\ , we refer to the\r\n**open access scientific publication**\\ :\r\n`\\ \"GemPy 1.0: open-source stochastic geological modeling and inversion\"\r\nby de la Varga et al. (2019) `_.\r\n\r\nPublications using GemPy\r\n------------------------\r\n\r\n- 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.\r\n\r\n- 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.\r\n\r\n- 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.\r\n\r\n- 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.\r\n\r\n- 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.\r\n\r\n- 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.\r\n\r\n- 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.\r\n\r\n- 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.\r\n\r\n- 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.\r\n\r\n- von Harten, J., de la Varga, M., Hillier, M., & Wellmann, F. (2021). `Informed Local Smoothing in 3D Implicit Geological Modeling `_. Minerals 2021, 11, 1281.\r\n\r\n- 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.\r\n\r\n- 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.\r\n\r\n- 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.\r\n\r\n- 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.\r\n\r\n- 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.\r\n\r\n\r\nReferences\r\n----------\r\n\r\n* 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.\r\n* 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.\r\n* `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.\r\n\r\n\r\n\r\nIndices and tables\r\n==================\r\n\r\n* :ref:`genindex`\r\n* :ref:`search`\r\n\r\n\r\n.. image:: _static/logos/logo_CGRE.png\r\n :width: 40%\r\n\r\n.. image:: _static/logos/Terranigma.png\r\n :width: 40%" }, { "path": "docs/source/installation.rst", "content": ".. role:: raw-html-m2r(raw)\n :format: html\n\nInstallation\n------------\n\nInstalling ``GemPy``\n^^^^^^^^^^^^^^^^^^^^\n\nThe latest release version of ``GemPy`` is available via **PyPi**. To install the core version, simply use pip:\n\n.. code-block:: bash\n\n $ pip install gempy\n\nThis will install ``GemPy`` with the minimal required dependencies.\n\nGemPy viewer\n^^^^^^^^^^^^\n\nFor visualization in 2-D and 3-D, the additional package ``gempy-viewer`` can be used and installed with:\n\n.. code-block:: bash\n\n $ pip install gempy-viewer\n\nThe 3-D visualization is based on `pyvista`. This package needs to be installed separately. Please check:\n\n- PyVista: Installation guide available on the `PyVista installation page `_.\n\n\n\nEnhanced Installation Options\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nFor additional features, ``GemPy`` offers enhanced installation options:\n\n- Base Features: For the majority of functionalities, install with base dependencies:\n\n .. code-block:: bash\n\n $ pip install gempy[base]\n\n- Optional Features: To access optional features such as data download support, install with optional dependencies:\n\n .. code-block:: bash\n\n $ pip install gempy[opt]\n\n- Development Version: For development purposes, including testing tools:\n\n .. code-block:: bash\n\n $ pip install gempy[dev]\n\n\nNote: Some advanced functionalities in ``GemPy`` require `PyTorch`. For installation, please follow the\ninstructions on the `PyTorch installation page `_.\n\nManual Installation\n^^^^^^^^^^^^^^^^^^^\n\nFor the latest, cutting-edge version of ``GemPy``, you can clone the repository and install it manually:\n\n1. Clone the repository:\n\n .. code-block:: bash\n\n $ git clone https://github.com/cgre-aachen/gempy.git\n\n2. Navigate to the root of the cloned repository and install using pip:\n\n .. code-block:: bash\n\n $ pip install -e .\n\nDependencies\n^^^^^^^^^^^^\n\n``GemPy`` relies on numerous open-source libraries. Core dependencies are automatically installed with the package. Enhanced functionalities require additional dependencies, structured as follows:\n\n- Core Dependencies: Installed with the basic package.\n- Base Dependencies: Additional libraries for extended functionalities (`base-requirements.txt`).\n- Optional Dependencies: Libraries for optional features (`optional-requirements.txt`).\n- Development Dependencies: Tools needed for development (`dev-requirements.txt`).\n\n" }, { "path": "docs/source/make_external_gallery.py", "content": "\"\"\"\nModified after https://github.com/pyvista/pyvista/blob/ab70c26edbcfb107286c827bd4914562056219fb/docs/make_external_gallery.py\n\nA helper script to generate the external 2-examples gallery.\n\"\"\"\nimport os\nfrom io import StringIO\n\n\ndef format_icon(title, description, link, image):\n body = r\"\"\"\n .. grid-item-card:: {}\n :link: {}\n :text-align: center\n :class-title: pyvista-card-title\n\n .. image:: {}\n\"\"\"\n content = body.format(title, link, image)\n return content\n\n\nclass Example():\n def __init__(self, title, description, link, image):\n self.title = title\n self.description = description\n self.link = link\n self.image = image\n\n def format(self):\n return format_icon(self.title, self.description, self.link, self.image)\n\n\n###############################################################################\n\narticles = dict(\n _1=Example(\n title=\"Vector Model 1: Construction\",\n description=\"Build a basic structural model\",\n 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\",\n image=\"https://vector-raw-materials.github.io/vector-geology/_images/sphx_glr_01_model_1_gempy_step_by_step_001.png\",\n ),\n _2=Example(\n title=\"Vector Model 1: Forward Gravity\",\n description=\"Get the gravity response of the model\",\n 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\",\n image=\"https://vector-raw-materials.github.io/vector-geology/_images/sphx_glr_02_model_1_gempy_fw_gravity_003.png\",\n ),\n _3=Example(\n title=\"Spremberg: Importing Boreholes\",\n description=\"Import data from well csv files\",\n 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\",\n image=\"https://vector-raw-materials.github.io/vector-geology/_images/sphx_glr_03_model_spremberg_import_003.png\",\n ),\n _4=Example(\n title=\"Spremberg: Constructing the Model\",\n description=\"Build a structural model from borehole data\",\n 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\",\n image=\"https://vector-raw-materials.github.io/vector-geology/_images/sphx_glr_04_model_spremberg_building_008.png\",\n ),\n)\n\n\n###############################################################################\n\ndef make_example_gallery():\n \"\"\"Make the example gallery.\"\"\"\n path = \"./external/external_examples.rst\"\n\n with StringIO() as new_fid:\n new_fid.write(\n \"\"\"\n.. _external_examples:\n\nExternal Examples\n==================\n\nVector Examples\n---------------\nThese are examples from the Vector project.\n\n.. grid:: 3\n :gutter: 1\n\n\"\"\"\n )\n # Reverse to put the latest items at the top\n for example in list(articles.values())[::-1]:\n new_fid.write(example.format())\n\n new_fid.write(\n \"\"\"\n\n.. raw:: html\n\n
\n\n\n\"\"\"\n )\n new_fid.seek(0)\n new_text = new_fid.read()\n\n # check if it's necessary to overwrite the table\n existing = \"\"\n if os.path.exists(path):\n with open(path) as existing_fid:\n existing = existing_fid.read()\n\n # write if different or does not exist\n if new_text != existing:\n with open(path, \"w\", encoding=\"utf-8\") as fid:\n fid.write(new_text)\n\n return\n" }, { "path": "examples/__init__.py", "content": "" }, { "path": "examples/data/gempy_models/Greenstone/Greenstone_faults.csv", "content": ",isFault,isFinite\nEarlyGranite_Series,False,False\nBIF_Series,False,False\nSimpleMafic_Series,False,False\nBasement,False,False\n" }, { "path": "examples/data/gempy_models/Greenstone/Greenstone_faults_relations.csv", "content": ",EarlyGranite_Series,BIF_Series,SimpleMafic_Series,Basement\nEarlyGranite_Series,False,False,False,False\nBIF_Series,False,False,False,False\nSimpleMafic_Series,False,False,False,False\nBasement,False,False,False,False\n" }, { "path": "examples/data/gempy_models/Greenstone/Greenstone_kriging_data.csv", "content": ",range,$C_o$,drift equations,nugget grad,nugget scalar\nvalues,86591.22357375486,178524761.9047619,[3 3 3 0],0.01,1e-06\n" }, { "path": "examples/data/gempy_models/Greenstone/Greenstone_options.csv", "content": ",dtype,output,aesara_optimizer,device,verbosity\nvalues,float64,geology,fast_compile,cpu,[]\n" }, { "path": "examples/data/gempy_models/Greenstone/Greenstone_orientations.csv", "content": ",X,Y,Z,X_r,Y_r,Z_r,G_x,G_y,G_z,dip,azimuth,polarity,surface,series,id,order_series,smooth\n15,743403.6875,6877878.25,454.74995199999995,0.7289193836681321,0.3393090442016824,0.5454662898901393,0.17435731030158103,-0.9692501424997381,0.17364817766793042,80.0,169.802194,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n16,738704.4065,6877046.0,458.14874000000003,0.6805345417666262,0.33074001317916457,0.5455012845669923,0.17005791273562937,-0.9700137198552169,0.17364817766793042,80.0,170.056246,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n17,735326.7815,6875283.75,452.96645599999994,0.6457577638664378,0.31259549540273673,0.5454479266188236,0.8938984296759075,-0.4132697760747355,0.17364817766793042,80.0,114.81218500000001,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n18,734151.969,6874304.75,448.179887,0.6336616331867841,0.30251549375534115,0.5453986430402685,-0.4396965705831378,-0.8811998843666176,0.17364817766793042,80.0,206.51804199999998,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n19,734200.938,6875724.5,461.98580400000003,0.6341658288973772,0.31713355538852794,0.5455407918309771,-0.857926514169013,0.4835373891210603,0.17364817766793042,80.0,299.406115,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n20,735082.063,6879102.25,480.551436,0.6432380877855911,0.35191162031650586,0.5457319477054868,-0.9791340537242994,0.10555953404893238,0.17364817766793042,80.0,276.153239,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n21,734592.563,6882039.25,487.287083,0.6381980869618933,0.38215162525869256,0.5458012994244411,-0.6723043953762564,-0.7196201153024675,0.17364817766793042,80.0,223.05309300000002,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n22,732683.469,6882332.75,481.71195199999994,0.6185416307156908,0.38517356650844803,0.5457438966362241,0.3108420554206961,-0.9344642994644278,0.17364817766793042,80.0,161.600709,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n23,730627.5315,6880472.5,477.40265800000003,0.5973732411478233,0.36602001894504904,0.545699527187175,0.8442658343469975,-0.5070123384573562,0.17364817766793042,80.0,120.98634799999999,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n24,728767.4065,6878759.25,470.031623,0.5782209806122138,0.3483800160621068,0.5456236333721157,0.23857012308693834,-0.9554740220235208,0.17364817766793042,80.0,165.980598,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n25,728571.594,6879934.25,472.536776,0.5762048515799557,0.3604780772834447,0.5456494269843395,-0.8624104574918853,0.4754939675756334,0.17364817766793042,80.0,298.87034700000004,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n26,729256.9065,6882724.5,471.72292400000003,0.5832609814359115,0.38920711160075366,0.5456410473832151,-0.9822538012829838,-0.07087863046064441,0.17364817766793042,80.0,265.87273700000003,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n27,727739.4375,6884242.0,474.25872599999997,0.567636783254224,0.4048316289653326,0.5456671565643565,-0.10193532939980383,-0.9795179932042825,0.17364817766793042,80.0,185.941204,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n28,724802.4065,6883312.0,473.707869,0.5373964591291459,0.39525614221142263,0.5456614848182202,0.5700993409362939,-0.8030149761097184,0.17364817766793042,80.0,144.627207,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n29,722403.813,6880913.25,470.70706500000006,0.5127000072073552,0.37055807893084025,0.5456305878731094,0.8192953473908405,-0.5464443650887268,0.17364817766793042,80.0,123.702047,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n30,721229.0005,6880766.25,477.680894,0.5006038765277017,0.3690445342503835,0.5457023919668873,-0.955039237532274,-0.24030473396155966,0.17364817766793042,80.0,255.876557,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n31,720690.563,6882822.25,489.90942300000006,0.495060004324413,0.39021356733214585,0.5458282996200694,-0.9487350298778479,-0.26409875704593466,0.17364817766793042,80.0,254.444427,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n32,718928.344,6883605.5,509.462245,0.4769158057308778,0.3982780834611781,0.5460296198222871,0.06399643026772625,-0.9827261914206341,0.17364817766793042,80.0,176.27408400000002,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n33,715991.2815,6882773.25,505.16586399999994,0.4466751572747963,0.3897090524386603,0.5459853833283568,0.4523816530880285,-0.8747554803151861,0.17364817766793042,80.0,152.65415900000002,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n34,712907.3755,6880766.25,512.582136,0.41492257549704975,0.3690445342503835,0.546061742913625,0.5981974408083341,-0.7823082079353031,0.17364817766793042,80.0,142.59641100000002,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n35,710459.844,6880521.5,511.839758,0.38972224704755865,0.36652453383853467,0.5460540992246945,-0.7829565734412656,-0.5973485703467495,0.17364817766793042,80.0,232.658556,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n36,709970.344,6882724.5,532.9679150000001,0.3846822462238609,0.38920711160075366,0.5462716394262945,-0.9555635021311216,0.23821146862908438,0.17364817766793042,80.0,283.99789599999997,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n37,709089.2505,6883312.0,519.457428,0.37561031166664915,0.39525614221142263,0.5461325324485446,0.4394815627262955,-0.8813071351213059,0.17364817766793042,80.0,153.495937,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n38,705858.5005,6880032.25,494.307044,0.34234579141912796,0.36148710707041587,0.545873578503547,0.7823117472581675,-0.5981928121393597,0.17364817766793042,80.0,127.40325,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n39,701844.5315,6875137.25,476.658525,0.3010170742254671,0.311087098833438,0.5456918654283743,0.7357966048102152,-0.6545606669689805,0.17364817766793042,80.0,131.656118,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n40,698466.9375,6871074.25,463.972201,0.26624061550817,0.269253547563399,0.5455612442160971,0.7825830449493052,-0.5978378443618282,0.17364817766793042,80.0,127.377257,1.0,EarlyGranite,EarlyGranite_Series,1,1,0.01\n0,739426.6276840001,6891935.451483999,75.422691,0.6879706916384385,0.48404511582219845,0.5415606520803518,-0.6040227735540536,-0.7198463103919542,0.3420201433266688,70.0,220.0,1.0,SimpleBIF,BIF_Series,3,2,0.01\n1,717311.112372,6891940.971392999,-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\n2,723415.321182,6891939.447817,-5298.1543090000005,0.5231147203237131,0.4840862629552179,0.4862331086560341,0.0593911746148847,0.33682408883446513,0.9396926207869084,20.0,10.0,1.0,SimpleMafic1,SimpleMafic_Series,4,3,0.01\n3,742907.686575,6891934.582632001,-2786.869309,0.7238124478753751,0.48403616992885884,0.5120898573149512,-0.8137976813483737,-0.29619813272502366,0.5000000000010001,60.0,250.0,1.0,SimpleMafic1,SimpleMafic_Series,4,3,0.01\n4,712584.536312,6891942.15112,-582.769334,0.4115985514450749,0.48411409676389305,0.5347837606643122,0.8660253779324091,-0.00021160992272160206,0.5000000000010001,60.0,90.014,1.0,SimpleMafic1,SimpleMafic_Series,4,3,0.01\n5,724309.279198,6905712.031145,-3763.8629950000004,0.532319110797648,0.625891842766388,0.5020305131225353,0.766044443119978,1.0000469066937634e-12,0.6427876096875393,50.0,90.0,1.0,SimpleMafic1,SimpleMafic_Series,4,3,0.01\n6,728653.9332569999,6905714.891064,-4196.807995,0.5770526348753636,0.6259212891282221,0.49757281515192076,-0.1579856938790021,-0.00010478009447487647,0.9874413955027208,9.09,269.962,1.0,SimpleMafic1,SimpleMafic_Series,4,3,0.01\n7,732788.182361,6905717.612482001,-1680.306995,0.6196197827600052,0.6259493094529728,0.5234832689064383,-0.8660252133152075,-0.0005743697555669816,0.5000000000010001,60.0,269.962,1.0,SimpleMafic1,SimpleMafic_Series,4,3,0.01\n8,727261.874621,6877214.496202,-5666.992176,0.5627196896821558,0.33247488753436794,0.48243547197883097,9.999162292252465e-13,0.3420201433266687,0.9396926207869084,20.0,360.0,1.0,SimpleMafic1,SimpleMafic_Series,4,3,0.01\n9,718902.5312979999,6900478.748653,-1882.8952969999998,0.4766500324125068,0.5720088027861601,0.5213973747104187,0.35135316564372676,0.042375304195105545,0.9352835327263932,20.726,83.12299999999999,1.0,SimpleMafic1,SimpleMafic_Series,4,3,0.01\n10,733335.732778,6903150.931884,-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\n11,730820.452963,6902599.428688001,-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\n12,725461.656,6915002.0,510.686328,0.5441842385428782,0.7215434274064846,0.5460422232529885,0.8435050562949495,-0.19621218108503954,0.5000000000010001,60.0,103.095,1.0,SimpleMafic1,SimpleMafic_Series,4,3,0.01\n13,726102.312,6917775.5,499.040304,0.5507805751469789,0.7501,0.5459223132007867,1e-12,-0.8660254037834386,0.5000000000010001,-60.0,0.0,1.0,SimpleMafic1,SimpleMafic_Series,4,3,0.01\n14,727390.156,6913486.75,517.5692429999999,0.5640405032793464,0.7059420765421167,0.5461130912760108,-0.8215644704637983,-0.2739193692847363,0.5000000000010001,-60.0,71.561,1.0,SimpleMafic1,SimpleMafic_Series,4,3,0.01\n" }, { "path": "examples/data/gempy_models/Greenstone/Greenstone_rescaling_data.csv", "content": ",rescaling factor,centers\nvalues,97123.0,[ 7.21180062e+05 6.89349475e+06 -3.95136022e+03]\n" }, { "path": "examples/data/gempy_models/Greenstone/Greenstone_series.csv", "content": ",order_series,BottomRelation,isActive\nEarlyGranite_Series,1,Erosion,True\nBIF_Series,2,Erosion,True\nSimpleMafic_Series,3,Erosion,True\nBasement,4,Erosion,False\n" }, { "path": "examples/data/gempy_models/Greenstone/Greenstone_surface_points.csv", "content": ",X,Y,Z,X_r,Y_r,Z_r,surface,series,id,order_series,smooth\n43,745312.75,6878221.0,452.733051,0.7485755155833325,0.34283807440050246,0.5454455234290538,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n44,741494.625,6877535.5,460.20235,0.7092632517529318,0.33578001400286234,0.5455224289921028,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n45,735914.188,6876556.5,456.48238899999996,0.6518058317803195,0.32570001235546675,0.5454841274466398,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n46,734739.375,6874011.0,446.031913,0.639709695952555,0.2994909784500067,0.5453765270224354,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n47,733564.563,6874598.5,452.620638,0.6276135704210122,0.30554000906067563,0.5454443659998147,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n48,734837.313,6876850.5,469.89565,0.6407180873737421,0.32872710171638025,0.5456222333638788,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n49,735326.813,6881354.0,492.347775,0.64575808819744,0.3750961389166315,0.5458534054343461,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n50,733858.313,6882724.5,486.360734,0.6306380857263466,0.38920711160075366,0.545791761529195,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n51,731508.625,6881941.0,476.162317,0.6064451757050338,0.3811400214161424,0.5456867563604914,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n52,729746.438,6879004.0,468.440123,0.5883013065906115,0.3509000164739557,0.5456072469342998,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n53,727788.375,6878514.5,465.16227599999996,0.5681406546338148,0.3458600156502579,0.5455734974928699,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n54,729354.813,6881354.0,469.46642099999997,0.5842690485260954,0.3750961389166315,0.5456178139266703,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n55,729159.0,6884095.0,480.639992,0.5822529143457266,0.40331808428487587,0.5457328594977503,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n56,726319.875,6884389.0,472.41144699999995,0.5530206521627214,0.40634517364578937,0.5456481365690928,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n57,723284.938,6882235.0,475.41346100000004,0.5217722660955691,0.3841671107770559,0.5456790459726326,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n58,721522.688,6879591.5,471.253042,0.5036277483191414,0.3569490470846246,0.5456362093736807,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n59,720935.313,6881941.0,492.156175,0.4975800047362619,0.3811400214161424,0.5458514326781504,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n60,720445.813,6883703.5,498.30804000000006,0.49254000391256414,0.39928711324814925,0.5459147736478486,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n61,717410.875,6883507.5,507.58461900000003,0.4612916075491902,0.39726905367420695,0.5460102873675649,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n62,714571.688,6882039.0,506.53461200000004,0.43205870700040117,0.3821490512031136,0.5459994762620595,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n63,711243.063,6879493.5,505.437117,0.39778644399369834,0.35594001729765345,0.5459881762095488,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n64,709676.625,6881549.5,515.202397,0.3816580501014178,0.3771090503794158,0.546088721703407,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n65,710264.063,6883899.5,524.0556650000001,0.3877064423463028,0.40130517282209155,0.5461798769189584,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n66,707914.438,6882724.5,507.24563600000005,0.3635141809869955,0.38920711160075366,0.5460067971232355,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n67,703802.563,6877340.0,483.05280700000003,0.3211774018512604,0.333767102540078,0.5457577023773977,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n68,699886.5,6872934.5,470.146586,0.2808567465996726,0.28840709512679796,0.5456248170567218,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n69,697047.375,6869214.0,456.299059,0.2516244844166675,0.2501,0.5454822398402026,EarlyGranite,EarlyGranite_Series,1,1,1e-06\n0,735484.817806,6891936.435338001,-1819.319309,0.647384940807018,0.4840552458017232,0.5220519672168281,SimpleMafic2,BIF_Series,2,2,1e-06\n1,729854.915982,6891937.840530001,-1432.263309,0.5894182200096779,0.4840697139709505,0.5260371818415823,SimpleMafic2,BIF_Series,2,2,1e-06\n2,724084.2671609999,6891939.280852,-4739.830309,0.5300023368409125,0.4840845438464651,0.4919817366844105,SimpleMafic2,BIF_Series,2,2,1e-06\n3,733521.625,6895282.5,521.55524,0.6271714712272067,0.5185070714454867,0.5461541319872738,SimpleMafic2,BIF_Series,2,2,1e-06\n4,721933.375,6884591.5,496.66929500000003,0.507856272973446,0.4084301586647859,0.5458979007650093,SimpleMafic2,BIF_Series,2,2,1e-06\n5,724251.0,6886909.0,484.550926,0.5317190552186403,0.4322916538821906,0.5457731273436776,SimpleMafic2,BIF_Series,2,2,1e-06\n6,727316.313,6886460.5,478.25442300000003,0.5632801993348637,0.42767379817345014,0.5457082971489761,SimpleMafic2,BIF_Series,2,2,1e-06\n7,729858.25,6887133.5,484.25957400000004,0.5894525478002121,0.4346031557921399,0.5457701275187133,SimpleMafic2,BIF_Series,2,2,1e-06\n8,732699.25,6885040.0,494.52648099999993,0.6187041154000597,0.41304801437352634,0.5458758378756834,SimpleMafic2,BIF_Series,2,2,1e-06\n9,716849.5,6887358.0,508.98189400000007,0.45551156574652757,0.4369146577020891,0.5460246740216015,SimpleMafic2,BIF_Series,2,2,1e-06\n10,719017.625,6892217.5,508.179387,0.477835062755475,0.4869491500468478,0.5460164112311193,SimpleMafic2,BIF_Series,2,2,1e-06\n11,739179.440691,6891935.513181,-552.591309,0.685425599404878,0.4840457510682332,0.535094480318771,SimpleBIF,BIF_Series,3,2,1e-06\n12,735564.599804,6891936.415425,-2652.196309,0.6482063939952432,0.4840550407730381,0.5134764804629182,SimpleBIF,BIF_Series,3,2,1e-06\n13,730009.0099770001,6891937.802069,-2088.409309,0.5910048060397647,0.48406931796793723,0.519281356753807,SimpleBIF,BIF_Series,3,2,1e-06\n14,718795.791326,6891940.600826,-2773.169309,0.47555101393078886,0.4840981345922167,0.5122309155606808,SimpleBIF,BIF_Series,3,2,1e-06\n15,724143.38616,6891939.266096001,-5569.907309,0.5306110391977182,0.48408439191541197,0.48343507935298535,SimpleBIF,BIF_Series,3,2,1e-06\n16,723877.188,6899768.5,529.1521690000001,0.5278702037622393,0.5646959247552072,0.546232351657177,SimpleBIF,BIF_Series,3,2,1e-06\n17,732998.313,6898049.0,521.619609,0.6217833345345589,0.5469915704827899,0.5461547947448081,SimpleBIF,BIF_Series,3,2,1e-06\n18,743689.438,6891769.0,512.811278,0.7318615343430492,0.48233129433810734,0.546064102210599,SimpleBIF,BIF_Series,3,2,1e-06\n19,712961.813,6882722.5,547.826016,0.4154830760993788,0.3891865191561216,0.5464246217373845,SimpleBIF,BIF_Series,3,2,1e-06\n20,716284.875,6891346.5,515.58686,0.4496980612213379,0.4779811404095837,0.5460926802199273,SimpleBIF,BIF_Series,3,2,1e-06\n21,718942.875,6897600.5,538.490136,0.4770654201373516,0.5423737147740494,0.5463284974413888,SimpleBIF,BIF_Series,3,2,1e-06\n22,722157.625,6882947.0,481.747055,0.5101652008278162,0.39149802106607084,0.5457442580645161,SimpleBIF,BIF_Series,3,2,1e-06\n23,723952.0,6885488.5,480.122832,0.5286404847461467,0.4176658700822668,0.5457275347034173,SimpleBIF,BIF_Series,3,2,1e-06\n24,728736.813,6885488.5,477.929009,0.5779059831347875,0.4176658700822668,0.5457049466140873,SimpleBIF,BIF_Series,3,2,1e-06\n25,738829.813,6878087.0,470.08143099999995,0.6818257549705009,0.3414583806101541,0.5456241462063569,SimpleBIF,BIF_Series,3,2,1e-06\n26,715522.708428,6891941.417769,-2810.185309,0.4418506247541775,0.48410654601896497,0.5118497905954306,SimpleMafic1,SimpleMafic_Series,4,3,1e-06\n27,722100.017223,6891939.776110001,-5640.110309000001,0.5095720583486912,0.4840896431329439,0.48271225365773296,SimpleMafic1,SimpleMafic_Series,4,3,1e-06\n28,728133.723035,6891938.27013,-8458.173309,0.57169643477858,0.4840741372280518,0.4536968505091482,SimpleMafic1,SimpleMafic_Series,4,3,1e-06\n29,733022.378883,6891937.049949001,-7358.592309000001,0.6220311222161589,0.4840615739732153,0.4650183809396332,SimpleMafic1,SimpleMafic_Series,4,3,1e-06\n30,737972.021729,6891935.814546,-5589.549309,0.6729937453435337,0.4840488539892735,0.4832328409542539,SimpleMafic1,SimpleMafic_Series,4,3,1e-06\n31,726443.009736,6905713.435698,-5023.656995,0.5542884747794032,0.6259063043563294,0.4890593940261318,SimpleMafic1,SimpleMafic_Series,4,3,1e-06\n32,727109.3352020001,6886862.614432001,-6720.406176,0.5611491099121743,0.4318140577618129,0.47158928724401017,SimpleMafic1,SimpleMafic_Series,4,3,1e-06\n33,726266.236469,6901753.8156550005,-5708.065297,0.5524683779228401,0.5851371761065918,0.48201257399380165,SimpleMafic1,SimpleMafic_Series,4,3,1e-06\n34,732617.810926,6902964.017319,-3068.116297,0.617865600588944,0.5975976814863659,0.5091940758007887,SimpleMafic1,SimpleMafic_Series,4,3,1e-06\n35,709728.25,6885850.5,532.9365769999999,0.38218959257848295,0.4213931025606705,0.5462713167632796,SimpleMafic1,SimpleMafic_Series,4,3,1e-06\n36,714271.063,6897386.0,536.8312540000001,0.428963405166644,0.5401651750872605,0.5463114172235207,SimpleMafic1,SimpleMafic_Series,4,3,1e-06\n37,722693.188,6907492.5,555.452867,0.5156794765400571,0.6442239459242404,0.5465031494908518,SimpleMafic1,SimpleMafic_Series,4,3,1e-06\n38,725092.188,6913005.0,514.864987,0.5403801138762184,0.7009818714413681,0.5460852476550353,SimpleMafic1,SimpleMafic_Series,4,3,1e-06\n39,744692.688,6890291.0,496.01971100000003,0.7421912193816085,0.46711347775501166,0.5458912125037324,SimpleMafic1,SimpleMafic_Series,4,3,1e-06\n40,738924.813,6900193.5,551.633725,0.682803896090524,0.569071819239521,0.5464638267557633,SimpleMafic1,SimpleMafic_Series,4,3,1e-06\n41,729430.813,6908717.5,517.178069,0.5850515614221139,0.6568368182613799,0.5461090636615425,SimpleMafic1,SimpleMafic_Series,4,3,1e-06\n42,726929.75,6916017.0,502.161614,0.5593000607477117,0.7319940930572573,0.5459544509024639,SimpleMafic1,SimpleMafic_Series,4,3,1e-06\n" }, { "path": "examples/data/gempy_models/Greenstone/Greenstone_surfaces.csv", "content": ",surface,series,order_surfaces,isBasement,isFault,isActive,color,vertices,edges,id,value_0\n3,EarlyGranite,EarlyGranite_Series,1,False,False,True,#728f02,\"[[ 6.96913326e+05 6.86300000e+06 -1.47480000e+04]\n [ 6.97020000e+05 6.86309351e+06 -1.47480000e+04]\n [ 6.97020000e+05 6.86300000e+06 -1.49709013e+04]\n ...\n [ 7.45781532e+05 6.87774000e+06 -2.04000000e+02]\n [ 7.45980000e+05 6.87774000e+06 -5.31339188e+02]\n [ 7.45980000e+05 6.87779400e+06 -2.04000000e+02]]\",\"[[ 2 1 0]\n [ 0 1 3]\n [ 3 1 4]\n ...\n [3577 3634 3635]\n [3576 3634 3577]\n [3636 3637 3635]]\",1,2.61\n0,SimpleMafic2,BIF_Series,1,False,False,True,#015482,\"[[ 7.17372390e+05 6.88444000e+06 -2.04000000e+02]\n [ 7.17420000e+05 6.88444000e+06 -2.19525421e+02]\n [ 7.17420000e+05 6.88416750e+06 -2.04000000e+02]\n ...\n [ 7.45980000e+05 6.89022786e+06 -1.01200000e+03]\n [ 7.45980000e+05 6.89058852e+06 -6.08000000e+02]\n [ 7.45980000e+05 6.89094654e+06 -2.04000000e+02]]\",\"[[ 2 1 0]\n [ 0 1 3]\n [ 3 1 4]\n ...\n [1049 1050 990]\n [ 991 1051 992]\n [1050 1051 991]]\",2,2.92\n1,SimpleBIF,BIF_Series,2,False,False,True,#9f0052,\"[[ 7.14839407e+05 6.88042000e+06 -2.04000000e+02]\n [ 7.15380000e+05 6.88042000e+06 -2.72209213e+02]\n [ 7.15380000e+05 6.87984794e+06 -2.04000000e+02]\n ...\n [ 7.45980000e+05 6.89114000e+06 -6.54912231e+02]\n [ 7.45980000e+05 6.89117842e+06 -6.08000000e+02]\n [ 7.45980000e+05 6.89150733e+06 -2.04000000e+02]]\",\"[[ 2 1 0]\n [ 0 1 3]\n [ 3 1 4]\n ...\n [1332 1334 1265]\n [1334 1266 1265]\n [1334 1335 1266]]\",3,3.1\n2,SimpleMafic1,SimpleMafic_Series,1,False,False,True,#ffbe00,\"[[ 6.96000000e+05 6.86300000e+06 -2.45178632e+03]\n [ 6.97020000e+05 6.86393305e+06 -2.22400000e+03]\n [ 6.97020000e+05 6.86300000e+06 -2.48451546e+03]\n ...\n [ 7.45267801e+05 6.88176000e+06 -2.04000000e+02]\n [ 7.45222338e+05 6.88310000e+06 -2.04000000e+02]\n [ 7.45091231e+05 6.88444000e+06 -2.04000000e+02]]\",\"[[ 2 1 0]\n [ 0 1 3]\n [ 4 3 1]\n ...\n [3030 3018 3029]\n [3029 3018 3014]\n [3030 3021 3018]]\",4,2.92\n4,basement,Basement,1,True,False,True,#443988,,,5,2.61\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-1_Basics/Tutorial_ch1-1_Basics_faults.csv", "content": ",isFault,isFinite\nFault_Series,True,False\nStrat_Series,False,False\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-1_Basics/Tutorial_ch1-1_Basics_faults_relations.csv", "content": ",Fault_Series,Strat_Series\nFault_Series,False,True\nStrat_Series,False,False\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-1_Basics/Tutorial_ch1-1_Basics_kriging_data.csv", "content": ",range,$C_o$,drift equations\nvalues,3464.1016151377544,285714.2857142857,[3 3]\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-1_Basics/Tutorial_ch1-1_Basics_options.csv", "content": ",dtype,output,aesara_optimizer,device,verbosity\nvalues,float64,geology,fast_compile,cpu,[]\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-1_Basics/Tutorial_ch1-1_Basics_orientations.csv", "content": ",X,Y,Z,X_r,Y_r,Z_r,G_x,G_y,G_z,dip,azimuth,polarity,surface,series,id,order_series,smooth\n2,500,1000,864.602,0.40799473684210524,0.5001,0.4907847368421052,-0.9486830155918029,9.998257297572324e-13,0.31622861338970926,71.565,270.0,1.0,Main_Fault,Fault_Series,1,1,0.01\n1,400,1000,1400.0,0.38167894736842106,0.5001,0.631678947368421,0.3162286133897092,1.0000193634179593e-12,0.9486830155938029,18.435,90.0,1.0,Sandstone_2,Strat_Series,2,2,0.01\n0,1000,1000,950.0,0.5395736842105263,0.5001,0.5132578947368421,0.3162286133897092,1.0000193634179593e-12,0.9486830155938029,18.435,90.0,1.0,Shale,Strat_Series,4,2,0.01\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-1_Basics/Tutorial_ch1-1_Basics_rescaling_data.csv", "content": ",rescaling factor,centers\nvalues,3800,[ 850. 1000. 900.]\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-1_Basics/Tutorial_ch1-1_Basics_series.csv", "content": ",order_series,BottomRelation,isActive\nFault_Series,1,Fault,True\nStrat_Series,2,Erosion,True\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-1_Basics/Tutorial_ch1-1_Basics_surface_points.csv", "content": ",X,Y,Z,X_r,Y_r,Z_r,surface,series,id,order_series,smooth\n52,700,1000,900,0.46062631578947366,0.5001,0.5001,Main_Fault,Fault_Series,1,1,2e-06\n53,600,1000,600,0.4343105263157895,0.5001,0.42115263157894733,Main_Fault,Fault_Series,1,1,2e-06\n54,500,1000,300,0.40799473684210524,0.5001,0.34220526315789473,Main_Fault,Fault_Series,1,1,2e-06\n55,800,1000,1200,0.4869421052631579,0.5001,0.5790473684210526,Main_Fault,Fault_Series,1,1,2e-06\n56,900,1000,1500,0.5132578947368421,0.5001,0.6579947368421053,Main_Fault,Fault_Series,1,1,2e-06\n44,0,1000,1500,0.27641578947368417,0.5001,0.6579947368421053,Sandstone_2,Strat_Series,2,2,2e-06\n45,300,1000,1400,0.3553631578947368,0.5001,0.631678947368421,Sandstone_2,Strat_Series,2,2,2e-06\n46,600,1000,1300,0.4343105263157895,0.5001,0.6053631578947368,Sandstone_2,Strat_Series,2,2,2e-06\n47,1100,1000,1300,0.5658894736842105,0.5001,0.6053631578947368,Sandstone_2,Strat_Series,2,2,2e-06\n48,1400,1000,1250,0.6448368421052632,0.5001,0.5922052631578947,Sandstone_2,Strat_Series,2,2,2e-06\n49,1700,1000,1300,0.7237842105263158,0.5001,0.6053631578947368,Sandstone_2,Strat_Series,2,2,2e-06\n50,1500,500,1200,0.6711526315789473,0.36852105263157897,0.5790473684210526,Sandstone_2,Strat_Series,2,2,2e-06\n51,1500,1500,1150,0.6711526315789473,0.631678947368421,0.5658894736842105,Sandstone_2,Strat_Series,2,2,2e-06\n31,900,150,920,0.5132578947368421,0.27641578947368417,0.5053631578947368,Siltstone,Strat_Series,3,2,2e-06\n32,900,300,920,0.5132578947368421,0.3158894736842105,0.5053631578947368,Siltstone,Strat_Series,3,2,2e-06\n33,900,1500,920,0.5132578947368421,0.631678947368421,0.5053631578947368,Siltstone,Strat_Series,3,2,2e-06\n34,900,1700,820,0.5132578947368421,0.6843105263157895,0.4790473684210526,Siltstone,Strat_Series,3,2,2e-06\n35,900,1950,870,0.5132578947368421,0.7501,0.4922052631578947,Siltstone,Strat_Series,3,2,2e-06\n36,0,1000,1300,0.27641578947368417,0.5001,0.6053631578947368,Siltstone,Strat_Series,3,2,2e-06\n37,300,1000,1200,0.3553631578947368,0.5001,0.5790473684210526,Siltstone,Strat_Series,3,2,2e-06\n38,600,1000,1100,0.4343105263157895,0.5001,0.5527315789473684,Siltstone,Strat_Series,3,2,2e-06\n39,1100,1000,1100,0.5658894736842105,0.5001,0.5527315789473684,Siltstone,Strat_Series,3,2,2e-06\n40,1400,1000,1050,0.6448368421052632,0.5001,0.5395736842105263,Siltstone,Strat_Series,3,2,2e-06\n41,1700,1000,1100,0.7237842105263158,0.5001,0.5527315789473684,Siltstone,Strat_Series,3,2,2e-06\n42,1500,500,1000,0.6711526315789473,0.36852105263157897,0.5264157894736842,Siltstone,Strat_Series,3,2,2e-06\n43,1500,1500,950,0.6711526315789473,0.631678947368421,0.5132578947368421,Siltstone,Strat_Series,3,2,2e-06\n0,800,50,750,0.4869421052631579,0.2501,0.46062631578947366,Shale,Strat_Series,4,2,2e-06\n1,800,150,700,0.4869421052631579,0.27641578947368417,0.44746842105263157,Shale,Strat_Series,4,2,2e-06\n2,800,300,700,0.4869421052631579,0.3158894736842105,0.44746842105263157,Shale,Strat_Series,4,2,2e-06\n3,800,500,800,0.4869421052631579,0.36852105263157897,0.4737842105263158,Shale,Strat_Series,4,2,2e-06\n4,800,1000,1000,0.4869421052631579,0.5001,0.5264157894736842,Shale,Strat_Series,4,2,2e-06\n5,800,1500,700,0.4869421052631579,0.631678947368421,0.44746842105263157,Shale,Strat_Series,4,2,2e-06\n6,800,1700,600,0.4869421052631579,0.6843105263157895,0.42115263157894733,Shale,Strat_Series,4,2,2e-06\n7,800,1950,650,0.4869421052631579,0.7501,0.4343105263157895,Shale,Strat_Series,4,2,2e-06\n8,0,1000,1100,0.27641578947368417,0.5001,0.5527315789473684,Shale,Strat_Series,4,2,2e-06\n9,300,1000,1000,0.3553631578947368,0.5001,0.5264157894736842,Shale,Strat_Series,4,2,2e-06\n10,450,1000,950,0.39483684210526315,0.5001,0.5132578947368421,Shale,Strat_Series,4,2,2e-06\n11,1100,1000,900,0.5658894736842105,0.5001,0.5001,Shale,Strat_Series,4,2,2e-06\n12,1400,1000,850,0.6448368421052632,0.5001,0.4869421052631579,Shale,Strat_Series,4,2,2e-06\n13,1700,1000,900,0.7237842105263158,0.5001,0.5001,Shale,Strat_Series,4,2,2e-06\n14,1500,500,800,0.6711526315789473,0.36852105263157897,0.4737842105263158,Shale,Strat_Series,4,2,2e-06\n15,1500,1500,750,0.6711526315789473,0.631678947368421,0.46062631578947366,Shale,Strat_Series,4,2,2e-06\n16,800,50,450,0.4869421052631579,0.2501,0.38167894736842106,Sandstone_1,Strat_Series,5,2,2e-06\n17,800,150,400,0.4869421052631579,0.27641578947368417,0.36852105263157897,Sandstone_1,Strat_Series,5,2,2e-06\n18,800,300,400,0.4869421052631579,0.3158894736842105,0.36852105263157897,Sandstone_1,Strat_Series,5,2,2e-06\n19,800,500,500,0.4869421052631579,0.36852105263157897,0.39483684210526315,Sandstone_1,Strat_Series,5,2,2e-06\n20,800,1000,700,0.4869421052631579,0.5001,0.44746842105263157,Sandstone_1,Strat_Series,5,2,2e-06\n21,800,1500,400,0.4869421052631579,0.631678947368421,0.36852105263157897,Sandstone_1,Strat_Series,5,2,2e-06\n22,800,1700,300,0.4869421052631579,0.6843105263157895,0.34220526315789473,Sandstone_1,Strat_Series,5,2,2e-06\n23,800,1950,350,0.4869421052631579,0.7501,0.3553631578947368,Sandstone_1,Strat_Series,5,2,2e-06\n24,0,1000,800,0.27641578947368417,0.5001,0.4737842105263158,Sandstone_1,Strat_Series,5,2,2e-06\n25,300,1000,700,0.3553631578947368,0.5001,0.44746842105263157,Sandstone_1,Strat_Series,5,2,2e-06\n26,1100,1000,600,0.5658894736842105,0.5001,0.42115263157894733,Sandstone_1,Strat_Series,5,2,2e-06\n27,1400,1000,550,0.6448368421052632,0.5001,0.40799473684210524,Sandstone_1,Strat_Series,5,2,2e-06\n28,1700,1000,600,0.7237842105263158,0.5001,0.42115263157894733,Sandstone_1,Strat_Series,5,2,2e-06\n29,1500,500,500,0.6711526315789473,0.36852105263157897,0.39483684210526315,Sandstone_1,Strat_Series,5,2,2e-06\n30,1500,1500,450,0.6711526315789473,0.631678947368421,0.38167894736842106,Sandstone_1,Strat_Series,5,2,2e-06\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-1_Basics/Tutorial_ch1-1_Basics_surfaces.csv", "content": ",surface,series,order_surfaces,isBasement,isFault,isActive,color,vertices,edges,sfai,id\n4,Main_Fault,Fault_Series,1,False,True,True,#527682,\"[[ 400. 0. 40. ]\n [ 386.66667938 0. 0. ]\n [ 400. 40. 40. ]\n ...\n [1013.33335876 1960. 1880. ]\n [1026.66664124 1960. 1920. ]\n [1040. 1960. 1960. ]]\",\"[[ 2 1 0]\n [ 3 1 2]\n [ 0 5 4]\n ...\n [3297 3294 3298]\n [3299 3295 3296]\n [3298 3295 3299]]\",-0.55768591019109,1\n3,Sandstone_2,Strat_Series,1,False,False,True,#728f02,\"[[ 0. 0. 1399.34921265]\n [ 0. 40. 1399.55947876]\n [ 40. 0. 1383.18862915]\n ...\n [1960. 1880. 1193.70231628]\n [1960. 1920. 1189.24514771]\n [1960. 1960. 1185.27381897]]\",\"[[ 2 1 0]\n [ 3 1 2]\n [ 5 1 4]\n ...\n [3763 3696 3762]\n [3763 3697 3696]\n [3764 3697 3763]]\",0.8179798867858623,2\n2,Siltstone,Strat_Series,2,False,False,True,#ffbe00,\"[[ 31.40995264 40. 1160. ]\n [ 40. 0. 1156.60209656]\n [ 32.15043068 0. 1160. ]\n ...\n [1960. 1880. 983.82293701]\n [1960. 1920. 978.76838684]\n [1960. 1960. 974.21592712]]\",\"[[ 2 1 0]\n [ 0 1 3]\n [ 0 5 4]\n ...\n [3905 3836 3904]\n [3905 3838 3836]\n [3906 3838 3905]]\",0.730487102963508,3\n0,Shale,Strat_Series,3,False,False,True,#015482,\"[[ 0. 0. 949.93782043]\n [ 0. 40. 949.11277771]\n [ 40. 0. 931.37695312]\n ...\n [1960. 1880. 781.52374268]\n [1960. 1920. 776.03805542]\n [1960. 1960. 771.07551575]]\",\"[[ 2 1 0]\n [ 3 1 2]\n [ 3 4 1]\n ...\n [4047 3978 4046]\n [4047 3980 3978]\n [4048 3980 4047]]\",0.6496588766460675,4\n1,Sandstone_1,Strat_Series,4,False,False,True,#9f0052,\"[[ 11.65150642 40. 640. ]\n [ 40. 0. 628.33042145]\n [ 17.65681148 0. 640. ]\n ...\n [1960. 1880. 512.40386963]\n [1960. 1920. 507.28397369]\n [1960. 1960. 502.67974854]]\",\"[[ 2 1 0]\n [ 0 1 3]\n [ 0 5 4]\n ...\n [4128 4061 4127]\n [4128 4062 4061]\n [4129 4062 4128]]\",0.5520920084971302,5\n5,basement,Strat_Series,5,True,False,True,#ff3f20,,,,6\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-8_Onlap_relations/Tutorial_ch1-8_Onlap_relations_faults.csv", "content": ",isFault,isFinite\nseafloor_series,False,False\nright_series,False,False\nonlap_series,False,False\nleft_series,False,False\nbasement,False,False\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-8_Onlap_relations/Tutorial_ch1-8_Onlap_relations_faults_relations.csv", "content": ",seafloor_series,right_series,onlap_series,left_series,basement\nseafloor_series,False,False,False,False,False\nright_series,False,False,False,False,False\nonlap_series,False,False,False,False,False\nleft_series,False,False,False,False,False\nbasement,False,False,False,False,False\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-8_Onlap_relations/Tutorial_ch1-8_Onlap_relations_kriging_data.csv", "content": ",range,$C_o$,drift equations,nugget grad,nugget scalar\nvalues,1854.7236990991407,81904.76190476191,[3 3 3 3],0.01,1e-06\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-8_Onlap_relations/Tutorial_ch1-8_Onlap_relations_options.csv", "content": ",dtype,output,aesara_optimizer,device,verbosity\nvalues,float64,geology,fast_compile,cpu,[]\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-8_Onlap_relations/Tutorial_ch1-8_Onlap_relations_orientations.csv", "content": ",X,Y,Z,X_r,Y_r,Z_r,G_x,G_y,G_z,dip,azimuth,polarity,surface,series,id,order_series\n0,200.0,50.0,-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\n1,500.0,50.0,-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\n2,800.0,50.0,-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\n4,700.0,50.0,-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\n3,900.0,50.0,-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\n6,700.0,50.0,-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\n5,900.0,50.0,-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\n7,300.0,50.0,-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\n8,700.0,50.0,-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\n9,550.0,50.0,-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\n10,100.0,50.0,-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\n11,450.0,50.0,-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\n12,300.0,50.0,-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\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-8_Onlap_relations/Tutorial_ch1-8_Onlap_relations_rescaling_data.csv", "content": ",rescaling factor,centers\nvalues,1600.0,[ 500. 50. -600.]\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-8_Onlap_relations/Tutorial_ch1-8_Onlap_relations_series.csv", "content": ",order_series,BottomRelation\nseafloor_series,1,Erosion\nright_series,2,Onlap\nonlap_series,3,Erosion\nleft_series,4,Erosion\nbasement,5,Erosion\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-8_Onlap_relations/Tutorial_ch1-8_Onlap_relations_surface_points.csv", "content": ",X,Y,Z,X_r,Y_r,Z_r,surface,series,id,order_series\n0,200.0,0.0,-200.0,0.3126,0.46885,0.7501,seafloor,seafloor_series,6,1\n1,200.0,100.0,-200.0,0.3126,0.53135,0.7501,seafloor,seafloor_series,6,1\n2,500.0,0.0,-200.0,0.5001,0.46885,0.7501,seafloor,seafloor_series,6,1\n3,500.0,100.0,-200.0,0.5001,0.53135,0.7501,seafloor,seafloor_series,6,1\n4,800.0,0.0,-200.0,0.6876,0.46885,0.7501,seafloor,seafloor_series,6,1\n5,800.0,100.0,-200.0,0.6876,0.53135,0.7501,seafloor,seafloor_series,6,1\n7,700.0,0.0,-450.0,0.6251,0.46885,0.59385,rock1,right_series,4,2\n6,700.0,100.0,-450.0,0.6251,0.53135,0.59385,rock1,right_series,4,2\n8,900.0,0.0,-450.0,0.7501,0.46885,0.59385,rock1,right_series,4,2\n9,900.0,100.0,-450.0,0.7501,0.53135,0.59385,rock1,right_series,4,2\n13,700.0,0.0,-700.0,0.6251,0.46885,0.4376,rock2,right_series,5,2\n12,700.0,100.0,-700.0,0.6251,0.53135,0.4376,rock2,right_series,5,2\n11,900.0,0.0,-700.0,0.7501,0.46885,0.4376,rock2,right_series,5,2\n10,900.0,100.0,-700.0,0.7501,0.53135,0.4376,rock2,right_series,5,2\n14,300.0,0.0,-200.0,0.3751,0.46885,0.7501,onlap_surface,onlap_series,3,3\n15,300.0,100.0,-200.0,0.3751,0.53135,0.7501,onlap_surface,onlap_series,3,3\n16,700.0,0.0,-1000.0,0.6251,0.46885,0.2501,onlap_surface,onlap_series,3,3\n17,700.0,100.0,-1000.0,0.6251,0.53135,0.2501,onlap_surface,onlap_series,3,3\n18,550.0,0.0,-500.0,0.53135,0.46885,0.5626,onlap_surface,onlap_series,3,3\n19,550.0,100.0,-500.0,0.53135,0.53135,0.5626,onlap_surface,onlap_series,3,3\n20,100.0,0.0,-400.0,0.2501,0.46885,0.6251,rock3,left_series,2,4\n21,100.0,100.0,-400.0,0.2501,0.53135,0.6251,rock3,left_series,2,4\n22,450.0,0.0,-1000.0,0.46885,0.46885,0.2501,rock3,left_series,2,4\n23,450.0,100.0,-1000.0,0.46885,0.53135,0.2501,rock3,left_series,2,4\n24,300.0,0.0,-500.0,0.3751,0.46885,0.5626,rock3,left_series,2,4\n25,300.0,100.0,-500.0,0.3751,0.53135,0.5626,rock3,left_series,2,4\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-8_Onlap_relations/Tutorial_ch1-8_Onlap_relations_surfaces.csv", "content": ",surface,series,order_surfaces,isBasement,color,vertices,edges,id\n4,basement,basement,1,False,#ff3f20,\"[[-200. -500. -210.]\n [-200. -480. -210.]\n [-176. -500. -210.]\n ...\n [ 976. 440. -210.]\n [ 976. 460. -210.]\n [ 976. 480. -210.]]\",\"[[ 2 1 0]\n [ 3 1 2]\n [ 3 4 1]\n ...\n [2498 2448 2497]\n [2498 2449 2448]\n [2499 2449 2498]]\",1\n3,rock3,left_series,1,False,#9f0052,\"[[ 400. -500. -460.00003815]\n [ 400. -480. -460.00003815]\n [ 424. -500. -460.00003815]\n ...\n [ 976. 440. -460.00003815]\n [ 976. 460. -460.00003815]\n [ 976. 480. -460.00003815]]\",\"[[ 2 1 0]\n [ 3 1 2]\n [ 3 4 1]\n ...\n [1111 1061 1110]\n [1111 1062 1061]\n [1112 1062 1111]]\",2\n5,onlap_surface,onlap_series,1,False,#ffbe00,\"[[ 544. -500. -710.]\n [ 544. -480. -710.]\n [ 568. -500. -710.]\n ...\n [ 976. 440. -710.]\n [ 976. 460. -710.]\n [ 976. 480. -710.]]\",\"[[ 2 1 0]\n [ 3 1 2]\n [ 3 4 1]\n ...\n [842 792 841]\n [842 793 792]\n [843 793 842]]\",3\n1,rock1,right_series,1,False,#728f02,\"[[ 52.39768219 -480. -220. ]\n [ 64. -500. -227.81341553]\n [ 45.62319183 -500. -220. ]\n ...\n [ 688.02554321 40. -980. ]\n [ 688.29644775 40. -1000. ]\n [ 688.20480347 60. -1000. ]]\",\"[[ 2 1 0]\n [ 0 1 3]\n [ 0 3 4]\n ...\n [3321 3322 3320]\n [3320 3173 3161]\n [3322 3185 3172]]\",4\n2,rock2,right_series,2,False,#443988,\"[[ -184.16520214 -480. -420. ]\n [ -176. -500. -425.53325653]\n [ -192.13578463 -500. -420. ]\n ...\n [ 427.72203064 280. -1000. ]\n [ 424.01483154 280. -960. ]\n [ 425.82922363 300. -1000. ]]\",\"[[ 2 1 0]\n [ 0 1 3]\n [ 0 5 4]\n ...\n [3199 2969 3201]\n [3201 2969 2958]\n [3202 2978 2967]]\",5\n0,seafloor,seafloor_series,1,True,#015482,\"[[-200. -500. -210.]\n [-200. -480. -210.]\n [-176. -500. -210.]\n ...\n [ 976. 440. -210.]\n [ 976. 460. -210.]\n [ 976. 480. -210.]]\",\"[[ 2 1 0]\n [ 3 1 2]\n [ 3 4 1]\n ...\n [2498 2448 2497]\n [2498 2449 2448]\n [2499 2449 2498]]\",6\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-9a_Fault_relations/Tutorial_ch1-9a_Fault_relations_faults.csv", "content": ",isFault,isFinite\nfault_series1,True,False\nseries2,False,False\nseries1,False,False\nbasement_series,False,False\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-9a_Fault_relations/Tutorial_ch1-9a_Fault_relations_faults_relations.csv", "content": ",fault_series1,series2,series1,basement_series\nfault_series1,False,False,True,True\nseries2,False,False,False,False\nseries1,False,False,False,False\nbasement_series,False,False,False,False\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-9a_Fault_relations/Tutorial_ch1-9a_Fault_relations_kriging_data.csv", "content": ",range,$C_o$,drift equations,nugget grad,nugget scalar\nvalues,1732.0508075688772,71428.57142857142,[3 3 3],0.01,1e-06\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-9a_Fault_relations/Tutorial_ch1-9a_Fault_relations_options.csv", "content": ",dtype,output,aesara_optimizer,device,verbosity\nvalues,float64,geology,fast_compile,cpu,[0]\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-9a_Fault_relations/Tutorial_ch1-9a_Fault_relations_orientations.csv", "content": ",X,Y,Z,X_r,Y_r,Z_r,G_x,G_y,G_z,dip,azimuth,polarity,surface,series,id,order_series\n10,500.0,500.0,-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\n9,850.0,500.0,-425.0,0.71885,0.5001,0.578225,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock5,series2,2,2\n8,150.0,500.0,-400.0,0.28135,0.5001,0.59385,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock5,series2,2,2\n7,150.0,500.0,-450.0,0.28135,0.5001,0.5626,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock4,series2,3,2\n6,850.0,500.0,-525.0,0.71885,0.5001,0.515725,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock4,series2,3,2\n5,150.0,500.0,-500.0,0.28135,0.5001,0.53135,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock3,series1,4,3\n4,850.0,500.0,-575.0,0.71885,0.5001,0.484475,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock3,series1,4,3\n3,850.0,500.0,-625.0,0.71885,0.5001,0.453225,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock2,series1,5,3\n2,150.0,500.0,-550.0,0.28135,0.5001,0.5001,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock2,series1,5,3\n0,150.0,500.0,-600.0,0.28135,0.5001,0.46885,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock1,series1,6,3\n1,850.0,500.0,-675.0,0.71885,0.5001,0.421975,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock1,series1,6,3\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-9a_Fault_relations/Tutorial_ch1-9a_Fault_relations_rescaling_data.csv", "content": ",rescaling factor,centers\nvalues,1600.0,[ 500. 500. -550.]\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-9a_Fault_relations/Tutorial_ch1-9a_Fault_relations_series.csv", "content": ",order_series,BottomRelation\nfault_series1,1,Fault\nseries2,2,Erosion\nseries1,3,Erosion\nbasement_series,4,Erosion\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-9a_Fault_relations/Tutorial_ch1-9a_Fault_relations_surface_points.csv", "content": ",X,Y,Z,X_r,Y_r,Z_r,surface,series,id,order_series\n57,450.0,200.0,-500.0,0.46885,0.3126,0.53135,fault1,fault_series1,2,1\n58,450.0,500.0,-500.0,0.46885,0.5001,0.53135,fault1,fault_series1,2,1\n59,450.0,800.0,-500.0,0.46885,0.6876,0.53135,fault1,fault_series1,2,1\n62,500.0,800.0,-600.0,0.5001,0.6876,0.46885,fault1,fault_series1,2,1\n61,500.0,500.0,-600.0,0.5001,0.5001,0.46885,fault1,fault_series1,2,1\n63,500.0,800.0,-600.0,0.5001,0.6876,0.46885,fault1,fault_series1,2,1\n64,550.0,800.0,-700.0,0.53135,0.6876,0.40635,fault1,fault_series1,2,1\n65,550.0,500.0,-700.0,0.53135,0.5001,0.40635,fault1,fault_series1,2,1\n60,550.0,200.0,-700.0,0.53135,0.3126,0.40635,fault1,fault_series1,2,1\n56,900.0,800.0,-425.0,0.7501,0.6876,0.578225,rock5,series2,6,2\n55,900.0,500.0,-425.0,0.7501,0.5001,0.578225,rock5,series2,6,2\n54,900.0,200.0,-425.0,0.7501,0.3126,0.578225,rock5,series2,6,2\n53,800.0,800.0,-425.0,0.6876,0.6876,0.578225,rock5,series2,6,2\n52,800.0,500.0,-425.0,0.6876,0.5001,0.578225,rock5,series2,6,2\n51,800.0,200.0,-425.0,0.6876,0.3126,0.578225,rock5,series2,6,2\n50,200.0,800.0,-400.0,0.3126,0.6876,0.59385,rock5,series2,6,2\n49,200.0,500.0,-400.0,0.3126,0.5001,0.59385,rock5,series2,6,2\n48,200.0,200.0,-400.0,0.3126,0.3126,0.59385,rock5,series2,6,2\n47,100.0,800.0,-400.0,0.2501,0.6876,0.59385,rock5,series2,6,2\n46,100.0,500.0,-400.0,0.2501,0.5001,0.59385,rock5,series2,6,2\n45,100.0,200.0,-400.0,0.2501,0.3126,0.59385,rock5,series2,6,2\n34,100.0,500.0,-450.0,0.2501,0.5001,0.5626,rock4,series2,7,2\n35,100.0,800.0,-450.0,0.2501,0.6876,0.5626,rock4,series2,7,2\n33,100.0,200.0,-450.0,0.2501,0.3126,0.5626,rock4,series2,7,2\n36,200.0,200.0,-450.0,0.3126,0.3126,0.5626,rock4,series2,7,2\n37,200.0,500.0,-450.0,0.3126,0.5001,0.5626,rock4,series2,7,2\n38,200.0,800.0,-450.0,0.3126,0.6876,0.5626,rock4,series2,7,2\n39,800.0,200.0,-525.0,0.6876,0.3126,0.515725,rock4,series2,7,2\n40,800.0,500.0,-525.0,0.6876,0.5001,0.515725,rock4,series2,7,2\n41,800.0,800.0,-525.0,0.6876,0.6876,0.515725,rock4,series2,7,2\n42,900.0,200.0,-525.0,0.7501,0.3126,0.515725,rock4,series2,7,2\n43,900.0,500.0,-525.0,0.7501,0.5001,0.515725,rock4,series2,7,2\n44,900.0,800.0,-525.0,0.7501,0.6876,0.515725,rock4,series2,7,2\n66,900.0,500.0,-575.0,0.7501,0.5001,0.484475,rock3,series1,3,3\n25,100.0,200.0,-500.0,0.2501,0.3126,0.53135,rock3,series1,3,3\n26,100.0,500.0,-500.0,0.2501,0.5001,0.53135,rock3,series1,3,3\n24,100.0,800.0,-500.0,0.2501,0.6876,0.53135,rock3,series1,3,3\n27,200.0,200.0,-500.0,0.3126,0.3126,0.53135,rock3,series1,3,3\n28,200.0,500.0,-500.0,0.3126,0.5001,0.53135,rock3,series1,3,3\n29,200.0,800.0,-500.0,0.3126,0.6876,0.53135,rock3,series1,3,3\n30,800.0,200.0,-575.0,0.6876,0.3126,0.484475,rock3,series1,3,3\n31,800.0,500.0,-575.0,0.6876,0.5001,0.484475,rock3,series1,3,3\n32,800.0,800.0,-575.0,0.6876,0.6876,0.484475,rock3,series1,3,3\n67,900.0,500.0,-575.0,0.7501,0.5001,0.484475,rock3,series1,3,3\n68,900.0,200.0,-575.0,0.7501,0.3126,0.484475,rock3,series1,3,3\n69,900.0,800.0,-575.0,0.7501,0.6876,0.484475,rock3,series1,3,3\n17,200.0,800.0,-550.0,0.3126,0.6876,0.5001,rock2,series1,4,3\n16,200.0,500.0,-550.0,0.3126,0.5001,0.5001,rock2,series1,4,3\n15,200.0,200.0,-550.0,0.3126,0.3126,0.5001,rock2,series1,4,3\n13,100.0,500.0,-550.0,0.2501,0.5001,0.5001,rock2,series1,4,3\n14,100.0,800.0,-550.0,0.2501,0.6876,0.5001,rock2,series1,4,3\n12,100.0,200.0,-550.0,0.2501,0.3126,0.5001,rock2,series1,4,3\n18,900.0,200.0,-625.0,0.7501,0.3126,0.453225,rock2,series1,4,3\n19,900.0,500.0,-625.0,0.7501,0.5001,0.453225,rock2,series1,4,3\n20,900.0,800.0,-625.0,0.7501,0.6876,0.453225,rock2,series1,4,3\n21,800.0,200.0,-625.0,0.6876,0.3126,0.453225,rock2,series1,4,3\n22,800.0,500.0,-625.0,0.6876,0.5001,0.453225,rock2,series1,4,3\n23,800.0,800.0,-625.0,0.6876,0.6876,0.453225,rock2,series1,4,3\n0,100.0,200.0,-600.0,0.2501,0.3126,0.46885,rock1,series1,5,3\n1,100.0,500.0,-600.0,0.2501,0.5001,0.46885,rock1,series1,5,3\n2,100.0,800.0,-600.0,0.2501,0.6876,0.46885,rock1,series1,5,3\n3,200.0,200.0,-600.0,0.3126,0.3126,0.46885,rock1,series1,5,3\n4,200.0,500.0,-600.0,0.3126,0.5001,0.46885,rock1,series1,5,3\n5,200.0,800.0,-600.0,0.3126,0.6876,0.46885,rock1,series1,5,3\n6,900.0,200.0,-675.0,0.7501,0.3126,0.421975,rock1,series1,5,3\n7,900.0,500.0,-675.0,0.7501,0.5001,0.421975,rock1,series1,5,3\n8,900.0,800.0,-675.0,0.7501,0.6876,0.421975,rock1,series1,5,3\n9,800.0,200.0,-675.0,0.6876,0.3126,0.421975,rock1,series1,5,3\n10,800.0,500.0,-675.0,0.6876,0.5001,0.421975,rock1,series1,5,3\n11,800.0,800.0,-675.0,0.6876,0.6876,0.421975,rock1,series1,5,3\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-9a_Fault_relations/Tutorial_ch1-9a_Fault_relations_surfaces.csv", "content": ",surface,series,order_surfaces,isBasement,color,vertices,edges,id\n4,basement,basement_series,1,False,#325916,,,1\n5,fault1,fault_series1,1,False,#015482,,,2\n0,rock3,series1,1,False,#728f02,,,3\n3,rock2,series1,2,False,#443988,,,4\n1,rock1,series1,3,False,#ff3f20,,,5\n7,rock5,series2,1,False,#9f0052,,,6\n6,rock4,series2,2,True,#ffbe00,,,7\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-9b_Fault_relations/Tutorial_ch1-9b_Fault_relations_faults.csv", "content": ",isFault,isFinite\nfault_series2,True,False\nfault_series1,True,False\nseries1,False,False\nbasement_series,False,False\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-9b_Fault_relations/Tutorial_ch1-9b_Fault_relations_faults_relations.csv", "content": ",fault_series2,fault_series1,series1,basement_series\nfault_series2,False,True,True,True\nfault_series1,False,False,True,True\nseries1,False,False,False,False\nbasement_series,False,False,False,False\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-9b_Fault_relations/Tutorial_ch1-9b_Fault_relations_kriging_data.csv", "content": ",range,$C_o$,drift equations,nugget grad,nugget scalar\nvalues,1732.0508075688772,71428.57142857142,[3 3 3],0.01,1e-06\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-9b_Fault_relations/Tutorial_ch1-9b_Fault_relations_options.csv", "content": ",dtype,output,aesara_optimizer,device,verbosity\nvalues,float64,geology,fast_compile,cpu,[0]\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-9b_Fault_relations/Tutorial_ch1-9b_Fault_relations_orientations.csv", "content": ",X,Y,Z,X_r,Y_r,Z_r,G_x,G_y,G_z,dip,azimuth,polarity,surface,series,id,order_series\n13,550.0,500.0,-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\n10,350.0,500.0,-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\n11,400.0,500.0,-625.0,0.4376,0.5001,0.515725,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock4,series1,3,3\n7,150.0,500.0,-550.0,0.28135,0.5001,0.5626,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock4,series1,3,3\n6,850.0,500.0,-525.0,0.71885,0.5001,0.578225,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock4,series1,3,3\n12,400.0,500.0,-675.0,0.4376,0.5001,0.484475,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock3,series1,4,3\n5,150.0,500.0,-600.0,0.28135,0.5001,0.53135,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock3,series1,4,3\n4,850.0,500.0,-575.0,0.71885,0.5001,0.546975,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock3,series1,4,3\n3,850.0,500.0,-625.0,0.71885,0.5001,0.515725,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock2,series1,5,3\n2,150.0,500.0,-650.0,0.28135,0.5001,0.5001,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock2,series1,5,3\n0,150.0,500.0,-700.0,0.28135,0.5001,0.46885,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock1,series1,6,3\n1,850.0,500.0,-675.0,0.71885,0.5001,0.484475,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,rock1,series1,6,3\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-9b_Fault_relations/Tutorial_ch1-9b_Fault_relations_rescaling_data.csv", "content": ",rescaling factor,centers\nvalues,1600.0,[ 500. 500. -650.]\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-9b_Fault_relations/Tutorial_ch1-9b_Fault_relations_series.csv", "content": ",order_series,BottomRelation\nfault_series2,1,Fault\nfault_series1,2,Fault\nseries1,3,Erosion\nbasement_series,4,Erosion\n" }, { "path": "examples/data/gempy_models/Tutorial_ch1-9b_Fault_relations/Tutorial_ch1-9b_Fault_relations_surface_points.csv", "content": 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}, { "path": "examples/data/gempy_models/Tutorial_ch1-9b_Fault_relations/Tutorial_ch1-9b_Fault_relations_surfaces.csv", "content": ",surface,series,order_surfaces,isBasement,color,vertices,edges,id\n4,basement,basement_series,1,False,#325916,,,1\n5,fault1,fault_series1,1,False,#9f0052,,,2\n7,fault2,fault_series2,1,False,#015482,,,3\n6,rock4,series1,1,False,#ffbe00,,,4\n0,rock3,series1,2,False,#728f02,,,5\n3,rock2,series1,3,False,#443988,,,6\n1,rock1,series1,4,True,#ff3f20,,,7\n" }, { "path": "examples/data/input_data/AlesModel/2018_interf.csv", "content": "X,Y,Z,X_r,Y_r,Z_r,formation,series,id,order_series,smooth\n737866.44,1916440.94,408.293632,0.4736471608999658,0.4589280664098209,0.4929178917721893,fault_left,fault_left,1,1,1e-08\n737393.89,1917771.71,321.804233,0.4598361365138869,0.4978219335609481,0.4903901016997599,fault_left,fault_left,1,1,1e-08\n737824.59,1917182.81,345.54013599999996,0.4724240281720912,0.4806103956052571,0.4910838211689349,fault_left,fault_left,1,1,1e-08\n739063.13,1914761.51,356.96948399999997,0.5086223270677365,0.4098440569193649,0.4914178620195221,fault_left,fault_left,1,1,1e-08\n738479.04,1915276.11,377.76717,0.4915513690026311,0.42488405935101975,0.4920257074295741,fault_left,fault_left,1,1,1e-08\n738375.75,1915524.08,379.181089,0.4885325546785621,0.4321313764963295,0.4920670314583308,fault_left,fault_left,1,1,1e-08\n737590.48,1917621.64,314.629286,0.4655817915431097,0.4934358994277979,0.4901804024764273,fault_left,fault_left,1,1,1e-08\n737692.8,1917370.45,344.002004,0.4685722560763542,0.4860944726675845,0.4910388668179434,fault_left,fault_left,1,1,1e-08\n737973.38,1916306.72,407.53772299999997,0.4767726522719865,0.4550052737040767,0.4928957991309181,fault_left,fault_left,1,1,1e-08\n736659.33,1919824.65,409.42467999999997,0.4383674536802389,0.5578223661144561,0.492950948446636,fault_left,fault_left,1,1,1e-08\n736175.65,1921407.13,617.1316879999998,0.4242311373643809,0.6040728578518069,0.4990215156938012,fault_left,fault_left,1,1,1e-08\n742298.34,1916230.43,227.104821,0.6031764783831367,0.4527755772689595,0.4876223609559666,fault_lr,fault_lr,2,2,1e-08\n743019.23,1915920.01,346.222435,0.624245633752266,0.4437030596730941,0.4911037624410281,fault_lr,fault_lr,2,2,1e-08\n743444.65,1915465.78,442.65879299999995,0.6366792090233,0.43042746638796175,0.4939222681907748,fault_lr,fault_lr,2,2,1e-08\n743159.56,1915758.03,352.647665,0.628347000913624,0.4389689368711533,0.4912915499941254,fault_lr,fault_lr,2,2,1e-08\n742804.77,1916009.65,285.059311,0.6179776995869113,0.4463229310644111,0.4893161729857473,fault_lr,fault_lr,2,2,1e-08\n739523.11,1918881.5,272.56482,0.5220659732366271,0.5302573091898324,0.4889510016390271,fault_right,fault_right,3,3,1e-08\n739523.11,1918881.5,272.56482,0.5220659732366271,0.5302573091898324,0.4889510016390271,fault_right,fault_right,3,3,1e-08\n739337.25,1919764.72,328.226726,0.5166339195017909,0.5560708166668697,0.4905778092596846,fault_right,fault_right,3,3,1e-08\n739368.8,1920230.31,412.295905,0.5175560183683268,0.5696784239971344,0.4930348645581437,fault_right,fault_right,3,3,1e-08\n739523.11,1918881.5,272.56482,0.5220659732366271,0.5302573091898324,0.4889510016390271,fault_right,fault_right,3,3,1e-08\n739520.42,1918475.21,293.752678,0.5219873537136184,0.5183828385564151,0.4895702504455593,fault_right,fault_right,3,3,1e-08\n739432.91,1921404.27,307.2002950000001,0.5194297349557597,0.6039892698087587,0.4899632784136509,fault_right,fault_right,3,3,1e-08\n739630.11,1922747.93,315.142413,0.5251932182039547,0.6432598676853862,0.4901953994280941,fault_right,fault_right,3,3,1e-08\n740296.43,1921980.39,362.909221,0.5446674786397484,0.6208272919636784,0.4915914602684988,TRIAS,Trias_Series,4,4,1e-08\n740508.41,1918165.96,285.94569,0.5508629308656741,0.5093445160690687,0.4893420788200174,TRIAS,Trias_Series,4,4,1e-08\n740514.08,1918162.75,286.344702,0.5510286456223519,0.50925069872005,0.4893537405795398,TRIAS,Trias_Series,4,4,1e-08\n740669.86,1917432.62,263.89051,0.5555815635757199,0.4879114896733719,0.4886974811503118,TRIAS,Trias_Series,4,4,1e-08\n740635.79,1917668.07,253.27375899999998,0.5545858137062181,0.4947928899309153,0.4883871897378433,TRIAS,Trias_Series,4,4,1e-08\n742991.43,1917577.86,294.191288,0.6234331345925325,0.4921563593841629,0.4895830695195324,TRIAS,Trias_Series,4,4,1e-08\n740634.56,1917780.73,240.40215800000001,0.5545498650023886,0.4980855573890355,0.4880109967523414,TRIAS,Trias_Series,4,4,1e-08\n740486.85,1918229.12,293.930121,0.5502328056180502,0.5111904673974508,0.4895754364990989,TRIAS,Trias_Series,4,4,1e-08\n738689.37,1913266.01,228.512532,0.4976985973575646,0.3661356938479776,0.4876635035460619,TRIAS,Trias_Series,4,4,1e-08\n736436.36,1914663.25,275.063473,0.4318508012469217,0.4069722523353495,0.4890240287420116,TRIAS,Trias_Series,4,4,1e-08\n739480.9,1914378.88,397.960997,0.5208323189369002,0.3986610873693748,0.4926159041011333,TRIAS,Trias_Series,4,4,1e-08\n739010.32,1913618.88,289.093957,0.5070788709301219,0.3764488801248049,0.4894340919240506,TRIAS,Trias_Series,4,4,1e-08\n737664.53,1915280.47,392.885671,0.4677460204200326,0.42501148727678745,0.492467569636731,TRIAS,Trias_Series,4,4,1e-08\n739235.16,1913852.37,341.805969,0.5136501770838996,0.3832729962689359,0.4909746842067114,TRIAS,Trias_Series,4,4,1e-08\n738837.12,1913660.75,277.421806,0.5020168258054398,0.37767259738445297,0.4890929547701007,TRIAS,Trias_Series,4,4,1e-08\n739393.81,1914213.3,388.044555,0.5182869753462043,0.3938217488489112,0.4923260803316165,TRIAS,Trias_Series,4,4,1e-08\n734994.57,1914860.35,319.97495499999997,0.3897121982427779,0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053100000001,0.6733167835437925,0.4316026675107293,0.4883356565986914,LIAS,Trias_Series,5,4,1e-08\n743849.68,1915939.44,391.370157,0.6485168541552326,0.4442709322872527,0.4924232763327178,LIAS,Trias_Series,5,4,1e-08\n743278.24,1917818.27,366.70364700000016,0.6318156124343927,0.4991827235205644,0.4917023583957175,LIAS,Trias_Series,5,4,1e-08\n742583.28,1917715.36,311.579231,0.6115043025045434,0.4961750153001212,0.4900912597741018,LIAS,Trias_Series,5,4,1e-08\n744683.28,1916855.67,306.412791,0.6728801383119075,0.4710492094500052,0.4899402623583168,LIAS,Trias_Series,5,4,1e-08\n744517.55,1917008.18,344.409197,0.6680364158031679,0.4755065564590441,0.4910507676801863,LIAS,Trias_Series,5,4,1e-08\n743775.88,1917781.2,384.146673,0.6463599319254296,0.4980992938856201,0.4922121585384601,LIAS,Trias_Series,5,4,1e-08\n741032.89,1918012.0,293.501641,0.5661916919915052,0.5048447905067356,0.4895629134904672,LIAS,Trias_Series,5,4,1e-08\n741563.92,1918256.34,385.419706,0.5817118872718796,0.5119860151358673,0.4922493649500722,LIAS,Trias_Series,5,4,1e-08\n742691.39,1920626.2,190.941086,0.614663988985083,0.5812489381103587,0.4865654183552328,LIAS,Trias_Series,5,4,1e-08\n742677.48,1920258.21,249.220857,0.6142574471393295,0.5704938458157153,0.4882687372389408,LIAS,Trias_Series,5,4,1e-08\n738269.72,1921452.93,445.53063899999995,0.4854336595020589,0.6054114356041783,0.4940062024519938,CARBO,Carbon_Series,6,5,1e-08\n738303.67,1921808.49,356.782866,0.4864259021809467,0.6158032414040211,0.4914124078120333,CARBO,Carbon_Series,6,5,1e-08\n738748.69,1922490.64,290.2778600000001,0.4994323185861787,0.6357401587354439,0.4894686933698314,CARBO,Carbon_Series,6,5,1e-08\n739081.0,1923227.79,190.943769,0.5091446062038686,0.6572845384332567,0.4865654967701697,CARBO,Carbon_Series,6,5,1e-08\n738610.18,1920687.66,450.18432800000005,0.4953841438158591,0.5830452042383198,0.4941422139053093,CARBO,Carbon_Series,6,5,1e-08\n738215.08,1920369.15,495.88862400000005,0.4838367187075279,0.5737362435416512,0.4954779945562556,CARBO,Carbon_Series,6,5,1e-08\n738756.07,1921036.97,322.8385990000001,0.4996480108091591,0.5932543438601645,0.4904203326891793,CARBO,Carbon_Series,6,5,1e-08\n739495.68,1918476.01,301.097111,0.5212642879146309,0.5184062198272003,0.489784903166467,CARBO,Carbon_Series,6,5,1e-08\n739360.42,1918338.41,332.001554,0.5173110995568667,0.5143846412523911,0.4906881346042223,CARBO,Carbon_Series,6,5,1e-08\n740287.77,1917340.35,309.376807,0.5444143763835133,0.4852147523543497,0.4900268904341961,CARBO,Carbon_Series,6,5,1e-08\n739371.21,1915126.02,362.180369,0.5176264544465604,0.4204974406860996,0.4915701584110322,CARBO,Carbon_Series,6,5,1e-08\n740150.49,1916464.03,470.111993,0.5404021503170197,0.4596029083378195,0.4947246315696256,CARBO,Carbon_Series,6,5,1e-08\n739938.44,1915893.33,360.803356,0.5342046522298985,0.4429232942924577,0.4915299130187501,CARBO,Carbon_Series,6,5,1e-08\n739566.06,1918295.28,280.233682,0.5233212552118321,0.5131240984912651,0.4891751363128086,CARBO,Carbon_Series,6,5,1e-08\n739406.24,1915202.86,344.144319,0.518650261841005,0.4227432117448819,0.4910430261998829,CARBO,Carbon_Series,6,5,1e-08\n739377.5,1915006.14,349.567567,0.5178102896880987,0.4169937572591499,0.4912015292374023,CARBO,Carbon_Series,6,5,1e-08\n740098.4,1915742.07,339.35886,0.5388797373231132,0.4385024805190184,0.4909031635590035,CARBO,Carbon_Series,6,5,1e-08\n732766.89,1916821.87,275.938602,0.3246047116183279,0.4700613507593968,0.4890496057771613,CARBO,Carbon_Series,6,5,1e-08\n732562.65,1915888.73,238.175373,0.3186354731872348,0.4427888519854483,0.4879459154235137,CARBO,Carbon_Series,6,5,1e-08\n732723.16,1918021.2,409.60850600000003,0.32332663290411123,0.5051136751207475,0.4929563210534899,CARBO,Carbon_Series,6,5,1e-08\n732676.24,1921256.41,467.96727000000004,0.3219553213726426,0.5996678264361477,0.4946619486331016,CARBO,Carbon_Series,6,5,1e-08\n732750.45,1919560.2,561.168737,0.32412422650371986,0.5500933947910015,0.4973859095548148,CARBO,Carbon_Series,6,5,1e-08\n732720.18,1920848.75,573.946948,0.32323953767044233,0.5877533153765168,0.4977593730692185,CARBO,Carbon_Series,6,5,1e-08\n736848.7,1920059.05,378.13127099999997,0.4439020927406391,0.5646730784541012,0.4920363488596662,CARBO,Carbon_Series,6,5,1e-08\n732632.45,1918000.37,418.83802599999996,0.3206754890631156,0.50450488528272,0.4932260684363951,CARBO,Carbon_Series,6,5,1e-08\n733045.34,1919166.4,509.82800999999995,0.3327428551804982,0.5385839642476954,0.4958853952545373,CARBO,Carbon_Series,6,5,1e-08\n737317.64,1918964.09,469.96870599999994,0.45760760914231,0.532671133132373,0.4947204437794421,CARBO,Carbon_Series,6,5,1e-08\n736114.5,1921563.35,596.432678,0.4224439314788467,0.6086386355041111,0.4984165542465941,CARBO,Carbon_Series,6,5,1e-08\n739398.95,1918627.46,295.403237,0.5184372000109869,0.522832586652449,0.4896184906542138,CARBO,Carbon_Series,6,5,1e-08\n739464.03,1918899.48,262.882472,0.5203392663892478,0.5307828032506968,0.4886680196385138,CARBO,Carbon_Series,6,5,1e-08\n738860.01,1921160.75,292.738664,0.50268582241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}, { "path": "examples/data/input_data/Claudius/APoints.csv", "content": 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}, { "path": "examples/data/input_data/jan_models/fixture_model_orientations.csv", "content": "X,Y,Z,azimuth,dip,polarity,formation\n0,500,450,90,330,1,rock2\n0,500,250,90,345,1,rock1\n500,500,800,90,0,1,rock3\n2000,500,400,90,55,1,fault\n" }, { "path": "examples/data/input_data/jan_models/fixture_model_surfaces.csv", "content": "X,Y,Z,formation\n0,0,800,rock3\n0,500,800,rock3\n0,1000,800,rock3\n500,0,800,rock3\n500,500,800,rock3\n500,1000,800,rock3\n1000,0,800,rock3\n1000,500,800,rock3\n1000,1000,800,rock3\n1500,0,800,rock3\n1500,500,800,rock3\n1500,1000,800,rock3\n2000,0,650,rock3\n2000,500,650,rock3\n2000,1000,650,rock3\n0,0,450,rock2\n0,500,450,rock2\n0,1000,450,rock2\n150,0,600,rock2\n150,500,600,rock2\n150,1000,600,rock2\n300,0,750,rock2\n300,500,750,rock2\n300,1000,750,rock2\n450,0,600,rock2\n450,500,600,rock2\n450,1000,600,rock2\n600,0,450,rock2\n600,500,450,rock2\n600,1000,450,rock2\n750,0,600,rock2\n750,500,600,rock2\n750,1000,600,rock2\n850,0,800,rock2\n850,500,800,rock2\n850,1000,800,rock2\n1500,0,800,rock2\n1500,500,800,rock2\n1500,1000,800,rock2\n1600,0,600,rock2\n1600,500,600,rock2\n1600,1000,600,rock2\n1750,0,450,rock2\n1750,500,450,rock2\n1750,1000,450,rock2\n1850,0,550,rock2\n1850,500,550,rock2\n1850,1000,550,rock2\n2200,0,550,rock2\n2200,500,550,rock2\n2200,1000,550,rock2\n2300,0,650,rock2\n2300,500,650,rock2\n2300,1000,650,rock2\n0,0,250,rock1\n0,500,250,rock1\n0,1000,250,rock1\n150,0,400,rock1\n150,500,400,rock1\n150,1000,400,rock1\n300,0,550,rock1\n300,500,550,rock1\n300,1000,550,rock1\n450,0,400,rock1\n450,500,400,rock1\n450,1000,400,rock1\n600,0,250,rock1\n600,500,250,rock1\n600,1000,250,rock1\n750,0,350,rock1\n750,500,350,rock1\n750,1000,350,rock1\n900,0,550,rock1\n900,500,550,rock1\n900,1000,550,rock1\n1450,0,550,rock1\n1450,500,550,rock1\n1450,1000,550,rock1\n1600,0,350,rock1\n1600,500,350,rock1\n1600,1000,350,rock1\n1750,0,250,rock1\n1750,500,250,rock1\n1750,1000,250,rock1\n1850,0,350,rock1\n1850,500,350,rock1\n1850,1000,350,rock1\n2200,0,350,rock1\n2200,500,350,rock1\n2200,1000,350,rock1\n2300,0,450,rock1\n2300,500,450,rock1\n2300,1000,450,rock1\n1050,0,799,rock1\n1050,500,799,rock1\n1050,1000,799,rock1\n1300,0,799,rock1\n1300,500,799,rock1\n1300,1000,799,rock1\n2000,0,400,fault\n2000,500,400,fault\n2000,1000,400,fault\n\n\n" }, { "path": "examples/data/input_data/jan_models/model1_orientations.csv", "content": "X,Y,Z,azimuth,dip,polarity,formation\n500,500,600,90,0,1,rock2\n500,500,400,90,0,1,rock1\n" }, { "path": "examples/data/input_data/jan_models/model1_surface_points.csv", "content": "X,Y,Z,formation\n100,200,600,rock2\n500,200,600,rock2\n900,200,600,rock2\n100,800,600,rock2\n500,800,600,rock2\n900,800,600,rock2\n100,200,400,rock1\n500,200,400,rock1\n900,200,400,rock1\n100,800,400,rock1\n500,800,400,rock1\n900,800,400,rock1\n" }, { "path": "examples/data/input_data/jan_models/model2_orientations.csv", "content": "X,Y,Z,azimuth,dip,polarity,formation\n500,500,820,90,0,1,rock2\n500,500,620,90,0,1,rock1\n" }, { "path": "examples/data/input_data/jan_models/model2_surface_points.csv", "content": "X,Y,Z,formation\n0,100,200,rock1\n0,500,200,rock1\n0,900,200,rock1\n400,100,600,rock1\n400,500,600,rock1\n400,900,600,rock1\n200,100,400,rock1\n200,500,400,rock1\n200,900,400,rock1\n800,100,400,rock1\n800,500,400,rock1\n800,900,400,rock1\n600,100,600,rock1\n600,500,600,rock1\n600,900,600,rock1\n1000,100,200,rock1\n1000,500,200,rock1\n1000,900,200,rock1\n0,100,400,rock2\n0,500,400,rock2\n0,900,400,rock2\n1000,100,400,rock2\n1000,500,400,rock2\n1000,900,400,rock2\n400,100,800,rock2\n400,500,800,rock2\n400,900,800,rock2\n200,100,600,rock2\n200,500,600,rock2\n200,900,600,rock2\n600,100,800,rock2\n600,500,800,rock2\n600,900,800,rock2\n800,100,600,rock2\n800,500,600,rock2\n800,900,600,rock2\n\n" }, { "path": "examples/data/input_data/jan_models/model3_orientations.csv", "content": "X,Y,Z,azimuth,dip,polarity,formation\n200,500,780,90,185.73,1,rock1\n200,500,220,90,354.27,1,rock1\n" }, { "path": "examples/data/input_data/jan_models/model3_surface_points.csv", "content": "X,Y,Z,formation\n0,200,800,rock1\n0,200,200,rock1\n100,200,790,rock1\n100,200,210,rock1\n200,200,780,rock1\n200,200,220,rock1\n300,200,770,rock1\n300,200,230,rock1\n400,200,760,rock1\n400,200,240,rock1\n700,200,500,rock1\n0,0,800,rock1\n0,0,200,rock1\n100,0,790,rock1\n100,0,210,rock1\n200,0,780,rock1\n200,0,220,rock1\n300,0,770,rock1\n300,0,230,rock1\n400,0,760,rock1\n400,0,240,rock1\n700,0,500,rock1\n0,500,800,rock1\n0,500,200,rock1\n100,500,790,rock1\n100,500,210,rock1\n200,500,780,rock1\n200,500,220,rock1\n300,500,770,rock1\n300,500,230,rock1\n400,500,760,rock1\n400,500,240,rock1\n700,500,500,rock1\n0,1000,800,rock1\n0,1000,200,rock1\n100,1000,790,rock1\n100,1000,210,rock1\n200,1000,780,rock1\n200,1000,220,rock1\n300,1000,770,rock1\n300,1000,230,rock1\n400,1000,760,rock1\n400,1000,240,rock1\n700,1000,500,rock1\n700,800,500,rock1\n0,800,800,rock1\n0,800,200,rock1\n100,800,790,rock1\n100,800,210,rock1\n200,800,780,rock1\n200,800,220,rock1\n300,800,770,rock1\n300,800,230,rock1\n400,800,760,rock1\n400,800,240,rock1\n0,200,600,rock2\n0,200,400,rock2\n100,200,590,rock2\n100,200,410,rock2\n200,200,580,rock2\n200,200,420,rock2\n300,200,570,rock2\n300,200,430,rock2\n400,200,560,rock2\n400,200,440,rock2\n0,0,600,rock2\n0,0,400,rock2\n100,0,590,rock2\n100,0,410,rock2\n200,0,580,rock2\n200,0,420,rock2\n300,0,570,rock2\n300,0,430,rock2\n400,0,560,rock2\n400,0,440,rock2\n0,500,600,rock2\n0,500,400,rock2\n100,500,590,rock2\n100,500,410,rock2\n200,500,580,rock2\n200,500,420,rock2\n300,500,570,rock2\n300,500,430,rock2\n400,500,560,rock2\n400,500,440,rock2\n0,1000,600,rock2\n0,1000,400,rock2\n100,1000,590,rock2\n100,1000,410,rock2\n200,1000,580,rock2\n200,1000,420,rock2\n300,1000,570,rock2\n300,1000,430,rock2\n400,1000,560,rock2\n400,1000,440,rock2\n0,800,600,rock2\n0,800,400,rock2\n100,800,590,rock2\n100,800,410,rock2\n200,800,580,rock2\n200,800,420,rock2\n300,800,570,rock2\n300,800,430,rock2\n400,800,560,rock2\n400,800,440,rock2\n" }, { "path": "examples/data/input_data/jan_models/model4_orientations.csv", "content": "X,Y,Z,azimuth,dip,polarity,formation\n500,500,625,90,8.54,1,rock2\n500,200,625,90,8.54,1,rock2\n500,800,625,90,8.54,1,rock2\n500,500,375,90,351.46,1,rock1\n500,200,375,90,351.46,1,rock1\n500,800,375,90,351.46,1,rock1\n" }, { "path": "examples/data/input_data/jan_models/model4_surface_points.csv", "content": "X,Y,Z,formation\n0,200,300,rock1\n0,800,300,rock1\n500,200,375,rock1\n500,800,375,rock1\n1000,200,450,rock1\n1000,800,450,rock1\n0,200,700,rock2\n0,800,700,rock2\n500,200,625,rock2\n500,800,625,rock2\n1000,200,550,rock2\n1000,800,550,rock2" }, { "path": "examples/data/input_data/jan_models/model5_orientations.csv", "content": "X,Y,Z,azimuth,dip,polarity,formation\n100,500,800,90,0,1,rock2\n100,500,600,90,0,1,rock1\n900,500,400,90,0,1,rock2\n900,500,200,90,0,1,rock1\n500,500,500,90,60,1,fault\n" }, { "path": "examples/data/input_data/jan_models/model5_surface_points.csv", "content": "X,Y,Z,formation\n0,200,600,rock1\n0,500,600,rock1\n0,800,600,rock1\n200,200,600,rock1\n200,500,600,rock1\n200,800,600,rock1\n800,200,200,rock1\n800,500,200,rock1\n800,800,200,rock1\n1000,200,200,rock1\n1000,500,200,rock1\n1000,800,200,rock1\n0,200,800,rock2\n0,800,800,rock2\n200,200,800,rock2\n200,800,800,rock2\n800,200,400,rock2\n800,800,400,rock2\n1000,200,400,rock2\n1000,800,400,rock2\n500,500,500,fault\n450,500,600,fault\n500,200,500,fault\n450,200,600,fault\n500,800,500,fault\n450,800,600,fault\n\n" }, { "path": "examples/data/input_data/jan_models/model6_orientations.csv", "content": "X,Y,Z,azimuth,dip,polarity,formation\n100,500,700,90,315,1,rock2\n900,500,700,90,45,1,rock2\n100,500,500,90,315,1,rock1\n900,500,500,90,45,1,rock1\n500,500,701,90,0,1,rock3\n" }, { "path": "examples/data/input_data/jan_models/model6_surface_points.csv", "content": "X,Y,Z,formation\n0,200,400,rock1\n0,500,400,rock1\n0,800,400,rock1\n100,200,500,rock1\n100,500,500,rock1\n100,800,500,rock1\n200,200,600,rock1\n200,500,600,rock1\n200,800,600,rock1\n800,200,600,rock1\n800,500,600,rock1\n800,800,600,rock1\n900,200,500,rock1\n900,500,500,rock1\n900,800,500,rock1\n1000,200,400,rock1\n1000,500,400,rock1\n1000,800,400,rock1\n0,200,600,rock2\n0,500,600,rock2\n0,800,600,rock2\n1000,200,600,rock2\n1000,500,600,rock2\n1000,800,600,rock2\n100,200,700,rock2\n100,500,700,rock2\n100,800,700,rock2\n900,200,700,rock2\n900,500,700,rock2\n900,800,700,rock2\n200,200,701,rock3\n200,500,701,rock3\n200,800,701,rock3\n500,200,701,rock3\n500,500,701,rock3\n500,800,701,rock3\n800,200,701,rock3\n800,500,701,rock3\n800,800,701,rock3\n\n\n\n" }, { "path": "examples/data/input_data/jan_models/model7_orientations.csv", "content": "X,Y,Z,azimuth,dip,polarity,formation\n0,500,450,90,335,1,rock2\n0,500,250,90,335,1,rock1\n0,0,450,90,335,1,rock2\n0,0,250,90,335,1,rock1\n0,1000,450,90,335,1,rock2\n0,1000,250,90,335,1,rock1\n500,500,800,90,0,1,rock3\n2000,500,400,90,55,1,fault\n" }, { "path": "examples/data/input_data/jan_models/model7_surface_points.csv", "content": "X,Y,Z,formation\n0,0,800,rock3\n0,500,800,rock3\n0,1000,800,rock3\n500,0,800,rock3\n500,500,800,rock3\n500,1000,800,rock3\n1000,0,800,rock3\n1000,500,800,rock3\n1000,1000,800,rock3\n1500,0,800,rock3\n1500,500,800,rock3\n1500,1000,800,rock3\n2000,0,650,rock3\n2000,500,650,rock3\n2000,1000,650,rock3\n0,0,450,rock2\n0,500,450,rock2\n0,1000,450,rock2\n150,0,600,rock2\n150,500,600,rock2\n150,1000,600,rock2\n300,0,750,rock2\n300,500,750,rock2\n300,1000,750,rock2\n450,0,600,rock2\n450,500,600,rock2\n450,1000,600,rock2\n600,0,450,rock2\n600,500,450,rock2\n600,1000,450,rock2\n750,0,600,rock2\n750,500,600,rock2\n750,1000,600,rock2\n850,0,800,rock2\n850,500,800,rock2\n850,1000,800,rock2\n1500,0,800,rock2\n1500,500,800,rock2\n1500,1000,800,rock2\n1600,0,600,rock2\n1600,500,600,rock2\n1600,1000,600,rock2\n1750,0,450,rock2\n1750,500,450,rock2\n1750,1000,450,rock2\n1850,0,550,rock2\n1850,500,550,rock2\n1850,1000,550,rock2\n2200,0,550,rock2\n2200,500,550,rock2\n2200,1000,550,rock2\n2300,0,650,rock2\n2300,500,650,rock2\n2300,1000,650,rock2\n0,0,250,rock1\n0,500,250,rock1\n0,1000,250,rock1\n150,0,400,rock1\n150,500,400,rock1\n150,1000,400,rock1\n300,0,550,rock1\n300,500,550,rock1\n300,1000,550,rock1\n450,0,400,rock1\n450,500,400,rock1\n450,1000,400,rock1\n600,0,250,rock1\n600,500,250,rock1\n600,1000,250,rock1\n750,0,350,rock1\n750,500,350,rock1\n750,1000,350,rock1\n900,0,550,rock1\n900,500,550,rock1\n900,1000,550,rock1\n1450,0,550,rock1\n1450,500,550,rock1\n1450,1000,550,rock1\n1600,0,350,rock1\n1600,500,350,rock1\n1600,1000,350,rock1\n1750,0,250,rock1\n1750,500,250,rock1\n1750,1000,250,rock1\n1850,0,350,rock1\n1850,500,350,rock1\n1850,1000,350,rock1\n2200,0,350,rock1\n2200,500,350,rock1\n2200,1000,350,rock1\n2300,0,450,rock1\n2300,500,450,rock1\n2300,1000,450,rock1\n1050,0,799,rock1\n1050,500,799,rock1\n1050,1000,799,rock1\n1300,0,799,rock1\n1300,500,799,rock1\n1300,1000,799,rock1\n2000,0,400,fault\n2000,500,400,fault\n2000,1000,400,fault" }, { "path": "examples/data/input_data/jan_models/tutorial_model_orientations.csv", "content": "X,Y,Z,azimuth,dip,polarity,formation\n0,500,450,90,335,1,rock2\n0,500,250,90,335,1,rock1\n0,0,450,90,335,1,rock2\n0,0,250,90,335,1,rock1\n0,1000,450,90,335,1,rock2\n0,1000,250,90,335,1,rock1\n500,500,800,90,0,1,rock3\n" }, { "path": "examples/data/input_data/jan_models/tutorial_model_surface_points.csv", "content": "X,Y,Z,formation\n0,0,800,rock3\n0,500,800,rock3\n0,1000,800,rock3\n500,0,800,rock3\n500,500,800,rock3\n500,1000,800,rock3\n1000,0,800,rock3\n1000,500,800,rock3\n1000,1000,800,rock3\n1500,0,800,rock3\n1500,500,800,rock3\n1500,1000,800,rock3\n2000,0,800,rock3\n2000,500,800,rock3\n2000,1000,800,rock3\n0,0,450,rock2\n0,500,450,rock2\n0,1000,450,rock2\n150,0,600,rock2\n150,500,600,rock2\n150,1000,600,rock2\n300,0,750,rock2\n300,500,750,rock2\n300,1000,750,rock2\n450,0,600,rock2\n450,500,600,rock2\n450,1000,600,rock2\n600,0,450,rock2\n600,500,450,rock2\n600,1000,450,rock2\n750,0,600,rock2\n750,500,600,rock2\n750,1000,600,rock2\n850,0,800,rock2\n850,500,800,rock2\n850,1000,800,rock2\n1500,0,800,rock2\n1500,500,800,rock2\n1500,1000,800,rock2\n1600,0,600,rock2\n1600,500,600,rock2\n1600,1000,600,rock2\n1750,0,450,rock2\n1750,500,450,rock2\n1750,1000,450,rock2\n1850,0,550,rock2\n1850,500,550,rock2\n1850,1000,550,rock2\n0,0,250,rock1\n0,500,250,rock1\n0,1000,250,rock1\n150,0,400,rock1\n150,500,400,rock1\n150,1000,400,rock1\n300,0,550,rock1\n300,500,550,rock1\n300,1000,550,rock1\n450,0,400,rock1\n450,500,400,rock1\n450,1000,400,rock1\n600,0,250,rock1\n600,500,250,rock1\n600,1000,250,rock1\n750,0,350,rock1\n750,500,350,rock1\n750,1000,350,rock1\n900,0,550,rock1\n900,500,550,rock1\n900,1000,550,rock1\n1450,0,550,rock1\n1450,500,550,rock1\n1450,1000,550,rock1\n1600,0,350,rock1\n1600,500,350,rock1\n1600,1000,350,rock1\n1750,0,250,rock1\n1750,500,250,rock1\n1750,1000,250,rock1\n1850,0,350,rock1\n1850,500,350,rock1\n1850,1000,350,rock1\n1050,0,799,rock1\n1050,500,799,rock1\n1050,1000,799,rock1\n1300,0,799,rock1\n1300,500,799,rock1\n1300,1000,799,rock1" }, { "path": "examples/data/input_data/lisa_models/foliations1.csv", "content": ",X,Y,Z,G_x,G_y,G_z,dip,azimuth,polarity,formation,series,formation_number,order_series,isFault,formation_value,annotations\n0,1000.0,1000.0,1500.0,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,Sandstone,Strat_Series_1,1,1,False,1,\"${\\bf{x}}_{\\beta \\,{\\bf{1}},0}$\"\n1,1000.0,1000.0,1400.0,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,Siltstone,Strat_Series_1,2,1,False,2,\"${\\bf{x}}_{\\beta \\,{\\bf{2}},0}$\"\n2,1000.0,1000.0,1100.0,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,Shale,Strat_Series_1,3,1,False,3,\"${\\bf{x}}_{\\beta \\,{\\bf{3}},0}$\"\n3,1000.0,1000.0,1000.0,0.6427876096875392,1.0000393593894367e-12,0.766044443119978,40.0,90.0,1.0,Sandstone2,Strat_Series_2,4,2,False,4,\"${\\bf{x}}_{\\beta \\,{\\bf{4}},0}$\"\n4,1000.0,1000.0,500.0,0.6427876096875392,1.0000393593894367e-12,0.766044443119978,40.0,90.0,1.0,Siltstone2,Strat_Series_2,5,2,False,5,\"${\\bf{x}}_{\\beta \\,{\\bf{5}},0}$\"\n5,1000.0,1000.0,100.0,0.6427876096875392,1.0000393593894367e-12,0.766044443119978,40.0,90.0,1.0,Shale2,Strat_Series_2,6,2,False,6,\"${\\bf{x}}_{\\beta \\,{\\bf{6}},0}$\"\n" }, { "path": "examples/data/input_data/lisa_models/foliations2.csv", "content": ",X,Y,Z,G_x,G_y,G_z,dip,azimuth,polarity,formation,series,formation_number,order_series,isFault,formation_value,annotations\n2,750.0,1000.0,1300.0,-0.8191520442879918,9.998495242106419e-13,0.573576436352046,55.0,270.0,1.0,Siltstone,Strat_Series,2,2,False,2,\"${\\bf{x}}_{\\beta \\,{\\bf{2}},0}$\"\n3,1250.0,1000.0,1300.0,0.8191520442899918,1.0000501585964528e-12,0.573576436352046,55.0,90.0,1.0,Siltstone,Strat_Series,2,2,False,2,\"${\\bf{x}}_{\\beta \\,{\\bf{2}},1}$\"\n0,750.0,1000.0,500.0,-0.8191520442879918,9.998495242106419e-13,0.573576436352046,55.0,270.0,1.0,Schist,Strat_Series,5,2,False,5,\"${\\bf{x}}_{\\beta \\,{\\bf{5}},0}$\"\n1,1250.0,1000.0,500.0,0.8191520442899918,1.0000501585964528e-12,0.573576436352046,55.0,90.0,1.0,Schist,Strat_Series,5,2,False,5,\"${\\bf{x}}_{\\beta \\,{\\bf{5}},1}$\"\n" }, { "path": "examples/data/input_data/lisa_models/foliations3.csv", "content": ",X,Y,Z,G_x,G_y,G_z,dip,azimuth,polarity,formation,series,formation_number,order_series,isFault,formation_value,annotations\n2,1000.0,1250.0,1500.0,0.31816239933811036,-0.4772448176609822,0.8191520442899918,35.0,146.31,1.0,Sandstone,Strat_Series,1,2,False,1,\"${\\bf{x}}_{\\beta \\,{\\bf{1}},0}$\"\n1,1000.0,1250.0,700.0,0.3181623993373182,-0.477244817659794,0.8191520442909918,34.99999999990011,146.31,1.0,Schist,Strat_Series,5,2,False,5,\"${\\bf{x}}_{\\beta \\,{\\bf{5}},0}$\"\n0,1000.0000000000001,1249.9999999999982,10.00000000000001,0.31816239933811036,-0.4772448176609822,0.8191520442899918,35.0,146.31,1.0,Gneiss,Strat_Series,6,2,False,6,\"${\\bf{x}}_{\\beta \\,{\\bf{6}},0}$\"\n" }, { "path": "examples/data/input_data/lisa_models/foliations5.csv", "content": ",X,Y,Z,G_x,G_y,G_z,dip,azimuth,polarity,formation,series,formation_number,order_series,isFault,formation_value,annotations\n0,500.0,1000.0,864.602,-0.9486830155920029,-0.31622861338710945,1.999261954502598e-12,89.99999999994274,251.56500000003624,1.0,Main_Fault,Fault_Series,1,1,True,1,\"${\\bf{x}}_{\\beta \\,{\\bf{1}},0}$\"\n1,1000.0,1000.0,1500.0,0.40557978767430714,-0.40557978767030683,0.8191520442903204,34.99999999996717,134.99999999985872,1.0,Sandstone,Strat_Series,2,2,False,2,\"${\\bf{x}}_{\\beta \\,{\\bf{2}},0}$\"\n2,1000.0,1000.0,500.0,0.4055797876736288,-0.4055797876696291,0.8191520442909919,34.999999999900105,134.99999999985874,1.0,Schist,Strat_Series,6,2,False,6,\"${\\bf{x}}_{\\beta \\,{\\bf{6}},0}$\"\n3,1000.0,1000.0,10.0,0.4055797876736288,-0.4055797876696291,0.8191520442909919,34.999999999900105,134.99999999985874,1.0,Gneiss,Strat_Series,7,2,False,7,\"${\\bf{x}}_{\\beta \\,{\\bf{7}},0}$\"\n" }, { "path": "examples/data/input_data/lisa_models/foliations6.csv", "content": ",X,Y,Z,G_x,G_y,G_z,dip,azimuth,polarity,formation,series,formation_number,order_series,isFault,annotations,formation_value\n2,1000,1000,1500,1e-12,1e-12,1.000000000001,0,90,1,Sandstone,Strat_Series,2,2,False,\"${\\bf{x}}_{\\beta \\,{\\bf{2}},0}$\",2\n1,1000,1000,1000,1e-12,1e-12,1.000000000001,0,90,1,Siltstone,Strat_Series,3,2,False,\"${\\bf{x}}_{\\beta \\,{\\bf{3}},0}$\",3\n0,1000,1000,500,1e-12,1e-12,1.000000000001,0,90,1,Shale,Strat_Series,4,2,False,\"${\\bf{x}}_{\\beta \\,{\\bf{4}},0}$\",4\n" }, { "path": "examples/data/input_data/lisa_models/foliations7.csv", "content": ",X,Y,Z,G_x,G_y,G_z,dip,azimuth,polarity,formation,series,formation_number,order_series,isFault,formation_value,annotations\n0,750.0,1000.0,500.0,0.9659258262900683,1.000059145898569e-12,0.2588190451035207,75.0,90.0,1.0,Fault_1,Fault_1,1,1,True,1,\"${\\bf{x}}_{\\beta \\,{\\bf{1}},0}$\"\n1,1250.0,1000.0,500.0,-0.9659258262880683,9.998225623042932e-13,0.2588190451035207,75.0,270.0,1.0,Fault_2,Fault_2,2,2,True,2,\"${\\bf{x}}_{\\beta \\,{\\bf{2}},0}$\"\n7,1000.0,1000.0,500.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Schist,Strat_Series,7,3,False,7,\"${\\bf{x}}_{\\beta \\,{\\bf{7}},2}$\"\n8,250.0,500.0,700.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Schist,Strat_Series,7,3,False,7,\"${\\bf{x}}_{\\beta \\,{\\bf{7}},1}$\"\n9,1750.0,500.0,700.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Schist,Strat_Series,7,3,False,7,\"${\\bf{x}}_{\\beta \\,{\\bf{7}},0}$\"\n5,1000.0,1000.0,700.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Sandstone_2,Strat_Series,6,3,False,6,\"${\\bf{x}}_{\\beta \\,{\\bf{6}},4}$\"\n6,250.0,500.0,900.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Sandstone_2,Strat_Series,6,3,False,6,\"${\\bf{x}}_{\\beta \\,{\\bf{6}},3}$\"\n10,1750.0,500.0,900.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Sandstone_2,Strat_Series,6,3,False,6,\"${\\bf{x}}_{\\beta \\,{\\bf{6}},2}$\"\n14,250.0,1500.0,900.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Sandstone_2,Strat_Series,6,3,False,6,\"${\\bf{x}}_{\\beta \\,{\\bf{6}},1}$\"\n15,1750.0,1500.0,900.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Sandstone_2,Strat_Series,6,3,False,6,\"${\\bf{x}}_{\\beta \\,{\\bf{6}},0}$\"\n3,1000.0,1000.0,1000.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Siltstone,Strat_Series,4,3,False,4,\"${\\bf{x}}_{\\beta \\,{\\bf{4}},4}$\"\n4,250.0,500.0,1300.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Siltstone,Strat_Series,4,3,False,4,\"${\\bf{x}}_{\\beta \\,{\\bf{4}},3}$\"\n11,1750.0,500.0,1300.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Siltstone,Strat_Series,4,3,False,4,\"${\\bf{x}}_{\\beta \\,{\\bf{4}},2}$\"\n12,250.0,1500.0,1300.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Siltstone,Strat_Series,4,3,False,4,\"${\\bf{x}}_{\\beta \\,{\\bf{4}},1}$\"\n13,1750.0,1500.0,1300.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Siltstone,Strat_Series,4,3,False,4,\"${\\bf{x}}_{\\beta \\,{\\bf{4}},0}$\"\n18,250.0,1500.0,1500.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Sandstone,Strat_Series,3,3,False,3,\"${\\bf{x}}_{\\beta \\,{\\bf{3}},3}$\"\n19,250.0,500.0,1500.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Sandstone,Strat_Series,3,3,False,3,\"${\\bf{x}}_{\\beta \\,{\\bf{3}},2}$\"\n20,1750.0,500.0,1500.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Sandstone,Strat_Series,3,3,False,3,\"${\\bf{x}}_{\\beta \\,{\\bf{3}},1}$\"\n21,1750.0,1500.0,1500.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Sandstone,Strat_Series,3,3,False,3,\"${\\bf{x}}_{\\beta \\,{\\bf{3}},0}$\"\n16,250.0,1500.0,700.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Schist,Strat_Series,7,3,False,7,\"${\\bf{x}}_{\\beta \\,{\\bf{7}},3}$\"\n17,1750.0,1500.0,700.0,1e-12,1e-12,1.000000000001,0.0,90.0,1.0,Schist,Strat_Series,7,3,False,7,\"${\\bf{x}}_{\\beta \\,{\\bf{7}},4}$\"\n" }, { "path": "examples/data/input_data/lisa_models/foliations8.csv", "content": ",X,Y,Z,G_x,G_y,G_z,dip,azimuth,polarity,formation,series,formation_number,order_series,isFault,formation_value,annotations\n0,500.0,1000.0,864.602,-0.9486830155920029,-0.31622861338710945,1.999261954502598e-12,89.99999999994274,251.56500000003624,1.0,Main_Fault,Fault_Series,1,1,True,1,\"${\\bf{x}}_{\\beta \\,{\\bf{1}},0}$\"\n1,1500.0,1000.0,1000.0,0.5389855446963152,0.19617469496985063,0.8191520442903204,34.99999999996717,70.0,1.0,Sandstone,Strat_Series,2,2,False,2,\"${\\bf{x}}_{\\beta \\,{\\bf{2}},0}$\"\n4,250.0,1000.0,1100.0,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,Sandstone,Strat_Series,2,2,False,2,\"${\\bf{x}}_{\\beta \\,{\\bf{2}},1}$\"\n5,250.0,1000.0,800.0,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,Shale,Strat_Series,4,2,False,4,\"${\\bf{x}}_{\\beta \\,{\\bf{4}},0}$\"\n6,250.0,1000.0,500.0,1e-12,1e-12,1.000000000001,0.0,0.0,1.0,Sandstone_2,Strat_Series,5,2,False,5,\"${\\bf{x}}_{\\beta 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\\,{\\bf{5}},7}$\",7\n49,500.0,1000.0,800.0,Sandstone_2,Strat_Series,5,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{5}},6}$\",5\n50,1500.0,1000.0,800.0,Sandstone_2,Strat_Series,5,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{5}},5}$\",5\n51,1000.0,1000.0,600.0,Sandstone_2,Strat_Series,5,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{5}},4}$\",5\n52,500.0,1000.0,700.0,Schist,Strat_Series,6,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{5}},3}$\",6\n53,1500.0,1000.0,700.0,Schist,Strat_Series,6,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{5}},2}$\",6\n54,1000.0,1000.0,500.0,Schist,Strat_Series,6,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{5}},1}$\",6\n57,1000.0,1000.0,300.0,Gneiss,Strat_Series,7,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{5}},8}$\",7\n55,500.0,1000.0,500.0,Gneiss,Strat_Series,7,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{5}},0}$\",7\n15,500.0,1000.0,1200.0,Shale,Strat_Series,4,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{4}},1}$\",4\n12,1500.0,1000.0,1200.0,Shale,Strat_Series,4,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{4}},0}$\",4\n19,500.0,1000.0,1300.0,Siltstone,Strat_Series,3,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{3}},2}$\",3\n20,1500.0,1000.0,1300.0,Siltstone,Strat_Series,3,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{3}},1}$\",3\n21,1000.0,1000.0,1100.0,Siltstone,Strat_Series,3,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{3}},0}$\",3\n47,1500.0,1000.0,1500.0,Sandstone,Strat_Series,2,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{2}},2}$\",2\n27,1000.0,1000.0,1200.0,Sandstone,Strat_Series,2,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{2}},1}$\",2\n9,1000.0,1000.0,1000.0,Shale,Strat_Series,4,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{4}},2}$\",4\n" }, { "path": "examples/data/input_data/lisa_models/interfaces7.csv", "content": ",X,Y,Z,formation,series,formation_number,order_series,isFault,annotations,formation_value\n0,750.0,1500.0,500.0,Fault_1,Fault_1,1,1,True,\"${\\bf{x}}_{\\alpha \\,{\\bf{1}},0}$\",1\n1,750.0,500.0,500.0,Fault_1,Fault_1,1,1,True,\"${\\bf{x}}_{\\alpha \\,{\\bf{1}},1}$\",1\n2,1250.0,1500.0,500.0,Fault_2,Fault_2,2,2,True,\"${\\bf{x}}_{\\alpha \\,{\\bf{2}},0}$\",2\n3,1250.0,1500.0,500.0,Fault_2,Fault_2,2,2,True,\"${\\bf{x}}_{\\alpha \\,{\\bf{2}},1}$\",2\n13,1000.0,1000.0,300.0,Gneiss,Strat_Series,8,3,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{8}},1}$\",8\n5,250.0,1000.0,500.0,Gneiss,Strat_Series,8,3,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{8}},0}$\",8\n8,250.0,1000.0,700.0,Schist,Strat_Series,7,3,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{7}},2}$\",7\n7,1750.0,1000.0,700.0,Schist,Strat_Series,7,3,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{7}},1}$\",7\n6,1000.0,1000.0,500.0,Schist,Strat_Series,7,3,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{7}},0}$\",7\n9,250.0,1000.0,900.0,Sandstone_2,Strat_Series,6,3,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{6}},2}$\",6\n10,1750.0,1000.0,900.0,Sandstone_2,Strat_Series,6,3,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{6}},1}$\",6\n11,1000.0,1000.0,700.0,Sandstone_2,Strat_Series,6,3,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{6}},0}$\",6\n12,1750.0,1000.0,1200.0,Shale,Strat_Series,5,3,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{5}},1}$\",5\n4,1750.0,1000.0,500.0,Gneiss,Strat_Series,8,3,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{8}},2}$\",8\n14,250.0,1000.0,1200.0,Shale,Strat_Series,5,3,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{5}},0}$\",5\n17,250.0,1000.0,1300.0,Siltstone,Strat_Series,4,3,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{4}},2}$\",4\n16,1750.0,1000.0,1300.0,Siltstone,Strat_Series,4,3,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{4}},1}$\",4\n15,1000.0,1000.0,1100.0,Siltstone,Strat_Series,4,3,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{4}},0}$\",4\n20,250.0,1000.0,1500.0,Sandstone,Strat_Series,3,3,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{3}},2}$\",3\n19,1000.0,1000.0,1200.0,Sandstone,Strat_Series,3,3,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{3}},1}$\",3\n18,1750.0,1000.0,1500.0,Sandstone,Strat_Series,3,3,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{3}},0}$\",3\n21,1000.0,1000.0,1000.0,Shale,Strat_Series,5,3,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{5}},2}$\",5\n" }, { "path": "examples/data/input_data/lisa_models/interfaces8.csv", "content": ",X,Y,Z,formation,series,formation_number,order_series,isFault,annotations,formation_value\n1,700.0,1000.0,900.0,Main_Fault,Fault_Series,1,1,True,\"${\\bf{x}}_{\\alpha \\,{\\bf{1}},0}$\",1\n2,800.0,1000.0,1200.0,Main_Fault,Fault_Series,1,1,True,\"${\\bf{x}}_{\\alpha \\,{\\bf{1}},1}$\",1\n3,900.0,1000.0,1500.0,Main_Fault,Fault_Series,1,1,True,\"${\\bf{x}}_{\\alpha \\,{\\bf{1}},2}$\",1\n4,500.0,1000.0,300.0,Main_Fault,Fault_Series,1,1,True,\"${\\bf{x}}_{\\alpha \\,{\\bf{1}},3}$\",1\n0,600.0,1000.0,600.0,Main_Fault,Fault_Series,1,1,True,\"${\\bf{x}}_{\\alpha \\,{\\bf{1}},4}$\",1\n22,250.0,500.0,1100.0,Sandstone,Strat_Series,2,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{2}},0}$\",2\n23,835.0,450.0,1100.0,Sandstone,Strat_Series,2,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{2}},1}$\",2\n24,250.0,1500.0,1100.0,Sandstone,Strat_Series,2,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{2}},2}$\",2\n25,1250.0,1000.0,1000.0,Sandstone,Strat_Series,2,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{2}},3}$\",2\n18,755.0,460.0,900.0,Siltstone,Strat_Series,3,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{3}},0}$\",3\n19,250.0,1500.0,900.0,Siltstone,Strat_Series,3,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{3}},1}$\",3\n20,1250.0,1000.0,800.0,Siltstone,Strat_Series,3,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{3}},2}$\",3\n21,250.0,500.0,900.0,Siltstone,Strat_Series,3,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{3}},3}$\",3\n16,250.0,500.0,800.0,Shale,Strat_Series,4,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{4}},0}$\",4\n15,1250.0,1000.0,700.0,Shale,Strat_Series,4,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{4}},1}$\",4\n26,715.0,505.0,800.0,Shale,Strat_Series,4,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{4}},2}$\",4\n13,250.0,1500.0,800.0,Shale,Strat_Series,4,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{4}},3}$\",4\n14,640.0,490.0,500.0,Sandstone_2,Strat_Series,5,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{5}},0}$\",5\n11,1250.0,1000.0,400.0,Sandstone_2,Strat_Series,5,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{5}},1}$\",5\n12,250.0,500.0,500.0,Sandstone_2,Strat_Series,5,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{5}},2}$\",5\n9,250.0,1500.0,500.0,Sandstone_2,Strat_Series,5,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{5}},3}$\",5\n10,250.0,1500.0,300.0,Schist,Strat_Series,6,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{6}},0}$\",6\n8,1250.0,1000.0,200.0,Schist,Strat_Series,6,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{6}},1}$\",6\n7,250.0,500.0,300.0,Schist,Strat_Series,6,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{6}},2}$\",6\n17,1250.0,1000.0,100.0,Gneiss,Strat_Series,7,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{7}},0}$\",7\n6,250.0,500.0,200.0,Gneiss,Strat_Series,7,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{7}},1}$\",7\n5,250.0,1500.0,200.0,Gneiss,Strat_Series,7,2,False,\"${\\bf{x}}_{\\alpha \\,{\\bf{7}},2}$\",7\n" }, { "path": "examples/data/input_data/lisa_models/interfaces9.csv", "content": ",Unnamed: 0,Unnamed: 0.1,X,Y,Z,annotations,formation,formation_number,formation_value,isFault,order_series,series\n10,10.0,7.0,2000.0,2000.0,10.0,\"${\\bf{x}}_{\\alpha \\,{\\bf{1}},0}$\",Gneiss,1,1,False,1,Default series\n11,11.0,8.0,0.0,500.0,10.0,\"${\\bf{x}}_{\\alpha \\,{\\bf{1}},1}$\",Gneiss,1,1,False,1,Default series\n0,0.0,11.0,2000.0,2000.0,1500.0,\"${\\bf{x}}_{\\alpha \\,{\\bf{2}},0}$\",Sandstone,2,2,False,1,Default series\n1,1.0,12.0,0.0,500.0,1500.0,\"${\\bf{x}}_{\\alpha \\,{\\bf{2}},1}$\",Sandstone,2,2,False,1,Default series\n6,6.0,3.0,2000.0,2000.0,1000.0,\"${\\bf{x}}_{\\alpha \\,{\\bf{3}},0}$\",Sandstone_2,3,3,False,1,Default series\n7,7.0,4.0,0.0,500.0,1000.0,\"${\\bf{x}}_{\\alpha \\,{\\bf{3}},1}$\",Sandstone_2,3,3,False,1,Default series\n8,8.0,5.0,2000.0,2000.0,700.0,\"${\\bf{x}}_{\\alpha \\,{\\bf{4}},0}$\",Schist,4,4,False,1,Default series\n9,9.0,6.0,0.0,500.0,700.0,\"${\\bf{x}}_{\\alpha \\,{\\bf{4}},1}$\",Schist,4,4,False,1,Default series\n4,4.0,1.0,2000.0,2000.0,1150.0,\"${\\bf{x}}_{\\alpha \\,{\\bf{5}},0}$\",Shale,5,5,False,1,Default series\n5,5.0,2.0,0.0,500.0,1150.0,\"${\\bf{x}}_{\\alpha \\,{\\bf{5}},1}$\",Shale,5,5,False,1,Default series\n2,2.0,0.0,0.0,500.0,1400.0,\"${\\bf{x}}_{\\alpha \\,{\\bf{6}},0}$\",Siltstone,6,6,False,1,Default series\n3,3.0,10.0,2000.0,2000.0,1400.0,\"${\\bf{x}}_{\\alpha \\,{\\bf{6}},1}$\",Siltstone,6,6,False,1,Default series\n" }, { "path": "examples/data/input_data/perth_basin/Paper_GU2F_sc_faults_topo_Foliations.csv", "content": "X,Y,Z,azimuth,dip,polarity,formation\n393936.142320,6693632.776940,957.702303,263.276747,1.193492,1,Permian\n396918.892567,6693984.396839,977.204803,83.276747,0.349367,1,Permian\n351487.758871,6688628.772226,-7385.646697,83.277000,6.546000,1,Permian\n338518.168564,6650708.570891,-4542.306605,256.590252,1.789915,1,Permian\n397464.032464,6663077.533819,959.378895,268.100891,1.432093,1,Permian\n388231.346475,6662771.397837,-5909.347105,88.100891,1.548158,1,Permian\n390067.013711,6693176.666808,-2027.085197,258.305884,56.113829,1,Darling\n387480.312066,6692871.734884,-5468.217697,258.453840,50.172150,1,Darling\n380685.471752,6692070.728810,-2475.219197,258.181256,59.136216,1,Urella_North\n378225.542286,6691780.741356,-5604.015697,258.491356,45.884855,1,Urella_North\n349524.174513,6688397.296146,-1722.315697,258.469633,48.978506,1,Eneabba_South\n346943.915258,6688093.123680,-4278.195197,258.462867,40.562225,1,Eneabba_South\n340944.288072,6687385.860875,-2356.005197,77.995123,62.486979,-1,Coomallo\n342643.483167,6687586.169570,-5228.729697,78.277337,56.597000,-1,Coomallo\n348362.594755,6688260.363945,-4800.701697,83.277000,7.496000,1,Lesueur\n347339.390828,6688139.744103,-5803.145697,83.277000,12.095000,1,Woodada_Kockatea\n352054.428028,6688695.573713,-3672.951697,83.277000,7.472000,1,Cattamarra\n377146.267897,6662140.215356,-3767.430605,252.583876,73.779822,1,Hypo_fault_E\n375633.492464,6661665.672702,-8051.546105,252.583876,66.705447,1,Hypo_fault_E\n358317.030388,6656233.670235,-3868.630105,72.583876,74.666723,-1,Hypo_fault_W\n359797.619749,6656698.116438,-8017.813105,72.583876,64.573130,-1,Hypo_fault_W\n386644.134759,6662718.769315,-2733.176605,262.967799,59.896750,1,Urella_South\n384322.099881,6662641.775640,-6850.435605,262.886991,61.379678,1,Urella_South\n349566.870409,6653488.831804,-1894.695784,72.584000,14.036000,1,Cretaceous\n393713.418971,6662953.171554,-1357.485252,88.101000,0.000000,1,Yarragadee\n362273.437500,6691196.250000,265.802829,37.647360,89.720212,-1,Abrolhos_Transfer\n382171.344000,6676036.000000,273.424125,216.987277,89.473746,1,Abrolhos_Transfer" }, { "path": "examples/data/input_data/perth_basin/Paper_GU2F_sc_faults_topo_Points.csv", "content": "X,Y,Z,formation\n383267.125849,6692375.065706,42.347303,Lesueur\n385656.999410,6692656.794658,42.347303,Lesueur\n388158.029281,6692951.627211,42.347303,Lesueur\n390510.850409,6693228.988256,-88.233697,Lesueur\n352976.841866,6688804.311969,-5833.794697,Lesueur\n349411.679351,6653440.149908,-4324.030105,Lesueur\n351841.686031,6654202.418911,-4392.337105,Lesueur\n380356.701646,6662510.291576,-6567.699105,Lesueur\n382545.931510,6662582.881723,-6806.655105,Lesueur\n392541.609621,6693468.383207,928.448303,Permian\n395330.675019,6693797.170673,986.956303,Permian\n398507.110115,6694171.623005,967.453303,Permian\n338267.783260,6687070.342554,-4782.514697,Permian\n340255.613728,6687304.676873,-5079.046697,Permian\n348501.431395,6688276.730627,-7154.775697,Permian\n352571.944537,6688756.580866,-7836.879697,Permian\n357602.562530,6689349.612546,-8273.556697,Permian\n372576.271591,6691114.780137,-7525.440697,Permian\n385092.242016,6692590.218538,-1668.921697,Permian\n388037.178103,6692937.380735,-1743.054697,Permian\n364190.685794,6658076.177701,-10068.796105,Permian\n370581.834459,6660081.017671,-10125.561105,Permian\n381550.826391,6662549.886180,-9355.525105,Permian\n337765.192857,6650305.529894,-4569.541105,Permian\n339365.910313,6650910.683888,-4515.072105,Permian\n348604.615926,6653186.982113,-6537.306105,Permian\n350975.088988,6653930.576010,-6662.921105,Permian\n396667.948468,6663051.137388,939.465895,Permian\n398260.116460,6663103.930249,979.291895,Permian\n387143.811652,6662735.337525,-5879.938105,Permian\n389318.881298,6662807.458150,-5938.756105,Permian\n344027.770807,6687749.355569,-6023.503697,Woodada_Kockatea\n350933.945695,6688563.486259,-7003.554697,Woodada_Kockatea\n343600.199481,6687698.951555,-2875.825697,Yarragadee\n344814.486275,6687842.097096,-2788.489697,Yarragadee\n347069.591029,6688107.938900,-2846.713697,Yarragadee\n353590.199732,6688876.617329,-3666.615697,Yarragadee\n380367.581667,6662510.652334,-3808.317105,Yarragadee\n383506.431648,6662614.729839,-4126.926105,Yarragadee\n364082.361402,6658042.197413,-4505.867105,Yarragadee\n371404.678673,6660339.135743,-4673.112105,Yarragadee\n350920.928223,6653913.586316,-1610.873105,Yarragadee\n353520.717446,6654729.114419,-1894.696105,Yarragadee\n342049.687219,6651550.532280,-2478.249105,Yarragadee\n343851.059886,6651980.003672,-2446.320105,Yarragadee\n345590.316523,6652394.665770,-2605.963105,Yarragadee\n388868.827634,6662792.535339,-830.809105,Yarragadee\n391549.358494,6662881.415969,-958.524105,Yarragadee\n394612.827902,6662982.994017,-990.453105,Yarragadee\n353033.649508,6688811.008707,-5424.239697,Eneabba\n341942.065605,6651524.873845,-4152.902105,Eneabba\n342823.407616,6651734.997595,-4152.902105,Eneabba\n337375.397545,6650010.823057,-1199.239105,Eneabba\n338177.661298,6650617.378871,-1227.176105,Eneabba\n353514.305255,6688867.670550,-4863.837697,Cattamarra\n353994.961003,6688924.332392,-3972.289697,Cadda\n358422.044112,6689446.216689,-737.241697,Cretaceous\n386659.170996,6662719.267885,-349.138105,Cretaceous\n362632.034534,6657587.244254,-303.620105,Cretaceous\n367192.073609,6659017.683301,-940.838105,Cretaceous\n373532.695890,6661006.673645,-713.260105,Cretaceous\n346487.344000,6707224.000000,229.783830,Cretaceous\n346724.219000,6709119.000000,236.946081,Cretaceous\n350389.250000,6683986.000000,253.713420,Cretaceous\n349437.156000,6693507.000000,238.379141,Cretaceous\n345866.844000,6701996.500000,209.750695,Cretaceous\n363848.313000,6642899.000000,248.562248,Cretaceous\n362850.906000,6651604.000000,222.729354,Cretaceous\n358589.188000,6662303.500000,280.211703,Cretaceous\n353762.938000,6672419.500000,254.338465,Cretaceous\n391199.876318,6693310.213703,-335.036697,Darling\n388934.151104,6693043.119912,-3719.133697,Darling\n386026.473028,6692700.349855,-7217.301697,Darling\n393786.593975,6662955.597879,-2703.767105,Darling\n391487.125000,6704623.000000,246.230082,Darling\n381591.762732,6692177.566432,-954.276697,Urella_North\n379779.180773,6691963.891188,-3996.161697,Urella_North\n376671.903799,6691597.591524,-7211.869697,Urella_North\n350677.949316,6688533.308264,-391.568697,Eneabba_South\n348370.399711,6688261.284028,-3053.062697,Eneabba_South\n345517.430806,6687924.963332,-5503.327697,Eneabba_South\n347559.222614,6652864.079376,-2586.131105,Eneabba_South\n354951.313000,6642333.000000,219.196929,Eneabba_South\n353707.750000,6648758.000000,196.407131,Eneabba_South\n340231.045846,6687301.780702,-983.011697,Coomallo\n341657.530298,6687469.941049,-3728.998697,Coomallo\n343629.436036,6687702.398090,-6728.460697,Coomallo\n338862.633991,6650790.696066,-1174.500105,Coomallo\n340922.351837,6651281.760412,-4585.943105,Coomallo\n343668.643595,6651936.513183,-7762.114105,Coomallo\n338001.531000,6703286.500000,151.117327,Coomallo\n377661.255065,6662301.761725,-1912.105105,Hypo_fault_E\n376631.280728,6661978.668988,-5622.756105,Hypo_fault_E\n374635.704200,6661352.676416,-10480.336105,Hypo_fault_E\n357769.856194,6656062.027115,-1777.172105,Hypo_fault_W\n358864.204582,6656405.313355,-5960.088105,Hypo_fault_W\n360731.034915,6656990.919521,-10075.538105,Hypo_fault_W\n387790.456275,6662756.778868,-762.774105,Urella_South\n385497.813244,6662680.759763,-4703.579105,Urella_South\n383146.386518,6662602.791518,-8997.292105,Urella_South\n352798.250000,6698505.500000,254.579992,Abrolhos_Transfer\n371748.625000,6683887.000000,282.857478,Abrolhos_Transfer\n392594.063000,6668185.000000,228.405954,Abrolhos_Transfer" }, { "path": "examples/data/input_data/striplog_integration/alpha_strip.tops", "content": "top, comp lith\n0, overburden\n50, miguel\n75, evan\n100, brian\n120, dieter\n" }, { "path": "examples/data/input_data/striplog_integration/beta_strip.tops", "content": "top, comp lith\n0, overburden\n50, miguel\n80, evan\n100, brian\n130, dieter\n" }, { "path": "examples/data/input_data/striplog_integration/epsilon_strip.tops", "content": "top, comp lith\n0, overburden\n50, miguel\n75, brian\n110, dieter\n" }, { "path": "examples/data/input_data/striplog_integration/gamma_strip.tops", "content": "top, comp lith\n0, overburden\n50, miguel\n75, evan\n100, brian\n130, dieter\n" }, { "path": "examples/data/input_data/tests/fault_relations_test_ori.csv", "content": "X,Y,Z,G_x,G_y,G_z,id,nugget,formation\n350.0,500.0,-700.0,0.8949343616020251,5.4798925069145e-17,0.4461978131098087,26244822,0.01,fault1\n550.0,500.0,-600.0,-0.8949343616015264,-1.6439677520734337e-16,0.44619781311080886,124053343,0.01,fault2\n400.0,500.0,-625.0,0.0,0.0,1.0,563652887,0.01,rock4\n150.0,500.0,-550.0,0.0,0.0,1.0,563652887,0.01,rock4\n850.0,500.0,-525.0,0.0,0.0,1.0,563652887,0.01,rock4\n400.0,500.0,-675.0,0.0,0.0,1.0,438217026,0.01,rock3\n150.0,500.0,-600.0,0.0,0.0,1.0,438217026,0.01,rock3\n850.0,500.0,-575.0,0.0,0.0,1.0,438217026,0.01,rock3\n850.0,500.0,-625.0,0.0,0.0,1.0,317776925,0.01,rock2\n150.0,500.0,-650.0,0.0,0.0,1.0,317776925,0.01,rock2\n150.0,500.0,-700.0,0.0,0.0,1.0,267239155,0.01,rock1\n850.0,500.0,-675.0,0.0,0.0,1.0,267239155,0.01,rock1\n" }, { "path": "examples/data/input_data/tests/fault_relations_test_surf.csv", "content": "X,Y,Z,id,nugget,formation\n300.0,200.0,-600.0,26244822,2e-05,fault1\n300.0,500.0,-600.0,26244822,2e-05,fault1\n300.0,800.0,-600.0,26244822,2e-05,fault1\n350.0,200.0,-700.0,26244822,2e-05,fault1\n350.0,500.0,-700.0,26244822,2e-05,fault1\n350.0,800.0,-700.0,26244822,2e-05,fault1\n400.0,800.0,-800.0,26244822,2e-05,fault1\n400.0,500.0,-800.0,26244822,2e-05,fault1\n400.0,200.0,-800.0,26244822,2e-05,fault1\n500.0,500.0,-700.0,124053343,2e-05,fault2\n500.0,200.0,-700.0,124053343,2e-05,fault2\n500.0,800.0,-700.0,124053343,2e-05,fault2\n550.0,200.0,-600.0,124053343,2e-05,fault2\n550.0,500.0,-600.0,124053343,2e-05,fault2\n550.0,800.0,-600.0,124053343,2e-05,fault2\n600.0,200.0,-500.0,124053343,2e-05,fault2\n600.0,500.0,-500.0,124053343,2e-05,fault2\n600.0,800.0,-500.0,124053343,2e-05,fault2\n400.0,200.0,-625.0,563652887,2e-05,rock4\n100.0,500.0,-550.0,563652887,2e-05,rock4\n100.0,800.0,-550.0,563652887,2e-05,rock4\n100.0,200.0,-550.0,563652887,2e-05,rock4\n200.0,200.0,-550.0,563652887,2e-05,rock4\n200.0,500.0,-550.0,563652887,2e-05,rock4\n200.0,800.0,-550.0,563652887,2e-05,rock4\n800.0,200.0,-525.0,563652887,2e-05,rock4\n800.0,500.0,-525.0,563652887,2e-05,rock4\n800.0,800.0,-525.0,563652887,2e-05,rock4\n900.0,200.0,-525.0,563652887,2e-05,rock4\n900.0,500.0,-525.0,563652887,2e-05,rock4\n900.0,800.0,-525.0,563652887,2e-05,rock4\n400.0,500.0,-625.0,563652887,2e-05,rock4\n400.0,800.0,-625.0,563652887,2e-05,rock4\n400.0,800.0,-675.0,438217026,2e-05,rock3\n100.0,200.0,-600.0,438217026,2e-05,rock3\n100.0,500.0,-600.0,438217026,2e-05,rock3\n100.0,800.0,-600.0,438217026,2e-05,rock3\n200.0,200.0,-600.0,438217026,2e-05,rock3\n200.0,500.0,-600.0,438217026,2e-05,rock3\n200.0,800.0,-600.0,438217026,2e-05,rock3\n800.0,200.0,-575.0,438217026,2e-05,rock3\n800.0,500.0,-575.0,438217026,2e-05,rock3\n800.0,800.0,-575.0,438217026,2e-05,rock3\n900.0,500.0,-575.0,438217026,2e-05,rock3\n900.0,200.0,-575.0,438217026,2e-05,rock3\n900.0,800.0,-575.0,438217026,2e-05,rock3\n400.0,200.0,-675.0,438217026,2e-05,rock3\n400.0,500.0,-675.0,438217026,2e-05,rock3\n200.0,800.0,-650.0,317776925,2e-05,rock2\n200.0,500.0,-650.0,317776925,2e-05,rock2\n200.0,200.0,-650.0,317776925,2e-05,rock2\n100.0,500.0,-650.0,317776925,2e-05,rock2\n100.0,800.0,-650.0,317776925,2e-05,rock2\n100.0,200.0,-650.0,317776925,2e-05,rock2\n900.0,200.0,-625.0,317776925,2e-05,rock2\n900.0,500.0,-625.0,317776925,2e-05,rock2\n900.0,800.0,-625.0,317776925,2e-05,rock2\n800.0,200.0,-625.0,317776925,2e-05,rock2\n800.0,500.0,-625.0,317776925,2e-05,rock2\n800.0,800.0,-625.0,317776925,2e-05,rock2\n100.0,200.0,-700.0,267239155,2e-05,rock1\n100.0,500.0,-700.0,267239155,2e-05,rock1\n100.0,800.0,-700.0,267239155,2e-05,rock1\n200.0,200.0,-700.0,267239155,2e-05,rock1\n200.0,500.0,-700.0,267239155,2e-05,rock1\n200.0,800.0,-700.0,267239155,2e-05,rock1\n900.0,200.0,-675.0,267239155,2e-05,rock1\n900.0,500.0,-675.0,267239155,2e-05,rock1\n900.0,800.0,-675.0,267239155,2e-05,rock1\n800.0,200.0,-675.0,267239155,2e-05,rock1\n800.0,500.0,-675.0,267239155,2e-05,rock1\n800.0,800.0,-675.0,267239155,2e-05,rock1\n" }, { "path": "examples/data/input_data/tut-ch1-4/tut_ch1-4_orientations.csv", "content": 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}, { "path": "examples/data/input_data/tut-ch1-4/tut_ch1-4_points.csv", "content": 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}, { "path": "examples/data/input_data/tut-ch1-7/onelayer_interfaces.csv", "content": "X,Y,Z,formation\n450000,79300,205,layer1\n458000,79300,-402.,layer1\n450760,70668,205,layer1\n460000,70668,-402.,layer1" }, { "path": "examples/data/input_data/tut-ch1-7/onelayer_orient.csv", "content": "X,Y,Z,formation,azimuth,dip,polarity\n450760,75484,205,layer1,0,0,1\n455000,75484,-402,layer1,0,0,1" }, { "path": "examples/data/input_data/tut_SandStone/SandStone_Foliations.csv", "content": 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6870000 33 -640.0246\n 738000 6870000 34 -641.3194\n 739000 6870000 35 -643.7433\n 740000 6870000 36 -644.2726\n 741000 6870000 37 -641.5922\n 742000 6870000 38 -638.9683\n 743000 6870000 39 -638.3649\n 744000 6870000 40 -639.3925\n 745000 6870000 41 -641.2292\n 746000 6870000 42 -643.5736\n 747000 6870000 43 -646.0243\n 705000 6871000 1 -754.3669\n 706000 6871000 2 -751.8713\n 707000 6871000 3 -748.5646\n 708000 6871000 4 -745.0576\n 709000 6871000 5 -742.519\n 710000 6871000 6 -742.7988\n 711000 6871000 7 -745.9435\n 712000 6871000 8 -748.6387\n 713000 6871000 9 -748.3953\n 714000 6871000 10 -744.114\n 715000 6871000 11 -737.5428\n 716000 6871000 12 -730.3738\n 717000 6871000 13 -723.9728\n 718000 6871000 14 -720.2718\n 719000 6871000 15 -720.1609\n 720000 6871000 16 -721.729\n 721000 6871000 17 -722.9586\n 722000 6871000 18 -721.7369\n 723000 6871000 19 -715.3544\n 724000 6871000 20 -703.9875\n 725000 6871000 21 -691.4811\n 726000 6871000 22 -681.1443\n 727000 6871000 23 -674.3745\n 728000 6871000 24 -671.047\n 729000 6871000 25 -670.0239\n 730000 6871000 26 -668.6172\n 731000 6871000 27 -665.035\n 732000 6871000 28 -659.4924\n 733000 6871000 29 -653.516\n 734000 6871000 30 -648.4738\n 735000 6871000 31 -644.7825\n 736000 6871000 32 -642.4282\n 737000 6871000 33 -641.1547\n 738000 6871000 34 -640.5202\n 739000 6871000 35 -639.2214\n 740000 6871000 36 -637.3436\n 741000 6871000 37 -636.9272\n 742000 6871000 38 -636.9487\n 743000 6871000 39 -637.5737\n 744000 6871000 40 -638.8878\n 745000 6871000 41 -640.1475\n 746000 6871000 42 -640.8153\n 747000 6871000 43 -641.6686\n 705000 6872000 1 -758.8167\n 706000 6872000 2 -754.5789\n 707000 6872000 3 -750.0815\n 708000 6872000 4 -747.4048\n 709000 6872000 5 -747.1372\n 710000 6872000 6 -748.2502\n 711000 6872000 7 -749.8807\n 712000 6872000 8 -751.609\n 713000 6872000 9 -751.3218\n 714000 6872000 10 -746.8555\n 715000 6872000 11 -739.8886\n 716000 6872000 12 -731.9398\n 717000 6872000 13 -724.1589\n 718000 6872000 14 -719.6393\n 719000 6872000 15 -720.7021\n 720000 6872000 16 -724.1617\n 721000 6872000 17 -725.8776\n 722000 6872000 18 -724.2868\n 723000 6872000 19 -717.4488\n 724000 6872000 20 -704.8765\n 725000 6872000 21 -691.2409\n 726000 6872000 22 -681.0361\n 727000 6872000 23 -674.9259\n 728000 6872000 24 -671.7219\n 729000 6872000 25 -670.296\n 730000 6872000 26 -668.0718\n 731000 6872000 27 -662.9372\n 732000 6872000 28 -655.8574\n 733000 6872000 29 -649.4745\n 734000 6872000 30 -645.7734\n 735000 6872000 31 -643.9171\n 736000 6872000 32 -641.8557\n 737000 6872000 33 -641.0311\n 738000 6872000 34 -639.8704\n 739000 6872000 35 -636.4351\n 740000 6872000 36 -632.9855\n 741000 6872000 37 -633.6514\n 742000 6872000 38 -635.0123\n 743000 6872000 39 -636.7814\n 744000 6872000 40 -639.3104\n 745000 6872000 41 -641.0886\n 746000 6872000 42 -640.8064\n 747000 6872000 43 -640.1898\n 705000 6873000 1 -762.621\n 706000 6873000 2 -756.4051\n 707000 6873000 3 -750.249\n 708000 6873000 4 -747.9705\n 709000 6873000 5 -749.3344\n 710000 6873000 6 -750.9233\n 711000 6873000 7 -751.0689\n 712000 6873000 8 -751.4086\n 713000 6873000 9 -751.3752\n 714000 6873000 10 -748.2352\n 715000 6873000 11 -742.1465\n 716000 6873000 12 -734.0484\n 717000 6873000 13 -725.4124\n 718000 6873000 14 -719.5511\n 719000 6873000 15 -719.3914\n 720000 6873000 16 -722.6395\n 721000 6873000 17 -724.3807\n 722000 6873000 18 -723.1212\n 723000 6873000 19 -717.2155\n 724000 6873000 20 -705.2951\n 725000 6873000 21 -691.8326\n 726000 6873000 22 -682.2547\n 727000 6873000 23 -676.756\n 728000 6873000 24 -673.2594\n 729000 6873000 25 -670.7527\n 730000 6873000 26 -667.2935\n 731000 6873000 27 -660.8227\n 732000 6873000 28 -652.3724\n 733000 6873000 29 -643.9525\n 734000 6873000 30 -637.527\n 735000 6873000 31 -634.0397\n 736000 6873000 32 -632.8763\n 737000 6873000 33 -634.7797\n 738000 6873000 34 -636.1608\n 739000 6873000 35 -635.1838\n 740000 6873000 36 -633.7174\n 741000 6873000 37 -632.6359\n 742000 6873000 38 -632.429\n 743000 6873000 39 -634.345\n 744000 6873000 40 -637.9949\n 745000 6873000 41 -640.9311\n 746000 6873000 42 -641.3262\n 747000 6873000 43 -640.3883\n 705000 6874000 1 -765.0767\n 706000 6874000 2 -756.6422\n 707000 6874000 3 -748.2858\n 708000 6874000 4 -745.7524\n 709000 6874000 5 -748.0393\n 710000 6874000 6 -749.7598\n 711000 6874000 7 -748.6334\n 712000 6874000 8 -747.6771\n 713000 6874000 9 -748.7573\n 714000 6874000 10 -748.4407\n 715000 6874000 11 -744.3113\n 716000 6874000 12 -736.4689\n 717000 6874000 13 -727.0186\n 718000 6874000 14 -718.7116\n 719000 6874000 15 -714.2886\n 720000 6874000 16 -714.1138\n 721000 6874000 17 -716.3112\n 722000 6874000 18 -718.2043\n 723000 6874000 19 -715.7236\n 724000 6874000 20 -706.6182\n 725000 6874000 21 -695.0385\n 726000 6874000 22 -685.9135\n 727000 6874000 23 -679.924\n 728000 6874000 24 -675.1736\n 729000 6874000 25 -670.5717\n 730000 6874000 26 -665.0458\n 731000 6874000 27 -657.7646\n 732000 6874000 28 -648.4885\n 733000 6874000 29 -636.8715\n 734000 6874000 30 -623.4494\n 735000 6874000 31 -613.1222\n 736000 6874000 32 -613.0426\n 737000 6874000 33 -618.9307\n 738000 6874000 34 -623.4331\n 739000 6874000 35 -626.3979\n 740000 6874000 36 -629.1527\n 741000 6874000 37 -626.9345\n 742000 6874000 38 -625.4094\n 743000 6874000 39 -628.1941\n 744000 6874000 40 -633.6397\n 745000 6874000 41 -638.4834\n 746000 6874000 42 -641.168\n 747000 6874000 43 -641.2292\n 705000 6875000 1 -765.915\n 706000 6875000 2 -755.3193\n 707000 6875000 3 -744.6825\n 708000 6875000 4 -741.1729\n 709000 6875000 5 -743.7126\n 710000 6875000 6 -745.5424\n 711000 6875000 7 -743.6848\n 712000 6875000 8 -742.3932\n 713000 6875000 9 -745.3513\n 714000 6875000 10 -747.8348\n 715000 6875000 11 -745.5787\n 716000 6875000 12 -738.1436\n 717000 6875000 13 -727.7284\n 718000 6875000 14 -716.0977\n 719000 6875000 15 -706.2346\n 720000 6875000 16 -701.6417\n 721000 6875000 17 -704.6215\n 722000 6875000 18 -711.3538\n 723000 6875000 19 -713.8907\n 724000 6875000 20 -708.5167\n 725000 6875000 21 -699.2614\n 726000 6875000 22 -690.5131\n 727000 6875000 23 -683.4747\n 728000 6875000 24 -676.9595\n 729000 6875000 25 -670.0574\n 730000 6875000 26 -662.4966\n 731000 6875000 27 -654.4025\n 732000 6875000 28 -644.6611\n 733000 6875000 29 -631.2206\n 734000 6875000 30 -612.9143\n 735000 6875000 31 -595.161\n 736000 6875000 32 -589.5483\n 737000 6875000 33 -592.2914\n 738000 6875000 34 -594.8001\n 739000 6875000 35 -598.5159\n 740000 6875000 36 -605.7584\n 741000 6875000 37 -607.683\n 742000 6875000 38 -609.8196\n 743000 6875000 39 -616.9475\n 744000 6875000 40 -626.8718\n 745000 6875000 41 -635.2043\n 746000 6875000 42 -640.0708\n 747000 6875000 43 -640.9233\n 705000 6876000 1 -763.8593\n 706000 6876000 2 -752.1906\n 707000 6876000 3 -739.5229\n 708000 6876000 4 -734.4942\n 709000 6876000 5 -735.9134\n 710000 6876000 6 -737.4503\n 711000 6876000 7 -737.1162\n 712000 6876000 8 -738.2546\n 713000 6876000 9 -742.7445\n 714000 6876000 10 -745.9056\n 715000 6876000 11 -744.4197\n 716000 6876000 12 -737.8077\n 717000 6876000 13 -726.979\n 718000 6876000 14 -712.2833\n 719000 6876000 15 -697.7259\n 720000 6876000 16 -689.7587\n 721000 6876000 17 -693.4755\n 722000 6876000 18 -704.6205\n 723000 6876000 19 -712.0062\n 724000 6876000 20 -709.9301\n 725000 6876000 21 -702.4492\n 726000 6876000 22 -694.1149\n 727000 6876000 23 -686.1976\n 728000 6876000 24 -677.9119\n 729000 6876000 25 -668.9717\n 730000 6876000 26 -659.9307\n 731000 6876000 27 -650.865\n 732000 6876000 28 -641.1273\n 733000 6876000 29 -629.0336\n 734000 6876000 30 -612.1245\n 735000 6876000 31 -589.9833\n 736000 6876000 32 -566.2075\n 737000 6876000 33 -547.181\n 738000 6876000 34 -539.9272\n 739000 6876000 35 -545.7938\n 740000 6876000 36 -559.8768\n 741000 6876000 37 -572.6641\n 742000 6876000 38 -585.1865\n 743000 6876000 39 -600.3456\n 744000 6876000 40 -616.8339\n 745000 6876000 41 -630.0739\n 746000 6876000 42 -636.593\n 747000 6876000 43 -638.0156\n 705000 6877000 1 -760.9938\n 706000 6877000 2 -750.0757\n 707000 6877000 3 -736.3839\n 708000 6877000 4 -729.2498\n 709000 6877000 5 -727.7697\n 710000 6877000 6 -727.3471\n 711000 6877000 7 -728.1642\n 712000 6877000 8 -732.1544\n 713000 6877000 9 -738.2259\n 714000 6877000 10 -740.9307\n 715000 6877000 11 -739.6845\n 716000 6877000 12 -734.5138\n 717000 6877000 13 -724.3141\n 718000 6877000 14 -708.0356\n 719000 6877000 15 -690.1644\n 720000 6877000 16 -679.1544\n 721000 6877000 17 -683.6207\n 722000 6877000 18 -698.975\n 723000 6877000 19 -710.3654\n 724000 6877000 20 -710.1371\n 725000 6877000 21 -703.6078\n 726000 6877000 22 -695.8905\n 727000 6877000 23 -687.504\n 728000 6877000 24 -677.2716\n 729000 6877000 25 -665.7302\n 730000 6877000 26 -654.9361\n 731000 6877000 27 -645.2728\n 732000 6877000 28 -636.8306\n 733000 6877000 29 -628.3115\n 734000 6877000 30 -615.2715\n 735000 6877000 31 -589.3489\n 736000 6877000 32 -543.5229\n 737000 6877000 33 -493.6918\n 738000 6877000 34 -472.1274\n 739000 6877000 35 -483.4118\n 740000 6877000 36 -506.1511\n 741000 6877000 37 -530.019\n 742000 6877000 38 -553.7886\n 743000 6877000 39 -577.1554\n 744000 6877000 40 -599.6539\n 745000 6877000 41 -617.958\n 746000 6877000 42 -627.3019\n 747000 6877000 43 -631.2473\n 705000 6878000 1 -760.041\n 706000 6878000 2 -752.4149\n 707000 6878000 3 -740.142\n 708000 6878000 4 -729.8929\n 709000 6878000 5 -722.4659\n 710000 6878000 6 -716.9059\n 711000 6878000 7 -715.3697\n 712000 6878000 8 -719.4546\n 713000 6878000 9 -726.452\n 714000 6878000 10 -729.6035\n 715000 6878000 11 -730.0408\n 716000 6878000 12 -727.8317\n 717000 6878000 13 -719.6916\n 718000 6878000 14 -704.3815\n 719000 6878000 15 -686.0613\n 720000 6878000 16 -673.579\n 721000 6878000 17 -677.3593\n 722000 6878000 18 -693.4105\n 723000 6878000 19 -706.1245\n 724000 6878000 20 -707.3817\n 725000 6878000 21 -702.3537\n 726000 6878000 22 -695.96\n 727000 6878000 23 -687.4113\n 728000 6878000 24 -674.4683\n 729000 6878000 25 -658.46\n 730000 6878000 26 -644.1451\n 731000 6878000 27 -634.9198\n 732000 6878000 28 -630.2053\n 733000 6878000 29 -626.3477\n 734000 6878000 30 -615.777\n 735000 6878000 31 -586.168\n 736000 6878000 32 -528.4104\n 737000 6878000 33 -462.5975\n 738000 6878000 34 -428.7493\n 739000 6878000 35 -434.7803\n 740000 6878000 36 -457.453\n 741000 6878000 37 -486.607\n 742000 6878000 38 -516.6805\n 743000 6878000 39 -544.8112\n 744000 6878000 40 -570.6968\n 745000 6878000 41 -592.5761\n 746000 6878000 42 -608.5021\n 747000 6878000 43 -619.4861\n 705000 6879000 1 -761.5499\n 706000 6879000 2 -758.2407\n 707000 6879000 3 -748.4412\n 708000 6879000 4 -735.0132\n 709000 6879000 5 -719.9836\n 710000 6879000 6 -706.1659\n 711000 6879000 7 -697.496\n 712000 6879000 8 -697.3132\n 713000 6879000 9 -703.8947\n 714000 6879000 10 -710.2087\n 715000 6879000 11 -715.7787\n 716000 6879000 12 -719.2794\n 717000 6879000 13 -715.3047\n 718000 6879000 14 -703.0548\n 719000 6879000 15 -687.0052\n 720000 6879000 16 -675.238\n 721000 6879000 17 -675.7753\n 722000 6879000 18 -686.2953\n 723000 6879000 19 -696.5989\n 724000 6879000 20 -700.6578\n 725000 6879000 21 -699.1854\n 726000 6879000 22 -695.2432\n 727000 6879000 23 -686.7808\n 728000 6879000 24 -670.1856\n 729000 6879000 25 -647.2323\n 730000 6879000 26 -626.6733\n 731000 6879000 27 -618.6165\n 732000 6879000 28 -619.9717\n 733000 6879000 29 -621.5306\n 734000 6879000 30 -612.551\n 735000 6879000 31 -581.4032\n 736000 6879000 32 -522.7476\n 737000 6879000 33 -456.9023\n 738000 6879000 34 -416.4697\n 739000 6879000 35 -408.4697\n 740000 6879000 36 -421.6115\n 741000 6879000 37 -449.9644\n 742000 6879000 38 -479.8852\n 743000 6879000 39 -507.109\n 744000 6879000 40 -533.0636\n 745000 6879000 41 -556.476\n 746000 6879000 42 -582.2613\n 747000 6879000 43 -605.1993\n 705000 6880000 1 -762.3885\n 706000 6880000 2 -763.1755\n 707000 6880000 3 -756.2756\n 708000 6880000 4 -740.6771\n 709000 6880000 5 -718.2197\n 710000 6880000 6 -694.1479\n 711000 6880000 7 -674.4216\n 712000 6880000 8 -666.3671\n 713000 6880000 9 -672.7328\n 714000 6880000 10 -686.1899\n 715000 6880000 11 -700.3036\n 716000 6880000 12 -710.444\n 717000 6880000 13 -711.77\n 718000 6880000 14 -703.804\n 719000 6880000 15 -690.4592\n 720000 6880000 16 -678.0956\n 721000 6880000 17 -672.6925\n 722000 6880000 18 -674.8248\n 723000 6880000 19 -682.2812\n 724000 6880000 20 -691.4479\n 725000 6880000 21 -696.2656\n 726000 6880000 22 -695.1216\n 727000 6880000 23 -686.327\n 728000 6880000 24 -665.8631\n 729000 6880000 25 -635.1652\n 730000 6880000 26 -607.2206\n 731000 6880000 27 -599.2183\n 732000 6880000 28 -605.9048\n 733000 6880000 29 -612.6025\n 734000 6880000 30 -605.9815\n 735000 6880000 31 -576.5212\n 736000 6880000 32 -520.6978\n 737000 6880000 33 -459.7212\n 738000 6880000 34 -418.6508\n 739000 6880000 35 -401.444\n 740000 6880000 36 -404.9449\n 741000 6880000 37 -428.6561\n 742000 6880000 38 -451.8113\n 743000 6880000 39 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6881000 30 -592.157\n 735000 6881000 31 -564.5209\n 736000 6881000 32 -512.7764\n 737000 6881000 33 -459.8852\n 738000 6881000 34 -425.0306\n 739000 6881000 35 -408.5395\n 740000 6881000 36 -407.788\n 741000 6881000 37 -421.1189\n 742000 6881000 38 -431.3094\n 743000 6881000 39 -439.9743\n 744000 6881000 40 -457.8586\n 745000 6881000 41 -486.2432\n 746000 6881000 42 -530.2421\n 747000 6881000 43 -576.7837\n 705000 6882000 1 -753.8176\n 706000 6882000 2 -761.7925\n 707000 6882000 3 -763.9332\n 708000 6882000 4 -748.9162\n 709000 6882000 5 -715.9006\n 710000 6882000 6 -672.1656\n 711000 6882000 7 -624.9985\n 712000 6882000 8 -591.585\n 713000 6882000 9 -595.395\n 714000 6882000 10 -627.4072\n 715000 6882000 11 -658.5958\n 716000 6882000 12 -674.673\n 717000 6882000 13 -685.0602\n 718000 6882000 14 -687.6356\n 719000 6882000 15 -684.3735\n 720000 6882000 16 -670.1614\n 721000 6882000 17 -645.3784\n 722000 6882000 18 -627.835\n 723000 6882000 19 -631.2886\n 724000 6882000 20 -657.8654\n 725000 6882000 21 -679.7535\n 726000 6882000 22 -682.9102\n 727000 6882000 23 -674.6832\n 728000 6882000 24 -653.234\n 729000 6882000 25 -616.6478\n 730000 6882000 26 -582.7672\n 731000 6882000 27 -562.5724\n 732000 6882000 28 -559.3673\n 733000 6882000 29 -569.9522\n 734000 6882000 30 -567.3097\n 735000 6882000 31 -543.2602\n 736000 6882000 32 -497.5714\n 737000 6882000 33 -454.571\n 738000 6882000 34 -428.2187\n 739000 6882000 35 -415.455\n 740000 6882000 36 -410.9371\n 741000 6882000 37 -409.8752\n 742000 6882000 38 -406.8226\n 743000 6882000 39 -409.282\n 744000 6882000 40 -426.6725\n 745000 6882000 41 -459.9451\n 746000 6882000 42 -511.7028\n 747000 6882000 43 -566.3795\n 705000 6883000 1 -748.2269\n 706000 6883000 2 -756.8132\n 707000 6883000 3 -762.4204\n 708000 6883000 4 -750.076\n 709000 6883000 5 -716.4781\n 710000 6883000 6 -668.8607\n 711000 6883000 7 -614.1481\n 712000 6883000 8 -570.3984\n 713000 6883000 9 -562.3948\n 714000 6883000 10 -583.7123\n 715000 6883000 11 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706000 6884000 2 -749.523\n 707000 6884000 3 -756.4178\n 708000 6884000 4 -747.9492\n 709000 6884000 5 -718.4492\n 710000 6884000 6 -672.8412\n 711000 6884000 7 -617.6673\n 712000 6884000 8 -568.6021\n 713000 6884000 9 -542.0935\n 714000 6884000 10 -537.1356\n 715000 6884000 11 -545.6413\n 716000 6884000 12 -562.4052\n 717000 6884000 13 -577.5117\n 718000 6884000 14 -587.3223\n 719000 6884000 15 -588.1257\n 720000 6884000 16 -578.9716\n 721000 6884000 17 -564.6756\n 722000 6884000 18 -553.479\n 723000 6884000 19 -557.3221\n 724000 6884000 20 -580.327\n 725000 6884000 21 -603.9573\n 726000 6884000 22 -615.4108\n 727000 6884000 23 -618.2803\n 728000 6884000 24 -615.3887\n 729000 6884000 25 -588.7186\n 730000 6884000 26 -541.3028\n 731000 6884000 27 -501.6441\n 732000 6884000 28 -477.5453\n 733000 6884000 29 -478.349\n 734000 6884000 30 -482.9331\n 735000 6884000 31 -475.0152\n 736000 6884000 32 -453.5399\n 737000 6884000 33 -429.3206\n 738000 6884000 34 -407.7904\n 739000 6884000 35 -388.2278\n 740000 6884000 36 -373.3095\n 741000 6884000 37 -366.6251\n 742000 6884000 38 -368.1217\n 743000 6884000 39 -379.6996\n 744000 6884000 40 -405.5162\n 745000 6884000 41 -448.1903\n 746000 6884000 42 -506.6763\n 747000 6884000 43 -566.5563\n 705000 6885000 1 -736.75\n 706000 6885000 2 -741.4056\n 707000 6885000 3 -748.5677\n 708000 6885000 4 -744.3896\n 709000 6885000 5 -722.0917\n 710000 6885000 6 -682.0337\n 711000 6885000 7 -628.296\n 712000 6885000 8 -574.4423\n 713000 6885000 9 -532.0338\n 714000 6885000 10 -505.4328\n 715000 6885000 11 -497.8382\n 716000 6885000 12 -508.0046\n 717000 6885000 13 -520.8041\n 718000 6885000 14 -531.8828\n 719000 6885000 15 -533.0125\n 720000 6885000 16 -526.6519\n 721000 6885000 17 -521.7437\n 722000 6885000 18 -516.9501\n 723000 6885000 19 -520.0192\n 724000 6885000 20 -537.4373\n 725000 6885000 21 -556.548\n 726000 6885000 22 -566.0923\n 727000 6885000 23 -566.1736\n 728000 6885000 24 -563.4062\n 729000 6885000 25 -543.9395\n 730000 6885000 26 -502.1642\n 731000 6885000 27 -465.3436\n 732000 6885000 28 -443.3148\n 733000 6885000 29 -438.3644\n 734000 6885000 30 -440.6096\n 735000 6885000 31 -436.4632\n 736000 6885000 32 -423.0532\n 737000 6885000 33 -405.3701\n 738000 6885000 34 -385.5156\n 739000 6885000 35 -363.8161\n 740000 6885000 36 -347.8412\n 741000 6885000 37 -347.0492\n 742000 6885000 38 -359.8062\n 743000 6885000 39 -380.3509\n 744000 6885000 40 -410.0252\n 745000 6885000 41 -455.5496\n 746000 6885000 42 -513.6635\n 747000 6885000 43 -571.4428\n 705000 6886000 1 -736.1649\n 706000 6886000 2 -736.6365\n 707000 6886000 3 -741.2521\n 708000 6886000 4 -740.2818\n 709000 6886000 5 -726.0593\n 710000 6886000 6 -692.3773\n 711000 6886000 7 -639.1216\n 712000 6886000 8 -579.9037\n 713000 6886000 9 -527.5802\n 714000 6886000 10 -489.8077\n 715000 6886000 11 -472.5688\n 716000 6886000 12 -475.5984\n 717000 6886000 13 -486.9527\n 718000 6886000 14 -498.902\n 719000 6886000 15 -499.951\n 720000 6886000 16 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711000 6887000 7 -646.4716\n 712000 6887000 8 -582.2631\n 713000 6887000 9 -524.7065\n 714000 6887000 10 -482.719\n 715000 6887000 11 -460.9957\n 716000 6887000 12 -460.3629\n 717000 6887000 13 -471.3401\n 718000 6887000 14 -483.86\n 719000 6887000 15 -484.3904\n 720000 6887000 16 -478.9699\n 721000 6887000 17 -477.6367\n 722000 6887000 18 -475.3954\n 723000 6887000 19 -471.4279\n 724000 6887000 20 -473.0879\n 725000 6887000 21 -479.9287\n 726000 6887000 22 -478.301\n 727000 6887000 23 -465.0894\n 728000 6887000 24 -453.2337\n 729000 6887000 25 -439.183\n 730000 6887000 26 -419.6025\n 731000 6887000 27 -402.6699\n 732000 6887000 28 -391.789\n 733000 6887000 29 -383.6115\n 734000 6887000 30 -375.237\n 735000 6887000 31 -366.16\n 736000 6887000 32 -355.4667\n 737000 6887000 33 -343.2252\n 738000 6887000 34 -330.7425\n 739000 6887000 35 -320.6939\n 740000 6887000 36 -317.2534\n 741000 6887000 37 -322.8514\n 742000 6887000 38 -339.7384\n 743000 6887000 39 -370.861\n 744000 6887000 40 -415.8036\n 745000 6887000 41 -468.959\n 746000 6887000 42 -527.7982\n 747000 6887000 43 -581.0529\n 705000 6888000 1 -746.0883\n 706000 6888000 2 -735.7146\n 707000 6888000 3 -729.6849\n 708000 6888000 4 -729.6115\n 709000 6888000 5 -726.9998\n 710000 6888000 6 -703.1713\n 711000 6888000 7 -649.3457\n 712000 6888000 8 -581.8063\n 713000 6888000 9 -522.2242\n 714000 6888000 10 -480.0982\n 715000 6888000 11 -456.6905\n 716000 6888000 12 -451.959\n 717000 6888000 13 -459.1847\n 718000 6888000 14 -469.3524\n 719000 6888000 15 -470.5497\n 720000 6888000 16 -466.168\n 721000 6888000 17 -465.8682\n 722000 6888000 18 -461.5335\n 723000 6888000 19 -449.9863\n 724000 6888000 20 -445.8073\n 725000 6888000 21 -448.903\n 726000 6888000 22 -443.2126\n 727000 6888000 23 -425.7929\n 728000 6888000 24 -411.6887\n 729000 6888000 25 -399.3151\n 730000 6888000 26 -386.4494\n 731000 6888000 27 -377.3762\n 732000 6888000 28 -371.1815\n 733000 6888000 29 -361.6748\n 734000 6888000 30 -348.0102\n 735000 6888000 31 -335.2703\n 736000 6888000 32 -324.2034\n 737000 6888000 33 -313.6884\n 738000 6888000 34 -305.1215\n 739000 6888000 35 -301.2089\n 740000 6888000 36 -304.5821\n 741000 6888000 37 -316.0584\n 742000 6888000 38 -338.6225\n 743000 6888000 39 -376.9432\n 744000 6888000 40 -429.9913\n 745000 6888000 41 -486.7816\n 746000 6888000 42 -541.8386\n 747000 6888000 43 -587.1686\n 705000 6889000 1 -750.3484\n 706000 6889000 2 -734.7048\n 707000 6889000 3 -722.1926\n 708000 6889000 4 -721.1226\n 709000 6889000 5 -723.6509\n 710000 6889000 6 -704.6424\n 711000 6889000 7 -650.5051\n 712000 6889000 8 -580.0803\n 713000 6889000 9 -520.2193\n 714000 6889000 10 -480.0486\n 715000 6889000 11 -456.021\n 716000 6889000 12 -445.7519\n 717000 6889000 13 -447.1492\n 718000 6889000 14 -455.0296\n 719000 6889000 15 -457.8226\n 720000 6889000 16 -454.2974\n 721000 6889000 17 -454.5417\n 722000 6889000 18 -450.5639\n 723000 6889000 19 -435.1855\n 724000 6889000 20 -422.622\n 725000 6889000 21 -417.5167\n 726000 6889000 22 -409.863\n 727000 6889000 23 -395.0352\n 728000 6889000 24 -380.0712\n 729000 6889000 25 -367.7372\n 730000 6889000 26 -359.1242\n 731000 6889000 27 -355.4793\n 732000 6889000 28 -352.1968\n 733000 6889000 29 -340.8312\n 734000 6889000 30 -323.2512\n 735000 6889000 31 -308.2857\n 736000 6889000 32 -297.6907\n 737000 6889000 33 -288.8623\n 738000 6889000 34 -283.3548\n 739000 6889000 35 -284.1377\n 740000 6889000 36 -293.469\n 741000 6889000 37 -313.8056\n 742000 6889000 38 -348.1608\n 743000 6889000 39 -397.1601\n 744000 6889000 40 -456.6643\n 745000 6889000 41 -515.876\n 746000 6889000 42 -562.4945\n 747000 6889000 43 -593.42\n 705000 6890000 1 -753.7894\n 706000 6890000 2 -733.7056\n 707000 6890000 3 -715.3792\n 708000 6890000 4 -712.6119\n 709000 6890000 5 -718.9236\n 710000 6890000 6 -704.6556\n 711000 6890000 7 -651.6267\n 712000 6890000 8 -580.6121\n 713000 6890000 9 -521.2042\n 714000 6890000 10 -482.3196\n 715000 6890000 11 -456.8488\n 716000 6890000 12 -439.9577\n 717000 6890000 13 -434.7486\n 718000 6890000 14 -441.8504\n 719000 6890000 15 -446.8011\n 720000 6890000 16 -443.509\n 721000 6890000 17 -441.0279\n 722000 6890000 18 -435.5248\n 723000 6890000 19 -418.444\n 724000 6890000 20 -399.4756\n 725000 6890000 21 -387.2655\n 726000 6890000 22 -378.5879\n 727000 6890000 23 -366.9512\n 728000 6890000 24 -352.2925\n 729000 6890000 25 -339.672\n 730000 6890000 26 -333.4167\n 731000 6890000 27 -333.3399\n 732000 6890000 28 -332.4493\n 733000 6890000 29 -321.7969\n 734000 6890000 30 -304.2296\n 735000 6890000 31 -289.2118\n 736000 6890000 32 -279.2176\n 737000 6890000 33 -271.7461\n 738000 6890000 34 -268.5978\n 739000 6890000 35 -272.8557\n 740000 6890000 36 -286.7969\n 741000 6890000 37 -314.9592\n 742000 6890000 38 -359.8727\n 743000 6890000 39 -418.6184\n 744000 6890000 40 -484.8153\n 745000 6890000 41 -546.5396\n 746000 6890000 42 -584.4149\n 747000 6890000 43 -599.5886\n 705000 6891000 1 -755.5894\n 706000 6891000 2 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6891000 36 -287.585\n 741000 6891000 37 -319.7881\n 742000 6891000 38 -368.2501\n 743000 6891000 39 -431.442\n 744000 6891000 40 -504.476\n 745000 6891000 41 -568.6085\n 746000 6891000 42 -601.1666\n 747000 6891000 43 -605.2297\n 705000 6892000 1 -758.5295\n 706000 6892000 2 -737.7301\n 707000 6892000 3 -717.6856\n 708000 6892000 4 -708.834\n 709000 6892000 5 -708.1837\n 710000 6892000 6 -696.2411\n 711000 6892000 7 -658.213\n 712000 6892000 8 -599.508\n 713000 6892000 9 -539.7326\n 714000 6892000 10 -497.0103\n 715000 6892000 11 -467.7604\n 716000 6892000 12 -438.2431\n 717000 6892000 13 -414.2237\n 718000 6892000 14 -408.8746\n 719000 6892000 15 -410.0806\n 720000 6892000 16 -406.6569\n 721000 6892000 17 -399.1258\n 722000 6892000 18 -387.1014\n 723000 6892000 19 -371.6872\n 724000 6892000 20 -355.1693\n 725000 6892000 21 -340.5204\n 726000 6892000 22 -328.8761\n 727000 6892000 23 -316.9166\n 728000 6892000 24 -301.8515\n 729000 6892000 25 -287.3063\n 730000 6892000 26 -283.0304\n 731000 6892000 27 -290.662\n 732000 6892000 28 -296.7216\n 733000 6892000 29 -293.2137\n 734000 6892000 30 -282.8144\n 735000 6892000 31 -272.7036\n 736000 6892000 32 -266.1236\n 737000 6892000 33 -264.5959\n 738000 6892000 34 -268.9244\n 739000 6892000 35 -278.6927\n 740000 6892000 36 -298.3139\n 741000 6892000 37 -333.3999\n 742000 6892000 38 -381.9025\n 743000 6892000 39 -444.3254\n 744000 6892000 40 -519.3377\n 745000 6892000 41 -582.2943\n 746000 6892000 42 -610.6711\n 747000 6892000 43 -608.3334\n 705000 6893000 1 -763.6896\n 706000 6893000 2 -744.2531\n 707000 6893000 3 -724.4937\n 708000 6893000 4 -712.0322\n 709000 6893000 5 -706.2461\n 710000 6893000 6 -696.2421\n 711000 6893000 7 -670.4063\n 712000 6893000 8 -622.0526\n 713000 6893000 9 -558.6788\n 714000 6893000 10 -509.1989\n 715000 6893000 11 -476.3383\n 716000 6893000 12 -443.5109\n 717000 6893000 13 -413.0331\n 718000 6893000 14 -398.9089\n 719000 6893000 15 -395.0957\n 720000 6893000 16 -390.2974\n 721000 6893000 17 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727000 6896000 23 -251.0303\n 728000 6896000 24 -246.0542\n 729000 6896000 25 -246.464\n 730000 6896000 26 -250.8627\n 731000 6896000 27 -259.6046\n 732000 6896000 28 -268.2632\n 733000 6896000 29 -271.0511\n 734000 6896000 30 -269.5236\n 735000 6896000 31 -270.3232\n 736000 6896000 32 -280.2003\n 737000 6896000 33 -302.1555\n 738000 6896000 34 -335.5317\n 739000 6896000 35 -376.8434\n 740000 6896000 36 -424.6426\n 741000 6896000 37 -479.7158\n 742000 6896000 38 -534.3527\n 743000 6896000 39 -574.8229\n 744000 6896000 40 -598.1163\n 745000 6896000 41 -604.6541\n 746000 6896000 42 -594.3217\n 747000 6896000 43 -573.17\n 705000 6897000 1 -776.2068\n 706000 6897000 2 -765.5167\n 707000 6897000 3 -751.0015\n 708000 6897000 4 -730.9739\n 709000 6897000 5 -705.3629\n 710000 6897000 6 -683.2637\n 711000 6897000 7 -678.8533\n 712000 6897000 8 -681.9785\n 713000 6897000 9 -659.5885\n 714000 6897000 10 -608.2853\n 715000 6897000 11 -550.4096\n 716000 6897000 12 -502.363\n 717000 6897000 13 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708000 6898000 4 -737.1212\n 709000 6898000 5 -710.6739\n 710000 6898000 6 -683.2192\n 711000 6898000 7 -673.6822\n 712000 6898000 8 -677.1313\n 713000 6898000 9 -665.1923\n 714000 6898000 10 -628.6254\n 715000 6898000 11 -580.5936\n 716000 6898000 12 -530.9074\n 717000 6898000 13 -484.8691\n 718000 6898000 14 -445.3963\n 719000 6898000 15 -411.4417\n 720000 6898000 16 -385.2642\n 721000 6898000 17 -367.9658\n 722000 6898000 18 -353.0005\n 723000 6898000 19 -333.3923\n 724000 6898000 20 -309.4915\n 725000 6898000 21 -286.2424\n 726000 6898000 22 -266.4265\n 727000 6898000 23 -251.011\n 728000 6898000 24 -242.0003\n 729000 6898000 25 -238.599\n 730000 6898000 26 -242.4914\n 731000 6898000 27 -255.4278\n 732000 6898000 28 -269.3606\n 733000 6898000 29 -281.7705\n 734000 6898000 30 -292.7725\n 735000 6898000 31 -305.4603\n 736000 6898000 32 -325.5329\n 737000 6898000 33 -357.7714\n 738000 6898000 34 -404.153\n 739000 6898000 35 -455.9435\n 740000 6898000 36 -503.269\n 741000 6898000 37 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6899000 28 -288.8335\n 733000 6899000 29 -303.5448\n 734000 6899000 30 -317.469\n 735000 6899000 31 -335.2996\n 736000 6899000 32 -360.1498\n 737000 6899000 33 -395.7527\n 738000 6899000 34 -446.4896\n 739000 6899000 35 -501.531\n 740000 6899000 36 -545.6655\n 741000 6899000 37 -575.8182\n 742000 6899000 38 -599.4439\n 743000 6899000 39 -615.2538\n 744000 6899000 40 -621.3937\n 745000 6899000 41 -617.9689\n 746000 6899000 42 -606.8922\n 747000 6899000 43 -594.7026\n 705000 6900000 1 -777.4224\n 706000 6900000 2 -769.9462\n 707000 6900000 3 -760.3835\n 708000 6900000 4 -745.2499\n 709000 6900000 5 -723.4526\n 710000 6900000 6 -697.2866\n 711000 6900000 7 -675.5865\n 712000 6900000 8 -671.6854\n 713000 6900000 9 -675.1704\n 714000 6900000 10 -668.275\n 715000 6900000 11 -647.8076\n 716000 6900000 12 -597.3036\n 717000 6900000 13 -539.5531\n 718000 6900000 14 -499.8518\n 719000 6900000 15 -471.5834\n 720000 6900000 16 -445.2801\n 721000 6900000 17 -413.8222\n 722000 6900000 18 -380.3062\n 723000 6900000 19 -354.4907\n 724000 6900000 20 -333.578\n 725000 6900000 21 -314.3491\n 726000 6900000 22 -295.8646\n 727000 6900000 23 -286.4351\n 728000 6900000 24 -278.6873\n 729000 6900000 25 -272.4437\n 730000 6900000 26 -277.0641\n 731000 6900000 27 -293.2478\n 732000 6900000 28 -313.2709\n 733000 6900000 29 -329.5197\n 734000 6900000 30 -346.1466\n 735000 6900000 31 -370.4175\n 736000 6900000 32 -402.8352\n 737000 6900000 33 -442.6594\n 738000 6900000 34 -492.2521\n 739000 6900000 35 -542.4113\n 740000 6900000 36 -579.1764\n 741000 6900000 37 -598.5265\n 742000 6900000 38 -609.5616\n 743000 6900000 39 -616.3501\n 744000 6900000 40 -621.0588\n 745000 6900000 41 -621.0521\n 746000 6900000 42 -616.7823\n 747000 6900000 43 -613.1161\n 705000 6901000 1 -778.1379\n 706000 6901000 2 -771.9238\n 707000 6901000 3 -763.8069\n 708000 6901000 4 -751.0319\n 709000 6901000 5 -733.4429\n 710000 6901000 6 -710.795\n 711000 6901000 7 -687.9707\n 712000 6901000 8 -678.8239\n 713000 6901000 9 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747000 6901000 43 -624.9174\n 705000 6902000 1 -778.0754\n 706000 6902000 2 -772.7811\n 707000 6902000 3 -766.5669\n 708000 6902000 4 -757.0671\n 709000 6902000 5 -743.5063\n 710000 6902000 6 -724.7568\n 711000 6902000 7 -702.75\n 712000 6902000 8 -688.2579\n 713000 6902000 9 -685.3362\n 714000 6902000 10 -686.5201\n 715000 6902000 11 -680.3033\n 716000 6902000 12 -654.2493\n 717000 6902000 13 -617.8743\n 718000 6902000 14 -580.8924\n 719000 6902000 15 -543.5831\n 720000 6902000 16 -509.1367\n 721000 6902000 17 -480.2406\n 722000 6902000 18 -451.8629\n 723000 6902000 19 -417.559\n 724000 6902000 20 -376.7885\n 725000 6902000 21 -342.1442\n 726000 6902000 22 -320.1546\n 727000 6902000 23 -312.7342\n 728000 6902000 24 -308.0745\n 729000 6902000 25 -304.9809\n 730000 6902000 26 -313.8137\n 731000 6902000 27 -336.3925\n 732000 6902000 28 -360.85\n 733000 6902000 29 -378.3667\n 734000 6902000 30 -400.7498\n 735000 6902000 31 -443.3786\n 736000 6902000 32 -500.5686\n 737000 6902000 33 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6903000 24 -335.2704\n 729000 6903000 25 -329.212\n 730000 6903000 26 -337.1389\n 731000 6903000 27 -359.2726\n 732000 6903000 28 -384.6938\n 733000 6903000 29 -405.0573\n 734000 6903000 30 -432.0563\n 735000 6903000 31 -483.1539\n 736000 6903000 32 -545.9446\n 737000 6903000 33 -585.6354\n 738000 6903000 34 -605.3915\n 739000 6903000 35 -617.0718\n 740000 6903000 36 -625.8333\n 741000 6903000 37 -631.4859\n 742000 6903000 38 -633.0786\n 743000 6903000 39 -630.9753\n 744000 6903000 40 -626.667\n 745000 6903000 41 -625.1128\n 746000 6903000 42 -627.9127\n 747000 6903000 43 -632.9771\n 705000 6904000 1 -777.386\n 706000 6904000 2 -772.8628\n 707000 6904000 3 -769.07\n 708000 6904000 4 -763.9391\n 709000 6904000 5 -755.2548\n 710000 6904000 6 -741.6342\n 711000 6904000 7 -723.2244\n 712000 6904000 8 -704.8118\n 713000 6904000 9 -692.5875\n 714000 6904000 10 -687.3907\n 715000 6904000 11 -685.0801\n 716000 6904000 12 -684.1858\n 717000 6904000 13 -678.786\n 718000 6904000 14 -659.0414\n 719000 6904000 15 -623.7553\n 720000 6904000 16 -586.067\n 721000 6904000 17 -553.2886\n 722000 6904000 18 -520.2827\n 723000 6904000 19 -483.533\n 724000 6904000 20 -445.2901\n 725000 6904000 21 -411.2472\n 726000 6904000 22 -387.709\n 727000 6904000 23 -375.1649\n 728000 6904000 24 -364.9692\n 729000 6904000 25 -359.2577\n 730000 6904000 26 -366.5754\n 731000 6904000 27 -386.5157\n 732000 6904000 28 -411.9777\n 733000 6904000 29 -441.2776\n 734000 6904000 30 -480.5313\n 735000 6904000 31 -534.2814\n 736000 6904000 32 -584.5436\n 737000 6904000 33 -607.1053\n 738000 6904000 34 -618.1309\n 739000 6904000 35 -627.0403\n 740000 6904000 36 -633.9052\n 741000 6904000 37 -637.5262\n 742000 6904000 38 -638.2237\n 743000 6904000 39 -634.8887\n 744000 6904000 40 -627.3361\n 745000 6904000 41 -624.2423\n 746000 6904000 42 -629.0316\n 747000 6904000 43 -635.7922\n 705000 6905000 1 -774.4816\n 706000 6905000 2 -770.4477\n 707000 6905000 3 -768.0563\n 708000 6905000 4 -765.0648\n 709000 6905000 5 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6905000 39 -639.1829\n 744000 6905000 40 -631.0168\n 745000 6905000 41 -628.156\n 746000 6905000 42 -635.1871\n 747000 6905000 43 -642.3609\n 705000 6906000 1 -769.2407\n 706000 6906000 2 -765.9749\n 707000 6906000 3 -765.6598\n 708000 6906000 4 -765.1884\n 709000 6906000 5 -761.5701\n 710000 6906000 6 -753.3408\n 711000 6906000 7 -739.7748\n 712000 6906000 8 -722.9935\n 713000 6906000 9 -706.8936\n 714000 6906000 10 -694.5732\n 715000 6906000 11 -689.5246\n 716000 6906000 12 -695.1341\n 717000 6906000 13 -703.4748\n 718000 6906000 14 -702.9111\n 719000 6906000 15 -689.3903\n 720000 6906000 16 -668.2379\n 721000 6906000 17 -641.92\n 722000 6906000 18 -609.1287\n 723000 6906000 19 -567.6189\n 724000 6906000 20 -520.7101\n 725000 6906000 21 -477.371\n 726000 6906000 22 -445.9419\n 727000 6906000 23 -428.7811\n 728000 6906000 24 -421.3133\n 729000 6906000 25 -423.0623\n 730000 6906000 26 -437.9799\n 731000 6906000 27 -467.3541\n 732000 6906000 28 -503.457\n 733000 6906000 29 -542.3038\n 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6908000 11 -699.5543\n 716000 6908000 12 -699.8994\n 717000 6908000 13 -707.3971\n 718000 6908000 14 -715.2269\n 719000 6908000 15 -720.1711\n 720000 6908000 16 -719.7236\n 721000 6908000 17 -709.1079\n 722000 6908000 18 -685.9202\n 723000 6908000 19 -644.0412\n 724000 6908000 20 -586.6362\n 725000 6908000 21 -530.7133\n 726000 6908000 22 -489.434\n 727000 6908000 23 -470.72\n 728000 6908000 24 -474.8091\n 729000 6908000 25 -497.2515\n 730000 6908000 26 -529.9908\n 731000 6908000 27 -566.2992\n 732000 6908000 28 -599.0749\n 733000 6908000 29 -621.0679\n 734000 6908000 30 -632.7683\n 735000 6908000 31 -640.4904\n 736000 6908000 32 -645.2806\n 737000 6908000 33 -646.0961\n 738000 6908000 34 -645.8002\n 739000 6908000 35 -646.431\n 740000 6908000 36 -646.4139\n 741000 6908000 37 -644.3117\n 742000 6908000 38 -643.1854\n 743000 6908000 39 -646.0289\n 744000 6908000 40 -651.5601\n 745000 6908000 41 -657.4271\n 746000 6908000 42 -662.7588\n 747000 6908000 43 -668.1602\n 705000 6909000 1 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6909000 35 -645.4199\n 740000 6909000 36 -644.0217\n 741000 6909000 37 -644.0255\n 742000 6909000 38 -645.4924\n 743000 6909000 39 -649.3932\n 744000 6909000 40 -655.0865\n 745000 6909000 41 -661.477\n 746000 6909000 42 -668.4027\n 747000 6909000 43 -675.5839\n 705000 6910000 1 -762.0343\n 706000 6910000 2 -757.1146\n 707000 6910000 3 -756.6089\n 708000 6910000 4 -758.5727\n 709000 6910000 5 -761.1663\n 710000 6910000 6 -762.885\n 711000 6910000 7 -762.8876\n 712000 6910000 8 -759.8763\n 713000 6910000 9 -751.502\n 714000 6910000 10 -737.8624\n 715000 6910000 11 -724.9535\n 716000 6910000 12 -717.457\n 717000 6910000 13 -714.3893\n 718000 6910000 14 -716.3757\n 719000 6910000 15 -723.0464\n 720000 6910000 16 -728.1182\n 721000 6910000 17 -728.872\n 722000 6910000 18 -717.3379\n 723000 6910000 19 -682.225\n 724000 6910000 20 -626.0471\n 725000 6910000 21 -567.7846\n 726000 6910000 22 -524.4022\n 727000 6910000 23 -506.3515\n 728000 6910000 24 -524.9136\n 729000 6910000 25 -574.0405\n 730000 6910000 26 -622.3079\n 731000 6910000 27 -643.5914\n 732000 6910000 28 -648.5961\n 733000 6910000 29 -650.6846\n 734000 6910000 30 -652.5571\n 735000 6910000 31 -652.7131\n 736000 6910000 32 -652.3135\n 737000 6910000 33 -651.1127\n 738000 6910000 34 -648.4026\n 739000 6910000 35 -645.1262\n 740000 6910000 36 -643.7283\n 741000 6910000 37 -646.0273\n 742000 6910000 38 -649.9622\n 743000 6910000 39 -654.6826\n 744000 6910000 40 -660.0717\n 745000 6910000 41 -666.4505\n 746000 6910000 42 -674.2485\n 747000 6910000 43 -682.4724\n 705000 6911000 1 -764.9401\n 706000 6911000 2 -758.6859\n 707000 6911000 3 -755.8604\n 708000 6911000 4 -756.262\n 709000 6911000 5 -759.2563\n 710000 6911000 6 -762.6635\n 711000 6911000 7 -764.6628\n 712000 6911000 8 -763.888\n 713000 6911000 9 -758.8356\n 714000 6911000 10 -749.7145\n 715000 6911000 11 -740.2831\n 716000 6911000 12 -732.5356\n 717000 6911000 13 -727.3787\n 718000 6911000 14 -726.1553\n 719000 6911000 15 -728.8169\n 720000 6911000 16 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6912000 7 -764.4636\n 712000 6912000 8 -764.496\n 713000 6912000 9 -761.8965\n 714000 6912000 10 -757.5464\n 715000 6912000 11 -752.5644\n 716000 6912000 12 -746.7952\n 717000 6912000 13 -741.9539\n 718000 6912000 14 -738.5677\n 719000 6912000 15 -736.3184\n 720000 6912000 16 -736.8618\n 721000 6912000 17 -738.4202\n 722000 6912000 18 -733.0433\n 723000 6912000 19 -708.5278\n 724000 6912000 20 -664.7603\n 725000 6912000 21 -618.3605\n 726000 6912000 22 -586.6124\n 727000 6912000 23 -577.2291\n 728000 6912000 24 -593.188\n 729000 6912000 25 -627.9825\n 730000 6912000 26 -660.6388\n 731000 6912000 27 -675.3406\n 732000 6912000 28 -676.1163\n 733000 6912000 29 -671.7856\n 734000 6912000 30 -666.7997\n 735000 6912000 31 -661.5368\n 736000 6912000 32 -655.9908\n 737000 6912000 33 -651.3325\n 738000 6912000 34 -649.3342\n 739000 6912000 35 -650.2393\n 740000 6912000 36 -653.0657\n 741000 6912000 37 -656.8472\n 742000 6912000 38 -662.011\n 743000 6912000 39 -668.1411\n 744000 6912000 40 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6913000 31 -663.3367\n 736000 6913000 32 -656.3887\n 737000 6913000 33 -651.0817\n 738000 6913000 34 -649.6895\n 739000 6913000 35 -652.1736\n 740000 6913000 36 -656.6078\n 741000 6913000 37 -661.4277\n 742000 6913000 38 -667.3689\n 743000 6913000 39 -674.4293\n 744000 6913000 40 -682.0163\n 745000 6913000 41 -689.1976\n 746000 6913000 42 -695.3354\n 747000 6913000 43 -701.4442\n 705000 6914000 1 -766.2286\n 706000 6914000 2 -760.6554\n 707000 6914000 3 -756.249\n 708000 6914000 4 -753.1471\n 709000 6914000 5 -752.7766\n 710000 6914000 6 -755.3228\n 711000 6914000 7 -759.1209\n 712000 6914000 8 -761.9673\n 713000 6914000 9 -762.9871\n 714000 6914000 10 -762.2465\n 715000 6914000 11 -760.6177\n 716000 6914000 12 -759.2847\n 717000 6914000 13 -756.68\n 718000 6914000 14 -750.378\n 719000 6914000 15 -742.0964\n 720000 6914000 16 -737.0046\n 721000 6914000 17 -740.0645\n 722000 6914000 18 -743.9828\n 723000 6914000 19 -735.4737\n 724000 6914000 20 -713.1089\n 725000 6914000 21 -687.8484\n 726000 6914000 22 -668.802\n 727000 6914000 23 -661.8043\n 728000 6914000 24 -665.4906\n 729000 6914000 25 -675.2615\n 730000 6914000 26 -684.9995\n 731000 6914000 27 -688.974\n 732000 6914000 28 -686.5041\n 733000 6914000 29 -679.897\n 734000 6914000 30 -671.694\n 735000 6914000 31 -663.5221\n 736000 6914000 32 -656.2584\n 737000 6914000 33 -651.1382\n 738000 6914000 34 -650.1824\n 739000 6914000 35 -653.1572\n 740000 6914000 36 -658.2562\n 741000 6914000 37 -664.3982\n 742000 6914000 38 -671.5559\n 743000 6914000 39 -679.6738\n 744000 6914000 40 -688.2629\n 745000 6914000 41 -696.1443\n 746000 6914000 42 -702.4662\n 747000 6914000 43 -708.4297\n 705000 6915000 1 -764.4474\n 706000 6915000 2 -759.6501\n 707000 6915000 3 -755.7032\n 708000 6915000 4 -751.9369\n 709000 6915000 5 -750.0691\n 710000 6915000 6 -751.6992\n 711000 6915000 7 -755.9747\n 712000 6915000 8 -760.062\n 713000 6915000 9 -762.1905\n 714000 6915000 10 -762.0507\n 715000 6915000 11 -760.9535\n 716000 6915000 12 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6890000 4 -580.6121\n 714000 6890000 5 -482.3196\n 716000 6890000 6 -439.9577\n 718000 6890000 7 -441.8504\n 720000 6890000 8 -443.509\n 722000 6890000 9 -435.5248\n 724000 6890000 10 -399.4756\n 726000 6890000 11 -378.5879\n 728000 6890000 12 -352.2925\n 730000 6890000 13 -333.4167\n 732000 6890000 14 -332.4493\n 734000 6890000 15 -304.2296\n 736000 6890000 16 -279.2176\n 738000 6890000 17 -268.5978\n 740000 6890000 18 -286.7969\n 742000 6890000 19 -359.8727\n 744000 6890000 20 -484.8153\n 746000 6890000 21 -584.4149\n 706000 6892000 1 -737.7301\n 708000 6892000 2 -708.834\n 710000 6892000 3 -696.2411\n 712000 6892000 4 -599.508\n 714000 6892000 5 -497.0103\n 716000 6892000 6 -438.2431\n 718000 6892000 7 -408.8746\n 720000 6892000 8 -406.6569\n 722000 6892000 9 -387.1014\n 724000 6892000 10 -355.1693\n 726000 6892000 11 -328.8761\n 728000 6892000 12 -301.8515\n 730000 6892000 13 -283.0304\n 732000 6892000 14 -296.7216\n 734000 6892000 15 -282.8144\n 736000 6892000 16 -266.1236\n 738000 6892000 17 -268.9244\n 740000 6892000 18 -298.3139\n 742000 6892000 19 -381.9025\n 744000 6892000 20 -519.3377\n 746000 6892000 21 -610.6711\n 706000 6894000 1 -751.7577\n 708000 6894000 2 -715.2292\n 710000 6894000 3 -694.9357\n 712000 6894000 4 -657.4186\n 714000 6894000 5 -531.732\n 716000 6894000 6 -449.096\n 718000 6894000 7 -398.0706\n 720000 6894000 8 -381.0529\n 722000 6894000 9 -358.3919\n 724000 6894000 10 -326.3704\n 726000 6894000 11 -294.286\n 728000 6894000 12 -273.1559\n 730000 6894000 13 -265.644\n 732000 6894000 14 -280.4622\n 734000 6894000 15 -270.7543\n 736000 6894000 16 -263.9767\n 738000 6894000 17 -291.2555\n 740000 6894000 18 -352.3661\n 742000 6894000 19 -456.5014\n 744000 6894000 20 -559.0276\n 746000 6894000 21 -606.2371\n 706000 6896000 1 -762.4843\n 708000 6896000 2 -724.3445\n 710000 6896000 3 -687.6819\n 712000 6896000 4 -683.7318\n 714000 6896000 5 -587.4675\n 716000 6896000 6 -479.2788\n 718000 6896000 7 -412.3017\n 720000 6896000 8 -371.082\n 722000 6896000 9 -348.377\n 724000 6896000 10 -309.7986\n 726000 6896000 11 -265.968\n 728000 6896000 12 -246.0542\n 730000 6896000 13 -250.8627\n 732000 6896000 14 -268.2632\n 734000 6896000 15 -269.5236\n 736000 6896000 16 -280.2003\n 738000 6896000 17 -335.5317\n 740000 6896000 18 -424.6426\n 742000 6896000 19 -534.3527\n 744000 6896000 20 -598.1163\n 746000 6896000 21 -594.3217\n 706000 6898000 1 -766.8432\n 708000 6898000 2 -737.1212\n 710000 6898000 3 -683.2192\n 712000 6898000 4 -677.1313\n 714000 6898000 5 -628.6254\n 716000 6898000 6 -530.9074\n 718000 6898000 7 -445.3963\n 720000 6898000 8 -385.2642\n 722000 6898000 9 -353.0005\n 724000 6898000 10 -309.4915\n 726000 6898000 11 -266.4265\n 728000 6898000 12 -242.0003\n 730000 6898000 13 -242.4914\n 732000 6898000 14 -269.3606\n 734000 6898000 15 -292.7725\n 736000 6898000 16 -325.5329\n 738000 6898000 17 -404.153\n 740000 6898000 18 -503.269\n 742000 6898000 19 -584.3758\n 744000 6898000 20 -619.6182\n 746000 6898000 21 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5 -694.5732\n 716000 6906000 6 -695.1341\n 718000 6906000 7 -702.9111\n 720000 6906000 8 -668.2379\n 722000 6906000 9 -609.1287\n 724000 6906000 10 -520.7101\n 726000 6906000 11 -445.9419\n 728000 6906000 12 -421.3133\n 730000 6906000 13 -437.9799\n 732000 6906000 14 -503.457\n 734000 6906000 15 -582.8282\n 736000 6906000 16 -632.3708\n 738000 6906000 17 -635.3553\n 740000 6906000 18 -643.3322\n 742000 6906000 19 -643.5975\n 744000 6906000 20 -639.4073\n 746000 6906000 21 -646.599\n 706000 6908000 1 -757.1274\n 708000 6908000 2 -763.1099\n 710000 6908000 3 -761.4848\n 712000 6908000 4 -742.5049\n 714000 6908000 5 -711.0689\n 716000 6908000 6 -699.8994\n 718000 6908000 7 -715.2269\n 720000 6908000 8 -719.7236\n 722000 6908000 9 -685.9202\n 724000 6908000 10 -586.6362\n 726000 6908000 11 -489.434\n 728000 6908000 12 -474.8091\n 730000 6908000 13 -529.9908\n 732000 6908000 14 -599.0749\n 734000 6908000 15 -632.7683\n 736000 6908000 16 -645.2806\n 738000 6908000 17 -645.8002\n 740000 6908000 18 -646.4139\n 742000 6908000 19 -643.1854\n 744000 6908000 20 -651.5601\n 746000 6908000 21 -662.7588\n 706000 6910000 1 -757.1146\n 708000 6910000 2 -758.5727\n 710000 6910000 3 -762.885\n 712000 6910000 4 -759.8763\n 714000 6910000 5 -737.8624\n 716000 6910000 6 -717.457\n 718000 6910000 7 -716.3757\n 720000 6910000 8 -728.1182\n 722000 6910000 9 -717.3379\n 724000 6910000 10 -626.0471\n 726000 6910000 11 -524.4022\n 728000 6910000 12 -524.9136\n 730000 6910000 13 -622.3079\n 732000 6910000 14 -648.5961\n 734000 6910000 15 -652.5571\n 736000 6910000 16 -652.3135\n 738000 6910000 17 -648.4026\n 740000 6910000 18 -643.7283\n 742000 6910000 19 -649.9622\n 744000 6910000 20 -660.0717\n 746000 6910000 21 -674.2485\n 706000 6912000 1 -759.9927\n 708000 6912000 2 -754.2757\n 710000 6912000 3 -761.6245\n 712000 6912000 4 -764.496\n 714000 6912000 5 -757.5464\n 716000 6912000 6 -746.7952\n 718000 6912000 7 -738.5677\n 720000 6912000 8 -736.8618\n 722000 6912000 9 -733.0433\n 724000 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708000 6916000 2 -749.6884\n 710000 6916000 3 -748.682\n 712000 6916000 4 -758.3638\n 714000 6916000 5 -761.7722\n 716000 6916000 6 -761.7597\n 718000 6916000 7 -758.3922\n 720000 6916000 8 -751.3932\n 722000 6916000 9 -755.72\n 724000 6916000 10 -747.7178\n 726000 6916000 11 -726.231\n 728000 6916000 12 -712.3225\n 730000 6916000 13 -706.9613\n 732000 6916000 14 -691.402\n 734000 6916000 15 -671.9666\n 736000 6916000 16 -658.1201\n 738000 6916000 17 -654.3087\n 740000 6916000 18 -663.0634\n 742000 6916000 19 -677.3807\n 744000 6916000 20 -695.8612\n 746000 6916000 21 -712.808\n 706000 6918000 1 -753.231\n 708000 6918000 2 -746.1266\n 710000 6918000 3 -745.5084\n 712000 6918000 4 -756.278\n 714000 6918000 5 -761.1682\n 716000 6918000 6 -762.3949\n 718000 6918000 7 -765.9335\n 720000 6918000 8 -772.5667\n 722000 6918000 9 -771.0844\n 724000 6918000 10 -767.3386\n 726000 6918000 11 -752.1813\n 728000 6918000 12 -732.538\n 730000 6918000 13 -725.7021\n 732000 6918000 14 -698.5851\n 734000 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724500 6865500 40 -688.1151\n 725000 6865500 41 -686.8765\n 725500 6865500 42 -685.1548\n 726000 6865500 43 -683.0378\n 726500 6865500 44 -680.7296\n 727000 6865500 45 -678.4142\n 727500 6865500 46 -676.1861\n 728000 6865500 47 -674.0938\n 728500 6865500 48 -672.1508\n 729000 6865500 49 -670.3408\n 729500 6865500 50 -668.6127\n 730000 6865500 51 -666.8656\n 730500 6865500 52 -665.0262\n 731000 6865500 53 -663.1589\n 731500 6865500 54 -661.3732\n 732000 6865500 55 -659.7682\n 732500 6865500 56 -658.4385\n 733000 6865500 57 -657.4059\n 733500 6865500 58 -656.5244\n 734000 6865500 59 -655.5597\n 734500 6865500 60 -654.2745\n 735000 6865500 61 -652.444\n 735500 6865500 62 -650.0639\n 736000 6865500 63 -647.4559\n 736500 6865500 64 -644.9688\n 737000 6865500 65 -642.7947\n 737500 6865500 66 -641.0004\n 738000 6865500 67 -639.5251\n 738500 6865500 68 -638.4841\n 739000 6865500 69 -638.037\n 739500 6865500 70 -638.4773\n 740000 6865500 71 -640.2437\n 740500 6865500 72 -643.3198\n 741000 6865500 73 -647.0573\n 741500 6865500 74 -650.7243\n 742000 6865500 75 -653.8254\n 742500 6865500 76 -655.9604\n 743000 6865500 77 -657.1927\n 743500 6865500 78 -657.9939\n 744000 6865500 79 -658.7497\n 744500 6865500 80 -659.612\n 745000 6865500 81 -660.5613\n 745500 6865500 82 -661.6575\n 746000 6865500 83 -663.0171\n 746500 6865500 84 -664.6807\n 747000 6865500 85 -666.6281\n 705000 6866000 1 -739.6504\n 705500 6866000 2 -739.4402\n 706000 6866000 3 -739.1899\n 706500 6866000 4 -738.811\n 707000 6866000 5 -738.1846\n 707500 6866000 6 -737.1826\n 708000 6866000 7 -735.66\n 708500 6866000 8 -733.6697\n 709000 6866000 9 -731.5015\n 709500 6866000 10 -729.5422\n 710000 6866000 11 -728.1897\n 710500 6866000 12 -727.7214\n 711000 6866000 13 -728.1157\n 711500 6866000 14 -728.8372\n 712000 6866000 15 -729.1428\n 712500 6866000 16 -728.5867\n 713000 6866000 17 -727.1031\n 713500 6866000 18 -724.8908\n 714000 6866000 19 -722.2621\n 714500 6866000 20 -719.5661\n 715000 6866000 21 -717.1272\n 715500 6866000 22 -715.2258\n 716000 6866000 23 -713.7672\n 716500 6866000 24 -712.562\n 717000 6866000 25 -711.3845\n 717500 6866000 26 -710.0089\n 718000 6866000 27 -708.2551\n 718500 6866000 28 -706.1175\n 719000 6866000 29 -703.771\n 719500 6866000 30 -701.4597\n 720000 6866000 31 -699.3611\n 720500 6866000 32 -697.5897\n 721000 6866000 33 -696.1456\n 721500 6866000 34 -694.9163\n 722000 6866000 35 -693.8103\n 722500 6866000 36 -692.8127\n 723000 6866000 37 -691.933\n 723500 6866000 38 -691.0756\n 724000 6866000 39 -690.0877\n 724500 6866000 40 -688.8304\n 725000 6866000 41 -687.1922\n 725500 6866000 42 -685.1175\n 726000 6866000 43 -682.7016\n 726500 6866000 44 -680.1652\n 727000 6866000 45 -677.7503\n 727500 6866000 46 -675.6482\n 728000 6866000 47 -673.9568\n 728500 6866000 48 -672.6234\n 729000 6866000 49 -671.5008\n 729500 6866000 50 -670.4087\n 730000 6866000 51 -669.1102\n 730500 6866000 52 -667.4728\n 731000 6866000 53 -665.5934\n 731500 6866000 54 -663.6353\n 732000 6866000 55 -661.7293\n 732500 6866000 56 -659.964\n 733000 6866000 57 -658.3696\n 733500 6866000 58 -656.8671\n 734000 6866000 59 -655.3212\n 734500 6866000 60 -653.5941\n 735000 6866000 61 -651.5746\n 735500 6866000 62 -649.2681\n 736000 6866000 63 -646.8954\n 736500 6866000 64 -644.7114\n 737000 6866000 65 -642.8697\n 737500 6866000 66 -641.4719\n 738000 6866000 67 -640.4749\n 738500 6866000 68 -639.9233\n 739000 6866000 69 -639.8943\n 739500 6866000 70 -640.5303\n 740000 6866000 71 -642.0357\n 740500 6866000 72 -644.4282\n 741000 6866000 73 -647.2793\n 741500 6866000 74 -650.0991\n 742000 6866000 75 -652.5623\n 742500 6866000 76 -654.4217\n 743000 6866000 77 -655.7138\n 743500 6866000 78 -656.7788\n 744000 6866000 79 -657.8908\n 744500 6866000 80 -659.1422\n 745000 6866000 81 -660.4846\n 745500 6866000 82 -661.9404\n 746000 6866000 83 -663.599\n 746500 6866000 84 -665.4694\n 747000 6866000 85 -667.4834\n 705000 6866500 1 -741.565\n 705500 6866500 2 -741.2386\n 706000 6866500 3 -740.8523\n 706500 6866500 4 -740.3333\n 707000 6866500 5 -739.5844\n 707500 6866500 6 -738.5104\n 708000 6866500 7 -737.0164\n 708500 6866500 8 -735.1872\n 709000 6866500 9 -733.2953\n 709500 6866500 10 -731.6705\n 710000 6866500 11 -730.6102\n 710500 6866500 12 -730.2546\n 711000 6866500 13 -730.5181\n 711500 6866500 14 -730.9978\n 712000 6866500 15 -731.1199\n 712500 6866500 16 -730.4998\n 713000 6866500 17 -729.004\n 713500 6866500 18 -726.7506\n 714000 6866500 19 -724.0264\n 714500 6866500 20 -721.1828\n 715000 6866500 21 -718.5751\n 715500 6866500 22 -716.5938\n 716000 6866500 23 -715.188\n 716500 6866500 24 -714.113\n 717000 6866500 25 -713.1082\n 717500 6866500 26 -711.9384\n 718000 6866500 27 -710.4258\n 718500 6866500 28 -708.5629\n 719000 6866500 29 -706.508\n 719500 6866500 30 -704.4655\n 720000 6866500 31 -702.5516\n 720500 6866500 32 -700.8134\n 721000 6866500 33 -699.2467\n 721500 6866500 34 -697.8004\n 722000 6866500 35 -696.4236\n 722500 6866500 36 -695.1156\n 723000 6866500 37 -693.8878\n 723500 6866500 38 -692.6382\n 724000 6866500 39 -691.2139\n 724500 6866500 40 -689.4932\n 725000 6866500 41 -687.4031\n 725500 6866500 42 -684.936\n 726000 6866500 43 -682.198\n 726500 6866500 44 -679.421\n 727000 6866500 45 -676.8864\n 727500 6866500 46 -674.8853\n 728000 6866500 47 -673.5667\n 728500 6866500 48 -672.8103\n 729000 6866500 49 -672.3426\n 729500 6866500 50 -671.8643\n 730000 6866500 51 -671.0065\n 730500 6866500 52 -669.5716\n 731000 6866500 53 -667.6939\n 731500 6866500 54 -665.5873\n 732000 6866500 55 -663.403\n 732500 6866500 56 -661.2142\n 733000 6866500 57 -659.0467\n 733500 6866500 58 -656.8795\n 734000 6866500 59 -654.6797\n 734500 6866500 60 -652.4303\n 735000 6866500 61 -650.1614\n 735500 6866500 62 -647.9282\n 736000 6866500 63 -645.8409\n 736500 6866500 64 -644.0342\n 737000 6866500 65 -642.6224\n 737500 6866500 66 -641.7645\n 738000 6866500 67 -641.4314\n 738500 6866500 68 -641.5956\n 739000 6866500 69 -642.209\n 739500 6866500 70 -643.1906\n 740000 6866500 71 -644.4243\n 740500 6866500 72 -645.8731\n 741000 6866500 73 -647.4478\n 741500 6866500 74 -649.0338\n 742000 6866500 75 -650.549\n 742500 6866500 76 -651.9521\n 743000 6866500 77 -653.2725\n 743500 6866500 78 -654.6396\n 744000 6866500 79 -656.1489\n 744500 6866500 80 -657.8152\n 745000 6866500 81 -659.5729\n 745500 6866500 82 -661.4338\n 746000 6866500 83 -663.4609\n 746500 6866500 84 -665.6243\n 747000 6866500 85 -667.7808\n 705000 6867000 1 -743.27\n 705500 6867000 2 -742.8435\n 706000 6867000 3 -742.3435\n 706500 6867000 4 -741.7078\n 707000 6867000 5 -740.8545\n 707500 6867000 6 -739.7168\n 708000 6867000 7 -738.2505\n 708500 6867000 8 -736.5714\n 709000 6867000 9 -734.939\n 709500 6867000 10 -733.6323\n 710000 6867000 11 -732.8619\n 710500 6867000 12 -732.6511\n 711000 6867000 13 -732.8603\n 711500 6867000 14 -733.1908\n 712000 6867000 15 -733.2065\n 712500 6867000 16 -732.5649\n 713000 6867000 17 -731.0715\n 713500 6867000 18 -728.7733\n 714000 6867000 19 -725.9368\n 714500 6867000 20 -722.9217\n 715000 6867000 21 -720.1178\n 715500 6867000 22 -718.0287\n 716000 6867000 23 -716.6469\n 716500 6867000 24 -715.6592\n 717000 6867000 25 -714.7747\n 717500 6867000 26 -713.7647\n 718000 6867000 27 -712.467\n 718500 6867000 28 -710.8723\n 719000 6867000 29 -709.1193\n 719500 6867000 30 -707.3682\n 720000 6867000 31 -705.6784\n 720500 6867000 32 -704.0368\n 721000 6867000 33 -702.4286\n 721500 6867000 34 -700.8368\n 722000 6867000 35 -699.2365\n 722500 6867000 36 -697.6313\n 723000 6867000 37 -696.0385\n 723500 6867000 38 -694.3594\n 724000 6867000 39 -692.4525\n 724500 6867000 40 -690.2267\n 725000 6867000 41 -687.653\n 725500 6867000 42 -684.7668\n 726000 6867000 43 -681.6905\n 726500 6867000 44 -678.6661\n 727000 6867000 45 -675.9991\n 727500 6867000 46 -674.0592\n 728000 6867000 47 -673.0421\n 728500 6867000 48 -672.7653\n 729000 6867000 49 -672.8461\n 729500 6867000 50 -672.8888\n 730000 6867000 51 -672.4194\n 730500 6867000 52 -671.1766\n 731000 6867000 53 -669.3245\n 731500 6867000 54 -667.1154\n 732000 6867000 55 -664.7068\n 732500 6867000 56 -662.1507\n 733000 6867000 57 -659.4589\n 733500 6867000 58 -656.6656\n 734000 6867000 59 -653.8413\n 734500 6867000 60 -651.0918\n 735000 6867000 61 -648.5748\n 735500 6867000 62 -646.4014\n 736000 6867000 63 -644.5888\n 736500 6867000 64 -643.1639\n 737000 6867000 65 -642.205\n 737500 6867000 66 -641.9119\n 738000 6867000 67 -642.2599\n 738500 6867000 68 -643.15\n 739000 6867000 69 -644.392\n 739500 6867000 70 -645.684\n 740000 6867000 71 -646.6224\n 740500 6867000 72 -647.1049\n 741000 6867000 73 -647.3414\n 741500 6867000 74 -647.5958\n 742000 6867000 75 -648.0603\n 742500 6867000 76 -648.922\n 743000 6867000 77 -650.2123\n 743500 6867000 78 -651.8403\n 744000 6867000 79 -653.7046\n 744500 6867000 80 -655.739\n 745000 6867000 81 -657.8735\n 745500 6867000 82 -660.1199\n 746000 6867000 83 -662.5239\n 746500 6867000 84 -665.0148\n 747000 6867000 85 -667.3595\n 705000 6867500 1 -744.7861\n 705500 6867500 2 -744.2829\n 706000 6867500 3 -743.6928\n 706500 6867500 4 -742.9589\n 707000 6867500 5 -742.0073\n 707500 6867500 6 -740.7913\n 708000 6867500 7 -739.3182\n 708500 6867500 8 -737.7399\n 709000 6867500 9 -736.3082\n 709500 6867500 10 -735.2642\n 710000 6867500 11 -734.7586\n 710500 6867500 12 -734.7285\n 711000 6867500 13 -734.9835\n 711500 6867500 14 -735.2863\n 712000 6867500 15 -735.2922\n 712500 6867500 16 -734.6876\n 713000 6867500 17 -733.238\n 713500 6867500 18 -730.9395\n 714000 6867500 19 -728.0416\n 714500 6867500 20 -724.9118\n 715000 6867500 21 -721.9515\n 715500 6867500 22 -719.7255\n 716000 6867500 23 -718.2877\n 716500 6867500 24 -717.2898\n 717000 6867500 25 -716.4274\n 717500 6867500 26 -715.4896\n 718000 6867500 27 -714.3444\n 718500 6867500 28 -712.9795\n 719000 6867500 29 -711.5061\n 719500 6867500 30 -710.0394\n 720000 6867500 31 -708.597\n 720500 6867500 32 -707.1231\n 721000 6867500 33 -705.5858\n 721500 6867500 34 -703.97\n 722000 6867500 35 -702.2515\n 722500 6867500 36 -700.4101\n 723000 6867500 37 -698.4536\n 723500 6867500 38 -696.3097\n 724000 6867500 39 -693.8745\n 724500 6867500 40 -691.1069\n 725000 6867500 41 -688.0289\n 725500 6867500 42 -684.7168\n 726000 6867500 43 -681.3132\n 726500 6867500 44 -678.0721\n 727000 6867500 45 -675.302\n 727500 6867500 46 -673.3823\n 728000 6867500 47 -672.5453\n 728500 6867500 48 -672.5638\n 729000 6867500 49 -672.9861\n 729500 6867500 50 -673.3611\n 730000 6867500 51 -673.1755\n 730500 6867500 52 -672.1196\n 731000 6867500 53 -670.3521\n 731500 6867500 54 -668.1276\n 732000 6867500 55 -665.5901\n 732500 6867500 56 -662.7642\n 733000 6867500 57 -659.6506\n 733500 6867500 58 -656.3383\n 734000 6867500 59 -653.0042\n 734500 6867500 60 -649.855\n 735000 6867500 61 -647.1283\n 735500 6867500 62 -644.9843\n 736000 6867500 63 -643.3883\n 736500 6867500 64 -642.2932\n 737000 6867500 65 -641.7388\n 737500 6867500 66 -641.9285\n 738000 6867500 67 -642.8234\n 738500 6867500 68 -644.2503\n 739000 6867500 69 -645.8969\n 739500 6867500 70 -647.3355\n 740000 6867500 71 -648.0116\n 740500 6867500 72 -647.7128\n 741000 6867500 73 -646.8304\n 741500 6867500 74 -645.9169\n 742000 6867500 75 -645.4164\n 742500 6867500 76 -645.7366\n 743000 6867500 77 -646.929\n 743500 6867500 78 -648.7084\n 744000 6867500 79 -650.7973\n 744500 6867500 80 -653.0656\n 745000 6867500 81 -655.4622\n 745500 6867500 82 -657.9978\n 746000 6867500 83 -660.6998\n 746500 6867500 84 -663.4616\n 747000 6867500 85 -665.9809\n 705000 6868000 1 -746.1437\n 705500 6868000 2 -745.5916\n 706000 6868000 3 -744.9362\n 706500 6868000 4 -744.1214\n 707000 6868000 5 -743.0731\n 707500 6868000 6 -741.7537\n 708000 6868000 7 -740.2125\n 708500 6868000 8 -738.6474\n 709000 6868000 9 -737.3142\n 709500 6868000 10 -736.4415\n 710000 6868000 11 -736.1534\n 710500 6868000 12 -736.3406\n 711000 6868000 13 -736.7551\n 711500 6868000 14 -737.1622\n 712000 6868000 15 -737.2551\n 712500 6868000 16 -736.7491\n 713000 6868000 17 -735.4113\n 713500 6868000 18 -733.2118\n 714000 6868000 19 -730.3822\n 714500 6868000 20 -727.2826\n 715000 6868000 21 -724.2855\n 715500 6868000 22 -721.9147\n 716000 6868000 23 -720.284\n 716500 6868000 24 -719.1082\n 717000 6868000 25 -718.1212\n 717500 6868000 26 -717.1425\n 718000 6868000 27 -716.054\n 718500 6868000 28 -714.8402\n 719000 6868000 29 -713.5837\n 719500 6868000 30 -712.3615\n 720000 6868000 31 -711.1713\n 720500 6868000 32 -709.9426\n 721000 6868000 33 -708.6161\n 721500 6868000 34 -707.1443\n 722000 6868000 35 -705.4636\n 722500 6868000 36 -703.4918\n 723000 6868000 37 -701.1832\n 723500 6868000 38 -698.5299\n 724000 6868000 39 -695.5107\n 724500 6868000 40 -692.1627\n 725000 6868000 41 -688.5659\n 725500 6868000 42 -684.8372\n 726000 6868000 43 -681.1393\n 726500 6868000 44 -677.7393\n 727000 6868000 45 -674.9142\n 727500 6868000 46 -672.9961\n 728000 6868000 47 -672.1929\n 728500 6868000 48 -672.272\n 729000 6868000 49 -672.7728\n 729500 6868000 50 -673.2369\n 730000 6868000 51 -673.1794\n 730500 6868000 52 -672.2956\n 731000 6868000 53 -670.6844\n 731500 6868000 54 -668.5517\n 732000 6868000 55 -666.0043\n 732500 6868000 56 -663.0326\n 733000 6868000 57 -659.6311\n 733500 6868000 58 -655.9413\n 734000 6868000 59 -652.2498\n 734500 6868000 60 -648.8354\n 735000 6868000 61 -645.974\n 735500 6868000 62 -643.8376\n 736000 6868000 63 -642.3809\n 736500 6868000 64 -641.5165\n 737000 6868000 65 -641.2592\n 737500 6868000 66 -641.7889\n 738000 6868000 67 -643.0374\n 738500 6868000 68 -644.7718\n 739000 6868000 69 -646.5942\n 739500 6868000 70 -647.9852\n 740000 6868000 71 -648.3993\n 740500 6868000 72 -647.6099\n 741000 6868000 73 -646.0201\n 741500 6868000 74 -644.3175\n 742000 6868000 75 -643.1124\n 742500 6868000 76 -642.9584\n 743000 6868000 77 -643.9417\n 743500 6868000 78 -645.6647\n 744000 6868000 79 -647.7448\n 744500 6868000 80 -650.0125\n 745000 6868000 81 -652.4603\n 745500 6868000 82 -655.0817\n 746000 6868000 83 -657.8663\n 746500 6868000 84 -660.6898\n 747000 6868000 85 -663.2643\n 705000 6868500 1 -747.3911\n 705500 6868500 2 -746.8135\n 706000 6868500 3 -746.1145\n 706500 6868500 4 -745.2339\n 707000 6868500 5 -744.0908\n 707500 6868500 6 -742.645\n 708000 6868500 7 -740.9765\n 708500 6868500 8 -739.3331\n 709000 6868500 9 -737.9902\n 709500 6868500 10 -737.1976\n 710000 6868500 11 -737.0878\n 710500 6868500 12 -737.5303\n 711000 6868500 13 -738.2075\n 711500 6868500 14 -738.8344\n 712000 6868500 15 -739.1003\n 712500 6868500 16 -738.746\n 713000 6868500 17 -737.5691\n 713500 6868500 18 -735.5443\n 714000 6868500 19 -732.8806\n 714500 6868500 20 -729.9072\n 715000 6868500 21 -726.9722\n 715500 6868500 22 -724.4586\n 716000 6868500 23 -722.5272\n 716500 6868500 24 -721.0358\n 717000 6868500 25 -719.8027\n 717500 6868500 26 -718.6868\n 718000 6868500 27 -717.5762\n 718500 6868500 28 -716.4425\n 719000 6868500 29 -715.3437\n 719500 6868500 30 -714.3297\n 720000 6868500 31 -713.3879\n 720500 6868500 32 -712.4598\n 721000 6868500 33 -711.4547\n 721500 6868500 34 -710.2631\n 722000 6868500 35 -708.7435\n 722500 6868500 36 -706.7374\n 723000 6868500 37 -704.1027\n 723500 6868500 38 -700.9166\n 724000 6868500 39 -697.282\n 724500 6868500 40 -693.3348\n 725000 6868500 41 -689.2187\n 725500 6868500 42 -685.089\n 726000 6868500 43 -681.1232\n 726500 6868500 44 -677.5962\n 727000 6868500 45 -674.7307\n 727500 6868500 46 -672.7953\n 728000 6868500 47 -671.934\n 728500 6868500 48 -671.9173\n 729000 6868500 49 -672.3179\n 729500 6868500 50 -672.7043\n 730000 6868500 51 -672.6453\n 730500 6868500 52 -671.8771\n 731000 6868500 53 -670.4328\n 731500 6868500 54 -668.4398\n 732000 6868500 55 -665.9579\n 732500 6868500 56 -662.9406\n 733000 6868500 57 -659.376\n 733500 6868500 58 -655.4482\n 734000 6868500 59 -651.5345\n 734500 6868500 60 -647.9631\n 735000 6868500 61 -645.0394\n 735500 6868500 62 -642.9334\n 736000 6868500 63 -641.5768\n 736500 6868500 64 -640.8517\n 737000 6868500 65 -640.7526\n 737500 6868500 66 -641.4665\n 738000 6868500 67 -642.905\n 738500 6868500 68 -644.7931\n 739000 6868500 69 -646.6826\n 739500 6868500 70 -648.0187\n 740000 6868500 71 -648.234\n 740500 6868500 72 -647.1522\n 741000 6868500 73 -645.2034\n 741500 6868500 74 -643.1298\n 742000 6868500 75 -641.5521\n 742500 6868500 76 -641.0192\n 743000 6868500 77 -641.6585\n 743500 6868500 78 -643.0784\n 744000 6868500 79 -644.8847\n 744500 6868500 80 -646.9091\n 745000 6868500 81 -649.1663\n 745500 6868500 82 -651.6266\n 746000 6868500 83 -654.2134\n 746500 6868500 84 -656.8068\n 747000 6868500 85 -659.2375\n 705000 6869000 1 -748.5695\n 705500 6869000 2 -747.976\n 706000 6869000 3 -747.245\n 706500 6869000 4 -746.3065\n 707000 6869000 5 -745.0706\n 707500 6869000 6 -743.4883\n 708000 6869000 7 -741.6584\n 708500 6869000 8 -739.8736\n 709000 6869000 9 -738.4399\n 709500 6869000 10 -737.6631\n 710000 6869000 11 -737.7232\n 710500 6869000 12 -738.4654\n 711000 6869000 13 -739.4814\n 711500 6869000 14 -740.4068\n 712000 6869000 15 -740.9081\n 712500 6869000 16 -740.7484\n 713000 6869000 17 -739.7539\n 713500 6869000 18 -737.9146\n 714000 6869000 19 -735.424\n 714500 6869000 20 -732.5814\n 715000 6869000 21 -729.6874\n 715500 6869000 22 -727.0231\n 716000 6869000 23 -724.7477\n 716500 6869000 24 -722.8752\n 717000 6869000 25 -721.3306\n 717500 6869000 26 -720.0213\n 718000 6869000 27 -718.8414\n 718500 6869000 28 -717.7496\n 719000 6869000 29 -716.789\n 719500 6869000 30 -715.9774\n 720000 6869000 31 -715.2968\n 720500 6869000 32 -714.7034\n 721000 6869000 33 -714.0759\n 721500 6869000 34 -713.2247\n 722000 6869000 35 -711.9163\n 722500 6869000 36 -709.9075\n 723000 6869000 37 -706.9794\n 723500 6869000 38 -703.2805\n 724000 6869000 39 -699.0474\n 724500 6869000 40 -694.5193\n 725000 6869000 41 -689.9049\n 725500 6869000 42 -685.3965\n 726000 6869000 43 -681.1829\n 726500 6869000 44 -677.5347\n 727000 6869000 45 -674.6178\n 727500 6869000 46 -672.6333\n 728000 6869000 47 -671.6725\n 728500 6869000 48 -671.5032\n 729000 6869000 49 -671.7305\n 729500 6869000 50 -671.9601\n 730000 6869000 51 -671.8185\n 730500 6869000 52 -671.0838\n 731000 6869000 53 -669.7419\n 731500 6869000 54 -667.8506\n 732000 6869000 55 -665.4342\n 732500 6869000 56 -662.4285\n 733000 6869000 57 -658.8151\n 733500 6869000 58 -654.805\n 734000 6869000 59 -650.8121\n 734500 6869000 60 -647.1937\n 735000 6869000 61 -644.2858\n 735500 6869000 62 -642.2584\n 736000 6869000 63 -640.9987\n 736500 6869000 64 -640.3433\n 737000 6869000 65 -640.2739\n 737500 6869000 66 -641.0144\n 738000 6869000 67 -642.4924\n 738500 6869000 68 -644.4027\n 739000 6869000 69 -646.2803\n 739500 6869000 70 -647.5598\n 740000 6869000 71 -647.6542\n 740500 6869000 72 -646.4218\n 741000 6869000 73 -644.3536\n 741500 6869000 74 -642.2151\n 742000 6869000 75 -640.5723\n 742500 6869000 76 -639.8351\n 743000 6869000 77 -640.0993\n 743500 6869000 78 -641.0745\n 744000 6869000 79 -642.435\n 744500 6869000 80 -644.0391\n 745000 6869000 81 -645.9333\n 745500 6869000 82 -648.0468\n 746000 6869000 83 -650.2281\n 746500 6869000 84 -652.38\n 747000 6869000 85 -654.4946\n 705000 6869500 1 -749.7357\n 705500 6869500 2 -749.1127\n 706000 6869500 3 -748.3368\n 706500 6869500 4 -747.3267\n 707000 6869500 5 -745.9949\n 707500 6869500 6 -744.2883\n 708000 6869500 7 -742.312\n 708500 6869500 8 -740.3817\n 709000 6869500 9 -738.8322\n 709500 6869500 10 -738.0464\n 710000 6869500 11 -738.2841\n 710500 6869500 12 -739.3561\n 711000 6869500 13 -740.7433\n 711500 6869500 14 -741.9947\n 712000 6869500 15 -742.7534\n 712500 6869500 16 -742.7999\n 713000 6869500 17 -741.9756\n 713500 6869500 18 -740.2791\n 714000 6869500 19 -737.8982\n 714500 6869500 20 -735.1119\n 715000 6869500 21 -732.1901\n 715500 6869500 22 -729.3351\n 716000 6869500 23 -726.71\n 716500 6869500 24 -724.4381\n 717000 6869500 25 -722.5509\n 717500 6869500 26 -721.0172\n 718000 6869500 27 -719.7599\n 718500 6869500 28 -718.7284\n 719000 6869500 29 -717.9361\n 719500 6869500 30 -717.3557\n 720000 6869500 31 -716.9592\n 720500 6869500 32 -716.7175\n 721000 6869500 33 -716.4697\n 721500 6869500 34 -715.9406\n 722000 6869500 35 -714.8157\n 722500 6869500 36 -712.7854\n 723000 6869500 37 -709.5898\n 723500 6869500 38 -705.4354\n 724000 6869500 39 -700.6645\n 724500 6869500 40 -695.6068\n 725000 6869500 41 -690.5359\n 725500 6869500 42 -685.6832\n 726000 6869500 43 -681.2468\n 726500 6869500 44 -677.4857\n 727000 6869500 45 -674.5087\n 727500 6869500 46 -672.4608\n 728000 6869500 47 -671.3916\n 728500 6869500 48 -671.0704\n 729000 6869500 49 -671.1127\n 729500 6869500 50 -671.1551\n 730000 6869500 51 -670.8723\n 730500 6869500 52 -670.0773\n 731000 6869500 53 -668.7264\n 731500 6869500 54 -666.8356\n 732000 6869500 55 -664.4195\n 732500 6869500 56 -661.442\n 733000 6869500 57 -657.897\n 733500 6869500 58 -653.9891\n 734000 6869500 59 -650.1019\n 734500 6869500 60 -646.5879\n 735000 6869500 61 -643.799\n 735500 6869500 62 -641.9085\n 736000 6869500 63 -640.7503\n 736500 6869500 64 -640.1128\n 737000 6869500 65 -639.9677\n 737500 6869500 66 -640.5792\n 738000 6869500 67 -641.9133\n 738500 6869500 68 -643.6511\n 739000 6869500 69 -645.342\n 739500 6869500 70 -646.4662\n 740000 6869500 71 -646.454\n 740500 6869500 72 -645.2004\n 741000 6869500 73 -643.2357\n 741500 6869500 74 -641.2816\n 742000 6869500 75 -639.7919\n 742500 6869500 76 -639.0374\n 743000 6869500 77 -639.0583\n 743500 6869500 78 -639.6468\n 744000 6869500 79 -640.5645\n 744500 6869500 80 -641.7095\n 745000 6869500 81 -643.1583\n 745500 6869500 82 -644.819\n 746000 6869500 83 -646.4943\n 746500 6869500 84 -648.1229\n 747000 6869500 85 -649.8501\n 705000 6870000 1 -750.9945\n 705500 6870000 2 -750.2965\n 706000 6870000 3 -749.4245\n 706500 6870000 4 -748.298\n 707000 6870000 5 -746.856\n 707500 6870000 6 -745.0648\n 708000 6870000 7 -743.0251\n 708500 6870000 8 -741.0379\n 709000 6870000 9 -739.4437\n 709500 6870000 10 -738.6841\n 710000 6870000 11 -739.0991\n 710500 6870000 12 -740.4645\n 711000 6870000 13 -742.1688\n 711500 6870000 14 -743.6967\n 712000 6870000 15 -744.678\n 712500 6870000 16 -744.9021\n 713000 6870000 17 -744.2037\n 713500 6870000 18 -742.575\n 714000 6870000 19 -740.2057\n 714500 6870000 20 -737.3721\n 715000 6870000 21 -734.3273\n 715500 6870000 22 -731.2455\n 716000 6870000 23 -728.2827\n 716500 6870000 24 -725.6191\n 717000 6870000 25 -723.3759\n 717500 6870000 26 -721.5983\n 718000 6870000 27 -720.2795\n 718500 6870000 28 -719.3724\n 719000 6870000 29 -718.8377\n 719500 6870000 30 -718.5724\n 720000 6870000 31 -718.511\n 720500 6870000 32 -718.6184\n 721000 6870000 33 -718.6995\n 721500 6870000 34 -718.408\n 722000 6870000 35 -717.3859\n 722500 6870000 36 -715.2645\n 723000 6870000 37 -711.8195\n 723500 6870000 38 -707.2749\n 724000 6870000 39 -702.0381\n 724500 6870000 40 -696.5125\n 725000 6870000 41 -691.0372\n 725500 6870000 42 -685.887\n 726000 6870000 43 -681.2697\n 726500 6870000 44 -677.4241\n 727000 6870000 45 -674.4028\n 727500 6870000 46 -672.301\n 728000 6870000 47 -671.1432\n 728500 6870000 48 -670.6955\n 729000 6870000 49 -670.5698\n 729500 6870000 50 -670.422\n 730000 6870000 51 -669.9548\n 730500 6870000 52 -669.0043\n 731000 6870000 53 -667.5189\n 731500 6870000 54 -665.5046\n 732000 6870000 55 -662.9973\n 732500 6870000 56 -660.033\n 733000 6870000 57 -656.6576\n 733500 6870000 58 -653.0452\n 734000 6870000 59 -649.4879\n 734500 6870000 60 -646.2832\n 735000 6870000 61 -643.7416\n 735500 6870000 62 -642.0303\n 736000 6870000 63 -640.9612\n 736500 6870000 64 -640.306\n 737000 6870000 65 -640.0246\n 737500 6870000 66 -640.364\n 738000 6870000 67 -641.3194\n 738500 6870000 68 -642.5784\n 739000 6870000 69 -643.7433\n 739500 6870000 70 -644.4412\n 740000 6870000 71 -644.2726\n 740500 6870000 72 -643.1702\n 741000 6870000 73 -641.5922\n 741500 6870000 74 -640.0878\n 742000 6870000 75 -638.9683\n 742500 6870000 76 -638.3911\n 743000 6870000 77 -638.3649\n 743500 6870000 78 -638.7527\n 744000 6870000 79 -639.3925\n 744500 6870000 80 -640.1963\n 745000 6870000 81 -641.2292\n 745500 6870000 82 -642.4169\n 746000 6870000 83 -643.5736\n 746500 6870000 84 -644.6952\n 747000 6870000 85 -646.0243\n 705000 6870500 1 -752.4981\n 705500 6870500 2 -751.6418\n 706000 6870500 3 -750.5826\n 706500 6870500 4 -749.2704\n 707000 6870500 5 -747.6968\n 707500 6870500 6 -745.8792\n 708000 6870500 7 -743.9199\n 708500 6870500 8 -742.0648\n 709000 6870500 9 -740.6168\n 709500 6870500 10 -739.9882\n 710000 6870500 11 -740.5422\n 710500 6870500 12 -742.053\n 711000 6870500 13 -743.8992\n 711500 6870500 14 -745.5598\n 712000 6870500 15 -746.6683\n 712500 6870500 16 -747.006\n 713000 6870500 17 -746.3737\n 713500 6870500 18 -744.736\n 714000 6870500 19 -742.2883\n 714500 6870500 20 -739.3222\n 715000 6870500 21 -736.0904\n 715500 6870500 22 -732.7559\n 716000 6870500 23 -729.47\n 716500 6870500 24 -726.4277\n 717000 6870500 25 -723.8198\n 717500 6870500 26 -721.7851\n 718000 6870500 27 -720.4266\n 718500 6870500 28 -719.7258\n 719000 6870500 29 -719.5706\n 719500 6870500 30 -719.7448\n 720000 6870500 31 -720.0985\n 720500 6870500 32 -720.5447\n 721000 6870500 33 -720.8668\n 721500 6870500 34 -720.7001\n 722000 6870500 35 -719.6916\n 722500 6870500 36 -717.4324\n 723000 6870500 37 -713.7157\n 723500 6870500 38 -708.8049\n 724000 6870500 39 -703.1433\n 724500 6870500 40 -697.1941\n 725000 6870500 41 -691.361\n 725500 6870500 42 -685.9697\n 726000 6870500 43 -681.2349\n 726500 6870500 44 -677.3638\n 727000 6870500 45 -674.3461\n 727500 6870500 46 -672.2285\n 728000 6870500 47 -671.0107\n 728500 6870500 48 -670.4613\n 729000 6870500 49 -670.1913\n 729500 6870500 50 -669.8628\n 730000 6870500 51 -669.1819\n 730500 6870500 52 -667.992\n 731000 6870500 53 -666.254\n 731500 6870500 54 -663.9984\n 732000 6870500 55 -661.314\n 732500 6870500 56 -658.3322\n 733000 6870500 57 -655.184\n 733500 6870500 58 -652.0151\n 734000 6870500 59 -648.9984\n 734500 6870500 60 -646.3187\n 735000 6870500 61 -644.1606\n 735500 6870500 62 -642.6401\n 736000 6870500 63 -641.6195\n 736500 6870500 64 -640.9203\n 737000 6870500 65 -640.4969\n 737500 6870500 66 -640.4813\n 738000 6870500 67 -640.8433\n 738500 6870500 68 -641.303\n 739000 6870500 69 -641.5651\n 739500 6870500 70 -641.4752\n 740000 6870500 71 -641.0391\n 740500 6870500 72 -640.2745\n 741000 6870500 73 -639.3798\n 741500 6870500 74 -638.5701\n 742000 6870500 75 -638.0009\n 742500 6870500 76 -637.7737\n 743000 6870500 77 -637.9033\n 743500 6870500 78 -638.3126\n 744000 6870500 79 -638.8893\n 744500 6870500 80 -639.5503\n 745000 6870500 81 -640.2797\n 745500 6870500 82 -641.0398\n 746000 6870500 83 -641.7237\n 746500 6870500 84 -642.4052\n 747000 6870500 85 -643.3632\n 705000 6871000 1 -754.3669\n 705500 6871000 2 -753.2387\n 706000 6871000 3 -751.8713\n 706500 6871000 4 -750.29\n 707000 6871000 5 -748.5646\n 707500 6871000 6 -746.7793\n 708000 6871000 7 -745.0576\n 708500 6871000 8 -743.5682\n 709000 6871000 9 -742.519\n 709500 6871000 10 -742.1757\n 710000 6871000 11 -742.7988\n 710500 6871000 12 -744.2199\n 711000 6871000 13 -745.9435\n 711500 6871000 14 -747.5311\n 712000 6871000 15 -748.6387\n 712500 6871000 16 -749.0153\n 713000 6871000 17 -748.3953\n 713500 6871000 18 -746.6944\n 714000 6871000 19 -744.114\n 714500 6871000 20 -740.9748\n 715000 6871000 21 -737.5428\n 715500 6871000 22 -733.9636\n 716000 6871000 23 -730.3738\n 716500 6871000 24 -726.9613\n 717000 6871000 25 -723.9728\n 717500 6871000 26 -721.6603\n 718000 6871000 27 -720.2718\n 718500 6871000 28 -719.8376\n 719000 6871000 29 -720.1609\n 719500 6871000 30 -720.8818\n 720000 6871000 31 -721.729\n 720500 6871000 32 -722.4976\n 721000 6871000 33 -722.9586\n 721500 6871000 34 -722.8057\n 722000 6871000 35 -721.7369\n 722500 6871000 36 -719.3446\n 723000 6871000 37 -715.3544\n 723500 6871000 38 -710.0715\n 724000 6871000 39 -703.9875\n 724500 6871000 40 -697.6323\n 725000 6871000 41 -691.4811\n 725500 6871000 42 -685.9148\n 726000 6871000 43 -681.1443\n 726500 6871000 44 -677.3267\n 727000 6871000 45 -674.3745\n 727500 6871000 46 -672.2927\n 728000 6871000 47 -671.047\n 728500 6871000 48 -670.4171\n 729000 6871000 49 -670.0239\n 729500 6871000 50 -669.5275\n 730000 6871000 51 -668.6172\n 730500 6871000 52 -667.1228\n 731000 6871000 53 -665.035\n 731500 6871000 54 -662.437\n 732000 6871000 55 -659.4924\n 732500 6871000 56 -656.4432\n 733000 6871000 57 -653.516\n 733500 6871000 58 -650.8397\n 734000 6871000 59 -648.4738\n 734500 6871000 60 -646.4579\n 735000 6871000 61 -644.7825\n 735500 6871000 62 -643.4461\n 736000 6871000 63 -642.4282\n 736500 6871000 64 -641.6782\n 737000 6871000 65 -641.1547\n 737500 6871000 66 -640.813\n 738000 6871000 67 -640.5202\n 738500 6871000 68 -640.0414\n 739000 6871000 69 -639.2214\n 739500 6871000 70 -638.1521\n 740000 6871000 71 -637.3436\n 740500 6871000 72 -637.0011\n 741000 6871000 73 -636.9272\n 741500 6871000 74 -636.9055\n 742000 6871000 75 -636.9487\n 742500 6871000 76 -637.1528\n 743000 6871000 77 -637.5737\n 743500 6871000 78 -638.1814\n 744000 6871000 79 -638.8878\n 744500 6871000 80 -639.5806\n 745000 6871000 81 -640.1475\n 745500 6871000 82 -640.5554\n 746000 6871000 83 -640.8153\n 746500 6871000 84 -641.0905\n 747000 6871000 85 -641.6686\n 705000 6871500 1 -756.5594\n 705500 6871500 2 -755.051\n 706000 6871500 3 -753.2557\n 706500 6871500 4 -751.3191\n 707000 6871500 5 -749.4113\n 707500 6871500 6 -747.6949\n 708000 6871500 7 -746.3188\n 708500 6871500 8 -745.3639\n 709000 6871500 9 -744.8953\n 709500 6871500 10 -744.9644\n 710000 6871500 11 -745.6058\n 710500 6871500 12 -746.7298\n 711000 6871500 13 -748.0857\n 711500 6871500 14 -749.4069\n 712000 6871500 15 -750.3969\n 712500 6871500 16 -750.7562\n 713000 6871500 17 -750.1274\n 713500 6871500 18 -748.3571\n 714000 6871500 19 -745.6505\n 714500 6871500 20 -742.3615\n 715000 6871500 21 -738.7701\n 715500 6871500 22 -734.993\n 716000 6871500 23 -731.1371\n 716500 6871500 24 -727.375\n 717000 6871500 25 -723.9977\n 717500 6871500 26 -721.3795\n 718000 6871500 27 -719.9334\n 718500 6871500 28 -719.7558\n 719000 6871500 29 -720.5659\n 719500 6871500 30 -721.8528\n 720000 6871500 31 -723.2006\n 720500 6871500 32 -724.2532\n 721000 6871500 33 -724.7617\n 721500 6871500 34 -724.5391\n 722000 6871500 35 -723.3773\n 722500 6871500 36 -720.8904\n 723000 6871500 37 -716.6679\n 723500 6871500 38 -711.0438\n 724000 6871500 39 -704.5655\n 724500 6871500 40 -697.8325\n 725000 6871500 41 -691.4042\n 725500 6871500 42 -685.7383\n 726000 6871500 43 -681.0346\n 726500 6871500 44 -677.3578\n 727000 6871500 45 -674.5302\n 727500 6871500 46 -672.5189\n 728000 6871500 47 -671.2712\n 728500 6871500 48 -670.5765\n 729000 6871500 49 -670.0723\n 729500 6871500 50 -669.4177\n 730000 6871500 51 -668.2693\n 730500 6871500 52 -666.4291\n 731000 6871500 53 -663.9263\n 731500 6871500 54 -660.9156\n 732000 6871500 55 -657.6394\n 732500 6871500 56 -654.4385\n 733000 6871500 57 -651.6295\n 733500 6871500 58 -649.348\n 734000 6871500 59 -647.5682\n 734500 6871500 60 -646.2029\n 735000 6871500 61 -645.0026\n 735500 6871500 62 -643.8253\n 736000 6871500 63 -642.7961\n 736500 6871500 64 -642.0491\n 737000 6871500 65 -641.5458\n 737500 6871500 66 -641.048\n 738000 6871500 67 -640.2792\n 738500 6871500 68 -639.0481\n 739000 6871500 69 -637.3453\n 739500 6871500 70 -635.4686\n 740000 6871500 71 -634.3066\n 740500 6871500 72 -634.2792\n 741000 6871500 73 -634.8425\n 741500 6871500 74 -635.421\n 742000 6871500 75 -635.9376\n 742500 6871500 76 -636.5167\n 743000 6871500 77 -637.2625\n 743500 6871500 78 -638.1699\n 744000 6871500 79 -639.1443\n 744500 6871500 80 -640.0166\n 745000 6871500 81 -640.5497\n 745500 6871500 82 -640.7061\n 746000 6871500 83 -640.6077\n 746500 6871500 84 -640.497\n 747000 6871500 85 -640.6716\n 705000 6872000 1 -758.8167\n 705500 6872000 2 -756.878\n 706000 6872000 3 -754.5789\n 706500 6872000 4 -752.2176\n 707000 6872000 5 -750.0815\n 707500 6872000 6 -748.4187\n 708000 6872000 7 -747.4048\n 708500 6872000 8 -747.0121\n 709000 6872000 9 -747.1372\n 709500 6872000 10 -747.6111\n 710000 6872000 11 -748.2502\n 710500 6872000 12 -749.0049\n 711000 6872000 13 -749.8807\n 711500 6872000 14 -750.8193\n 712000 6872000 15 -751.609\n 712500 6872000 16 -751.9219\n 713000 6872000 17 -751.3218\n 713500 6872000 18 -749.5707\n 714000 6872000 19 -746.8555\n 714500 6872000 20 -743.5355\n 715000 6872000 21 -739.8886\n 715500 6872000 22 -735.9955\n 716000 6872000 23 -731.9398\n 716500 6872000 24 -727.8854\n 717000 6872000 25 -724.1589\n 717500 6872000 26 -721.2279\n 718000 6872000 27 -719.6393\n 718500 6872000 28 -719.5699\n 719000 6872000 29 -720.7021\n 719500 6872000 30 -722.418\n 720000 6872000 31 -724.1617\n 720500 6872000 32 -725.405\n 721000 6872000 33 -725.8776\n 721500 6872000 34 -725.5386\n 722000 6872000 35 -724.2868\n 722500 6872000 36 -721.7748\n 723000 6872000 37 -717.4488\n 723500 6872000 38 -711.6232\n 724000 6872000 39 -704.8765\n 724500 6872000 40 -697.8643\n 725000 6872000 41 -691.2409\n 725500 6872000 42 -685.5693\n 726000 6872000 43 -681.0361\n 726500 6872000 44 -677.5835\n 727000 6872000 45 -674.9259\n 727500 6872000 46 -672.9902\n 728000 6872000 47 -671.7219\n 728500 6872000 48 -670.9357\n 729000 6872000 49 -670.296\n 729500 6872000 50 -669.4681\n 730000 6872000 51 -668.0718\n 730500 6872000 52 -665.8699\n 731000 6872000 53 -662.9372\n 731500 6872000 54 -659.5009\n 732000 6872000 55 -655.8574\n 732500 6872000 56 -652.3915\n 733000 6872000 57 -649.4745\n 733500 6872000 58 -647.2762\n 734000 6872000 59 -645.7734\n 734500 6872000 60 -644.8001\n 735000 6872000 61 -643.9171\n 735500 6872000 62 -642.8397\n 736000 6872000 63 -641.8557\n 736500 6872000 64 -641.2709\n 737000 6872000 65 -641.0311\n 737500 6872000 66 -640.7018\n 738000 6872000 67 -639.8704\n 738500 6872000 68 -638.4078\n 739000 6872000 69 -636.4351\n 739500 6872000 70 -634.3283\n 740000 6872000 71 -632.9855\n 740500 6872000 72 -632.9752\n 741000 6872000 73 -633.6514\n 741500 6872000 74 -634.3564\n 742000 6872000 75 -635.0123\n 742500 6872000 76 -635.7781\n 743000 6872000 77 -636.7814\n 743500 6872000 78 -638.0038\n 744000 6872000 79 -639.3104\n 744500 6872000 80 -640.4595\n 745000 6872000 81 -641.0886\n 745500 6872000 82 -641.1376\n 746000 6872000 83 -640.8064\n 746500 6872000 84 -640.3902\n 747000 6872000 85 -640.1898\n 705000 6872500 1 -760.8854\n 705500 6872500 2 -758.5078\n 706000 6872500 3 -755.6708\n 706500 6872500 4 -752.833\n 707000 6872500 5 -750.4084\n 707500 6872500 6 -748.7398\n 708000 6872500 7 -748.0209\n 708500 6872500 8 -748.1008\n 709000 6872500 9 -748.7062\n 709500 6872500 10 -749.4951\n 710000 6872500 11 -750.1175\n 710500 6872500 12 -750.5391\n 711000 6872500 13 -750.9451\n 711500 6872500 14 -751.4599\n 712000 6872500 15 -751.9943\n 712500 6872500 16 -752.2485\n 713000 6872500 17 -751.7649\n 713500 6872500 18 -750.2178\n 714000 6872500 19 -747.7183\n 714500 6872500 20 -744.5666\n 715000 6872500 21 -741.0038\n 715500 6872500 22 -737.0853\n 716000 6872500 23 -732.9068\n 716500 6872500 24 -728.6429\n 717000 6872500 25 -724.644\n 717500 6872500 26 -721.4161\n 718000 6872500 27 -719.5494\n 718500 6872500 28 -719.3044\n 719000 6872500 29 -720.4146\n 719500 6872500 30 -722.2512\n 720000 6872500 31 -724.1664\n 720500 6872500 32 -725.4828\n 721000 6872500 33 -725.8965\n 721500 6872500 34 -725.4852\n 722000 6872500 35 -724.2286\n 722500 6872500 36 -721.8019\n 723000 6872500 37 -717.5975\n 723500 6872500 38 -711.8199\n 724000 6872500 39 -705.0431\n 724500 6872500 40 -697.9443\n 725000 6872500 41 -691.2621\n 725500 6872500 42 -685.6729\n 726000 6872500 43 -681.3614\n 726500 6872500 44 -678.1487\n 727000 6872500 45 -675.653\n 727500 6872500 46 -673.7598\n 728000 6872500 47 -672.4067\n 728500 6872500 48 -671.439\n 729000 6872500 49 -670.5761\n 729500 6872500 50 -669.5066\n 730000 6872500 51 -667.8339\n 730500 6872500 52 -665.2832\n 731000 6872500 53 -661.9632\n 731500 6872500 54 -658.1487\n 732000 6872500 55 -654.1442\n 732500 6872500 56 -650.2955\n 733000 6872500 57 -646.9634\n 733500 6872500 58 -644.3643\n 734000 6872500 59 -642.5734\n 734500 6872500 60 -641.4494\n 735000 6872500 61 -640.5588\n 735500 6872500 62 -639.5461\n 736000 6872500 63 -638.7867\n 736500 6872500 64 -638.6418\n 737000 6872500 65 -638.9785\n 737500 6872500 66 -639.1876\n 738000 6872500 67 -638.754\n 738500 6872500 68 -637.6391\n 739000 6872500 69 -636.0681\n 739500 6872500 70 -634.4011\n 740000 6872500 71 -633.1853\n 740500 6872500 72 -632.9196\n 741000 6872500 73 -633.1687\n 741500 6872500 74 -633.5182\n 742000 6872500 75 -633.9809\n 742500 6872500 76 -634.7376\n 743000 6872500 77 -635.8984\n 743500 6872500 78 -637.3928\n 744000 6872500 79 -639.0175\n 744500 6872500 80 -640.4712\n 745000 6872500 81 -641.3165\n 745500 6872500 82 -641.4581\n 746000 6872500 83 -641.1113\n 746500 6872500 84 -640.5706\n 747000 6872500 85 -640.1169\n 705000 6873000 1 -762.621\n 705500 6873000 2 -759.8061\n 706000 6873000 3 -756.4051\n 706500 6873000 4 -753.0361\n 707000 6873000 5 -750.249\n 707500 6873000 6 -748.4914\n 708000 6873000 7 -747.9705\n 708500 6873000 8 -748.3953\n 709000 6873000 9 -749.3344\n 709500 6873000 10 -750.3224\n 710000 6873000 11 -750.9233\n 710500 6873000 12 -751.0794\n 711000 6873000 13 -751.0689\n 711500 6873000 14 -751.1558\n 712000 6873000 15 -751.4086\n 712500 6873000 16 -751.6254\n 713000 6873000 17 -751.3752\n 713500 6873000 18 -750.2568\n 714000 6873000 19 -748.2352\n 714500 6873000 20 -745.4778\n 715000 6873000 21 -742.1465\n 715500 6873000 22 -738.2855\n 716000 6873000 23 -734.0484\n 716500 6873000 24 -729.6393\n 717000 6873000 25 -725.4124\n 717500 6873000 26 -721.8686\n 718000 6873000 27 -719.5511\n 718500 6873000 28 -718.7692\n 719000 6873000 29 -719.3914\n 719500 6873000 30 -720.8912\n 720000 6873000 31 -722.6395\n 720500 6873000 32 -723.9184\n 721000 6873000 33 -724.3807\n 721500 6873000 34 -724.1213\n 722000 6873000 35 -723.1212\n 722500 6873000 36 -721.0134\n 723000 6873000 37 -717.2155\n 723500 6873000 38 -711.7954\n 724000 6873000 39 -705.2951\n 724500 6873000 40 -698.3839\n 725000 6873000 41 -691.8326\n 725500 6873000 42 -686.3872\n 726000 6873000 43 -682.2547\n 726500 6873000 44 -679.1896\n 727000 6873000 45 -676.756\n 727500 6873000 46 -674.8075\n 728000 6873000 47 -673.2594\n 728500 6873000 48 -671.9806\n 729000 6873000 49 -670.7527\n 729500 6873000 50 -669.3064\n 730000 6873000 51 -667.2935\n 730500 6873000 52 -664.433\n 731000 6873000 53 -660.8227\n 731500 6873000 54 -656.7188\n 732000 6873000 55 -652.3724\n 732500 6873000 56 -648.0275\n 733000 6873000 57 -643.9525\n 733500 6873000 58 -640.3751\n 734000 6873000 59 -637.527\n 734500 6873000 60 -635.4373\n 735000 6873000 61 -634.0397\n 735500 6873000 62 -633.0963\n 736000 6873000 63 -632.8763\n 736500 6873000 64 -633.5424\n 737000 6873000 65 -634.7797\n 737500 6873000 66 -635.8372\n 738000 6873000 67 -636.1608\n 738500 6873000 68 -635.8359\n 739000 6873000 69 -635.1838\n 739500 6873000 70 -634.4544\n 740000 6873000 71 -633.7174\n 740500 6873000 72 -633.1187\n 741000 6873000 73 -632.6359\n 741500 6873000 74 -632.3459\n 742000 6873000 75 -632.429\n 742500 6873000 76 -633.076\n 743000 6873000 77 -634.345\n 743500 6873000 78 -636.0805\n 744000 6873000 79 -637.9949\n 744500 6873000 80 -639.7477\n 745000 6873000 81 -640.9311\n 745500 6873000 82 -641.4072\n 746000 6873000 83 -641.3262\n 746500 6873000 84 -640.9114\n 747000 6873000 85 -640.3883\n 705000 6873500 1 -764.005\n 705500 6873500 2 -760.7298\n 706000 6873500 3 -756.7205\n 706500 6873500 4 -752.7531\n 707000 6873500 5 -749.5254\n 707500 6873500 6 -747.6022\n 708000 6873500 7 -747.1975\n 708500 6873500 8 -747.8791\n 709000 6873500 9 -749.0674\n 709500 6873500 10 -750.1844\n 710000 6873500 11 -750.7368\n 710500 6873500 12 -750.6508\n 711000 6873500 13 -750.2541\n 711500 6873500 14 -749.9186\n 712000 6873500 15 -749.909\n 712500 6873500 16 -750.1702\n 713000 6873500 17 -750.2955\n 713500 6873500 18 -749.7947\n 714000 6873500 19 -748.4519\n 714500 6873500 20 -746.2599\n 715000 6873500 21 -743.2761\n 715500 6873500 22 -739.5356\n 716000 6873500 23 -735.2792\n 716500 6873500 24 -730.7438\n 717000 6873500 25 -726.2684\n 717500 6873500 26 -722.3107\n 718000 6873500 27 -719.3445\n 718500 6873500 28 -717.681\n 719000 6873500 29 -717.3515\n 719500 6873500 30 -718.02\n 720000 6873500 31 -719.2162\n 720500 6873500 32 -720.3715\n 721000 6873500 33 -721.1089\n 721500 6873500 34 -721.384\n 722000 6873500 35 -721.042\n 722500 6873500 36 -719.6258\n 723000 6873500 37 -716.5354\n 723500 6873500 38 -711.76\n 724000 6873500 39 -705.82\n 724500 6873500 40 -699.368\n 725000 6873500 41 -693.1499\n 725500 6873500 42 -687.8854\n 726000 6873500 43 -683.8234\n 726500 6873500 44 -680.7408\n 727000 6873500 45 -678.2139\n 727500 6873500 46 -676.0715\n 728000 6873500 47 -674.2014\n 728500 6873500 48 -672.4797\n 729000 6873500 49 -670.738\n 729500 6873500 50 -668.767\n 730000 6873500 51 -666.3132\n 730500 6873500 52 -663.183\n 731000 6873500 53 -659.4004\n 731500 6873500 54 -655.1188\n 732000 6873500 55 -650.4679\n 732500 6873500 56 -645.5447\n 733000 6873500 57 -640.4745\n 733500 6873500 58 -635.4435\n 734000 6873500 59 -630.8115\n 734500 6873500 60 -626.9294\n 735000 6873500 61 -624.3934\n 735500 6873500 62 -623.4602\n 736000 6873500 63 -624.0349\n 736500 6873500 64 -625.7839\n 737000 6873500 65 -628.1067\n 737500 6873500 66 -630.1187\n 738000 6873500 67 -631.3036\n 738500 6873500 68 -631.9299\n 739000 6873500 69 -632.4485\n 739500 6873500 70 -632.9474\n 740000 6873500 71 -632.9545\n 740500 6873500 72 -632.1608\n 741000 6873500 73 -630.9594\n 741500 6873500 74 -630.0367\n 742000 6873500 75 -629.7786\n 742500 6873500 76 -630.3886\n 743000 6873500 77 -631.8535\n 743500 6873500 78 -633.9089\n 744000 6873500 79 -636.1749\n 744500 6873500 80 -638.2782\n 745000 6873500 81 -639.9138\n 745500 6873500 82 -640.9318\n 746000 6873500 83 -641.3513\n 746500 6873500 84 -641.2693\n 747000 6873500 85 -640.8374\n 705000 6874000 1 -765.0767\n 705500 6874000 2 -761.3064\n 706000 6874000 3 -756.6422\n 706500 6874000 4 -752.0214\n 707000 6874000 5 -748.2858\n 707500 6874000 6 -746.1162\n 708000 6874000 7 -745.7524\n 708500 6874000 8 -746.6323\n 709000 6874000 9 -748.0393\n 709500 6874000 10 -749.2738\n 710000 6874000 11 -749.7598\n 710500 6874000 12 -749.4157\n 711000 6874000 13 -748.6334\n 711500 6874000 14 -747.8923\n 712000 6874000 15 -747.6771\n 712500 6874000 16 -748.1056\n 713000 6874000 17 -748.7573\n 713500 6874000 18 -749.0035\n 714000 6874000 19 -748.4407\n 714500 6874000 20 -746.8915\n 715000 6874000 21 -744.3113\n 715500 6874000 22 -740.7269\n 716000 6874000 23 -736.4689\n 716500 6874000 24 -731.7975\n 717000 6874000 25 -727.0186\n 717500 6874000 26 -722.5211\n 718000 6874000 27 -718.7116\n 718500 6874000 28 -715.9058\n 719000 6874000 29 -714.2886\n 719500 6874000 30 -713.7615\n 720000 6874000 31 -714.1138\n 720500 6874000 32 -715.0796\n 721000 6874000 33 -716.3112\n 721500 6874000 34 -717.4922\n 722000 6874000 35 -718.2043\n 722500 6874000 36 -717.8369\n 723000 6874000 37 -715.7236\n 723500 6874000 38 -711.8119\n 724000 6874000 39 -706.6182\n 724500 6874000 40 -700.7937\n 725000 6874000 41 -695.0385\n 725500 6874000 42 -689.9893\n 726000 6874000 43 -685.9135\n 726500 6874000 44 -682.6783\n 727000 6874000 45 -679.924\n 727500 6874000 46 -677.4688\n 728000 6874000 47 -675.1736\n 728500 6874000 48 -672.9168\n 729000 6874000 49 -670.5717\n 729500 6874000 50 -667.9947\n 730000 6874000 51 -665.0458\n 730500 6874000 52 -661.6507\n 731000 6874000 53 -657.7646\n 731500 6874000 54 -653.3854\n 732000 6874000 55 -648.4885\n 732500 6874000 56 -642.9991\n 733000 6874000 57 -636.8715\n 733500 6874000 58 -630.2042\n 734000 6874000 59 -623.4494\n 734500 6874000 60 -617.3116\n 735000 6874000 61 -613.1222\n 735500 6874000 62 -611.856\n 736000 6874000 63 -613.0426\n 736500 6874000 64 -615.7021\n 737000 6874000 65 -618.9307\n 737500 6874000 66 -621.6642\n 738000 6874000 67 -623.4331\n 738500 6874000 68 -624.7928\n 739000 6874000 69 -626.3979\n 739500 6874000 70 -628.1931\n 740000 6874000 71 -629.1527\n 740500 6874000 72 -628.5087\n 741000 6874000 73 -626.9345\n 741500 6874000 74 -625.7097\n 742000 6874000 75 -625.4094\n 742500 6874000 76 -626.2643\n 743000 6874000 77 -628.1941\n 743500 6874000 78 -630.8132\n 744000 6874000 79 -633.6397\n 744500 6874000 80 -636.2698\n 745000 6874000 81 -638.4834\n 745500 6874000 82 -640.1534\n 746000 6874000 83 -641.168\n 746500 6874000 84 -641.5003\n 747000 6874000 85 -641.2292\n 705000 6874500 1 -765.7789\n 705500 6874500 2 -761.5137\n 706000 6874500 3 -756.2001\n 706500 6874500 4 -750.9338\n 707000 6874500 5 -746.6699\n 707500 6874500 6 -744.175\n 708000 6874500 7 -743.7383\n 708500 6874500 8 -744.715\n 709000 6874500 9 -746.2891\n 709500 6874500 10 -747.6411\n 710000 6874500 11 -748.0726\n 710500 6874500 12 -747.4932\n 711000 6874500 13 -746.3834\n 711500 6874500 14 -745.3312\n 712000 6874500 15 -745.0212\n 712500 6874500 16 -745.7408\n 713000 6874500 17 -747.0132\n 713500 6874500 18 -748.0363\n 714000 6874500 19 -748.2407\n 714500 6874500 20 -747.316\n 715000 6874500 21 -745.136\n 715500 6874500 22 -741.7203\n 716000 6874500 23 -737.4734\n 716500 6874500 24 -732.6581\n 717000 6874500 25 -727.5306\n 717500 6874500 26 -722.3939\n 718000 6874500 27 -717.6075\n 718500 6874500 28 -713.5178\n 719000 6874500 29 -710.4601\n 719500 6874500 30 -708.5939\n 720000 6874500 31 -708.0075\n 720500 6874500 32 -708.7646\n 721000 6874500 33 -710.5932\n 721500 6874500 34 -712.8813\n 722000 6874500 35 -714.8867\n 722500 6874500 36 -715.8028\n 723000 6874500 37 -714.8412\n 723500 6874500 38 -711.9303\n 724000 6874500 39 -707.5684\n 724500 6874500 40 -702.4301\n 725000 6874500 41 -697.1691\n 725500 6874500 42 -692.3469\n 726000 6874500 43 -688.2322\n 726500 6874500 44 -684.7857\n 727000 6874500 45 -681.7312\n 727500 6874500 46 -678.8923\n 728000 6874500 47 -676.1169\n 728500 6874500 48 -673.2916\n 729000 6874500 49 -670.3295\n 729500 6874500 50 -667.1522\n 730000 6874500 51 -663.7186\n 730500 6874500 52 -660.0554\n 731000 6874500 53 -656.0667\n 731500 6874500 54 -651.6128\n 732000 6874500 55 -646.5353\n 732500 6874500 56 -640.6162\n 733000 6874500 57 -633.652\n 733500 6874500 58 -625.6371\n 734000 6874500 59 -616.9902\n 734500 6874500 60 -608.6885\n 735000 6874500 61 -602.5756\n 735500 6874500 62 -600.3066\n 736000 6874500 63 -601.1883\n 736500 6874500 64 -603.8336\n 737000 6874500 65 -607.1765\n 737500 6874500 66 -609.9556\n 738000 6874500 67 -611.6624\n 738500 6874500 68 -613.2027\n 739000 6874500 69 -615.4993\n 739500 6874500 70 -618.4116\n 740000 6874500 71 -620.5074\n 740500 6874500 72 -620.5942\n 741000 6874500 73 -619.4103\n 741500 6874500 74 -618.5825\n 742000 6874500 75 -618.8223\n 742500 6874500 76 -620.4077\n 743000 6874500 77 -623.2298\n 743500 6874500 78 -626.7952\n 744000 6874500 79 -630.5229\n 744500 6874500 80 -633.9563\n 745000 6874500 81 -636.8873\n 745500 6874500 82 -639.2158\n 746000 6874500 83 -640.7657\n 746500 6874500 84 -641.4538\n 747000 6874500 85 -641.3216\n 705000 6875000 1 -765.915\n 705500 6875000 2 -761.2118\n 706000 6875000 3 -755.3193\n 706500 6875000 4 -749.4548\n 707000 6875000 5 -744.6825\n 707500 6875000 6 -741.806\n 708000 6875000 7 -741.1729\n 708500 6875000 8 -742.1015\n 709000 6875000 9 -743.7126\n 709500 6875000 10 -745.1183\n 710000 6875000 11 -745.5424\n 710500 6875000 12 -744.8891\n 711000 6875000 13 -743.6848\n 711500 6875000 14 -742.5987\n 712000 6875000 15 -742.3932\n 712500 6875000 16 -743.4927\n 713000 6875000 17 -745.3513\n 713500 6875000 18 -747.0236\n 714000 6875000 19 -747.8348\n 714500 6875000 20 -747.4108\n 715000 6875000 21 -745.5787\n 715500 6875000 22 -742.3444\n 716000 6875000 23 -738.1436\n 716500 6875000 24 -733.2105\n 717000 6875000 25 -727.7284\n 717500 6875000 26 -721.9112\n 718000 6875000 27 -716.0977\n 718500 6875000 28 -710.7065\n 719000 6875000 29 -706.2346\n 719500 6875000 30 -703.0932\n 720000 6875000 31 -701.6417\n 720500 6875000 32 -702.2139\n 721000 6875000 33 -704.6215\n 721500 6875000 34 -708.0157\n 722000 6875000 35 -711.3538\n 722500 6875000 36 -713.624\n 723000 6875000 37 -713.8907\n 723500 6875000 38 -712.0331\n 724000 6875000 39 -708.5167\n 724500 6875000 40 -704.0513\n 725000 6875000 41 -699.2614\n 725500 6875000 42 -694.6582\n 726000 6875000 43 -690.5131\n 726500 6875000 44 -686.8516\n 727000 6875000 45 -683.4747\n 727500 6875000 46 -680.2256\n 728000 6875000 47 -676.9595\n 728500 6875000 48 -673.5849\n 729000 6875000 49 -670.0574\n 729500 6875000 50 -666.3585\n 730000 6875000 51 -662.4966\n 730500 6875000 52 -658.5471\n 731000 6875000 53 -654.4025\n 731500 6875000 54 -649.8505\n 732000 6875000 55 -644.6611\n 732500 6875000 56 -638.5502\n 733000 6875000 57 -631.2206\n 733500 6875000 58 -622.5751\n 734000 6875000 59 -612.9143\n 734500 6875000 60 -603.2239\n 735000 6875000 61 -595.161\n 735500 6875000 62 -590.7078\n 736000 6875000 63 -589.5483\n 736500 6875000 64 -590.3875\n 737000 6875000 65 -592.2914\n 737500 6875000 66 -593.9757\n 738000 6875000 67 -594.8001\n 738500 6875000 68 -595.9449\n 739000 6875000 69 -598.5159\n 739500 6875000 70 -602.3018\n 740000 6875000 71 -605.7584\n 740500 6875000 72 -607.3741\n 741000 6875000 73 -607.683\n 741500 6875000 74 -608.243\n 742000 6875000 75 -609.8196\n 742500 6875000 76 -612.7474\n 743000 6875000 77 -616.9475\n 743500 6875000 78 -621.8724\n 744000 6875000 79 -626.8718\n 744500 6875000 80 -631.4221\n 745000 6875000 81 -635.2043\n 745500 6875000 82 -638.1321\n 746000 6875000 83 -640.0708\n 746500 6875000 84 -640.9854\n 747000 6875000 85 -640.9233\n 705000 6875500 1 -765.1998\n 705500 6875500 2 -760.2047\n 706000 6875500 3 -753.8889\n 706500 6875500 4 -747.5019\n 707000 6875500 5 -742.1996\n 707500 6875500 6 -738.9026\n 708000 6875500 7 -737.9766\n 708500 6875500 8 -738.6848\n 709000 6875500 9 -740.1135\n 709500 6875500 10 -741.4287\n 710000 6875500 11 -741.9132\n 710500 6875500 12 -741.474\n 711000 6875500 13 -740.6163\n 711500 6875500 14 -739.9987\n 712000 6875500 15 -740.2476\n 712500 6875500 16 -741.7606\n 713000 6875500 17 -744.0047\n 713500 6875500 18 -746.0027\n 714000 6875500 19 -747.0957\n 714500 6875500 20 -746.9403\n 715000 6875500 21 -745.3681\n 715500 6875500 22 -742.3501\n 716000 6875500 23 -738.2788\n 716500 6875500 24 -733.3165\n 717000 6875500 25 -727.5419\n 717500 6875500 26 -721.0786\n 718000 6875500 27 -714.2761\n 718500 6875500 28 -707.6716\n 719000 6875500 29 -701.9362\n 719500 6875500 30 -697.7089\n 720000 6875500 31 -695.5598\n 720500 6875500 32 -695.9915\n 721000 6875500 33 -698.8911\n 721500 6875500 34 -703.2756\n 722000 6875500 35 -707.8669\n 722500 6875500 36 -711.4388\n 723000 6875500 37 -712.9039\n 723500 6875500 38 -712.0591\n 724000 6875500 39 -709.3215\n 724500 6875500 40 -705.4484\n 725000 6875500 41 -701.0632\n 725500 6875500 42 -696.6607\n 726000 6875500 43 -692.5135\n 726500 6875500 44 -688.6716\n 727000 6875500 45 -684.9894\n 727500 6875500 46 -681.3328\n 728000 6875500 47 -677.5851\n 728500 6875500 48 -673.6931\n 729000 6875500 49 -669.6647\n 729500 6875500 50 -665.5333\n 730000 6875500 51 -661.3333\n 730500 6875500 52 -657.0924\n 731000 6875500 53 -652.7382\n 731500 6875500 54 -648.0801\n 732000 6875500 55 -642.8905\n 732500 6875500 56 -636.8893\n 733000 6875500 57 -629.7693\n 733500 6875500 58 -621.3157\n 734000 6875500 59 -611.6123\n 734500 6875500 60 -601.3308\n 735000 6875500 61 -591.5587\n 735500 6875500 62 -583.695\n 736000 6875500 63 -578.1057\n 736500 6875500 64 -574.4231\n 737000 6875500 65 -572.3843\n 737500 6875500 66 -571.361\n 738000 6875500 67 -570.7415\n 738500 6875500 68 -571.5656\n 739000 6875500 69 -574.6691\n 739500 6875500 70 -579.5407\n 740000 6875500 71 -584.7095\n 740500 6875500 72 -588.6873\n 741000 6875500 73 -591.6074\n 741500 6875500 74 -594.6134\n 742000 6875500 75 -598.4007\n 742500 6875500 76 -603.2977\n 743000 6875500 77 -609.2875\n 743500 6875500 78 -615.8746\n 744000 6875500 79 -622.4224\n 744500 6875500 80 -628.3474\n 745000 6875500 81 -633.1157\n 745500 6875500 82 -636.593\n 746000 6875500 83 -638.7954\n 746500 6875500 84 -639.8461\n 747000 6875500 85 -639.847\n 705000 6876000 1 -763.8593\n 705500 6876000 2 -758.7505\n 706000 6876000 3 -752.1906\n 706500 6876000 4 -745.3663\n 707000 6876000 5 -739.5229\n 707500 6876000 6 -735.7653\n 708000 6876000 7 -734.4942\n 708500 6876000 8 -734.8568\n 709000 6876000 9 -735.9134\n 709500 6876000 10 -736.9495\n 710000 6876000 11 -737.4503\n 710500 6876000 12 -737.3647\n 711000 6876000 13 -737.1162\n 711500 6876000 14 -737.2484\n 712000 6876000 15 -738.2546\n 712500 6876000 16 -740.2642\n 713000 6876000 17 -742.7445\n 713500 6876000 18 -744.8044\n 714000 6876000 19 -745.9056\n 714500 6876000 20 -745.813\n 715000 6876000 21 -744.4197\n 715500 6876000 22 -741.655\n 716000 6876000 23 -737.8077\n 716500 6876000 24 -732.9324\n 717000 6876000 25 -726.979\n 717500 6876000 26 -719.972\n 718000 6876000 27 -712.2833\n 718500 6876000 28 -704.5912\n 719000 6876000 29 -697.7259\n 719500 6876000 30 -692.5265\n 720000 6876000 31 -689.7587\n 720500 6876000 32 -690.0958\n 721000 6876000 33 -693.4755\n 721500 6876000 34 -698.8113\n 722000 6876000 35 -704.6205\n 722500 6876000 36 -709.4487\n 723000 6876000 37 -712.0062\n 723500 6876000 38 -712.0236\n 724000 6876000 39 -709.9301\n 724500 6876000 40 -706.5161\n 725000 6876000 41 -702.4492\n 725500 6876000 42 -698.2266\n 726000 6876000 43 -694.1149\n 726500 6876000 44 -690.147\n 727000 6876000 45 -686.1976\n 727500 6876000 46 -682.147\n 728000 6876000 47 -677.9119\n 728500 6876000 48 -673.4954\n 729000 6876000 49 -668.9717\n 729500 6876000 50 -664.4357\n 730000 6876000 51 -659.9307\n 730500 6876000 52 -655.4204\n 731000 6876000 53 -650.865\n 731500 6876000 54 -646.1602\n 732000 6876000 55 -641.1273\n 732500 6876000 56 -635.5188\n 733000 6876000 57 -629.0336\n 733500 6876000 58 -621.3283\n 734000 6876000 59 -612.1245\n 734500 6876000 60 -601.5903\n 735000 6876000 61 -589.9833\n 735500 6876000 62 -577.9285\n 736000 6876000 63 -566.2075\n 736500 6876000 64 -555.6744\n 737000 6876000 65 -547.181\n 737500 6876000 66 -541.9714\n 738000 6876000 67 -539.9272\n 738500 6876000 68 -541.2493\n 739000 6876000 69 -545.7938\n 739500 6876000 70 -552.5068\n 740000 6876000 71 -559.8768\n 740500 6876000 72 -566.6332\n 741000 6876000 73 -572.6641\n 741500 6876000 74 -578.6748\n 742000 6876000 75 -585.1865\n 742500 6876000 76 -592.4099\n 743000 6876000 77 -600.3456\n 743500 6876000 78 -608.6599\n 744000 6876000 79 -616.8339\n 744500 6876000 80 -624.2377\n 745000 6876000 81 -630.0739\n 745500 6876000 82 -634.1195\n 746000 6876000 83 -636.593\n 746500 6876000 84 -637.8281\n 747000 6876000 85 -638.0156\n 705000 6876500 1 -762.3191\n 705500 6876500 2 -757.3102\n 706000 6876500 3 -750.7224\n 706500 6876500 4 -743.6182\n 707000 6876500 5 -737.2895\n 707500 6876500 6 -733.0462\n 708000 6876500 7 -731.3305\n 708500 6876500 8 -731.1697\n 709000 6876500 9 -731.6323\n 709500 6876500 10 -732.1716\n 710000 6876500 11 -732.5089\n 710500 6876500 12 -732.6846\n 711000 6876500 13 -733.0244\n 711500 6876500 14 -733.9455\n 712000 6876500 15 -735.7413\n 712500 6876500 16 -738.3264\n 713000 6876500 17 -741.0249\n 713500 6876500 18 -743.0247\n 714000 6876500 19 -743.9824\n 714500 6876500 20 -743.8469\n 715000 6876500 21 -742.5964\n 715500 6876500 22 -740.1331\n 716000 6876500 23 -736.6047\n 716500 6876500 24 -731.9354\n 717000 6876500 25 -725.9471\n 717500 6876500 26 -718.5669\n 718000 6876500 27 -710.184\n 718500 6876500 28 -701.587\n 719000 6876500 29 -693.7247\n 719500 6876500 30 -687.6021\n 720000 6876500 31 -684.1941\n 720500 6876500 32 -684.431\n 721000 6876500 33 -688.3297\n 721500 6876500 34 -694.6499\n 722000 6876500 35 -701.6776\n 722500 6876500 36 -707.7247\n 723000 6876500 37 -711.2371\n 723500 6876500 38 -711.9106\n 724000 6876500 39 -710.2624\n 724500 6876500 40 -707.1541\n 725000 6876500 41 -703.3205\n 725500 6876500 42 -699.2698\n 726000 6876500 43 -695.2454\n 726500 6876500 44 -691.2178\n 727000 6876500 45 -687.0431\n 727500 6876500 46 -682.6011\n 728000 6876500 47 -677.84\n 728500 6876500 48 -672.8332\n 729000 6876500 49 -667.7476\n 729500 6876500 50 -662.7592\n 730000 6876500 51 -657.9415\n 730500 6876500 52 -653.1846\n 731000 6876500 53 -648.4928\n 731500 6876500 54 -643.8655\n 732000 6876500 55 -639.1797\n 732500 6876500 56 -634.2184\n 733000 6876500 57 -628.6773\n 733500 6876500 58 -622.0527\n 734000 6876500 59 -613.6503\n 734500 6876500 60 -603.0388\n 735000 6876500 61 -589.5692\n 735500 6876500 62 -572.9605\n 736000 6876500 63 -554.3405\n 736500 6876500 64 -535.7982\n 737000 6876500 65 -519.4982\n 737500 6876500 66 -508.8849\n 738000 6876500 67 -505.2299\n 738500 6876500 68 -507.6305\n 739000 6876500 69 -514.2628\n 739500 6876500 70 -523.2644\n 740000 6876500 71 -533.0947\n 740500 6876500 72 -542.693\n 741000 6876500 73 -551.848\n 741500 6876500 74 -560.9544\n 742000 6876500 75 -570.309\n 742500 6876500 76 -579.9415\n 743000 6876500 77 -589.7966\n 743500 6876500 78 -599.7563\n 744000 6876500 79 -609.498\n 744500 6876500 80 -618.3808\n 745000 6876500 81 -625.3389\n 745500 6876500 82 -630.054\n 746000 6876500 83 -632.9597\n 746500 6876500 84 -634.5986\n 747000 6876500 85 -635.2278\n 705000 6877000 1 -760.9938\n 705500 6877000 2 -756.3818\n 706000 6877000 3 -750.0757\n 706500 6877000 4 -742.9985\n 707000 6877000 5 -736.3839\n 707500 6877000 6 -731.6337\n 708000 6877000 7 -729.2498\n 708500 6877000 8 -728.2413\n 709000 6877000 9 -727.7697\n 709500 6877000 10 -727.4857\n 710000 6877000 11 -727.3471\n 710500 6877000 12 -727.4898\n 711000 6877000 13 -728.1642\n 711500 6877000 14 -729.6739\n 712000 6877000 15 -732.1544\n 712500 6877000 16 -735.2852\n 713000 6877000 17 -738.2259\n 713500 6877000 18 -740.1521\n 714000 6877000 19 -740.9307\n 714500 6877000 20 -740.7448\n 715000 6877000 21 -739.6845\n 715500 6877000 22 -737.6168\n 716000 6877000 23 -734.5138\n 716500 6877000 24 -730.1672\n 717000 6877000 25 -724.3141\n 717500 6877000 26 -716.8091\n 718000 6877000 27 -708.0356\n 718500 6877000 28 -698.8177\n 719000 6877000 29 -690.1644\n 719500 6877000 30 -683.2103\n 720000 6877000 31 -679.1544\n 720500 6877000 32 -679.2442\n 721000 6877000 33 -683.6207\n 721500 6877000 34 -690.8535\n 722000 6877000 35 -698.975\n 722500 6877000 36 -706.0832\n 723000 6877000 37 -710.3654\n 723500 6877000 38 -711.4967\n 724000 6877000 39 -710.1371\n 724500 6877000 40 -707.241\n 725000 6877000 41 -703.6078\n 725500 6877000 42 -699.7565\n 726000 6877000 43 -695.8905\n 726500 6877000 44 -691.8732\n 727000 6877000 45 -687.504\n 727500 6877000 46 -682.6454\n 728000 6877000 47 -677.2716\n 728500 6877000 48 -671.5366\n 729000 6877000 49 -665.7302\n 729500 6877000 50 -660.1418\n 730000 6877000 51 -654.9361\n 730500 6877000 52 -649.9664\n 731000 6877000 53 -645.2728\n 731500 6877000 54 -640.9299\n 732000 6877000 55 -636.8306\n 732500 6877000 56 -632.7405\n 733000 6877000 57 -628.3115\n 733500 6877000 58 -622.854\n 734000 6877000 59 -615.2715\n 734500 6877000 60 -604.5743\n 735000 6877000 61 -589.3489\n 735500 6877000 62 -568.5566\n 736000 6877000 63 -543.5229\n 736500 6877000 64 -517.4174\n 737000 6877000 65 -493.6918\n 737500 6877000 66 -477.7217\n 738000 6877000 67 -472.1274\n 738500 6877000 68 -475.1615\n 739000 6877000 69 -483.4118\n 739500 6877000 70 -494.2744\n 740000 6877000 71 -506.1511\n 740500 6877000 72 -518.1489\n 741000 6877000 73 -530.019\n 741500 6877000 74 -541.8842\n 742000 6877000 75 -553.7886\n 742500 6877000 76 -565.5865\n 743000 6877000 77 -577.1554\n 743500 6877000 78 -588.5538\n 744000 6877000 79 -599.6539\n 744500 6877000 80 -609.8524\n 745000 6877000 81 -617.958\n 745500 6877000 82 -623.5824\n 746000 6877000 83 -627.3019\n 746500 6877000 84 -629.7718\n 747000 6877000 85 -631.2473\n 705000 6877500 1 -760.1796\n 705500 6877500 2 -756.3053\n 706000 6877500 3 -750.6395\n 706500 6877500 4 -743.9722\n 707000 6877500 5 -737.3914\n 707500 6877500 6 -732.1389\n 708000 6877500 7 -728.7811\n 708500 6877500 8 -726.5028\n 709000 6877500 9 -724.6782\n 709500 6877500 10 -723.1652\n 710000 6877500 11 -722.1224\n 710500 6877500 12 -721.7773\n 711000 6877500 13 -722.3381\n 711500 6877500 14 -724.0347\n 712000 6877500 15 -726.8981\n 712500 6877500 16 -730.425\n 713000 6877500 17 -733.6105\n 713500 6877500 18 -735.5485\n 714000 6877500 19 -736.2774\n 714500 6877500 20 -736.2032\n 715000 6877500 21 -735.5222\n 715500 6877500 22 -734.039\n 716000 6877500 23 -731.5269\n 716500 6877500 24 -727.6531\n 717000 6877500 25 -722.1179\n 717500 6877500 26 -714.7836\n 718000 6877500 27 -706.0033\n 718500 6877500 28 -696.5499\n 719000 6877500 29 -687.4528\n 719500 6877500 30 -679.9214\n 720000 6877500 31 -675.3413\n 720500 6877500 32 -675.2047\n 721000 6877500 33 -679.7966\n 721500 6877500 34 -687.5497\n 722000 6877500 35 -696.3099\n 722500 6877500 36 -704.0667\n 723000 6877500 37 -708.861\n 723500 6877500 38 -710.3381\n 724000 6877500 39 -709.2635\n 724500 6877500 40 -706.6406\n 725000 6877500 41 -703.2791\n 725500 6877500 42 -699.7122\n 726000 6877500 43 -696.0967\n 726500 6877500 44 -692.1542\n 727000 6877500 45 -687.6006\n 727500 6877500 46 -682.2651\n 728000 6877500 47 -676.1408\n 728500 6877500 48 -669.4639\n 729000 6877500 49 -662.6725\n 729500 6877500 50 -656.2119\n 730000 6877500 51 -650.4438\n 730500 6877500 52 -645.3084\n 731000 6877500 53 -640.8384\n 731500 6877500 54 -637.0954\n 732000 6877500 55 -633.8942\n 732500 6877500 56 -630.8877\n 733000 6877500 57 -627.6072\n 733500 6877500 58 -623.1555\n 734000 6877500 59 -616.1062\n 734500 6877500 60 -605.087\n 735000 6877500 61 -588.3284\n 735500 6877500 62 -564.4108\n 736000 6877500 63 -534.7401\n 736500 6877500 64 -503.1877\n 737000 6877500 65 -474.1626\n 737500 6877500 66 -453.8426\n 738000 6877500 67 -445.7985\n 738500 6877500 68 -448.0416\n 739000 6877500 69 -456.3322\n 739500 6877500 70 -467.7047\n 740000 6877500 71 -480.5765\n 740500 6877500 72 -494.1284\n 741000 6877500 73 -507.9801\n 741500 6877500 74 -521.9165\n 742000 6877500 75 -535.732\n 742500 6877500 76 -549.1868\n 743000 6877500 77 -562.0978\n 743500 6877500 78 -574.603\n 744000 6877500 79 -586.6855\n 744500 6877500 80 -597.8418\n 745000 6877500 81 -607.0689\n 745500 6877500 82 -613.9921\n 746000 6877500 83 -619.1458\n 746500 6877500 84 -623.0823\n 747000 6877500 85 -625.9416\n 705000 6878000 1 -760.041\n 705500 6878000 2 -757.1769\n 706000 6878000 3 -752.4149\n 706500 6878000 4 -746.4387\n 707000 6878000 5 -740.142\n 707500 6878000 6 -734.4524\n 708000 6878000 7 -729.8929\n 708500 6878000 8 -726.0051\n 709000 6878000 9 -722.4659\n 709500 6878000 10 -719.3293\n 710000 6878000 11 -716.9059\n 710500 6878000 12 -715.5135\n 711000 6878000 13 -715.3697\n 711500 6878000 14 -716.6854\n 712000 6878000 15 -719.4546\n 712500 6878000 16 -723.062\n 713000 6878000 17 -726.452\n 713500 6878000 18 -728.6019\n 714000 6878000 19 -729.6035\n 714500 6878000 20 -729.9921\n 715000 6878000 21 -730.0408\n 715500 6878000 22 -729.4699\n 716000 6878000 23 -727.8317\n 716500 6878000 24 -724.6755\n 717000 6878000 25 -719.6916\n 717500 6878000 26 -712.8054\n 718000 6878000 27 -704.3815\n 718500 6878000 28 -695.1368\n 719000 6878000 29 -686.0613\n 719500 6878000 30 -678.394\n 720000 6878000 31 -673.579\n 720500 6878000 32 -673.1002\n 721000 6878000 33 -677.3593\n 721500 6878000 34 -684.8431\n 722000 6878000 35 -693.4105\n 722500 6878000 36 -701.125\n 723000 6878000 37 -706.1245\n 723500 6878000 38 -707.9726\n 724000 6878000 39 -707.3817\n 724500 6878000 40 -705.2598\n 725000 6878000 41 -702.3537\n 725500 6878000 42 -699.2135\n 726000 6878000 43 -695.96\n 726500 6878000 44 -692.1534\n 727000 6878000 45 -687.4113\n 727500 6878000 46 -681.5157\n 728000 6878000 47 -674.4683\n 728500 6878000 48 -666.5821\n 729000 6878000 49 -658.46\n 729500 6878000 50 -650.7595\n 730000 6878000 51 -644.1451\n 730500 6878000 52 -638.8831\n 731000 6878000 53 -634.9198\n 731500 6878000 54 -632.1579\n 732000 6878000 55 -630.2053\n 732500 6878000 56 -628.4969\n 733000 6878000 57 -626.3477\n 733500 6878000 58 -622.6594\n 734000 6878000 59 -615.777\n 734500 6878000 60 -604.196\n 735000 6878000 61 -586.168\n 735500 6878000 62 -560.4387\n 736000 6878000 63 -528.4104\n 736500 6878000 64 -494.2142\n 737000 6878000 65 -462.5975\n 737500 6878000 66 -439.6889\n 738000 6878000 67 -428.7493\n 738500 6878000 68 -428.4554\n 739000 6878000 69 -434.7803\n 739500 6878000 70 -444.924\n 740000 6878000 71 -457.453\n 740500 6878000 72 -471.5779\n 741000 6878000 73 -486.607\n 741500 6878000 74 -501.7966\n 742000 6878000 75 -516.6805\n 742500 6878000 76 -531.0463\n 743000 6878000 77 -544.8112\n 743500 6878000 78 -558.0459\n 744000 6878000 79 -570.6968\n 744500 6878000 80 -582.3646\n 745000 6878000 81 -592.5761\n 745500 6878000 82 -601.207\n 746000 6878000 83 -608.5021\n 746500 6878000 84 -614.637\n 747000 6878000 85 -619.4861\n 705000 6878500 1 -760.5768\n 705500 6878500 2 -758.8587\n 706000 6878500 3 -755.1093\n 706500 6878500 4 -749.9515\n 707000 6878500 5 -744.0341\n 707500 6878500 6 -737.9712\n 708000 6878500 7 -732.141\n 708500 6878500 8 -726.481\n 709000 6878500 9 -720.9931\n 709500 6878500 10 -715.9099\n 710000 6878500 11 -711.6487\n 710500 6878500 12 -708.6263\n 711000 6878500 13 -707.1386\n 711500 6878500 14 -707.4592\n 712000 6878500 15 -709.6127\n 712500 6878500 16 -712.9656\n 713000 6878500 17 -716.4823\n 713500 6878500 18 -719.1119\n 714000 6878500 19 -720.8196\n 714500 6878500 20 -722.1312\n 715000 6878500 21 -723.3513\n 715500 6878500 22 -724.0886\n 716000 6878500 23 -723.6639\n 716500 6878500 24 -721.519\n 717000 6878500 25 -717.352\n 717500 6878500 26 -711.1655\n 718000 6878500 27 -703.3713\n 718500 6878500 28 -694.6794\n 719000 6878500 29 -686.0171\n 719500 6878500 30 -678.6302\n 720000 6878500 31 -673.8748\n 720500 6878500 32 -672.9816\n 721000 6878500 33 -676.3008\n 721500 6878500 34 -682.6455\n 722000 6878500 35 -690.1566\n 722500 6878500 36 -697.0905\n 723000 6878500 37 -701.9637\n 723500 6878500 38 -704.272\n 724000 6878500 39 -704.4511\n 724500 6878500 40 -703.1288\n 725000 6878500 41 -700.916\n 725500 6878500 42 -698.3781\n 726000 6878500 43 -695.6109\n 726500 6878500 44 -692.0063\n 727000 6878500 45 -687.0795\n 727500 6878500 46 -680.5488\n 728000 6878500 47 -672.4043\n 728500 6878500 48 -663.0313\n 729000 6878500 49 -653.2149\n 729500 6878500 50 -643.8853\n 730000 6878500 51 -636.1051\n 730500 6878500 52 -630.6786\n 731000 6878500 53 -627.4674\n 731500 6878500 54 -626.0204\n 732000 6878500 55 -625.6105\n 732500 6878500 56 -625.384\n 733000 6878500 57 -624.3856\n 733500 6878500 58 -621.3411\n 734000 6878500 59 -614.5067\n 734500 6878500 60 -602.4471\n 735000 6878500 61 -583.6019\n 735500 6878500 62 -557.1207\n 736000 6878500 63 -524.5132\n 736500 6878500 64 -489.781\n 737000 6878500 65 -457.5672\n 737500 6878500 66 -433.3827\n 738000 6878500 67 -419.7578\n 738500 6878500 68 -415.9376\n 739000 6878500 69 -418.9182\n 739500 6878500 70 -426.4709\n 740000 6878500 71 -437.5109\n 740500 6878500 72 -451.3513\n 741000 6878500 73 -466.8513\n 741500 6878500 74 -482.5388\n 742000 6878500 75 -497.6595\n 742500 6878500 76 -512.1376\n 743000 6878500 77 -526.1197\n 743500 6878500 78 -539.6324\n 744000 6878500 79 -552.4822\n 744500 6878500 80 -564.3381\n 745000 6878500 81 -575.3722\n 745500 6878500 82 -585.9477\n 746000 6878500 83 -595.9518\n 746500 6878500 84 -604.9396\n 747000 6878500 85 -612.3754\n 705000 6879000 1 -761.5499\n 705500 6879000 2 -760.9766\n 706000 6879000 3 -758.2407\n 706500 6879000 4 -753.922\n 707000 6879000 5 -748.4412\n 707500 6879000 6 -742.0613\n 708000 6879000 7 -735.0132\n 708500 6879000 8 -727.5567\n 709000 6879000 9 -719.9836\n 709500 6879000 10 -712.6841\n 710000 6879000 11 -706.1659\n 710500 6879000 12 -700.9546\n 711000 6879000 13 -697.496\n 711500 6879000 14 -696.225\n 712000 6879000 15 -697.3132\n 712500 6879000 16 -700.1954\n 713000 6879000 17 -703.8947\n 713500 6879000 18 -707.3129\n 714000 6879000 19 -710.2087\n 714500 6879000 20 -712.9412\n 715000 6879000 21 -715.7787\n 715500 6879000 22 -718.1891\n 716000 6879000 23 -719.2794\n 716500 6879000 24 -718.412\n 717000 6879000 25 -715.3047\n 717500 6879000 26 -710.0281\n 718000 6879000 27 -703.0548\n 718500 6879000 28 -695.1044\n 719000 6879000 29 -687.0052\n 719500 6879000 30 -679.9652\n 720000 6879000 31 -675.238\n 720500 6879000 32 -673.7706\n 721000 6879000 33 -675.7753\n 721500 6879000 34 -680.4089\n 722000 6879000 35 -686.2953\n 722500 6879000 36 -692.0648\n 723000 6879000 37 -696.5989\n 723500 6879000 38 -699.4442\n 724000 6879000 39 -700.6578\n 724500 6879000 40 -700.4513\n 725000 6879000 41 -699.1854\n 725500 6879000 42 -697.4225\n 726000 6879000 43 -695.2432\n 726500 6879000 44 -691.8856\n 727000 6879000 45 -686.7808\n 727500 6879000 46 -679.5692\n 728000 6879000 47 -670.1856\n 728500 6879000 48 -659.0745\n 729000 6879000 49 -647.2323\n 729500 6879000 50 -635.9009\n 730000 6879000 51 -626.6733\n 730500 6879000 52 -620.9487\n 731000 6879000 53 -618.6165\n 731500 6879000 54 -618.6758\n 732000 6879000 55 -619.9717\n 732500 6879000 56 -621.3293\n 733000 6879000 57 -621.5306\n 733500 6879000 58 -619.1691\n 734000 6879000 59 -612.551\n 734500 6879000 60 -600.4167\n 735000 6879000 61 -581.4032\n 735500 6879000 62 -554.9519\n 736000 6879000 63 -522.7476\n 736500 6879000 64 -488.621\n 737000 6879000 65 -456.9023\n 737500 6879000 66 -432.2523\n 738000 6879000 67 -416.4697\n 738500 6879000 68 -409.198\n 739000 6879000 69 -408.4697\n 739500 6879000 70 -412.7733\n 740000 6879000 71 -421.6115\n 740500 6879000 72 -434.5729\n 741000 6879000 73 -449.9644\n 741500 6879000 74 -465.41\n 742000 6879000 75 -479.8852\n 742500 6879000 76 -493.6051\n 743000 6879000 77 -507.109\n 743500 6879000 78 -520.4009\n 744000 6879000 79 -533.0636\n 744500 6879000 80 -544.7535\n 745000 6879000 81 -556.476\n 745500 6879000 82 -569.1563\n 746000 6879000 83 -582.2613\n 746500 6879000 84 -594.6284\n 747000 6879000 85 -605.1993\n 705000 6879500 1 -762.3618\n 705500 6879500 2 -762.8543\n 706000 6879500 3 -761.1191\n 706500 6879500 4 -757.6591\n 707000 6879500 5 -752.6754\n 707500 6879500 6 -746.0936\n 708000 6879500 7 -737.9733\n 708500 6879500 8 -728.8058\n 709000 6879500 9 -719.1136\n 709500 6879500 10 -709.4176\n 710000 6879500 11 -700.3195\n 710500 6879500 12 -692.4723\n 711000 6879500 13 -686.5213\n 711500 6879500 14 -683.1503\n 712000 6879500 15 -682.8158\n 712500 6879500 16 -685.1241\n 713000 6879500 17 -689.209\n 713500 6879500 18 -693.8584\n 714000 6879500 19 -698.5016\n 714500 6879500 20 -703.1719\n 715000 6879500 21 -707.963\n 715500 6879500 22 -712.1973\n 716000 6879500 23 -714.8994\n 716500 6879500 24 -715.4319\n 717000 6879500 25 -713.5472\n 717500 6879500 26 -709.344\n 718000 6879500 27 -703.3117\n 718500 6879500 28 -696.1599\n 719000 6879500 29 -688.5857\n 719500 6879500 30 -681.7359\n 720000 6879500 31 -676.7743\n 720500 6879500 32 -674.4009\n 721000 6879500 33 -674.7484\n 721500 6879500 34 -677.3419\n 722000 6879500 35 -681.3296\n 722500 6879500 36 -685.8251\n 723000 6879500 37 -690.124\n 723500 6879500 38 -693.7317\n 724000 6879500 39 -696.3135\n 724500 6879500 40 -697.6076\n 725000 6879500 41 -697.5865\n 725500 6879500 42 -696.7073\n 726000 6879500 43 -695.092\n 726500 6879500 44 -691.9156\n 727000 6879500 45 -686.593\n 727500 6879500 46 -678.6805\n 728000 6879500 47 -668.0107\n 728500 6879500 48 -655.0579\n 729000 6879500 49 -641.034\n 729500 6879500 50 -627.5222\n 730000 6879500 51 -616.6555\n 730500 6879500 52 -610.4714\n 731000 6879500 53 -608.9376\n 731500 6879500 54 -610.4306\n 732000 6879500 55 -613.3701\n 732500 6879500 56 -616.2754\n 733000 6879500 57 -617.6686\n 733500 6879500 58 -616.0528\n 734000 6879500 59 -609.8674\n 734500 6879500 60 -598.0556\n 735000 6879500 61 -579.3718\n 735500 6879500 62 -553.3794\n 736000 6879500 63 -521.9417\n 736500 6879500 64 -488.8735\n 737000 6879500 65 -458.1721\n 737500 6879500 66 -433.6796\n 738000 6879500 67 -416.5684\n 738500 6879500 68 -406.6667\n 739000 6879500 69 -402.7596\n 739500 6879500 70 -403.9833\n 740000 6879500 71 -410.4876\n 740500 6879500 72 -422.2553\n 741000 6879500 73 -437.0244\n 741500 6879500 74 -451.482\n 742000 6879500 75 -464.4263\n 742500 6879500 76 -476.489\n 743000 6879500 77 -488.742\n 743500 6879500 78 -501.2752\n 744000 6879500 79 -513.4662\n 744500 6879500 80 -524.8843\n 745000 6879500 81 -537.2178\n 745500 6879500 82 -551.9858\n 746000 6879500 83 -568.2842\n 746500 6879500 84 -584.2271\n 747000 6879500 85 -598.2368\n 705000 6880000 1 -762.3885\n 705500 6880000 2 -763.8607\n 706000 6880000 3 -763.1755\n 706500 6880000 4 -760.6533\n 707000 6880000 5 -756.2756\n 707500 6880000 6 -749.6547\n 708000 6880000 7 -740.6771\n 708500 6880000 8 -729.971\n 709000 6880000 9 -718.2197\n 709500 6880000 10 -706.0477\n 710000 6880000 11 -694.1479\n 710500 6880000 12 -683.3131\n 711000 6880000 13 -674.4216\n 711500 6880000 14 -668.4788\n 712000 6880000 15 -666.3671\n 712500 6880000 16 -668.0143\n 713000 6880000 17 -672.7328\n 713500 6880000 18 -679.1467\n 714000 6880000 19 -686.1899\n 714500 6880000 20 -693.3516\n 715000 6880000 21 -700.3036\n 715500 6880000 22 -706.2648\n 716000 6880000 23 -710.444\n 716500 6880000 24 -712.3468\n 717000 6880000 25 -711.77\n 717500 6880000 26 -708.7781\n 718000 6880000 27 -703.804\n 718500 6880000 28 -697.5076\n 719000 6880000 29 -690.4592\n 719500 6880000 30 -683.6362\n 720000 6880000 31 -678.0956\n 720500 6880000 32 -674.3875\n 721000 6880000 33 -672.6925\n 721500 6880000 34 -672.9321\n 722000 6880000 35 -674.8248\n 722500 6880000 36 -678.0327\n 723000 6880000 37 -682.2812\n 723500 6880000 38 -687.0125\n 724000 6880000 39 -691.4479\n 724500 6880000 40 -694.7194\n 725000 6880000 41 -696.2656\n 725500 6880000 42 -696.319\n 726000 6880000 43 -695.1216\n 726500 6880000 44 -691.9545\n 727000 6880000 45 -686.327\n 727500 6880000 46 -677.7264\n 728000 6880000 47 -665.8631\n 728500 6880000 48 -651.2101\n 729000 6880000 49 -635.1652\n 729500 6880000 50 -619.6498\n 730000 6880000 51 -607.2206\n 730500 6880000 52 -600.3792\n 731000 6880000 53 -599.2183\n 731500 6880000 54 -601.6873\n 732000 6880000 55 -605.9048\n 732500 6880000 56 -610.1387\n 733000 6880000 57 -612.6025\n 733500 6880000 58 -611.6779\n 734000 6880000 59 -605.9815\n 734500 6880000 60 -594.6484\n 735000 6880000 61 -576.5212\n 735500 6880000 62 -551.1855\n 736000 6880000 63 -520.6978\n 736500 6880000 64 -488.9692\n 737000 6880000 65 -459.7212\n 737500 6880000 66 -436.0545\n 738000 6880000 67 -418.6508\n 738500 6880000 68 -407.3656\n 739000 6880000 69 -401.444\n 739500 6880000 70 -400.4521\n 740000 6880000 71 -404.9449\n 740500 6880000 72 -415.2343\n 741000 6880000 73 -428.6561\n 741500 6880000 74 -441.252\n 742000 6880000 75 -451.8113\n 742500 6880000 76 -461.3659\n 743000 6880000 77 -471.5599\n 743500 6880000 78 -482.7259\n 744000 6880000 79 -494.2441\n 744500 6880000 80 -505.6198\n 745000 6880000 81 -518.7529\n 745500 6880000 82 -535.4904\n 746000 6880000 83 -554.6666\n 746500 6880000 84 -573.8719\n 747000 6880000 85 -591.1487\n 705000 6880500 1 -761.3199\n 705500 6880500 2 -763.6838\n 706000 6880500 3 -764.1432\n 706500 6880500 4 -762.7055\n 707000 6880500 5 -759.08\n 707500 6880500 6 -752.6312\n 708000 6880500 7 -743.034\n 708500 6880500 8 -730.9967\n 709000 6880500 9 -717.3118\n 709500 6880500 10 -702.6754\n 710000 6880500 11 -687.8508\n 710500 6880500 12 -673.7599\n 711000 6880500 13 -661.5281\n 711500 6880500 14 -652.5381\n 712000 6880500 15 -648.2498\n 712500 6880500 16 -649.1212\n 713000 6880500 17 -654.6789\n 713500 6880500 18 -663.3094\n 714000 6880500 19 -673.3064\n 714500 6880500 20 -683.4079\n 715000 6880500 21 -692.5941\n 715500 6880500 22 -700.0021\n 716000 6880500 23 -705.3732\n 716500 6880500 24 -708.5171\n 717000 6880500 25 -709.2734\n 717500 6880500 26 -707.5988\n 718000 6880500 27 -703.837\n 718500 6880500 28 -698.5795\n 719000 6880500 29 -692.2889\n 719500 6880500 30 -685.5167\n 720000 6880500 31 -679.0625\n 720500 6880500 32 -673.4901\n 721000 6880500 33 -669.2733\n 721500 6880500 34 -666.8216\n 722000 6880500 35 -666.4158\n 722500 6880500 36 -668.3046\n 723000 6880500 37 -672.7098\n 723500 6880500 38 -678.9608\n 724000 6880500 39 -685.7454\n 724500 6880500 40 -691.4182\n 725000 6880500 41 -694.7513\n 725500 6880500 42 -695.733\n 726000 6880500 43 -694.7959\n 726500 6880500 44 -691.4949\n 727000 6880500 45 -685.5119\n 727500 6880500 46 -676.3121\n 728000 6880500 47 -663.5251\n 728500 6880500 48 -647.595\n 729000 6880500 49 -630.0418\n 729500 6880500 50 -613.0961\n 730000 6880500 51 -599.5095\n 730500 6880500 52 -591.7947\n 731000 6880500 53 -590.1527\n 731500 6880500 54 -592.6798\n 732000 6880500 55 -597.4846\n 732500 6880500 56 -602.6616\n 733000 6880500 57 -605.971\n 733500 6880500 58 -605.5632\n 734000 6880500 59 -600.2353\n 734500 6880500 60 -589.3087\n 735000 6880500 61 -571.7335\n 735500 6880500 62 -547.1555\n 736000 6880500 63 -517.8243\n 736500 6880500 64 -487.777\n 737000 6880500 65 -460.4887\n 737500 6880500 66 -438.4001\n 738000 6880500 67 -421.7856\n 738500 6880500 68 -410.4738\n 739000 6880500 69 -403.9252\n 739500 6880500 70 -401.8702\n 740000 6880500 71 -404.8254\n 740500 6880500 72 -413.1637\n 741000 6880500 73 -424.1959\n 741500 6880500 74 -433.9519\n 742000 6880500 75 -441.424\n 742500 6880500 76 -447.9311\n 743000 6880500 77 -455.5122\n 743500 6880500 78 -464.8786\n 744000 6880500 79 -475.6342\n 744500 6880500 80 -487.4106\n 745000 6880500 81 -501.7649\n 745500 6880500 82 -520.3301\n 746000 6880500 83 -541.8723\n 746500 6880500 84 -563.7576\n 747000 6880500 85 -583.7993\n 705000 6881000 1 -759.331\n 705500 6881000 2 -762.4482\n 706000 6881000 3 -764.1096\n 706500 6881000 4 -763.9121\n 707000 6881000 5 -761.2294\n 707500 6881000 6 -755.1739\n 708000 6881000 7 -745.1561\n 708500 6881000 8 -731.9786\n 709000 6881000 9 -716.5279\n 709500 6881000 10 -699.5346\n 710000 6881000 11 -681.7821\n 710500 6881000 12 -664.2809\n 711000 6881000 13 -648.389\n 711500 6881000 14 -635.8911\n 712000 6881000 15 -628.9767\n 712500 6881000 16 -628.878\n 713000 6881000 17 -635.3493\n 713500 6881000 18 -646.4025\n 714000 6881000 19 -659.5925\n 714500 6881000 20 -672.7507\n 715000 6881000 21 -684.0258\n 715500 6881000 22 -692.4778\n 716000 6881000 23 -698.6898\n 716500 6881000 24 -702.9062\n 717000 6881000 25 -704.9814\n 717500 6881000 26 -704.6741\n 718000 6881000 27 -702.2535\n 718500 6881000 28 -698.3077\n 719000 6881000 29 -693.1795\n 719500 6881000 30 -686.7417\n 720000 6881000 31 -679.1617\n 720500 6881000 32 -671.2468\n 721000 6881000 33 -664.0803\n 721500 6881000 34 -658.6728\n 722000 6881000 35 -655.8232\n 722500 6881000 36 -656.3397\n 723000 6881000 37 -661.0389\n 723500 6881000 38 -669.1545\n 724000 6881000 39 -678.7153\n 724500 6881000 40 -687.0797\n 725000 6881000 41 -692.2364\n 725500 6881000 42 -694.0353\n 726000 6881000 43 -693.2578\n 726500 6881000 44 -689.826\n 727000 6881000 45 -683.6056\n 727500 6881000 46 -674.0482\n 728000 6881000 47 -660.7368\n 728500 6881000 48 -644.0626\n 729000 6881000 49 -625.5984\n 729500 6881000 50 -607.8323\n 730000 6881000 51 -593.6649\n 730500 6881000 52 -584.8541\n 731000 6881000 53 -581.7167\n 731500 6881000 54 -583.1042\n 732000 6881000 55 -587.623\n 732500 6881000 56 -593.3101\n 733000 6881000 57 -597.2539\n 733500 6881000 58 -597.2106\n 734000 6881000 59 -592.157\n 734500 6881000 60 -581.5881\n 735000 6881000 61 -564.5209\n 735500 6881000 62 -540.7529\n 736000 6881000 63 -512.7764\n 736500 6881000 64 -484.7613\n 737000 6881000 65 -459.8852\n 737500 6881000 66 -440.0061\n 738000 6881000 67 -425.0306\n 738500 6881000 68 -414.7124\n 739000 6881000 69 -408.5395\n 739500 6881000 70 -406.1693\n 740000 6881000 71 -407.788\n 740500 6881000 72 -413.5392\n 741000 6881000 73 -421.1189\n 741500 6881000 74 -427.2437\n 742000 6881000 75 -431.3094\n 742500 6881000 76 -434.8319\n 743000 6881000 77 -439.9743\n 743500 6881000 78 -447.7033\n 744000 6881000 79 -457.8586\n 744500 6881000 80 -470.3105\n 745000 6881000 81 -486.2432\n 745500 6881000 82 -506.6493\n 746000 6881000 83 -530.2421\n 746500 6881000 84 -554.3893\n 747000 6881000 85 -576.7837\n 705000 6881500 1 -756.7407\n 705500 6881500 2 -760.4445\n 706000 6881500 3 -763.2818\n 706500 6881500 4 -764.4255\n 707000 6881500 5 -762.8763\n 707500 6881500 6 -757.4296\n 708000 6881500 7 -747.1528\n 708500 6881500 8 -733.0215\n 709000 6881500 9 -716.0322\n 709500 6881500 10 -696.9118\n 710000 6881500 11 -676.3962\n 710500 6881500 12 -655.5209\n 711000 6881500 13 -635.8287\n 711500 6881500 14 -619.4907\n 712000 6881500 15 -609.5104\n 712500 6881500 16 -608.1066\n 713000 6881500 17 -615.2629\n 713500 6881500 18 -628.4831\n 714000 6881500 19 -644.5589\n 714500 6881500 20 -660.4119\n 715000 6881500 21 -673.312\n 715500 6881500 22 -682.3182\n 716000 6881500 23 -689.0167\n 716500 6881500 24 -694.1306\n 717000 6881500 25 -697.4729\n 717500 6881500 26 -698.519\n 718000 6881500 27 -697.4711\n 718500 6881500 28 -695.0107\n 719000 6881500 29 -691.348\n 719500 6881500 30 -685.5939\n 720000 6881500 31 -676.9538\n 720500 6881500 32 -666.5786\n 721000 6881500 33 -656.4119\n 721500 6881500 34 -648.1071\n 722000 6881500 35 -642.9008\n 722500 6881500 36 -642.0856\n 723000 6881500 37 -647.125\n 723500 6881500 38 -657.2268\n 724000 6881500 39 -669.6994\n 724500 6881500 40 -680.8014\n 725000 6881500 41 -687.7076\n 725500 6881500 42 -690.2141\n 726000 6881500 43 -689.6206\n 726500 6881500 44 -686.2664\n 727000 6881500 45 -680.1636\n 727500 6881500 46 -670.7052\n 728000 6881500 47 -657.355\n 728500 6881500 48 -640.373\n 729000 6881500 49 -621.2971\n 729500 6881500 50 -602.9894\n 730000 6881500 51 -588.4756\n 730500 6881500 52 -578.4504\n 731000 6881500 53 -572.9285\n 731500 6881500 54 -571.9803\n 732000 6881500 55 -575.3203\n 732500 6881500 56 -581.132\n 733000 6881500 57 -585.6273\n 733500 6881500 58 -585.9905\n 734000 6881500 59 -581.3649\n 734500 6881500 60 -571.3677\n 735000 6881500 61 -555.0173\n 735500 6881500 62 -532.2325\n 736000 6881500 63 -505.8076\n 736500 6881500 64 -480.0684\n 737000 6881500 65 -457.8165\n 737500 6881500 66 -440.4258\n 738000 6881500 67 -427.4426\n 738500 6881500 68 -418.5219\n 739000 6881500 69 -413.0685\n 739500 6881500 70 -410.5031\n 740000 6881500 71 -410.5996\n 740500 6881500 72 -413.2016\n 741000 6881500 73 -416.7055\n 741500 6881500 74 -418.9168\n 742000 6881500 75 -419.7699\n 742500 6881500 76 -420.879\n 743000 6881500 77 -424.4179\n 743500 6881500 78 -431.2716\n 744000 6881500 79 -441.3183\n 744500 6881500 80 -454.5896\n 745000 6881500 81 -472.1402\n 745500 6881500 82 -494.4376\n 746000 6881500 83 -520.0386\n 746500 6881500 84 -546.295\n 747000 6881500 85 -570.8075\n 705000 6882000 1 -753.8176\n 705500 6882000 2 -757.9052\n 706000 6882000 3 -761.7925\n 706500 6882000 4 -764.2573\n 707000 6882000 5 -763.9332\n 707500 6882000 6 -759.2688\n 708000 6882000 7 -748.9162\n 708500 6882000 8 -734.0881\n 709000 6882000 9 -715.9006\n 709500 6882000 10 -695.0469\n 710000 6882000 11 -672.1656\n 710500 6882000 12 -648.2627\n 711000 6882000 13 -624.9985\n 711500 6882000 14 -604.8696\n 712000 6882000 15 -591.585\n 712500 6882000 16 -588.3504\n 713000 6882000 17 -595.395\n 713500 6882000 18 -609.6873\n 714000 6882000 19 -627.4072\n 714500 6882000 20 -644.8592\n 715000 6882000 21 -658.5958\n 715500 6882000 22 -667.7191\n 716000 6882000 23 -674.673\n 716500 6882000 24 -680.5472\n 717000 6882000 25 -685.0602\n 717500 6882000 26 -687.3854\n 718000 6882000 27 -687.6356\n 718500 6882000 28 -686.6116\n 719000 6882000 29 -684.3735\n 719500 6882000 30 -679.4734\n 720000 6882000 31 -670.1614\n 720500 6882000 32 -657.8799\n 721000 6882000 33 -645.3784\n 721500 6882000 34 -634.8455\n 722000 6882000 35 -627.835\n 722500 6882000 36 -625.9907\n 723000 6882000 37 -631.2886\n 723500 6882000 38 -643.004\n 724000 6882000 39 -657.8654\n 724500 6882000 40 -671.2745\n 725000 6882000 41 -679.7535\n 725500 6882000 42 -683.0367\n 726000 6882000 43 -682.9102\n 726500 6882000 44 -680.0884\n 727000 6882000 45 -674.6832\n 727500 6882000 46 -665.9917\n 728000 6882000 47 -653.234\n 728500 6882000 48 -636.3268\n 729000 6882000 49 -616.6478\n 729500 6882000 50 -597.6699\n 730000 6882000 51 -582.7672\n 730500 6882000 52 -571.2324\n 731000 6882000 53 -562.5724\n 731500 6882000 54 -558.1577\n 732000 6882000 55 -559.3673\n 732500 6882000 56 -564.8618\n 733000 6882000 57 -569.9522\n 733500 6882000 58 -571.0491\n 734000 6882000 59 -567.3097\n 734500 6882000 60 -558.3677\n 735000 6882000 61 -543.2602\n 735500 6882000 62 -522.0224\n 736000 6882000 63 -497.5714\n 736500 6882000 64 -474.2762\n 737000 6882000 65 -454.571\n 737500 6882000 66 -439.4641\n 738000 6882000 67 -428.2187\n 738500 6882000 68 -420.4338\n 739000 6882000 69 -415.455\n 739500 6882000 70 -412.4983\n 740000 6882000 71 -410.9371\n 740500 6882000 72 -410.3402\n 741000 6882000 73 -409.8752\n 741500 6882000 74 -408.5581\n 742000 6882000 75 -406.8226\n 742500 6882000 76 -406.3613\n 743000 6882000 77 -409.282\n 743500 6882000 78 -416.1741\n 744000 6882000 79 -426.6725\n 744500 6882000 80 -440.8842\n 745000 6882000 81 -459.9451\n 745500 6882000 82 -484.1021\n 746000 6882000 83 -511.7028\n 746500 6882000 84 -539.9674\n 747000 6882000 85 -566.3795\n 705000 6882500 1 -750.8972\n 705500 6882500 2 -755.046\n 706000 6882500 3 -759.6527\n 706500 6882500 4 -763.1744\n 707000 6882500 5 -763.9497\n 707500 6882500 6 -760.1084\n 708000 6882500 7 -750.0377\n 708500 6882500 8 -734.9412\n 709000 6882500 9 -716.077\n 709500 6882500 10 -694.0956\n 710000 6882500 11 -669.5532\n 710500 6882500 12 -643.4188\n 711000 6882500 13 -617.4077\n 711500 6882500 14 -594.2255\n 712000 6882500 15 -577.8572\n 712500 6882500 16 -572.0383\n 713000 6882500 17 -577.2663\n 713500 6882500 18 -590.2914\n 714000 6882500 19 -607.1064\n 714500 6882500 20 -624.0402\n 715000 6882500 21 -637.5192\n 715500 6882500 22 -646.5934\n 716000 6882500 23 -653.8748\n 716500 6882500 24 -660.4614\n 717000 6882500 25 -665.9969\n 717500 6882500 26 -669.4908\n 718000 6882500 27 -670.8992\n 718500 6882500 28 -671.0073\n 719000 6882500 29 -669.6885\n 719500 6882500 30 -665.5189\n 720000 6882500 31 -656.2797\n 720500 6882500 32 -643.4412\n 721000 6882500 33 -630.124\n 721500 6882500 34 -618.7546\n 722000 6882500 35 -611.0631\n 722500 6882500 36 -608.8435\n 723000 6882500 37 -614.1802\n 723500 6882500 38 -626.4963\n 724000 6882500 39 -642.3743\n 724500 6882500 40 -657.0649\n 725000 6882500 41 -666.7961\n 725500 6882500 42 -671.2106\n 726000 6882500 43 -672.1077\n 726500 6882500 44 -670.4425\n 727000 6882500 45 -666.4555\n 727500 6882500 46 -659.3912\n 728000 6882500 47 -648.1202\n 728500 6882500 48 -631.9055\n 729000 6882500 49 -611.6468\n 729500 6882500 50 -591.525\n 730000 6882500 51 -575.7027\n 730500 6882500 52 -562.2385\n 731000 6882500 53 -549.892\n 731500 6882500 54 -541.173\n 732000 6882500 55 -539.2812\n 732500 6882500 56 -543.7863\n 733000 6882500 57 -549.4401\n 733500 6882500 58 -551.7211\n 734000 6882500 59 -549.4821\n 734500 6882500 60 -542.1993\n 735000 6882500 61 -529.0383\n 735500 6882500 62 -510.247\n 736000 6882500 63 -488.5095\n 736500 6882500 64 -467.868\n 737000 6882500 65 -450.4177\n 737500 6882500 66 -436.9802\n 738000 6882500 67 -426.7309\n 738500 6882500 68 -419.3004\n 739000 6882500 69 -414.1149\n 739500 6882500 70 -410.3957\n 740000 6882500 71 -407.293\n 740500 6882500 72 -404.1998\n 741000 6882500 73 -400.8016\n 741500 6882500 74 -397.1226\n 742000 6882500 75 -393.9282\n 742500 6882500 76 -392.9373\n 743000 6882500 77 -396.1102\n 743500 6882500 78 -403.7455\n 744000 6882500 79 -415.1555\n 744500 6882500 80 -430.4498\n 745000 6882500 81 -450.8657\n 745500 6882500 82 -476.6523\n 746000 6882500 83 -505.9837\n 746500 6882500 84 -535.9227\n 747000 6882500 85 -563.8408\n 705000 6883000 1 -748.2269\n 705500 6883000 2 -752.0195\n 706000 6883000 3 -756.8132\n 706500 6883000 4 -760.8876\n 707000 6883000 5 -762.4204\n 707500 6883000 6 -759.4203\n 708000 6883000 7 -750.076\n 708500 6883000 8 -735.3312\n 709000 6883000 9 -716.4781\n 709500 6883000 10 -694.1371\n 710000 6883000 11 -668.8607\n 710500 6883000 12 -641.6179\n 711000 6883000 13 -614.1481\n 711500 6883000 14 -589.1603\n 712000 6883000 15 -570.3984\n 712500 6883000 16 -561.2185\n 713000 6883000 17 -562.3948\n 713500 6883000 18 -571.0599\n 714000 6883000 19 -583.7123\n 714500 6883000 20 -597.3437\n 715000 6883000 21 -609.2983\n 715500 6883000 22 -618.4001\n 716000 6883000 23 -626.3181\n 716500 6883000 24 -633.6399\n 717000 6883000 25 -639.9791\n 717500 6883000 26 -644.4812\n 718000 6883000 27 -646.9275\n 718500 6883000 28 -647.7942\n 719000 6883000 29 -646.8188\n 719500 6883000 30 -642.9286\n 720000 6883000 31 -634.6096\n 720500 6883000 32 -622.8799\n 721000 6883000 33 -610.5847\n 721500 6883000 34 -599.9816\n 722000 6883000 35 -592.8131\n 722500 6883000 36 -590.9139\n 723000 6883000 37 -596.1654\n 723500 6883000 38 -608.0433\n 724000 6883000 39 -623.4698\n 724500 6883000 40 -638.1704\n 725000 6883000 41 -648.7029\n 725500 6883000 42 -654.452\n 726000 6883000 43 -656.7833\n 726500 6883000 44 -656.7098\n 727000 6883000 45 -654.7054\n 727500 6883000 46 -650.1166\n 728000 6883000 47 -641.4546\n 728500 6883000 48 -627.0112\n 729000 6883000 49 -606.6371\n 729500 6883000 50 -584.7785\n 730000 6883000 51 -566.8949\n 730500 6883000 52 -550.9344\n 731000 6883000 53 -535.1504\n 731500 6883000 54 -522.5598\n 732000 6883000 55 -517.3696\n 732500 6883000 56 -520.0092\n 733000 6883000 57 -525.5552\n 733500 6883000 58 -528.9507\n 734000 6883000 59 -528.4261\n 734500 6883000 60 -523.162\n 735000 6883000 61 -512.4063\n 735500 6883000 62 -496.7195\n 736000 6883000 63 -478.3553\n 736500 6883000 64 -460.5557\n 737000 6883000 65 -445.1224\n 737500 6883000 66 -432.7097\n 738000 6883000 67 -422.6751\n 738500 6883000 68 -414.7185\n 739000 6883000 69 -408.4989\n 739500 6883000 70 -403.4912\n 740000 6883000 71 -399.0294\n 740500 6883000 72 -394.5441\n 741000 6883000 73 -390.0132\n 741500 6883000 74 -385.8596\n 742000 6883000 75 -382.9073\n 742500 6883000 76 -382.6362\n 743000 6883000 77 -386.7564\n 743500 6883000 78 -395.4996\n 744000 6883000 79 -408.0875\n 744500 6883000 80 -424.6492\n 745000 6883000 81 -446.2787\n 745500 6883000 82 -473.1888\n 746000 6883000 83 -503.573\n 746500 6883000 84 -534.486\n 747000 6883000 85 -563.2668\n 705000 6883500 1 -745.5788\n 705500 6883500 2 -748.7629\n 706000 6883500 3 -753.4156\n 706500 6883500 4 -757.7266\n 707000 6883500 5 -759.7424\n 707500 6883500 6 -757.5208\n 708000 6883500 7 -749.2494\n 708500 6883500 8 -735.4127\n 709000 6883500 9 -717.2171\n 709500 6883500 10 -695.2214\n 710000 6883500 11 -669.9921\n 710500 6883500 12 -642.5218\n 711000 6883500 13 -614.5352\n 711500 6883500 14 -588.5579\n 712000 6883500 15 -567.9256\n 712500 6883500 16 -555.0851\n 713000 6883500 17 -550.8223\n 713500 6883500 18 -553.1364\n 714000 6883500 19 -559.5216\n 714500 6883500 20 -567.9339\n 715000 6883500 21 -577.1859\n 715500 6883500 22 -586.1394\n 716000 6883500 23 -594.7023\n 716500 6883500 24 -602.637\n 717000 6883500 25 -609.5062\n 717500 6883500 26 -614.7773\n 718000 6883500 27 -618.1196\n 718500 6883500 28 -619.5304\n 719000 6883500 29 -618.6032\n 719500 6883500 30 -614.8388\n 720000 6883500 31 -607.8239\n 720500 6883500 32 -598.2711\n 721000 6883500 33 -588.1979\n 721500 6883500 34 -579.3751\n 722000 6883500 35 -573.4031\n 722500 6883500 36 -572.0652\n 723000 6883500 37 -577.1234\n 723500 6883500 38 -588.1146\n 724000 6883500 39 -602.4362\n 724500 6883500 40 -616.4535\n 725000 6883500 41 -627.2534\n 725500 6883500 42 -634.0523\n 726000 6883500 43 -637.6454\n 726500 6883500 44 -638.9814\n 727000 6883500 45 -638.8806\n 727500 6883500 46 -636.9795\n 728000 6883500 47 -631.5529\n 728500 6883500 48 -619.8625\n 729000 6883500 49 -600.293\n 729500 6883500 50 -576.6959\n 730000 6883500 51 -555.7603\n 730500 6883500 52 -537.1025\n 731000 6883500 53 -519.0736\n 731500 6883500 54 -504.3282\n 732000 6883500 55 -496.6372\n 732500 6883500 56 -496.993\n 733000 6883500 57 -501.3486\n 733500 6883500 58 -505.0408\n 734000 6883500 59 -505.7683\n 734500 6883500 60 -502.393\n 735000 6883500 61 -494.1711\n 735500 6883500 62 -481.6583\n 736000 6883500 63 -466.7137\n 736500 6883500 64 -451.7379\n 737000 6883500 65 -438.1848\n 737500 6883500 66 -426.5264\n 738000 6883500 67 -416.2769\n 738500 6883500 68 -407.2697\n 739000 6883500 69 -399.4926\n 739500 6883500 70 -392.8262\n 740000 6883500 71 -387.1315\n 740500 6883500 72 -382.2781\n 741000 6883500 73 -378.3214\n 741500 6883500 74 -375.5117\n 742000 6883500 75 -374.3922\n 742500 6883500 76 -375.9901\n 743000 6883500 77 -381.5524\n 743500 6883500 78 -391.45\n 744000 6883500 79 -405.2239\n 744500 6883500 80 -423.081\n 745000 6883500 81 -445.8338\n 745500 6883500 82 -473.3744\n 746000 6883500 83 -504.1045\n 746500 6883500 84 -535.295\n 747000 6883500 85 -564.3358\n 705000 6884000 1 -742.6258\n 705500 6884000 2 -745.1173\n 706000 6884000 3 -749.523\n 706500 6884000 4 -753.9504\n 707000 6884000 5 -756.4178\n 707500 6884000 6 -754.9846\n 708000 6884000 7 -747.9492\n 708500 6884000 8 -735.442\n 709000 6884000 9 -718.4492\n 709500 6884000 10 -697.416\n 710000 6884000 11 -672.8412\n 710500 6884000 12 -645.7009\n 711000 6884000 13 -617.6673\n 711500 6884000 14 -591.0575\n 712000 6884000 15 -568.6021\n 712500 6884000 16 -552.0752\n 713000 6884000 17 -542.0935\n 713500 6884000 18 -537.588\n 714000 6884000 19 -537.1356\n 714500 6884000 20 -539.8257\n 715000 6884000 21 -545.6413\n 715500 6884000 22 -553.7772\n 716000 6884000 23 -562.4052\n 716500 6884000 24 -570.4796\n 717000 6884000 25 -577.5117\n 717500 6884000 26 -583.2745\n 718000 6884000 27 -587.3223\n 718500 6884000 28 -589.1309\n 719000 6884000 29 -588.1257\n 719500 6884000 30 -584.5363\n 720000 6884000 31 -578.9716\n 720500 6884000 32 -572.0184\n 721000 6884000 33 -564.6756\n 721500 6884000 34 -558.0524\n 722000 6884000 35 -553.479\n 722500 6884000 36 -552.6361\n 723000 6884000 37 -557.3221\n 723500 6884000 38 -567.2832\n 724000 6884000 39 -580.327\n 724500 6884000 40 -593.3705\n 725000 6884000 41 -603.9573\n 725500 6884000 42 -611.1884\n 726000 6884000 43 -615.4108\n 726500 6884000 44 -617.3715\n 727000 6884000 45 -618.2803\n 727500 6884000 46 -618.1899\n 728000 6884000 47 -615.3887\n 728500 6884000 48 -606.5177\n 729000 6884000 49 -588.7186\n 729500 6884000 50 -564.7187\n 730000 6884000 51 -541.3028\n 730500 6884000 52 -520.5244\n 731000 6884000 53 -501.6441\n 731500 6884000 54 -486.449\n 732000 6884000 55 -477.5453\n 732500 6884000 56 -475.7483\n 733000 6884000 57 -478.349\n 733500 6884000 58 -481.5944\n 734000 6884000 59 -482.9331\n 734500 6884000 60 -480.9848\n 735000 6884000 61 -475.0152\n 735500 6884000 62 -465.4102\n 736000 6884000 63 -453.5399\n 736500 6884000 64 -441.1341\n 737000 6884000 65 -429.3206\n 737500 6884000 66 -418.3619\n 738000 6884000 67 -407.7904\n 738500 6884000 68 -397.6247\n 739000 6884000 69 -388.2278\n 739500 6884000 70 -379.9572\n 740000 6884000 71 -373.3095\n 740500 6884000 72 -368.8257\n 741000 6884000 73 -366.6251\n 741500 6884000 74 -366.3768\n 742000 6884000 75 -368.1217\n 742500 6884000 76 -372.336\n 743000 6884000 77 -379.6996\n 743500 6884000 78 -390.7032\n 744000 6884000 79 -405.5162\n 744500 6884000 80 -424.5109\n 745000 6884000 81 -448.1903\n 745500 6884000 82 -476.0543\n 746000 6884000 83 -506.6763\n 746500 6884000 84 -537.6755\n 747000 6884000 85 -566.5563\n 705000 6884500 1 -739.462\n 705500 6884500 2 -741.2416\n 706000 6884500 3 -745.3466\n 706500 6884500 4 -749.7864\n 707000 6884500 5 -752.6071\n 707500 6884500 6 -751.9872\n 708000 6884500 7 -746.3015\n 708500 6884500 8 -735.4858\n 709000 6884500 9 -720.1323\n 709500 6884500 10 -700.5427\n 710000 6884500 11 -677.0423\n 710500 6884500 12 -650.5124\n 711000 6884500 13 -622.5856\n 711500 6884500 14 -595.3851\n 712000 6884500 15 -571.1583\n 712500 6884500 16 -551.1542\n 713000 6884500 17 -535.9771\n 713500 6884500 18 -525.3618\n 714000 6884500 19 -518.854\n 714500 6884500 20 -516.3569\n 715000 6884500 21 -518.5516\n 715500 6884500 22 -524.9734\n 716000 6884500 23 -532.7261\n 716500 6884500 24 -540.2496\n 717000 6884500 25 -547.0193\n 717500 6884500 26 -553.0192\n 718000 6884500 27 -557.6085\n 718500 6884500 28 -559.7038\n 719000 6884500 29 -558.5977\n 719500 6884500 30 -555.1777\n 720000 6884500 31 -551.0014\n 720500 6884500 32 -546.569\n 721000 6884500 33 -541.9149\n 721500 6884500 34 -537.4571\n 722000 6884500 35 -534.1899\n 722500 6884500 36 -533.6702\n 723000 6884500 37 -537.794\n 723500 6884500 38 -546.6205\n 724000 6884500 39 -558.2855\n 724500 6884500 40 -570.1309\n 725000 6884500 41 -580.0439\n 725500 6884500 42 -587.0372\n 726000 6884500 43 -591.1138\n 726500 6884500 44 -592.7194\n 727000 6884500 45 -593.364\n 727500 6884500 46 -593.5504\n 728000 6884500 47 -591.819\n 728500 6884500 48 -584.8584\n 729000 6884500 49 -569.348\n 729500 6884500 50 -546.7582\n 730000 6884500 51 -523.0576\n 730500 6884500 52 -501.8017\n 731000 6884500 53 -483.4253\n 731500 6884500 54 -468.8961\n 732000 6884500 55 -459.6968\n 732500 6884500 56 -456.3316\n 733000 6884500 57 -457.1799\n 733500 6884500 58 -459.5719\n 734000 6884500 59 -460.999\n 734500 6884500 60 -459.9264\n 735000 6884500 61 -455.6359\n 735500 6884500 62 -448.315\n 736000 6884500 63 -438.9193\n 736500 6884500 64 -428.6774\n 737000 6884500 65 -418.4046\n 737500 6884500 66 -408.1667\n 738000 6884500 67 -397.473\n 738500 6884500 68 -386.5042\n 739000 6884500 69 -375.978\n 739500 6884500 70 -366.6833\n 740000 6884500 71 -359.6477\n 740500 6884500 72 -355.9969\n 741000 6884500 73 -355.9627\n 741500 6884500 74 -358.6313\n 742000 6884500 75 -363.5217\n 742500 6884500 76 -370.5314\n 743000 6884500 77 -379.7835\n 743500 6884500 78 -391.8548\n 744000 6884500 79 -407.5553\n 744500 6884500 80 -427.4566\n 745000 6884500 81 -451.814\n 745500 6884500 82 -479.8243\n 746000 6884500 83 -510.1493\n 746500 6884500 84 -540.7039\n 747000 6884500 85 -569.1346\n 705000 6885000 1 -736.75\n 705500 6885000 2 -737.7686\n 706000 6885000 3 -741.4056\n 706500 6885000 4 -745.6242\n 707000 6885000 5 -748.5677\n 707500 6885000 6 -748.6537\n 708000 6885000 7 -744.3896\n 708500 6885000 8 -735.4976\n 709000 6885000 9 -722.0917\n 709500 6885000 10 -704.248\n 710000 6885000 11 -682.0337\n 710500 6885000 12 -656.1791\n 711000 6885000 13 -628.296\n 711500 6885000 14 -600.3989\n 712000 6885000 15 -574.4423\n 712500 6885000 16 -551.4346\n 713000 6885000 17 -532.0338\n 713500 6885000 18 -516.6209\n 714000 6885000 19 -505.4328\n 714500 6885000 20 -498.9926\n 715000 6885000 21 -497.8382\n 715500 6885000 22 -501.8819\n 716000 6885000 23 -508.0046\n 716500 6885000 24 -514.5073\n 717000 6885000 25 -520.8041\n 717500 6885000 26 -526.8948\n 718000 6885000 27 -531.8828\n 718500 6885000 28 -534.176\n 719000 6885000 29 -533.0125\n 719500 6885000 30 -529.7625\n 720000 6885000 31 -526.6519\n 720500 6885000 32 -524.2007\n 721000 6885000 33 -521.7437\n 721500 6885000 34 -519.1113\n 722000 6885000 35 -516.9501\n 722500 6885000 36 -516.6401\n 723000 6885000 37 -520.0192\n 723500 6885000 38 -527.4531\n 724000 6885000 39 -537.4373\n 724500 6885000 40 -547.7433\n 725000 6885000 41 -556.548\n 725500 6885000 42 -562.7657\n 726000 6885000 43 -566.0923\n 726500 6885000 44 -566.6832\n 727000 6885000 45 -566.1736\n 727500 6885000 46 -565.4462\n 728000 6885000 47 -563.4062\n 728500 6885000 48 -557.2353\n 729000 6885000 49 -543.9395\n 729500 6885000 50 -524.0768\n 730000 6885000 51 -502.1642\n 730500 6885000 52 -482.1742\n 731000 6885000 53 -465.3436\n 731500 6885000 54 -452.1756\n 732000 6885000 55 -443.3148\n 732500 6885000 56 -438.9978\n 733000 6885000 57 -438.3644\n 733500 6885000 58 -439.6425\n 734000 6885000 59 -440.6096\n 734500 6885000 60 -439.7767\n 735000 6885000 61 -436.4632\n 735500 6885000 62 -430.6853\n 736000 6885000 63 -423.0532\n 736500 6885000 64 -414.4268\n 737000 6885000 65 -405.3701\n 737500 6885000 66 -395.8835\n 738000 6885000 67 -385.5156\n 738500 6885000 68 -374.5002\n 739000 6885000 69 -363.8161\n 739500 6885000 70 -354.514\n 740000 6885000 71 -347.8412\n 740500 6885000 72 -345.2108\n 741000 6885000 73 -347.0492\n 741500 6885000 74 -352.188\n 742000 6885000 75 -359.8062\n 742500 6885000 76 -369.3204\n 743000 6885000 77 -380.3509\n 743500 6885000 78 -393.5062\n 744000 6885000 79 -410.0252\n 744500 6885000 80 -430.6781\n 745000 6885000 81 -455.5496\n 745500 6885000 82 -483.6545\n 746000 6885000 83 -513.6635\n 746500 6885000 84 -543.6641\n 747000 6885000 85 -571.4428\n 705000 6885500 1 -735.4846\n 705500 6885500 2 -735.5795\n 706000 6885500 3 -738.3807\n 706500 6885500 4 -741.9714\n 707000 6885500 5 -744.7004\n 707500 6885500 6 -745.2582\n 708000 6885500 7 -742.3503\n 708500 6885500 8 -735.4435\n 709000 6885500 9 -724.1215\n 709500 6885500 10 -708.153\n 710000 6885500 11 -687.2755\n 710500 6885500 12 -662.009\n 711000 6885500 13 -633.9695\n 711500 6885500 14 -605.1735\n 712000 6885500 15 -577.5129\n 712500 6885500 16 -552.0204\n 713000 6885500 17 -529.4688\n 713500 6885500 18 -510.637\n 714000 6885500 19 -496.1633\n 714500 6885500 20 -486.7464\n 715000 6885500 21 -482.9652\n 715500 6885500 22 -484.6699\n 716000 6885500 23 -489.1219\n 716500 6885500 24 -494.727\n 717000 6885500 25 -500.7542\n 717500 6885500 26 -506.97\n 718000 6885500 27 -512.2203\n 718500 6885500 28 -514.5985\n 719000 6885500 29 -513.438\n 719500 6885500 30 -510.3687\n 720000 6885500 31 -507.825\n 720500 6885500 32 -506.489\n 721000 6885500 33 -505.4358\n 721500 6885500 34 -504.1066\n 722000 6885500 35 -502.8096\n 722500 6885500 36 -502.6627\n 723000 6885500 37 -505.1522\n 723500 6885500 38 -510.7978\n 724000 6885500 39 -518.634\n 724500 6885500 40 -526.9919\n 725000 6885500 41 -534.3338\n 725500 6885500 42 -539.4111\n 726000 6885500 43 -541.5973\n 726500 6885500 44 -540.8807\n 727000 6885500 45 -538.9096\n 727500 6885500 46 -536.7547\n 728000 6885500 47 -533.7188\n 728500 6885500 48 -527.6259\n 729000 6885500 49 -516.1478\n 729500 6885500 50 -499.3969\n 730000 6885500 51 -480.4766\n 730500 6885500 52 -462.867\n 731000 6885500 53 -448.1415\n 731500 6885500 54 -436.6393\n 732000 6885500 55 -428.5198\n 732500 6885500 56 -423.8411\n 733000 6885500 57 -422.0446\n 733500 6885500 58 -422.0171\n 734000 6885500 59 -422.0215\n 734500 6885500 60 -420.8057\n 735000 6885500 61 -417.7732\n 735500 6885500 62 -412.8319\n 736000 6885500 63 -406.3217\n 736500 6885500 64 -398.7963\n 737000 6885500 65 -390.6524\n 737500 6885500 66 -381.8559\n 738000 6885500 67 -372.2402\n 738500 6885500 68 -362.0254\n 739000 6885500 69 -352.2257\n 739500 6885500 70 -343.9459\n 740000 6885500 71 -338.4041\n 740500 6885500 72 -336.8145\n 741000 6885500 73 -339.8274\n 741500 6885500 74 -346.4678\n 742000 6885500 75 -355.8694\n 742500 6885500 76 -367.2518\n 743000 6885500 77 -379.9246\n 743500 6885500 78 -394.3996\n 744000 6885500 79 -411.9492\n 744500 6885500 80 -433.4279\n 745000 6885500 81 -458.8001\n 745500 6885500 82 -487.0731\n 746000 6885500 83 -516.8861\n 746500 6885500 84 -546.3842\n 747000 6885500 85 -573.4711\n 705000 6886000 1 -736.1649\n 705500 6886000 2 -735.1297\n 706000 6886000 3 -736.6365\n 706500 6886000 4 -739.1196\n 707000 6886000 5 -741.2521\n 707500 6886000 6 -742.0082\n 708000 6886000 7 -740.2818\n 708500 6886000 8 -735.255\n 709000 6886000 9 -726.0593\n 709500 6886000 10 -711.978\n 710000 6886000 11 -692.3773\n 710500 6886000 12 -667.5424\n 711000 6886000 13 -639.1216\n 711500 6886000 14 -609.2452\n 712000 6886000 15 -579.9037\n 712500 6886000 16 -552.3727\n 713000 6886000 17 -527.5802\n 713500 6886000 18 -506.4646\n 714000 6886000 19 -489.8077\n 714500 6886000 20 -478.3286\n 715000 6886000 21 -472.5688\n 715500 6886000 22 -472.4162\n 716000 6886000 23 -475.5984\n 716500 6886000 24 -480.7473\n 717000 6886000 25 -486.9527\n 717500 6886000 26 -493.4964\n 718000 6886000 27 -498.902\n 718500 6886000 28 -501.2056\n 719000 6886000 29 -499.951\n 719500 6886000 30 -497.0719\n 720000 6886000 31 -494.6645\n 720500 6886000 32 -493.6252\n 721000 6886000 33 -493.1988\n 721500 6886000 34 -492.5935\n 722000 6886000 35 -491.7839\n 722500 6886000 36 -491.5957\n 723000 6886000 37 -493.0748\n 723500 6886000 38 -496.6704\n 724000 6886000 39 -502.1018\n 724500 6886000 40 -508.3379\n 725000 6886000 41 -514.0677\n 725500 6886000 42 -517.7839\n 726000 6886000 43 -518.5162\n 726500 6886000 44 -516.2347\n 727000 6886000 45 -512.6927\n 727500 6886000 46 -509.0416\n 728000 6886000 47 -504.8912\n 728500 6886000 48 -498.6169\n 729000 6886000 49 -488.5819\n 729500 6886000 50 -474.7273\n 730000 6886000 51 -459.0838\n 730500 6886000 52 -444.3562\n 731000 6886000 53 -431.9957\n 731500 6886000 54 -422.2855\n 732000 6886000 55 -415.1469\n 732500 6886000 56 -410.4821\n 733000 6886000 57 -407.8189\n 733500 6886000 58 -406.4117\n 734000 6886000 59 -405.1169\n 734500 6886000 60 -403.0558\n 735000 6886000 61 -399.7648\n 735500 6886000 62 -395.0938\n 736000 6886000 63 -389.1935\n 736500 6886000 64 -382.3963\n 737000 6886000 65 -374.9701\n 737500 6886000 66 -366.9384\n 738000 6886000 67 -358.3294\n 738500 6886000 68 -349.4767\n 739000 6886000 69 -341.2512\n 739500 6886000 70 -334.6243\n 740000 6886000 71 -330.5741\n 740500 6886000 72 -330.0185\n 741000 6886000 73 -333.5554\n 741500 6886000 74 -340.6667\n 742000 6886000 75 -350.7322\n 742500 6886000 76 -363.2261\n 743000 6886000 77 -377.5211\n 743500 6886000 78 -393.8253\n 744000 6886000 79 -412.9602\n 744500 6886000 80 -435.5713\n 745000 6886000 81 -461.5829\n 745500 6886000 82 -490.1629\n 746000 6886000 83 -519.9556\n 746500 6886000 84 -549.0944\n 747000 6886000 85 -575.5966\n 705000 6886500 1 -738.3612\n 705500 6886500 2 -736.1248\n 706000 6886500 3 -736.0288\n 706500 6886500 4 -737.0432\n 707000 6886500 5 -738.2496\n 707500 6886500 6 -738.9211\n 708000 6886500 7 -738.1298\n 708500 6886500 8 -734.7896\n 709000 6886500 9 -727.6173\n 709500 6886500 10 -715.3472\n 710000 6886500 11 -696.9086\n 710500 6886500 12 -672.3302\n 711000 6886500 13 -643.3579\n 711500 6886500 14 -612.3301\n 712000 6886500 15 -581.4721\n 712500 6886500 16 -552.3466\n 713000 6886500 17 -526.0587\n 713500 6886500 18 -503.5497\n 714000 6886500 19 -485.5647\n 714500 6886500 20 -472.7048\n 715000 6886500 21 -465.6376\n 715500 6886500 22 -464.2822\n 716000 6886500 23 -466.6781\n 716500 6886500 24 -471.5966\n 717000 6886500 25 -478.0535\n 717500 6886500 26 -484.9956\n 718000 6886500 27 -490.5515\n 718500 6886500 28 -492.681\n 719000 6886500 29 -491.2322\n 719500 6886500 30 -488.31\n 720000 6886500 31 -485.8559\n 720500 6886500 32 -484.7244\n 721000 6886500 33 -484.4755\n 721500 6886500 34 -484.0755\n 722000 6886500 35 -483.1257\n 722500 6886500 36 -482.257\n 723000 6886500 37 -482.3798\n 723500 6886500 38 -483.9893\n 724000 6886500 39 -487.3288\n 724500 6886500 40 -491.836\n 725000 6886500 41 -496.187\n 725500 6886500 42 -498.5072\n 726000 6886500 43 -497.5142\n 726500 6886500 44 -493.334\n 727000 6886500 45 -488.0398\n 727500 6886500 46 -483.0108\n 728000 6886500 47 -477.9758\n 728500 6886500 48 -471.6328\n 729000 6886500 49 -462.7802\n 729500 6886500 50 -451.3499\n 730000 6886500 51 -438.6856\n 730500 6886500 52 -426.8138\n 731000 6886500 53 -416.8764\n 731500 6886500 54 -409.0317\n 732000 6886500 55 -403.0351\n 732500 6886500 56 -398.5952\n 733000 6886500 57 -395.2355\n 733500 6886500 58 -392.4584\n 734000 6886500 59 -389.6982\n 734500 6886500 60 -386.5089\n 735000 6886500 61 -382.5985\n 735500 6886500 62 -377.7982\n 736000 6886500 63 -372.1335\n 736500 6886500 64 -365.7952\n 737000 6886500 65 -358.9837\n 737500 6886500 66 -351.7905\n 738000 6886500 67 -344.3882\n 738500 6886500 68 -337.153\n 739000 6886500 69 -330.8116\n 739500 6886500 70 -326.1192\n 740000 6886500 71 -323.7323\n 740500 6886500 72 -324.1309\n 741000 6886500 73 -327.8527\n 741500 6886500 74 -334.7974\n 742000 6886500 75 -344.7522\n 742500 6886500 76 -357.7194\n 743000 6886500 77 -373.6559\n 743500 6886500 78 -392.3474\n 744000 6886500 79 -413.7012\n 744500 6886500 80 -437.7821\n 745000 6886500 81 -464.5623\n 745500 6886500 82 -493.5353\n 746000 6886500 83 -523.3807\n 746500 6886500 84 -552.2056\n 747000 6886500 85 -578.1384\n 705000 6887000 1 -741.0441\n 705500 6887000 2 -737.6454\n 706000 6887000 3 -735.9132\n 706500 6887000 4 -735.3547\n 707000 6887000 5 -735.4784\n 707500 6887000 6 -735.857\n 708000 6887000 7 -735.7447\n 708500 6887000 8 -733.8151\n 709000 6887000 9 -728.4033\n 709500 6887000 10 -717.7239\n 710000 6887000 11 -700.327\n 710500 6887000 12 -675.9681\n 711000 6887000 13 -646.4716\n 711500 6887000 14 -614.4\n 712000 6887000 15 -582.2631\n 712500 6887000 16 -551.9442\n 713000 6887000 17 -524.7065\n 713500 6887000 18 -501.426\n 714000 6887000 19 -482.719\n 714500 6887000 20 -469.0421\n 715000 6887000 21 -460.9957\n 715500 6887000 22 -458.6922\n 716000 6887000 23 -460.3629\n 716500 6887000 24 -464.9228\n 717000 6887000 25 -471.3401\n 717500 6887000 26 -478.3196\n 718000 6887000 27 -483.86\n 718500 6887000 28 -485.9057\n 719000 6887000 29 -484.3904\n 719500 6887000 30 -481.4878\n 720000 6887000 31 -478.9699\n 720500 6887000 32 -477.7742\n 721000 6887000 33 -477.6367\n 721500 6887000 34 -477.1187\n 722000 6887000 35 -475.3954\n 722500 6887000 36 -473.0719\n 723000 6887000 37 -471.4279\n 723500 6887000 38 -471.2484\n 724000 6887000 39 -473.0879\n 724500 6887000 40 -476.5068\n 725000 6887000 41 -479.9287\n 725500 6887000 42 -481.04\n 726000 6887000 43 -478.301\n 726500 6887000 44 -472.1276\n 727000 6887000 45 -465.0894\n 727500 6887000 46 -458.8661\n 728000 6887000 47 -453.2337\n 728500 6887000 48 -446.9913\n 729000 6887000 49 -439.183\n 729500 6887000 50 -429.7381\n 730000 6887000 51 -419.6025\n 730500 6887000 52 -410.3047\n 731000 6887000 53 -402.6699\n 731500 6887000 54 -396.6462\n 732000 6887000 55 -391.789\n 732500 6887000 56 -387.6175\n 733000 6887000 57 -383.6115\n 733500 6887000 58 -379.4958\n 734000 6887000 59 -375.237\n 734500 6887000 60 -370.8268\n 735000 6887000 61 -366.16\n 735500 6887000 62 -361.0634\n 736000 6887000 63 -355.4667\n 736500 6887000 64 -349.4672\n 737000 6887000 65 -343.2252\n 737500 6887000 66 -336.9054\n 738000 6887000 67 -330.7425\n 738500 6887000 68 -325.138\n 739000 6887000 69 -320.6939\n 739500 6887000 70 -317.9464\n 740000 6887000 71 -317.2534\n 740500 6887000 72 -318.7521\n 741000 6887000 73 -322.8514\n 741500 6887000 74 -329.7847\n 742000 6887000 75 -339.7384\n 742500 6887000 76 -353.2586\n 743000 6887000 77 -370.861\n 743500 6887000 78 -392.0552\n 744000 6887000 79 -415.8036\n 744500 6887000 80 -441.4514\n 745000 6887000 81 -468.959\n 745500 6887000 82 -498.1582\n 746000 6887000 83 -527.7982\n 746500 6887000 84 -556.0009\n 747000 6887000 85 -581.0529\n 705000 6887500 1 -743.6907\n 705500 6887500 2 -739.1967\n 706000 6887500 3 -735.8917\n 706500 6887500 4 -733.7578\n 707000 6887500 5 -732.7177\n 707500 6887500 6 -732.6368\n 708000 6887500 7 -732.9546\n 708500 6887500 8 -732.1288\n 709000 6887500 9 -728.1373\n 709500 6887500 10 -718.7647\n 710000 6887500 11 -702.2817\n 710500 6887500 12 -678.2145\n 711000 6887500 13 -648.3673\n 711500 6887500 14 -615.465\n 712000 6887500 15 -582.3201\n 712500 6887500 16 -551.168\n 713000 6887500 17 -523.4376\n 713500 6887500 18 -499.9061\n 714000 6887500 19 -480.9931\n 714500 6887500 20 -466.9698\n 715000 6887500 21 -458.1826\n 715500 6887500 22 -454.9514\n 716000 6887500 23 -455.6797\n 716500 6887500 24 -459.4383\n 717000 6887500 25 -465.209\n 717500 6887500 26 -471.6452\n 718000 6887500 27 -476.8608\n 718500 6887500 28 -478.9431\n 719000 6887500 29 -477.6628\n 719500 6887500 30 -474.9828\n 720000 6887500 31 -472.6314\n 720500 6887500 32 -471.593\n 721000 6887500 33 -471.6563\n 721500 6887500 34 -470.9207\n 722000 6887500 35 -468.0855\n 722500 6887500 36 -463.8438\n 723000 6887500 37 -460.1235\n 723500 6887500 38 -458.3408\n 724000 6887500 39 -459.0609\n 724500 6887500 40 -461.7083\n 725000 6887500 41 -464.4195\n 725500 6887500 42 -464.5597\n 726000 6887500 43 -460.3991\n 726500 6887500 44 -452.6343\n 727000 6887500 45 -444.2138\n 727500 6887500 46 -437.0819\n 728000 6887500 47 -431.0478\n 728500 6887500 48 -424.9617\n 729000 6887500 49 -418.0201\n 729500 6887500 50 -410.1768\n 730000 6887500 51 -402.1401\n 730500 6887500 52 -395.063\n 731000 6887500 53 -389.4798\n 731500 6887500 54 -385.091\n 732000 6887500 55 -381.2153\n 732500 6887500 56 -377.2062\n 733000 6887500 57 -372.5189\n 733500 6887500 58 -367.1079\n 734000 6887500 59 -361.402\n 734500 6887500 60 -355.796\n 735000 6887500 61 -350.3779\n 735500 6887500 62 -344.9722\n 736000 6887500 63 -339.4136\n 736500 6887500 64 -333.7214\n 737000 6887500 65 -328.029\n 737500 6887500 66 -322.5636\n 738000 6887500 67 -317.5899\n 738500 6887500 68 -313.5029\n 739000 6887500 69 -310.8052\n 739500 6887500 70 -309.8747\n 740000 6887500 71 -310.8823\n 740500 6887500 72 -313.764\n 741000 6887500 73 -318.8602\n 741500 6887500 74 -326.6117\n 742000 6887500 75 -337.4609\n 742500 6887500 76 -352.182\n 743000 6887500 77 -371.5794\n 743500 6887500 78 -395.0441\n 744000 6887500 79 -420.9332\n 744500 6887500 80 -448.0607\n 745000 6887500 81 -476.1158\n 745500 6887500 82 -505.082\n 746000 6887500 83 -533.8389\n 746500 6887500 84 -560.6622\n 747000 6887500 85 -584.1107\n 705000 6888000 1 -746.0883\n 705500 6888000 2 -740.55\n 706000 6888000 3 -735.7146\n 706500 6888000 4 -731.9822\n 707000 6888000 5 -729.6849\n 707500 6888000 6 -729.0127\n 708000 6888000 7 -729.6115\n 708500 6888000 8 -729.7346\n 709000 6888000 9 -726.9998\n 709500 6888000 10 -718.8304\n 710000 6888000 11 -703.1713\n 710500 6888000 12 -679.4332\n 711000 6888000 13 -649.3457\n 711500 6888000 14 -615.7612\n 712000 6888000 15 -581.8063\n 712500 6888000 16 -550.0942\n 713000 6888000 17 -522.2242\n 713500 6888000 18 -498.8382\n 714000 6888000 19 -480.0982\n 714500 6888000 20 -466.0422\n 715000 6888000 21 -456.6905\n 715500 6888000 22 -452.4446\n 716000 6888000 23 -451.959\n 716500 6888000 24 -454.5109\n 717000 6888000 25 -459.1847\n 717500 6888000 26 -464.6987\n 718000 6888000 27 -469.3524\n 718500 6888000 28 -471.4741\n 719000 6888000 29 -470.5497\n 719500 6888000 30 -468.1783\n 720000 6888000 31 -466.168\n 720500 6888000 32 -465.4939\n 721000 6888000 33 -465.8682\n 721500 6888000 34 -465.1272\n 722000 6888000 35 -461.5335\n 722500 6888000 36 -455.7266\n 723000 6888000 37 -449.9863\n 723500 6888000 38 -446.4753\n 724000 6888000 39 -445.8073\n 724500 6888000 40 -447.2582\n 725000 6888000 41 -448.903\n 725500 6888000 42 -448.1571\n 726000 6888000 43 -443.2126\n 726500 6888000 44 -434.8177\n 727000 6888000 45 -425.7929\n 727500 6888000 46 -418.0911\n 728000 6888000 47 -411.6887\n 728500 6888000 48 -405.6299\n 729000 6888000 49 -399.3151\n 729500 6888000 50 -392.7402\n 730000 6888000 51 -386.4494\n 730500 6888000 52 -381.2397\n 731000 6888000 53 -377.3762\n 731500 6888000 54 -374.3326\n 732000 6888000 55 -371.1815\n 732500 6888000 56 -367.118\n 733000 6888000 57 -361.6748\n 733500 6888000 58 -355.039\n 734000 6888000 59 -348.0102\n 734500 6888000 60 -341.3261\n 735000 6888000 61 -335.2703\n 735500 6888000 62 -329.6559\n 736000 6888000 63 -324.2034\n 736500 6888000 64 -318.8452\n 737000 6888000 65 -313.6884\n 737500 6888000 66 -309.0169\n 738000 6888000 67 -305.1215\n 738500 6888000 68 -302.3783\n 739000 6888000 69 -301.2089\n 739500 6888000 70 -301.9075\n 740000 6888000 71 -304.5821\n 740500 6888000 72 -309.1879\n 741000 6888000 73 -316.0584\n 741500 6888000 74 -325.7049\n 742000 6888000 75 -338.6225\n 742500 6888000 76 -355.4893\n 743000 6888000 77 -376.9432\n 743500 6888000 78 -402.3654\n 744000 6888000 79 -429.9913\n 744500 6888000 80 -458.3849\n 745000 6888000 81 -486.7816\n 745500 6888000 82 -514.8857\n 746000 6888000 83 -541.8386\n 746500 6888000 84 -566.2738\n 747000 6888000 85 -587.1686\n 705000 6888500 1 -748.2779\n 705500 6888500 2 -741.6956\n 706000 6888500 3 -735.2982\n 706500 6888500 4 -729.8698\n 707000 6888500 5 -726.1572\n 707500 6888500 6 -724.7689\n 708000 6888500 7 -725.6313\n 708500 6888500 8 -726.7726\n 709000 6888500 9 -725.4185\n 709500 6888500 10 -718.6075\n 710000 6888500 11 -703.817\n 710500 6888500 12 -680.3386\n 711000 6888500 13 -649.933\n 711500 6888500 14 -615.6498\n 712000 6888500 15 -580.9597\n 712500 6888500 16 -548.8586\n 713000 6888500 17 -521.0992\n 713500 6888500 18 -498.1365\n 714000 6888500 19 -479.8169\n 714500 6888500 20 -465.8904\n 715000 6888500 21 -456.0758\n 715500 6888500 22 -450.6774\n 716000 6888500 23 -448.7443\n 716500 6888500 24 -449.8108\n 717000 6888500 25 -453.1724\n 717500 6888500 26 -457.7286\n 718000 6888500 27 -461.9401\n 718500 6888500 28 -464.2102\n 719000 6888500 29 -463.6853\n 719500 6888500 30 -461.5486\n 720000 6888500 31 -459.8427\n 720500 6888500 32 -459.5398\n 721000 6888500 33 -460.1271\n 721500 6888500 34 -459.5539\n 722000 6888500 35 -455.8935\n 722500 6888500 36 -449.3129\n 723000 6888500 37 -442.0215\n 723500 6888500 38 -436.5496\n 724000 6888500 39 -433.8268\n 724500 6888500 40 -433.2525\n 725000 6888500 41 -433.1681\n 725500 6888500 42 -431.4235\n 726000 6888500 43 -426.3518\n 726500 6888500 44 -418.3683\n 727000 6888500 45 -409.6821\n 727500 6888500 46 -401.7738\n 728000 6888500 47 -394.9568\n 728500 6888500 48 -388.714\n 729000 6888500 49 -382.7562\n 729500 6888500 50 -377.1463\n 730000 6888500 51 -372.298\n 730500 6888500 52 -368.6519\n 731000 6888500 53 -366.1964\n 731500 6888500 54 -364.2273\n 732000 6888500 55 -361.5877\n 732500 6888500 56 -357.2958\n 733000 6888500 57 -351.0629\n 733500 6888500 58 -343.3251\n 734000 6888500 59 -335.1631\n 734500 6888500 60 -327.5902\n 735000 6888500 61 -321.0782\n 735500 6888500 62 -315.4053\n 736000 6888500 63 -310.1571\n 736500 6888500 64 -305.1671\n 737000 6888500 65 -300.517\n 737500 6888500 66 -296.5487\n 738000 6888500 67 -293.5984\n 738500 6888500 68 -292.0273\n 739000 6888500 69 -292.1884\n 739500 6888500 70 -294.3502\n 740000 6888500 71 -298.651\n 740500 6888500 72 -305.1998\n 741000 6888500 73 -314.4171\n 741500 6888500 74 -326.7432\n 742000 6888500 75 -342.555\n 742500 6888500 76 -362.2552\n 743000 6888500 77 -386.0367\n 743500 6888500 78 -413.2481\n 744000 6888500 79 -442.3548\n 744500 6888500 80 -471.8541\n 745000 6888500 81 -500.4223\n 745500 6888500 82 -527.2288\n 746000 6888500 83 -551.5977\n 746500 6888500 84 -572.7675\n 747000 6888500 85 -590.2762\n 705000 6889000 1 -750.3484\n 705500 6889000 2 -742.7141\n 706000 6889000 3 -734.7048\n 706500 6889000 4 -727.4753\n 707000 6889000 5 -722.1926\n 707500 6889000 6 -719.9697\n 708000 6889000 7 -721.1226\n 708500 6889000 8 -723.4189\n 709000 6889000 9 -723.6509\n 709500 6889000 10 -718.4364\n 710000 6889000 11 -704.6424\n 710500 6889000 12 -681.3536\n 711000 6889000 13 -650.5051\n 711500 6889000 14 -615.4698\n 712000 6889000 15 -580.0803\n 712500 6889000 16 -547.7042\n 713000 6889000 17 -520.2193\n 713500 6889000 18 -497.8375\n 714000 6889000 19 -480.0486\n 714500 6889000 20 -466.296\n 715000 6889000 21 -456.021\n 715500 6889000 22 -449.3266\n 716000 6889000 23 -445.7519\n 716500 6889000 24 -445.1521\n 717000 6889000 25 -447.1492\n 717500 6889000 26 -450.9196\n 718000 6889000 27 -455.0296\n 718500 6889000 28 -457.7551\n 719000 6889000 29 -457.8226\n 719500 6889000 30 -455.9474\n 720000 6889000 31 -454.2974\n 720500 6889000 32 -454.0257\n 721000 6889000 33 -454.5417\n 721500 6889000 34 -454.0066\n 722000 6889000 35 -450.5639\n 722500 6889000 36 -443.6963\n 723000 6889000 37 -435.1855\n 723500 6889000 38 -427.7508\n 724000 6889000 39 -422.622\n 724500 6889000 40 -419.5938\n 725000 6889000 41 -417.5167\n 725500 6889000 42 -414.7374\n 726000 6889000 43 -409.863\n 726500 6889000 44 -402.8635\n 727000 6889000 45 -395.0352\n 727500 6889000 46 -387.265\n 728000 6889000 47 -380.0712\n 728500 6889000 48 -373.5359\n 729000 6889000 49 -367.7372\n 729500 6889000 50 -362.8299\n 730000 6889000 51 -359.1242\n 730500 6889000 52 -356.7658\n 731000 6889000 53 -355.4793\n 731500 6889000 54 -354.4056\n 732000 6889000 55 -352.1968\n 732500 6889000 56 -347.7126\n 733000 6889000 57 -340.8312\n 733500 6889000 58 -332.2451\n 734000 6889000 59 -323.2512\n 734500 6889000 60 -315.0521\n 735000 6889000 61 -308.2857\n 735500 6889000 62 -302.6804\n 736000 6889000 63 -297.6907\n 736500 6889000 64 -293.0635\n 737000 6889000 65 -288.8623\n 737500 6889000 66 -285.4915\n 738000 6889000 67 -283.3548\n 738500 6889000 68 -282.8117\n 739000 6889000 69 -284.1377\n 739500 6889000 70 -287.608\n 740000 6889000 71 -293.469\n 740500 6889000 72 -302.0173\n 741000 6889000 73 -313.8056\n 741500 6889000 74 -329.1331\n 742000 6889000 75 -348.1608\n 742500 6889000 76 -370.9317\n 743000 6889000 77 -397.1601\n 743500 6889000 78 -426.1493\n 744000 6889000 79 -456.6643\n 744500 6889000 80 -487.2024\n 745000 6889000 81 -515.876\n 745500 6889000 82 -541.1388\n 746000 6889000 83 -562.4945\n 746500 6889000 84 -579.8632\n 747000 6889000 85 -593.42\n 705000 6889500 1 -752.2693\n 705500 6889500 2 -743.6382\n 706000 6889500 3 -734.0938\n 706500 6889500 4 -725.1235\n 707000 6889500 5 -718.2728\n 707500 6889500 6 -715.1511\n 708000 6889500 7 -716.5156\n 708500 6889500 8 -719.8807\n 709000 6889500 9 -721.6024\n 709500 6889500 10 -717.9704\n 710000 6889500 11 -705.2858\n 710500 6889500 12 -682.269\n 711000 6889500 13 -651.123\n 711500 6889500 14 -615.554\n 712000 6889500 15 -579.6844\n 712500 6889500 16 -547.1696\n 713000 6889500 17 -520.0032\n 713500 6889500 18 -498.1669\n 714000 6889500 19 -480.8217\n 714500 6889500 20 -467.1342\n 715000 6889500 21 -456.2962\n 715500 6889500 22 -448.1978\n 716000 6889500 23 -442.8184\n 716500 6889500 24 -440.4119\n 717000 6889500 25 -441.0378\n 717500 6889500 26 -444.2241\n 718000 6889500 27 -448.5352\n 718500 6889500 28 -451.9126\n 719000 6889500 29 -452.6506\n 719500 6889500 30 -451.1299\n 720000 6889500 31 -449.3323\n 720500 6889500 32 -448.5977\n 721000 6889500 33 -448.5439\n 721500 6889500 34 -447.6885\n 722000 6889500 35 -444.3111\n 722500 6889500 36 -437.2593\n 723000 6889500 37 -427.8023\n 723500 6889500 38 -418.6601\n 724000 6889500 39 -411.3133\n 724500 6889500 40 -405.9919\n 725000 6889500 41 -402.1107\n 725500 6889500 42 -398.4858\n 726000 6889500 43 -393.9124\n 726500 6889500 44 -387.9099\n 727000 6889500 45 -380.9974\n 727500 6889500 46 -373.5076\n 728000 6889500 47 -366.0807\n 728500 6889500 48 -359.2974\n 729000 6889500 49 -353.5696\n 729500 6889500 50 -349.162\n 730000 6889500 51 -346.3302\n 730500 6889500 52 -344.9928\n 731000 6889500 53 -344.6576\n 731500 6889500 54 -344.3383\n 732000 6889500 55 -342.5876\n 732500 6889500 56 -338.1617\n 733000 6889500 57 -331.0547\n 733500 6889500 58 -322.1259\n 734000 6889500 59 -312.794\n 734500 6889500 60 -304.327\n 735000 6889500 61 -297.4832\n 735500 6889500 62 -291.987\n 736000 6889500 63 -287.2276\n 736500 6889500 64 -282.9108\n 737000 6889500 65 -279.0934\n 737500 6889500 66 -276.232\n 738000 6889500 67 -274.7988\n 738500 6889500 68 -275.1525\n 739000 6889500 69 -277.4848\n 739500 6889500 70 -282.0947\n 740000 6889500 71 -289.3633\n 740500 6889500 72 -299.7797\n 741000 6889500 73 -314.0187\n 741500 6889500 74 -332.2193\n 742000 6889500 75 -354.3113\n 742500 6889500 76 -380.0095\n 743000 6889500 77 -408.629\n 743500 6889500 78 -439.4239\n 744000 6889500 79 -471.4014\n 744500 6889500 80 -503.0127\n 745000 6889500 81 -531.8148\n 745500 6889500 82 -555.5255\n 746000 6889500 83 -573.7618\n 746500 6889500 84 -587.163\n 747000 6889500 85 -596.5323\n 705000 6890000 1 -753.7894\n 705500 6890000 2 -744.3882\n 706000 6890000 3 -733.7056\n 706500 6890000 4 -723.4438\n 707000 6890000 5 -715.3792\n 707500 6890000 6 -711.3977\n 708000 6890000 7 -712.6119\n 708500 6890000 8 -716.4191\n 709000 6890000 9 -718.9236\n 709500 6890000 10 -716.3054\n 710000 6890000 11 -704.6556\n 710500 6890000 12 -682.3342\n 711000 6890000 13 -651.6267\n 711500 6890000 14 -616.3267\n 712000 6890000 15 -580.6121\n 712500 6890000 16 -548.1965\n 713000 6890000 17 -521.2042\n 713500 6890000 18 -499.5775\n 714000 6890000 19 -482.3196\n 714500 6890000 20 -468.412\n 715000 6890000 21 -456.8488\n 715500 6890000 22 -447.2787\n 716000 6890000 23 -439.9577\n 716500 6890000 24 -435.5843\n 717000 6890000 25 -434.7486\n 717500 6890000 26 -437.3556\n 718000 6890000 27 -441.8504\n 718500 6890000 28 -445.6547\n 719000 6890000 29 -446.8011\n 719500 6890000 30 -445.5055\n 720000 6890000 31 -443.509\n 720500 6890000 32 -442.0571\n 721000 6890000 33 -441.0279\n 721500 6890000 34 -439.3162\n 722000 6890000 35 -435.5248\n 722500 6890000 36 -428.3342\n 723000 6890000 37 -418.444\n 723500 6890000 38 -408.3265\n 724000 6890000 39 -399.4756\n 724500 6890000 40 -392.4774\n 725000 6890000 41 -387.2655\n 725500 6890000 42 -383.0136\n 726000 6890000 43 -378.5879\n 726500 6890000 44 -373.2271\n 727000 6890000 45 -366.9512\n 727500 6890000 46 -359.7805\n 728000 6890000 47 -352.2925\n 728500 6890000 48 -345.3727\n 729000 6890000 49 -339.672\n 729500 6890000 50 -335.5938\n 730000 6890000 51 -333.4167\n 730500 6890000 52 -332.8964\n 731000 6890000 53 -333.3399\n 731500 6890000 54 -333.6587\n 732000 6890000 55 -332.4493\n 732500 6890000 56 -328.4958\n 733000 6890000 57 -321.7969\n 733500 6890000 58 -313.2491\n 734000 6890000 59 -304.2296\n 734500 6890000 60 -295.9641\n 735000 6890000 61 -289.2118\n 735500 6890000 62 -283.8101\n 736000 6890000 63 -279.2176\n 736500 6890000 64 -275.1754\n 737000 6890000 65 -271.7461\n 737500 6890000 66 -269.3933\n 738000 6890000 67 -268.5978\n 738500 6890000 68 -269.7082\n 739000 6890000 69 -272.8557\n 739500 6890000 70 -278.3825\n 740000 6890000 71 -286.7969\n 740500 6890000 72 -298.7404\n 741000 6890000 73 -314.9592\n 741500 6890000 74 -335.4187\n 742000 6890000 75 -359.8727\n 742500 6890000 76 -387.8873\n 743000 6890000 77 -418.6184\n 743500 6890000 78 -451.2503\n 744000 6890000 79 -484.8153\n 744500 6890000 80 -517.5484\n 745000 6890000 81 -546.5396\n 745500 6890000 82 -568.9756\n 746000 6890000 83 -584.4149\n 746500 6890000 84 -594.1793\n 747000 6890000 85 -599.5886\n 705000 6890500 1 -754.7719\n 705500 6890500 2 -744.9421\n 706000 6890500 3 -733.7266\n 706500 6890500 4 -722.8536\n 707000 6890500 5 -714.1437\n 707500 6890500 6 -709.4523\n 708000 6890500 7 -710.016\n 708500 6890500 8 -713.365\n 709000 6890500 9 -715.6508\n 709500 6890500 10 -713.205\n 710000 6890500 11 -702.3481\n 710500 6890500 12 -681.2932\n 711000 6890500 13 -652.025\n 711500 6890500 14 -618.0133\n 712000 6890500 15 -583.1998\n 712500 6890500 16 -551.1606\n 713000 6890500 17 -524.1106\n 713500 6890500 18 -502.2558\n 714000 6890500 19 -484.7001\n 714500 6890500 20 -470.3142\n 715000 6890500 21 -457.9254\n 715500 6890500 22 -446.9044\n 716000 6890500 23 -437.5255\n 716500 6890500 24 -430.9752\n 717000 6890500 25 -428.4088\n 717500 6890500 26 -430.0704\n 718000 6890500 27 -434.3319\n 718500 6890500 28 -438.0962\n 719000 6890500 29 -439.2614\n 719500 6890500 30 -438.0609\n 720000 6890500 31 -435.9005\n 720500 6890500 32 -433.7827\n 721000 6890500 33 -431.6966\n 721500 6890500 34 -428.8959\n 722000 6890500 35 -424.2755\n 722500 6890500 36 -416.9413\n 723000 6890500 37 -407.1852\n 723500 6890500 38 -396.9248\n 724000 6890500 39 -387.4608\n 724500 6890500 40 -379.5476\n 725000 6890500 41 -373.4807\n 725500 6890500 42 -368.7365\n 726000 6890500 43 -364.2487\n 726500 6890500 44 -359.1184\n 727000 6890500 45 -353.0645\n 727500 6890500 46 -346.0512\n 728000 6890500 47 -338.5478\n 728500 6890500 48 -331.503\n 729000 6890500 49 -325.7215\n 729500 6890500 50 -321.8057\n 730000 6890500 51 -320.1833\n 730500 6890500 52 -320.4684\n 731000 6890500 53 -321.726\n 731500 6890500 54 -322.747\n 732000 6890500 55 -322.2015\n 732500 6890500 56 -319.0351\n 733000 6890500 57 -313.2156\n 733500 6890500 58 -305.5614\n 734000 6890500 59 -297.311\n 734500 6890500 60 -289.6324\n 735000 6890500 61 -283.1788\n 735500 6890500 62 -277.9374\n 736000 6890500 63 -273.5515\n 736500 6890500 64 -269.8687\n 737000 6890500 65 -266.9749\n 737500 6890500 66 -265.2758\n 738000 6890500 67 -265.1317\n 738500 6890500 68 -266.8704\n 739000 6890500 69 -270.6491\n 739500 6890500 70 -276.8549\n 740000 6890500 71 -286.118\n 740500 6890500 72 -299.1672\n 741000 6890500 73 -316.7218\n 741500 6890500 74 -338.5413\n 742000 6890500 75 -364.2869\n 742500 6890500 76 -393.6604\n 743000 6890500 77 -426.0407\n 743500 6890500 78 -460.5116\n 744000 6890500 79 -495.7682\n 744500 6890500 80 -529.6096\n 745000 6890500 81 -558.7988\n 745500 6890500 82 -580.3533\n 746000 6890500 83 -593.6326\n 746500 6890500 84 -600.4879\n 747000 6890500 85 -602.5652\n 705000 6891000 1 -755.5894\n 705500 6891000 2 -745.5767\n 706000 6891000 3 -734.2714\n 706500 6891000 4 -723.2495\n 707000 6891000 5 -714.2563\n 707500 6891000 6 -709.015\n 708000 6891000 7 -708.6474\n 708500 6891000 8 -710.9717\n 709000 6891000 9 -712.4388\n 709500 6891000 10 -709.6861\n 710000 6891000 11 -699.4402\n 710500 6891000 12 -680.0021\n 711000 6891000 13 -652.8139\n 711500 6891000 14 -620.7148\n 712000 6891000 15 -587.1854\n 712500 6891000 16 -555.6332\n 713000 6891000 17 -528.3522\n 713500 6891000 18 -506.0068\n 714000 6891000 19 -488.0027\n 714500 6891000 20 -473.136\n 715000 6891000 21 -459.9958\n 715500 6891000 22 -447.616\n 716000 6891000 23 -436.1425\n 716500 6891000 24 -427.1773\n 717000 6891000 25 -422.4582\n 717500 6891000 26 -422.5092\n 718000 6891000 27 -425.8073\n 718500 6891000 28 -429.0792\n 719000 6891000 29 -430.0667\n 719500 6891000 30 -428.9342\n 720000 6891000 31 -426.7077\n 720500 6891000 32 -424.0827\n 721000 6891000 33 -421.0709\n 721500 6891000 34 -417.199\n 722000 6891000 35 -411.7652\n 722500 6891000 36 -404.2996\n 723000 6891000 37 -395.0398\n 723500 6891000 38 -385.2402\n 724000 6891000 39 -375.89\n 724500 6891000 40 -367.7158\n 725000 6891000 41 -361.1575\n 725500 6891000 42 -355.943\n 726000 6891000 43 -351.1771\n 726500 6891000 44 -345.9758\n 727000 6891000 45 -339.9179\n 727500 6891000 46 -332.8726\n 728000 6891000 47 -325.2382\n 728500 6891000 48 -317.9242\n 729000 6891000 49 -311.8586\n 729500 6891000 50 -307.913\n 730000 6891000 51 -306.8261\n 730500 6891000 52 -308.052\n 731000 6891000 53 -310.318\n 731500 6891000 54 -312.2547\n 732000 6891000 55 -312.5624\n 732500 6891000 56 -310.3271\n 733000 6891000 57 -305.5406\n 733500 6891000 58 -298.9389\n 734000 6891000 59 -291.6378\n 734500 6891000 60 -284.7156\n 735000 6891000 61 -278.7609\n 735500 6891000 62 -273.8467\n 736000 6891000 63 -269.8215\n 736500 6891000 64 -266.6806\n 737000 6891000 65 -264.5481\n 737500 6891000 66 -263.7373\n 738000 6891000 67 -264.3513\n 738500 6891000 68 -266.6655\n 739000 6891000 69 -270.9506\n 739500 6891000 70 -277.6745\n 740000 6891000 71 -287.585\n 740500 6891000 72 -301.4241\n 741000 6891000 73 -319.7881\n 741500 6891000 74 -342.1888\n 742000 6891000 75 -368.2501\n 742500 6891000 76 -398.0116\n 743000 6891000 77 -431.442\n 743500 6891000 78 -467.5831\n 744000 6891000 79 -504.476\n 744500 6891000 80 -539.3437\n 745000 6891000 81 -568.6085\n 745500 6891000 82 -589.4614\n 746000 6891000 83 -601.1666\n 746500 6891000 84 -605.806\n 747000 6891000 85 -605.2297\n 705000 6891500 1 -756.7645\n 705500 6891500 2 -746.7308\n 706000 6891500 3 -735.5521\n 706500 6891500 4 -724.5922\n 707000 6891500 5 -715.4234\n 707500 6891500 6 -709.6369\n 708000 6891500 7 -708.2487\n 708500 6891500 8 -709.365\n 709000 6891500 9 -709.8636\n 709500 6891500 10 -706.765\n 710000 6891500 11 -697.2303\n 710500 6891500 12 -679.5843\n 711000 6891500 13 -654.7132\n 711500 6891500 14 -624.731\n 712000 6891500 15 -592.4946\n 712500 6891500 16 -561.2641\n 713000 6891500 17 -533.5366\n 713500 6891500 18 -510.5653\n 714000 6891500 19 -492.1537\n 714500 6891500 20 -477.0028\n 715000 6891500 21 -463.3629\n 715500 6891500 22 -449.8414\n 716000 6891500 23 -436.3723\n 716500 6891500 24 -424.8268\n 717000 6891500 25 -417.5415\n 717500 6891500 26 -415.3439\n 718000 6891500 27 -416.9267\n 718500 6891500 28 -419.2463\n 719000 6891500 29 -419.9464\n 719500 6891500 30 -418.8742\n 720000 6891500 31 -416.6422\n 720500 6891500 32 -413.6603\n 721000 6891500 33 -409.9182\n 721500 6891500 34 -405.1453\n 722000 6891500 35 -399.0345\n 722500 6891500 36 -391.5503\n 723000 6891500 37 -382.9443\n 723500 6891500 38 -373.9001\n 724000 6891500 39 -365.0711\n 724500 6891500 40 -357.0541\n 725000 6891500 41 -350.2662\n 725500 6891500 42 -344.6081\n 726000 6891500 43 -339.437\n 726500 6891500 44 -334.008\n 727000 6891500 45 -327.8318\n 727500 6891500 46 -320.6852\n 728000 6891500 47 -312.8561\n 728500 6891500 48 -305.1929\n 729000 6891500 49 -298.7229\n 729500 6891500 50 -294.6125\n 730000 6891500 51 -294.0465\n 730500 6891500 52 -296.3156\n 731000 6891500 53 -299.7288\n 731500 6891500 54 -302.7223\n 732000 6891500 55 -303.9903\n 732500 6891500 56 -302.6993\n 733000 6891500 57 -298.8865\n 733500 6891500 58 -293.2701\n 734000 6891500 59 -286.8998\n 734500 6891500 60 -280.7732\n 735000 6891500 61 -275.4325\n 735500 6891500 62 -271.0171\n 736000 6891500 63 -267.5342\n 736500 6891500 64 -265.1294\n 737000 6891500 65 -263.9711\n 737500 6891500 66 -264.2684\n 738000 6891500 67 -265.8321\n 738500 6891500 68 -268.8344\n 739000 6891500 69 -273.6909\n 739500 6891500 70 -280.9762\n 740000 6891500 71 -291.5151\n 740500 6891500 72 -306.0591\n 741000 6891500 73 -325.0073\n 741500 6891500 74 -347.5997\n 742000 6891500 75 -373.4324\n 742500 6891500 76 -402.9229\n 743000 6891500 77 -436.7204\n 743500 6891500 78 -473.938\n 744000 6891500 79 -511.9364\n 744500 6891500 80 -547.3494\n 745000 6891500 81 -576.4106\n 745500 6891500 82 -596.463\n 746000 6891500 83 -606.9431\n 746500 6891500 84 -609.8851\n 747000 6891500 85 -607.2828\n 705000 6892000 1 -758.5295\n 705500 6892000 2 -748.6506\n 706000 6892000 3 -737.7301\n 706500 6892000 4 -726.9486\n 707000 6892000 5 -717.6856\n 707500 6892000 6 -711.331\n 708000 6892000 7 -708.834\n 708500 6892000 8 -708.6583\n 709000 6892000 9 -708.1837\n 709500 6892000 10 -704.8651\n 710000 6892000 11 -696.2411\n 710500 6892000 12 -680.5707\n 711000 6892000 13 -658.213\n 711500 6892000 14 -630.5016\n 712000 6892000 15 -599.508\n 712500 6892000 16 -568.3025\n 713000 6892000 17 -539.7326\n 713500 6892000 18 -515.871\n 714000 6892000 19 -497.0103\n 714500 6892000 20 -481.6938\n 715000 6892000 21 -467.7604\n 715500 6892000 22 -453.388\n 716000 6892000 23 -438.2431\n 716500 6892000 24 -424.2459\n 717000 6892000 25 -414.2237\n 717500 6892000 26 -409.4289\n 718000 6892000 27 -408.8746\n 718500 6892000 28 -409.8649\n 719000 6892000 29 -410.0806\n 719500 6892000 30 -408.939\n 720000 6892000 31 -406.6569\n 720500 6892000 32 -403.4038\n 721000 6892000 33 -399.1258\n 721500 6892000 34 -393.6958\n 722000 6892000 35 -387.1014\n 722500 6892000 36 -379.6671\n 723000 6892000 37 -371.6872\n 723500 6892000 38 -363.404\n 724000 6892000 39 -355.1693\n 724500 6892000 40 -347.4242\n 725000 6892000 41 -340.5204\n 725500 6892000 42 -334.4674\n 726000 6892000 43 -328.8761\n 726500 6892000 44 -323.19\n 727000 6892000 45 -316.9166\n 727500 6892000 46 -309.7426\n 728000 6892000 47 -301.8515\n 728500 6892000 48 -294.0082\n 729000 6892000 49 -287.3063\n 729500 6892000 50 -283.1185\n 730000 6892000 51 -283.0304\n 730500 6892000 52 -286.2266\n 731000 6892000 53 -290.662\n 731500 6892000 54 -294.6021\n 732000 6892000 55 -296.7216\n 732500 6892000 56 -296.231\n 733000 6892000 57 -293.2137\n 733500 6892000 58 -288.4001\n 734000 6892000 59 -282.8144\n 734500 6892000 60 -277.4045\n 735000 6892000 61 -272.7036\n 735500 6892000 62 -268.9037\n 736000 6892000 63 -266.1236\n 736500 6892000 64 -264.6221\n 737000 6892000 65 -264.5959\n 737500 6892000 66 -266.168\n 738000 6892000 67 -268.9244\n 738500 6892000 68 -272.9276\n 739000 6892000 69 -278.6927\n 739500 6892000 70 -286.8896\n 740000 6892000 71 -298.3139\n 740500 6892000 72 -313.7462\n 741000 6892000 73 -333.3999\n 741500 6892000 74 -356.2752\n 742000 6892000 75 -381.9025\n 742500 6892000 76 -410.8893\n 743000 6892000 77 -444.3254\n 743500 6892000 78 -481.4849\n 744000 6892000 79 -519.3377\n 744500 6892000 80 -554.175\n 745000 6892000 81 -582.2943\n 745500 6892000 82 -601.2784\n 746000 6892000 83 -610.6711\n 746500 6892000 84 -612.3505\n 747000 6892000 85 -608.3334\n 705000 6892500 1 -760.8865\n 705500 6892500 2 -751.3072\n 706000 6892500 3 -740.7229\n 706500 6892500 4 -730.1779\n 707000 6892500 5 -720.8801\n 707500 6892500 6 -713.9734\n 708000 6892500 7 -710.2714\n 708500 6892500 8 -708.6833\n 709000 6892500 9 -707.1779\n 709500 6892500 10 -703.7\n 710000 6892500 11 -696.1868\n 710500 6892500 12 -682.8442\n 711000 6892500 13 -663.494\n 711500 6892500 14 -638.5479\n 712000 6892500 15 -609.0372\n 712500 6892500 16 -577.639\n 713000 6892500 17 -547.6478\n 713500 6892500 18 -522.2888\n 714000 6892500 19 -502.5249\n 714500 6892500 20 -486.7201\n 715000 6892500 21 -472.3209\n 715500 6892500 22 -457.2617\n 716000 6892500 23 -440.9518\n 716500 6892500 24 -425.1386\n 717000 6892500 25 -412.7682\n 717500 6892500 26 -405.4649\n 718000 6892500 27 -402.6795\n 718500 6892500 28 -402.1156\n 719000 6892500 29 -401.5762\n 719500 6892500 30 -400.1325\n 720000 6892500 31 -397.6668\n 720500 6892500 32 -394.1581\n 721000 6892500 33 -389.5105\n 721500 6892500 34 -383.6907\n 722000 6892500 35 -376.838\n 722500 6892500 36 -369.4667\n 723000 6892500 37 -361.9125\n 723500 6892500 38 -354.1483\n 724000 6892500 39 -346.3209\n 724500 6892500 40 -338.7393\n 725000 6892500 41 -331.6893\n 725500 6892500 42 -325.2553\n 726000 6892500 43 -319.2594\n 726500 6892500 44 -313.3433\n 727000 6892500 45 -307.0753\n 727500 6892500 46 -300.1084\n 728000 6892500 47 -292.5299\n 728500 6892500 48 -284.9931\n 729000 6892500 49 -278.5862\n 729500 6892500 50 -274.6519\n 730000 6892500 51 -274.9195\n 730500 6892500 52 -278.6333\n 731000 6892500 53 -283.6252\n 731500 6892500 54 -288.1158\n 732000 6892500 55 -290.7904\n 732500 6892500 56 -290.8605\n 733000 6892500 57 -288.4073\n 733500 6892500 58 -284.1696\n 734000 6892500 59 -279.1622\n 734500 6892500 60 -274.309\n 735000 6892500 61 -270.1761\n 735500 6892500 62 -267.0422\n 736000 6892500 63 -265.0977\n 736500 6892500 64 -264.6412\n 737000 6892500 65 -265.8565\n 737500 6892500 66 -268.8299\n 738000 6892500 67 -273.0592\n 738500 6892500 68 -278.5192\n 739000 6892500 69 -285.7367\n 739500 6892500 70 -295.4079\n 740000 6892500 71 -308.2277\n 740500 6892500 72 -324.9464\n 741000 6892500 73 -345.6643\n 741500 6892500 74 -369.241\n 742000 6892500 75 -395.0785\n 742500 6892500 76 -423.6496\n 743000 6892500 77 -456.0089\n 743500 6892500 78 -491.6403\n 744000 6892500 79 -527.6223\n 744500 6892500 80 -560.344\n 745000 6892500 81 -586.3919\n 745500 6892500 82 -603.7751\n 746000 6892500 83 -612.053\n 746500 6892500 84 -612.8162\n 747000 6892500 85 -607.8563\n 705000 6893000 1 -763.6896\n 705500 6893000 2 -754.501\n 706000 6893000 3 -744.2531\n 706500 6893000 4 -733.8992\n 707000 6893000 5 -724.4937\n 707500 6893000 6 -716.982\n 708000 6893000 7 -712.0322\n 708500 6893000 8 -708.9236\n 709000 6893000 9 -706.2461\n 709500 6893000 10 -702.5211\n 710000 6893000 11 -696.2421\n 710500 6893000 12 -685.8101\n 711000 6893000 13 -670.4063\n 711500 6893000 14 -649.2748\n 712000 6893000 15 -622.0526\n 712500 6893000 16 -590.6342\n 713000 6893000 17 -558.6788\n 713500 6893000 18 -530.8858\n 714000 6893000 19 -509.1989\n 714500 6893000 20 -491.9166\n 715000 6893000 21 -476.3383\n 715500 6893000 22 -460.4412\n 716000 6893000 23 -443.5109\n 716500 6893000 24 -426.8231\n 717000 6893000 25 -413.0331\n 717500 6893000 26 -403.7671\n 718000 6893000 27 -398.9089\n 718500 6893000 28 -396.656\n 719000 6893000 29 -395.0957\n 719500 6893000 30 -393.1078\n 720000 6893000 31 -390.2974\n 720500 6893000 32 -386.4949\n 721000 6893000 33 -381.5916\n 721500 6893000 34 -375.6062\n 722000 6893000 35 -368.7235\n 722500 6893000 36 -361.389\n 723000 6893000 37 -353.9605\n 723500 6893000 38 -346.3403\n 724000 6893000 39 -338.5937\n 724500 6893000 40 -330.9444\n 725000 6893000 41 -323.6271\n 725500 6893000 42 -316.7775\n 726000 6893000 43 -310.3815\n 726500 6893000 44 -304.2852\n 727000 6893000 45 -298.1512\n 727500 6893000 46 -291.685\n 728000 6893000 47 -284.869\n 728500 6893000 48 -278.2026\n 729000 6893000 49 -272.6578\n 729500 6893000 50 -269.4594\n 730000 6893000 51 -269.9818\n 730500 6893000 52 -273.7155\n 731000 6893000 53 -278.7012\n 731500 6893000 54 -283.2754\n 732000 6893000 55 -286.1496\n 732500 6893000 56 -286.5168\n 733000 6893000 57 -284.4048\n 733500 6893000 58 -280.5241\n 734000 6893000 59 -275.874\n 734500 6893000 60 -271.3939\n 735000 6893000 61 -267.7193\n 735500 6893000 62 -265.25\n 736000 6893000 63 -264.2545\n 736500 6893000 64 -264.9837\n 737000 6893000 65 -267.5629\n 737500 6893000 66 -272.0587\n 738000 6893000 67 -278.0106\n 738500 6893000 68 -285.3642\n 739000 6893000 69 -294.5612\n 739500 6893000 70 -306.2309\n 740000 6893000 71 -320.9746\n 740500 6893000 72 -339.4446\n 741000 6893000 73 -361.6427\n 741500 6893000 74 -386.3649\n 742000 6893000 75 -412.7931\n 742500 6893000 76 -441.0333\n 743000 6893000 77 -471.7571\n 743500 6893000 78 -504.5957\n 744000 6893000 79 -537.1901\n 744500 6893000 80 -566.443\n 745000 6893000 81 -589.3693\n 745500 6893000 82 -604.4662\n 746000 6893000 83 -611.3378\n 746500 6893000 84 -611.1647\n 747000 6893000 85 -605.3818\n 705000 6893500 1 -766.7399\n 705500 6893500 2 -757.9863\n 706000 6893500 3 -748.033\n 706500 6893500 4 -737.7733\n 707000 6893500 5 -728.1387\n 707500 6893500 6 -719.9227\n 708000 6893500 7 -713.6888\n 708500 6893500 8 -709.0214\n 709000 6893500 9 -705.0422\n 709500 6893500 10 -700.8828\n 710000 6893500 11 -695.8262\n 710500 6893500 12 -688.8156\n 711000 6893500 13 -678.3965\n 711500 6893500 14 -662.4514\n 712000 6893500 15 -638.8879\n 712500 6893500 16 -608.416\n 713000 6893500 17 -574.5823\n 713500 6893500 18 -543.5252\n 714000 6893500 19 -518.5621\n 714500 6893500 20 -498.3256\n 715000 6893500 21 -480.446\n 715500 6893500 22 -463.2126\n 716000 6893500 23 -445.8639\n 716500 6893500 24 -429.0356\n 717000 6893500 25 -414.6884\n 717500 6893500 26 -404.1578\n 718000 6893500 27 -397.5409\n 718500 6893500 28 -393.603\n 719000 6893500 29 -390.8438\n 719500 6893500 30 -388.1183\n 720000 6893500 31 -384.8148\n 720500 6893500 32 -380.6435\n 721000 6893500 33 -375.5209\n 721500 6893500 34 -369.4948\n 722000 6893500 35 -362.7148\n 722500 6893500 36 -355.4015\n 723000 6893500 37 -347.8198\n 723500 6893500 38 -339.9833\n 724000 6893500 39 -331.9845\n 724500 6893500 40 -324.0118\n 725000 6893500 41 -316.2638\n 725500 6893500 42 -308.9202\n 726000 6893500 43 -302.1068\n 726500 6893500 44 -295.8818\n 727000 6893500 45 -290.0232\n 727500 6893500 46 -284.3074\n 728000 6893500 47 -278.6014\n 728500 6893500 48 -273.1603\n 729000 6893500 49 -268.793\n 729500 6893500 50 -266.5224\n 730000 6893500 51 -267.3426\n 730500 6893500 52 -270.8394\n 731000 6893500 53 -275.5183\n 731500 6893500 54 -279.9349\n 732000 6893500 55 -282.8049\n 732500 6893500 56 -283.2451\n 733000 6893500 57 -281.2397\n 733500 6893500 58 -277.4973\n 734000 6893500 59 -273.0283\n 734500 6893500 60 -268.7918\n 735000 6893500 61 -265.506\n 735500 6893500 62 -263.7037\n 736000 6893500 63 -263.7436\n 736500 6893500 64 -265.8002\n 737000 6893500 65 -269.8946\n 737500 6893500 66 -276.101\n 738000 6893500 67 -283.9854\n 738500 6893500 68 -293.505\n 739000 6893500 69 -304.9887\n 739500 6893500 70 -318.9556\n 740000 6893500 71 -335.9267\n 740500 6893500 72 -356.4592\n 741000 6893500 73 -380.4975\n 741500 6893500 74 -406.6878\n 742000 6893500 75 -433.8292\n 742500 6893500 76 -461.54\n 743000 6893500 77 -490.1683\n 743500 6893500 78 -519.4891\n 744000 6893500 79 -547.8495\n 744500 6893500 80 -572.8414\n 745000 6893500 81 -592.02\n 745500 6893500 82 -604.2203\n 746000 6893500 83 -609.1738\n 746500 6893500 84 -607.6959\n 747000 6893500 85 -600.9373\n 705000 6894000 1 -769.7299\n 705500 6894000 2 -761.4346\n 706000 6894000 3 -751.7577\n 706500 6894000 4 -741.5474\n 707000 6894000 5 -731.6323\n 707500 6894000 6 -722.6926\n 708000 6894000 7 -715.2292\n 708500 6894000 8 -709.0506\n 709000 6894000 9 -703.7269\n 709500 6894000 10 -698.9991\n 710000 6894000 11 -694.9357\n 710500 6894000 12 -691.1797\n 711000 6894000 13 -685.9761\n 711500 6894000 14 -676.013\n 712000 6894000 15 -657.4186\n 712500 6894000 16 -629.502\n 713000 6894000 17 -595.069\n 713500 6894000 18 -560.8776\n 714000 6894000 19 -531.732\n 714500 6894000 20 -507.3737\n 715000 6894000 21 -486.1815\n 715500 6894000 22 -467.1234\n 716000 6894000 23 -449.096\n 716500 6894000 24 -432.1952\n 717000 6894000 25 -417.5574\n 717500 6894000 26 -406.1374\n 718000 6894000 27 -398.0706\n 718500 6894000 28 -392.5652\n 719000 6894000 29 -388.5538\n 719500 6894000 30 -384.97\n 720000 6894000 31 -381.0529\n 720500 6894000 32 -376.4301\n 721000 6894000 33 -371.0712\n 721500 6894000 34 -365.0479\n 722000 6894000 35 -358.3919\n 722500 6894000 36 -351.0739\n 723000 6894000 37 -343.1651\n 723500 6894000 38 -334.8689\n 724000 6894000 39 -326.3704\n 724500 6894000 40 -317.8641\n 725000 6894000 41 -309.525\n 725500 6894000 42 -301.5776\n 726000 6894000 43 -294.286\n 726500 6894000 44 -287.9329\n 727000 6894000 45 -282.423\n 727500 6894000 46 -277.5956\n 728000 6894000 47 -273.1559\n 728500 6894000 48 -269.0393\n 729000 6894000 49 -265.8788\n 729500 6894000 50 -264.5311\n 730000 6894000 51 -265.644\n 730500 6894000 52 -268.9156\n 731000 6894000 53 -273.303\n 731500 6894000 54 -277.589\n 732000 6894000 55 -280.4622\n 732500 6894000 56 -280.8979\n 733000 6894000 57 -278.8526\n 733500 6894000 58 -275.1158\n 734000 6894000 59 -270.7543\n 734500 6894000 60 -266.7379\n 735000 6894000 61 -263.8623\n 735500 6894000 62 -262.7989\n 736000 6894000 63 -263.9767\n 736500 6894000 64 -267.4739\n 737000 6894000 65 -273.2168\n 737500 6894000 66 -281.2894\n 738000 6894000 67 -291.2555\n 738500 6894000 68 -303.0491\n 739000 6894000 69 -316.8808\n 739500 6894000 70 -333.1614\n 740000 6894000 71 -352.3661\n 740500 6894000 72 -374.9926\n 741000 6894000 73 -401.0014\n 741500 6894000 74 -428.7403\n 742000 6894000 75 -456.5014\n 742500 6894000 76 -483.3859\n 743000 6894000 77 -509.5727\n 743500 6894000 78 -535.1174\n 744000 6894000 79 -559.0276\n 744500 6894000 80 -579.5446\n 745000 6894000 81 -594.7224\n 745500 6894000 82 -603.6797\n 746000 6894000 83 -606.2371\n 746500 6894000 84 -603.0907\n 747000 6894000 85 -595.2564\n 705000 6894500 1 -772.2635\n 705500 6894500 2 -764.4742\n 706000 6894500 3 -755.1569\n 706500 6894500 4 -745.0934\n 707000 6894500 5 -735.0038\n 707500 6894500 6 -725.4567\n 708000 6894500 7 -716.9027\n 708500 6894500 8 -709.3481\n 709000 6894500 9 -702.733\n 709500 6894500 10 -697.2976\n 710000 6894500 11 -693.6947\n 710500 6894500 12 -692.1609\n 711000 6894500 13 -690.9076\n 711500 6894500 14 -686.0458\n 712000 6894500 15 -672.3828\n 712500 6894500 16 -648.1971\n 713000 6894500 17 -615.1752\n 713500 6894500 18 -579.6201\n 714000 6894500 19 -547.0768\n 714500 6894500 20 -518.7511\n 715000 6894500 21 -494.1596\n 715500 6894500 22 -473.0043\n 716000 6894500 23 -453.993\n 716500 6894500 24 -436.7137\n 717000 6894500 25 -421.6394\n 717500 6894500 26 -409.3454\n 718000 6894500 27 -399.9318\n 718500 6894500 28 -392.9164\n 719000 6894500 29 -387.5911\n 719500 6894500 30 -383.0272\n 720000 6894500 31 -378.3878\n 720500 6894500 32 -373.2564\n 721000 6894500 33 -367.6627\n 721500 6894500 34 -361.6688\n 722000 6894500 35 -355.1556\n 722500 6894500 36 -347.8024\n 723000 6894500 37 -339.4988\n 723500 6894500 38 -330.6201\n 724000 6894500 39 -321.4796\n 724500 6894500 40 -312.3025\n 725000 6894500 41 -303.2526\n 725500 6894500 42 -294.5965\n 726000 6894500 43 -286.7288\n 726500 6894500 44 -280.1505\n 727000 6894500 45 -274.9047\n 727500 6894500 46 -270.9042\n 728000 6894500 47 -267.6821\n 728500 6894500 48 -264.8178\n 729000 6894500 49 -262.76\n 729500 6894500 50 -262.2546\n 730000 6894500 51 -263.6975\n 730500 6894500 52 -266.8755\n 731000 6894500 53 -271.1154\n 731500 6894500 54 -275.4003\n 732000 6894500 55 -278.3883\n 732500 6894500 56 -278.902\n 733000 6894500 57 -276.8868\n 733500 6894500 58 -273.2503\n 734000 6894500 59 -269.1286\n 734500 6894500 60 -265.4841\n 735000 6894500 61 -263.1557\n 735500 6894500 62 -262.9511\n 736000 6894500 63 -265.3673\n 736500 6894500 64 -270.4024\n 737000 6894500 65 -277.8795\n 737500 6894500 66 -287.8766\n 738000 6894500 67 -299.9401\n 738500 6894500 68 -313.961\n 739000 6894500 69 -330.0325\n 739500 6894500 70 -348.4649\n 740000 6894500 71 -369.6795\n 740500 6894500 72 -394.1585\n 741000 6894500 73 -421.877\n 741500 6894500 74 -450.864\n 742000 6894500 75 -478.958\n 742500 6894500 76 -504.7961\n 743000 6894500 77 -528.5005\n 743500 6894500 78 -550.4066\n 744000 6894500 79 -570.0561\n 744500 6894500 80 -586.2359\n 745000 6894500 81 -597.4584\n 745500 6894500 82 -603.0743\n 746000 6894500 83 -603.0258\n 746500 6894500 84 -598.0619\n 747000 6894500 85 -589.1965\n 705000 6895000 1 -774.059\n 705500 6895000 2 -766.8466\n 706000 6895000 3 -758.0603\n 706500 6895000 4 -748.3604\n 707000 6895000 5 -738.3275\n 707500 6895000 6 -728.3992\n 708000 6895000 7 -718.951\n 708500 6895000 8 -710.1759\n 709000 6895000 9 -702.3328\n 709500 6895000 10 -696.0189\n 710000 6895000 11 -692.1635\n 710500 6895000 12 -691.3406\n 711000 6895000 13 -692.0568\n 711500 6895000 14 -690.3586\n 712000 6895000 15 -680.6645\n 712500 6895000 16 -660.4634\n 713000 6895000 17 -630.5134\n 713500 6895000 18 -595.9296\n 714000 6895000 19 -562.1225\n 714500 6895000 20 -531.3209\n 715000 6895000 21 -504.1284\n 715500 6895000 22 -481.0254\n 716000 6895000 23 -460.8139\n 716500 6895000 24 -442.8338\n 717000 6895000 25 -427.0729\n 717500 6895000 26 -413.7679\n 718000 6895000 27 -402.9328\n 718500 6895000 28 -394.2819\n 719000 6895000 29 -387.4224\n 719500 6895000 30 -381.6323\n 720000 6895000 31 -376.1349\n 720500 6895000 32 -370.5054\n 721000 6895000 33 -364.7643\n 721500 6895000 34 -358.8776\n 722000 6895000 35 -352.5388\n 722500 6895000 36 -345.1625\n 723000 6895000 37 -336.4744\n 723500 6895000 38 -326.972\n 724000 6895000 39 -317.1134\n 724500 6895000 40 -307.1775\n 725000 6895000 41 -297.3427\n 725500 6895000 42 -287.9048\n 726000 6895000 43 -279.3479\n 726500 6895000 44 -272.3392\n 727000 6895000 45 -267.0811\n 727500 6895000 46 -263.6168\n 728000 6895000 47 -261.3883\n 728500 6895000 48 -259.6349\n 729000 6895000 49 -258.5598\n 729500 6895000 50 -258.8246\n 730000 6895000 51 -260.6723\n 730500 6895000 52 -263.9378\n 731000 6895000 53 -268.1881\n 731500 6895000 54 -272.564\n 732000 6895000 55 -275.7785\n 732500 6895000 56 -276.5984\n 733000 6895000 57 -274.9466\n 733500 6895000 58 -271.7842\n 734000 6895000 59 -268.2689\n 734500 6895000 60 -265.335\n 735000 6895000 61 -263.8403\n 735500 6895000 62 -264.6748\n 736000 6895000 63 -268.3966\n 736500 6895000 64 -274.9773\n 737000 6895000 65 -284.1858\n 737500 6895000 66 -296.1026\n 738000 6895000 67 -310.2184\n 738500 6895000 68 -326.3367\n 739000 6895000 69 -344.4514\n 739500 6895000 70 -364.7727\n 740000 6895000 71 -387.6365\n 740500 6895000 72 -413.4661\n 741000 6895000 73 -442.205\n 741500 6895000 74 -471.7438\n 742000 6895000 75 -499.7203\n 742500 6895000 76 -524.4409\n 743000 6895000 77 -545.8993\n 743500 6895000 78 -564.5783\n 744000 6895000 79 -580.3893\n 744500 6895000 80 -592.5585\n 745000 6895000 81 -600.0263\n 745500 6895000 82 -602.4115\n 746000 6895000 83 -599.791\n 746500 6895000 84 -593.0314\n 747000 6895000 85 -583.2619\n 705000 6895500 1 -775.1441\n 705500 6895500 2 -768.56\n 706000 6895500 3 -760.4673\n 706500 6895500 4 -751.3561\n 707000 6895500 5 -741.6363\n 707500 6895500 6 -731.5856\n 708000 6895500 7 -721.4431\n 708500 6895500 8 -711.5463\n 709000 6895500 9 -702.4246\n 709500 6895500 10 -694.9274\n 710000 6895500 11 -690.1672\n 710500 6895500 12 -688.8911\n 711000 6895500 13 -690.1107\n 711500 6895500 14 -690.1511\n 712000 6895500 15 -683.5507\n 712500 6895500 16 -667.1771\n 713000 6895500 17 -641.053\n 713500 6895500 18 -608.9315\n 714000 6895500 19 -575.5908\n 714500 6895500 20 -543.8565\n 715000 6895500 21 -515.1875\n 715500 6895500 22 -490.6665\n 716000 6895500 23 -469.3828\n 716500 6895500 24 -450.5985\n 717000 6895500 25 -433.9917\n 717500 6895500 26 -419.4981\n 718000 6895500 27 -407.029\n 718500 6895500 28 -396.4364\n 719000 6895500 29 -387.6312\n 719500 6895500 30 -380.2104\n 720000 6895500 31 -373.685\n 720500 6895500 32 -367.6629\n 721000 6895500 33 -361.9831\n 721500 6895500 34 -356.3521\n 722000 6895500 35 -350.2427\n 722500 6895500 36 -342.8832\n 723000 6895500 37 -333.874\n 723500 6895500 38 -323.7726\n 724000 6895500 39 -313.1743\n 724500 6895500 40 -302.4398\n 725000 6895500 41 -291.8098\n 725500 6895500 42 -281.5891\n 726000 6895500 43 -272.2654\n 726500 6895500 44 -264.5747\n 727000 6895500 45 -258.9019\n 727500 6895500 46 -255.5294\n 728000 6895500 47 -253.9468\n 728500 6895500 48 -253.1314\n 729000 6895500 49 -252.9408\n 729500 6895500 50 -253.9074\n 730000 6895500 51 -256.2521\n 730500 6895500 52 -259.8035\n 731000 6895500 53 -264.213\n 731500 6895500 54 -268.7284\n 732000 6895500 55 -272.2405\n 732500 6895500 56 -273.6631\n 733000 6895500 57 -272.8771\n 733500 6895500 58 -270.7403\n 734000 6895500 59 -268.3268\n 734500 6895500 60 -266.5337\n 735000 6895500 61 -266.2194\n 735500 6895500 62 -268.2939\n 736000 6895500 63 -273.3547\n 736500 6895500 64 -281.4275\n 737000 6895500 65 -292.2976\n 737500 6895500 66 -306.0466\n 738000 6895500 67 -322.1005\n 738500 6895500 68 -340.1632\n 739000 6895500 69 -360.0985\n 739500 6895500 70 -381.982\n 740000 6895500 71 -406.0519\n 740500 6895500 72 -432.6009\n 741000 6895500 73 -461.5029\n 741500 6895500 74 -490.7628\n 742000 6895500 75 -518.0635\n 742500 6895500 76 -541.683\n 743000 6895500 77 -561.2848\n 743500 6895500 78 -577.2633\n 744000 6895500 79 -589.7458\n 744500 6895500 80 -598.3348\n 745000 6895500 81 -602.3685\n 745500 6895500 82 -601.7723\n 746000 6895500 83 -596.7713\n 746500 6895500 84 -588.3434\n 747000 6895500 85 -577.8087\n 705000 6896000 1 -775.7528\n 705500 6896000 2 -769.8021\n 706000 6896000 3 -762.4843\n 706500 6896000 4 -754.1156\n 707000 6896000 5 -744.923\n 707500 6896000 6 -734.9881\n 708000 6896000 7 -724.3445\n 708500 6896000 8 -713.4015\n 709000 6896000 9 -702.8986\n 709500 6896000 10 -693.8708\n 710000 6896000 11 -687.6819\n 710500 6896000 12 -685.3589\n 711000 6896000 13 -686.4044\n 711500 6896000 14 -687.6668\n 712000 6896000 15 -683.7318\n 712500 6896000 16 -670.9864\n 713000 6896000 17 -648.6497\n 713500 6896000 18 -619.4439\n 714000 6896000 19 -587.4675\n 714500 6896000 20 -555.7958\n 715000 6896000 21 -526.5322\n 715500 6896000 22 -501.2531\n 716000 6896000 23 -479.2788\n 716500 6896000 24 -459.8312\n 717000 6896000 25 -442.3962\n 717500 6896000 26 -426.6222\n 718000 6896000 27 -412.3017\n 718500 6896000 28 -399.4466\n 719000 6896000 29 -388.2873\n 719500 6896000 30 -378.826\n 720000 6896000 31 -371.082\n 720500 6896000 32 -364.8011\n 721000 6896000 33 -359.42\n 721500 6896000 34 -354.2118\n 722000 6896000 35 -348.377\n 722500 6896000 36 -341.05\n 723000 6896000 37 -331.7643\n 723500 6896000 38 -321.115\n 724000 6896000 39 -309.7986\n 724500 6896000 40 -298.2857\n 725000 6896000 41 -286.9282\n 725500 6896000 42 -276.0276\n 726000 6896000 43 -265.968\n 726500 6896000 44 -257.4418\n 727000 6896000 45 -251.0303\n 727500 6896000 46 -247.3592\n 728000 6896000 47 -246.0542\n 728500 6896000 48 -245.9278\n 729000 6896000 49 -246.464\n 729500 6896000 50 -248.0002\n 730000 6896000 51 -250.8627\n 730500 6896000 52 -254.8703\n 731000 6896000 53 -259.6046\n 731500 6896000 54 -264.3606\n 732000 6896000 55 -268.2632\n 732500 6896000 56 -270.5354\n 733000 6896000 57 -271.0511\n 733500 6896000 58 -270.4198\n 734000 6896000 59 -269.5236\n 734500 6896000 60 -269.2101\n 735000 6896000 61 -270.3232\n 735500 6896000 62 -273.7771\n 736000 6896000 63 -280.2003\n 736500 6896000 64 -289.704\n 737000 6896000 65 -302.1555\n 737500 6896000 66 -317.6537\n 738000 6896000 67 -335.5317\n 738500 6896000 68 -355.3609\n 739000 6896000 69 -376.8434\n 739500 6896000 70 -399.8961\n 740000 6896000 71 -424.6426\n 740500 6896000 72 -451.3081\n 741000 6896000 73 -479.7158\n 741500 6896000 74 -508.1047\n 742000 6896000 75 -534.3527\n 742500 6896000 76 -556.8166\n 743000 6896000 77 -574.8229\n 743500 6896000 78 -588.501\n 744000 6896000 79 -598.1163\n 744500 6896000 80 -603.6052\n 745000 6896000 81 -604.6541\n 745500 6896000 82 -601.4347\n 746000 6896000 83 -594.3217\n 746500 6896000 84 -584.3923\n 747000 6896000 85 -573.17\n 705000 6896500 1 -776.0753\n 705500 6896500 2 -770.7224\n 706000 6896500 3 -764.1776\n 706500 6896500 4 -756.6216\n 707000 6896500 5 -748.1106\n 707500 6896500 6 -738.5074\n 708000 6896500 7 -727.5765\n 708500 6896500 8 -715.7212\n 709000 6896500 9 -703.8211\n 709500 6896500 10 -693.0645\n 710000 6896500 11 -685.1898\n 710500 6896500 12 -681.6719\n 711000 6896500 13 -682.3759\n 711500 6896500 14 -684.6089\n 712000 6896500 15 -683.0443\n 712500 6896500 16 -673.5809\n 713000 6896500 17 -654.6848\n 713500 6896500 18 -628.3485\n 714000 6896500 19 -598.1698\n 714500 6896500 20 -567.251\n 715000 6896500 21 -538.0795\n 715500 6896500 22 -512.5913\n 716000 6896500 23 -490.2514\n 716500 6896500 24 -470.2635\n 717000 6896500 25 -452.0316\n 717500 6896500 26 -435.0008\n 718000 6896500 27 -418.83\n 718500 6896500 28 -403.678\n 719000 6896500 29 -390.0683\n 719500 6896500 30 -378.4357\n 720000 6896500 31 -369.3749\n 720500 6896500 32 -362.8349\n 721000 6896500 33 -357.7988\n 721500 6896500 34 -353.0239\n 722000 6896500 35 -347.4525\n 722500 6896500 36 -340.0919\n 723000 6896500 37 -330.487\n 723500 6896500 38 -319.3068\n 724000 6896500 39 -307.3274\n 724500 6896500 40 -295.145\n 725000 6896500 41 -283.2351\n 725500 6896500 42 -271.89\n 726000 6896500 43 -261.3089\n 726500 6896500 44 -252.0466\n 727000 6896500 45 -244.8414\n 727500 6896500 46 -240.6695\n 728000 6896500 47 -239.3188\n 728500 6896500 48 -239.4888\n 729000 6896500 49 -240.3905\n 729500 6896500 50 -242.2248\n 730000 6896500 51 -245.5038\n 730500 6896500 52 -250.0568\n 731000 6896500 53 -255.2681\n 731500 6896500 54 -260.4193\n 732000 6896500 55 -264.8622\n 732500 6896500 56 -268.1573\n 733000 6896500 57 -270.2087\n 733500 6896500 58 -271.335\n 734000 6896500 59 -272.1727\n 734500 6896500 60 -273.4976\n 735000 6896500 61 -276.1275\n 735500 6896500 62 -280.9923\n 736000 6896500 63 -288.7735\n 736500 6896500 64 -299.6602\n 737000 6896500 65 -313.6234\n 737500 6896500 66 -330.8107\n 738000 6896500 67 -350.4363\n 738500 6896500 68 -371.8869\n 739000 6896500 69 -394.6525\n 739500 6896500 70 -418.4593\n 740000 6896500 71 -443.3245\n 740500 6896500 72 -469.5243\n 741000 6896500 73 -496.9688\n 741500 6896500 74 -524.1402\n 742000 6896500 75 -549.1166\n 742500 6896500 76 -570.346\n 743000 6896500 77 -586.8372\n 743500 6896500 78 -598.4387\n 744000 6896500 79 -605.5524\n 744500 6896500 80 -608.4377\n 745000 6896500 81 -607.0507\n 745500 6896500 82 -601.6945\n 746000 6896500 83 -592.8313\n 746500 6896500 84 -581.6292\n 747000 6896500 85 -569.7825\n 705000 6897000 1 -776.2068\n 705500 6897000 2 -771.362\n 706000 6897000 3 -765.5167\n 706500 6897000 4 -758.7574\n 707000 6897000 5 -751.0015\n 707500 6897000 6 -741.915\n 708000 6897000 7 -730.9739\n 708500 6897000 8 -718.485\n 709000 6897000 9 -705.3629\n 709500 6897000 10 -692.8958\n 710000 6897000 11 -683.2637\n 710500 6897000 12 -678.575\n 711000 6897000 13 -678.8533\n 711500 6897000 14 -681.69\n 712000 6897000 15 -681.9785\n 712500 6897000 16 -675.3466\n 713000 6897000 17 -659.5885\n 713500 6897000 18 -636.1697\n 714000 6897000 19 -608.2853\n 714500 6897000 20 -578.8527\n 715000 6897000 21 -550.4096\n 715500 6897000 22 -525.0391\n 716000 6897000 23 -502.363\n 716500 6897000 24 -481.6826\n 717000 6897000 25 -462.5179\n 717500 6897000 26 -444.285\n 718000 6897000 27 -426.5469\n 718500 6897000 28 -409.5585\n 719000 6897000 29 -394.0149\n 719500 6897000 30 -380.6173\n 720000 6897000 31 -370.2835\n 720500 6897000 32 -363.2181\n 721000 6897000 33 -358.1543\n 721500 6897000 34 -353.4822\n 722000 6897000 35 -347.9353\n 722500 6897000 36 -340.3869\n 723000 6897000 37 -330.3664\n 723500 6897000 38 -318.6706\n 724000 6897000 39 -306.1546\n 724500 6897000 40 -293.5248\n 725000 6897000 41 -281.3749\n 725500 6897000 42 -269.9769\n 726000 6897000 43 -259.3169\n 726500 6897000 44 -249.7625\n 727000 6897000 45 -242.0557\n 727500 6897000 46 -237.3355\n 728000 6897000 47 -235.5639\n 728500 6897000 48 -235.4649\n 729000 6897000 49 -236.1741\n 729500 6897000 50 -237.8753\n 730000 6897000 51 -241.3892\n 730500 6897000 52 -246.4951\n 731000 6897000 53 -252.2954\n 731500 6897000 54 -257.9916\n 732000 6897000 55 -263.114\n 732500 6897000 56 -267.5196\n 733000 6897000 57 -271.1443\n 733500 6897000 58 -274.0478\n 734000 6897000 59 -276.6333\n 734500 6897000 60 -279.5937\n 735000 6897000 61 -283.7101\n 735500 6897000 62 -289.9376\n 736000 6897000 63 -299.0275\n 736500 6897000 64 -311.2319\n 737000 6897000 65 -326.6279\n 737500 6897000 66 -345.4534\n 738000 6897000 67 -366.7845\n 738500 6897000 68 -389.7642\n 739000 6897000 69 -413.615\n 739500 6897000 70 -437.8308\n 740000 6897000 71 -462.3161\n 740500 6897000 72 -487.501\n 741000 6897000 73 -513.5315\n 741500 6897000 74 -539.1415\n 742000 6897000 75 -562.5803\n 742500 6897000 76 -582.3661\n 743000 6897000 77 -597.2653\n 743500 6897000 78 -606.9254\n 744000 6897000 79 -611.9238\n 744500 6897000 80 -612.7714\n 745000 6897000 81 -609.5883\n 745500 6897000 82 -602.7175\n 746000 6897000 83 -592.6423\n 746500 6897000 84 -580.5905\n 747000 6897000 85 -568.4026\n 705000 6897500 1 -776.1641\n 705500 6897500 2 -771.6683\n 706000 6897500 3 -766.3768\n 706500 6897500 4 -760.2921\n 707000 6897500 5 -753.2512\n 707500 6897500 6 -744.8147\n 708000 6897500 7 -734.235\n 708500 6897500 8 -721.6079\n 709000 6897500 9 -707.7297\n 709500 6897500 10 -693.8681\n 710000 6897500 11 -682.543\n 710500 6897500 12 -676.4943\n 711000 6897500 13 -675.9576\n 711500 6897500 14 -678.6426\n 712000 6897500 15 -679.9453\n 712500 6897500 16 -675.6027\n 713000 6897500 17 -662.9769\n 713500 6897500 18 -642.9941\n 714000 6897500 19 -618.3689\n 714500 6897500 20 -591.5342\n 715000 6897500 21 -564.5869\n 715500 6897500 22 -539.4024\n 716000 6897500 23 -515.9669\n 716500 6897500 24 -494.0054\n 717000 6897500 25 -473.4981\n 717500 6897500 26 -454.137\n 718000 6897500 27 -435.4446\n 718500 6897500 28 -417.615\n 719000 6897500 29 -401.2854\n 719500 6897500 30 -387.0803\n 720000 6897500 31 -375.7884\n 720500 6897500 32 -367.731\n 721000 6897500 33 -361.7201\n 721500 6897500 34 -356.2168\n 722000 6897500 35 -350.0365\n 722500 6897500 36 -341.972\n 723000 6897500 37 -331.5073\n 723500 6897500 38 -319.5103\n 724000 6897500 39 -306.8446\n 724500 6897500 40 -294.2308\n 725000 6897500 41 -282.305\n 725500 6897500 42 -271.3318\n 726000 6897500 43 -261.1688\n 726500 6897500 44 -252.0216\n 727000 6897500 45 -244.4219\n 727500 6897500 46 -239.2554\n 728000 6897500 47 -236.6196\n 728500 6897500 48 -235.5626\n 729000 6897500 49 -235.4705\n 729500 6897500 50 -236.6178\n 730000 6897500 51 -240.136\n 730500 6897500 52 -245.713\n 731000 6897500 53 -252.1524\n 731500 6897500 54 -258.5298\n 732000 6897500 55 -264.4448\n 732500 6897500 56 -269.9173\n 733000 6897500 57 -274.9201\n 733500 6897500 58 -279.3646\n 734000 6897500 59 -283.5115\n 734500 6897500 60 -287.9789\n 735000 6897500 61 -293.5058\n 735500 6897500 62 -301.0018\n 736000 6897500 63 -311.2832\n 736500 6897500 64 -324.6821\n 737000 6897500 65 -341.3747\n 737500 6897500 66 -361.7532\n 738000 6897500 67 -384.7534\n 738500 6897500 68 -409.2271\n 739000 6897500 69 -434.0797\n 739500 6897500 70 -458.5075\n 740000 6897500 71 -482.2241\n 740500 6897500 72 -505.7987\n 741000 6897500 73 -529.7283\n 741500 6897500 74 -553.0945\n 742000 6897500 75 -574.3748\n 742500 6897500 76 -592.2198\n 743000 6897500 77 -605.3391\n 743500 6897500 78 -613.258\n 744000 6897500 79 -616.6523\n 744500 6897500 80 -616.1858\n 745000 6897500 81 -612.0671\n 745500 6897500 82 -604.5835\n 746000 6897500 83 -594.2164\n 746500 6897500 84 -582.2167\n 747000 6897500 85 -570.5048\n 705000 6898000 1 -776.0328\n 705500 6898000 2 -771.7353\n 706000 6898000 3 -766.8432\n 706500 6898000 4 -761.2844\n 707000 6898000 5 -754.8585\n 707500 6898000 6 -747.0866\n 708000 6898000 7 -737.1212\n 708500 6898000 8 -724.7874\n 709000 6898000 9 -710.6739\n 709500 6898000 10 -695.9462\n 710000 6898000 11 -683.2192\n 710500 6898000 12 -675.6085\n 711000 6898000 13 -673.6822\n 711500 6898000 14 -675.4448\n 712000 6898000 15 -677.1313\n 712500 6898000 16 -674.6342\n 713000 6898000 17 -665.1923\n 713500 6898000 18 -649.152\n 714000 6898000 19 -628.6254\n 714500 6898000 20 -605.3419\n 715000 6898000 21 -580.5936\n 715500 6898000 22 -555.5914\n 716000 6898000 23 -530.9074\n 716500 6898000 24 -507.0688\n 717000 6898000 25 -484.8691\n 717500 6898000 26 -464.4625\n 718000 6898000 27 -445.3963\n 718500 6898000 28 -427.6018\n 719000 6898000 29 -411.4417\n 719500 6898000 30 -397.1967\n 720000 6898000 31 -385.2642\n 720500 6898000 32 -375.7932\n 721000 6898000 33 -367.9658\n 721500 6898000 34 -360.6466\n 722000 6898000 35 -353.0005\n 722500 6898000 36 -344.0818\n 723000 6898000 37 -333.3923\n 723500 6898000 38 -321.631\n 724000 6898000 39 -309.4915\n 724500 6898000 40 -297.5316\n 725000 6898000 41 -286.2424\n 725500 6898000 42 -275.8929\n 726000 6898000 43 -266.4265\n 726500 6898000 44 -258.0474\n 727000 6898000 45 -251.011\n 727500 6898000 46 -245.6416\n 728000 6898000 47 -242.0003\n 728500 6898000 48 -239.6941\n 729000 6898000 49 -238.599\n 729500 6898000 50 -239.1089\n 730000 6898000 51 -242.4914\n 730500 6898000 52 -248.3909\n 731000 6898000 53 -255.4278\n 731500 6898000 54 -262.577\n 732000 6898000 55 -269.3606\n 732500 6898000 56 -275.7553\n 733000 6898000 57 -281.7705\n 733500 6898000 58 -287.352\n 734000 6898000 59 -292.7725\n 734500 6898000 60 -298.5916\n 735000 6898000 61 -305.4603\n 735500 6898000 62 -314.1656\n 736000 6898000 63 -325.5329\n 736500 6898000 64 -339.9659\n 737000 6898000 65 -357.7714\n 737500 6898000 66 -379.561\n 738000 6898000 67 -404.153\n 738500 6898000 68 -430.0905\n 739000 6898000 69 -455.9435\n 739500 6898000 70 -480.5356\n 740000 6898000 71 -503.269\n 740500 6898000 72 -524.7239\n 741000 6898000 73 -545.7905\n 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6899000 38 -329.5206\n 724000 6899000 39 -320.0599\n 724500 6899000 40 -310.8069\n 725000 6899000 41 -301.321\n 725500 6899000 42 -291.7756\n 726000 6899000 43 -283.1071\n 726500 6899000 44 -276.264\n 727000 6899000 45 -270.8376\n 727500 6899000 46 -265.7868\n 728000 6899000 47 -260.7482\n 728500 6899000 48 -256.4677\n 729000 6899000 49 -253.9438\n 729500 6899000 50 -253.9291\n 730000 6899000 51 -257.1629\n 730500 6899000 52 -263.4203\n 731000 6899000 53 -271.4655\n 731500 6899000 54 -280.2418\n 732000 6899000 55 -288.8335\n 732500 6899000 56 -296.5927\n 733000 6899000 57 -303.5448\n 733500 6899000 58 -310.2526\n 734000 6899000 59 -317.469\n 734500 6899000 60 -325.7011\n 735000 6899000 61 -335.2996\n 735500 6899000 62 -346.6207\n 736000 6899000 63 -360.1498\n 736500 6899000 64 -376.3271\n 737000 6899000 65 -395.7527\n 737500 6899000 66 -419.5854\n 738000 6899000 67 -446.4896\n 738500 6899000 68 -474.471\n 739000 6899000 69 -501.531\n 739500 6899000 70 -525.722\n 740000 6899000 71 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730000 6900500 51 -286.5499\n 730500 6900500 52 -294.2094\n 731000 6900500 53 -304.093\n 731500 6900500 54 -314.8246\n 732000 6900500 55 -325.0298\n 732500 6900500 56 -333.998\n 733000 6900500 57 -341.9916\n 733500 6900500 58 -350.2812\n 734000 6900500 59 -360.3972\n 734500 6900500 60 -373.1482\n 735000 6900500 61 -388.7845\n 735500 6900500 62 -406.8907\n 736000 6900500 63 -426.6744\n 736500 6900500 64 -447.5624\n 737000 6900500 65 -469.3901\n 737500 6900500 66 -492.2842\n 738000 6900500 67 -515.7601\n 738500 6900500 68 -538.6147\n 739000 6900500 69 -559.5906\n 739500 6900500 70 -577.4037\n 740000 6900500 71 -590.9871\n 740500 6900500 72 -600.412\n 741000 6900500 73 -606.6904\n 741500 6900500 74 -610.9725\n 742000 6900500 75 -613.8688\n 742500 6900500 76 -615.8398\n 743000 6900500 77 -617.5925\n 743500 6900500 78 -619.5817\n 744000 6900500 79 -621.374\n 744500 6900500 80 -622.3955\n 745000 6900500 81 -622.4468\n 745500 6900500 82 -621.7629\n 746000 6900500 83 -620.7478\n 746500 6900500 84 -619.9908\n 747000 6900500 85 -620.0256\n 705000 6901000 1 -778.1379\n 705500 6901000 2 -775.1224\n 706000 6901000 3 -771.9238\n 706500 6901000 4 -768.2819\n 707000 6901000 5 -763.8069\n 707500 6901000 6 -758.1315\n 708000 6901000 7 -751.0319\n 708500 6901000 8 -742.7314\n 709000 6901000 9 -733.4429\n 709500 6901000 10 -722.7986\n 710000 6901000 11 -710.795\n 710500 6901000 12 -698.579\n 711000 6901000 13 -687.9707\n 711500 6901000 14 -681.0068\n 712000 6901000 15 -678.8239\n 712500 6901000 16 -679.9987\n 713000 6901000 17 -681.3095\n 713500 6901000 18 -681.5108\n 714000 6901000 19 -680.7953\n 714500 6901000 20 -677.4871\n 715000 6901000 21 -668.7612\n 715500 6901000 22 -651.8088\n 716000 6901000 23 -628.6536\n 716500 6901000 24 -602.8716\n 717000 6901000 25 -578.4456\n 717500 6901000 26 -557.3\n 718000 6901000 27 -538.858\n 718500 6901000 28 -522.2574\n 719000 6901000 29 -507.0678\n 719500 6901000 30 -492.491\n 720000 6901000 31 -477.9792\n 720500 6901000 32 -462.9785\n 721000 6901000 33 -446.8509\n 721500 6901000 34 -430.0259\n 722000 6901000 35 -414.0268\n 722500 6901000 36 -398.7364\n 723000 6901000 37 -382.8842\n 723500 6901000 38 -366.2491\n 724000 6901000 39 -349.6505\n 724500 6901000 40 -334.3604\n 725000 6901000 41 -321.1036\n 725500 6901000 42 -309.9302\n 726000 6901000 43 -301.2054\n 726500 6901000 44 -295.9836\n 727000 6901000 45 -293.7191\n 727500 6901000 46 -292.3068\n 728000 6901000 47 -290.2306\n 728500 6901000 48 -288.2516\n 729000 6901000 49 -287.8761\n 729500 6901000 50 -290.0846\n 730000 6901000 51 -295.4781\n 730500 6901000 52 -303.9102\n 731000 6901000 53 -314.7491\n 731500 6901000 54 -326.2306\n 732000 6901000 55 -336.7248\n 732500 6901000 56 -345.7977\n 733000 6901000 57 -354.0902\n 733500 6901000 58 -362.9728\n 734000 6901000 59 -374.1733\n 734500 6901000 60 -388.762\n 735000 6901000 61 -407.1473\n 735500 6901000 62 -428.5451\n 736000 6901000 63 -451.3653\n 736500 6901000 64 -474.3682\n 737000 6901000 65 -496.8652\n 737500 6901000 66 -518.3376\n 738000 6901000 67 -538.7261\n 738500 6901000 68 -557.7203\n 739000 6901000 69 -574.7849\n 739500 6901000 70 -589.1992\n 740000 6901000 71 -600.3319\n 740500 6901000 72 -608.1774\n 741000 6901000 73 -613.2524\n 741500 6901000 74 -616.3919\n 742000 6901000 75 -618.1885\n 742500 6901000 76 -619.1631\n 743000 6901000 77 -619.9747\n 743500 6901000 78 -621.133\n 744000 6901000 79 -622.3892\n 744500 6901000 80 -623.3115\n 745000 6901000 81 -623.7399\n 745500 6901000 82 -623.7954\n 746000 6901000 83 -623.7455\n 746500 6901000 84 -624.0029\n 747000 6901000 85 -624.9174\n 705000 6901500 1 -778.1097\n 705500 6901500 2 -775.283\n 706000 6901500 3 -772.4131\n 706500 6901500 4 -769.2043\n 707000 6901500 5 -765.2748\n 707500 6901500 6 -760.3319\n 708000 6901500 7 -754.1982\n 708500 6901500 8 -747.004\n 709000 6901500 9 -738.8165\n 709500 6901500 10 -729.2568\n 710000 6901500 11 -718.181\n 710500 6901500 12 -706.431\n 711000 6901500 13 -695.6339\n 711500 6901500 14 -687.6593\n 712000 6901500 15 -683.7031\n 712500 6901500 16 -682.9964\n 713000 6901500 17 -683.6164\n 713500 6901500 18 -684.3082\n 714000 6901500 19 -684.4578\n 714500 6901500 20 -682.3506\n 715000 6901500 21 -675.5632\n 715500 6901500 22 -661.6517\n 716000 6901500 23 -642.3219\n 716500 6901500 24 -620.3628\n 717000 6901500 25 -598.6495\n 717500 6901500 26 -578.5667\n 718000 6901500 27 -559.8583\n 718500 6901500 28 -542.0652\n 719000 6901500 29 -525.1547\n 719500 6901500 30 -508.9373\n 720000 6901500 31 -493.4761\n 720500 6901500 32 -478.601\n 721000 6901500 33 -463.5692\n 721500 6901500 34 -448.1306\n 722000 6901500 35 -432.9321\n 722500 6901500 36 -417.3345\n 723000 6901500 37 -399.7871\n 723500 6901500 38 -380.7195\n 724000 6901500 39 -361.5381\n 724500 6901500 40 -343.873\n 725000 6901500 41 -328.8772\n 725500 6901500 42 -316.8586\n 726000 6901500 43 -307.9033\n 726500 6901500 44 -302.7587\n 727000 6901500 45 -300.8318\n 727500 6901500 46 -299.6966\n 728000 6901500 47 -297.7121\n 728500 6901500 48 -295.8505\n 729000 6901500 49 -295.7704\n 729500 6901500 50 -298.4101\n 730000 6901500 51 -304.3441\n 730500 6901500 52 -313.5357\n 731000 6901500 53 -325.4696\n 731500 6901500 54 -337.8925\n 732000 6901500 55 -348.6917\n 732500 6901500 56 -357.7525\n 733000 6901500 57 -366.1485\n 733500 6901500 58 -375.3969\n 734000 6901500 59 -387.501\n 734500 6901500 60 -403.9067\n 735000 6901500 61 -425.2858\n 735500 6901500 62 -450.2904\n 736000 6901500 63 -476.1936\n 736500 6901500 64 -501.0179\n 737000 6901500 65 -523.7141\n 737500 6901500 66 -543.2416\n 738000 6901500 67 -560.1702\n 738500 6901500 68 -575.0904\n 739000 6901500 69 -588.194\n 739500 6901500 70 -599.2865\n 740000 6901500 71 -608.0991\n 740500 6901500 72 -614.5487\n 741000 6901500 73 -618.7575\n 741500 6901500 74 -621.229\n 742000 6901500 75 -622.448\n 742500 6901500 76 -622.878\n 743000 6901500 77 -623.0226\n 743500 6901500 78 -623.3232\n 744000 6901500 79 -623.7993\n 744500 6901500 80 -624.3063\n 745000 6901500 81 -624.7929\n 745500 6901500 82 -625.2606\n 746000 6901500 83 -625.8203\n 746500 6901500 84 -626.7277\n 747000 6901500 85 -628.1999\n 705000 6902000 1 -778.0754\n 705500 6902000 2 -775.3815\n 706000 6902000 3 -772.7811\n 706500 6902000 4 -769.9638\n 707000 6902000 5 -766.5669\n 707500 6902000 6 -762.3298\n 708000 6902000 7 -757.0671\n 708500 6902000 8 -750.8036\n 709000 6902000 9 -743.5063\n 709500 6902000 10 -734.887\n 710000 6902000 11 -724.7568\n 710500 6902000 12 -713.6078\n 711000 6902000 13 -702.75\n 711500 6902000 14 -693.8994\n 712000 6902000 15 -688.2579\n 712500 6902000 16 -685.6932\n 713000 6902000 17 -685.3362\n 713500 6902000 18 -685.9871\n 714000 6902000 19 -686.5201\n 714500 6902000 20 -685.2852\n 715000 6902000 21 -680.3033\n 715500 6902000 22 -669.541\n 716000 6902000 23 -654.2493\n 716500 6902000 24 -636.416\n 717000 6902000 25 -617.8743\n 717500 6902000 26 -599.4244\n 718000 6902000 27 -580.8924\n 718500 6902000 28 -562.1577\n 719000 6902000 29 -543.5831\n 719500 6902000 30 -525.672\n 720000 6902000 31 -509.1367\n 720500 6902000 32 -494.2707\n 721000 6902000 33 -480.2406\n 721500 6902000 34 -466.1886\n 722000 6902000 35 -451.8629\n 722500 6902000 36 -436.1273\n 723000 6902000 37 -417.559\n 723500 6902000 38 -397.1771\n 724000 6902000 39 -376.7885\n 724500 6902000 40 -358.0908\n 725000 6902000 41 -342.1442\n 725500 6902000 42 -329.4362\n 726000 6902000 43 -320.1546\n 726500 6902000 44 -314.8847\n 727000 6902000 45 -312.7342\n 727500 6902000 46 -311.0114\n 728000 6902000 47 -308.0745\n 728500 6902000 48 -305.4135\n 729000 6902000 49 -304.9809\n 729500 6902000 50 -307.6262\n 730000 6902000 51 -313.8137\n 730500 6902000 52 -323.5408\n 731000 6902000 53 -336.3925\n 731500 6902000 54 -349.7234\n 732000 6902000 55 -360.85\n 732500 6902000 56 -369.9034\n 733000 6902000 57 -378.3667\n 733500 6902000 58 -387.8788\n 734000 6902000 59 -400.7498\n 734500 6902000 60 -418.9092\n 735000 6902000 61 -443.3786\n 735500 6902000 62 -472.0108\n 736000 6902000 63 -500.5686\n 736500 6902000 64 -526.3549\n 737000 6902000 65 -548.4073\n 737500 6902000 66 -565.5666\n 738000 6902000 67 -579.0537\n 738500 6902000 68 -590.2138\n 739000 6902000 69 -599.8057\n 739500 6902000 70 -608.0095\n 740000 6902000 71 -614.7887\n 740500 6902000 72 -620.0154\n 741000 6902000 73 -623.5325\n 741500 6902000 74 -625.5594\n 742000 6902000 75 -626.4327\n 742500 6902000 76 -626.4952\n 743000 6902000 77 -626.0802\n 743500 6902000 78 -625.5435\n 744000 6902000 79 -625.1495\n 744500 6902000 80 -625.0726\n 745000 6902000 81 -625.4212\n 745500 6902000 82 -626.1077\n 746000 6902000 83 -627.0845\n 746500 6902000 84 -628.4645\n 747000 6902000 85 -630.3699\n 705000 6902500 1 -778.1172\n 705500 6902500 2 -775.5107\n 706000 6902500 3 -773.0994\n 706500 6902500 4 -770.5913\n 707000 6902500 5 -767.6516\n 707500 6902500 6 -764.0216\n 708000 6902500 7 -759.4721\n 708500 6902500 8 -753.9383\n 709000 6902500 9 -747.3383\n 709500 6902500 10 -739.4368\n 710000 6902500 11 -730.083\n 710500 6902500 12 -719.56\n 711000 6902500 13 -708.838\n 711500 6902500 14 -699.3716\n 712000 6902500 15 -692.3886\n 712500 6902500 16 -688.203\n 713000 6902500 17 -686.6816\n 713500 6902500 18 -686.8456\n 714000 6902500 19 -687.3037\n 714500 6902500 20 -686.553\n 715000 6902500 21 -683.0964\n 715500 6902500 22 -675.4152\n 716000 6902500 23 -664.2125\n 716500 6902500 24 -650.6468\n 717000 6902500 25 -635.6417\n 717500 6902500 26 -619.3898\n 718000 6902500 27 -601.6169\n 718500 6902500 28 -582.4756\n 719000 6902500 29 -562.7007\n 719500 6902500 30 -543.4352\n 720000 6902500 31 -525.8989\n 720500 6902500 32 -510.7944\n 721000 6902500 33 -497.2361\n 721500 6902500 34 -483.9301\n 722000 6902500 35 -470.0276\n 722500 6902500 36 -454.1873\n 723000 6902500 37 -435.2398\n 723500 6902500 38 -414.5509\n 724000 6902500 39 -394.0677\n 724500 6902500 40 -375.39\n 725000 6902500 41 -359.26\n 725500 6902500 42 -346.1323\n 726000 6902500 43 -336.4594\n 726500 6902500 44 -330.8553\n 727000 6902500 45 -327.9929\n 727500 6902500 46 -325.2125\n 728000 6902500 47 -320.9664\n 728500 6902500 48 -317.1906\n 729000 6902500 49 -316.1032\n 729500 6902500 50 -318.4641\n 730000 6902500 51 -324.615\n 730500 6902500 52 -334.4359\n 731000 6902500 53 -347.5526\n 731500 6902500 54 -361.363\n 732000 6902500 55 -372.8179\n 732500 6902500 56 -382.1554\n 733000 6902500 57 -391.0594\n 733500 6902500 58 -401.1583\n 734000 6902500 59 -414.9731\n 734500 6902500 60 -434.882\n 735000 6902500 61 -462.2096\n 735500 6902500 62 -493.8793\n 736000 6902500 63 -523.9583\n 736500 6902500 64 -549.1817\n 737000 6902500 65 -569.3428\n 737500 6902500 66 -583.7989\n 738000 6902500 67 -594.2879\n 738500 6902500 68 -602.5082\n 739000 6902500 69 -609.4587\n 739500 6902500 70 -615.4909\n 740000 6902500 71 -620.6697\n 740500 6902500 72 -624.8511\n 741000 6902500 73 -627.7493\n 741500 6902500 74 -629.3978\n 742000 6902500 75 -629.9841\n 742500 6902500 76 -629.7086\n 743000 6902500 77 -628.7568\n 743500 6902500 78 -627.4111\n 744000 6902500 79 -626.1289\n 744500 6902500 80 -625.387\n 745000 6902500 81 -625.5048\n 745500 6902500 82 -626.3426\n 746000 6902500 83 -627.689\n 746500 6902500 84 -629.5043\n 747000 6902500 85 -631.8375\n 705000 6903000 1 -778.1332\n 705500 6903000 2 -775.5903\n 706000 6903000 3 -773.306\n 706500 6903000 4 -771.0292\n 707000 6903000 5 -768.4669\n 707500 6903000 6 -765.3475\n 708000 6903000 7 -761.3943\n 708500 6903000 8 -756.4647\n 709000 6903000 9 -750.4407\n 709500 6903000 10 -743.1271\n 710000 6903000 11 -734.4082\n 710500 6903000 12 -724.4739\n 711000 6903000 13 -714.0475\n 711500 6903000 14 -704.3003\n 712000 6903000 15 -696.4037\n 712500 6903000 16 -690.9205\n 713000 6903000 17 -688.1675\n 713500 6903000 18 -687.4434\n 714000 6903000 19 -687.3842\n 714500 6903000 20 -686.7332\n 715000 6903000 21 -684.4595\n 715500 6903000 22 -679.5999\n 716000 6903000 23 -672.3409\n 716500 6903000 24 -663.0211\n 717000 6903000 25 -651.762\n 717500 6903000 26 -638.1827\n 718000 6903000 27 -621.79\n 718500 6903000 28 -602.9391\n 719000 6903000 29 -582.7269\n 719500 6903000 30 -562.7665\n 720000 6903000 31 -544.5002\n 720500 6903000 32 -528.8254\n 721000 6903000 33 -514.9276\n 721500 6903000 34 -501.3468\n 722000 6903000 35 -487.0091\n 722500 6903000 36 -470.795\n 723000 6903000 37 -451.887\n 723500 6903000 38 -431.5657\n 724000 6903000 39 -411.5623\n 724500 6903000 40 -393.2483\n 725000 6903000 41 -377.2899\n 725500 6903000 42 -364.049\n 726000 6903000 43 -354.0973\n 726500 6903000 44 -347.9586\n 727000 6903000 45 -344.1541\n 727500 6903000 46 -340.3564\n 728000 6903000 47 -335.2704\n 728500 6903000 48 -330.8053\n 729000 6903000 49 -329.212\n 729500 6903000 50 -331.2314\n 730000 6903000 51 -337.1389\n 730500 6903000 52 -346.6612\n 731000 6903000 53 -359.2726\n 731500 6903000 54 -372.8723\n 732000 6903000 55 -384.6938\n 732500 6903000 56 -394.8968\n 733000 6903000 57 -405.0573\n 733500 6903000 58 -416.6311\n 734000 6903000 59 -432.0563\n 734500 6903000 60 -453.8107\n 735000 6903000 61 -483.1539\n 735500 6903000 62 -516.2716\n 736000 6903000 63 -545.9446\n 736500 6903000 64 -568.7987\n 737000 6903000 65 -585.6354\n 737500 6903000 66 -597.2012\n 738000 6903000 67 -605.3915\n 738500 6903000 68 -611.7261\n 739000 6903000 69 -617.0718\n 739500 6903000 70 -621.7613\n 740000 6903000 71 -625.8333\n 740500 6903000 72 -629.1627\n 741000 6903000 73 -631.4859\n 741500 6903000 74 -632.7702\n 742000 6903000 75 -633.0786\n 742500 6903000 76 -632.4615\n 743000 6903000 77 -630.9753\n 743500 6903000 78 -628.8447\n 744000 6903000 79 -626.667\n 744500 6903000 80 -625.222\n 745000 6903000 81 -625.1128\n 745500 6903000 82 -626.1464\n 746000 6903000 83 -627.9127\n 746500 6903000 84 -630.1918\n 747000 6903000 85 -632.9771\n 705000 6903500 1 -777.9465\n 705500 6903500 2 -775.454\n 706000 6903500 3 -773.2697\n 706500 6903500 4 -771.1874\n 707000 6903500 5 -768.9532\n 707500 6903500 6 -766.2823\n 708000 6903500 7 -762.8638\n 708500 6903500 8 -758.4944\n 709000 6903500 9 -753.0286\n 709500 6903500 10 -746.2896\n 710000 6903500 11 -738.1741\n 710500 6903500 12 -728.8121\n 711000 6903500 13 -718.7635\n 711500 6903500 14 -708.9886\n 712000 6903500 15 -700.5526\n 712500 6903500 16 -694.1287\n 713000 6903500 17 -690.1454\n 713500 6903500 18 -688.2289\n 714000 6903500 19 -687.2894\n 714500 6903500 20 -686.3916\n 715000 6903500 21 -684.9599\n 715500 6903500 22 -682.5588\n 716000 6903500 23 -678.9178\n 716500 6903500 24 -673.6547\n 717000 6903500 25 -666.1966\n 717500 6903500 26 -655.6059\n 718000 6903500 27 -641.1099\n 718500 6903500 28 -623.2047\n 719000 6903500 29 -603.3462\n 719500 6903500 30 -583.4005\n 720000 6903500 31 -564.8494\n 720500 6903500 32 -548.455\n 721000 6903500 33 -533.5818\n 721500 6903500 34 -518.8774\n 722000 6903500 35 -503.3395\n 722500 6903500 36 -486.346\n 723000 6903500 37 -467.5942\n 723500 6903500 38 -447.916\n 724000 6903500 39 -428.5194\n 724500 6903500 40 -410.493\n 725000 6903500 41 -394.622\n 725500 6903500 42 -381.4214\n 726000 6903500 43 -371.3477\n 726500 6903500 44 -364.679\n 727000 6903500 45 -359.9752\n 727500 6903500 46 -355.4126\n 728000 6903500 47 -350.0867\n 728500 6903500 48 -345.5618\n 729000 6903500 49 -343.8192\n 729500 6903500 50 -345.6145\n 730000 6903500 51 -351.2347\n 730500 6903500 52 -360.3005\n 731000 6903500 53 -372.1232\n 731500 6903500 54 -385.1365\n 732000 6903500 55 -397.4\n 732500 6903500 56 -409.1415\n 733000 6903500 57 -421.6272\n 733500 6903500 58 -435.9322\n 734000 6903500 59 -453.9473\n 734500 6903500 60 -477.5327\n 735000 6903500 61 -507.2834\n 735500 6903500 62 -539.2813\n 736000 6903500 63 -566.203\n 736500 6903500 64 -585.1136\n 737000 6903500 65 -597.7985\n 737500 6903500 66 -606.4229\n 738000 6903500 67 -612.9351\n 738500 6903500 68 -618.2213\n 739000 6903500 69 -622.7906\n 739500 6903500 70 -626.823\n 740000 6903500 71 -630.2429\n 740500 6903500 72 -632.9294\n 741000 6903500 73 -634.7488\n 741500 6903500 74 -635.7126\n 742000 6903500 75 -635.798\n 742500 6903500 76 -634.9062\n 743000 6903500 77 -632.9357\n 743500 6903500 78 -630.0409\n 744000 6903500 79 -626.9434\n 744500 6903500 80 -624.7747\n 745000 6903500 81 -624.5271\n 745500 6903500 82 -625.8987\n 746000 6903500 83 -628.1797\n 746500 6903500 84 -630.9416\n 747000 6903500 85 -634.1595\n 705000 6904000 1 -777.386\n 705500 6904000 2 -774.9418\n 706000 6904000 3 -772.8628\n 706500 6904000 4 -770.9805\n 707000 6904000 5 -769.07\n 707500 6904000 6 -766.8364\n 708000 6904000 7 -763.9391\n 708500 6904000 8 -760.1347\n 709000 6904000 9 -755.2548\n 709500 6904000 10 -749.1253\n 710000 6904000 11 -741.6342\n 710500 6904000 12 -732.852\n 711000 6904000 13 -723.2244\n 711500 6904000 14 -713.5581\n 712000 6904000 15 -704.8118\n 712500 6904000 16 -697.6703\n 713000 6904000 17 -692.5875\n 713500 6904000 18 -689.3973\n 714000 6904000 19 -687.3907\n 714500 6904000 20 -685.9982\n 715000 6904000 21 -685.0801\n 715500 6904000 22 -684.6895\n 716000 6904000 23 -684.1858\n 716500 6904000 24 -682.5984\n 717000 6904000 25 -678.786\n 717500 6904000 26 -671.2971\n 718000 6904000 27 -659.0414\n 718500 6904000 28 -642.6166\n 719000 6904000 29 -623.7553\n 719500 6904000 30 -604.4476\n 720000 6904000 31 -586.067\n 720500 6904000 32 -569.1997\n 721000 6904000 33 -553.2886\n 721500 6904000 34 -537.236\n 722000 6904000 35 -520.2827\n 722500 6904000 36 -502.3078\n 723000 6904000 37 -483.533\n 723500 6904000 38 -464.324\n 724000 6904000 39 -445.2901\n 724500 6904000 40 -427.2874\n 725000 6904000 41 -411.2472\n 725500 6904000 42 -397.945\n 726000 6904000 43 -387.709\n 726500 6904000 44 -380.5545\n 727000 6904000 45 -375.1649\n 727500 6904000 46 -370.1636\n 728000 6904000 47 -364.9692\n 728500 6904000 48 -360.8003\n 729000 6904000 49 -359.2577\n 729500 6904000 50 -361.076\n 730000 6904000 51 -366.5754\n 730500 6904000 52 -375.308\n 731000 6904000 53 -386.5157\n 731500 6904000 54 -399.0926\n 732000 6904000 55 -411.9777\n 732500 6904000 56 -425.7003\n 733000 6904000 57 -441.2776\n 733500 6904000 58 -459.2491\n 734000 6904000 59 -480.5313\n 734500 6904000 60 -505.7924\n 735000 6904000 61 -534.2814\n 735500 6904000 62 -562.4328\n 736000 6904000 63 -584.5436\n 736500 6904000 64 -598.658\n 737000 6904000 65 -607.1053\n 737500 6904000 66 -612.9882\n 738000 6904000 67 -618.1309\n 738500 6904000 68 -622.7896\n 739000 6904000 69 -627.0403\n 739500 6904000 70 -630.8338\n 740000 6904000 71 -633.9052\n 740500 6904000 72 -636.122\n 741000 6904000 73 -637.5262\n 741500 6904000 74 -638.2493\n 742000 6904000 75 -638.2237\n 742500 6904000 76 -637.2025\n 743000 6904000 77 -634.8887\n 743500 6904000 78 -631.3258\n 744000 6904000 79 -627.3361\n 744500 6904000 80 -624.4728\n 745000 6904000 81 -624.2423\n 745500 6904000 82 -626.1442\n 746000 6904000 83 -629.0316\n 746500 6904000 84 -632.2422\n 747000 6904000 85 -635.7922\n 705000 6904500 1 -776.2743\n 705500 6904500 2 -773.8884\n 706000 6904500 3 -771.9532\n 706500 6904500 4 -770.3212\n 707000 6904500 5 -768.7745\n 707500 6904500 6 -767.0137\n 708000 6904500 7 -764.6613\n 708500 6904500 8 -761.4473\n 709000 6904500 9 -757.1875\n 709500 6904500 10 -751.7034\n 710000 6904500 11 -744.8652\n 710500 6904500 12 -736.6862\n 711000 6904500 13 -727.5225\n 711500 6904500 14 -718.0657\n 712000 6904500 15 -709.1665\n 712500 6904500 16 -701.4813\n 713000 6904500 17 -695.4542\n 713500 6904500 18 -691.1075\n 714000 6904500 19 -688.0723\n 714500 6904500 20 -686.1157\n 715000 6904500 21 -685.409\n 715500 6904500 22 -686.4286\n 716000 6904500 23 -688.2968\n 716500 6904500 24 -689.6328\n 717000 6904500 25 -688.9061\n 717500 6904500 26 -684.3384\n 718000 6904500 27 -674.5729\n 718500 6904500 28 -660.1873\n 719000 6904500 29 -643.0115\n 719500 6904500 30 -624.9965\n 720000 6904500 31 -607.3771\n 720500 6904500 32 -590.5932\n 721000 6904500 33 -574.1676\n 721500 6904500 34 -557.2173\n 722000 6904500 35 -539.2186\n 722500 6904500 36 -520.3664\n 723000 6904500 37 -501.2409\n 723500 6904500 38 -481.9407\n 724000 6904500 39 -462.7113\n 724500 6904500 40 -444.2807\n 725000 6904500 41 -427.7001\n 725500 6904500 42 -413.9131\n 726000 6904500 43 -403.2269\n 726500 6904500 44 -395.502\n 727000 6904500 45 -389.5625\n 727500 6904500 46 -384.3888\n 728000 6904500 47 -379.5783\n 728500 6904500 48 -376.0366\n 729000 6904500 49 -375.0229\n 729500 6904500 50 -377.2141\n 730000 6904500 51 -382.9676\n 730500 6904500 52 -391.8158\n 731000 6904500 53 -402.958\n 731500 6904500 54 -415.5914\n 732000 6904500 55 -429.3286\n 732500 6904500 56 -445.0928\n 733000 6904500 57 -463.752\n 733500 6904500 58 -485.2139\n 734000 6904500 59 -509.2995\n 734500 6904500 60 -535.3652\n 735000 6904500 61 -561.4351\n 735500 6904500 62 -584.3835\n 736000 6904500 63 -600.8004\n 736500 6904500 64 -610.1912\n 737000 6904500 65 -615.0022\n 737500 6904500 66 -618.4807\n 738000 6904500 67 -622.3583\n 738500 6904500 68 -626.3967\n 739000 6904500 69 -630.3807\n 739500 6904500 70 -634.0579\n 740000 6904500 71 -636.9174\n 740500 6904500 72 -638.7587\n 741000 6904500 73 -639.8114\n 741500 6904500 74 -640.3651\n 742000 6904500 75 -640.3472\n 742500 6904500 76 -639.387\n 743000 6904500 77 -636.9928\n 743500 6904500 78 -633.0564\n 744000 6904500 79 -628.4496\n 744500 6904500 80 -625.1067\n 745000 6904500 81 -625.1005\n 745500 6904500 82 -627.674\n 746000 6904500 83 -631.1639\n 746500 6904500 84 -634.6974\n 747000 6904500 85 -638.3837\n 705000 6905000 1 -774.4816\n 705500 6905000 2 -772.1707\n 706000 6905000 3 -770.4477\n 706500 6905000 4 -769.1562\n 707000 6905000 5 -768.0563\n 707500 6905000 6 -766.8307\n 708000 6905000 7 -765.0648\n 708500 6905000 8 -762.468\n 709000 6905000 9 -758.8474\n 709500 6905000 10 -754.0213\n 710000 6905000 11 -747.8514\n 710500 6905000 12 -740.3125\n 711000 6905000 13 -731.6822\n 711500 6905000 14 -722.5477\n 712000 6905000 15 -713.6526\n 712500 6905000 16 -705.6027\n 713000 6905000 17 -698.842\n 713500 6905000 18 -693.5352\n 714000 6905000 19 -689.6208\n 714500 6905000 20 -687.1556\n 715000 6905000 21 -686.4561\n 715500 6905000 22 -688.1688\n 716000 6905000 23 -691.3615\n 716500 6905000 24 -694.5036\n 717000 6905000 25 -695.9168\n 717500 6905000 26 -693.7971\n 718000 6905000 27 -686.7081\n 718500 6905000 28 -675.0289\n 719000 6905000 29 -660.3492\n 719500 6905000 30 -644.419\n 720000 6905000 31 -628.2939\n 720500 6905000 32 -612.3791\n 721000 6905000 33 -596.2604\n 721500 6905000 34 -579.2401\n 722000 6905000 35 -560.9413\n 722500 6905000 36 -541.5358\n 723000 6905000 37 -521.6583\n 723500 6905000 38 -501.5196\n 724000 6905000 39 -481.3663\n 724500 6905000 40 -461.9609\n 725000 6905000 41 -444.3788\n 725500 6905000 42 -429.6198\n 726000 6905000 43 -418.0856\n 726500 6905000 44 -409.6193\n 727000 6905000 45 -403.175\n 727500 6905000 46 -397.9824\n 728000 6905000 47 -393.7268\n 728500 6905000 48 -391.0379\n 729000 6905000 49 -390.8697\n 729500 6905000 50 -393.8277\n 730000 6905000 51 -400.3629\n 730500 6905000 52 -410.0413\n 731000 6905000 53 -422.003\n 731500 6905000 54 -435.5171\n 732000 6905000 55 -450.4706\n 732500 6905000 56 -467.9248\n 733000 6905000 57 -488.6223\n 733500 6905000 58 -511.9981\n 734000 6905000 59 -537.075\n 734500 6905000 60 -562.3429\n 735000 6905000 61 -585.1427\n 735500 6905000 62 -602.9146\n 736000 6905000 63 -614.3085\n 736500 6905000 64 -619.949\n 737000 6905000 65 -622.2064\n 737500 6905000 66 -623.9045\n 738000 6905000 67 -626.5955\n 738500 6905000 68 -629.8909\n 739000 6905000 69 -633.425\n 739500 6905000 70 -636.8254\n 740000 6905000 71 -639.4\n 740500 6905000 72 -640.8785\n 741000 6905000 73 -641.6063\n 741500 6905000 74 -641.9955\n 742000 6905000 75 -642.0189\n 742500 6905000 76 -641.2724\n 743000 6905000 77 -639.1829\n 743500 6905000 78 -635.4957\n 744000 6905000 79 -631.0168\n 744500 6905000 80 -627.7538\n 745000 6905000 81 -628.156\n 745500 6905000 82 -631.3168\n 746000 6905000 83 -635.1871\n 746500 6905000 84 -638.8002\n 747000 6905000 85 -642.3609\n 705000 6905500 1 -772.0834\n 705500 6905500 2 -769.8558\n 706000 6905500 3 -768.4129\n 706500 6905500 4 -767.5555\n 707000 6905500 5 -766.9899\n 707500 6905500 6 -766.3636\n 708000 6905500 7 -765.2233\n 708500 6905500 8 -763.2637\n 709000 6905500 9 -760.2961\n 709500 6905500 10 -756.1411\n 710000 6905500 11 -750.6538\n 710500 6905500 12 -743.7869\n 711000 6905500 13 -735.7507\n 711500 6905500 14 -727.0313\n 712000 6905500 15 -718.2642\n 712500 6905500 16 -710.0058\n 713000 6905500 17 -702.6782\n 713500 6905500 18 -696.5593\n 714000 6905500 19 -691.8542\n 714500 6905500 20 -688.8403\n 715000 6905500 21 -687.9671\n 715500 6905500 22 -689.8613\n 716000 6905500 23 -693.6186\n 716500 6905500 24 -697.7165\n 717000 6905500 25 -700.4859\n 717500 6905500 26 -700.2924\n 718000 6905500 27 -695.8744\n 718500 6905500 28 -687.3322\n 719000 6905500 29 -675.7849\n 719500 6905500 30 -662.6367\n 720000 6905500 31 -648.6786\n 720500 6905500 32 -634.257\n 721000 6905500 33 -619.0872\n 721500 6905500 34 -602.6512\n 722000 6905500 35 -584.646\n 722500 6905500 36 -565.0332\n 723000 6905500 37 -544.1812\n 723500 6905500 38 -522.6223\n 724000 6905500 39 -500.9315\n 724500 6905500 40 -480.0521\n 725000 6905500 41 -461.0955\n 725500 6905500 42 -445.0294\n 726000 6905500 43 -432.3733\n 726500 6905500 44 -423.0892\n 727000 6905500 45 -416.2218\n 727500 6905500 46 -411.1463\n 728000 6905500 47 -407.5776\n 728500 6905500 48 -405.903\n 729000 6905500 49 -406.8203\n 729500 6905500 50 -410.8764\n 730000 6905500 51 -418.6924\n 730500 6905500 52 -429.9342\n 731000 6905500 53 -443.6286\n 731500 6905500 54 -458.9205\n 732000 6905500 55 -475.5443\n 732500 6905500 56 -494.1138\n 733000 6905500 57 -515.1836\n 733500 6905500 58 -538.1224\n 734000 6905500 59 -561.7807\n 734500 6905500 60 -584.4642\n 735000 6905500 61 -603.5829\n 735500 6905500 62 -617.0808\n 736000 6905500 63 -624.8023\n 736500 6905500 64 -628.0007\n 737000 6905500 65 -628.7338\n 737500 6905500 66 -629.3199\n 738000 6905500 67 -631.0319\n 738500 6905500 68 -633.5618\n 739000 6905500 69 -636.478\n 739500 6905500 70 -639.369\n 740000 6905500 71 -641.5154\n 740500 6905500 72 -642.5899\n 741000 6905500 73 -642.9639\n 741500 6905500 74 -643.1135\n 742000 6905500 75 -643.1033\n 742500 6905500 76 -642.6201\n 743000 6905500 77 -641.1685\n 743500 6905500 78 -638.4295\n 744000 6905500 79 -634.9783\n 744500 6905500 80 -632.5427\n 745000 6905500 81 -633.4167\n 745500 6905500 82 -636.8479\n 746000 6905500 83 -640.7173\n 746500 6905500 84 -644.1458\n 747000 6905500 85 -647.3977\n 705000 6906000 1 -769.2407\n 705500 6906000 2 -767.0908\n 706000 6906000 3 -765.9749\n 706500 6906000 4 -765.6237\n 707000 6906000 5 -765.6598\n 707500 6906000 6 -765.6785\n 708000 6906000 7 -765.1884\n 708500 6906000 8 -763.8734\n 709000 6906000 9 -761.5701\n 709500 6906000 10 -758.1113\n 710000 6906000 11 -753.3408\n 710500 6906000 12 -747.1769\n 711000 6906000 13 -739.7748\n 711500 6906000 14 -731.5345\n 712000 6906000 15 -722.9935\n 712500 6906000 16 -714.6494\n 713000 6906000 17 -706.8936\n 713500 6906000 18 -700.0676\n 714000 6906000 19 -694.5732\n 714500 6906000 20 -690.8712\n 715000 6906000 21 -689.5246\n 715500 6906000 22 -691.2505\n 716000 6906000 23 -695.1341\n 716500 6906000 24 -699.7336\n 717000 6906000 25 -703.4748\n 717500 6906000 26 -704.8896\n 718000 6906000 27 -702.9111\n 718500 6906000 28 -697.5195\n 719000 6906000 29 -689.3903\n 719500 6906000 30 -679.4913\n 720000 6906000 31 -668.2379\n 720500 6906000 32 -655.7454\n 721000 6906000 33 -641.92\n 721500 6906000 34 -626.4738\n 722000 6906000 35 -609.1287\n 722500 6906000 36 -589.5127\n 723000 6906000 37 -567.6189\n 723500 6906000 38 -544.3284\n 724000 6906000 39 -520.7101\n 724500 6906000 40 -497.9871\n 725000 6906000 41 -477.371\n 725500 6906000 42 -459.8408\n 726000 6906000 43 -445.9419\n 726500 6906000 44 -435.8994\n 727000 6906000 45 -428.7811\n 727500 6906000 46 -424.0274\n 728000 6906000 47 -421.3133\n 728500 6906000 48 -420.8321\n 729000 6906000 49 -423.0623\n 729500 6906000 50 -428.4968\n 730000 6906000 51 -437.9799\n 730500 6906000 52 -451.3163\n 731000 6906000 53 -467.3541\n 731500 6906000 54 -484.9612\n 732000 6906000 55 -503.457\n 732500 6906000 56 -522.5132\n 733000 6906000 57 -542.3038\n 733500 6906000 58 -562.6342\n 734000 6906000 59 -582.8282\n 734500 6906000 60 -601.5344\n 735000 6906000 61 -616.7444\n 735500 6906000 62 -626.9539\n 736000 6906000 63 -632.3708\n 736500 6906000 64 -634.2665\n 737000 6906000 65 -634.3307\n 737500 6906000 66 -634.3409\n 738000 6906000 67 -635.3553\n 738500 6906000 68 -637.2147\n 739000 6906000 69 -639.4951\n 739500 6906000 70 -641.749\n 740000 6906000 71 -643.3322\n 740500 6906000 72 -643.9365\n 741000 6906000 73 -643.9037\n 741500 6906000 74 -643.7331\n 742000 6906000 75 -643.5975\n 742500 6906000 76 -643.367\n 743000 6906000 77 -642.693\n 743500 6906000 78 -641.2861\n 744000 6906000 79 -639.4073\n 744500 6906000 80 -638.3138\n 745000 6906000 81 -639.6909\n 745500 6906000 82 -643.0513\n 746000 6906000 83 -646.599\n 746500 6906000 84 -649.6774\n 747000 6906000 85 -652.5981\n 705000 6906500 1 -766.2075\n 705500 6906500 2 -764.1141\n 706000 6906500 3 -763.3357\n 706500 6906500 4 -763.5146\n 707000 6906500 5 -764.171\n 707500 6906500 6 -764.8373\n 708000 6906500 7 -764.9899\n 708500 6906500 8 -764.3021\n 709000 6906500 9 -762.6609\n 709500 6906500 10 -759.9196\n 710000 6906500 11 -755.9038\n 710500 6906500 12 -750.4796\n 711000 6906500 13 -743.7477\n 711500 6906500 14 -736.0458\n 712000 6906500 15 -727.8243\n 712500 6906500 16 -719.5134\n 713000 6906500 17 -711.4532\n 713500 6906500 18 -704.0145\n 714000 6906500 19 -697.7128\n 714500 6906500 20 -693.1666\n 715000 6906500 21 -691.0862\n 715500 6906500 22 -692.3648\n 716000 6906500 23 -696.1136\n 716500 6906500 24 -700.9434\n 717000 6906500 25 -705.3887\n 717500 6906500 26 -708.153\n 718000 6906500 27 -708.3276\n 718500 6906500 28 -705.8102\n 719000 6906500 29 -700.9819\n 719500 6906500 30 -694.4152\n 720000 6906500 31 -686.1188\n 720500 6906500 32 -675.814\n 721000 6906500 33 -663.5838\n 721500 6906500 34 -649.4052\n 722000 6906500 35 -632.9785\n 722500 6906500 36 -613.5322\n 723000 6906500 37 -590.6402\n 723500 6906500 38 -565.5562\n 724000 6906500 39 -539.8775\n 724500 6906500 40 -515.155\n 725000 6906500 41 -492.747\n 725500 6906500 42 -473.6967\n 726000 6906500 43 -458.5732\n 726500 6906500 44 -447.9022\n 727000 6906500 45 -440.749\n 727500 6906500 46 -436.5857\n 728000 6906500 47 -435.0097\n 728500 6906500 48 -436.0316\n 729000 6906500 49 -439.9133\n 729500 6906500 50 -447.0385\n 730000 6906500 51 -458.4081\n 730500 6906500 52 -473.9439\n 731000 6906500 53 -492.2801\n 731500 6906500 54 -511.9709\n 732000 6906500 55 -531.8607\n 732500 6906500 56 -550.6679\n 733000 6906500 57 -568.0651\n 733500 6906500 58 -584.5369\n 734000 6906500 59 -600.2468\n 734500 6906500 60 -614.4699\n 735000 6906500 61 -625.8983\n 735500 6906500 62 -633.5407\n 736000 6906500 63 -637.5467\n 736500 6906500 64 -638.854\n 737000 6906500 65 -638.7283\n 737500 6906500 66 -638.5419\n 738000 6906500 67 -639.142\n 738500 6906500 68 -640.4938\n 739000 6906500 69 -642.2319\n 739500 6906500 70 -643.8936\n 740000 6906500 71 -644.8825\n 740500 6906500 72 -644.9523\n 741000 6906500 73 -644.4451\n 741500 6906500 74 -643.8932\n 742000 6906500 75 -643.592\n 742500 6906500 76 -643.6121\n 743000 6906500 77 -643.7399\n 743500 6906500 78 -643.6736\n 744000 6906500 79 -643.4622\n 744500 6906500 80 -643.7531\n 745000 6906500 81 -645.5888\n 745500 6906500 82 -648.666\n 746000 6906500 83 -651.7783\n 746500 6906500 84 -654.5035\n 747000 6906500 85 -657.1844\n 705000 6907000 1 -763.3392\n 705500 6907000 2 -761.2622\n 706000 6907000 3 -760.7721\n 706500 6907000 4 -761.4223\n 707000 6907000 5 -762.6313\n 707500 6907000 6 -763.8774\n 708000 6907000 7 -764.6119\n 708500 6907000 8 -764.5027\n 709000 6907000 9 -763.4959\n 709500 6907000 10 -761.4692\n 710000 6907000 11 -758.2297\n 710500 6907000 12 -753.5847\n 711000 6907000 13 -747.5806\n 711500 6907000 14 -740.5034\n 712000 6907000 15 -732.7204\n 712500 6907000 16 -724.5841\n 713000 6907000 17 -716.3677\n 713500 6907000 18 -708.4445\n 714000 6907000 19 -701.3807\n 714500 6907000 20 -695.9357\n 715000 6907000 21 -692.9572\n 715500 6907000 22 -693.5718\n 716000 6907000 23 -696.9407\n 716500 6907000 24 -701.7155\n 717000 6907000 25 -706.5456\n 717500 6907000 26 -710.2992\n 718000 6907000 27 -712.1939\n 718500 6907000 28 -712.0865\n 719000 6907000 29 -710.1605\n 719500 6907000 30 -706.632\n 720000 6907000 31 -701.1615\n 720500 6907000 32 -693.1128\n 721000 6907000 33 -682.6146\n 721500 6907000 34 -669.8492\n 722000 6907000 35 -654.4728\n 722500 6907000 36 -635.3987\n 723000 6907000 37 -611.7907\n 723500 6907000 38 -585.1682\n 724000 6907000 39 -557.6207\n 724500 6907000 40 -531.011\n 725000 6907000 41 -506.8686\n 725500 6907000 42 -486.3483\n 726000 6907000 43 -470.0706\n 726500 6907000 44 -458.8736\n 727000 6907000 45 -451.8752\n 727500 6907000 46 -448.6106\n 728000 6907000 47 -448.6096\n 728500 6907000 48 -451.6811\n 729000 6907000 49 -457.8008\n 729500 6907000 50 -467.0915\n 730000 6907000 51 -480.4175\n 730500 6907000 52 -497.7138\n 731000 6907000 53 -517.4866\n 731500 6907000 54 -538.1103\n 732000 6907000 55 -558.1868\n 732500 6907000 56 -575.8565\n 733000 6907000 57 -590.4014\n 733500 6907000 58 -602.8536\n 734000 6907000 59 -614.1273\n 734500 6907000 60 -624.1481\n 735000 6907000 61 -632.2706\n 735500 6907000 62 -637.9043\n 736000 6907000 63 -640.9973\n 736500 6907000 64 -642.0596\n 737000 6907000 65 -641.9583\n 737500 6907000 66 -641.7523\n 738000 6907000 67 -642.1368\n 738500 6907000 68 -643.1333\n 739000 6907000 69 -644.4461\n 739500 6907000 70 -645.6165\n 740000 6907000 71 -646.0709\n 740500 6907000 72 -645.6173\n 741000 6907000 73 -644.6423\n 741500 6907000 74 -643.7292\n 742000 6907000 75 -643.2859\n 742500 6907000 76 -643.567\n 743000 6907000 77 -644.4572\n 743500 6907000 78 -645.5956\n 744000 6907000 79 -646.8234\n 744500 6907000 80 -648.3434\n 745000 6907000 81 -650.5685\n 745500 6907000 82 -653.3387\n 746000 6907000 83 -656.0433\n 746500 6907000 84 -658.5139\n 747000 6907000 85 -661.079\n 705000 6907500 1 -761.0701\n 705500 6907500 2 -758.9509\n 706000 6907500 3 -758.6144\n 706500 6907500 4 -759.5601\n 707000 6907500 5 -761.1382\n 707500 6907500 6 -762.8005\n 708000 6907500 7 -763.9971\n 708500 6907500 8 -764.3898\n 709000 6907500 9 -763.9603\n 709500 6907500 10 -762.607\n 710000 6907500 11 -760.1331\n 710500 6907500 12 -756.3107\n 711000 6907500 13 -751.1295\n 711500 6907500 14 -744.8119\n 712000 6907500 15 -737.628\n 712500 6907500 16 -729.8466\n 713000 6907500 17 -721.6676\n 713500 6907500 18 -713.4526\n 714000 6907500 19 -705.7943\n 714500 6907500 20 -699.5631\n 715000 6907500 21 -695.6732\n 715500 6907500 22 -695.4193\n 716000 6907500 23 -698.0832\n 716500 6907500 24 -702.3898\n 717000 6907500 25 -707.1352\n 717500 6907500 26 -711.3753\n 718000 6907500 27 -714.4521\n 718500 6907500 28 -716.1683\n 719000 6907500 29 -716.5286\n 719500 6907500 30 -715.4427\n 720000 6907500 31 -712.4085\n 720500 6907500 32 -706.5579\n 721000 6907500 33 -697.8668\n 721500 6907500 34 -686.5436\n 722000 6907500 35 -672.1584\n 722500 6907500 36 -653.548\n 723000 6907500 37 -629.6641\n 723500 6907500 38 -602.1418\n 724000 6907500 39 -573.2947\n 724500 6907500 40 -545.191\n 725000 6907500 41 -519.5436\n 725500 6907500 42 -497.6799\n 726000 6907500 43 -480.3492\n 726500 6907500 44 -468.6223\n 727000 6907500 45 -461.8842\n 727500 6907500 46 -459.812\n 728000 6907500 47 -461.9443\n 728500 6907500 48 -467.828\n 729000 6907500 49 -477.0074\n 729500 6907500 50 -489.1148\n 730000 6907500 51 -504.3858\n 730500 6907500 52 -522.5679\n 731000 6907500 53 -542.2878\n 731500 6907500 54 -562.0704\n 732000 6907500 55 -580.6306\n 732500 6907500 56 -596.2562\n 733000 6907500 57 -608.0245\n 733500 6907500 58 -617.107\n 734000 6907500 59 -624.7507\n 734500 6907500 60 -631.3646\n 735000 6907500 61 -636.8457\n 735500 6907500 62 -640.9261\n 736000 6907500 63 -643.3721\n 736500 6907500 64 -644.314\n 737000 6907500 65 -644.2825\n 737500 6907500 66 -644.089\n 738000 6907500 67 -644.3101\n 738500 6907500 68 -644.9893\n 739000 6907500 69 -645.9031\n 739500 6907500 70 -646.6407\n 740000 6907500 71 -646.6663\n 740500 6907500 72 -645.8322\n 741000 6907500 73 -644.5655\n 741500 6907500 74 -643.472\n 742000 6907500 75 -643.0187\n 742500 6907500 76 -643.5841\n 743000 6907500 77 -645.1165\n 743500 6907500 78 -647.1946\n 744000 6907500 79 -649.4961\n 744500 6907500 80 -651.9401\n 745000 6907500 81 -654.5032\n 745500 6907500 82 -657.124\n 746000 6907500 83 -659.6221\n 746500 6907500 84 -662.0157\n 747000 6907500 85 -664.6089\n 705000 6908000 1 -759.7402\n 705500 6908000 2 -757.5124\n 706000 6908000 3 -757.1274\n 706500 6908000 4 -758.1\n 707000 6908000 5 -759.7776\n 707500 6908000 6 -761.619\n 708000 6908000 7 -763.1099\n 708500 6908000 8 -763.9119\n 709000 6908000 9 -763.983\n 709500 6908000 10 -763.2341\n 710000 6908000 11 -761.4848\n 710500 6908000 12 -758.5213\n 711000 6908000 13 -754.2822\n 711500 6908000 14 -748.8942\n 712000 6908000 15 -742.5049\n 712500 6908000 16 -735.2878\n 713000 6908000 17 -727.3672\n 713500 6908000 18 -719.0856\n 714000 6908000 19 -711.0689\n 714500 6908000 20 -704.2333\n 715000 6908000 21 -699.5543\n 715500 6908000 22 -698.2827\n 716000 6908000 23 -699.8994\n 716500 6908000 24 -703.275\n 717000 6908000 25 -707.3971\n 717500 6908000 26 -711.5439\n 718000 6908000 27 -715.2269\n 718500 6908000 28 -718.179\n 719000 6908000 29 -720.1711\n 719500 6908000 30 -720.8735\n 720000 6908000 31 -719.7236\n 720500 6908000 32 -715.9116\n 721000 6908000 33 -709.1079\n 721500 6908000 34 -699.3034\n 722000 6908000 35 -685.9202\n 722500 6908000 36 -667.787\n 723000 6908000 37 -644.0412\n 723500 6908000 38 -616.2115\n 724000 6908000 39 -586.6362\n 724500 6908000 40 -557.515\n 725000 6908000 41 -530.7133\n 725500 6908000 42 -507.7116\n 726000 6908000 43 -489.434\n 726500 6908000 44 -477.1657\n 727000 6908000 45 -470.72\n 727500 6908000 46 -470.0315\n 728000 6908000 47 -474.8091\n 728500 6908000 48 -484.2442\n 729000 6908000 49 -497.2515\n 729500 6908000 50 -512.7747\n 730000 6908000 51 -529.9908\n 730500 6908000 52 -548.1389\n 731000 6908000 53 -566.2992\n 731500 6908000 54 -583.5651\n 732000 6908000 55 -599.0749\n 732500 6908000 56 -611.8253\n 733000 6908000 57 -621.0679\n 733500 6908000 58 -627.6827\n 734000 6908000 59 -632.7683\n 734500 6908000 60 -636.953\n 735000 6908000 61 -640.4904\n 735500 6908000 62 -643.3716\n 736000 6908000 63 -645.2806\n 736500 6908000 64 -646.0999\n 737000 6908000 65 -646.0961\n 737500 6908000 66 -645.8373\n 738000 6908000 67 -645.8002\n 738500 6908000 68 -646.043\n 739000 6908000 69 -646.431\n 739500 6908000 70 -646.6877\n 740000 6908000 71 -646.4139\n 740500 6908000 72 -645.4913\n 741000 6908000 73 -644.3117\n 741500 6908000 74 -643.4012\n 742000 6908000 75 -643.1854\n 742500 6908000 76 -644.0622\n 743000 6908000 77 -646.0289\n 743500 6908000 78 -648.6606\n 744000 6908000 79 -651.5601\n 744500 6908000 80 -654.549\n 745000 6908000 81 -657.4271\n 745500 6908000 82 -660.1514\n 746000 6908000 83 -662.7588\n 746500 6908000 84 -665.3514\n 747000 6908000 85 -668.1602\n 705000 6908500 1 -759.4261\n 705500 6908500 2 -757.0411\n 706000 6908500 3 -756.4157\n 706500 6908500 4 -757.1472\n 707000 6908500 5 -758.6505\n 707500 6908500 6 -760.4211\n 708000 6908500 7 -762.0129\n 708500 6908500 8 -763.098\n 709000 6908500 9 -763.577\n 709500 6908500 10 -763.356\n 710000 6908500 11 -762.2798\n 710500 6908500 12 -760.1858\n 711000 6908500 13 -756.989\n 711500 6908500 14 -752.6973\n 712000 6908500 15 -747.316\n 712500 6908500 16 -740.8984\n 713000 6908500 17 -733.4675\n 713500 6908500 18 -725.342\n 714000 6908500 19 -717.1957\n 714500 6908500 20 -709.9402\n 715000 6908500 21 -704.5864\n 715500 6908500 22 -702.2883\n 716000 6908500 23 -702.637\n 716500 6908500 24 -704.711\n 717000 6908500 25 -707.723\n 717500 6908500 26 -711.2151\n 718000 6908500 27 -714.9279\n 718500 6908500 28 -718.5401\n 719000 6908500 29 -721.5605\n 719500 6908500 30 -723.4479\n 720000 6908500 31 -723.685\n 720500 6908500 32 -721.6144\n 721000 6908500 33 -716.7045\n 721500 6908500 34 -708.5519\n 722000 6908500 35 -696.3868\n 722500 6908500 36 -679.0053\n 723000 6908500 37 -655.6324\n 723500 6908500 38 -627.8406\n 724000 6908500 39 -597.8931\n 724500 6908500 40 -568.0649\n 725000 6908500 41 -540.39\n 725500 6908500 42 -516.5216\n 726000 6908500 43 -497.4985\n 726500 6908500 44 -484.7417\n 727000 6908500 45 -478.551\n 727500 6908500 46 -479.3278\n 728000 6908500 47 -487.067\n 728500 6908500 48 -500.5525\n 729000 6908500 49 -517.8759\n 729500 6908500 50 -537.0745\n 730000 6908500 51 -556.0878\n 730500 6908500 52 -573.4892\n 731000 6908500 53 -588.9835\n 731500 6908500 54 -602.5922\n 732000 6908500 55 -614.2258\n 732500 6908500 56 -623.629\n 733000 6908500 57 -630.5293\n 733500 6908500 58 -635.3973\n 734000 6908500 59 -638.8849\n 734500 6908500 60 -641.5611\n 735000 6908500 61 -643.7988\n 735500 6908500 62 -645.7435\n 736000 6908500 63 -647.132\n 736500 6908500 64 -647.7523\n 737000 6908500 65 -647.6943\n 737500 6908500 66 -647.2498\n 738000 6908500 67 -646.7855\n 738500 6908500 68 -646.4003\n 739000 6908500 69 -646.0844\n 739500 6908500 70 -645.7549\n 740000 6908500 71 -645.3036\n 740500 6908500 72 -644.66\n 741000 6908500 73 -644.0349\n 741500 6908500 74 -643.7307\n 742000 6908500 75 -644.0482\n 742500 6908500 76 -645.2843\n 743000 6908500 77 -647.4611\n 743500 6908500 78 -650.2421\n 744000 6908500 79 -653.2852\n 744500 6908500 80 -656.4145\n 745000 6908500 81 -659.5486\n 745500 6908500 82 -662.6039\n 746000 6908500 83 -665.6193\n 746500 6908500 84 -668.6636\n 747000 6908500 85 -671.8734\n 705000 6909000 1 -759.7986\n 705500 6909000 2 -757.2067\n 706000 6909000 3 -756.247\n 706500 6909000 4 -756.5947\n 707000 6909000 5 -757.7604\n 707500 6909000 6 -759.309\n 708000 6909000 7 -760.8569\n 708500 6909000 8 -762.1064\n 709000 6909000 9 -762.9073\n 709500 6909000 10 -763.1511\n 710000 6909000 11 -762.709\n 710500 6909000 12 -761.4671\n 711000 6909000 13 -759.335\n 711500 6909000 14 -756.2169\n 712000 6909000 15 -751.9804\n 712500 6909000 16 -746.5396\n 713000 6909000 17 -739.7786\n 713500 6909000 18 -731.9883\n 714000 6909000 19 -723.9004\n 714500 6909000 20 -716.4295\n 715000 6909000 21 -710.5474\n 715500 6909000 22 -707.3045\n 716000 6909000 23 -706.3577\n 716500 6909000 24 -706.9755\n 717000 6909000 25 -708.6025\n 717500 6909000 26 -711.0784\n 718000 6909000 27 -714.4199\n 718500 6909000 28 -718.2678\n 719000 6909000 29 -721.8651\n 719500 6909000 30 -724.4946\n 720000 6909000 31 -725.7315\n 720500 6909000 32 -725.1322\n 721000 6909000 33 -722.01\n 721500 6909000 34 -715.5673\n 722000 6909000 35 -704.8294\n 722500 6909000 36 -688.462\n 723000 6909000 37 -665.6561\n 723500 6909000 38 -638.0295\n 724000 6909000 39 -607.8742\n 724500 6909000 40 -577.5607\n 725000 6909000 41 -549.2833\n 725500 6909000 42 -524.8479\n 726000 6909000 43 -505.3354\n 726500 6909000 44 -492.2349\n 727000 6909000 45 -486.1646\n 727500 6909000 46 -488.2905\n 728000 6909000 47 -498.9606\n 728500 6909000 48 -516.493\n 729000 6909000 49 -538.0799\n 729500 6909000 50 -560.8156\n 730000 6909000 51 -581.3269\n 730500 6909000 52 -597.5488\n 731000 6909000 53 -609.942\n 731500 6909000 54 -619.5723\n 732000 6909000 55 -627.1808\n 732500 6909000 56 -633.2815\n 733000 6909000 57 -638.0408\n 733500 6909000 58 -641.5368\n 734000 6909000 59 -643.9374\n 734500 6909000 60 -645.6229\n 735000 6909000 61 -646.9566\n 735500 6909000 62 -648.1339\n 736000 6909000 63 -648.9959\n 736500 6909000 64 -649.3376\n 737000 6909000 65 -649.1388\n 737500 6909000 66 -648.4441\n 738000 6909000 67 -647.4918\n 738500 6909000 68 -646.4416\n 739000 6909000 69 -645.4199\n 739500 6909000 70 -644.5483\n 740000 6909000 71 -644.0217\n 740500 6909000 72 -643.8617\n 741000 6909000 73 -644.0255\n 741500 6909000 74 -644.5225\n 742000 6909000 75 -645.4924\n 742500 6909000 76 -647.1105\n 743000 6909000 77 -649.3932\n 743500 6909000 78 -652.1274\n 744000 6909000 79 -655.0865\n 744500 6909000 80 -658.1821\n 745000 6909000 81 -661.477\n 745500 6909000 82 -664.908\n 746000 6909000 83 -668.4027\n 746500 6909000 84 -671.9472\n 747000 6909000 85 -675.5839\n 705000 6909500 1 -760.7206\n 705500 6909500 2 -757.8798\n 706000 6909500 3 -756.5135\n 706500 6909500 4 -756.3811\n 707000 6909500 5 -757.0965\n 707500 6909500 6 -758.3072\n 708000 6909500 7 -759.7073\n 708500 6909500 8 -761.0241\n 709000 6909500 9 -762.0776\n 709500 6909500 10 -762.7328\n 710000 6909500 11 -762.8768\n 710500 6909500 12 -762.4359\n 711000 6909500 13 -761.3156\n 711500 6909500 14 -759.3452\n 712000 6909500 15 -756.2835\n 712500 6909500 16 -751.9124\n 713000 6909500 17 -745.9675\n 713500 6909500 18 -738.7073\n 714000 6909500 19 -730.9277\n 714500 6909500 20 -723.5402\n 715000 6909500 21 -717.4111\n 715500 6909500 22 -713.4057\n 716000 6909500 23 -711.2693\n 716500 6909500 24 -710.4502\n 717000 6909500 25 -710.6221\n 717500 6909500 26 -711.9038\n 718000 6909500 27 -714.5847\n 718500 6909500 28 -718.2958\n 719000 6909500 29 -722.0558\n 719500 6909500 30 -725.0289\n 720000 6909500 31 -726.8987\n 720500 6909500 32 -727.4316\n 721000 6909500 33 -725.8459\n 721500 6909500 34 -721.017\n 722000 6909500 35 -711.7602\n 722500 6909500 36 -696.5789\n 723000 6909500 37 -674.5015\n 723500 6909500 38 -647.2177\n 724000 6909500 39 -617.1057\n 724500 6909500 40 -586.6354\n 725000 6909500 41 -558.1381\n 725500 6909500 42 -533.5476\n 726000 6909500 43 -513.946\n 726500 6909500 44 -500.7903\n 727000 6909500 45 -494.891\n 727500 6909500 46 -498.1286\n 728000 6909500 47 -511.2077\n 728500 6909500 48 -532.0516\n 729000 6909500 49 -557.0635\n 729500 6909500 50 -582.5436\n 730000 6909500 51 -604.0726\n 730500 6909500 52 -618.9843\n 731000 6909500 53 -628.4426\n 731500 6909500 54 -634.4623\n 732000 6909500 55 -638.5109\n 732500 6909500 56 -641.673\n 733000 6909500 57 -644.5142\n 733500 6909500 58 -646.8349\n 734000 6909500 59 -648.3742\n 734500 6909500 60 -649.3023\n 735000 6909500 61 -649.9194\n 735500 6909500 62 -650.4301\n 736000 6909500 63 -650.7578\n 736500 6909500 64 -650.7452\n 737000 6909500 65 -650.3172\n 737500 6909500 66 -649.364\n 738000 6909500 67 -648.012\n 738500 6909500 68 -646.4688\n 739000 6909500 69 -644.9682\n 739500 6909500 70 -643.782\n 740000 6909500 71 -643.3152\n 740500 6909500 72 -643.6769\n 741000 6909500 73 -644.616\n 741500 6909500 74 -645.8882\n 742000 6909500 75 -647.4802\n 742500 6909500 76 -649.4533\n 743000 6909500 77 -651.8127\n 743500 6909500 78 -654.4587\n 744000 6909500 79 -657.2908\n 744500 6909500 80 -660.3059\n 745000 6909500 81 -663.683\n 745500 6909500 82 -667.3882\n 746000 6909500 83 -671.2481\n 746500 6909500 84 -675.1739\n 747000 6909500 85 -679.1468\n 705000 6910000 1 -762.0343\n 705500 6910000 2 -758.935\n 706000 6910000 3 -757.1146\n 706500 6910000 4 -756.4298\n 707000 6910000 5 -756.6089\n 707500 6910000 6 -757.3954\n 708000 6910000 7 -758.5727\n 708500 6910000 8 -759.8979\n 709000 6910000 9 -761.1663\n 709500 6910000 10 -762.203\n 710000 6910000 11 -762.885\n 710500 6910000 12 -763.1464\n 711000 6910000 13 -762.8876\n 711500 6910000 14 -761.8984\n 712000 6910000 15 -759.8763\n 712500 6910000 16 -756.5311\n 713000 6910000 17 -751.502\n 713500 6910000 18 -745.0057\n 714000 6910000 19 -737.8624\n 714500 6910000 20 -730.9423\n 715000 6910000 21 -724.9535\n 715500 6910000 22 -720.5101\n 716000 6910000 23 -717.457\n 716500 6910000 24 -715.4557\n 717000 6910000 25 -714.3893\n 717500 6910000 26 -714.5441\n 718000 6910000 27 -716.3757\n 718500 6910000 28 -719.5621\n 719000 6910000 29 -723.0464\n 719500 6910000 30 -725.9811\n 720000 6910000 31 -728.1182\n 720500 6910000 32 -729.3542\n 721000 6910000 33 -728.872\n 721500 6910000 34 -725.3211\n 722000 6910000 35 -717.3379\n 722500 6910000 36 -703.3497\n 723000 6910000 37 -682.225\n 723500 6910000 38 -655.6451\n 724000 6910000 39 -626.0471\n 724500 6910000 40 -595.9587\n 725000 6910000 41 -567.7846\n 725500 6910000 42 -543.5591\n 726000 6910000 43 -524.4022\n 726500 6910000 44 -511.713\n 727000 6910000 45 -506.3515\n 727500 6910000 46 -510.4521\n 728000 6910000 47 -524.9136\n 728500 6910000 48 -547.4555\n 729000 6910000 49 -574.0405\n 729500 6910000 50 -600.5842\n 730000 6910000 51 -622.3079\n 730500 6910000 52 -636.1683\n 731000 6910000 53 -643.5914\n 731500 6910000 54 -647.0967\n 732000 6910000 55 -648.5961\n 732500 6910000 56 -649.5129\n 733000 6910000 57 -650.6846\n 733500 6910000 58 -651.8622\n 734000 6910000 59 -652.5571\n 734500 6910000 60 -652.7709\n 735000 6910000 61 -652.7131\n 735500 6910000 62 -652.566\n 736000 6910000 63 -652.3135\n 736500 6910000 64 -651.8538\n 737000 6910000 65 -651.1127\n 737500 6910000 66 -649.9446\n 738000 6910000 67 -648.4026\n 738500 6910000 68 -646.7053\n 739000 6910000 69 -645.1262\n 739500 6910000 70 -643.986\n 740000 6910000 71 -643.7283\n 740500 6910000 72 -644.5188\n 741000 6910000 73 -646.0273\n 741500 6910000 74 -647.8828\n 742000 6910000 75 -649.9622\n 742500 6910000 76 -652.2326\n 743000 6910000 77 -654.6826\n 743500 6910000 78 -657.2966\n 744000 6910000 79 -660.0717\n 744500 6910000 80 -663.0594\n 745000 6910000 81 -666.4505\n 745500 6910000 82 -670.2468\n 746000 6910000 83 -674.2485\n 746500 6910000 84 -678.335\n 747000 6910000 85 -682.4724\n 705000 6910500 1 -763.511\n 705500 6910500 2 -760.1708\n 706000 6910500 3 -757.8911\n 706500 6910500 4 -756.6239\n 707000 6910500 5 -756.2161\n 707500 6910500 6 -756.5173\n 708000 6910500 7 -757.4278\n 708500 6910500 8 -758.7429\n 709000 6910500 9 -760.2229\n 709500 6910500 10 -761.6379\n 710000 6910500 11 -762.8141\n 710500 6910500 12 -763.6421\n 711000 6910500 13 -764.0079\n 711500 6910500 14 -763.7006\n 712000 6910500 15 -762.4247\n 712500 6910500 16 -759.9193\n 713000 6910500 17 -755.8488\n 713500 6910500 18 -750.3732\n 714000 6910500 19 -744.232\n 714500 6910500 20 -738.1799\n 715000 6910500 21 -732.736\n 715500 6910500 22 -728.2699\n 716000 6910500 23 -724.7184\n 716500 6910500 24 -721.9935\n 717000 6910500 25 -720.1448\n 717500 6910500 26 -719.4606\n 718000 6910500 27 -720.3496\n 718500 6910500 28 -722.6068\n 719000 6910500 29 -725.354\n 719500 6910500 30 -727.8784\n 720000 6910500 31 -729.9495\n 720500 6910500 32 -731.4401\n 721000 6910500 33 -731.5518\n 721500 6910500 34 -728.8192\n 722000 6910500 35 -721.7795\n 722500 6910500 36 -708.8607\n 723000 6910500 37 -688.9383\n 723500 6910500 38 -663.5347\n 724000 6910500 39 -635.0351\n 724500 6910500 40 -605.958\n 725000 6910500 41 -578.7176\n 725500 6910500 42 -555.4033\n 726000 6910500 43 -537.2144\n 726500 6910500 44 -525.5706\n 727000 6910500 45 -521.1871\n 727500 6910500 46 -525.9619\n 728000 6910500 47 -540.6243\n 728500 6910500 48 -562.9835\n 729000 6910500 49 -589.0175\n 729500 6910500 50 -614.6574\n 730000 6910500 51 -635.427\n 730500 6910500 52 -648.5104\n 731000 6910500 53 -654.9966\n 731500 6910500 54 -657.3367\n 732000 6910500 55 -657.524\n 732500 6910500 56 -657.0244\n 733000 6910500 57 -656.8196\n 733500 6910500 58 -656.8427\n 734000 6910500 59 -656.6307\n 734500 6910500 60 -656.0944\n 735000 6910500 61 -655.3436\n 735500 6910500 62 -654.5092\n 736000 6910500 63 -653.6169\n 736500 6910500 64 -652.6304\n 737000 6910500 65 -651.5124\n 737500 6910500 66 -650.1937\n 738000 6910500 67 -648.6996\n 738500 6910500 68 -647.2093\n 739000 6910500 69 -645.9549\n 739500 6910500 70 -645.2211\n 740000 6910500 71 -645.3234\n 740500 6910500 72 -646.4151\n 741000 6910500 73 -648.2316\n 741500 6910500 74 -650.4221\n 742000 6910500 75 -652.816\n 742500 6910500 76 -655.317\n 743000 6910500 77 -657.8898\n 743500 6910500 78 -660.5629\n 744000 6910500 79 -663.3808\n 744500 6910500 80 -666.4142\n 745000 6910500 81 -669.7874\n 745500 6910500 82 -673.5127\n 746000 6910500 83 -677.44\n 746500 6910500 84 -681.4698\n 747000 6910500 85 -685.591\n 705000 6911000 1 -764.9401\n 705500 6911000 2 -761.3903\n 706000 6911000 3 -758.6859\n 706500 6911000 4 -756.8596\n 707000 6911000 5 -755.8604\n 707500 6911000 6 -755.6443\n 708000 6911000 7 -756.262\n 708500 6911000 8 -757.5634\n 709000 6911000 9 -759.2563\n 709500 6911000 10 -761.0411\n 710000 6911000 11 -762.6635\n 710500 6911000 12 -763.9186\n 711000 6911000 13 -764.6628\n 711500 6911000 14 -764.726\n 712000 6911000 15 -763.888\n 712500 6911000 16 -761.9838\n 713000 6911000 17 -758.8356\n 713500 6911000 18 -754.565\n 714000 6911000 19 -749.7145\n 714500 6911000 20 -744.8437\n 715000 6911000 21 -740.2831\n 715500 6911000 22 -736.1622\n 716000 6911000 23 -732.5356\n 716500 6911000 24 -729.5528\n 717000 6911000 25 -727.3787\n 717500 6911000 26 -726.1912\n 718000 6911000 27 -726.1553\n 718500 6911000 28 -727.1858\n 719000 6911000 29 -728.8169\n 719500 6911000 30 -730.6069\n 720000 6911000 31 -732.3405\n 720500 6911000 32 -733.7645\n 721000 6911000 33 -734.0986\n 721500 6911000 34 -731.8726\n 722000 6911000 35 -725.6024\n 722500 6911000 36 -713.7189\n 723000 6911000 37 -695.1693\n 723500 6911000 38 -671.2977\n 724000 6911000 39 -644.327\n 724500 6911000 40 -616.7167\n 725000 6911000 41 -590.8884\n 725500 6911000 42 -568.9511\n 726000 6911000 43 -552.1425\n 726500 6911000 44 -541.8577\n 727000 6911000 45 -538.5587\n 727500 6911000 46 -543.7042\n 728000 6911000 47 -557.7487\n 728500 6911000 48 -578.5992\n 729000 6911000 49 -602.6457\n 729500 6911000 50 -626.1548\n 730000 6911000 51 -645.1745\n 730500 6911000 52 -657.3547\n 731000 6911000 53 -663.4403\n 731500 6911000 54 -665.4144\n 732000 6911000 55 -665.0798\n 732500 6911000 56 -663.8375\n 733000 6911000 57 -662.6335\n 733500 6911000 58 -661.6113\n 734000 6911000 59 -660.4995\n 734500 6911000 60 -659.2043\n 735000 6911000 61 -657.753\n 735500 6911000 62 -656.2195\n 736000 6911000 63 -654.6624\n 736500 6911000 64 -653.1182\n 737000 6911000 65 -651.6199\n 737500 6911000 66 -650.2157\n 738000 6911000 67 -648.9451\n 738500 6911000 68 -647.9157\n 739000 6911000 69 -647.2621\n 739500 6911000 70 -647.1473\n 740000 6911000 71 -647.7188\n 740500 6911000 72 -649.0386\n 741000 6911000 73 -650.9785\n 741500 6911000 74 -653.3086\n 742000 6911000 75 -655.8721\n 742500 6911000 76 -658.5504\n 743000 6911000 77 -661.2848\n 743500 6911000 78 -664.1031\n 744000 6911000 79 -667.0524\n 744500 6911000 80 -670.1771\n 745000 6911000 81 -673.525\n 745500 6911000 82 -677.0912\n 746000 6911000 83 -680.8044\n 746500 6911000 84 -684.6236\n 747000 6911000 85 -688.5858\n 705000 6911500 1 -766.164\n 705500 6911500 2 -762.459\n 706000 6911500 3 -759.406\n 706500 6911500 4 -757.0965\n 707000 6911500 5 -755.558\n 707500 6911500 6 -754.8408\n 708000 6911500 7 -755.1478\n 708500 6911500 8 -756.4039\n 709000 6911500 9 -758.2617\n 709500 6911500 10 -760.3498\n 710000 6911500 11 -762.327\n 710500 6911500 12 -763.8853\n 711000 6911500 13 -764.8358\n 711500 6911500 14 -765.0754\n 712000 6911500 15 -764.4939\n 712500 6911500 16 -763.0551\n 713000 6911500 17 -760.7393\n 713500 6911500 18 -757.6762\n 714000 6911500 19 -754.1885\n 714500 6911500 20 -750.6082\n 715000 6911500 21 -747.1004\n 715500 6911500 22 -743.5752\n 716000 6911500 23 -740.1871\n 716500 6911500 24 -737.2711\n 717000 6911500 25 -735.0463\n 717500 6911500 26 -733.5416\n 718000 6911500 27 -732.667\n 718500 6911500 28 -732.4035\n 719000 6911500 29 -732.756\n 719500 6911500 30 -733.6232\n 720000 6911500 31 -734.8301\n 720500 6911500 32 -736.0356\n 721000 6911500 33 -736.4656\n 721500 6911500 34 -734.7154\n 722000 6911500 35 -729.2993\n 722500 6911500 36 -718.6234\n 723000 6911500 37 -701.5839\n 723500 6911500 38 -679.4068\n 724000 6911500 39 -654.1572\n 724500 6911500 40 -628.2314\n 725000 6911500 41 -604.0777\n 725500 6911500 42 -583.8341\n 726000 6911500 43 -568.6826\n 726500 6911500 44 -559.8256\n 727000 6911500 45 -557.4141\n 727500 6911500 46 -562.5075\n 728000 6911500 47 -575.3927\n 728500 6911500 48 -594.0952\n 729000 6911500 49 -615.4997\n 729500 6911500 50 -636.3505\n 730000 6911500 51 -653.2719\n 730500 6911500 52 -664.3451\n 731000 6911500 53 -670.0243\n 731500 6911500 54 -671.8234\n 732000 6911500 55 -671.2685\n 732500 6911500 56 -669.6039\n 733000 6911500 57 -667.6996\n 733500 6911500 58 -665.8468\n 734000 6911500 59 -663.9625\n 734500 6911500 60 -661.9766\n 735000 6911500 61 -659.8613\n 735500 6911500 62 -657.655\n 736000 6911500 63 -655.4551\n 736500 6911500 64 -653.3751\n 737000 6911500 65 -651.5292\n 737500 6911500 66 -650.1057\n 738000 6911500 67 -649.1589\n 738500 6911500 68 -648.7146\n 739000 6911500 69 -648.7834\n 739500 6911500 70 -649.3774\n 740000 6911500 71 -650.4482\n 740500 6911500 72 -651.9706\n 741000 6911500 73 -653.9554\n 741500 6911500 74 -656.328\n 742000 6911500 75 -658.982\n 742500 6911500 76 -661.8093\n 743000 6911500 77 -664.7379\n 743500 6911500 78 -667.7653\n 744000 6911500 79 -670.9059\n 744500 6911500 80 -674.1562\n 745000 6911500 81 -677.4816\n 745500 6911500 82 -680.8666\n 746000 6911500 83 -684.3095\n 746500 6911500 84 -687.8401\n 747000 6911500 85 -691.5514\n 705000 6912000 1 -767.0073\n 705500 6912000 2 -763.251\n 706000 6912000 3 -759.9927\n 706500 6912000 4 -757.3564\n 707000 6912000 5 -755.4185\n 707500 6912000 6 -754.288\n 708000 6912000 7 -754.2757\n 708500 6912000 8 -755.3843\n 709000 6912000 9 -757.2462\n 709500 6912000 10 -759.4545\n 710000 6912000 11 -761.6245\n 710500 6912000 12 -763.3729\n 711000 6912000 13 -764.4636\n 711500 6912000 14 -764.8429\n 712000 6912000 15 -764.496\n 712500 6912000 16 -763.4821\n 713000 6912000 17 -761.8965\n 713500 6912000 18 -759.8653\n 714000 6912000 19 -757.5464\n 714500 6912000 20 -755.0922\n 715000 6912000 21 -752.5644\n 715500 6912000 22 -749.7429\n 716000 6912000 23 -746.7952\n 716500 6912000 24 -744.132\n 717000 6912000 25 -741.9539\n 717500 6912000 26 -740.181\n 718000 6912000 27 -738.5677\n 718500 6912000 28 -737.1595\n 719000 6912000 29 -736.3184\n 719500 6912000 30 -736.2437\n 720000 6912000 31 -736.8618\n 720500 6912000 32 -737.834\n 721000 6912000 33 -738.4202\n 721500 6912000 34 -737.334\n 722000 6912000 35 -733.0433\n 722500 6912000 36 -723.863\n 723000 6912000 37 -708.5278\n 723500 6912000 38 -688.1616\n 724000 6912000 39 -664.7603\n 724500 6912000 40 -640.6707\n 725000 6912000 41 -618.3605\n 725500 6912000 42 -599.9957\n 726000 6912000 43 -586.6124\n 726500 6912000 44 -579.0675\n 727000 6912000 45 -577.2291\n 727500 6912000 46 -581.8631\n 728000 6912000 47 -593.188\n 728500 6912000 48 -609.4616\n 729000 6912000 49 -627.9825\n 729500 6912000 50 -645.9914\n 730000 6912000 51 -660.6388\n 730500 6912000 52 -670.3184\n 731000 6912000 53 -675.3406\n 731500 6912000 54 -676.8596\n 732000 6912000 55 -676.1163\n 732500 6912000 56 -674.1699\n 733000 6912000 57 -671.7856\n 733500 6912000 58 -669.3118\n 734000 6912000 59 -666.7997\n 734500 6912000 60 -664.2244\n 735000 6912000 61 -661.5368\n 735500 6912000 62 -658.7482\n 736000 6912000 63 -655.9908\n 736500 6912000 64 -653.4543\n 737000 6912000 65 -651.3325\n 737500 6912000 66 -649.9368\n 738000 6912000 67 -649.3342\n 738500 6912000 68 -649.4724\n 739000 6912000 69 -650.2393\n 739500 6912000 70 -651.5076\n 740000 6912000 71 -653.0657\n 740500 6912000 72 -654.8043\n 741000 6912000 73 -656.8472\n 741500 6912000 74 -659.2656\n 742000 6912000 75 -662.011\n 742500 6912000 76 -664.9953\n 743000 6912000 77 -668.1411\n 743500 6912000 78 -671.4105\n 744000 6912000 79 -674.772\n 744500 6912000 80 -678.1685\n 745000 6912000 81 -681.4981\n 745500 6912000 82 -684.7352\n 746000 6912000 83 -687.9326\n 746500 6912000 84 -691.1828\n 747000 6912000 85 -694.6293\n 705000 6912500 1 -767.3754\n 705500 6912500 2 -763.704\n 706000 6912500 3 -760.4238\n 706500 6912500 4 -757.664\n 707000 6912500 5 -755.5196\n 707500 6912500 6 -754.1161\n 708000 6912500 7 -753.7789\n 708500 6912500 8 -754.5879\n 709000 6912500 9 -756.2205\n 709500 6912500 10 -758.3023\n 710000 6912500 11 -760.4612\n 710500 6912500 12 -762.3041\n 711000 6912500 13 -763.5613\n 711500 6912500 14 -764.1766\n 712000 6912500 15 -764.1602\n 712500 6912500 16 -763.6014\n 713000 6912500 17 -762.6235\n 713500 6912500 18 -761.35\n 714000 6912500 19 -759.8545\n 714500 6912500 20 -758.1922\n 715000 6912500 21 -756.3859\n 715500 6912500 22 -754.247\n 716000 6912500 23 -751.847\n 716500 6912500 24 -749.5266\n 717000 6912500 25 -747.3916\n 717500 6912500 26 -745.317\n 718000 6912500 27 -743.0789\n 718500 6912500 28 -740.7662\n 719000 6912500 29 -738.9281\n 719500 6912500 30 -737.9835\n 720000 6912500 31 -738.0229\n 720500 6912500 32 -738.8322\n 721000 6912500 33 -739.7339\n 721500 6912500 34 -739.5175\n 722000 6912500 35 -736.573\n 722500 6912500 36 -729.1442\n 723000 6912500 37 -715.7603\n 723500 6912500 38 -697.4645\n 724000 6912500 39 -676.2162\n 724500 6912500 40 -654.2826\n 725000 6912500 41 -634.0502\n 725500 6912500 42 -617.636\n 726000 6912500 43 -605.9288\n 726500 6912500 44 -599.4549\n 727000 6912500 45 -597.8979\n 727500 6912500 46 -601.7577\n 728000 6912500 47 -611.2164\n 728500 6912500 48 -624.8322\n 729000 6912500 49 -640.2881\n 729500 6912500 50 -655.3054\n 730000 6912500 51 -667.5381\n 730500 6912500 52 -675.6151\n 731000 6912500 53 -679.7507\n 731500 6912500 54 -680.8371\n 732000 6912500 55 -679.8553\n 732500 6912500 56 -677.6644\n 733000 6912500 57 -674.9158\n 733500 6912500 58 -671.9636\n 734000 6912500 59 -668.9374\n 734500 6912500 60 -665.8608\n 735000 6912500 61 -662.6958\n 735500 6912500 62 -659.4502\n 736000 6912500 63 -656.2878\n 736500 6912500 64 -653.4426\n 737000 6912500 65 -651.1559\n 737500 6912500 66 -649.8171\n 738000 6912500 67 -649.4988\n 738500 6912500 68 -650.092\n 739000 6912500 69 -651.3928\n 739500 6912500 70 -653.1863\n 740000 6912500 71 -655.1697\n 740500 6912500 72 -657.1743\n 741000 6912500 73 -659.3768\n 741500 6912500 74 -661.9305\n 742000 6912500 75 -664.831\n 742500 6912500 76 -668.0118\n 743000 6912500 77 -671.3949\n 743500 6912500 78 -674.9152\n 744000 6912500 79 -678.5017\n 744500 6912500 80 -682.0552\n 745000 6912500 81 -685.4286\n 745500 6912500 82 -688.5902\n 746000 6912500 83 -691.6287\n 746500 6912500 84 -694.6845\n 747000 6912500 85 -697.9394\n 705000 6913000 1 -767.3026\n 705500 6913000 2 -763.8315\n 706000 6913000 3 -760.6863\n 706500 6913000 4 -757.9754\n 707000 6913000 5 -755.778\n 707500 6913000 6 -754.2111\n 708000 6913000 7 -753.5569\n 708500 6913000 8 -753.9457\n 709000 6913000 9 -755.1658\n 709500 6913000 10 -756.9359\n 710000 6913000 11 -758.9396\n 710500 6913000 12 -760.8129\n 711000 6913000 13 -762.2669\n 711500 6913000 14 -763.1981\n 712000 6913000 15 -763.5866\n 712500 6913000 16 -763.5031\n 713000 6913000 17 -763.0341\n 713500 6913000 18 -762.2775\n 714000 6913000 19 -761.2902\n 714500 6913000 20 -760.1168\n 715000 6913000 21 -758.7726\n 715500 6913000 22 -757.2083\n 716000 6913000 23 -755.4054\n 716500 6913000 24 -753.4993\n 717000 6913000 25 -751.4366\n 717500 6913000 26 -749.0576\n 718000 6913000 27 -746.2589\n 718500 6913000 28 -743.2145\n 719000 6913000 29 -740.5228\n 719500 6913000 30 -738.7458\n 720000 6913000 31 -738.2009\n 720500 6913000 32 -738.9097\n 721000 6913000 33 -740.2667\n 721500 6913000 34 -741.0375\n 722000 6913000 35 -739.5185\n 722500 6913000 36 -733.9505\n 723000 6913000 37 -722.8034\n 723500 6913000 38 -706.9954\n 724000 6913000 39 -688.4122\n 724500 6913000 40 -669.1558\n 725000 6913000 41 -651.3428\n 725500 6913000 42 -636.8748\n 726000 6913000 43 -626.5822\n 726500 6913000 44 -620.8739\n 727000 6913000 45 -619.3707\n 727500 6913000 46 -622.2646\n 728000 6913000 47 -629.5979\n 728500 6913000 48 -640.2613\n 729000 6913000 49 -652.3929\n 729500 6913000 50 -664.2071\n 730000 6913000 51 -673.866\n 730500 6913000 52 -680.2272\n 731000 6913000 53 -683.3787\n 731500 6913000 54 -683.9639\n 732000 6913000 55 -682.7136\n 732500 6913000 56 -680.2897\n 733000 6913000 57 -677.2291\n 733500 6913000 58 -673.8701\n 734000 6913000 59 -670.3965\n 734500 6913000 60 -666.8837\n 735000 6913000 61 -663.3367\n 735500 6913000 62 -659.7821\n 736000 6913000 63 -656.3887\n 736500 6913000 64 -653.4034\n 737000 6913000 65 -651.0817\n 737500 6913000 66 -649.821\n 738000 6913000 67 -649.6895\n 738500 6913000 68 -650.5569\n 739000 6913000 69 -652.1736\n 739500 6913000 70 -654.2861\n 740000 6913000 71 -656.6078\n 740500 6913000 72 -658.938\n 741000 6913000 73 -661.4277\n 741500 6913000 74 -664.2323\n 742000 6913000 75 -667.3689\n 742500 6913000 76 -670.7915\n 743000 6913000 77 -674.4293\n 743500 6913000 78 -678.2033\n 744000 6913000 79 -682.0163\n 744500 6913000 80 -685.7371\n 745000 6913000 81 -689.1976\n 745500 6913000 82 -692.3607\n 746000 6913000 83 -695.3354\n 746500 6913000 84 -698.2944\n 747000 6913000 85 -701.4442\n 705000 6913500 1 -766.8882\n 705500 6913500 2 -763.6818\n 706000 6913500 3 -760.7708\n 706500 6913500 4 -758.2213\n 707000 6913500 5 -756.0668\n 707500 6913500 6 -754.3773\n 708000 6913500 7 -753.4003\n 708500 6913500 8 -753.3098\n 709000 6913500 9 -754.027\n 709500 6913500 10 -755.3956\n 710000 6913500 11 -757.191\n 710500 6913500 12 -759.0764\n 711000 6913500 13 -760.7438\n 711500 6913500 14 -762.0251\n 712000 6913500 15 -762.8375\n 712500 6913500 16 -763.1971\n 713000 6913500 17 -763.1354\n 713500 6913500 18 -762.7182\n 714000 6913500 19 -762.0181\n 714500 6913500 20 -761.1106\n 715000 6913500 21 -760.0729\n 715500 6913500 22 -758.9792\n 716000 6913500 23 -757.7759\n 716500 6913500 24 -756.3392\n 717000 6913500 25 -754.4229\n 717500 6913500 26 -751.8215\n 718000 6913500 27 -748.5459\n 718500 6913500 28 -744.8718\n 719000 6913500 29 -741.3967\n 719500 6913500 30 -738.787\n 720000 6913500 31 -737.6283\n 720500 6913500 32 -738.2754\n 721000 6913500 33 -740.184\n 721500 6913500 34 -741.9683\n 722000 6913500 35 -741.8672\n 722500 6913500 36 -738.1133\n 723000 6913500 37 -729.3698\n 723500 6913500 38 -716.402\n 724000 6913500 39 -700.931\n 724500 6913500 40 -684.8007\n 725000 6913500 41 -669.7119\n 725500 6913500 42 -657.1497\n 726000 6913500 43 -647.9584\n 726500 6913500 44 -642.7\n 727000 6913500 45 -641.0552\n 727500 6913500 46 -642.9114\n 728000 6913500 47 -647.9877\n 728500 6913500 48 -655.4964\n 729000 6913500 49 -664.1465\n 729500 6913500 50 -672.6458\n 730000 6913500 51 -679.636\n 730500 6913500 52 -684.2353\n 731000 6913500 53 -686.3739\n 731500 6913500 54 -686.4222\n 732000 6913500 55 -684.8704\n 732500 6913500 56 -682.203\n 733000 6913500 57 -678.8491\n 733500 6913500 58 -675.1305\n 734000 6913500 59 -671.2708\n 734500 6913500 60 -667.3943\n 735000 6913500 61 -663.5574\n 735500 6913500 62 -659.8242\n 736000 6913500 63 -656.3467\n 736500 6913500 64 -653.3492\n 737000 6913500 65 -651.0788\n 737500 6913500 66 -649.9197\n 738000 6913500 67 -649.9125\n 738500 6913500 68 -650.9239\n 739000 6913500 69 -652.6972\n 739500 6913500 70 -654.9747\n 740000 6913500 71 -657.5313\n 740500 6913500 72 -660.1996\n 741000 6913500 73 -663.0495\n 741500 6913500 74 -666.1781\n 742000 6913500 75 -669.603\n 742500 6913500 76 -673.2961\n 743000 6913500 77 -677.1989\n 743500 6913500 78 -681.231\n 744000 6913500 79 -685.2817\n 744500 6913500 80 -689.1952\n 745000 6913500 81 -692.7803\n 745500 6913500 82 -696.0004\n 746000 6913500 83 -698.9769\n 746500 6913500 84 -701.9025\n 747000 6913500 85 -704.9914\n 705000 6914000 1 -766.2286\n 705500 6914000 2 -763.2964\n 706000 6914000 3 -760.6554\n 706500 6914000 4 -758.3174\n 707000 6914000 5 -756.249\n 707500 6914000 6 -754.4527\n 708000 6914000 7 -753.1471\n 708500 6914000 8 -752.5715\n 709000 6914000 9 -752.7766\n 709500 6914000 10 -753.7335\n 710000 6914000 11 -755.3228\n 710500 6914000 12 -757.2339\n 711000 6914000 13 -759.1209\n 711500 6914000 14 -760.75\n 712000 6914000 15 -761.9673\n 712500 6914000 16 -762.7228\n 713000 6914000 17 -762.9871\n 713500 6914000 18 -762.796\n 714000 6914000 19 -762.2465\n 714500 6914000 20 -761.4678\n 715000 6914000 21 -760.6177\n 715500 6914000 22 -759.9196\n 716000 6914000 23 -759.2847\n 716500 6914000 24 -758.357\n 717000 6914000 25 -756.68\n 717500 6914000 26 -753.9834\n 718000 6914000 27 -750.378\n 718500 6914000 28 -746.2232\n 719000 6914000 29 -742.0964\n 719500 6914000 30 -738.7415\n 720000 6914000 31 -737.0046\n 720500 6914000 32 -737.6058\n 721000 6914000 33 -740.0645\n 721500 6914000 34 -742.7909\n 722000 6914000 35 -743.9828\n 722500 6914000 36 -741.91\n 723000 6914000 37 -735.4737\n 723500 6914000 38 -725.3815\n 724000 6914000 39 -713.1089\n 724500 6914000 40 -700.1815\n 725000 6914000 41 -687.8484\n 725500 6914000 42 -677.1116\n 726000 6914000 43 -668.802\n 726500 6914000 44 -663.7579\n 727000 6914000 45 -661.8043\n 727500 6914000 46 -662.5898\n 728000 6914000 47 -665.4906\n 728500 6914000 48 -669.9487\n 729000 6914000 49 -675.2615\n 729500 6914000 50 -680.5862\n 730000 6914000 51 -684.9995\n 730500 6914000 52 -687.8639\n 731000 6914000 53 -688.974\n 731500 6914000 54 -688.4283\n 732000 6914000 55 -686.5041\n 732500 6914000 56 -683.5453\n 733000 6914000 57 -679.897\n 733500 6914000 58 -675.8636\n 734000 6914000 59 -671.694\n 734500 6914000 60 -667.5458\n 735000 6914000 61 -663.5221\n 735500 6914000 62 -659.718\n 736000 6914000 63 -656.2584\n 736500 6914000 64 -653.3239\n 737000 6914000 65 -651.1382\n 737500 6914000 66 -650.0828\n 738000 6914000 67 -650.1824\n 738500 6914000 68 -651.2936\n 739000 6914000 69 -653.1572\n 739500 6914000 70 -655.5348\n 740000 6914000 71 -658.2562\n 740500 6914000 72 -661.2099\n 741000 6914000 73 -664.3982\n 741500 6914000 74 -667.8452\n 742000 6914000 75 -671.5559\n 742500 6914000 76 -675.5137\n 743000 6914000 77 -679.6738\n 743500 6914000 78 -683.962\n 744000 6914000 79 -688.2629\n 744500 6914000 80 -692.4005\n 745000 6914000 81 -696.1443\n 745500 6914000 82 -699.4533\n 746000 6914000 83 -702.4662\n 746500 6914000 84 -705.3896\n 747000 6914000 85 -708.4297\n 705000 6914500 1 -765.3958\n 705500 6914500 2 -762.6962\n 706000 6914500 3 -760.2971\n 706500 6914500 4 -758.1568\n 707000 6914500 5 -756.1746\n 707500 6914500 6 -754.2838\n 708000 6914500 7 -752.6829\n 708500 6914500 8 -751.6805\n 709000 6914500 9 -751.438\n 709500 6914500 10 -752.0364\n 710000 6914500 11 -753.4543\n 710500 6914500 12 -755.4058\n 711000 6914500 13 -757.5026\n 711500 6914500 14 -759.4497\n 712000 6914500 15 -761.0259\n 712500 6914500 16 -762.1195\n 713000 6914500 17 -762.6517\n 713500 6914500 18 -762.6369\n 714000 6914500 19 -762.1962\n 714500 6914500 20 -761.5102\n 715000 6914500 21 -760.8\n 715500 6914500 22 -760.4063\n 716000 6914500 23 -760.236\n 716500 6914500 24 -759.7723\n 717000 6914500 25 -758.4036\n 717500 6914500 26 -755.7958\n 718000 6914500 27 -752.1141\n 718500 6914500 28 -747.7757\n 719000 6914500 29 -743.3285\n 719500 6914500 30 -739.5522\n 720000 6914500 31 -737.4423\n 720500 6914500 32 -737.9835\n 721000 6914500 33 -740.7798\n 721500 6914500 34 -744.1271\n 722000 6914500 35 -746.2482\n 722500 6914500 36 -745.5372\n 723000 6914500 37 -741.1051\n 723500 6914500 38 -733.5947\n 724000 6914500 39 -724.2228\n 724500 6914500 40 -714.1815\n 725000 6914500 41 -704.3229\n 725500 6914500 42 -695.2405\n 726000 6914500 43 -687.7012\n 726500 6914500 44 -682.7404\n 727000 6914500 45 -680.3398\n 727500 6914500 46 -680.0549\n 728000 6914500 47 -681.0623\n 728500 6914500 48 -682.945\n 729000 6914500 49 -685.4223\n 729500 6914500 50 -688.0036\n 730000 6914500 51 -690.124\n 730500 6914500 52 -691.3731\n 731000 6914500 53 -691.441\n 731500 6914500 54 -690.2061\n 732000 6914500 55 -687.7892\n 732500 6914500 56 -684.452\n 733000 6914500 57 -680.4933\n 733500 6914500 58 -676.2\n 734000 6914500 59 -671.8171\n 734500 6914500 60 -667.5076\n 735000 6914500 61 -663.4054\n 735500 6914500 62 -659.6233\n 736000 6914500 63 -656.2522\n 736500 6914500 64 -653.4248\n 737000 6914500 65 -651.3411\n 737500 6914500 66 -650.3902\n 738000 6914500 67 -650.6008\n 738500 6914500 68 -651.8129\n 739000 6914500 69 -653.7589\n 739500 6914500 70 -656.2159\n 740000 6914500 71 -659.0538\n 740500 6914500 72 -662.2025\n 741000 6914500 73 -665.6359\n 741500 6914500 74 -669.3259\n 742000 6914500 75 -673.2672\n 742500 6914500 76 -677.447\n 743000 6914500 77 -681.8298\n 743500 6914500 78 -686.3514\n 744000 6914500 79 -690.8957\n 744500 6914500 80 -695.2689\n 745000 6914500 81 -699.1944\n 745500 6914500 82 -702.6185\n 746000 6914500 83 -705.6987\n 746500 6914500 84 -708.6492\n 747000 6914500 85 -711.6552\n 705000 6915000 1 -764.4474\n 705500 6915000 2 -761.8935\n 706000 6915000 3 -759.6501\n 706500 6915000 4 -757.6332\n 707000 6915000 5 -755.7032\n 707500 6915000 6 -753.7456\n 708000 6915000 7 -751.9369\n 708500 6915000 8 -750.6314\n 709000 6915000 9 -750.0691\n 709500 6915000 10 -750.4072\n 710000 6915000 11 -751.6992\n 710500 6915000 12 -753.6946\n 711000 6915000 13 -755.9747\n 711500 6915000 14 -758.1907\n 712000 6915000 15 -760.062\n 712500 6915000 16 -761.4296\n 713000 6915000 17 -762.1905\n 713500 6915000 18 -762.3513\n 714000 6915000 19 -762.0507\n 714500 6915000 20 -761.5047\n 715000 6915000 21 -760.9535\n 715500 6915000 22 -760.7649\n 716000 6915000 23 -760.8739\n 716500 6915000 24 -760.7217\n 717000 6915000 25 -759.675\n 717500 6915000 26 -757.3726\n 718000 6915000 27 -753.9897\n 718500 6915000 28 -749.9467\n 719000 6915000 29 -745.7452\n 719500 6915000 30 -742.1363\n 720000 6915000 31 -740.0521\n 720500 6915000 32 -740.4745\n 721000 6915000 33 -743.1487\n 721500 6915000 34 -746.4891\n 722000 6915000 35 -748.8833\n 722500 6915000 36 -749.0104\n 723000 6915000 37 -746.11\n 723500 6915000 38 -740.7027\n 724000 6915000 39 -733.7101\n 724500 6915000 40 -725.9955\n 725000 6915000 41 -718.1369\n 725500 6915000 42 -710.4811\n 726000 6915000 43 -703.6594\n 726500 6915000 44 -698.7176\n 727000 6915000 45 -695.7745\n 727500 6915000 46 -694.468\n 728000 6915000 47 -694.0238\n 728500 6915000 48 -694.0651\n 729000 6915000 49 -694.4894\n 729500 6915000 50 -695.0046\n 730000 6915000 51 -695.2759\n 730500 6915000 52 -695.0814\n 731000 6915000 53 -694.0699\n 731500 6915000 54 -691.997\n 732000 6915000 55 -688.9129\n 732500 6915000 56 -685.071\n 733000 6915000 57 -680.7733\n 733500 6915000 58 -676.2846\n 734000 6915000 59 -671.8056\n 734500 6915000 60 -667.4667\n 735000 6915000 61 -663.3995\n 735500 6915000 62 -659.7177\n 736000 6915000 63 -656.4855\n 736500 6915000 64 -653.8009\n 737000 6915000 65 -651.8461\n 737500 6915000 66 -651.01\n 738000 6915000 67 -651.3327\n 738500 6915000 68 -652.6402\n 739000 6915000 69 -654.658\n 739500 6915000 70 -657.1779\n 740000 6915000 71 -660.0853\n 740500 6915000 72 -663.3286\n 741000 6915000 73 -666.8846\n 741500 6915000 74 -670.7028\n 742000 6915000 75 -674.7746\n 742500 6915000 76 -679.0954\n 743000 6915000 77 -683.6384\n 743500 6915000 78 -688.3409\n 744000 6915000 79 -693.0806\n 744500 6915000 80 -697.6583\n 745000 6915000 81 -701.7731\n 745500 6915000 82 -705.3535\n 746000 6915000 83 -708.5604\n 746500 6915000 84 -711.5979\n 747000 6915000 85 -714.6157\n 705000 6915500 1 -763.4304\n 705500 6915500 2 -760.9142\n 706000 6915500 3 -758.716\n 706500 6915500 4 -756.7245\n 707000 6915500 5 -754.7936\n 707500 6915500 6 -752.8051\n 708000 6915500 7 -750.9023\n 708500 6915500 8 -749.4418\n 709000 6915500 9 -748.7032\n 709500 6915500 10 -748.8961\n 710000 6915500 11 -750.116\n 710500 6915500 12 -752.1619\n 711000 6915500 13 -754.6013\n 711500 6915500 14 -757.041\n 712000 6915500 15 -759.1469\n 712500 6915500 16 -760.7245\n 713000 6915500 17 -761.675\n 713500 6915500 18 -762.0186\n 714000 6915500 19 -761.9032\n 714500 6915500 20 -761.5485\n 715000 6915500 21 -761.2028\n 715500 6915500 22 -761.1514\n 716000 6915500 23 -761.3652\n 716500 6915500 24 -761.3661\n 717000 6915500 25 -760.6139\n 717500 6915500 26 -758.8114\n 718000 6915500 27 -756.0898\n 718500 6915500 28 -752.822\n 719000 6915500 29 -749.4627\n 719500 6915500 30 -746.6338\n 720000 6915500 31 -745.0485\n 720500 6915500 32 -745.3531\n 721000 6915500 33 -747.422\n 721500 6915500 34 -750.0732\n 722000 6915500 35 -752.0592\n 722500 6915500 36 -752.3781\n 723000 6915500 37 -750.4644\n 723500 6915500 38 -746.6249\n 724000 6915500 39 -741.4399\n 724500 6915500 40 -735.4552\n 725000 6915500 41 -729.0579\n 725500 6915500 42 -722.5293\n 726000 6915500 43 -716.3579\n 726500 6915500 44 -711.4149\n 727000 6915500 45 -707.926\n 727500 6915500 46 -705.7103\n 728000 6915500 47 -704.2828\n 728500 6915500 48 -703.2893\n 729000 6915500 49 -702.5687\n 729500 6915500 50 -701.8543\n 730000 6915500 51 -700.8359\n 730500 6915500 52 -699.3755\n 731000 6915500 53 -697.184\n 731500 6915500 54 -694.0503\n 732000 6915500 55 -690.0715\n 732500 6915500 56 -685.5661\n 733000 6915500 57 -680.8856\n 733500 6915500 58 -676.2674\n 734000 6915500 59 -671.8231\n 734500 6915500 60 -667.6051\n 735000 6915500 61 -663.6888\n 735500 6915500 62 -660.1682\n 736000 6915500 63 -657.1076\n 736500 6915500 64 -654.6063\n 737000 6915500 65 -652.8354\n 737500 6915500 66 -652.1404\n 738000 6915500 67 -652.5571\n 738500 6915500 68 -653.9185\n 739000 6915500 69 -655.9625\n 739500 6915500 70 -658.496\n 740000 6915500 71 -661.4128\n 740500 6915500 72 -664.6605\n 741000 6915500 73 -668.2158\n 741500 6915500 74 -672.038\n 742000 6915500 75 -676.1218\n 742500 6915500 76 -680.4766\n 743000 6915500 77 -685.0812\n 743500 6915500 78 -689.8676\n 744000 6915500 79 -694.7078\n 744500 6915500 80 -699.4131\n 745000 6915500 81 -703.7073\n 745500 6915500 82 -707.5078\n 746000 6915500 83 -710.9409\n 746500 6915500 84 -714.1661\n 747000 6915500 85 -717.2811\n 705000 6916000 1 -762.3834\n 705500 6916000 2 -759.8188\n 706000 6916000 3 -757.5883\n 706500 6916000 4 -755.5643\n 707000 6916000 5 -753.6032\n 707500 6916000 6 -751.6064\n 708000 6916000 7 -749.6884\n 708500 6916000 8 -748.1703\n 709000 6916000 9 -747.3455\n 709500 6916000 10 -747.4718\n 710000 6916000 11 -748.682\n 710500 6916000 12 -750.8118\n 711000 6916000 13 -753.4164\n 711500 6916000 14 -756.0618\n 712000 6916000 15 -758.3638\n 712500 6916000 16 -760.1012\n 713000 6916000 17 -761.1966\n 713500 6916000 18 -761.7023\n 714000 6916000 19 -761.7722\n 714500 6916000 20 -761.6141\n 715000 6916000 21 -761.4498\n 715500 6916000 22 -761.507\n 716000 6916000 23 -761.7597\n 716500 6916000 24 -761.8668\n 717000 6916000 25 -761.4285\n 717500 6916000 26 -760.2368\n 718000 6916000 27 -758.3922\n 718500 6916000 28 -756.1867\n 719000 6916000 29 -753.9926\n 719500 6916000 30 -752.2584\n 720000 6916000 31 -751.3932\n 720500 6916000 32 -751.6824\n 721000 6916000 33 -752.9733\n 721500 6916000 34 -754.5747\n 722000 6916000 35 -755.72\n 722500 6916000 36 -755.7824\n 723000 6916000 37 -754.358\n 723500 6916000 38 -751.5949\n 724000 6916000 39 -747.7178\n 724500 6916000 40 -742.9875\n 725000 6916000 41 -737.6421\n 725500 6916000 42 -731.922\n 726000 6916000 43 -726.231\n 726500 6916000 44 -721.2385\n 727000 6916000 45 -717.2537\n 727500 6916000 46 -714.3389\n 728000 6916000 47 -712.3225\n 728500 6916000 48 -710.9333\n 729000 6916000 49 -709.8517\n 729500 6916000 50 -708.6879\n 730000 6916000 51 -706.9613\n 730500 6916000 52 -704.4048\n 731000 6916000 53 -700.913\n 731500 6916000 54 -696.4911\n 732000 6916000 55 -691.402\n 732500 6916000 56 -686.088\n 733000 6916000 57 -680.9793\n 733500 6916000 58 -676.2761\n 734000 6916000 59 -671.9666\n 734500 6916000 60 -667.9867\n 735000 6916000 61 -664.3184\n 735500 6916000 62 -660.996\n 736000 6916000 63 -658.1201\n 736500 6916000 64 -655.8348\n 737000 6916000 65 -654.3078\n 737500 6916000 66 -653.8062\n 738000 6916000 67 -654.3087\n 738500 6916000 68 -655.681\n 739000 6916000 69 -657.7015\n 739500 6916000 70 -660.1976\n 740000 6916000 71 -663.0634\n 740500 6916000 72 -666.2384\n 741000 6916000 73 -669.693\n 741500 6916000 74 -673.4042\n 742000 6916000 75 -677.3807\n 742500 6916000 76 -681.6616\n 743000 6916000 77 -686.2385\n 743500 6916000 78 -691.0208\n 744000 6916000 79 -695.8612\n 744500 6916000 80 -700.5822\n 745000 6916000 81 -705.0101\n 745500 6916000 82 -709.0694\n 746000 6916000 83 -712.808\n 746500 6916000 84 -716.309\n 747000 6916000 85 -719.6113\n 705000 6916500 1 -761.3406\n 705500 6916500 2 -758.684\n 706000 6916500 3 -756.3962\n 706500 6916500 4 -754.3362\n 707000 6916500 5 -752.3596\n 707500 6916500 6 -750.3748\n 708000 6916500 7 -748.4725\n 708500 6916500 8 -746.9316\n 709000 6916500 9 -746.0488\n 709500 6916500 10 -746.1362\n 710000 6916500 11 -747.3838\n 710500 6916500 12 -749.6337\n 711000 6916500 13 -752.4202\n 711500 6916500 14 -755.2661\n 712000 6916500 15 -757.7396\n 712500 6916500 16 -759.5976\n 713000 6916500 17 -760.7947\n 713500 6916500 18 -761.4243\n 714000 6916500 19 -761.6475\n 714500 6916500 20 -761.6529\n 715000 6916500 21 -761.6322\n 715500 6916500 22 -761.7683\n 716000 6916500 23 -762.057\n 716500 6916500 24 -762.2846\n 717000 6916500 25 -762.1914\n 717500 6916500 26 -761.6494\n 718000 6916500 27 -760.7346\n 718500 6916500 28 -759.6638\n 719000 6916500 29 -758.7161\n 719500 6916500 30 -758.1415\n 720000 6916500 31 -758.0419\n 720500 6916500 32 -758.3428\n 721000 6916500 33 -758.8949\n 721500 6916500 34 -759.4349\n 722000 6916500 35 -759.6384\n 722500 6916500 36 -759.2199\n 723000 6916500 37 -758.0204\n 723500 6916500 38 -756.0015\n 724000 6916500 39 -753.0865\n 724500 6916500 40 -749.2867\n 725000 6916500 41 -744.7112\n 725500 6916500 42 -739.567\n 726000 6916500 43 -734.1767\n 726500 6916500 44 -729.0562\n 727000 6916500 45 -724.6128\n 727500 6916500 46 -721.1487\n 728000 6916500 47 -718.8033\n 728500 6916500 48 -717.3657\n 729000 6916500 49 -716.3616\n 729500 6916500 50 -715.2081\n 730000 6916500 51 -713.1735\n 730500 6916500 52 -709.7229\n 731000 6916500 53 -704.9489\n 731500 6916500 54 -699.1876\n 732000 6916500 55 -692.9263\n 732500 6916500 56 -686.754\n 733000 6916500 57 -681.1906\n 733500 6916500 58 -676.3953\n 734000 6916500 59 -672.2258\n 734500 6916500 60 -668.4969\n 735000 6916500 61 -665.0978\n 735500 6916500 62 -661.9899\n 736000 6916500 63 -659.3142\n 736500 6916500 64 -657.2758\n 737000 6916500 65 -656.0358\n 737500 6916500 66 -655.7759\n 738000 6916500 67 -656.3993\n 738500 6916500 68 -657.7949\n 739000 6916500 69 -659.7915\n 739500 6916500 70 -662.2374\n 740000 6916500 71 -665.0251\n 740500 6916500 72 -668.0833\n 741000 6916500 73 -671.3709\n 741500 6916500 74 -674.8832\n 742000 6916500 75 -678.6564\n 742500 6916500 76 -682.7781\n 743000 6916500 77 -687.2645\n 743500 6916500 78 -691.9937\n 744000 6916500 79 -696.7853\n 744500 6916500 80 -701.4775\n 745000 6916500 81 -705.9705\n 745500 6916500 82 -710.2316\n 746000 6916500 83 -714.2546\n 746500 6916500 84 -718.0444\n 747000 6916500 85 -721.5731\n 705000 6917000 1 -760.3417\n 705500 6917000 2 -757.5746\n 706000 6917000 3 -755.2325\n 706500 6917000 4 -753.1582\n 707000 6917000 5 -751.1998\n 707500 6917000 6 -749.2596\n 708000 6917000 7 -747.4017\n 708500 6917000 8 -745.8665\n 709000 6917000 9 -744.9501\n 709500 6917000 10 -745.0133\n 710000 6917000 11 -746.311\n 710500 6917000 12 -748.6805\n 711000 6917000 13 -751.6283\n 711500 6917000 14 -754.6377\n 712000 6917000 15 -757.2437\n 712500 6917000 16 -759.1849\n 713000 6917000 17 -760.4487\n 713500 6917000 18 -761.1688\n 714000 6917000 19 -761.5113\n 714500 6917000 20 -761.6435\n 715000 6917000 21 -761.728\n 715500 6917000 22 -761.9241\n 716000 6917000 23 -762.2581\n 716500 6917000 24 -762.6198\n 717000 6917000 25 -762.8689\n 717500 6917000 26 -762.9432\n 718000 6917000 27 -762.9016\n 718500 6917000 28 -762.8909\n 719000 6917000 29 -763.095\n 719500 6917000 30 -763.579\n 720000 6917000 31 -764.1849\n 720500 6917000 32 -764.5609\n 721000 6917000 33 -764.5453\n 721500 6917000 34 -764.2132\n 722000 6917000 35 -763.6134\n 722500 6917000 36 -762.7341\n 723000 6917000 37 -761.634\n 723500 6917000 38 -760.1476\n 724000 6917000 39 -757.9543\n 724500 6917000 40 -754.8583\n 725000 6917000 41 -750.8506\n 725500 6917000 42 -746.0857\n 726000 6917000 43 -740.8328\n 726500 6917000 44 -735.5142\n 727000 6917000 45 -730.6425\n 727500 6917000 46 -726.7205\n 728000 6917000 47 -724.1342\n 728500 6917000 48 -722.7344\n 729000 6917000 49 -721.9149\n 729500 6917000 50 -720.9022\n 730000 6917000 51 -718.7313\n 730500 6917000 52 -714.6279\n 731000 6917000 53 -708.8109\n 731500 6917000 54 -701.9241\n 732000 6917000 55 -694.6555\n 732500 6917000 56 -687.7098\n 733000 6917000 57 -681.6818\n 733500 6917000 58 -676.7097\n 734000 6917000 59 -672.5624\n 734500 6917000 60 -668.9707\n 735000 6917000 61 -665.7743\n 735500 6917000 62 -662.8807\n 736000 6917000 63 -660.4478\n 736500 6917000 64 -658.7103\n 737000 6917000 65 -657.8058\n 737500 6917000 66 -657.8409\n 738000 6917000 67 -658.6522\n 738500 6917000 68 -660.1379\n 739000 6917000 69 -662.1631\n 739500 6917000 70 -664.5907\n 740000 6917000 71 -667.308\n 740500 6917000 72 -670.2342\n 741000 6917000 73 -673.3193\n 741500 6917000 74 -676.5728\n 742000 6917000 75 -680.0735\n 742500 6917000 76 -683.972\n 743000 6917000 77 -688.3214\n 743500 6917000 78 -692.964\n 744000 6917000 79 -697.6783\n 744500 6917000 80 -702.3043\n 745000 6917000 81 -706.7908\n 745500 6917000 82 -711.1427\n 746000 6917000 83 -715.3328\n 746500 6917000 84 -719.326\n 747000 6917000 85 -723.0515\n 705000 6917500 1 -759.4329\n 705500 6917500 2 -756.5488\n 706000 6917500 3 -754.1638\n 706500 6917500 4 -752.1021\n 707000 6917500 5 -750.2024\n 707500 6917500 6 -748.3546\n 708000 6917500 7 -746.5981\n 708500 6917500 8 -745.1353\n 709000 6917500 9 -744.2516\n 709500 6917500 10 -744.3203\n 710000 6917500 11 -745.6392\n 710500 6917500 12 -748.048\n 711000 6917500 13 -751.0524\n 711500 6917500 14 -754.1204\n 712000 6917500 15 -756.7867\n 712500 6917500 16 -758.7773\n 713000 6917500 17 -760.0973\n 713500 6917500 18 -760.9017\n 714000 6917500 19 -761.3513\n 714500 6917500 20 -761.5903\n 715000 6917500 21 -761.7555\n 715500 6917500 22 -761.9932\n 716000 6917500 23 -762.3663\n 716500 6917500 24 -762.846\n 717000 6917500 25 -763.3885\n 717500 6917500 26 -763.9871\n 718000 6917500 27 -764.6802\n 718500 6917500 28 -765.5463\n 719000 6917500 29 -766.6709\n 719500 6917500 30 -767.9708\n 720000 6917500 31 -769.1317\n 720500 6917500 32 -769.6548\n 721000 6917500 33 -769.3657\n 721500 6917500 34 -768.5447\n 722000 6917500 35 -767.4921\n 722500 6917500 36 -766.3721\n 723000 6917500 37 -765.3773\n 723500 6917500 38 -764.3011\n 724000 6917500 39 -762.6591\n 724500 6917500 40 -760.1083\n 725000 6917500 41 -756.5245\n 725500 6917500 42 -751.9882\n 726000 6917500 43 -746.7347\n 726500 6917500 44 -741.1638\n 727000 6917500 45 -735.8941\n 727500 6917500 46 -731.5501\n 728000 6917500 47 -728.6653\n 728500 6917500 48 -727.1459\n 729000 6917500 49 -726.3224\n 729500 6917500 50 -725.2857\n 730000 6917500 51 -722.9751\n 730500 6917500 52 -718.483\n 731000 6917500 53 -712.0507\n 731500 6917500 54 -704.4865\n 732000 6917500 55 -696.5831\n 732500 6917500 56 -689.0783\n 733000 6917500 57 -682.5937\n 733500 6917500 58 -677.2904\n 734000 6917500 59 -672.9425\n 734500 6917500 60 -669.273\n 735000 6917500 61 -666.1428\n 735500 6917500 62 -663.4567\n 736000 6917500 63 -661.3386\n 736500 6917500 64 -659.9828\n 737000 6917500 65 -659.4745\n 737500 6917500 66 -659.8635\n 738000 6917500 67 -660.9479\n 738500 6917500 68 -662.6195\n 739000 6917500 69 -664.7581\n 739500 6917500 70 -667.2305\n 740000 6917500 71 -669.9155\n 740500 6917500 72 -672.7233\n 741000 6917500 73 -675.6053\n 741500 6917500 74 -678.5891\n 742000 6917500 75 -681.7974\n 742500 6917500 76 -685.434\n 743000 6917500 77 -689.5942\n 743500 6917500 78 -694.095\n 744000 6917500 79 -698.6813\n 744500 6917500 80 -703.1885\n 745000 6917500 81 -707.5671\n 745500 6917500 82 -711.8434\n 746000 6917500 83 -716.011\n 746500 6917500 84 -720.0399\n 747000 6917500 85 -723.8467\n 705000 6918000 1 -758.6549\n 705500 6918000 2 -755.6515\n 706000 6918000 3 -753.231\n 706500 6918000 4 -751.2003\n 707000 6918000 5 -749.3943\n 707500 6918000 6 -747.6974\n 708000 6918000 7 -746.1266\n 708500 6918000 8 -744.8438\n 709000 6918000 9 -744.1031\n 709500 6918000 10 -744.2312\n 710000 6918000 11 -745.5084\n 710500 6918000 12 -747.8062\n 711000 6918000 13 -750.6842\n 711500 6918000 14 -753.6525\n 712000 6918000 15 -756.278\n 712500 6918000 16 -758.2918\n 713000 6918000 17 -759.6838\n 713500 6918000 18 -760.5969\n 714000 6918000 19 -761.1682\n 714500 6918000 20 -761.5152\n 715000 6918000 21 -761.7519\n 715500 6918000 22 -762.0115\n 716000 6918000 23 -762.3949\n 716500 6918000 24 -762.9408\n 717000 6918000 25 -763.6887\n 717500 6918000 26 -764.6814\n 718000 6918000 27 -765.9335\n 718500 6918000 28 -767.4495\n 719000 6918000 29 -769.2157\n 719500 6918000 30 -771.045\n 720000 6918000 31 -772.5667\n 720500 6918000 32 -773.2928\n 721000 6918000 33 -773.0508\n 721500 6918000 34 -772.1793\n 722000 6918000 35 -771.0844\n 722500 6918000 36 -770.0382\n 723000 6918000 37 -769.2431\n 723500 6918000 38 -768.5252\n 724000 6918000 39 -767.3386\n 724500 6918000 40 -765.2457\n 725000 6918000 41 -762.0023\n 725500 6918000 42 -757.5756\n 726000 6918000 43 -752.1813\n 726500 6918000 44 -746.2744\n 727000 6918000 45 -740.597\n 727500 6918000 46 -735.8367\n 728000 6918000 47 -732.538\n 728500 6918000 48 -730.6644\n 729000 6918000 49 -729.5506\n 729500 6918000 50 -728.2661\n 730000 6918000 51 -725.7021\n 730500 6918000 52 -721.0546\n 731000 6918000 53 -714.4568\n 731500 6918000 54 -706.705\n 732000 6918000 55 -698.5851\n 732500 6918000 56 -690.7929\n 733000 6918000 57 -683.8997\n 733500 6918000 58 -678.1281\n 734000 6918000 59 -673.362\n 734500 6918000 60 -669.4028\n 735000 6918000 61 -666.1897\n 735500 6918000 62 -663.6957\n 736000 6918000 63 -661.9693\n 736500 6918000 64 -661.0806\n 737000 6918000 65 -661.0303\n 737500 6918000 66 -661.8239\n 738000 6918000 67 -663.2548\n 738500 6918000 68 -665.201\n 739000 6918000 69 -667.5356\n 739500 6918000 70 -670.1207\n 740000 6918000 71 -672.8239\n 740500 6918000 72 -675.5491\n 741000 6918000 73 -678.2655\n 741500 6918000 74 -681.0281\n 742000 6918000 75 -683.9874\n 742500 6918000 76 -687.3501\n 743000 6918000 77 -691.2403\n 743500 6918000 78 -695.4858\n 744000 6918000 79 -699.8343\n 744500 6918000 80 -704.1213\n 745000 6918000 81 -708.2643\n 745500 6918000 82 -712.2768\n 746000 6918000 83 -716.1929\n 746500 6918000 84 -720.0264\n 747000 6918000 85 -723.7334\n 705000 6918500 1 -758.0273\n 705500 6918500 2 -754.9111\n 706000 6918500 3 -752.461\n 706500 6918500 4 -750.4704\n 707000 6918500 5 -748.7775\n 707500 6918500 6 -747.2745\n 708000 6918500 7 -745.9585\n 708500 6918500 8 -744.9437\n 709000 6918500 9 -744.4356\n 709500 6918500 10 -744.6779\n 710000 6918500 11 -745.8566\n 710500 6918500 12 -747.8962\n 711000 6918500 13 -750.4749\n 711500 6918500 14 -753.2064\n 712000 6918500 15 -755.7019\n 712500 6918500 16 -757.7172\n 713000 6918500 17 -759.2051\n 713500 6918500 18 -760.2614\n 714000 6918500 19 -760.982\n 714500 6918500 20 -761.4506\n 715000 6918500 21 -761.757\n 715500 6918500 22 -762.0176\n 716000 6918500 23 -762.3676\n 716500 6918500 24 -762.9064\n 717000 6918500 25 -763.7518\n 717500 6918500 26 -765.0002\n 718000 6918500 27 -766.6391\n 718500 6918500 28 -768.596\n 719000 6918500 29 -770.7703\n 719500 6918500 30 -772.9163\n 720000 6918500 31 -774.6698\n 720500 6918500 32 -775.6207\n 721000 6918500 33 -775.6349\n 721500 6918500 34 -775.0201\n 722000 6918500 35 -774.1838\n 722500 6918500 36 -773.4367\n 723000 6918500 37 -772.9783\n 723500 6918500 38 -772.6409\n 724000 6918500 39 -771.8971\n 724500 6918500 40 -770.2448\n 725000 6918500 41 -767.3098\n 725500 6918500 42 -762.9073\n 726000 6918500 43 -757.2277\n 726500 6918500 44 -750.8401\n 727000 6918500 45 -744.6404\n 727500 6918500 46 -739.3745\n 728000 6918500 47 -735.589\n 728500 6918500 48 -733.2523\n 729000 6918500 49 -731.7234\n 729500 6918500 50 -730.1004\n 730000 6918500 51 -727.3072\n 730500 6918500 52 -722.6143\n 731000 6918500 53 -716.1295\n 731500 6918500 54 -708.5095\n 732000 6918500 55 -700.4584\n 732500 6918500 56 -692.5886\n 733000 6918500 57 -685.3865\n 733500 6918500 58 -679.1168\n 734000 6918500 59 -673.8318\n 734500 6918500 60 -669.4694\n 735000 6918500 61 -666.0937\n 735500 6918500 62 -663.7748\n 736000 6918500 63 -662.4892\n 736500 6918500 64 -662.1278\n 737000 6918500 65 -662.5767\n 737500 6918500 66 -663.8043\n 738000 6918500 67 -665.6188\n 738500 6918500 68 -667.888\n 739000 6918500 69 -670.467\n 739500 6918500 70 -673.2087\n 740000 6918500 71 -675.9681\n 740500 6918500 72 -678.6459\n 741000 6918500 73 -681.2439\n 741500 6918500 74 -683.8591\n 742000 6918500 75 -686.6411\n 742500 6918500 76 -689.7419\n 743000 6918500 77 -693.2641\n 743500 6918500 78 -697.1029\n 744000 6918500 79 -701.0668\n 744500 6918500 80 -705.007\n 745000 6918500 81 -708.7828\n 745500 6918500 82 -712.3676\n 746000 6918500 83 -715.8378\n 746500 6918500 84 -719.2758\n 747000 6918500 85 -722.7008\n 705000 6919000 1 -757.5653\n 705500 6919000 2 -754.3525\n 706000 6919000 3 -751.8751\n 706500 6919000 4 -749.9205\n 707000 6919000 5 -748.3391\n 707500 6919000 6 -747.0353\n 708000 6919000 7 -745.9901\n 708500 6919000 8 -745.2602\n 709000 6919000 9 -744.9988\n 709500 6919000 10 -745.3562\n 710000 6919000 11 -746.4084\n 710500 6919000 12 -748.1171\n 711000 6919000 13 -750.3162\n 711500 6919000 14 -752.7524\n 712000 6919000 15 -755.0933\n 712500 6919000 16 -757.101\n 713000 6919000 17 -758.7018\n 713500 6919000 18 -759.9304\n 714000 6919000 19 -760.8302\n 714500 6919000 20 -761.4413\n 715000 6919000 21 -761.8212\n 715500 6919000 22 -762.0638\n 716000 6919000 23 -762.334\n 716500 6919000 24 -762.787\n 717000 6919000 25 -763.621\n 717500 6919000 26 -764.997\n 718000 6919000 27 -766.8731\n 718500 6919000 28 -769.0989\n 719000 6919000 29 -771.5101\n 719500 6919000 30 -773.8447\n 720000 6919000 31 -775.7756\n 720500 6919000 32 -776.9659\n 721000 6919000 33 -777.3168\n 721500 6919000 34 -777.0882\n 722000 6919000 35 -776.6489\n 722500 6919000 36 -776.3101\n 723000 6919000 37 -776.2729\n 723500 6919000 38 -776.3761\n 724000 6919000 39 -776.1157\n 724500 6919000 40 -774.9307\n 725000 6919000 41 -772.3029\n 725500 6919000 42 -767.8574\n 726000 6919000 43 -761.7607\n 726500 6919000 44 -754.7397\n 727000 6919000 45 -747.8734\n 727500 6919000 46 -741.9877\n 728000 6919000 47 -737.6563\n 728500 6919000 48 -734.8535\n 729000 6919000 49 -732.9479\n 729500 6919000 50 -731.0699\n 730000 6919000 51 -728.1496\n 730500 6919000 52 -723.5182\n 731000 6919000 53 -717.2498\n 731500 6919000 54 -709.8943\n 732000 6919000 55 -702.0577\n 732500 6919000 56 -694.2456\n 733000 6919000 57 -686.8478\n 733500 6919000 58 -680.1595\n 734000 6919000 59 -674.3875\n 734500 6919000 60 -669.6246\n 735000 6919000 61 -666.087\n 735500 6919000 62 -663.9565\n 736000 6919000 63 -663.1381\n 736500 6919000 64 -663.3279\n 737000 6919000 65 -664.2819\n 737500 6919000 66 -665.9341\n 738000 6919000 67 -668.1211\n 738500 6919000 68 -670.7032\n 739000 6919000 69 -673.5192\n 739500 6919000 70 -676.4137\n 740000 6919000 71 -679.23\n 740500 6919000 72 -681.8669\n 741000 6919000 73 -684.3668\n 741500 6919000 74 -686.8733\n 742000 6919000 75 -689.514\n 742500 6919000 76 -692.3554\n 743000 6919000 77 -695.4346\n 743500 6919000 78 -698.7391\n 744000 6919000 79 -702.1896\n 744500 6919000 80 -705.6733\n 745000 6919000 81 -708.9894\n 745500 6919000 82 -712.059\n 746000 6919000 83 -714.999\n 746500 6919000 84 -717.9634\n 747000 6919000 85 -721.0521\n 705000 6919500 1 -757.3887\n 705500 6919500 2 -754.0664\n 706000 6919500 3 -751.524\n 706500 6919500 4 -749.5558\n 707000 6919500 5 -748.0339\n 707500 6919500 6 -746.8772\n 708000 6919500 7 -746.0426\n 708500 6919500 8 -745.5316\n 709000 6919500 9 -745.4554\n 709500 6919500 10 -745.884\n 710000 6919500 11 -746.7918\n 710500 6919500 12 -748.1817\n 711000 6919500 13 -750.0291\n 711500 6919500 14 -752.2086\n 712000 6919500 15 -754.4342\n 712500 6919500 16 -756.4663\n 713000 6919500 17 -758.2073\n 713500 6919500 18 -759.6448\n 714000 6919500 19 -760.7678\n 714500 6919500 20 -761.5607\n 715000 6919500 21 -762.0358\n 715500 6919500 22 -762.2557\n 716000 6919500 23 -762.4144\n 716500 6919500 24 -762.7167\n 717000 6919500 25 -763.4391\n 717500 6919500 26 -764.8159\n 718000 6919500 27 -766.778\n 718500 6919500 28 -769.1031\n 719000 6919500 29 -771.5953\n 719500 6919500 30 -774.0141\n 720000 6919500 31 -776.0806\n 720500 6919500 32 -777.501\n 721000 6919500 33 -778.1877\n 721500 6919500 34 -778.3615\n 722000 6919500 35 -778.348\n 722500 6919500 36 -778.4394\n 723000 6919500 37 -778.8448\n 723500 6919500 38 -779.4078\n 724000 6919500 39 -779.6289\n 724500 6919500 40 -778.8907\n 725000 6919500 41 -776.5405\n 725500 6919500 42 -772.0054\n 726000 6919500 43 -765.4256\n 726500 6919500 44 -757.703\n 727000 6919500 45 -750.1107\n 727500 6919500 46 -743.5646\n 728000 6919500 47 -738.7034\n 728500 6919500 48 -735.5046\n 729000 6919500 49 -733.3149\n 729500 6919500 50 -731.2722\n 730000 6919500 51 -728.3378\n 730500 6919500 52 -723.8257\n 731000 6919500 53 -717.8061\n 731500 6919500 54 -710.7617\n 732000 6919500 55 -703.2172\n 732500 6919500 56 -695.5806\n 733000 6919500 57 -688.1543\n 733500 6919500 58 -681.239\n 734000 6919500 59 -675.16\n 734500 6919500 60 -670.1471\n 735000 6919500 61 -666.5481\n 735500 6919500 62 -664.6441\n 736000 6919500 63 -664.2895\n 736500 6919500 64 -665.0024\n 737000 6919500 65 -666.4157\n 737500 6919500 66 -668.4244\n 738000 6919500 67 -670.8948\n 738500 6919500 68 -673.6891\n 739000 6919500 69 -676.6405\n 739500 6919500 70 -679.598\n 740000 6919500 71 -682.4048\n 740500 6919500 72 -684.9611\n 741000 6919500 73 -687.3408\n 741500 6919500 74 -689.718\n 742000 6919500 75 -692.1871\n 742500 6919500 76 -694.7316\n 743000 6919500 77 -697.322\n 743500 6919500 78 -700.0203\n 744000 6919500 79 -702.8662\n 744500 6919500 80 -705.8049\n 745000 6919500 81 -708.6294\n 745500 6919500 82 -711.2261\n 746000 6919500 83 -713.7355\n 746500 6919500 84 -716.3539\n 747000 6919500 85 -719.2266\n 705000 6920000 1 -757.5566\n 705500 6920000 2 -754.0858\n 706000 6920000 3 -751.3958\n 706500 6920000 4 -749.3154\n 707000 6920000 5 -747.7479\n 707500 6920000 6 -746.6238\n 708000 6920000 7 -745.8771\n 708500 6920000 8 -745.455\n 709000 6920000 9 -745.445\n 709500 6920000 10 -745.8768\n 710000 6920000 11 -746.6634\n 710500 6920000 12 -747.8307\n 711000 6920000 13 -749.4533\n 711500 6920000 14 -751.4828\n 712000 6920000 15 -753.6791\n 712500 6920000 16 -755.7916\n 713000 6920000 17 -757.7184\n 713500 6920000 18 -759.417\n 714000 6920000 19 -760.8231\n 714500 6920000 20 -761.8553\n 715000 6920000 21 -762.4685\n 715500 6920000 22 -762.6934\n 716000 6920000 23 -762.756\n 716500 6920000 24 -762.9036\n 717000 6920000 25 -763.4686\n 717500 6920000 26 -764.735\n 718000 6920000 27 -766.626\n 718500 6920000 28 -768.8766\n 719000 6920000 29 -771.288\n 719500 6920000 30 -773.6724\n 720000 6920000 31 -775.8076\n 720500 6920000 32 -777.425\n 721000 6920000 33 -778.4244\n 721500 6920000 34 -778.9857\n 722000 6920000 35 -779.3897\n 722500 6920000 36 -779.891\n 723000 6920000 37 -780.6889\n 723500 6920000 38 -781.6268\n 724000 6920000 39 -782.2001\n 724500 6920000 40 -781.7472\n 725000 6920000 41 -779.5485\n 725500 6920000 42 -774.91\n 726000 6920000 43 -767.94\n 726500 6920000 44 -759.6596\n 727000 6920000 45 -751.4808\n 727500 6920000 46 -744.3796\n 728000 6920000 47 -739.0508\n 728500 6920000 48 -735.4944\n 729000 6920000 49 -733.0463\n 729500 6920000 50 -730.84\n 730000 6920000 51 -727.9127\n 730500 6920000 52 -723.537\n 731000 6920000 53 -717.7729\n 731500 6920000 54 -711.0646\n 732000 6920000 55 -703.8691\n 732500 6920000 56 -696.5172\n 733000 6920000 57 -689.2506\n 733500 6920000 58 -682.3715\n 734000 6920000 59 -676.277\n 734500 6920000 60 -671.2874\n 735000 6920000 61 -667.809\n 735500 6920000 62 -666.1849\n 736000 6920000 63 -666.2302\n 736500 6920000 64 -667.3714\n 737000 6920000 65 -669.153\n 737500 6920000 66 -671.4074\n 738000 6920000 67 -674.0124\n 738500 6920000 68 -676.8431\n 739000 6920000 69 -679.7504\n 739500 6920000 70 -682.6083\n 740000 6920000 71 -685.2858\n 740500 6920000 72 -687.693\n 741000 6920000 73 -689.9091\n 741500 6920000 74 -692.1057\n 742000 6920000 75 -694.3382\n 742500 6920000 76 -696.5397\n 743000 6920000 77 -698.6326\n 743500 6920000 78 -700.715\n 744000 6920000 79 -702.9117\n 744500 6920000 80 -705.2446\n 745000 6920000 81 -707.5975\n 745500 6920000 82 -709.8742\n 746000 6920000 83 -712.2003\n 746500 6920000 84 -714.7504\n 747000 6920000 85 -717.6503\n 705000 6920500 1 -757.9907\n 705500 6920500 2 -754.3473\n 706000 6920500 3 -751.4338\n 706500 6920500 4 -749.1298\n 707000 6920500 5 -747.374\n 707500 6920500 6 -746.1145\n 708000 6920500 7 -745.2783\n 708500 6920500 8 -744.7673\n 709000 6920500 9 -744.6573\n 709500 6920500 10 -745.0048\n 710000 6920500 11 -745.7324\n 710500 6920500 12 -746.8617\n 711000 6920500 13 -748.4697\n 711500 6920500 14 -750.5118\n 712000 6920500 15 -752.785\n 712500 6920500 16 -755.0499\n 713000 6920500 17 -757.2211\n 713500 6920500 18 -759.2462\n 714000 6920500 19 -761.0109\n 714500 6920500 20 -762.3563\n 715000 6920500 21 -763.1687\n 715500 6920500 22 -763.4554\n 716000 6920500 23 -763.4861\n 716500 6920500 24 -763.5365\n 717000 6920500 25 -763.9417\n 717500 6920500 26 -764.9954\n 718000 6920500 27 -766.6381\n 718500 6920500 28 -768.6107\n 719000 6920500 29 -770.7475\n 719500 6920500 30 -772.9406\n 720000 6920500 31 -775.0405\n 720500 6920500 32 -776.8045\n 721000 6920500 33 -778.098\n 721500 6920500 34 -779.0401\n 722000 6920500 35 -779.8525\n 722500 6920500 36 -780.7339\n 723000 6920500 37 -781.8326\n 723500 6920500 38 -782.9879\n 724000 6920500 39 -783.6882\n 724500 6920500 40 -783.2574\n 725000 6920500 41 -780.999\n 725500 6920500 42 -776.2562\n 726000 6920500 43 -769.0989\n 726500 6920500 44 -760.5512\n 727000 6920500 45 -752.0551\n 727500 6920500 46 -744.6017\n 728000 6920500 47 -738.9044\n 728500 6920500 48 -735.0057\n 729000 6920500 49 -732.2685\n 729500 6920500 50 -729.8477\n 730000 6920500 51 -726.8663\n 730500 6920500 52 -722.6232\n 731000 6920500 53 -717.1342\n 731500 6920500 54 -710.7866\n 732000 6920500 55 -703.9793\n 732500 6920500 56 -697.0137\n 733000 6920500 57 -690.1094\n 733500 6920500 58 -683.5594\n 734000 6920500 59 -677.7901\n 734500 6920500 60 -673.1471\n 735000 6920500 61 -670.0198\n 735500 6920500 62 -668.726\n 736000 6920500 63 -669.0873\n 736500 6920500 64 -670.535\n 737000 6920500 65 -672.5674\n 737500 6920500 66 -674.9382\n 738000 6920500 67 -677.4958\n 738500 6920500 68 -680.1475\n 739000 6920500 69 -682.7942\n 739500 6920500 70 -685.3588\n 740000 6920500 71 -687.7609\n 740500 6920500 72 -689.9372\n 741000 6920500 73 -691.9464\n 741500 6920500 74 -693.9147\n 742000 6920500 75 -695.8588\n 742500 6920500 76 -697.6869\n 743000 6920500 77 -699.3041\n 743500 6920500 78 -700.8004\n 744000 6920500 79 -702.3434\n 744500 6920500 80 -704.0405\n 745000 6920500 81 -705.9551\n 745500 6920500 82 -708.0913\n 746000 6920500 83 -710.5125\n 746500 6920500 84 -713.2875\n 747000 6920500 85 -716.4636\n 705000 6921000 1 -758.5811\n 705500 6921000 2 -754.7693\n 706000 6921000 3 -751.5885\n 706500 6921000 4 -748.9749\n 707000 6921000 5 -746.9034\n 707500 6921000 6 -745.3459\n 708000 6921000 7 -744.2359\n 708500 6921000 8 -743.4592\n 709000 6921000 9 -743.079\n 709500 6921000 10 -743.2407\n 710000 6921000 11 -743.9869\n 710500 6921000 12 -745.2968\n 711000 6921000 13 -747.1246\n 711500 6921000 14 -749.3492\n 712000 6921000 15 -751.7886\n 712500 6921000 16 -754.2603\n 713000 6921000 17 -756.7208\n 713500 6921000 18 -759.1272\n 714000 6921000 19 -761.3176\n 714500 6921000 20 -763.0455\n 715000 6921000 21 -764.1173\n 715500 6921000 22 -764.5298\n 716000 6921000 23 -764.6115\n 716500 6921000 24 -764.649\n 717000 6921000 25 -764.9344\n 717500 6921000 26 -765.7111\n 718000 6921000 27 -766.9529\n 718500 6921000 28 -768.4622\n 719000 6921000 29 -770.1426\n 719500 6921000 30 -771.9893\n 720000 6921000 31 -773.9406\n 720500 6921000 32 -775.7806\n 721000 6921000 33 -777.3318\n 721500 6921000 34 -778.6288\n 722000 6921000 35 -779.8139\n 722500 6921000 36 -781.0229\n 723000 6921000 37 -782.3281\n 723500 6921000 38 -783.5539\n 724000 6921000 39 -784.1947\n 724500 6921000 40 -783.5858\n 725000 6921000 41 -781.0966\n 725500 6921000 42 -776.2249\n 726000 6921000 43 -769.0463\n 726500 6921000 44 -760.4696\n 727000 6921000 45 -751.8715\n 727500 6921000 46 -744.243\n 728000 6921000 47 -738.3018\n 728500 6921000 48 -734.1272\n 729000 6921000 49 -731.1255\n 729500 6921000 50 -728.488\n 730000 6921000 51 -725.4031\n 730500 6921000 52 -721.2552\n 731000 6921000 53 -716.027\n 731500 6921000 54 -710.0383\n 732000 6921000 55 -703.6454\n 732500 6921000 56 -697.1388\n 733000 6921000 57 -690.7455\n 733500 6921000 58 -684.7575\n 734000 6921000 59 -679.585\n 734500 6921000 60 -675.539\n 735000 6921000 61 -672.9574\n 735500 6921000 62 -672.0756\n 736000 6921000 63 -672.7221\n 736500 6921000 64 -674.3856\n 737000 6921000 65 -676.5602\n 737500 6921000 66 -678.9188\n 738000 6921000 67 -681.2599\n 738500 6921000 68 -683.5334\n 739000 6921000 69 -685.7277\n 739500 6921000 70 -687.8355\n 740000 6921000 71 -689.8344\n 740500 6921000 72 -691.6994\n 741000 6921000 73 -693.4621\n 741500 6921000 74 -695.1806\n 742000 6921000 75 -696.8314\n 742500 6921000 76 -698.3029\n 743000 6921000 77 -699.4907\n 743500 6921000 78 -700.4594\n 744000 6921000 79 -701.3844\n 744500 6921000 80 -702.4571\n 745000 6921000 81 -703.9783\n 745500 6921000 82 -706.1154\n 746000 6921000 83 -708.8459\n 746500 6921000 84 -712.0607\n 747000 6921000 85 -715.6724\n 705000 6921500 1 -759.2837\n 705500 6921500 2 -755.3266\n 706000 6921500 3 -751.8721\n 706500 6921500 4 -748.9159\n 707000 6921500 5 -746.4702\n 707500 6921500 6 -744.5401\n 708000 6921500 7 -743.0705\n 708500 6921500 8 -741.939\n 709000 6921500 9 -741.2082\n 709500 6921500 10 -741.1279\n 710000 6921500 11 -741.9208\n 710500 6921500 12 -743.5308\n 711000 6921500 13 -745.7107\n 711500 6921500 14 -748.2094\n 712000 6921500 15 -750.8477\n 712500 6921500 16 -753.5278\n 713000 6921500 17 -756.2698\n 713500 6921500 18 -759.0569\n 714000 6921500 19 -761.6862\n 714500 6921500 20 -763.8246\n 715000 6921500 21 -765.1959\n 715500 6921500 22 -765.7933\n 716000 6921500 23 -766.0031\n 716500 6921500 24 -766.1048\n 717000 6921500 25 -766.3265\n 717500 6921500 26 -766.8311\n 718000 6921500 27 -767.6075\n 718500 6921500 28 -768.5518\n 719000 6921500 29 -769.665\n 719500 6921500 30 -771.0578\n 720000 6921500 31 -772.7654\n 720500 6921500 32 -774.5943\n 721000 6921500 33 -776.3228\n 721500 6921500 34 -777.8958\n 722000 6921500 35 -779.3691\n 722500 6921500 36 -780.8124\n 723000 6921500 37 -782.238\n 723500 6921500 38 -783.4459\n 724000 6921500 39 -783.947\n 724500 6921500 40 -783.1\n 725000 6921500 41 -780.3475\n 725500 6921500 42 -775.3364\n 726000 6921500 43 -768.1904\n 726500 6921500 44 -759.6676\n 727000 6921500 45 -751.0439\n 727500 6921500 46 -743.3143\n 728000 6921500 47 -737.2394\n 728500 6921500 48 -732.9197\n 729000 6921500 49 -729.7674\n 729500 6921500 50 -726.9813\n 730000 6921500 51 -723.8005\n 730500 6921500 52 -719.6984\n 731000 6921500 53 -714.6543\n 731500 6921500 54 -708.9582\n 732000 6921500 55 -702.9576\n 732500 6921500 56 -696.951\n 733000 6921500 57 -691.1715\n 733500 6921500 58 -685.903\n 734000 6921500 59 -681.5089\n 734500 6921500 60 -678.2213\n 735000 6921500 61 -676.3006\n 735500 6921500 62 -675.9131\n 736000 6921500 63 -676.8722\n 736500 6921500 64 -678.7042\n 737000 6921500 65 -680.9327\n 737500 6921500 66 -683.1569\n 738000 6921500 67 -685.1349\n 738500 6921500 68 -686.876\n 739000 6921500 69 -688.4813\n 739500 6921500 70 -690.0261\n 740000 6921500 71 -691.543\n 740500 6921500 72 -693.0399\n 741000 6921500 73 -694.5253\n 741500 6921500 74 -695.9952\n 742000 6921500 75 -697.3899\n 742500 6921500 76 -698.5772\n 743000 6921500 77 -699.4288\n 743500 6921500 78 -699.9717\n 744000 6921500 79 -700.3679\n 744500 6921500 80 -700.8726\n 745000 6921500 81 -702.0515\n 745500 6921500 82 -704.2776\n 746000 6921500 83 -707.4313\n 746500 6921500 84 -711.1879\n 747000 6921500 85 -715.301\n 705000 6922000 1 -760.1052\n 705500 6922000 2 -756.0378\n 706000 6922000 3 -752.3426\n 706500 6922000 4 -749.0709\n 707000 6922000 5 -746.274\n 707500 6922000 6 -743.9979\n 708000 6922000 7 -742.2076\n 708500 6922000 8 -740.7752\n 709000 6922000 9 -739.7655\n 709500 6922000 10 -739.4984\n 710000 6922000 11 -740.3357\n 710500 6922000 12 -742.2048\n 711000 6922000 13 -744.687\n 711500 6922000 14 -747.4162\n 712000 6922000 15 -750.2047\n 712500 6922000 16 -753.0255\n 713000 6922000 17 -755.9604\n 713500 6922000 18 -759.0292\n 714000 6922000 19 -762.0057\n 714500 6922000 20 -764.5052\n 715000 6922000 21 -766.1908\n 715500 6922000 22 -767.0463\n 716000 6922000 23 -767.4791\n 716500 6922000 24 -767.7343\n 717000 6922000 25 -767.9644\n 717500 6922000 26 -768.2537\n 718000 6922000 27 -768.5987\n 718500 6922000 28 -768.9824\n 719000 6922000 29 -769.5157\n 719500 6922000 30 -770.4095\n 720000 6922000 31 -771.7931\n 720500 6922000 32 -773.5028\n 721000 6922000 33 -775.2857\n 721500 6922000 34 -777.0093\n 722000 6922000 35 -778.6476\n 722500 6922000 36 -780.2101\n 723000 6922000 37 -781.673\n 723500 6922000 38 -782.8234\n 724000 6922000 39 -783.1879\n 724500 6922000 40 -782.1449\n 725000 6922000 41 -779.1859\n 725500 6922000 42 -774.0682\n 726000 6922000 43 -766.9482\n 726500 6922000 44 -758.4661\n 727000 6922000 45 -749.8137\n 727500 6922000 46 -742.006\n 728000 6922000 47 -735.8831\n 728500 6922000 48 -731.5604\n 729000 6922000 49 -728.3967\n 729500 6922000 50 -725.5563\n 730000 6922000 51 -722.2971\n 730500 6922000 52 -718.1701\n 731000 6922000 53 -713.175\n 731500 6922000 54 -707.6357\n 732000 6922000 55 -701.9391\n 732500 6922000 56 -696.4195\n 733000 6922000 57 -691.3204\n 733500 6922000 58 -686.9046\n 734000 6922000 59 -683.4493\n 734500 6922000 60 -681.0601\n 735000 6922000 61 -679.8955\n 735500 6922000 62 -680.0535\n 736000 6922000 63 -681.3233\n 736500 6922000 64 -683.256\n 737000 6922000 65 -685.4213\n 737500 6922000 66 -687.3871\n 738000 6922000 67 -688.8874\n 738500 6922000 68 -690.0001\n 739000 6922000 69 -690.9515\n 739500 6922000 70 -691.8953\n 740000 6922000 71 -692.9058\n 740500 6922000 72 -694.01\n 741000 6922000 73 -695.203\n 741500 6922000 74 -696.4393\n 742000 6922000 75 -697.6368\n 742500 6922000 76 -698.65\n 743000 6922000 77 -699.3188\n 743500 6922000 78 -699.6238\n 744000 6922000 79 -699.682\n 744500 6922000 80 -699.7637\n 745000 6922000 81 -700.6651\n 745500 6922000 82 -702.9951\n 746000 6922000 83 -706.556\n 746500 6922000 84 -710.8204\n 747000 6922000 85 -715.397\n 705000 6922500 1 -761.0756\n 705500 6922500 2 -756.9406\n 706000 6922500 3 -753.0661\n 706500 6922500 4 -749.5559\n 707000 6922500 5 -746.4989\n 707500 6922500 6 -743.9802\n 708000 6922500 7 -742.0045\n 708500 6922500 8 -740.452\n 709000 6922500 9 -739.3759\n 709500 6922500 10 -739.0698\n 710000 6922500 11 -739.9211\n 710500 6922500 12 -741.8727\n 711000 6922500 13 -744.4479\n 711500 6922500 14 -747.2439\n 712000 6922500 15 -750.0705\n 712500 6922500 16 -752.9254\n 713000 6922500 17 -755.9003\n 713500 6922500 18 -759.0432\n 714000 6922500 19 -762.1539\n 714500 6922500 20 -764.8792\n 715000 6922500 21 -766.8754\n 715500 6922500 22 -768.0913\n 716000 6922500 23 -768.8759\n 716500 6922500 24 -769.4019\n 717000 6922500 25 -769.7393\n 717500 6922500 26 -769.9064\n 718000 6922500 27 -769.9106\n 718500 6922500 28 -769.8143\n 719000 6922500 29 -769.8327\n 719500 6922500 30 -770.2403\n 720000 6922500 31 -771.2297\n 720500 6922500 32 -772.6979\n 721000 6922500 33 -774.3944\n 721500 6922500 34 -776.1308\n 722000 6922500 35 -777.8054\n 722500 6922500 36 -779.3715\n 723000 6922500 37 -780.79\n 723500 6922500 38 -781.8532\n 724000 6922500 39 -782.1096\n 724500 6922500 40 -780.9525\n 725000 6922500 41 -777.8929\n 725500 6922500 42 -772.7332\n 726000 6922500 43 -765.6425\n 726500 6922500 44 -757.199\n 727000 6922500 45 -748.5454\n 727500 6922500 46 -740.7133\n 728000 6922500 47 -734.6144\n 728500 6922500 48 -730.3754\n 729000 6922500 49 -727.2783\n 729500 6922500 50 -724.4335\n 730000 6922500 51 -721.0886\n 730500 6922500 52 -716.826\n 731000 6922500 53 -711.6845\n 731500 6922500 54 -706.0911\n 732000 6922500 55 -700.5295\n 732500 6922500 56 -695.4202\n 733000 6922500 57 -691.0507\n 733500 6922500 58 -687.6517\n 734000 6922500 59 -685.353\n 734500 6922500 60 -684.0663\n 735000 6922500 61 -683.7817\n 735500 6922500 62 -684.4898\n 736000 6922500 63 -685.9612\n 736500 6922500 64 -687.8123\n 737000 6922500 65 -689.6918\n 737500 6922500 66 -691.2255\n 738000 6922500 67 -692.1661\n 738500 6922500 68 -692.6573\n 739000 6922500 69 -692.9999\n 739500 6922500 70 -693.3946\n 740000 6922500 71 -693.9363\n 740500 6922500 72 -694.6558\n 741000 6922500 73 -695.5469\n 741500 6922500 74 -696.5589\n 742000 6922500 75 -697.6134\n 742500 6922500 76 -698.5773\n 743000 6922500 77 -699.2762\n 743500 6922500 78 -699.638\n 744000 6922500 79 -699.6884\n 744500 6922500 80 -699.6355\n 745000 6922500 81 -700.3688\n 745500 6922500 82 -702.7277\n 746000 6922500 83 -706.5172\n 746500 6922500 84 -711.0969\n 747000 6922500 85 -715.9873\n 705000 6923000 1 -762.2321\n 705500 6923000 2 -758.0699\n 706000 6923000 3 -754.0822\n 706500 6923000 4 -750.4256\n 707000 6923000 5 -747.2339\n 707500 6923000 6 -744.6173\n 708000 6923000 7 -742.6303\n 708500 6923000 8 -741.1754\n 709000 6923000 9 -740.2744\n 709500 6923000 10 -740.1231\n 710000 6923000 11 -740.9746\n 710500 6923000 12 -742.7914\n 711000 6923000 13 -745.1907\n 711500 6923000 14 -747.8335\n 712000 6923000 15 -750.5456\n 712500 6923000 16 -753.2983\n 713000 6923000 17 -756.1562\n 713500 6923000 18 -759.1556\n 714000 6923000 19 -762.159\n 714500 6923000 20 -764.91\n 715000 6923000 21 -767.1764\n 715500 6923000 22 -768.8497\n 716000 6923000 23 -770.1157\n 716500 6923000 24 -771.0346\n 717000 6923000 25 -771.5829\n 717500 6923000 26 -771.7196\n 718000 6923000 27 -771.4774\n 718500 6923000 28 -771.0036\n 719000 6923000 29 -770.5928\n 719500 6923000 30 -770.5712\n 720000 6923000 31 -771.1338\n 720500 6923000 32 -772.2688\n 721000 6923000 33 -773.7713\n 721500 6923000 34 -775.412\n 722000 6923000 35 -777.0161\n 722500 6923000 36 -778.4791\n 723000 6923000 37 -779.7498\n 723500 6923000 38 -780.6547\n 724000 6923000 39 -780.7849\n 724500 6923000 40 -779.5884\n 725000 6923000 41 -776.5667\n 725500 6923000 42 -771.4754\n 726000 6923000 43 -764.473\n 726500 6923000 44 -756.1542\n 727000 6923000 45 -747.6494\n 727500 6923000 46 -739.9495\n 728000 6923000 47 -733.9427\n 728500 6923000 48 -729.7692\n 729000 6923000 49 -726.6909\n 729500 6923000 50 -723.8028\n 730000 6923000 51 -720.3141\n 730500 6923000 52 -715.7545\n 731000 6923000 53 -710.2191\n 731500 6923000 54 -704.2806\n 732000 6923000 55 -698.5966\n 732500 6923000 56 -693.7498\n 733000 6923000 57 -690.1546\n 733500 6923000 58 -688.0007\n 734000 6923000 59 -687.1779\n 734500 6923000 60 -687.3035\n 735000 6923000 61 -688.0842\n 735500 6923000 62 -689.3001\n 736000 6923000 63 -690.7286\n 736500 6923000 64 -692.1547\n 737000 6923000 65 -693.3961\n 737500 6923000 66 -694.2578\n 738000 6923000 67 -694.5921\n 738500 6923000 68 -694.5473\n 739000 6923000 69 -694.4304\n 739500 6923000 70 -694.4318\n 740000 6923000 71 -694.6166\n 740500 6923000 72 -694.9906\n 741000 6923000 73 -695.5718\n 741500 6923000 74 -696.3528\n 742000 6923000 75 -697.2948\n 742500 6923000 76 -698.3192\n 743000 6923000 77 -699.2834\n 743500 6923000 78 -700.0552\n 744000 6923000 79 -700.5276\n 744500 6923000 80 -700.7476\n 745000 6923000 81 -701.5059\n 745500 6923000 82 -703.7631\n 746000 6923000 83 -707.4746\n 746500 6923000 84 -712.0508\n 747000 6923000 85 -717.0187\n 705000 6923500 1 -763.5626\n 705500 6923500 2 -759.3953\n 706000 6923500 3 -755.3348\n 706500 6923500 4 -751.5952\n 707000 6923500 5 -748.3593\n 707500 6923500 6 -745.7678\n 708000 6923500 7 -743.9125\n 708500 6923500 8 -742.7086\n 709000 6923500 9 -742.122\n 709500 6923500 10 -742.216\n 710000 6923500 11 -743.0722\n 710500 6923500 12 -744.6435\n 711000 6923500 13 -746.7064\n 711500 6923500 14 -749.0505\n 712000 6923500 15 -751.5282\n 712500 6923500 16 -754.0641\n 713000 6923500 17 -756.6814\n 713500 6923500 18 -759.4111\n 714000 6923500 19 -762.1779\n 714500 6923500 20 -764.8505\n 715000 6923500 21 -767.3138\n 715500 6923500 22 -769.4521\n 716000 6923500 23 -771.2477\n 716500 6923500 24 -772.6235\n 717000 6923500 25 -773.4453\n 717500 6923500 26 -773.6036\n 718000 6923500 27 -773.1718\n 718500 6923500 28 -772.3912\n 719000 6923500 29 -771.6229\n 719500 6923500 30 -771.213\n 720000 6923500 31 -771.3735\n 720500 6923500 32 -772.1652\n 721000 6923500 33 -773.4471\n 721500 6923500 34 -774.9468\n 722000 6923500 35 -776.4092\n 722500 6923500 36 -777.6723\n 723000 6923500 37 -778.666\n 723500 6923500 38 -779.2818\n 724000 6923500 39 -779.2011\n 724500 6923500 40 -777.9747\n 725000 6923500 41 -775.0913\n 725500 6923500 42 -770.1775\n 726000 6923500 43 -763.3541\n 726500 6923500 44 -755.2993\n 727000 6923500 45 -747.1494\n 727500 6923500 46 -739.7926\n 728000 6923500 47 -733.9636\n 728500 6923500 48 -729.7885\n 729000 6923500 49 -726.6113\n 729500 6923500 50 -723.5848\n 730000 6923500 51 -719.8761\n 730500 6923500 52 -714.8802\n 731000 6923500 53 -708.738\n 731500 6923500 54 -702.2132\n 732000 6923500 55 -696.1879\n 732500 6923500 56 -691.4565\n 733000 6923500 57 -688.65\n 733500 6923500 58 -687.916\n 734000 6923500 59 -688.813\n 734500 6923500 60 -690.5809\n 735000 6923500 61 -692.5768\n 735500 6923500 62 -694.2576\n 736000 6923500 63 -695.4262\n 736500 6923500 64 -696.1389\n 737000 6923500 65 -696.4851\n 737500 6923500 66 -696.471\n 738000 6923500 67 -696.1653\n 738500 6923500 68 -695.6766\n 739000 6923500 69 -695.233\n 739500 6923500 70 -694.9705\n 740000 6923500 71 -694.9068\n 740500 6923500 72 -695.0054\n 741000 6923500 73 -695.3051\n 741500 6923500 74 -695.8679\n 742000 6923500 75 -696.7156\n 742500 6923500 76 -697.8497\n 743000 6923500 77 -699.204\n 743500 6923500 78 -700.5734\n 744000 6923500 79 -701.7138\n 744500 6923500 80 -702.4764\n 745000 6923500 81 -703.4775\n 745500 6923500 82 -705.6056\n 746000 6923500 83 -709.0269\n 746500 6923500 84 -713.3533\n 747000 6923500 85 -718.2053\n 705000 6924000 1 -765.0118\n 705500 6924000 2 -760.8465\n 706000 6924000 3 -756.7327\n 706500 6924000 4 -752.946\n 707000 6924000 5 -749.7191\n 707500 6924000 6 -747.2274\n 708000 6924000 7 -745.5918\n 708500 6924000 8 -744.7184\n 709000 6924000 9 -744.502\n 709500 6924000 10 -744.8642\n 710000 6924000 11 -745.7166\n 710500 6924000 12 -747.007\n 711000 6924000 13 -748.6788\n 711500 6924000 14 -750.6646\n 712000 6924000 15 -752.8541\n 712500 6924000 16 -755.121\n 713000 6924000 17 -757.4493\n 713500 6924000 18 -759.8666\n 714000 6924000 19 -762.3555\n 714500 6924000 20 -764.8997\n 715000 6924000 21 -767.5084\n 715500 6924000 22 -770.0551\n 716000 6924000 23 -772.3364\n 716500 6924000 24 -774.1503\n 717000 6924000 25 -775.2513\n 717500 6924000 26 -775.452\n 718000 6924000 27 -774.8707\n 718500 6924000 28 -773.8533\n 719000 6924000 29 -772.8035\n 719500 6924000 30 -772.0753\n 720000 6924000 31 -771.8884\n 720500 6924000 32 -772.3809\n 721000 6924000 33 -773.4606\n 721500 6924000 34 -774.7954\n 722000 6924000 35 -776.0396\n 722500 6924000 36 -776.976\n 723000 6924000 37 -777.5264\n 723500 6924000 38 -777.6672\n 724000 6924000 39 -777.2172\n 724500 6924000 40 -775.8651\n 725000 6924000 41 -773.1204\n 725500 6924000 42 -768.4491\n 726000 6924000 43 -761.9052\n 726500 6924000 44 -754.2695\n 727000 6924000 45 -746.6603\n 727500 6924000 46 -739.8165\n 728000 6924000 47 -734.2645\n 728500 6924000 48 -730.0679\n 729000 6924000 49 -726.6985\n 729500 6924000 50 -723.4244\n 730000 6924000 51 -719.4081\n 730500 6924000 52 -713.9035\n 731000 6924000 53 -707.0879\n 731500 6924000 54 -699.9272\n 732000 6924000 55 -693.5281\n 732500 6924000 56 -688.8831\n 733000 6924000 57 -686.8452\n 733500 6924000 58 -687.5329\n 734000 6924000 59 -690.1424\n 734500 6924000 60 -693.5263\n 735000 6924000 61 -696.7037\n 735500 6924000 62 -698.8123\n 736000 6924000 63 -699.6616\n 736500 6924000 64 -699.6048\n 737000 6924000 65 -699.0118\n 737500 6924000 66 -698.1103\n 738000 6924000 67 -697.145\n 738500 6924000 68 -696.2283\n 739000 6924000 69 -695.4871\n 739500 6924000 70 -695.0001\n 740000 6924000 71 -694.7473\n 740500 6924000 72 -694.6504\n 741000 6924000 73 -694.7552\n 741500 6924000 74 -695.1754\n 742000 6924000 75 -695.9758\n 742500 6924000 76 -697.246\n 743000 6924000 77 -699.0056\n 743500 6924000 78 -700.9895\n 744000 6924000 79 -702.7961\n 744500 6924000 80 -704.1602\n 745000 6924000 81 -705.4647\n 745500 6924000 82 -707.5032\n 746000 6924000 83 -710.562\n 746500 6924000 84 -714.5125\n 747000 6924000 85 -719.1268\n 705000 6924500 1 -766.4787\n 705500 6924500 2 -762.3233\n 706000 6924500 3 -758.1772\n 706500 6924500 4 -754.372\n 707000 6924500 5 -751.1858\n 707500 6924500 6 -748.8272\n 708000 6924500 7 -747.4367\n 708500 6924500 8 -746.8952\n 709000 6924500 9 -747.0255\n 709500 6924500 10 -747.6266\n 710000 6924500 11 -748.4695\n 710500 6924500 12 -749.4953\n 711000 6924500 13 -750.7931\n 711500 6924500 14 -752.4263\n 712000 6924500 15 -754.334\n 712500 6924500 16 -756.3561\n 713000 6924500 17 -758.444\n 713500 6924500 18 -760.6139\n 714000 6924500 19 -762.8786\n 714500 6924500 20 -765.2991\n 715000 6924500 21 -767.9805\n 715500 6924500 22 -770.7958\n 716000 6924500 23 -773.4043\n 716500 6924500 24 -775.5167\n 717000 6924500 25 -776.8193\n 717500 6924500 26 -777.0689\n 718000 6924500 27 -776.4154\n 718500 6924500 28 -775.2823\n 719000 6924500 29 -774.0884\n 719500 6924500 30 -773.1715\n 720000 6924500 31 -772.7557\n 720500 6924500 32 -773.0269\n 721000 6924500 33 -773.9281\n 721500 6924500 34 -775.0568\n 722000 6924500 35 -775.9732\n 722500 6924500 36 -776.4012\n 723000 6924500 37 -776.2745\n 723500 6924500 38 -775.6751\n 724000 6924500 39 -774.5814\n 724500 6924500 40 -772.842\n 725000 6924500 41 -770.0749\n 725500 6924500 42 -765.6557\n 726000 6924500 43 -759.5555\n 726500 6924500 44 -752.5887\n 727000 6924500 45 -745.7504\n 727500 6924500 46 -739.5768\n 728000 6924500 47 -734.3892\n 728500 6924500 48 -730.1732\n 729000 6924500 49 -726.5154\n 729500 6924500 50 -722.8226\n 730000 6924500 51 -718.3561\n 730500 6924500 52 -712.3484\n 731000 6924500 53 -705.012\n 731500 6924500 54 -697.459\n 732000 6924500 55 -690.9344\n 732500 6924500 56 -686.5274\n 733000 6924500 57 -685.2145\n 733500 6924500 58 -687.0952\n 734000 6924500 59 -691.0816\n 734500 6924500 60 -695.7324\n 735000 6924500 61 -699.8446\n 735500 6924500 62 -702.3373\n 736000 6924500 63 -702.9968\n 736500 6924500 64 -702.3723\n 737000 6924500 65 -701.0408\n 737500 6924500 66 -699.3819\n 738000 6924500 67 -697.7874\n 738500 6924500 68 -696.3943\n 739000 6924500 69 -695.2788\n 739500 6924500 70 -694.5049\n 740000 6924500 71 -694.0672\n 740500 6924500 72 -693.8825\n 741000 6924500 73 -693.9771\n 741500 6924500 74 -694.4324\n 742000 6924500 75 -695.3057\n 742500 6924500 76 -696.7612\n 743000 6924500 77 -698.9119\n 743500 6924500 78 -701.4398\n 744000 6924500 79 -703.7844\n 744500 6924500 80 -705.614\n 745000 6924500 81 -707.1403\n 745500 6924500 82 -709.0056\n 746000 6924500 83 -711.5979\n 746500 6924500 84 -715.033\n 747000 6924500 85 -719.2686\n 705000 6925000 1 -767.8219\n 705500 6925000 2 -763.702\n 706000 6925000 3 -759.5692\n 706500 6925000 4 -755.7879\n 707000 6925000 5 -752.6683\n 707500 6925000 6 -750.4419\n 708000 6925000 7 -749.2583\n 708500 6925000 8 -748.9691\n 709000 6925000 9 -749.3412\n 709500 6925000 10 -750.0983\n 710000 6925000 11 -750.9264\n 710500 6925000 12 -751.7524\n 711000 6925000 13 -752.7565\n 711500 6925000 14 -754.0947\n 712000 6925000 15 -755.7755\n 712500 6925000 16 -757.6525\n 713000 6925000 17 -759.6516\n 713500 6925000 18 -761.7514\n 714000 6925000 19 -763.9501\n 714500 6925000 20 -766.3121\n 715000 6925000 21 -768.9762\n 715500 6925000 22 -771.8208\n 716000 6925000 23 -774.4587\n 716500 6925000 24 -776.5862\n 717000 6925000 25 -777.9168\n 717500 6925000 26 -778.217\n 718000 6925000 27 -777.6345\n 718500 6925000 28 -776.588\n 719000 6925000 29 -775.4675\n 719500 6925000 30 -774.5763\n 720000 6925000 31 -774.1235\n 720500 6925000 32 -774.2846\n 721000 6925000 33 -775.0115\n 721500 6925000 34 -775.8536\n 722000 6925000 35 -776.2987\n 722500 6925000 36 -776.0027\n 723000 6925000 37 -774.9085\n 723500 6925000 38 -773.2197\n 724000 6925000 39 -771.0778\n 724500 6925000 40 -768.514\n 725000 6925000 41 -765.3741\n 725500 6925000 42 -761.1683\n 726000 6925000 43 -755.7853\n 726500 6925000 44 -749.8943\n 727000 6925000 45 -744.1657\n 727500 6925000 46 -738.8726\n 728000 6925000 47 -734.14\n 728500 6925000 48 -729.8813\n 729000 6925000 49 -725.7789\n 729500 6925000 50 -721.4115\n 730000 6925000 51 -716.2687\n 730500 6925000 52 -709.8146\n 731000 6925000 53 -702.291\n 731500 6925000 54 -694.8215\n 732000 6925000 55 -688.6339\n 732500 6925000 56 -684.7685\n 733000 6925000 57 -684.1331\n 733500 6925000 58 -686.8244\n 734000 6925000 59 -691.6371\n 734500 6925000 60 -697.0076\n 735000 6925000 61 -701.6655\n 735500 6925000 62 -704.4901\n 736000 6925000 63 -705.2039\n 736500 6925000 64 -704.374\n 737000 6925000 65 -702.6452\n 737500 6925000 66 -700.4656\n 738000 6925000 67 -698.3013\n 738500 6925000 68 -696.3626\n 739000 6925000 69 -694.7473\n 739500 6925000 70 -693.5701\n 740000 6925000 71 -692.9161\n 740500 6925000 72 -692.7798\n 741000 6925000 73 -693.1425\n 741500 6925000 74 -693.9347\n 742000 6925000 75 -695.1215\n 742500 6925000 76 -696.8679\n 743000 6925000 77 -699.3538\n 743500 6925000 78 -702.2449\n 744000 6925000 79 -704.8959\n 744500 6925000 80 -706.9296\n 745000 6925000 81 -708.43\n 745500 6925000 82 -709.8936\n 746000 6925000 83 -711.7782\n 746500 6925000 84 -714.4371\n 747000 6925000 85 -718.046\n" }, { "path": "examples/data/input_data/tut_chapter1/.~lock.fault_unconformity_model_orientations.csv#", "content": ",bl9,bl9,24.01.2018 18:51,file:///home/bl9/.config/libreoffice/4;" }, { "path": "examples/data/input_data/tut_chapter1/.~lock.fault_unconformity_model_points.csv#", "content": ",bl9,bl9,24.01.2018 18:39,file:///home/bl9/.config/libreoffice/4;" }, { "path": "examples/data/input_data/tut_chapter1/fault_unconformity_model_orientations.csv", "content": "X,Y,Z,azimuth,dip,polarity,formation\n1000,1000,950,90,18.435,1,Shale\n400,1000,1400,90,18.435,1,Sandstone_2\n1000,1500,1600,270,20,1,Carbonate\n500,1000,864.602,270,71.565,1,Main_Fault\n" }, { "path": "examples/data/input_data/tut_chapter1/fault_unconformity_model_points.csv", "content": "X,Y,Z,formation\n800,50,750,Shale\n800,150,700,Shale\n800,300,700,Shale\n800,500,800,Shale\n800,1000,1000,Shale\n800,1500,700,Shale\n800,1700,600,Shale\n800,1950,650,Shale\n0,1000,1100,Shale\n300,1000,1000,Shale\n450,1000,950,Shale\n1100,1000,900,Shale\n1400,1000,850,Shale\n1700,1000,900,Shale\n1500,500,800,Shale\n1500,1500,750,Shale\n800,50,450,Sandstone_1\n800,150,400,Sandstone_1\n800,300,400,Sandstone_1\n800,500,500,Sandstone_1\n800,1000,700,Sandstone_1\n800,1500,400,Sandstone_1\n800,1700,300,Sandstone_1\n800,1950,350,Sandstone_1\n0,1000,800,Sandstone_1\n300,1000,700,Sandstone_1\n1100,1000,600,Sandstone_1\n1400,1000,550,Sandstone_1\n1700,1000,600,Sandstone_1\n1500,500,500,Sandstone_1\n1500,1500,450,Sandstone_1\n900,150,920,Siltstone\n900,300,920,Siltstone\n900,1500,920,Siltstone\n900,1700,820,Siltstone\n900,1950,870,Siltstone\n0,1000,1300,Siltstone\n300,1000,1200,Siltstone\n600,1000,1100,Siltstone\n1100,1000,1100,Siltstone\n1400,1000,1050,Siltstone\n1700,1000,1100,Siltstone\n1500,500,1000,Siltstone\n1500,1500,950,Siltstone\n0,1000,1500,Sandstone_2\n300,1000,1400,Sandstone_2\n600,1000,1300,Sandstone_2\n1100,1000,1300,Sandstone_2\n1400,1000,1250,Sandstone_2\n1700,1000,1300,Sandstone_2\n1500,500,1200,Sandstone_2\n1500,1500,1150,Sandstone_2\n300,300,1500,Carbonate\n1700,1700,1600,Carbonate\n1000,1000,1300,Carbonate\n300,1700,1400,Carbonate\n1700,300,1500,Carbonate\n700,1000,900,Main_Fault\n600,1000,600,Main_Fault\n500,1000,300,Main_Fault\n800,1000,1200,Main_Fault\n900,1000,1500,Main_Fault\n" }, { "path": "examples/data/input_data/tut_chapter1/simple_fault_model_orientations.csv", "content": "X,Y,Z,azimuth,dip,polarity,formation\n1000,1000,950,90,18.435,1,Shale\n400,1000,1400,90,18.435,1,Sandstone_2\n500,1000,864.602,270,71.565,1,Main_Fault\n" }, { "path": "examples/data/input_data/tut_chapter1/simple_fault_model_orientations_geophy.csv", "content": "X,Y,Z,azimuth,dip,polarity,formation\n1000,1000,-1050,90,18.435,1,target\n400,1000,-600,90,18.435,1,seawater\n500,1000,-1135.398,270,71.565,1,fault\n" }, { "path": "examples/data/input_data/tut_chapter1/simple_fault_model_points.csv", "content": "X,Y,Z,formation\n800,50,750,Shale\n800,150,700,Shale\n800,300,700,Shale\n800,500,800,Shale\n800,1000,1000,Shale\n800,1500,700,Shale\n800,1700,600,Shale\n800,1950,650,Shale\n0,1000,1100,Shale\n300,1000,1000,Shale\n450,1000,950,Shale\n1100,1000,900,Shale\n1400,1000,850,Shale\n1700,1000,900,Shale\n1500,500,800,Shale\n1500,1500,750,Shale\n800,50,450,Sandstone_1\n800,150,400,Sandstone_1\n800,300,400,Sandstone_1\n800,500,500,Sandstone_1\n800,1000,700,Sandstone_1\n800,1500,400,Sandstone_1\n800,1700,300,Sandstone_1\n800,1950,350,Sandstone_1\n0,1000,800,Sandstone_1\n300,1000,700,Sandstone_1\n1100,1000,600,Sandstone_1\n1400,1000,550,Sandstone_1\n1700,1000,600,Sandstone_1\n1500,500,500,Sandstone_1\n1500,1500,450,Sandstone_1\n900,150,920,Siltstone\n900,300,920,Siltstone\n900,1500,920,Siltstone\n900,1700,820,Siltstone\n900,1950,870,Siltstone\n0,1000,1300,Siltstone\n300,1000,1200,Siltstone\n600,1000,1100,Siltstone\n1100,1000,1100,Siltstone\n1400,1000,1050,Siltstone\n1700,1000,1100,Siltstone\n1500,500,1000,Siltstone\n1500,1500,950,Siltstone\n0,1000,1500,Sandstone_2\n300,1000,1400,Sandstone_2\n600,1000,1300,Sandstone_2\n1100,1000,1300,Sandstone_2\n1400,1000,1250,Sandstone_2\n1700,1000,1300,Sandstone_2\n1500,500,1200,Sandstone_2\n1500,1500,1150,Sandstone_2\n700,1000,900,Main_Fault\n600,1000,600,Main_Fault\n500,1000,300,Main_Fault\n800,1000,1200,Main_Fault\n900,1000,1500,Main_Fault\n" }, { "path": "examples/data/input_data/tut_chapter1/simple_fault_model_points_geophy.csv", "content": "X,Y,Z,formation\n800,50,-1250,target\n800,150,-1300,target\n800,300,-1300,target\n800,500,-1200,target\n800,1000,-1000,target\n800,1500,-1300,target\n800,1700,-1400,target\n800,1950,-1350,target\n0,1000,-900,target\n300,1000,-1000,target\n450,1000,-1050,target\n1100,1000,-1100,target\n1400,1000,-1150,target\n1700,1000,-1100,target\n1500,500,-1200,target\n1500,1500,-1250,target\n800,50,-1550,underburden\n800,150,-1600,underburden\n800,300,-1600,underburden\n800,500,-1500,underburden\n800,1000,-1300,underburden\n800,1500,-1600,underburden\n800,1700,-1700,underburden\n800,1950,-1650,underburden\n0,1000,-1200,underburden\n300,1000,-1300,underburden\n1100,1000,-1400,underburden\n1400,1000,-1450,underburden\n1700,1000,-1400,underburden\n1500,500,-1500,underburden\n1500,1500,-1550,underburden\n900,150,-1080,overburden\n900,300,-1080,overburden\n900,1500,-1080,overburden\n900,1700,-1180,overburden\n900,1950,-1130,overburden\n0,1000,-700,overburden\n300,1000,-800,overburden\n600,1000,-900,overburden\n1100,1000,-900,overburden\n1400,1000,-950,overburden\n1700,1000,-900,overburden\n1500,500,-1000,overburden\n1500,1500,-1050,overburden\n0,1000,-500,seawater\n300,1000,-600,seawater\n600,1000,-700,seawater\n1100,1000,-700,seawater\n1400,1000,-750,seawater\n1700,1000,-700,seawater\n1500,500,-800,seawater\n1500,1500,-850,seawater\n700,1000,-1100,fault\n600,1000,-1400,fault\n500,1000,-1700,fault\n800,1000,-800,fault\n900,1000,-500,fault\n" }, { "path": "examples/data/input_data/tut_chapter6/ch6_data_fol.csv", "content": "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\n0.8682431421244591,-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\n0.25881904510252074,1.5848095757158825e-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\n0.25881904510252074,1.5848095757158825e-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\n" }, { "path": "examples/data/input_data/tut_chapter6/ch6_data_interf.csv", "content": "X,X_std,Y,Y_std,Z,Z_std,annotations,formation,formation_number,group_id,isFault,order_series,series\n1700,0.0,0,0.0,640,0.0,\"${\\bf{x}}_{\\alpha \\,{\\bf{1}},0}$\",Fault,1,fault,True,1,fault\n1500,0.0,10,0.0,990,0.0,\"${\\bf{x}}_{\\alpha \\,{\\bf{1}},1}$\",Fault,1,fault,True,1,fault\n1300,0.0,10,0.0,1340,0.0,\"${\\bf{x}}_{\\alpha \\,{\\bf{1}},2}$\",Fault,1,fault,True,1,fault\n2500,0.0,10,0.0,911,0.0,\"${\\bf{x}}_{\\alpha \\,{\\bf{2}},0}$\",Layer 2,2,l2_b,False,2,Rest\n500,0.0,10,0.0,1681,0.0,\"${\\bf{x}}_{\\alpha \\,{\\bf{2}},1}$\",Layer 2,2,l2_a,False,2,Rest\n2500,0.0,10,0.0,704,0.0,\"${\\bf{x}}_{\\alpha \\,{\\bf{3}},0}$\",Layer 3,3,l3_b,False,2,Rest\n500,0.0,10,0.0,1474,0.0,\"${\\bf{x}}_{\\alpha \\,{\\bf{3}},1}$\",Layer 3,3,l3_a,False,2,Rest\n2500,0.0,10,0.0,497,0.0,\"${\\bf{x}}_{\\alpha \\,{\\bf{4}},0}$\",Layer 4,4,l4_b,False,2,Rest\n500,0.0,10,0.0,1267,0.0,\"${\\bf{x}}_{\\alpha \\,{\\bf{4}},1}$\",Layer 4,4,l4_a,False,2,Rest\n2500,0.0,10,0.0,290,0.0,\"${\\bf{x}}_{\\alpha \\,{\\bf{5}},0}$\",Layer 5,5,l5_b,False,2,Rest\n500,0.0,10,0.0,1060,0.0,\"${\\bf{x}}_{\\alpha \\,{\\bf{5}},1}$\",Layer 5,5,l5_a,False,2,Rest\n" }, { "path": "examples/examples/README.rst", "content": "Examples\n========\n\nThis examples intend to be a gallery of what other people has done with GemPy.\nHopefully with the help of the community we can create a complete library of\nstructures that can serve as template for new users.\n" }, { "path": "examples/examples/__init__.py", "content": "" }, { "path": "examples/examples/geometries/README.rst", "content": "Simple geometries\n-----------------\nSome simple geometries." }, { "path": "examples/examples/geometries/__init__.py", "content": "" }, { "path": "examples/examples/geometries/a01_horizontal_stratigraphic.py", "content": "\"\"\"\nModel 1 - Horizontal Stratigraphy\n===================================\n\nThis example demonstrates how to build a basic geological model with horizontally stacked layers using GemPy,\nan open-source Python library for implicit geological modeling. In this script, we will:\n\n- Set up and compute a simple geological model using input data (orientations and surface points).\n- Visualize the model in 2D and 3D.\n- Export model components (formations, orientations, surface points, and the regular grid) to VTK files.\n- Extract mesh vertices and normals, save them to Excel files, and convert the vertex data to an XYZ file.\n\nEach section includes detailed comments to explain its purpose and functionality.\n\"\"\"\n\n# %%\n# Import necessary libraries\nimport gempy as gp\nimport gempy_viewer as gpv\nimport pyvista as pv\nimport vtk\nimport pandas as pd\n\n# sphinx_gallery_thumbnail_number = 2\n\n# %%\n# Generate the Geological Model\n# -----------------------------\n# In this section, we define the data source and create a GeoModel instance.\n# The model is defined with a specific spatial extent and resolution (refinement).\n# Input data (orientations and surface points) are loaded from a remote repository.\n\n# Define the path to the dataset hosted online\ndata_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'\n\n# Create a GeoModel instance with the given project name, extent, and refinement level.\ndata = gp.create_geomodel(\n project_name='horizontal',\n extent=[0, 1000, 0, 1000, 0, 1000],\n refinement=6,\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=data_path + \"/data/input_data/jan_models/model1_orientations.csv\",\n path_to_surface_points=data_path + \"/data/input_data/jan_models/model1_surface_points.csv\"\n )\n)\n\n# Map a geological series to the corresponding surfaces.\n# Here, the \"Strat_Series\" is associated with two formations ('rock2' and 'rock1'),\n# which defines the stacking order of horizontal layers.\ngp.map_stack_to_surfaces(\n gempy_model=data,\n mapping_object={\"Strat_Series\": ('rock2', 'rock1')}\n)\n\n# Compute the geological model using the provided data and mappings.\ngp.compute_model(data)\ngeo_data = data\n\n# %%\n# 2D Visualization of the Geological Model\n# -----------------------------------------\n# The following plots provide 2D views of the model.\n# - The first plot shows the initial model without computed results.\n# - The subsequent plots display the computed geological data in the x and y directions.\n# Note: The boundaries are hidden in the latter plots for a cleaner visualization.\n\n# Plot the initial model in the y direction (without displaying computed results)\ngpv.plot_2d(geo_data, direction=['y'], show_results=False)\n\n# Plot the computed model results in the x and y directions, including data but excluding boundaries\ngpv.plot_2d(geo_data, direction=['x'], show_data=True, show_boundaries=False)\ngpv.plot_2d(geo_data, direction=['y'], show_data=True, show_boundaries=False)\n\n# %%\n# 3D Visualization and Export to VTK\n# ----------------------------------\n# Visualize the model in 3D with data, results, and boundaries.\n# The generated 3D plot is also used to export various components of the model to VTK files.\np = gpv.plot_3d(geo_data, show_data=True, show_results=True, show_boundaries=True, image=True)\n\n# Export different components of the model to VTK files:\np.surface_poly['rock1'].save('rock1.vtk') # Save formation 'rock1'\np.surface_poly['rock2'].save('rock2.vtk') # Save formation 'rock2'\np.orientations_mesh.save('orientations.vtk') # Save the orientations mesh\np.surface_points_mesh.save('surface_points.vtk') # Save the surface points mesh\n\n# Retrieve and export the regular grid (volume representation) of the model\nbox = p.regular_grid_actor.GetMapper().GetInput()\nbox.save('box.vtk')\n\n# %%\n# Display the Exported VTK Files with PyVista\n# -------------------------------------------\n# Load and plot each exported VTK file to verify the export.\npv.read('rock1.vtk').plot(show_edges=False)\npv.read('rock2.vtk').plot(show_edges=False)\npv.read('orientations.vtk').plot(show_edges=False)\npv.read('surface_points.vtk').plot(show_edges=False)\npv.read('box.vtk').plot(show_edges=False)\n\n\n# %%\n# Extract and Save Mesh Vertices and Normals\n# ------------------------------------------\n# The following functions extract vertex coordinates and corresponding normal vectors\n# from a mesh (vtkPolyData), and then save this data into Excel files for further analysis.\n\ndef generate_normals(polydata):\n \"\"\"\n Generate normals for the given polydata if they are not already computed.\n\n Parameters:\n polydata (vtk.vtkPolyData): Input polydata for which normals are to be computed.\n\n Returns:\n vtk.vtkPolyData: The polydata with computed point normals.\n \"\"\"\n normal_generator = vtk.vtkPolyDataNormals()\n normal_generator.SetInputData(polydata)\n normal_generator.ComputePointNormalsOn()\n normal_generator.ComputeCellNormalsOff()\n normal_generator.Update()\n return normal_generator.GetOutput()\n\n\ndef get_vertices_and_normals(mesh):\n \"\"\"\n Extract vertices and normals from a mesh.\n\n Parameters:\n mesh: The mesh object (typically from a formation) to extract surface vertices and normals.\n\n Returns:\n tuple: Two lists containing vertices and normals respectively.\n \"\"\"\n # Extract the surface of the mesh\n surface_mesh = mesh.extract_surface()\n polydata = surface_mesh\n\n # Ensure normals are computed for the polydata\n polydata_with_normals = generate_normals(polydata)\n\n # Extract points (vertices)\n points = polydata_with_normals.GetPoints()\n vertices = [points.GetPoint(i) for i in range(points.GetNumberOfPoints())]\n\n # Extract normals associated with the points\n normals_array = polydata_with_normals.GetPointData().GetNormals()\n normals = [normals_array.GetTuple(i) for i in range(normals_array.GetNumberOfTuples())]\n\n return vertices, normals\n\n\ndef save_to_excel(vertices, normals, vertices_file, normals_file):\n \"\"\"\n Save the vertices and normals to separate Excel files.\n\n Parameters:\n vertices (list): List of vertex coordinates.\n normals (list): List of normal vectors.\n vertices_file (str): File name for saving vertices.\n normals_file (str): File name for saving normals.\n \"\"\"\n # Create DataFrames from the lists\n vertices_df = pd.DataFrame(vertices, columns=['X', 'Y', 'Z'])\n normals_df = pd.DataFrame(normals, columns=['x', 'y', 'z'])\n\n # Write the DataFrames to Excel files\n vertices_df.to_excel(vertices_file, index=False)\n normals_df.to_excel(normals_file, index=False)\n\n\n# Extract vertices and normals from the mesh of formation 'rock1'\nmesh = p.surface_poly['rock1']\nvertices, normals = get_vertices_and_normals(mesh)\n\n# Create DataFrames for later processing or verification\nvertices_df = pd.DataFrame(vertices, columns=['X', 'Y', 'Z'])\nnormals_df = pd.DataFrame(normals, columns=['x', 'y', 'z'])\n\n# Define file names for the Excel outputs\nvertices_file = \"rock1_vertices.xlsx\"\nnormals_file = \"rock1_norms.xlsx\"\n\n# Save the extracted data to Excel files\nsave_to_excel(vertices, normals, vertices_file, normals_file)\n\n# Optionally, read back the Excel files to verify their content\npd.read_excel(vertices_file)\npd.read_excel(normals_file)\n\n\n# %%\n# Convert Vertices DataFrame to an XYZ File\n# -----------------------------------------\n# This function converts a DataFrame containing vertex coordinates to an XYZ format file,\n# which is a simple text file with one point per line.\n\ndef dataframe_to_xyz(df, filename):\n \"\"\"\n Write vertex coordinates from a DataFrame to an XYZ file.\n\n Parameters:\n df (pandas.DataFrame): DataFrame containing 'X', 'Y', and 'Z' columns.\n filename (str): Output filename for the XYZ file.\n \"\"\"\n with open(filename, 'w') as f:\n for index, row in df.iterrows():\n f.write(f\"{row['X']} {row['Y']} {row['Z']}\\n\")\n\n\n# Specify the output file name for the XYZ file\nfilename = \"output.xyz\"\n\n# Convert the vertices DataFrame to an XYZ file\ndataframe_to_xyz(vertices_df, filename)\n" }, { "path": "examples/examples/geometries/b02_fold.py", "content": "\"\"\"\nModel 2 - Anticline\n===================\n\nSimple model of an anticline\n\nThis script demonstrates how to create a geological model of an anticline structure using GemPy,\na Python-based, open-source library for implicit geological modeling.\n\"\"\"\n\n# Import necessary libraries\nimport gempy as gp\nimport gempy_viewer as gpv\n\n\n# sphinx_gallery_thumbnail_number = 2\n\n\n# %%\n# Generate the model\n# Define the path to data\ndata_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'\npath_to_data = data_path + \"/data/input_data/jan_models/\"\n# Create a GeoModel instance\ndata: gp.data.GeoModel = gp.create_geomodel(\n project_name='fold',\n extent=[0, 1000, 0, 1000, 0, 1000],\n refinement=6,\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=path_to_data + \"model2_orientations.csv\",\n path_to_surface_points=path_to_data + \"model2_surface_points.csv\"\n )\n)\n# Map geological series to surfaces\ngp.map_stack_to_surfaces(\n gempy_model=data,\n mapping_object={\"Strat_Series\": ('rock2', 'rock1')}\n)\n# Compute the geological model\ngp.compute_model(data)\ngeo_data = data\n\n# %%\n# Plot the initial geological model in the y direction without results\ngpv.plot_2d(geo_data, direction=['y'], show_results=False)\n\n# Plot the result of the model in the y and x direction with data and scalar\ngpv.plot_2d(geo_data, direction='y', show_data=True, show_scalar=False)\ngpv.plot_2d(geo_data, direction='x', show_data=True, show_scalar=True)\n" }, { "path": "examples/examples/geometries/c03_recumbent_fold.py", "content": "\"\"\"\nModel 3 - Recumbent Fold\n========================\n\nModeling a recumbent fold\n\nThis script demonstrates how to create a geological model of a recumbent fold using GemPy,\na Python-based, open-source library for implicit geological modeling.\n\"\"\"\n\n# Import necessary libraries\nimport gempy as gp\nimport gempy_viewer as gpv\n\n\n# sphinx_gallery_thumbnail_number = 2\n\n\n# %%\n# Generate the model\n# Define the path to data\ndata_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'\npath_to_data = data_path + \"/data/input_data/jan_models/\"\n# Create a GeoModel instance\ndata = gp.create_geomodel(\n project_name='recumbent',\n extent=[0, 1000, 0, 1000, 0, 1000],\n resolution=[50, 50, 50],\n refinement=6,\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=path_to_data + \"model3_orientations.csv\",\n path_to_surface_points=path_to_data + \"model3_surface_points.csv\"\n )\n)\n# Map geological series to surfaces\ngp.map_stack_to_surfaces(\n gempy_model=data,\n mapping_object={\"Strat_Series\": ('rock2', 'rock1')}\n)\n# Compute the geological model\ngp.compute_model(data)\ngeo_data = data\n\n# %%\n# Plot the initial geological model in the y direction\ngpv.plot_2d(geo_data, direction=['y'], show_results=False)\n\n# Plot the result of the model in the y direction with data\ngpv.plot_2d(geo_data, direction=['y'], show_data=True)\n\n# %%\ngpv.plot_3d(geo_data, show_surfaces=True)\n" }, { "path": "examples/examples/geometries/d04_pinchout.py", "content": "\"\"\"\nModel 4 - Pinchout\n==================\n\nModeling of a pinchout: combining scalar fields\n\nThis script demonstrates how to create a geological model with a layer of varying thickness (pinchout) using GemPy,\na Python-based, open-source library for implicit geological modeling.\n\"\"\"\n\n# Import necessary libraries\nimport gempy as gp\nimport gempy_viewer as gpv\n\n\n# sphinx_gallery_thumbnail_number = 3\n\n\n# %%\n# Generate the model\n# Define the path to data\ndata_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'\npath_to_data = data_path + \"/data/input_data/jan_models/\"\n# Create a GeoModel instance\ndata = gp.create_geomodel(\n project_name='pinchout',\n extent=[0, 1000, 0, 1000, 0, 1000],\n resolution=[50, 50, 50],\n refinement=6,\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=path_to_data + \"model4_orientations.csv\",\n path_to_surface_points=path_to_data + \"model4_surface_points.csv\"\n )\n)\n# Map geological series to surfaces\ngp.map_stack_to_surfaces(\n gempy_model=data,\n mapping_object={\"Strat_Series\": ('rock2', 'rock1')}\n)\n# Compute the geological model\ngp.compute_model(data)\ngeo_data = data\n\n# %%\n# Plot the initial geological model in the y direction\ngpv.plot_2d(geo_data, direction=['y'], show_results=False)\n\n# Plot the result of the model in the x and y direction with data\ngpv.plot_2d(geo_data, direction=['x'], show_data=True)\ngpv.plot_2d(geo_data, direction=['y'], show_data=True)\n\n# %%\ngpv.plot_3d(geo_data, show_surfaces=True)\n" }, { "path": "examples/examples/geometries/e05_fault.py", "content": "\"\"\"\nModel 5 - Fault\n===============\n\nModeling a fault\n\nThis script demonstrates how to create a simple fault model with constant offset using GemPy,\na Python-based, open-source library for implicit geological modeling.\n\"\"\"\n\n# Import necessary libraries\nimport gempy as gp\nimport gempy_viewer as gpv\nimport numpy as np\nfrom gempy_engine.core.data.stack_relation_type import StackRelationType\n\n# sphinx_gallery_thumbnail_number = 2\n\n\n# %%\n# Generate the model\n# Define the path to data\ndata_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'\npath_to_data = data_path + \"/data/input_data/jan_models/\"\n# Create a GeoModel instance\ndata = gp.create_geomodel(\n project_name='fault',\n extent=[0, 1000, 0, 1000, 0, 1000],\n refinement=6,\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=path_to_data + \"model5_orientations.csv\",\n path_to_surface_points=path_to_data + \"model5_surface_points.csv\"\n )\n)\n# Map geological series to surfaces\ngp.map_stack_to_surfaces(\n gempy_model=data,\n mapping_object={\n \"Fault_Series\": 'fault',\n \"Strat_Series\": ('rock2', 'rock1')\n }\n)\n# Define fault groups\ndata.structural_frame.structural_groups[0].structural_relation = StackRelationType.FAULT\ndata.structural_frame.fault_relations = np.array([[0, 1], [0, 0]])\n# Compute the geological model\ngp.compute_model(data)\ngeo_data = data\n\n# %%\n# Plot the initial geological model in the y direction\ngpv.plot_2d(geo_data, direction=['y'], show_results=False)\n\n# %%\n\n# Plot the result of the model in the x and y direction with data\ngpv.plot_2d(geo_data, direction='y', show_data=True)\ngpv.plot_2d(geo_data, direction='x', show_data=True)\n\ngpv.plot_3d(geo_data, show_data=True, show_boundaries=True, show_lith=True)\n" }, { "path": "examples/examples/geometries/f06_unconformity.py", "content": "\"\"\"\nModel 6 - Unconformity\n======================\n\nModeling unconformities through the combination of scalar fields\n\nThis script creates an unconformity cutting an anticline structure using GemPy,\nan open-source, Python-based library for building implicit geological models.\n\"\"\"\n\n# Importing necessary libraries\nimport gempy as gp\nimport gempy_viewer as gpv\nfrom gempy_engine.core.data.stack_relation_type import StackRelationType\n\n# sphinx_gallery_thumbnail_number = 2\n\n\n# %%\n# Generate the model\n# Define the path to data\ndata_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'\npath_to_data = data_path + \"/data/input_data/jan_models/\"\n# Create a GeoModel instance\ndata = gp.create_geomodel(\n project_name='unconformity',\n extent=[0, 1000, 0, 1000, 0, 1000],\n refinement=6,\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=path_to_data + \"model6_orientations.csv\",\n path_to_surface_points=path_to_data + \"model6_surface_points.csv\"\n )\n)\n# Map geological series to surfaces\ngp.map_stack_to_surfaces(\n gempy_model=data,\n mapping_object={\n \"Strat_Series1\": 'rock3',\n \"Strat_Series2\": ('rock2', 'rock1')\n }\n)\n# Define the structural relation\ndata.structural_frame.structural_groups[0].structural_relation = StackRelationType.ERODE\n\n# Compute the geological model\ngp.compute_model(data)\ngeo_data = data\n\n# %%\n# Plot the initial geological model in the y direction\ngpv.plot_2d(geo_data, direction=['y'], show_results=False)\n\n# %%\n# Plot the result of the model in the y and x directions with data\ngpv.plot_2d(geo_data, direction='y', show_data=True)\ngpv.plot_2d(geo_data, direction='x', show_data=True)\n\n" }, { "path": "examples/examples/geometries/g07_combination.py", "content": "\"\"\"\nModel 7 - Combination\n======================\n\nCombining faults and unconformities\n\nThis script creates a folded domain featuring an unconformity and a fault using GemPy,\nan open-source, Python-based library for building implicit geological models.\n\"\"\"\n\n# Importing necessary libraries\nimport numpy as np\nimport gempy as gp\nimport gempy_viewer as gpv\nfrom gempy_engine.core.data.stack_relation_type import StackRelationType\n\n# sphinx_gallery_thumbnail_number = 2\n\n# Generate the model\n# Define the path to data\ndata_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'\npath_to_data = data_path + \"/data/input_data/jan_models/\"\n# Create a GeoModel instance\ndata = gp.create_geomodel(\n project_name='combination',\n extent=[0, 2500, 0, 1000, 0, 1000],\n refinement=6,\n resolution=[20, 20, 20],\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=path_to_data + \"model7_orientations.csv\",\n path_to_surface_points=path_to_data + \"model7_surface_points.csv\"\n )\n)\n# Map geological series to surfaces\ngp.map_stack_to_surfaces(\n gempy_model=data,\n mapping_object={\n \"Fault_Series\" : ('fault'),\n \"Strat_Series1\": ('rock3'),\n \"Strat_Series2\": ('rock2', 'rock1'),\n }\n)\n# Define the structural relation\ndata.structural_frame.structural_groups[0].structural_relation = StackRelationType.FAULT\ndata.structural_frame.fault_relations = np.array(\n [[0, 1, 1],\n [0, 0, 0],\n [0, 0, 0]]\n)\n# Compute the geological model\ndata.interpolation_options.number_octree_levels_surface = 5\ngp.compute_model(data)\ndata.structural_frame\n\n# %%\n# Plot the initial geological model in the y direction\ngpv.plot_2d(data, direction=['y'], show_results=False)\n\n# %%\n# Plot the result of the model in the y and x directions with data and boundaries\ngpv.plot_2d(data, direction='y', show_data=True, show_boundaries=True)\ngpv.plot_2d(data, direction='x', show_data=True)\n\n# Plot the blocks accounting for fault blocks\ngpv.plot_2d(\n model=data,\n override_regular_grid=data.solutions.raw_arrays.litho_faults_block,\n show_data=True, kwargs_lithology={'cmap': 'Set1', 'norm': None}\n)\n\n# %%\n# The 3D plot is commented out due to a bug.\ngpv.plot_3d(data)\n" }, { "path": "examples/examples/geometries/h08_more_examples.py", "content": "\"\"\"\nGemPy Models - Some More Complex Examples\n-----------------------------------------\n\nA set of more complex examples\n\n\"\"\"\n\n# %%\n# Importing gempy\nimport gempy as gp\nimport gempy_viewer as gpv\n\n# Aux imports\nimport numpy as np\nimport os\n\n# %%\n# Choose a model and load the corresponding data set in the line below\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# \n# Model 1: Discordant layering\n# \n# Model 2: Anticlinal layering\n# \n# Model 3: Parallel steep dipping layers\n# \n# Model 5: Normal faulting\n# \n# Model 6: Synclinal layering\n# \n# Model 7: Graben structure with two faults\n# \n# Model 8: Similar to Model 5\n# \n# Model 9: Parallel flat dipping layers\n# \n\n# %%\n\ndata_path = os.path.abspath('../..')\n\n\n# %%\ndef create_example(name_model, plot_section=True):\n \"\"\"\n Create an inter_data from one of the examples data_set\n \n Attr:\n 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',\n 'Model 8', 'Model 9']\n save_pickle (bool, str): Save to a pickle the interp_data object. You can pass the path as a string otherwse\n the default name will be given\n plot_section (bool)\n \n \"\"\"\n name_list = np.array(['Model 1', 'Model 2', 'Model 3', 'Model 4', 'Model 5', 'Model 6', 'Model 7', 'Model 8', 'Model 9'])\n assert name_model in name_list, 'Name model must be in the following list: ' + str(name_list)\n # Extract number of the model\n n_model = name_model[-1]\n\n # # Load right gempy geodata\n geo_data: gp.data.GeoModel = gp.create_geomodel(\n project_name=name_model,\n extent=[0, 2000, 0, 2000, 0, 1600],\n resolution=[50, 50, 50],\n refinement=6, # * For this model is better not to use octrees because we want to see what is happening in the scalar fields\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=data_path + \"/data/input_data/lisa_models/foliations\" + n_model + \".csv\",\n path_to_surface_points=data_path + \"/data/input_data/lisa_models/interfaces\" + n_model + \".csv\",\n )\n )\n\n # Set the right sequential pile\n subset_list_1 = np.array(['Model 1'])\n subset_list_2 = np.array(['Model 5', 'Model 8'])\n subset_list_3 = np.array(['Model 2', 'Model 3', 'Model 9', 'Model 6'])\n subset_list_4 = np.array(['Model 7'])\n \n ### Model 1 - Discordant layering ###\n if name_model in subset_list_1:\n gp.map_stack_to_surfaces(\n gempy_model=geo_data,\n mapping_object={\n \"Strat_Series_1\": ('Sandstone', 'Siltstone', 'Shale'),\n \"Strat_Series_2\": ('Sandstone2', 'Siltstone2', 'Shale2')\n },\n )\n ### Model 5 - One normal Fault ###\n ### Model 8 - ###\n elif name_model in subset_list_2:\n gp.map_stack_to_surfaces(\n gempy_model=geo_data,\n mapping_object={\n \"Fault_Series\": 'Main_Fault',\n \"Strat_Series\": ('Sandstone', 'Siltstone', 'Shale', 'Sandstone_2', 'Schist', 'Gneiss')\n },\n )\n \n gp.set_is_fault(geo_data, ['Fault_Series'])\n \n elif name_model in subset_list_3:\n ### Model 2 - Aufwölbung (durch Salzstock?) ###\n ### Model 3+9 - Parallele NNE Schichtung ohne Verwerfung ###\n ### Model 6 - Mulde ###\n gp.map_stack_to_surfaces(\n gempy_model=geo_data,\n mapping_object={\n \"Strat_Series\": ('Sandstone', 'Siltstone', 'Shale', 'Sandstone_2', 'Schist', 'Gneiss')\n },\n )\n elif name_model in subset_list_4:\n ### Model 7 - Graben ###\n gp.map_stack_to_surfaces(\n gempy_model=geo_data,\n mapping_object={\n \"Fault_1\": 'Fault_1', \"Fault_2\": 'Fault_2',\n \"Strat_Series\": ('Sandstone', 'Siltstone', 'Shale', 'Sandstone_2', 'Schist', 'Gneiss')\n },\n )\n \n gp.set_is_fault(geo_data, ['Fault_1', 'Fault_2'])\n else:\n print('You would never reach this point. Look for the bug')\n\n # Interpolation and Computation\n sol = gp.compute_model(\n gempy_model=geo_data,\n engine_config=gp.data.GemPyEngineConfig(backend=gp.data.AvailableBackends.PYTORCH)\n )\n\n if plot_section is True:\n # 2D Plot\n gpv.plot_2d(geo_data, cell_number=['mid'], direction='y', show_data=True)\n gpv.plot_3d(geo_data, image=False)\n\n return geo_data.structural_frame\n\n\n# %%\n# Discordant layering 1\n# =====================\n\ncreate_example('Model 1')\n\n# %%\n# Discordant layering 2\n# =====================\n\ncreate_example('Model 2')\n\n# %%\n# Discordant layering 3\n# =====================\n\ncreate_example('Model 3')\n\n# %%\n# One normal Fault 1\n# ==================\n\ncreate_example('Model 5')\n\n# %%\n# Fold\n# ====\n\ncreate_example('Model 6')\n\n# %%\n# Graben\n# ======\n\ncreate_example('Model 7')\n\n# %%\n# One normal Fault 2\n# ===================\n\ncreate_example('Model 8')\n\n# %%\n# Horizontal tilted\n# =================\n\ncreate_example('Model 9')\n\n# %%\n\n# generate_all_models(['Model 1', 'Model 2', 'Model 3',\n# 'Model 5', 'Model 6', 'Model 7',\n# 'Model 8', 'Model 9'])\n" }, { "path": "examples/examples/real/Alesmodel.py", "content": "\"\"\"\nModel of Ales, France: Plotting Sections and Maps\n======================================\n\"\"\"\n\n# %%\n# .. admonition:: Explanation\n#\n# This model is generally quite unstable and requires float64 precision to determine a solution.\n# The lack of data in one corner for the TRIAS and LIAS series causes the model to bend unrealistically,\n# eroding the CARBO layer, which disappears in that section. \n#\n\nimport gempy as gp\nimport gempy_viewer as gpv\nimport os\n\n# %%\n# Setting up paths\ncwd = os.getcwd()\ndata_path = os.path.join(cwd, 'examples') if 'examples' not in cwd else os.path.join(cwd, '../..')\n\npath_interf = os.path.join(data_path, \"data/input_data/AlesModel/2018_interf.csv\")\npath_orient = os.path.join(data_path, \"data/input_data/AlesModel/2018_orient_clust_n_init5_0.csv\")\npath_dem = os.path.join(data_path, \"data/input_data/AlesModel/_cropped_DEM_coarse.tif\")\n\n# %%\n# Creating the geological model\ngeo_model: gp.data.GeoModel = gp.create_geomodel(\n project_name='Claudius',\n extent=[729550.0, 751500.0, 1913500.0, 1923650.0, -1800.0, 800.0],\n resolution=None,\n refinement=6,\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=path_orient,\n path_to_surface_points=path_interf,\n )\n)\n\n# %%\n# Setting up the section grid\ngp.set_section_grid(\n grid=geo_model.grid,\n section_dict={\n 'section1': ([732000, 1916000], [745000, 1916000], [200, 150])\n }\n)\n\n# %%\n# Sorting lithologies\ngp.map_stack_to_surfaces(\n gempy_model=geo_model,\n mapping_object={\n 'fault_left': 'fault_left',\n 'fault_right': 'fault_right',\n 'fault_lr': 'fault_lr',\n 'Trias_Series': ('TRIAS', 'LIAS'),\n 'Carbon_Series': 'CARBO',\n 'Basement_Series': 'basement'\n },\n remove_unused_series=True\n)\n\n# %%\n# Changing colors\ngeo_model.structural_frame.get_element_by_name(\"LIAS\").color = \"#015482\"\ngeo_model.structural_frame.get_element_by_name(\"TRIAS\").color = \"#9f0052\"\ngeo_model.structural_frame.get_element_by_name(\"CARBO\").color = \"#ffbe00\"\n\n# %%\n# Plotting the 2D model\ngpv.plot_2d(geo_model, direction='y')\n\n# %%\n# Plotting section traces\ngpv.plot_section_traces(geo_model)\n\n# %%\n# Setting faults\ngp.set_is_fault(\n frame=geo_model.structural_frame,\n fault_groups=[\n geo_model.structural_frame.get_group_by_name('fault_left'),\n geo_model.structural_frame.get_group_by_name('fault_right'),\n geo_model.structural_frame.get_group_by_name('fault_lr')\n ],\n change_color=True\n)\n\n# %%\n# Setting topography from file\ngp.set_topography_from_file(\n grid=geo_model.grid,\n filepath=path_dem,\n crop_to_extent=[729550.0, 751500.0, 1913500.0, 1923650.0]\n)\n\n# Plotting 3D model with topography\ngpv.plot_3d(geo_model, show_topography=True, ve=1, image=True)\n\n# %%\n# Getting the Carbon Series\ncarbo = geo_model.structural_frame.get_group_by_name(\"Carbon_Series\")\n\n# %%\n# Modifying interpolation options for better model fitting\ngeo_model.interpolation_options.number_octree_levels_surface = 4\ngeo_model.interpolation_options.kernel_options.range = 0.8\ngp.modify_surface_points(\n geo_model=geo_model,\n elements_names=[\"CARBO\", \"LIAS\", \"TRIAS\"],\n nugget=0.005\n)\n\n# %%\n# Displaying the structural frame\nprint(geo_model.structural_frame)\ngeo_model.structural_frame\n\n# %%\n# Explanation of model characteristics and adjustments\n# This model has characteristics that make it difficult to get the right default values:\n# - It is large, and we want high resolution\n# - Some series have a large conditional number (i.e., the model input is not very stable)\n# To address these issues:\n# - Reduce the chunk size during evaluation to trade speed for memory\n# - Reduce the std of the error parameter in octree refinement, which evaluates fewer voxels but may leave some without refinement\n# Enable debugging options to help tune these parameters.\n\n# %%\n# Setting verbose and condition number options for debugging\ngeo_model.interpolation_options.evaluation_options.verbose = True\ngeo_model.interpolation_options.kernel_options.compute_condition_number = True\n\n# %%\n# Observations and parameter adjustments\n# The octree refinement is making the octree grid almost dense, and smaller chunks are needed to avoid running out of memory.\n# Adjusting parameters accordingly:\n\ngeo_model.interpolation_options.evaluation_options.octree_error_threshold = 0.5\ngeo_model.interpolation_options.evaluation_options.evaluation_chunk_size = 50_000\n\n# %%\n# Computing the model with the adjusted settings\ngeo_model.interpolation_options.mesh_extraction = False\n_ = gp.compute_model(\n geo_model,\n engine_config=gp.data.GemPyEngineConfig(\n backend=gp.data.AvailableBackends.PYTORCH,\n use_gpu=True,\n dtype=\"float64\"\n )\n)\n\n# %%\n# Plotting the 2D model with and without topography\ngpv.plot_2d(geo_model, show_topography=False, section_names=['topography'], show_lith=True)\ngpv.plot_2d(geo_model, cell_number=[4], direction=['y'], show_topography=True, show_data=True)\ngpv.plot_2d(geo_model, cell_number=[-4], direction=['y'], show_topography=True, show_data=True)\n\n# %%\n# Setting thumbnail number for Sphinx-Gallery\n# sphinx_gallery_thumbnail_number = -1\ngpv.plot_3d(geo_model, show_lith=True, show_topography=True, kwargs_plot_structured_grid={'opacity': 0.8})\n" }, { "path": "examples/examples/real/Claudius.py", "content": "\"\"\"\nGeomodeling benchmark: the \"Claudius\"-Model\n========\n\nThis model is part of a geomodeling benchmaring effort. More information (and, hopefully, publication) coming.\n\"\"\"\n\n# %%\nimport sys, os\n\n# Importing gempy\nimport gempy as gp\nimport gempy_viewer as gpv\n\n# Aux imports\nimport numpy as np\nimport pandas as pn\n\n# %%\n# Loading data from repository:\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# \n# With pandas we can do it directly from the web and with the right args\n# we can directly tidy the data in gempy style:\n# \n\n# %% \n\ndata_path = os.path.abspath('../../data/input_data/Claudius')\n\nreduce_data_by = 30\n\ndfs = []\nfor letter in 'ABCD':\n dfs.append(\n pn.read_csv(\n filepath_or_buffer=f\"{data_path}/{letter}Points.csv\",\n sep=';',\n names=['X', 'Y', 'Z', 'surface', 'cutoff'],\n header=0\n )[::reduce_data_by]\n )\n\n# Add fault:\ndfs.append(\n pn.read_csv(\n filepath_or_buffer=f\"{data_path}/Fault.csv\",\n names=['X', 'Y', 'Z', 'surface'],\n header=0,\n sep=','\n )\n)\n\nsurface_points = pn.concat(dfs, sort=True)\nsurface_points['surface'] = surface_points['surface'].astype('str')\nsurface_points.reset_index(inplace=True, drop=False)\nsurface_points.tail()\n\n# %%\nsurface_points.dtypes\n\n# %%\n# How many points are per surface\n# \n\n# %% \nsurface_points.groupby('surface').count()\n\n# %%\n# Now we do the same with the orientations:\n# \n\n# %% \ndfs = []\n\nfor surf in ['0', '330']:\n o = pn.read_csv(\n filepath_or_buffer=f\"{data_path}/Dips.csv\",\n sep=';',\n names=['X', 'Y', 'Z', 'G_x', 'G_y', 'G_z', '-'],\n header=1\n )\n\n # Orientation needs to belong to a surface. This is mainly to categorize to which series belong and to\n # use the same color\n o['surface'] = surf\n dfs.append(o)\norientations = pn.concat(dfs, sort=True)\norientations.reset_index(inplace=True, drop=False)\n\norientations.tail()\n\n# %%\norientations.dtypes\n\n# %%\n# Data initialization:\n# ~~~~~~~~~~~~~~~~~~~~\n# \n# Suggested size of the axis-aligned modeling box: Origin: 548800 7816600\n# -8400 Maximum: 552500 7822000 -11010\n# \n# Suggested resolution: 100m x 100m x -90m (grid size 38 x 55 x 30)\n# \n\n# %% \n# Number of voxels:\nnp.array([38, 55, 30]).prod()\n\nsurface_points_table: gp.data.SurfacePointsTable = gp.data.SurfacePointsTable.from_arrays(\n x=surface_points['X'].values,\n y=surface_points['Y'].values,\n z=surface_points['Z'].values,\n names=surface_points['surface'].values\n)\n\norientations_table: gp.data.OrientationsTable = gp.data.OrientationsTable.from_arrays(\n x=orientations['X'].values,\n y=orientations['Y'].values,\n z=orientations['Z'].values,\n G_x=orientations['G_x'].values,\n G_y=orientations['G_y'].values,\n G_z=orientations['G_z'].values,\n names=orientations['surface'].values,\n name_id_map=surface_points_table.name_id_map # ! Make sure that ids and names are shared\n)\n\nstructural_frame: gp.data.StructuralFrame = gp.data.StructuralFrame.from_data_tables(\n surface_points=surface_points_table,\n orientations=orientations_table\n)\n\ngeo_model: gp.data.GeoModel = gp.create_geomodel(\n project_name='Claudius',\n extent=[548800, 552500, 7816600, 7822000, -11010, -8400],\n resolution=[38, 55, 30],\n refinement=5,\n structural_frame=structural_frame\n)\n\ngroup_fault = gp.data.StructuralGroup(\n name='Fault1',\n elements=[geo_model.structural_frame.structural_elements.pop(-2)],\n structural_relation=gp.data.StackRelationType.FAULT,\n fault_relations=gp.data.FaultsRelationSpecialCase.OFFSET_ALL\n)\n\ngeo_model.structural_frame.get_group_by_name(\"default_formation\").elements.pop(-1)\n\n# Insert the fault group into the structural frame:\ngeo_model.structural_frame.insert_group(0, group_fault)\n\ngp.set_is_fault(\n frame=geo_model.structural_frame,\n fault_groups=[geo_model.structural_frame.get_group_by_name('Fault1')]\n)\n\nprint(geo_model)\n\n# %%\n# We are going to increase the smoothness (nugget) of the data to increase\n# the conditional number of the matrix:\n# \n\n# %% \ngp.modify_surface_points(geo_model, nugget=0.01)\n\n# %%\n# Also the original poles are pointing downwards. We can change the\n# direction by calling the following:\n# \n\n# %%\ngp.modify_orientations(geo_model, polarity=-1)\n\n# %%\n# We need an orientation per series/fault. The faults does not have\n# orientation so the easiest is to create an orientation from the surface\n# points availablle:\n# \n\nelement = geo_model.structural_frame.get_element_by_name(\"Claudius_fault\")\nnew_orientations: gp.data.OrientationsTable = gp.create_orientations_from_surface_points_coords(\n xyz_coords=element.surface_points.xyz\n)\ngp.add_orientations(\n geo_model=geo_model,\n x=new_orientations.data['X'],\n y=new_orientations.data['Y'],\n z=new_orientations.data['Z'],\n pole_vector=new_orientations.grads,\n elements_names=\"Claudius_fault\"\n)\n\n# %% \ngpv.plot_2d(geo_model, direction='y')\n\n# %%\n# We will need to separate with surface belong to each series:\n# \n\ngp.map_stack_to_surfaces(\n gempy_model=geo_model,\n mapping_object={\n 'Default series': ('0', '60', '250'),\n 'Fault': 'Claudius_fault',\n 'Uncomformity': '330',\n }\n)\n# %%\n# So far we did not specify which series/faults are actula faults:\n# \n\n# %%\ngp.set_is_fault(\n frame=geo_model.structural_frame,\n fault_groups=[geo_model.structural_frame.get_group_by_name('Fault')]\n)\n\ngeo_model.structural_frame\n\n# %%\ngeo_model.interpolation_options.kernel_options.range = 1\ngp.compute_model(\n geo_model,\n gp.data.GemPyEngineConfig(\n backend=gp.data.AvailableBackends.numpy,\n use_gpu=False,\n dtype='float64'\n )\n)\n\n# %% \nsect = ['mid']\n\ngpv.plot_2d(geo_model, cell_number=sect, series_n=1, show_scalar=True, direction='x')\n\n# %% \ngpv.plot_2d(geo_model, cell_number=sect, show_data=True, direction='x')\n\n# %% \ngpv.plot_2d(geo_model, cell_number=[28], series_n=0, direction='y', show_scalar=True)\ngpv.plot_2d(geo_model, cell_number=[28], series_n=1, direction='y', show_scalar=True)\ngpv.plot_2d(geo_model, cell_number=[28], series_n=2, direction='y', show_scalar=True)\n\n# %% \ngpv.plot_2d(geo_model, cell_number=[28], show_data=True, direction='y')\n\n# %%\n\n# sphinx_gallery_thumbnail_number = 8\ngpv.plot_3d(geo_model, show_lith=True, show_data=True, show_boundaries=True)\n" }, { "path": "examples/examples/real/Greenstone.py", "content": "\"\"\"\nGreenstone.\n===========\n\"\"\"\nimport os\n\n# Importing gempy\nimport gempy as gp\nimport gempy_viewer as gpv\n\nprint(gp.__version__)\n\n# %%\n\ndata_path = os.path.abspath('../../data/input_data/tut_SandStone')\n\n# Importing the data from csv\n\ngeo_model: gp.data.GeoModel = gp.create_geomodel(\n project_name='Greenstone',\n extent=[696000, 747000, 6863000, 6930000, -20000, 200], # * Here we define the extent of the model\n refinement=6,\n # * Here we define the number of octree levels. If octree levels are defined, the resolution is ignored.\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=data_path + \"/SandStone_Foliations.csv\",\n path_to_surface_points=data_path + \"/SandStone_Points.csv\",\n hash_surface_points=None,\n hash_orientations=None\n )\n)\n\n# %% \ngpv.plot_2d(geo_model, direction=['z'])\n\n# %% \ngp.map_stack_to_surfaces(\n gempy_model=geo_model,\n mapping_object={\n \"EarlyGranite_Series\": 'EarlyGranite',\n \"BIF_Series\": ('SimpleMafic2', 'SimpleBIF'),\n \"SimpleMafic_Series\": 'SimpleMafic1', 'Basement': 'basement'\n }\n)\n\n# %% \ngp.compute_model(geo_model)\n\n# %% \ngpv.plot_2d(geo_model, cell_number=[-1], direction=['z'], show_data=False)\n\n# %% \ngpv.plot_2d(geo_model, cell_number=['mid'], direction='x')\n\n# %%\n# sphinx_gallery_thumbnail_number = -1\ngpv.plot_3d(geo_model)\n" }, { "path": "examples/examples/real/Hecho.py", "content": "\"\"\"\nGeomodeling benchmark: the \"Hecho\"-Model\n========\n\nThis model is part of a geomodeling benchmaring effort. More information (and, hopefully, publication) coming.\n\"\"\"\n\nimport os\n\nimport numpy as np\n# %% \n\n# Aux imports\nimport pandas as pn\nfrom gempy.modules.serialization.save_load import _validate_serialization\n\n# Importing gempy\nimport gempy as gp\nimport gempy_viewer as gpv\n\n# %%\n# Loading surface points from repository:\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# \n# With pandas we can do it directly from the web and with the right args\n# we can directly tidy the data in gempy style:\n# \n\n# %% \n\ndata_path = os.path.abspath('../../data/input_data/Hecho')\ndfs = []\n\n# First stratigraphic data\nfor letter in range(1, 10):\n dfs.append(pn.read_csv(\n filepath_or_buffer=data_path + '/H' + str(letter) + '.csv',\n sep=';',\n names=['X', 'Y', 'Z', 'surface', '_'],\n header=0\n ))\n\n# Also faults\nfor f in range(1, 4):\n fault_df = pn.read_csv(\n filepath_or_buffer=data_path + '/F' + str(f) + 'Line.csv',\n sep=';',\n names=['X', 'Y', 'Z'],\n header=0\n )\n fault_df['surface'] = 'f' + str(f)\n dfs.append(fault_df)\n\n# We put all the surfaces points together because is how gempy likes it:\nsurface_points = pn.concat(dfs, sort=True)\nsurface_points.reset_index(inplace=True, drop=False)\nsurface_points.tail()\n\n# %%\n# Now we do the same with the orientations:\n# \n\n# %% \norientations = pn.read_csv(\n filepath_or_buffer=data_path + '/Dips.csv',\n sep=';',\n names=['X', 'Y', 'Z', 'G_x', 'G_z', '_'],\n header=0\n)\n# Orientation needs to belong to a surface. This is mainly to categorize to which series belong and to\n# use the same color\norientations['surface'] = 0\n\n# We fill the laking direction with a dummy value:\norientations['G_y'] = 0\n\n# Replace -99999.00000 with NaN\norientations.replace(-99999.00000, np.nan, inplace=True)\n\n# Drop irrelevant columns\norientations.drop(columns=['_'], inplace=True)\n\n# Remove rows containing NaN\norientations.dropna(inplace=True)\n\n# %%\n# Data initialization:\n# ~~~~~~~~~~~~~~~~~~~~\n# \n# Suggested size of the axis-aligned modeling box: Origin: 0 -0.5 0\n# Maximum: 16 0.5 4.5\n# \n# Suggested resolution: 0.05m (grid size 321 x 21 x 91)\n# \n# %%\n\nsurface_points_table: gp.data.SurfacePointsTable = gp.data.SurfacePointsTable.from_arrays(\n x=surface_points['X'].values,\n y=surface_points['Y'].values,\n z=surface_points['Z'].values,\n names=surface_points['surface'].values.astype(str)\n)\n\norientations_table: gp.data.OrientationsTable = gp.data.OrientationsTable.from_arrays(\n x=orientations['X'].values,\n y=orientations['Y'].values,\n z=orientations['Z'].values,\n G_x=orientations['G_x'].values,\n G_y=orientations['G_y'].values,\n G_z=orientations['G_z'].values,\n names=orientations['surface'].values.astype(str),\n name_id_map=surface_points_table.name_id_map # ! Make sure that ids and names are shared\n)\n\nstructural_frame: gp.data.StructuralFrame = gp.data.StructuralFrame.from_data_tables(\n surface_points=surface_points_table,\n orientations=orientations_table\n)\n\ngeo_model: gp.data.GeoModel = gp.create_geomodel(\n project_name='Moureze',\n extent=[0, 16, -0.5, 0.5, 0, 4.5],\n resolution=[321, 21, 91],\n structural_frame=structural_frame\n)\n\ngp.set_section_grid(\n grid=geo_model.grid,\n section_dict={\n 'section': ((0., 0.), (16., 0.), (321, 91))\n },\n)\n\n# %%\n# We need an orientation per series/fault. The faults does not have\n# orientation so the easiest is to create an orientation from the surface\n# points availablle:\n# \n\n# %% \nf_names = ['f1', 'f2', 'f3']\nfor fn in f_names:\n element = geo_model.structural_frame.get_element_by_name(fn)\n new_orientations = gp.create_orientations_from_surface_points_coords(\n xyz_coords=element.surface_points.xyz\n )\n gp.add_orientations(\n geo_model=geo_model,\n x=new_orientations.data['X'],\n y=new_orientations.data['Y'],\n z=new_orientations.data['Z'],\n pole_vector=new_orientations.grads,\n elements_names=fn,\n name_id_map=element.surface_points.name_id_map \n )\n\n# %%\n# Now we can see how the data looks so far:\n# \n\n# %% \ngpv.plot_2d(geo_model)\n\n# %%\n# By default all surfaces belong to one unique series.\n# \n\n# %% \ngeo_model.structural_frame\n\n# %%\n# We will need to separate with surface belong to each series:\n# \n\n# %% \ngp.map_stack_to_surfaces(\n gempy_model=geo_model,\n mapping_object={'Fault1': 'f1', 'Fault2': 'f2', 'Fault3': 'f3'}\n)\n\n# %%\n# However if we want the faults to offset the “Default series”, they will\n# need to be more recent (higher on the pile). We can modify the order by:\n# \n\n# %% \n\n# %%\n# Lastly, so far we did not specify which series/faults are actula faults:\n# \n\n# %% \ngp.set_is_fault(\n frame=geo_model,\n fault_groups=['Fault1', 'Fault2', 'Fault3']\n)\n\n# %%\n# The default range is always the diagonal of the extent. Since in this\n# model data is very close we will need to reduce the range to 5-10% of\n# that value:\n# \n\n# %%\ngeo_model.interpolation_options.kernel_options.range *= 0.2\n\n# %%\n# Explanation of model characteristics and adjustments\n# This model has characteristics that make it difficult to get the right default values:\n# - It is large, and we want high resolution\n# - Some series have a large conditional number (i.e., the model input is not very stable)\n# To address these issues:\n# - Reduce the chunk size during evaluation to trade speed for memory\n# - Reduce the std of the error parameter in octree refinement, which evaluates fewer voxels but may leave some without refinement\n# Enable debugging options to help tune these parameters.\n\n# %%\n# Setting verbose and condition number options for debugging\ngeo_model.interpolation_options.kernel_options.compute_condition_number = True\n\n\n# %% \ngp.compute_model(\n gempy_model=geo_model,\n engine_config=gp.data.GemPyEngineConfig(\n backend=gp.data.AvailableBackends.PYTORCH,\n dtype='float64'\n ),\n validate_serialization=True\n)\n\n# %% \ngpv.plot_2d(geo_model, cell_number=[10], series_n=3, show_scalar=True)\n\n# %% \ngpv.plot_2d(geo_model, cell_number=[10], show_data=True)\n\n# %%\ngpv.plot_2d(geo_model, section_names=['section'], show_data=True)\n\n# %%\n# sphinx_gallery_thumbnail_number = 3\ngpv.plot_3d(geo_model, kwargs_plot_structured_grid={'opacity': .2})\n" }, { "path": "examples/examples/real/Moureze.py", "content": "\"\"\"\nGeomodeling benchmark: the \"Moureze\"-Model\n==========================================\n\nThis model is part of a geomodeling benchmaring effort. More information (and, hopefully, publication) coming.\n\"\"\"\n\nimport os\n\n# %% \n# These two lines are necessary only if gempy is not installed\n\n# Importing gempy\nimport gempy as gp\nimport gempy_viewer as gpv\n\n# Aux imports\nimport numpy as np\nimport pandas as pd\n\nfrom gempy_engine.config import AvailableBackends\n\n# %%\n# Loading surface points from repository:\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# \n# With pandas we can do it directly from the web and with the right args\n# we can directly tidy the data in gempy style:\n# \n\n# %% \n\ndata_path = os.path.abspath('../../data/input_data/Moureze')\nMoureze_points = pd.read_csv(\n filepath_or_buffer=data_path + '/Moureze_Points.csv',\n sep=';',\n names=['X', 'Y', 'Z', 'G_x', 'G_y', 'G_z', '_'],\n header=0,\n)\n\nSections_EW = pd.read_csv(\n filepath_or_buffer=data_path + '/Sections_EW.csv',\n sep=';',\n names=['X', 'Y', 'Z', 'ID', '_'], header=1\n).dropna()\n\nSections_NS = pd.read_csv(\n filepath_or_buffer=data_path + '/Sections_NS.csv',\n sep=';',\n names=['X', 'Y', 'Z', 'ID', '_'], header=1\n).dropna()\n\n# %%\n# Extracting the orientatins:\n# \n\n# %% \nmask_surfpoints = Moureze_points['G_x'] < -9999\nsurface_points = Moureze_points[mask_surfpoints][::10]\norientations = Moureze_points[~mask_surfpoints][::10]\n\n# %%\n# Giving an arbitrary value name to the surface\n# \n\n# %% \nsurface_points['surface'] = '0'\norientations['surface'] = '0'\n\n# %% \nsurface_points.tail()\n\n# %% \norientations.tail()\n\n# %%\n# Data initialization:\n# ~~~~~~~~~~~~~~~~~~~~\n# \n# Suggested size of the axis-aligned modeling box:\n# \n# Origin: -5 -5 -200\n# \n# Maximum: 305 405 -50\n# \n# Suggested resolution: 2m (grid size 156 x 206 x 76)\n# \n\n\n# %%\n# Only using one orientation because otherwhise it gets a mess\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# \n\n# %% \n# Number voxels\nnp.array([156, 206, 76]).prod()\n\n# %% \nresolution_requ = [156, 206, 76]\nresolution = [77, 103, 38]\nresolution_low = [45, 51, 38]\n\n\nsurface_points_table: gp.data.SurfacePointsTable = gp.data.SurfacePointsTable.from_arrays(\n x=surface_points['X'].values,\n y=surface_points['Y'].values,\n z=surface_points['Z'].values,\n names=surface_points['surface'].values.astype(str)\n)\n\norientations_table: gp.data.OrientationsTable = gp.data.OrientationsTable.from_arrays(\n x=orientations['X'].values,\n y=orientations['Y'].values,\n z=orientations['Z'].values,\n G_x=orientations['G_x'].values,\n G_y=orientations['G_y'].values,\n G_z=orientations['G_z'].values,\n names=orientations['surface'].values.astype(str),\n name_id_map=surface_points_table.name_id_map # ! Make sure that ids and names are shared\n)\n\nstructural_frame: gp.data.StructuralFrame = gp.data.StructuralFrame.from_data_tables(\n surface_points=surface_points_table,\n orientations=orientations_table\n)\n\ngeo_model: gp.data.GeoModel = gp.create_geomodel(\n project_name='Moureze',\n extent=[-5, 305, -5, 405, -200, -50],\n # resolution=resolution_low,\n refinement=5,\n structural_frame=structural_frame\n)\n\n# %%\n# Now we can see how the data looks so far:\n# \n\n# %% \ngpv.plot_2d(geo_model, direction='y')\n\n\n# %%\n# The default range is always the diagonal of the extent. Since in this\n# model data is very close we will need to reduce the range to 5-10% of\n# that value:\n# \n\n# %% \ngeo_model.interpolation_options.kernel_options.range *= 0.2\ngeo_model.interpolation_options.evaluation_options.verbose = True\ngeo_model.interpolation_options.evaluation_options.octree_error_threshold = 1.5\ngeo_model.interpolation_options.evaluation_options.number_octree_levels_surface = 5\n\n# %%\ngp.compute_model(\n gempy_model=geo_model,\n engine_config=gp.data.GemPyEngineConfig(\n use_gpu=False, \n dtype='float32',\n backend=AvailableBackends.PYTORCH\n )\n)\n\n\n# %%\n# Time\n# ~~~~\n# \n# 300k voxels 3.5k points\n# ^^^^^^^^^^^^^^^^^^^^^^^\n# \n# - Nvidia 2080: 500 ms ± 1.3 ms per loop (mean ± std. dev. of 7 runs, 1\n# loop each), Memory 1 Gb\n# - CPU 14.2 s ± 82.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop\n# each), Memory: 1.3 Gb\n# \n# 2.4 M voxels, 3.5k points\n# ^^^^^^^^^^^^^^^^^^^^^^^^^\n# \n# - CPU 2min 33s ± 216 ms per loop (mean ± std. dev. of 7 runs, 1 loop\n# each) Memory: 1.3 GB\n# - Nvidia 2080: 1.92 s ± 6.74 ms per loop (mean ± std. dev. of 7 runs, 1\n# loop each) 1 Gb\n# \n# 2.4 M voxels, 3.5k points 3.5 k orientations\n# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n# \n# - Nvidia 2080: 2.53 s ± 1.31 ms per loop (mean ± std. dev. of 7 runs, 1\n# loop each)\n# \n\n# %% \n\ngpv.plot_2d(geo_model, cell_number='mid', series_n=0, show_scalar=True)\n# %% \ngpv.plot_2d(geo_model, cell_number='mid', show_data=True, direction='y')\n\n# %%\n# sphinx_gallery_thumbnail_number = 4\ngpv.plot_3d(geo_model)\n" }, { "path": "examples/examples/real/Perth_basin.py", "content": "\"\"\"\nA geological model of the Perth basin, Australia\n============\n\"\"\"\nimport os\n\n# Importing GemPy\nimport gempy as gp\nimport gempy_viewer as gpv\n\n# Importing auxiliary libraries\nimport matplotlib\n\nmatplotlib.rcParams['figure.figsize'] = (20.0, 10.0)\nos.environ[\"aesara_FLAGS\"] = \"mode=FAST_RUN,device=cuda\"\n\n# %%\ncwd = os.getcwd()\nif 'examples' not in cwd:\n data_path = os.getcwd() + '/examples'\nelse:\n data_path = cwd + '/../..'\n\n# %% \ngeo_model: gp.data.GeoModel = gp.create_geomodel(\n project_name='Perth_Basin',\n extent=[337000, 400000, 6640000, 6710000, -18000, 1000],\n refinement=6,\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=data_path + \"/data/input_data/perth_basin/Paper_GU2F_sc_faults_topo_Foliations.csv\",\n path_to_surface_points=data_path + \"/data/input_data/perth_basin/Paper_GU2F_sc_faults_topo_Points.csv\",\n )\n)\n\n# %%\ngeo_model.structural_frame\n\n# %% \ndel_surfaces = ['Cadda', 'Woodada_Kockatea', 'Cattamarra']\nfor s in del_surfaces:\n gp.remove_element_by_name(geo_model, s)\n\ngeo_model.structural_frame\n\n# %% \ngp.map_stack_to_surfaces(\n gempy_model=geo_model,\n mapping_object={\n \"fault_Abrolhos_Transfer\": [\"Abrolhos_Transfer\"],\n \"fault_Coomallo\": [\"Coomallo\"],\n \"fault_Eneabba_South\": [\"Eneabba_South\"],\n \"fault_Hypo_fault_W\": [\"Hypo_fault_W\"],\n \"fault_Hypo_fault_E\": [\"Hypo_fault_E\"],\n \"fault_Urella_North\": [\"Urella_North\"],\n \"fault_Darling\": [\"Darling\"],\n \"fault_Urella_South\": [\"Urella_South\"],\n \"Sedimentary_Series\": ['Cretaceous', 'Yarragadee', 'Eneabba', 'Lesueur', 'Permian']\n }\n)\n\n# %%\n# Select which series are faults\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# \n\ngp.set_is_fault(\n geo_model,\n fault_groups=[\n \"fault_Abrolhos_Transfer\",\n \"fault_Coomallo\",\n \"fault_Eneabba_South\",\n \"fault_Hypo_fault_W\",\n \"fault_Hypo_fault_E\",\n \"fault_Urella_North\",\n \"fault_Darling\",\n \"fault_Urella_South\"\n ],\n)\n\n\n# %% \n# gp.set_fault_relation(geo_model, fr)\n\nprint(geo_model.structural_frame.fault_relations)\n\n# %% \n# %matplotlib inline\ngpv.plot_2d(geo_model, direction=['z'])\n\n# %% \ngp.set_topography_from_random(geo_model.grid)\n\n# %% \ngpv.plot_3d(geo_model)\n\n# %% \ngp.compute_model(\n gempy_model=geo_model,\n engine_config= gp.data.GemPyEngineConfig(\n backend=gp.data.AvailableBackends.PYTORCH,\n dtype=\"float64\",\n )\n)\n\n# %% \ngpv.plot_2d(geo_model, cell_number=\"mid\")\n\n# %% \ngpv.plot_2d(geo_model, cell_number=\"mid\", series_n=-1, show_scalar=True)\n\n# %% \ngpv.plot_2d(geo_model, cell_number=[12], direction=[\"y\"], show_data=True, show_topography=True)\n\n# %%\n# sphinx_gallery_thumbnail_number = 6\ngpv.plot_3d(geo_model, show_topography=True)" }, { "path": "examples/examples/real/README.rst", "content": "Real case studies\n-----------------\n\nThese models are built with real data\n" }, { "path": "examples/examples/real/__init__.py", "content": "" }, { "path": "examples/examples/real/mik.py", "content": "\"\"\"\nUnknown Model: Importing Borehole Data and Building a 3D Geological Model with GemPy\n====================================================================================\n\"\"\"\n\n# %%\n# In this section, we will explore how to take borehole or drillhole data and convert it into a format compatible with GemPy,\n# creating a 3D geological model. Borehole data is commonly collected in the mining, oil, and gas industries and contains \n# information about subsurface geological formations, including lithologies, stratigraphy, and the geometry of layers or faults.\n#\n# For this, we will rely on several helper functions from the `subsurface` package to extract borehole data and translate it \n# into a 3D structure that GemPy can use for modeling.\n# We will cover:\n#\n# - Downloading the borehole data\n# - Processing the data using `subsurface`\n# - Visualizing borehole locations and lithology data in 3D\n#\n# Downloading Borehole Data\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# The borehole data is hosted online, and we can use the `pooch` library to download it directly. `pooch` is a library for \n# fetching datasets from the internet. We will download a CSV file that contains the borehole information, including collar\n# positions, survey data, and lithology logs.\n#\n# Let's start by downloading the dataset and inspecting its content.\n\n# %%\n# sphinx_gallery_thumbnail_number = -1\n\n# List of relative paths used during the workshop\nimport numpy as np\nimport pandas as pd\nimport pooch\nimport pyvista\n\nfrom subsurface.core.geological_formats import Collars, Survey, BoreholeSet\nfrom subsurface.core.geological_formats.boreholes._combine_trajectories import MergeOptions\nfrom subsurface.core.reader_helpers.readers_data import GenericReaderFilesHelper\nfrom subsurface.core.structs.base_structures.base_structures_enum import SpecialCellCase\nfrom subsurface.modules.reader.wells.read_borehole_interface import read_collar, read_survey, read_lith\nimport subsurface as ss\nfrom subsurface.modules.visualization import to_pyvista_points, pv_plot, to_pyvista_line, init_plotter\n\n# Importing GemPy\nimport gempy as gp\n\n# %% md\n# We use `pooch` to download the dataset into a temp file:\n\n# %%\nurl = \"https://raw.githubusercontent.com/softwareunderground/subsurface/main/tests/data/borehole/kim_ready.csv\"\nknown_hash = \"a91445cb960526398e25d8c1d2ab3b3a32f7d35feaf33e18887629b242256ab6\"\n\n# Your code here:\nraw_borehole_data_csv = pooch.retrieve(url, known_hash)\n\n# %% md\n# Now we can use `subsurface` function to help us reading csv files into pandas dataframes that the package can understand. \n# Since the combination of styles data is provided can highly vary from project to project, `subsurface` provides some *helpers* \n# functions to parse different combination of .csv\n\n# %%\n# Read the collar data from the CSV\ncollar_df: pd.DataFrame = read_collar(\n GenericReaderFilesHelper(\n file_or_buffer=raw_borehole_data_csv,\n index_col=\"name\",\n usecols=['x', 'y', 'altitude', \"name\"],\n columns_map={\n \"name\": \"id\", # ? Index name is not mapped\n \"X\": \"x\",\n \"Y\": \"y\",\n \"altitude\": \"z\"\n }\n )\n)\n\n# Convert to UnstructuredData\nunstruc: ss.UnstructuredData = ss.UnstructuredData.from_array(\n vertex=collar_df[[\"x\", \"y\", \"z\"]].values,\n cells=SpecialCellCase.POINTS\n)\n\npoints = ss.PointSet(data=unstruc)\ncollars: Collars = Collars(\n ids=collar_df.index.to_list(),\n collar_loc=points\n)\n\n# %%\n# Visualizing the Borehole Collars\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# Once we have the borehole collars data, we can visualize them in 3D using the `pyvista` package. This gives us a good \n# overview of where the boreholes are located in space. In this visualization, each borehole will be represented by a point, \n# and we will label the boreholes using their IDs.\nwell_mesh = to_pyvista_points(collars.collar_loc)\n\n# Plot the collar points\npv_plot([well_mesh], image_2d=False)\n\n# %%\n# Reading Borehole Survey Data\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# Borehole surveys give us information about the trajectory of each borehole, including its depth (measured depth). \n# The survey data allows us to compute the full 3D path of each wellbore. We will use the `read_survey` function from \n# `subsurface` to read this data.\n\n# %%\n# Create the Survey object\nsurvey_df: pd.DataFrame = read_survey(\n GenericReaderFilesHelper(\n file_or_buffer=raw_borehole_data_csv,\n index_col=\"name\",\n usecols=[\"name\", \"md\"]\n )\n)\n\nsurvey: Survey = Survey.from_df(survey_df)\n\nsurvey\n\n# %%\n# Reading Lithology Data\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# Next, we will read the lithology data. Lithology logs describe the rock type or geological unit encountered at different \n# depths within each borehole. Using `read_lith`, we will extract the lithology data, which includes the top and base depths\n# of each geological formation within the borehole, as well as the formation name.\n\n# %%\n# Your code here:\nlith = read_lith(\n GenericReaderFilesHelper(\n file_or_buffer=raw_borehole_data_csv,\n usecols=['name', 'top', 'base', 'formation'],\n columns_map={\n 'top': 'top',\n 'base': 'base',\n 'formation': 'component lith',\n }\n )\n)\n\n# Update survey data with lithology information\nsurvey.update_survey_with_lith(lith)\n\nlith\n# %%\n# Creating a Borehole Set and Visualizing in 3D\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# Now that we have both the collar data and the lithology logs, we can combine them into a `BoreholeSet` object. This object \n# combines the collar, survey, and lithology data and allows us to create a 3D visualization of the borehole trajectories and\n# their associated lithologies.\n#\n# We will use `pyvista` to plot the borehole trajectories as lines, and we will color them according to their lithologies. \n# Additionally, we will label the collars for easy identification.\n\n# Combine collar and survey into a BoreholeSet\nborehole_set = BoreholeSet(\n collars=collars,\n survey=survey,\n merge_option=MergeOptions.INTERSECT\n)\n\n# %%\n# Visualize boreholes with pyvista\nimport matplotlib.pyplot as plt\n\nwell_mesh = to_pyvista_line(\n line_set=borehole_set.combined_trajectory,\n active_scalar=\"lith_ids\",\n radius=40\n)\n\np = init_plotter()\n\n# Set colormap for lithologies\nboring_cmap = plt.get_cmap(name=\"viridis\", lut=14)\np.add_mesh(well_mesh, cmap=boring_cmap)\n\ncollar_mesh = to_pyvista_points(collars.collar_loc)\n\np.add_mesh(collar_mesh, render_points_as_spheres=True)\np.add_point_labels(\n points=collars.collar_loc.points,\n labels=collars.ids,\n point_size=10,\n shape_opacity=0.5,\n font_size=12,\n bold=True\n)\n\n\np.show()\n\n# %% md\n# Structural Elements from Borehole Set\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n#\n# Now that we have successfully imported and visualized the borehole data, we can move on to creating the geological \n# formations (or structural elements) based on the borehole data. Each lithological unit will be associated with a unique \n# identifier and a color, allowing us to distinguish between different formations when we visualize the model.\n#\n# GemPy offers the function `gempy.structural_elements_from_borehole_set`, which extracts these structural elements from \n# the borehole data and associates each one with a name, ID, and color.\n\n# %%\n# Initialize the color generator for formations\ncolors_generator = gp.data.ColorsGenerator()\n\n# Define formations and colors\nelements = gp.structural_elements_from_borehole_set(\n borehole_set=borehole_set,\n elements_dict={\n \"Basement\": {\n \"id\": -1,\n \"color\": next(colors_generator)\n },\n \"etchgoin\": {\n \"id\": 1,\n \"color\": next(colors_generator)\n },\n \"macoma\": {\n \"id\": 2,\n \"color\": next(colors_generator)\n },\n \"chanac\": {\n \"id\": 3,\n \"color\": next(colors_generator)\n },\n \"mclure\": {\n \"id\": 4,\n \"color\": next(colors_generator)\n },\n \"santa_margarita\": {\n \"id\": 5,\n \"color\": next(colors_generator)\n },\n \"fruitvale\": {\n \"id\": 6,\n \"color\": next(colors_generator)\n },\n \"round_mountain\": {\n \"id\": 7,\n \"color\": next(colors_generator)\n },\n \"olcese\": {\n \"id\": 8,\n \"color\": next(colors_generator)\n },\n \"freeman_jewett\": {\n \"id\": 9,\n \"color\": next(colors_generator)\n },\n \"vedder\": {\n \"id\": 10,\n \"color\": next(colors_generator)\n },\n \"eocene\": {\n \"id\": 11,\n \"color\": next(colors_generator)\n },\n \"cretaceous\": {\n \"id\": 12,\n \"color\": next(colors_generator)\n },\n }\n)\n\n\n# %% md\n# Initializing the GemPy Model\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# After defining the geological formations, we need to initialize the GemPy model. The first step in this process is to \n# create a `GeoModel` object, which serves as the core container for all data related to the geological model.\n#\n# We will also define a **regular grid** to interpolate the geological layers. GemPy uses a meshless interpolator to \n# create geological models in 3D space, but grids are convenient for visualization and computation.\n#\n# GemPy supports various grid types, such as regular grids for visualization, custom grids, topographic grids, and more.\n# For this example, we will use a regular grid with a medium resolution.\n\n# %%\nimport gempy_viewer as gpv\n\n# Create a structural group with the elements\ngroup = gp.data.StructuralGroup(\n name=\"Stratigraphic Pile\",\n elements=elements,\n structural_relation=gp.data.StackRelationType.ERODE\n)\n\n# Define the structural frame\nstructural_frame = gp.data.StructuralFrame(\n structural_groups=[group],\n color_gen=colors_generator\n)\n\n# %% md\n# Defining Model Extent and Grid Resolution\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# We now determine the extent of our model based on the surface points provided. This ensures that the grid covers the\n# entire area where the geological data points are located. Additionally, we set a grid resolution of 50x50x50 for a \n# balance between performance and model detail.\n\nall_surface_points_coords: gp.data.SurfacePointsTable = structural_frame.surface_points_copy\nextent_from_data = all_surface_points_coords.xyz.min(axis=0), all_surface_points_coords.xyz.max(axis=0)\n# Initialize GeoModel\ngeo_model = gp.data.GeoModel.from_args(\n name=\"Stratigraphic Pile\",\n structural_frame=structural_frame,\n grid=gp.data.Grid(\n 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]],\n resolution=(50, 50, 50)\n ),\n interpolation_options=gp.data.InterpolationOptions.from_args(\n range=5,\n c_o=10,\n mesh_extraction=True,\n number_octree_levels=3,\n ),\n)\n\n# %% md\n# 3D Visualization of the Model\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# After initializing the GeoModel, we can proceed to visualize it in 3D using GemPy's `plot_3d` function. This function \n# allows us to see the full 3D geological model with all the defined formations.\n\ngempy_plot = gpv.plot_3d(\n model=geo_model,\n kwargs_pyvista_bounds={\n 'show_xlabels': False,\n 'show_ylabels': False,\n },\n show=True,\n image=False\n)\n\n# %% md\n# Adding Boreholes and Collars to the Visualization\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# To enhance the 3D model, we can combine the geological formations with the borehole trajectories and collar points that we\n# visualized earlier. This will give us a complete picture of the subsurface, showing both the lithological units and the borehole paths.\n\nsp_mesh: pyvista.PolyData = gempy_plot.surface_points_mesh\n\npyvista_plotter = init_plotter()\npyvista_plotter.show_bounds(all_edges=True)\n\n# Set limits for the units to visualize\nunits_limit = [0, 13]\npyvista_plotter.add_mesh(\n well_mesh.threshold(units_limit),\n cmap=\"tab20c\",\n clim=units_limit\n)\n\n# Add collar points\npyvista_plotter.add_mesh(\n collar_mesh,\n point_size=10,\n render_points_as_spheres=True\n)\n\n# Label the collars with their names\npyvista_plotter.add_point_labels(\n points=collars.collar_loc.points,\n labels=collars.ids,\n point_size=10,\n shape_opacity=0.5,\n font_size=12,\n bold=True\n)\n# Add surface points from the geological model\npyvista_plotter.add_actor(gempy_plot.surface_points_actor)\n\n# Show the final 3D plot\npyvista_plotter.show()\n\n\n\n# %% md\n# Step-by-Step Model Building\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# When building a geological model, it's often better to proceed step by step, adding one surface at a time, rather\n# than trying to interpolate all formations at once. This allows for better control over the model and helps avoid \n# potential issues from noisy or irregular data.\n#\n\n# %% md\n# Adding Surfaces and Formations\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# In GemPy, surfaces mark the bottom of each geological unit. For our model, we will add the first two formations \n# along with the basement, which always needs to be defined. After this, we can visualize the surfaces in 2D.\n\n# %%\ngroup = gp.data.StructuralGroup(\n name=\"Stratigraphic Pile Top\",\n elements=elements[:3],\n structural_relation=gp.data.StackRelationType.ERODE\n)\ngeo_model.structural_frame.structural_groups[0] = group\n\n\n# Visualize the surfaces in 2D\n\ng2d = gpv.plot_2d(geo_model)\n# %%\ng2d.fig\n# %% md\n# Minimum Input Data for Interpolation\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# To interpolate the geological layers, GemPy requires at least:\n#\n# - Two surface points per geological unit\n# - One orientation measurement per series\n#\n# Let's add an orientation for one of the units.\n\ngp.add_orientations(\n x=[300000],\n y=[3930000],\n z=[0],\n elements_names=elements[0].name,\n pole_vector=np.array([0, 0, 1]),\n geo_model=geo_model\n)\n\n# %% md\n# Model Computation\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# Now that we have the necessary surface points and orientations, we can compute the final geological model. The \n# `compute_model` function will take all the input data and perform the interpolation to generate the 3D subsurface structure.\n\ngeo_model.interpolation_options\n\n# %%\ngp.compute_model(geo_model)\n\n# %% md\n# Final 3D Visualization\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# Let's take a look at the final model, combining the borehole data and geological formations in 3D.\n\ng3d = gpv.plot_3d(geo_model, show_lith=False, show=False)\ng3d.p.add_mesh(well_mesh)\ng3d.p.show()\n\n\n# %% md\n# Conclusion\n# \"\"\"\"\"\"\"\"\"\"\n#\n# In this tutorial, we have demonstrated how to take borehole data and create a 3D geological model in GemPy. We explored \n# how to extract structural elements from borehole data, set up a regular grid for interpolation, and visualize the \n# resulting model in both 2D and 3D.\n#\n# GemPy's flexibility allows you to iteratively build models and refine your inputs for more accurate results, and it\n# integrates seamlessly with borehole data for subsurface geological modeling.\n#\n# For further reading and resources, check out:\n#\n# \n# Extra Resources\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# Page:\n# https://www.gempy.org/\n# \n# Paper:\n# https://www.gempy.org/theory\n# \n# Gitub:\n# https://github.com/cgre-aachen/gempy\n# \n# Further training and collaborations\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# https://www.terranigma-solutions.com/\n# \n# \n#\n" }, { "path": "examples/integrations/README.rst", "content": "Integrations\n============\n\nHere there are examples showing GemPy working together with other libraries\nof geoscientific stack. This needs to grow!" }, { "path": "examples/integrations/__init__.py", "content": "" }, { "path": "examples/integrations/gempy_export_MOOSE.py", "content": "\"\"\"\nExport a geological model from GemPy to use in MOOSE\n====================================================\n\n\"\"\"\n\n# %%\nimport gempy as gp\n\n# %%\n# Creating a geological model\n# ---------------------------\n# \n# The procedure of generating a geological model is presented in detail in\n# `Chapter\n# 1-1 `__\n# of the GemPy tutorials, so it will only be briefly presented here\n# \n\n# %% \n# Initiate a model\ngeo_model = gp.create_model('tutorial_moose_exp')\ndata_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'\n\n# %% \n# Import data from CSV-files with setting the resolution and model extent\ngp.init_data(geo_model, [0, 2000., 0, 2000., 0, 2000.], [50, 50, 80],\n path_o=data_path + \"/data/input_data/tut_chapter1/simple_fault_model_orientations.csv\",\n path_i=data_path + \"/data/input_data/tut_chapter1/simple_fault_model_points.csv\",\n default_values=True)\n\n# %% \n# present the units and series\ngeo_model.surfaces\n\n# %% \n# combine units in series and make two series, as the fault needs its own\ngp.map_stack_to_surfaces(geo_model,\n {\"Fault_Series\": 'Main_Fault',\n \"Strat_Series\": ('Sandstone_2', 'Siltstone', 'Shale', 'Sandstone_1', 'basement')},\n remove_unused_series=True)\n\n# set the fault series to be fault object\ngeo_model.set_is_fault(['Fault_Series'], change_color=False)\n\n# %% \n# check whether series were assigned correctly\ngeo_model.surfaces\n\n# %%\n# Model generation\n# ----------------\n# \n# After loading in the data, we set it up for interpolation and compute\n# the model.\n# \n\n# %% \n# set up interpolator\ngp.set_interpolator(geo_model,\n compile_aesara=True,\n aesara_optimizer='fast_compile',\n verbose=[])\n\n# %% \n# compute the model\ngp.compute_model(geo_model, compute_mesh=False);\n\n# %% \n# have a look at the data and computed model\ngp.plot_3d(geo_model)\n\n# %%\n# Exporting the Model to MOOSE\n# ----------------------------\n# \n# The voxel-model above already is the same as a model discretized in a\n# hexahedral grid, so my immediately be used as input in a simulation\n# tool, e.g. `MOOSE `__. For this, we need to\n# access to the unit IDs assigned to each voxel in GemPy. The array\n# containing these IDs is called ``lith_block``.\n# \n\n# %% \nids = geo_model.solutions.lith_block\nprint(ids)\n\n# %%\n# This array has the shape of ``(x,)`` and would be immediately useful, if\n# GemPy and the chosen simulation code would *populate* a grid in the same\n# way. Of course, however, that is not the case. This is why we have to\n# restructure the ``lith_block`` array, so it can be read correctly by\n# MOOSE.\n# \n\n# %% \n# model resolution\nnx, ny, nz = geo_model.grid.octree_grid.resolution\n\n# model extent\nxmin, xmax, ymin, ymax, zmin, zmax = geo_model.grid.octree_grid.extent\n\n# %%\n# These two parameters are important to, a) restructure ``lith_block``,\n# and b) write the input file for MOOSE correctly. For a), we need to\n# reshape ``lith_block`` again to its three dimensions and *re-flatten* it\n# in a *MOOSE-conform* way.\n# \n\n# %% \n# reshape to 3D array\nunits = ids.reshape((nx, ny, nz))\n# flatten MOOSE conform\nunits = units.flatten('F')\n\n# %%\n# | The importance of ``nx, ny, nz`` is apparent from the cell above. But\n# what about ``xmin``, …, ``zmax``?\n# | A MOOSE input-file for mesh generation has the following syntax:\n# \n# .. code:: python\n# \n# [MeshGenerators]\n# [./gmg]\n# type = GeneratedMeshGenerator\n# dim = 3\n# nx = 50\n# ny = 50\n# nz = 80\n# xmin = 0.0\n# xmax = 2000.0\n# yim = 0.0\n# ymax = 2000.0\n# zmin = 0.0\n# zmax = 2000.0\n# block_id = '1 2 3 4 5 6'\n# block_name = 'Main_Fault Sandstone_2 Siltstone Shale Sandstone_1 basement'\n# [../]\n# \n# [./subdomains]\n# type = ElementSubdomainIDGenerator\n# input = gmg\n# subdomain_ids = ' ' # here you paste the transformed lith_block vector\n# [../]\n# []\n# \n# [Mesh]\n# type = MeshGeneratorMesh\n# []\n# \n# So these parameters are required inputs in the ``[MeshGenerators]``\n# object in the MOOSE input file. ``GemPy`` has a method to directly\n# create such an input file, stored in ``gempy.utils.export.py``.\n# \n# The following cell shows how to call the method:\n# \n\n# %%\n\n# sphinx_gallery_thumbnail_path = '_static/GemPy_model_combined.png'\nimport gempy_plugins.utils.export as export\nexport.export_moose_input(geo_model, path='')\n\n# %%\n# This method automatically stores a file\n# ``geo_model_units_moose_input.i`` at the specified path. Either this\n# input file could be extended with parameters to directly run a\n# simulation, or it is used just for creating a mesh. In the latter case,\n# the next step would be, to run the compiled MOOSE executable witch the\n# optional flag ``--mesh-only``.\n# \n# E.g. with using the `PorousFlow\n# module `__:\n# \n# .. code:: bash\n# \n# $path_to_moose/moose/modules/porous_flow/porous_flow-opt -i pct_voxel_mesh.i --mesh-only\n# \n# How to compile MOOSE is described in their\n# `documentation `__.\n# \n# The now generated mesh with the name\n# ``geo_model_units_moose_input_in.e`` can be used as input for another\n# MOOSE input file, which contains the main simulation parameters. To call\n# the file with the grid, the following part has to be added in the MOOSE\n# simulation input file:\n# \n# .. code:: python\n# \n# [Mesh]\n# file = geo_model_units_moose_input_in.e\n# []\n# \n# .. raw:: html\n# \n#
\n# \n# The final output of the simulation may also be such an ``.e``, which\n# can, for instance, be opened with\n# `paraview `__. A simulated temperature field\n# (purely conductive) of the created model would look like this:\n# \n# .. figure:: https://raw.githubusercontent.com/Japhiolite/a-Moose-and-you/master/imgs/GemPy_model_combined.png\n# :alt: gempy_temperature\n#\n#\n" }, { "path": "examples/integrations/gempy_subsurface.py", "content": "\"\"\"\nGemPy - Subsurface Link\n=======================\n\"\"\"\n\nimport pooch\nimport numpy as np\nimport pandas as pd\n\nimport subsurface as sb\nfrom subsurface.reader import read_netcdf\n\ndata_url = \"https://raw.githubusercontent.com/softwareunderground/subsurface/main\" \\\n \"/examples/tutorials/wells_unstructured.nc\"\n\ndata_hash = \"206290db4e563e379361725349ebf4a02628f4700d361599aedff37fab9cf5b9\"\nborehole_unstructured_data_file = pooch.retrieve(url=data_url,\n known_hash=data_hash)\n\nunstruct = read_netcdf.read_unstruct(borehole_unstructured_data_file)\nunstruct\n\n# %%\nelement = sb.LineSet(unstruct)\nlines_mesh = sb.visualization.to_pyvista_line(element, radius=50)\n\n# Plot default LITH\nsb.visualization.pv_plot([lines_mesh])\n\n\n# %% md\n# Findig the boreholes bases\n# --------------------------\n# GemPy interpolates the bottom of a unit, therefore we need to be able to extract those points to be able tointerpolate them.\n# 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.\n# Lets find the base points of each well:\n\n# %%\n\n# Creating references to the xarray.DataArray\ncells_attr = unstruct.data.cell_attrs\ncells = unstruct.data.cells\nvertex = unstruct.data.vertex\n# Find vertex points at the boundary of two units\n# Marking each vertex\nbool_prop_change = cells_attr.values[1:] != cells_attr.values[:-1]\n# Getting the index of the vertex\nargs_prop_change = np.where(bool_prop_change)[0]\n# Getting the attr values at those points\nvals_prop_change = cells_attr[args_prop_change]\nvals_prop_change.to_pandas()\n\n# %%\n# Getting the vertex values at those points\nvertex_args_prop_change = cells[args_prop_change, 1]\ninterface_points = vertex[vertex_args_prop_change]\ninterface_points\n\n\n# %%\n# Creating a new UnstructuredData\ninterf_us= sb.UnstructuredData.from_array(vertex=interface_points.values, cells=\"points\",\n cells_attr=vals_prop_change.to_pandas())\ninterf_us\n\n# %% md\n# This new UnstructuredData object instead containing data that represent lines, contain point data at the bottom of each\n# unit. We can plot it very similar as before:\n\nelement = sb.PointSet(interf_us)\npoint_mesh = sb.visualization.to_pyvista_points(element)\nsb.visualization.pv_plot([lines_mesh, point_mesh])\n\n# %% md\n# GemPy: Initialize model\n# -----------------------\n# The first step to create a GemPy model is create a gempy.\n\n# %%\n\nimport gempy as gp\ngeo_model = gp.create_model(\"getting started\")\ngeo_model.set_regular_grid(extent=[275619, 323824, 3914125, 3961793, -3972.6, 313.922], resolution=[50,50,50])\ngp.set_interpolator(geo_model, aesara_optimizer='fast_compile', verbose=[])\n\n# %% md\n# Making a model step by step.\n# ----------------------------\n\n# The temptation at this point is to bring all the points into gempy and just interpolate. However, often that strategy\n# results in ill posed problems due to noise or irregularities in the data. gempy has been design to being able to\n# iterate rapidly and therefore a much better workflow use to be creating the model step by step.\n#\n# To do that, lets define a function that we can pass the name of the formation and get the assotiated vertex. Grab from\n# the interf_us the XYZ coordinates of the first layer:\n\n# %%\ndef get_interface_coord_from_surfaces(surface_names: list, verbose=False):\n df = pd.DataFrame(columns=[\"X\", \"Y\", \"Z\", \"surface\"])\n\n for e, surface_name in enumerate(surface_names):\n # The properties in subsurface start at 1\n val_property = formations.index(surface_name) + 1\n # Find the cells with the surface id\n args_from_first_surface = np.where(vals_prop_change == val_property)[0]\n if verbose: print(args_from_first_surface)\n # Find the vertex\n points_from_first_surface = interface_points[args_from_first_surface]\n if verbose: print(points_from_first_surface)\n\n # xarray.DataArray to pandas.DataFrame\n surface_pandas = points_from_first_surface.to_pandas()\n\n # Add formation column\n surface_pandas[\"surface\"] = surface_name\n df = df.append(surface_pandas)\n\n return df.reset_index()\n\n# %% md\n# Surfaces\n# ++++++++\n\nformations = [\"topo\", \"etchegoin\", \"macoma\", \"chanac\", \"mclure\",\n \"santa_margarita\", \"fruitvale\",\n \"round_mountain\", \"olcese\", \"freeman_jewett\", \"vedder\", \"eocene\",\n \"cretaceous\",\n \"basement\", \"null\"]\n\n# %%\ngeo_model.add_features(\"Formations\")\none_formation_every = 3\ngeo_model.add_surfaces(formations[0:4*one_formation_every:one_formation_every])\n\ngeo_model.map_stack_to_surfaces({\"Formations\": [\"etchegoin\", \"macoma\", \"chanac\", \"mclure\"],\n \"Default series\": [\"topo\"]},\n set_series=False)\n\n\n# %%\ngempy_surface_points = get_interface_coord_from_surfaces(formations[0:3*one_formation_every:one_formation_every])\n\n# %%\ngeo_model.set_surface_points(gempy_surface_points, update_surfaces=False)\ngeo_model.update_to_interpolator()\n\n\n# %% md\n# Adding orientations\n# -------------------\n\n# %%\n# find neighbours\nneighbours = gp.select_nearest_surfaces_points(geo_model, geo_model._surface_points.df, 2)\n\n# calculate all fault orientations\ngp.set_orientation_from_neighbours_all(geo_model, neighbours)\n\n# %% md\n# Using the flag to subsurface, the result of the interpolation will get stored in `subsurface` data objects. In the\n# future exporting to subsurface will be the default behaviour.\n\n# %%\ngp.compute_model(geo_model, to_subsurface=True)\n\n# %%\np3d = gp.plot_3d(geo_model)\n\n# %%\ngeo_model.solutions.s_regular_grid\n\n# %%\ngeo_model.solutions.meshes\n\n\n\n\n" }, { "path": "examples/integrations/geo_model_units_moose_input.i", "content": "[MeshGenerators]\n [./gmg]\n type = GeneratedMeshGenerator\n dim = 3\n nx = 50\n ny = 50\n nz = 80\n xmin = 0.0\n xmax = 2000.0\n yim = 0.0\n ymax = 2000.0\n zmin = 0.0\n zmax = 2000.0\n block_id = '1 2 3 4 5 6'\n block_name = 'Main_Fault Sandstone_2 Siltstone Shale Sandstone_1 basement'\n [../]\n \n [./subdomains]\n type = ElementSubdomainIDGenerator\n input = gmg\n subdomain_ids = '6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 6\n 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4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 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5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 5\n 5\n 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5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 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4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 5\n 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5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 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4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 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4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 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3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 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4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 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4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 5\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 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3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 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3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 4\n 4\n 4\n 4\n 4\n 4\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 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2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 3\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 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2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2\n 2'\n [../]\n[]\n\n[Mesh]\n type = MeshGeneratorMesh\n[]\n" }, { "path": "examples/integrations/pflotran_perth_bassin.in", "content": "#Sample simulation of the flow in the Perth bassin modeled with gempy\n#Illustrate how to set up properly a PFLOTRAN simulation with the\n# meshes outputted by gempy\n#Note: formation properties are not realistic\n\n\nSIMULATION\n SIMULATION_TYPE SUBSURFACE\n PROCESS_MODELS\n SUBSURFACE_FLOW flow\n MODE RICHARDS\n /\n /\nEND\n\nSUBSURFACE\n\n\n#=========================== discretization ===================================\n\n################################################\n#Import mesh from GemPy in ASCII format \nGRID\n TYPE UNSTRUCTURED ./perth_basin_mesh.ugi #path to the grid outputted by gempy\nEND\n#for each stratigraphic formation, create a PFLOTRAN region \n#and assign the right gempy file \nREGION Permian\n FILE ./Permian.vs\nEND\nREGION Lesueur\n FILE ./Lesueur.vs\nEND\nREGION Eneabba\n FILE ./Eneabba.vs\nEND\nREGION Cretaceous\n FILE ./Cretaceous.vs\nEND\nREGION basement\n FILE ./basement.vs\nEND\nREGION Yarragadee\n FILE ./Yarragadee.vs\nEND\n#create a region corresponding to the topographic surface (z normal faces)\nREGION topo_surface\n FILE topography_surface.ss\nEND\n\n################################################\n#Import mesh from GemPy in HDF5 format\nSKIP\nGRID\n TYPE UNSTRUCTURED ./perth_basin_mesh.h5\nEND\nREGION Permian\n FILE ./perth_basin_mesh.h5\nEND\nREGION Lesueur\n FILE ./perth_basin_mesh.h5\nEND\nREGION Eneabba\n FILE ./perth_basin_mesh.h5\nEND\nREGION Cretaceous\n FILE ./perth_basin_mesh.h5\nEND\nREGION basement\n FILE ./perth_basin_mesh.h5\nEND\nREGION Yarragadee\n FILE ./perth_basin_mesh.h5\nEND\nREGION Topography_surface\n FILE ./perth_basin_mesh.h5\nEND\nNOSKIP\n################################################\n\n\n#create region on the model boundary to further apply a boundary condition\nREGION xmin\n COORDINATES \n 337000. -1e20 -1e20\n 337000. 1e20 1e20\n /\nEND\n\nREGION xmax\n COORDINATES \n 400000. -1e20 -1e20\n 400000. 1e20 1e20\n /\nEND\n\nREGION all\n COORDINATES \n -1e20 -1e20 -1e20\n 1e20 1e20 1e20\n /\nEND\n\n\n\n#=========================== material properties ==============================\n#Define material property for each formation\nMATERIAL_PROPERTY Permian\n ID 1\n SATURATION_FUNCTION default\n POROSITY 0.3\n PERMEABILITY\n PERM_ISO 1e-12\n /\nEND\n\nMATERIAL_PROPERTY Lesueur\n ID 2\n SATURATION_FUNCTION default\n POROSITY 0.3\n PERMEABILITY\n PERM_ISO 5e-12\n /\nEND\n\nMATERIAL_PROPERTY Eneabba\n ID 3\n SATURATION_FUNCTION default\n POROSITY 0.3\n PERMEABILITY\n PERM_ISO 5e-14\n /\nEND\n\nMATERIAL_PROPERTY Cretaceous\n ID 4\n SATURATION_FUNCTION default\n POROSITY 0.3\n PERMEABILITY\n PERM_ISO 1e-13\n /\nEND\n\nMATERIAL_PROPERTY Yarragadee\n ID 5\n SATURATION_FUNCTION default\n POROSITY 0.3\n PERMEABILITY\n PERM_ISO 1e-12\n /\nEND\n\nMATERIAL_PROPERTY basement\n ID 6\n SATURATION_FUNCTION default\n POROSITY 0.3\n PERMEABILITY\n PERM_ISO 5e-13\n /\nEND\n\n\n#=========================== characteristic curves ============================\nCHARACTERISTIC_CURVES default\n DEFAULT\nEND\n\n\n#=========================== output options ===================================\nOUTPUT\n FORMAT HDF5\n VARIABLES\n PERMEABILITY_X\n /\n NO_PRINT_INITIAL\nEND\n\n\n#=========================== times ============================================\nTIME\n FINAL_TIME 50 y\n INITIAL_TIMESTEP_SIZE 50 y\n MAXIMUM_TIMESTEP_SIZE 1e3 y\nEND\n\n\n\n\n\n\n\n#=========================== flow conditions ==================================\nFLOW_CONDITION initial\n TYPE\n PRESSURE hydrostatic\n /\n DATUM 0 0 20000.\n GRADIENT \n PRESSURE -0.01 0. 0.\n /\n PRESSURE 101325.\nEND\n\nFLOW_CONDITION rain\n TYPE\n FLUX NEUMANN\n /\n FLUX 1.757d-9 ! [m/s]\nEND\n\n\n#=========================== condition couplers ===============================\n# initial condition\nINITIAL_CONDITION\n FLOW_CONDITION initial\n REGION all\nEND\n\n\nBOUNDARY_CONDITION west\n FLOW_CONDITION initial\n REGION xmin\nEND\n\nBOUNDARY_CONDITION east\n FLOW_CONDITION initial\n REGION xmax\nEND\n\n#BOUNDARY_CONDITION topo #apply rain on the topography\n# FLOW_CONDITION rain\n# REGION topo_surface\n#END\n\n\n#=========================== stratigraphy couplers ============================\n#Couple the PFLOTRAN region and material\nSTRATA\n REGION Permian\n MATERIAL Permian\nEND\n\nSTRATA\n REGION Lesueur\n MATERIAL Lesueur\nEND\n\nSTRATA\n REGION Eneabba\n MATERIAL Eneabba\nEND\n\nSTRATA\n REGION Cretaceous\n MATERIAL Cretaceous\nEND\n\nSTRATA\n REGION Yarragadee\n MATERIAL Yarragadee\nEND\n\nSTRATA\n REGION basement\n MATERIAL basement\nEND\n\n\nEND_SUBSURFACE\n" }, { "path": "examples/tutorials/README.rst", "content": "GemPy Tutorials\n===============\n\nHere you will find a selection of examples showing what can you do and how\nyou can do it in GemPy\n\n" }, { "path": "examples/tutorials/__init__.py", "content": "" }, { "path": "examples/tutorials/a_getting_started/README.rst", "content": "Getting Started\n---------------\n\nHere you can see how to run a simple GemPy example." }, { "path": "examples/tutorials/a_getting_started/__init__.py", "content": "" }, { "path": "examples/tutorials/a_getting_started/get_started.py", "content": "\"\"\"\nGetting Started\n===============\n\n\"\"\"\n\n# %%\n# Welcome to our introductory notebook on GemPy! Here, we will cover the essentials of GemPy, introducing you to the core concepts of\n# geomodeling and demonstrating how you can leverage these to create your own geological models. We will guide you through building a\n# model from scratch, based on a conceptual 2D cross-section with boreholes. This simple example will highlight key workflow steps \n# and structural features that GemPy can model.\n#\n# Installation\n# \"\"\"\"\"\"\"\"\"\"\"\"\n# \n# https://docs.gempy.org/installation.html\n#\n# Setting Up Our Environment: Importing Libraries\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# \n# 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\n# import GemPy and its GemPy viewer module, which we will be using extensively. Additionally, we will use NumPy for various functions,\n# as well as Matplotlib and some of its specific modules/functions for visualizing our data and results in 2D and 3D.\n\n# %%\n# Set environmental variable DEFAULT_BACKEND = PYTORCH\nimport os\n\nos.environ[\"DEFAULT_BACKEND\"] = \"PYTORCH\"\n\n# Importing GemPy and viewer\nimport gempy as gp\nimport gempy_viewer as gpv\n\n# Auxiliary libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n\n# sphinx_gallery_thumbnail_number = 11\n\n# %% md\n# Main Classes and Objects in GemPy\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\" \n# GemPy uses Python classes and objects to store and manipulate the data used for modeling. This object-oriented approach helps make\n# the code more modular, reusable, and easier to understand. Each class represents a different aspect of the geological modeling process,\n# and objects are instances of these classes that contain specific data and methods to operate on that data.\n# \n# Here are the main data classes:\n# \n# - :obj:`gempy.core.data.GeoModel`\n# - :obj:`gempy.core.data.StructuralFrame`\n# - :obj:`gempy.core.data.StructuralGroup`\n# - :obj:`gempy.core.data.StructuralElement`\n# - :obj:`gempy.core.data.SurfacePointsTable`\n# - :obj:`gempy.core.data.OrientationsTable`\n# - :obj:`gempy.core.data.Grid`\n#\n# Setting Up a Model: Initializing Objects and Input Data\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# 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 \n# be input in various ways. One way is to start from scratch and manually input surface points and orientations. Another way is to import \n# 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 \n# to build a model in GemPy.\n# \n# Starting a Model from Scratch Based on a Cross-Section\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# 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 \n# the image of this cross-section and plotting it using Matplotlib. For this example, we assume that the size of the image corresponds to \n# 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:\n\n# %%\nimg = mpimg.imread('boreholes_concept.png')\nplt.imshow(img, origin='upper', alpha=.8)\nimg.shape[:2]\n# %% md\n# 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. \n# This object will contain all other data structures and necessary functionality. Here’s what we will do:\n# \n# 1. **Name the Model**: Assign a name to our model.\n#\n# 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 \n# relevant data in a representative space. For this example, we align the extent with the cross-section we imported:\n#\n# - **X** is parallel to the section.\n# - **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, \n# placing the cross-section at y=0 (in the middle).\n# - **Z** representing depth, takes a negative value since we are modeling the subsurface.\n#\n# 3. **Initialize Structural Framework**: Set up a default structural framework.\n#\n# 4. **Define either resolution or refinement**: In GemPy 3, you can use either regular grids or octrees.\n# - **Regular grids**: Define a resolution (and refinement=None). A medium resolution of 50x50x50, for example, results in 125,000 voxels.\n# Model voxels are prisms, not cubes, so resolution can differ from extent. Avoid exceeding 100 cells in each direction (1,000,000 voxels)\n# to prevent high computational costs.\n# - **Octrees**: Define a level of refinement (and resolution=None). Higher refinement levels increase computational costs.\n#\n# .. admonition:: Note on choice of modeling grids \n#\n# Which type of grid is used depends on the use case. Note that as of the current version of GemPy 3, \n# the rendering of surfaces uses dual-contouring, which is based on octrees. So even if you choose regular grids, octree-based computing will\n# be executed additionally in order to render the surfaces in 3D.\n\n# %%\ngeo_model = gp.create_geomodel(\n project_name='Model1',\n extent=[0, 780, -200, 200, -582, 0],\n resolution=(50, 50, 50),\n # refinement=4, # We will use octrees\n structural_frame=gp.data.StructuralFrame.initialize_default_structure()\n)\n# %% md\n# The `geo_model` Object\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# The :obj:`gempy.core.data.GeoModel` object we just initialized contains all the essential information for constructing our model, \n# such as the parameters defined above,\n# and the input data we will introduce further below. You can access different types of information by accessing the attributes. For instance, you\n# can retrieve the coordinates of our modeling grid as follows:\n\n# %%\ngeo_model.grid\n\n# %% md\n# The `geo_model` object also contains the structural frame of our model, i.e., information about our main structural groups (also referred to as\n# series or stacks in our model), and their sequential and geological relationships with one another. Each group can contain several elements,\n# which can be surfaces representing the **bottom** interfaces of lithological units or fault planes. Each structural group also has a relation\n# 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' \n# (more about this later). The structural frame also contains information about fault relationships, i.e., which elements are affected by which \n# faults. Let's look at our default structural frame:\n\n# %%\ngeo_model.structural_frame\n\n# %% md\n# 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 \n# with this 'surface 1' but also a 'basement' unit which is always present. We can see this using the following function:\n\n# %%\ngeo_model.structural_frame.structural_elements\n\n# %% md\n# 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\n# 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\n# represented by the same color as displayed in the cross-section. For this, we input hex color codes.\n\n# %%\ngeo_model.structural_frame.structural_elements[0].color = '#33ABFF' # Set 'surface 1' color to blue.\ngeo_model.structural_frame.structural_elements[1].color = '#570987' # Set basement color to purple.\n\ngeo_model.structural_frame.structural_elements[0].name = 'Limestone' # Renaming 'surface 1' to 'Limestone'.\ngeo_model.structural_frame.structural_elements[0]\n\n# %%\ngeo_model.structural_frame.structural_groups[0].name = 'Deposit. Series'\n\n# %% md\n# Manually Inputting Data\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# Now that the `geo_model` object has been created and our first structural element renamed, we can start inputting data by reading it from\n# the cross-section. We start with location points that represent the boundaries between two lithological units. Let's look at the cross-section \n# 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 \n# `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.\n# \n# The image cross-section indicates that there are three lithological boundaries, with one dot for each boundary shown in the first two boreholes.\n# 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\n# of a boundary surface in depth. Remember also that in GemPy, each surface represents the bottom of the unit it is assigned to.\n\n# %%\np2d = gpv.plot_2d(geo_model, show=False)\np2d.axes[0].imshow(img, origin='upper', alpha=.8, extent=(0, 780, -582, 0))\n\n# Enable grid with minor ticks\np2d.axes[0].grid(which='both') # Enable both major and minor grids\np2d.axes[0].minorticks_on() # Enable minor ticks\n\n# Customize the appearance of the grid if needed\np2d.axes[0].grid(which='major', linestyle='--', linewidth='0.8', color='gray')\np2d.axes[0].grid(which='minor', linestyle=':', linewidth='0.4', color='gray')\n\nplt.show()\n\n# %% md\n# 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 \n# only the uppermost layer, the limestone, with its bottom boundary represented by the blue dots. Using the function `add_surface_points`, \n# 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\n# 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,\n# we can leave y as 0. We can input that as follows:\n# %%\ngp.add_surface_points(\n geo_model=geo_model,\n x=[225],\n y=[0],\n z=[-95],\n elements_names=['Limestone']\n)\n\n# %% md\n# Now let's plot the data with the section and see how we did:\n\n# %%\np2d = gpv.plot_2d(geo_model, show=False)\np2d.axes[0].imshow(img, origin='upper', alpha=.8, extent=(0, 780, -582, 0))\n\n# Enable grid with minor ticks\np2d.axes[0].grid(which='both') # Enable both major and minor grids\np2d.axes[0].minorticks_on() # Enable minor ticks\n\n# Customize the appearance of the grid if needed\np2d.axes[0].grid(which='major', linestyle='--', linewidth='0.8', color='gray')\np2d.axes[0].grid(which='minor', linestyle=':', linewidth='0.4', color='gray')\n\nplt.show()\n\n# %% md\n# 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\n# borehole dots. Conveniently, we can use the same function to add several points at a time:\n\n# %%\ngp.add_surface_points(\n geo_model=geo_model,\n x=[460, 617],\n y=[0, 0],\n z=[-100, -10],\n elements_names=['Limestone', 'Limestone']\n)\n\np2d = gpv.plot_2d(geo_model, show=False)\np2d.axes[0].imshow(img, origin='upper', alpha=.8, extent=(0, 780, -582, 0))\n\n# Enable grid with minor ticks\np2d.axes[0].grid(which='both') # Enable both major and minor grids\np2d.axes[0].minorticks_on() # Enable minor ticks\n\n# Customize the appearance of the grid if needed\np2d.axes[0].grid(which='major', linestyle='--', linewidth='0.8', color='gray')\np2d.axes[0].grid(which='minor', linestyle=':', linewidth='0.4', color='gray')\n\nplt.show()\n\n# %% md\n# Minimum Data for Model Computation\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# 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? \n# Well, no. GemPy's approach is based on an implicit interpolation method that requires the following minimum data:\n#\n# - **Two surface points** for at least one surface in a structural group/series\n# - **One orientation** per structural group/series\n# \n# Thanks to GemPy's global interpolation approach, once you have one surface defined by two surface points and an orientation in a structural \n# 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\n# from the other surface.\n# \n# 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 \n# surface points (apparent, as we have no information about the y-direction). Let's add a corresponding orientation using the function \n# `add_orientations`:\n\n# %%\ngp.add_orientations(\n geo_model=geo_model,\n x=[350],\n y=[0],\n z=[-120],\n elements_names=['Limestone'],\n pole_vector=[np.array([0, 0, 1])]\n)\n\np2d = gpv.plot_2d(geo_model, show=False)\np2d.axes[0].imshow(img, origin='upper', alpha=.8, extent=(0, 780, -582, 0))\n\n# Enable grid with minor ticks\np2d.axes[0].grid(which='both') # Enable both major and minor grids\np2d.axes[0].minorticks_on() # Enable minor ticks\n\n# Customize the appearance of the grid if needed\np2d.axes[0].grid(which='major', linestyle='--', linewidth='0.8', color='gray')\np2d.axes[0].grid(which='minor', linestyle=':', linewidth='0.4', color='gray')\n\nplt.show()\n\n# %% md\n# 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`:\n\n# %%\ngeo_model.update_transform(gp.data.GlobalAnisotropy.NONE)\n\n# %%\ngp.compute_model(geo_model, engine_config=gp.data.GemPyEngineConfig())\n\n# %% md\n# Now, the model has been computed and is ready to be visualized. Let's see what it looks like in a 2D section:\n\n# %%\n# 2D visualization:\ngpv.plot_2d(geo_model, cell_number='mid')\n\n# %% md\n# 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\n# visualization.\n\n# %%\np2d = gpv.plot_2d(geo_model, show=False)\np2d.axes[0].imshow(img, origin='upper', alpha=.8, extent=(0, 780, -582, 0))\n\n# Enable grid with minor ticks\np2d.axes[0].grid(which='both') # Enable both major and minor grids\np2d.axes[0].minorticks_on() # Enable minor ticks\n\n# Customize the appearance of the grid if needed\np2d.axes[0].grid(which='major', linestyle='--', linewidth='0.8', color='gray')\np2d.axes[0].grid(which='minor', linestyle=':', linewidth='0.4', color='gray')\n\nplt.show()\n\n# %% md\n# You can see how the computed interface runs through the points we defined and is furthermore determined by the one orientation we placed.\n# Now, let's look at our 3D model:\n\n# %%\n# 3D visualization:\ngpv.plot_3d(geo_model, show_surfaces=True, image=False)\n\n# %% md\n# Very good, we have successfully computed the first iteration of our model including one lithological interface. You could now go ahead and\n# fine-tune the model by adding further points or orientations. For now, let's continue.\n# \n# About Scalar Fields\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# GemPy's global interpolation approach uses scalar fields to compute models. Each structural group or series has its own scalar field, which\n# 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 \n# already be created \"globally\", i.e., for the full extent of the modeling space. The defined surface follows one value along that field as\n# an isosurface. A separate structural element will follow a different isosurface, and once you input additional points and orientations, \n# the scalar field will be altered accordingly.\n# \n# As mentioned, each structural group has its own scalar field. These can be combined to achieve more complex structures and relationships, \n# as we will see later.\n# \n# 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\n# elements and data.\n\n# %%\np2d = gpv.plot_2d(\n model=geo_model,\n series_n=0, \n show_data=True,\n show_scalar=True,\n show_lith=False\n)\n\nplt.show()\n\n# %% md\n# Adding a Second Lithological Unit\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# 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\n# 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 \n# 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\n# adding it to our first (and currently only) structural group/series, i.e., it will be in the same stack as our limestone.\n\n# %%\nelement2 = gp.data.StructuralElement(\n name='Siltstone',\n color='#FFA833', # color=next(geo_model.structural_frame.color_generator),\n surface_points=gp.data.SurfacePointsTable.from_arrays(\n x=np.array([460]),\n y=np.array([0]),\n z=np.array([-280]),\n names='Siltstone'\n ),\n orientations=gp.data.OrientationsTable.initialize_empty()\n)\n\ngeo_model.structural_frame.structural_groups[0].append_element(element2)\n\n# %% md\n# 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. \n# The order of structural elements within one group only affects the default color assigned. We recommend being consistent in the way you \n# choose to order them, and to order them in accordance with geological age. The order of structural groups actually represents geological \n# time relationships, with groups at the top being the youngest and lower ones being older. This, together with their `StackRelationType`, \n# decides how they affect each other via their individual scalar fields, as we will see later.\n\n# %%\ngeo_model.structural_frame\n\n# %%\ngp.compute_model(geo_model, engine_config=gp.data.GemPyEngineConfig())\n\n# %%\np2d = gpv.plot_2d(geo_model, cell='mid', show=False)\np2d.axes[0].imshow(img, origin='upper', alpha=.8, extent=(0, 780, -582, 0))\n\n# Enable grid with minor ticks\np2d.axes[0].grid(which='both') # Enable both major and minor grids\np2d.axes[0].minorticks_on() # Enable minor ticks\n\n# Customize the appearance of the grid if needed\np2d.axes[0].grid(which='major', linestyle='--', linewidth='0.8', color='gray')\np2d.axes[0].grid(which='minor', linestyle=':', linewidth='0.4', color='gray')\n\nplt.show()\n\n# %% md\n# The 2D visualization of our updated model now shows that the next surface follows the respective point we added. Based on the implicit\n# modeling approach GemPy uses, given this sparse information we have put in, the siltstone bottom interface otherwise follows the course \n# 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\n# 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,\n# and while we are at it, let's also add the third lithological unit marked in green.\n\n# %%\n# First, we add the two missing points to the Siltstone\ngp.add_surface_points(\n geo_model=geo_model,\n x=[225],\n y=[0],\n z=[-270],\n elements_names=['Siltstone']\n)\n\nelement3 = gp.data.StructuralElement(\n name='Sandstone',\n color='#72A533', # next(geo_model.structural_frame.color_generator),\n surface_points=gp.data.SurfacePointsTable.from_arrays(\n x=np.array([225, 460]),\n y=np.array([0, 0]),\n z=np.array([-436, -441]),\n names='Sandstone'\n ),\n orientations=gp.data.OrientationsTable.initialize_empty()\n)\n\ngeo_model.structural_frame.structural_groups[0].append_element(element3)\n# %%\ngp.compute_model(geo_model)\n\n# %%\np2d = gpv.plot_2d(geo_model, show=False)\np2d.axes[0].imshow(img, origin='upper', alpha=.8, extent=(0, 780, -582, 0))\n\n# Enable grid with minor ticks\np2d.axes[0].grid(which='both') # Enable both major and minor grids\np2d.axes[0].minorticks_on() # Enable minor ticks\n\n# Customize the appearance of the grid if needed\np2d.axes[0].grid(which='major', linestyle='--', linewidth='0.8', color='gray')\np2d.axes[0].grid(which='minor', linestyle=':', linewidth='0.4', color='gray')\n\nplt.show()\n\n# %%\n# 3D visualization:\ngpv.plot_3d(geo_model, show_surfaces=True, image=False)\n\n# %% md\n# Discontinuities: Combining Scalar Fields\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# \n# 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\n# simple geological model. However, geological scenarios are usually more complex. In GemPy, you can not only combine numerous surface\n# points and orientations to create elaborate structures but also create various structural groups that affect each other through combinations\n# 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\n# see how we can add a new structural group to create various types of discontinuities, and with that, possibly even meaningful alternative \n# model hypotheses.\n# \n# So, let's define another structural element that will serve to showcase the different types of discontinuities we can implement in GemPy:\n\n# %%\nelement_discont = gp.data.StructuralElement(\n name='Discont_Surface',\n color='#990000', # next(geo_model.structural_frame.color_generator),\n surface_points=gp.data.SurfacePointsTable.from_arrays(\n x=np.array([550, 650]),\n y=np.array([0, 0]),\n z=np.array([-30, -200]),\n names='Discont_Surface'\n ),\n orientations=gp.data.OrientationsTable.from_arrays(\n x=np.array([600]),\n y=np.array([0]),\n z=np.array([-100]),\n G_x=np.array([.3]),\n G_y=np.array([0]),\n G_z=np.array([.3]),\n names='Discont_Surface'\n )\n)\n\n# %% md\n# 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 \n# 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\n# the group into the structural frame.\n\n# %%\ngroup_discont = gp.data.StructuralGroup(\n name='Discontinuity',\n elements=[element_discont],\n structural_relation=gp.data.StackRelationType.ERODE,\n)\n\n# Insert the fault group into the structural frame:\ngeo_model.structural_frame.insert_group(0, group_discont)\n\n# %% md\n# Let's take a quick look at the state of our structural frame.\n\n# %%\ngeo_model.structural_frame\n\n# %% md\n# We can now see the two different structural groups: the default one containing the deposition series, and the group containing the\n# discontinuity. Let's go ahead and compute the model. Once the model has been computed, we can plot the scalar fields of both structural\n# groups independently.\n\n# %%\ngp.compute_model(geo_model, engine_config=gp.data.GemPyEngineConfig())\n\n# %%\np2d = gpv.plot_2d(\n model=geo_model,\n series_n=1, # This setting will now plot the scalar field used for the fault\n show_data=True,\n show_scalar=True,\n show_lith=False\n)\n\nplt.show()\n\n# %%\np2d = gpv.plot_2d(\n model=geo_model,\n series_n=0, # This setting will plot the scalar field used for the discontinuity.\n show_data=True,\n show_scalar=True,\n show_lith=False\n)\n\nplt.show()\n\n# %% md\n# We now have two very different scalar fields. Note how they are each defined by the input data assigned to their respective structural \n# elements. Multiple scalar fields in GemPy influence each other depending on (1) their order and (2) their `StackRelationType`. The latter \n# defines how a younger (upper) structural group will relate to the older (lower) structural groups and possibly affect their scalar field.\n# \n# The parameter `StackRelationType` can take the following values:\n#\n# - `BASEMENT`: Treats all lower groups as the basement.\n# - `ERODE`: Defines erosive contact/unconformity.\n# - `ONLAP`: Defines the younger group to be onlapping onto the older groups.\n# - `FAULT`: Defines the group to be a fault.\n# \n# We will now take a look at each of these relation types except for the basement type.\n\n# %% md\n# Erosive Contact\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# 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 \n# how this younger structural group erodes all older elements and basically \"cuts them out\" in our model.\n\n# %%\np2d = gpv.plot_2d(geo_model, show=False)\np2d.axes[0].imshow(img, origin='upper', alpha=.8, extent=(0, 780, -582, 0))\n\n# Enable grid with minor ticks\np2d.axes[0].grid(which='both') # Enable both major and minor grids\np2d.axes[0].minorticks_on() # Enable minor ticks\n\n# Customize the appearance of the grid if needed\np2d.axes[0].grid(which='major', linestyle='--', linewidth='0.8', color='gray')\np2d.axes[0].grid(which='minor', linestyle=':', linewidth='0.4', color='gray')\n\nplt.show()\n\n# %%\n# 3D visualization:\ngpv.plot_3d(geo_model, show_surfaces=True, image=False, show_lith=False)\n\n# %% md\n# We can see how all units of the depositional series stop at the contact with the new discontinuity group. However, this doesn't look \n# 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.\n# \n# Onlapping\n# \"\"\"\"\"\"\"\"\"\n# Let's change the relation type from `ERODE` to `ONLAP` to achieve a different type of discontinuity and then plot it.\n\n# %%\ngeo_model.structural_frame.structural_groups[0].structural_relation = gp.data.StackRelationType.ONLAP\n\n# %%\ngp.compute_model(geo_model, engine_config=gp.data.GemPyEngineConfig())\n\n# %%\np2d = gpv.plot_2d(geo_model, show=False)\np2d.axes[0].imshow(img, origin='upper', alpha=.8, extent=(0, 780, -582, 0))\n\n# Enable grid with minor ticks\np2d.axes[0].grid(which='both') # Enable both major and minor grids\np2d.axes[0].minorticks_on() # Enable minor ticks\n\n# Customize the appearance of the grid if needed\np2d.axes[0].grid(which='major', linestyle='--', linewidth='0.8', color='gray')\np2d.axes[0].grid(which='minor', linestyle=':', linewidth='0.4', color='gray')\n\nplt.show()\n\n# %%\n# 3D visualization:\ngpv.plot_3d(geo_model, show_surfaces=True, image=False, show_lith=False)\n\n# %% md\n# Now the unit defined as part of the discontinuity group is onlapping onto the uppermost surface of the default group and ends there. \n# This also doesn't really make sense considering the data given, so let's try the last relation type.\n# \n# Faults\n# \"\"\"\"\"\"\n# 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`.\n\n# %%\ngeo_model.structural_frame.structural_groups[0].structural_relation = gp.data.StackRelationType.FAULT\n# %%\ngp.set_is_fault(\n frame=geo_model.structural_frame,\n fault_groups=['Discontinuity']\n)\n\n# %% md\n# See that the fault relations field in the structural frame now indicates that the fault affects the default formation, i.e., offsets it.\n# Let's compute our model including the fault and see what that looks like.\n\n# %%\ngp.compute_model(geo_model, engine_config=gp.data.GemPyEngineConfig())\n\n# %%\np2d = gpv.plot_2d(geo_model, show=False)\np2d.axes[0].imshow(img, origin='upper', alpha=.8, extent=(0, 780, -582, 0))\n\n# Enable grid with minor ticks\np2d.axes[0].grid(which='both') # Enable both major and minor grids\np2d.axes[0].minorticks_on() # Enable minor ticks\n\n# Customize the appearance of the grid if needed\np2d.axes[0].grid(which='major', linestyle='--', linewidth='0.8', color='gray')\np2d.axes[0].grid(which='minor', linestyle=':', linewidth='0.4', color='gray')\n\nplt.show()\n\n# %%\n# 3D visualization:\ngpv.plot_3d(geo_model, show_surfaces=True, image=False, show_lith=False)\n\n# %% md\n# 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 \n# original data in the cross-section. Instead of a larger syncline structure and upward-bending of the whole depositional series, we can \n# now explain the shallower depth of the limestone in the third borehole by the effects of a reverse fault. In this model realization,\n# all lithological units are oriented near-horizontal but are offset by the fault. Note that in GemPy, the degree of offset is defined \n# 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.\n# \n# Note also that by implementing a fault, the scalar field of the depositional series has been affected by the offset.\n\n# %%\np2d = gpv.plot_2d(\n model=geo_model,\n series_n=1, # This will plot the scalar field used for the fault\n show_data=True,\n show_scalar=True,\n show_lith=False\n)\n\nplt.show()\n\n# %% md\n# Topography and Geological Maps\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# In GemPy, we can add more grid types for different purposes, such as to add topography to our model. In this following section,\n# we will exemplify this by creating a random topography grid which allows us to intersect the surfaces as well as compute a high-resolution\n# geological map. GemPy has a built-in function to generate random topography. After executing it, a topography grid will be added to the \n# `geo_model`. It can be directly visualized in 2D and 3D.\n\n# %%\n# GemPy offers built-in tools to manage topographic data through gdal.\n# For demonstration, we'll create a random topography:\n\ngp.set_topography_from_random(\n grid=geo_model.grid,\n fractal_dimension=1.9,\n d_z=np.array([-150, 0]),\n topography_resolution=np.array([200, 200])\n)\n# %%\ngpv.plot_2d(geo_model, show_topography=True)\n\n# %%\ngpv.plot_3d(geo_model, show_surfaces=True, image=False, show_topography=True, show_lith=False)\n\n# %% md\n# If we now also re-compute our geological model, the generated topography grid will display the lithological units that intersect it, \n# i.e., which outcrop at the surface. We can therefore display a geological map based on our topography and the underlying 3D geological model. \n# To plot a top-down view of this map, you can pass the arguments `section_names=['topography']` and `show_topography=True` in the plotting\n# function as shown below.\n\n# %%\ngp.compute_model(geo_model)\n\n# %%\ngpv.plot_2d(geo_model, section_names=['topography'], show_topography=True)\n\n# %%\ngpv.plot_3d(geo_model, show_surfaces=True, image=False, show_topography=True)\n\n# %% md\n# We can now see how the topography displays the color of the lithologies outcropping at the surface, together with topographical contour lines.\n# \n# While this topography is random, GemPy also has the capability to include real topography files and arrays via the functions \n# `set_topography_from_file` and `set_topography_from_arrays`.\n\n# %% md\n# Extracting Model Solutions\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# 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. \n# 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\n# includes the following outputs in particular:\n# - `geo_model.solutions.dc_meshes`: A list of the surface meshes in the model with the location of vertices and edges for each.\n# - `geo_model.solutions.raw_arrays`: An object containing numerous arrays that define various parts of the model. Of particular importance\n# are the lithology block (`lith_block`), the fault block (`fault_block`), and the scalar field matrix (`scalar_field_matrix`).\n# \n# Mesh Solutions\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# 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 \n# `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 \n# in our `structural_frame`, i.e., the index `[0]` will return the first and top surface, in our case, the discontinuity surface.\n\n# %%\nvertices_0 = geo_model.solutions.dc_meshes[0].vertices\nedges_0 = geo_model.solutions.dc_meshes[0].edges\nprint(type(vertices_0), vertices_0, edges_0)\n\n# %% md\n# 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. \n# However, the values don't correspond with our model extent. That is because they have been transformed in GemPy. To return the values\n# corresponding to the original coordinate system, we can invert this transformation as follows:\n\n# %%\ngeo_model.input_transform.apply_inverse(vertices_0)\n\n# %% md\n# Lithology Block\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# The lithology block is an array that, for a given model realization/solution, returns the ID of the lithology for each voxel. Note below\n# 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,\n# you can reshape it using the resolution used in `geo_model`.\n# \n\n# %%\nlith_block = geo_model.solutions.raw_arrays.lith_block\nprint(lith_block.shape, lith_block)\n\n# %%\nlith_block = lith_block.reshape(50, 50, 50)\nprint(lith_block.shape, lith_block)\n\n# %% md\n# Grid Values\n# \"\"\"\"\"\"\"\"\"\"\"\n# Apart from these solutions, you might also need to return grid values. You can access the values for each grid in your `geo_model` \n# object via `geo_model.grid` as shown below.\n# %%\ngeo_model.grid.regular_grid.values\n# %%\ngeo_model.grid.topography.values\n" }, { "path": "examples/tutorials/ch1_fundamentals/README.rst", "content": "Chapter 1: GemPy fundamentals\n-----------------------------\n\nThese examples show the basic functionality to create geometries.\n" }, { "path": "examples/tutorials/ch1_fundamentals/__init__.py", "content": "" }, { "path": "examples/tutorials/ch1_fundamentals/ch1_1_basics.py", "content": "\"\"\"\n1.1 -Basics of geological modeling with GemPy\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n\"\"\"\nimport numpy as np\n\n# %%\n# Importing Necessary Libraries\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\nimport gempy as gp\nimport gempy_viewer as gpv\n\n# %%\n# Importing and Defining Input Data\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# :obj:`gempy.core.data.GeoModel`\n# GemPy uses Python objects to store the data that builds the geological model. The main data classes include:\n#\n# - :obj:`gempy.core.data.GeoModel`\n# - :obj:`gempy.core.data.StructuralFrame`\n# - :obj:`gempy.core.data.StructuralGroup`\n# - :obj:`gempy.core.data.StructuralElement`\n# - :obj:`gempy.core.data.SurfacePointsTable`\n# - :obj:`gempy.core.data.OrientationsTable`\n# - :obj:`gempy.core.data.Grid`\n#\n# You can also create data from raw CSV files (comma-separated values). This could be useful if you are exporting model data\n# from a different program or creating it in a spreadsheet software like Microsoft Excel or LibreOffice Calc.\n#\n# In this tutorial, we'll use CSV files to generate input data. You can find these example files in the `gempy data`\n# repository on GitHub. The data consists of x, y, and z positional values for all surface points and orientation\n# measurements. Additional data includes poles, azimuth and polarity (or the gradient components). Surface points are\n# assigned a formation, which can be a lithological unit (like \"Sandstone\") or a structural feature (like \"Main Fault\"). \n#\n# It's important to note that, in GemPy, interface position points mark the **bottom** of a layer. If you need points\n# to represent the top of a formation (for example, when modeling an intrusion), you can define an inverted orientation measurement.\n#\n# While generating data from CSV files, we also need to define the model's real extent in x, y, and z. This extent\n# defines the area used for interpolation and many of the plotting functions. We also set a resolution to establish a\n# regular grid right away. This resolution will dictate the number of voxels used during modeling. We're using a medium\n# resolution of 50x50x50 here, which results in 125,000 voxels. The model extent should enclose all relevant data in a\n# representative space. As our model voxels are prisms rather than cubes, the resolution can differ from the extent.\n# However, it is recommended to avoid going beyond 100 cells in each direction (1,000,000 voxels) to prevent excessive\n# computational costs.\n#\n#\n\n# %%\ndata_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'\n\ngeo_model: gp.data.GeoModel = gp.create_geomodel(\n project_name='Tutorial_ch1_1_Basics',\n extent=[0, 2000, 0, 2000, 0, 750],\n refinement=6, # * Here we define the number of octree levels. If octree levels are defined, the resolution is ignored.\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=data_path + \"/data/input_data/getting_started/simple_fault_model_orientations.csv\",\n path_to_surface_points=data_path + \"/data/input_data/getting_started/simple_fault_model_points.csv\",\n hash_surface_points=\"4cdd54cd510cf345a583610585f2206a2936a05faaae05595b61febfc0191563\",\n hash_orientations=\"7ba1de060fc8df668d411d0207a326bc94a6cdca9f5fe2ed511fd4db6b3f3526\"\n )\n)\n\n# %% \n# .. admonition:: New in GemPy 3! \n#\n# GemPy 3 has introduced the ``ImporterHelper`` class to streamline importing data from various sources. This class\n# simplifies the process of passing multiple arguments needed for importing data and will likely see further \n# extensions in the future. Currently, one of its uses is to handle `pooch` arguments for downloading data from the internet.\n#\n#\n#\n# Reviewing the Imported Data\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# 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`.\n#\n# Using `structural_frame.element_id_name_map`, we can see which ID corresponds to which structural element name in the data.\n\n\n# %% \ngeo_model.surface_points_copy\n\n# %% \ngeo_model.orientations_copy\n\n\n# %%\n# Declaring the Sequential Order of Structural Elements (Geological Formations)\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n# In our model, we want the geological units to appear in the correct chronological order. This order could be determined by a sequence\n# of stratigraphic deposition, unconformities due to erosion, or other lithological genesis events like igneous intrusions. A similar\n# age-related order is declared for faults in our model. In GemPy, we use the function `gempy.map_stack_to_surfaces` to assign formations\n# or faults to different sequential series by declaring them in a Python dictionary.\n#\n#\n# The correct ordering of series is crucial for model construction! It's possible to assign several surfaces to one series. The order of\n# units within a series only affects the color code, so we recommend maintaining consistency. The order can be defined by simply changing\n# the order of the lists within the `gempy.core.data.StructuralFrame.structural_groups` and `gempy.core.data.StructuralGroups.elements` attributes.\n#\n# Faults are treated as independent groups and must be younger than the groups they affect. The relative order between different faults\n# defines their tectonic relationship (the first entry is the youngest).\n#\n# For a model with simple sequential stratigraphy, all layer formations can be assigned to one series without an issue. All unit\n# boundaries and their order would then be determined by interface points. However, to model more complex lithostratigraphical\n# relations and interactions, separate series definitions become important. For example, modeling an unconformity or an intrusion\n# that disrupts older stratigraphy would require declaring a younger series.\n#\n# By default, a simple sequence/group is created inferred from the data as shown above.\n#\n# Our example model comprises four main layers (plus an underlying basement that is automatically generated by GemPy) and one main\n# normal fault displacing those layers. Assuming a simple stratigraphy where each younger unit was deposited onto the underlying\n# older one, we can assign these layer formations to one structural group called \"Strat_Series\". For the fault, we declare a\n# respective \"Fault_Series\" as the first key entry in the mapping dictionary. We could give any other names to these series;\n# the formations, however, have to be referred to as named in the input data.\n#\n# In the following, we map the \"Main Fault\" to the \"Fault Series\" and the individual formations to the \"Strat Series\".\n\n# %%\ngp.map_stack_to_surfaces(\n gempy_model=geo_model,\n mapping_object= # TODO: This mapping I do not like it too much. We should be able to do it passing the data objects directly\n {\n \"Fault_Series\": 'Main_Fault',\n \"Strat_Series\": ('Sandstone_2', 'Siltstone', 'Shale', 'Sandstone_1')\n }\n)\n\n# %%\n# Note how the structural frame still indicates the \"Fault Series\" group to have a relation type \"erode\".\n# We still need to tell GemPy that we want this group to be a fault. We do this using the function `set_is_fault`.\n\n# %% \ngp.set_is_fault(\n frame=geo_model.structural_frame,\n fault_groups=['Fault_Series']\n)\n\n# %%\n# Now, all surfaces have been assigned to a series and are displayed in the correct order \n# (from young to old).\n#\n# Returning Information from Our Input Data\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Our model input data, named \"geo_model\", contains all essential information for constructing \n# our model. You can access different types of information by accessing the attributes.\n# For instance, you can retrieve the coordinates of our modeling grid as follows:\n\n\n# %% \ngeo_model.grid\n\n# %%\n# \n# Visualizing input data\n# ~~~~~~~~~~~~~~~~~~~~~~\n# \n# 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\n# and orientations are defined the way we want them to be. Using the function `gempy_viewer.plot2d`, we obtain a 2D projection of our\n# 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).\n# Below, we can freely switch the direction and check out the projection from different sides to get a good idea.\n\n\n# %%\nplot = gpv.plot_2d(geo_model, show_lith=False, show_boundaries=False)\n\n# %%\n# Beyond 2D, however, we can also visualize our input data in full 3D using `gempy_viewer.plot_3d`. Note that direct 3D visualization\n# in GemPy requires [the Visualization Toolkit](https://www.vtk.org/) (VTK) to be installed.\n\n\n# %%\ngpv.plot_3d(geo_model, image=False, plotter_type='basic')\n\n# %%\n# Model Generation\n# ~~~~~~~~~~~~~~~~\n# Once we've correctly defined all our primary information in our `gempy.core.data.GeoModel` object (referred to as `geo_model`\n# in these tutorials), we can proceed with the model computation step. We can go ahead and save the solution of a specific computation\n# as we do below, but solutions are also stored within the `gempy.core.data.GeoModel` object for future reference.\n#\n#\n# .. admonition:: New in GemPy 3! Numpy and TensorFlow backend\n#\n# Unlike previous versions, GemPy 3 doesn't rely on `theano` or `asera`. \n# Instead, it utilizes `numpy` or `tensorflow`. Consequently, we no longer need \n# to recompile all theano functions (TensorFlow uses eager execution; we found no \n# notable speed difference after profiling the XLA compiler).\n\n# %%\n# The parameters used for the interpolation are stored in \n# `gempy.core.data.GeoModel.interpolation_options`. These parameters have sensible default values \n# that you can modify if necessary. However, we advise caution when changing these parameters \n# unless you fully understand their implications.\n\n# %%\n# Display the current interpolation options\ngeo_model.interpolation_options\n\n# %%\n# With all our prerequisites in place, we can now compute our complete geological model \n# using :func:`gempy.compute_model`. This function returns a :obj:`gempy.core.data.Solutions` object.\n#\n# The following sections illustrate these different model solutions and how to utilize them.\n\n# %%\n# Compute the geological model and get the solutions\nsol = gp.compute_model(geo_model)\nsol\n\n# %% \n# Solutions are also stored within the :obj:`gempy.core.data.GeoModel` object, for future reference.\n\n# %%\ngeo_model.solutions\n\n# %%\n# Direct model visualization in GemPy\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# \n# Model solutions can be easily visualized in 2D sections in GemPy\n# directly. Let's take a look at our lithology block:\n#\n\n# %%\ngpv.plot_2d(geo_model, show_data=True, cell_number=\"mid\", direction='y')\n\n# %%\n# With ``cell_number=mid``, we have chosen a section going through\n# the middle of our block. We have moved in ``direction='y'``,\n# the plot thus depicts a plane parallel to the :math:`x`- and\n# :math:`y`-axes. Setting ``show_data=True``, we could plot original data\n# together with the results. Changing the values for ``cell_number`` and\n# ``direction``, we can move through our 3D block model and explore it by\n# looking at different 2D planes.\n# \n# We can do the same with the underlying scalar-field solution:\n# \n\n\n# %%\ngpv.plot_2d(\n model=geo_model,\n series_n=0, # This will plot the scalar field used for the fault\n show_data=False,\n show_scalar=True,\n show_lith=False\n)\n\n# %%\ngpv.plot_2d(\n model=geo_model,\n series_n=1, # This will plot the scalar field used for the stratigraphy\n show_data=False,\n show_scalar=True,\n show_lith=False\n)\n\n# %%\n# Dual Contouring and vtk visualization\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# \n# In addition to 2D sections we can extract surfaces to visualize in 3D\n# renderers. Surfaces can be visualized as 3D triangle complexes in VTK\n# (see function plot\\_surfaces\\_3D below). To create these triangles, we\n# need to extract respective vertices and simplices from the potential\n# fields of lithologies and faults. This process is automatized in GemPy\n# using dual contouring in the :obj:`gempy_engine`.\n# \n# .. admonition:: New in GemPy 3! Dual Contouring\n#\n# GemPy 3 uses dual contouring to extract surfaces from the scalar fields. The method is completely coded in :obj:`gempy_engine` what also\n# enables further improvements in the midterm. This method is more efficient to use\n# together with octrees and suited better the new capabilities of gempy3. \n\n# %% \ngpv.plot_3d(geo_model, show_data=False, image=False, plotter_type='basic')\n\n# %%\n# Adding topography\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# In gempy we can add more grid types for different purposes. We will explore this concept in more detail in the\n# 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\n# surfaces as well as compute a high resolution geological mal.\n\n\n# %%\ngp.set_topography_from_random(\n grid=geo_model.grid,\n fractal_dimension=1.2,\n d_z=np.array([350, 750]),\n topography_resolution=np.array([50, 50]),\n)\n\ngp.compute_model(geo_model)\ngpv.plot_2d(geo_model, show_topography=True)\n\ngpv.plot_3d(\n model=geo_model,\n plotter_type='basic',\n show_topography=True,\n show_surfaces=True,\n show_lith=True,\n image=False\n)\n\n# %%\n# Compute at a given location\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# \n# This is done by modifying the grid to a custom grid and recomputing.\n# \n\n# %% \nx_i = np.array([[1000, 1000, 1000]])\nlith_values_at_coords: np.ndarray = gp.compute_model_at(\n gempy_model=geo_model,\n at=x_i\n)\nlith_values_at_coords\n\n# %%\n# Therefore if we just want the value at **x\\_i**: \n\n# %%\ngeo_model.solutions.raw_arrays.custom\n\n# %%\n# .. admonition:: Work in progress \n#\n# GemPy3 model serialization is currently being redisigned. Therefore, at the current version, there is not a build in\n# method to save the model. However, since now the data model should be completely robust, you should be able to save the\n# :obj:`gempy.core.data.GeoModel` and all its attributes using the standard python library [pickle](https://docs.python.org/3/library/pickle.html)\n#\n# sphinx_gallery_thumbnail_number = -2\n" }, { "path": "examples/tutorials/ch1_fundamentals/ch1_3a_grids.py", "content": "\n\"\"\"\n1.3a: Grids.\n============\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport gempy as gp\nfrom gempy.core.data import Grid\nfrom gempy.core.data.grid_modules import RegularGrid\n\nnp.random.seed(55500)\n\n# %%\n# The Grid Class\n# --------------\n# \n# The Grid class interacts with the rest of the data classes and grid\n# subclasses. Its main purpose is to feed coordinates XYZ to the\n# interpolator.\n# \n\n# %% \ngrid = Grid()\n\n# %%\n# The most important attribute of Grid is ``values`` (and ``values_r``\n# which are the values rescaled) which are the 3D points in space that\n# kriging will be evaluated on. This array will be fed by \"grid types\" on\n# a **composition** relation with Grid:\n# \n\n# %%\n# .. image:: /_static/grids.jpg\n# \n\n# %% \nprint(grid.values)\n\n# %%\n# At the moment of writing this tutorial, there are 5 grid types. The\n# number of grid types is scalable, and down the road we aim to connect\n# other grid packages (like `Discretize `_) as an extra Grid type.\n# \n\n# %% \n# This is an enum now\nprint(grid.GridTypes)\n\n# %%\n# Each grid contains its own ``values`` attribute as well as other\n# methods to manipulate them depending on the type of grid.\n# \n\n# %% \nprint(grid.values)\n\n# %%\n# We can see which grids are activated (i.e. they are going to be\n# interpolated and therefore will live on ``Grid().values``) by:\n# \n\n# %% \nprint(grid.active_grids)\n\n# %%\n# By default, only the *regular grid* (``grid.regular_grid``) is active. However, since the regular\n# grid is still empty, ``Grid().values`` is empty too.\n# \n\n# %% \nprint(grid.values)\n\n# %%\n# The last important attribute of Grid is the length:\n# \n\n# %% \nprint(grid.length)\n\n# %%\n# Length gives back the interface indices between grids on the\n# ``Grid().values`` attribute. This can be used after interpolation to\n# know which interpolated values and coordinates correspond to each grid\n# type. You can use the method ``get_grid_args`` to return the indices by\n# name:\n# \n\n# %% \nprint(grid.topography)\n\n# %%\n# By now all is a bit confusing because we have no values. Let's start\n# adding values to the different grids:\n# \n\n# %%\n# Regular Grid\n# ~~~~~~~~~~~~\n# \n# The ``Grid`` class has a bunch of methods to set each grid type and\n# activate them.\n# \n\n# %% \nhelp(RegularGrid)\n\n# %% \nextent = np.array([0, 100, 0, 100, -100, 0])\nresolution = np.array([20, 20, 20])\ngrid.dense_grid = RegularGrid(extent, resolution)\nprint(grid.regular_grid) # RegularGrid will return either dense grid or octree grid depending on what is set\n\n# %%\n# Now the regular grid object composed in ``Grid`` has been filled:\n# \n\n# %% \nprint(grid.regular_grid.values)\n\n# %%\n# And the regular grid has been set active (it was already active in any\n# case):\n# \n\n# %% \nprint(grid.active_grids)\n\n# %%\n# Therefore, the grid values will be equal to the regular grid:\n# \n\n# %% \nprint(grid.values)\n\n# %%\n# And the indices to extract the different arrays:\n# \n\n# %% \nprint(grid.length)\n\n# %%\n# Custom Grid\n# ~~~~~~~~~~~\n# \n# Completely free XYZ values.\n# \n\n# %% \ngp.set_custom_grid(grid, np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))\n\n# %%\n# Again, ``set_any_grid`` will create a grid and activate it. So now the\n# composed object will contain values:\n# \n\n# %% \nprint(grid.custom_grid.values)\n\n# %%\n# And since it is active, it will be added to the grid.values stack:\n# \n\n# %% \nprint(grid.active_grids)\n\n# %% \nprint(grid.values.shape)\n\n# %%\n# We can still recover those values with ``get_grid`` or by getting the\n# slicing args:\n# \n\n# %% \nprint(grid.custom_grid)\n\n# %% \nprint(grid.custom_grid.values)\n\n# %%\n# Topography\n# ~~~~~~~~~~\n# \n# Now we can set the topography. :class:`Topography `\n# contains methods to create manual topographies as well as using gdal for\n# dealing with raster data. By default, we will create a random topography:\n# \n\n# %%\ngp.set_topography_from_random(grid)\n\n# %% \nprint(grid.active_grids)\n\n# %%\n# Now the grid values will contain both the regular grid and topography:\n# \n\n# %% \nprint(grid.values, grid.length)\n\n# %% \nprint(grid.topography.values)\n\n# %%\n# We can compare it to the topography.values:\n# \n\n# %% \nprint(grid.topography.values)\n\n# %%\n# Now that we have more than one grid, we can activate and deactivate any\n# of them in real time:\n# \n\n# %% \ngrid.active_grids ^= grid.GridTypes.TOPOGRAPHY\ngrid.active_grids ^= grid.GridTypes.DENSE\n\n# %%\n# Since now all grids are deactivated, the values will be empty:\n# \n\n# %% \nprint(grid.values)\n\n# %% \ngrid.active_grids |= grid.GridTypes.TOPOGRAPHY\n\n# %% \nprint(grid.values, grid.values.shape)\n\n# %% \ngrid.active_grids |= grid.GridTypes.DENSE\n\n# %% \nprint(grid.values)\n\n# %%\n# Centered Grid\n# ~~~~~~~~~~~~~\n# \n# This grid contains an irregular grid where the majority of voxels are\n# centered around a value (or values). This type of grid is usually used\n# to compute certain types of forward physics where the influence\n# decreases with distance (e.g. gravity: Check `tutorial 2.2-Cell-selection `_\n# )\n# \n\n# %% \ngp.set_centered_grid(\n grid,\n centers=np.array([[300, 0, 0], [0, 0, 0]]),\n resolution=[10, 10, 20],\n radius=np.array([100, 100, 100])\n)\n\n# %%\n# Resolution and radius create a geometrically spaced kernel (blue dots) which\n# will be used to create a grid around each of the center points (red\n# dots):\n# \n\n# %% \n\nfig = plt.figure()\nax = fig.add_subplot(111, projection='3d')\n\nax.scatter(\n grid.centered_grid.values[:, 0],\n grid.centered_grid.values[:, 1],\n grid.centered_grid.values[:, 2],\n '.',\n alpha=.2\n)\n\nax.scatter(\n np.array([[300, 0, 0], [0, 0, 0]])[:, 0],\n np.array([[300, 0, 0], [0, 0, 0]])[:, 1],\n np.array([[300, 0, 0], [0, 0, 0]])[:, 2],\n c='r',\n alpha=1,\n s=30\n)\n\nax.set_xlim(-100, 400)\nax.set_ylim(-100, 100)\nax.set_zlim(-120, 0)\nax.set_xlabel('X Label')\nax.set_ylabel('Y Label')\nax.set_zlabel('Z Label')\nplt.show()\n\n# %%\n# Section Grid\n# ~~~~~~~~~~~~\n# \n# This grid type has its own tutorial. See :doc:`ch1_3b_cross_sections`\n#\n" }, { "path": "examples/tutorials/ch1_fundamentals/ch1_3b_cross_sections.py", "content": "\"\"\"\n1.3b: 2-D sections\n==================\n\n\"\"\"\n\n# %%\n# Importing\nimport gempy as gp\nimport gempy_viewer as gpv\nimport numpy as np\n\nnp.random.seed(1234)\n\n# %%\n# Setup the model\n# ---------------\n# \n\n# %% \n# Importing the data from CSV-files and setting extent and resolution\ndata_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'\n\ngeo_model: gp.data.GeoModel = gp.create_geomodel(\n project_name='Tutorial_ch1_1_Basics',\n extent=[0, 2000, 0, 2000, 0, 750],\n resolution=[20, 20, 20], # * Here we define the resolution of the voxels\n refinement=4, # * Here we define the number of octree levels. If octree levels are defined, the resolution is ignored.\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=data_path + \"/data/input_data/getting_started/simple_fault_model_orientations.csv\",\n path_to_surface_points=data_path + \"/data/input_data/getting_started/simple_fault_model_points.csv\",\n hash_surface_points=\"4cdd54cd510cf345a583610585f2206a2936a05faaae05595b61febfc0191563\",\n hash_orientations=\"7ba1de060fc8df668d411d0207a326bc94a6cdca9f5fe2ed511fd4db6b3f3526\"\n )\n)\n\ngp.map_stack_to_surfaces(\n gempy_model=geo_model,\n mapping_object= # TODO: This mapping I do not like it too much. We should be able to do it passing the data objects directly\n {\n \"Fault_Series\": 'Main_Fault',\n \"Strat_Series\": ('Sandstone_2', 'Siltstone', 'Shale', 'Sandstone_1')\n }\n)\n\ngp.set_is_fault(\n frame=geo_model.structural_frame,\n fault_groups=['Fault_Series']\n)\n\n# %%\n# Add sections\n# ~~~~~~~~~~~~\n# \n\n\n# %%\n# pass section dictionary with startpoint, endpoint and resolution for\n# every section:\n# \n\n# %% \ngp.set_section_grid(\n grid=geo_model.grid,\n section_dict={\n 'section1': ([0, 0], [2000, 2000], [100, 80]),\n 'section2': ([800, 0], [800, 2000], [150, 100]),\n 'section3': ([0, 200], [1500, 500], [200, 150])\n } # p1,p2,resolution\n)\n\n# %%\n# Add topography\n# ~~~~~~~~~~~~~~\n# \n\n# %% \ngp.set_topography_from_random(\n grid=geo_model.grid,\n fractal_dimension=1.2,\n d_z=np.array([300, 750]),\n topography_resolution=np.array([50, 50])\n)\n\n# %%\n# Active grids:\n# \n\n# %% \ngeo_model.grid.active_grids\n\n# %% \ngpv.plot_section_traces(geo_model)\n\n# %% \n\n# %% \ngeo_model.interpolation_options.mesh_extraction = False\nsol = gp.compute_model(geo_model)\n\n# %% \ngpv.plot_2d(geo_model, section_names=['topography'])\n\n# %% \ngpv.plot_2d(geo_model, section_names=['section1'])\n\n# %%\ngpv.plot_2d(\n model=geo_model,\n section_names=['section1', 'section2', 'section3', 'topography'],\n show_topography=True\n)\n\n\n# sphinx_gallery_thumbnail_number = 4\n" }, { "path": "examples/tutorials/ch1_fundamentals/ch1_3c_topography.py", "content": "\"\"\"\n1.3c: Adding topography to geological models\n============================================\n\n\"\"\"\n\n# %%\nimport gempy as gp\nimport gempy_viewer as gpv\nimport numpy as np\nimport os\n\n# %%\n# 1. The common procedure to set up a model:\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# \n\n# %%\ndata_path = os.path.abspath('../../')\n\ngeo_model: gp.data.GeoModel = gp.create_geomodel(\n project_name='Single_layer_topo',\n extent=[450000, 460000, 70000, 80000, -1000, 500],\n resolution=[50, 50, 50],\n refinement=4,\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=data_path + \"/data/input_data/tut-ch1-7/onelayer_orient.csv\",\n path_to_surface_points=data_path + \"/data/input_data/tut-ch1-7/onelayer_interfaces.csv\",\n )\n)\n\n# %% \n\n# %% \ngp.set_section_grid(\n grid=geo_model.grid,\n section_dict={\n 'section1': ([450000, 75000], [460000, 75500], [100, 100]),\n }\n)\n\n# %%\n# 2. Adding topography\n# ~~~~~~~~~~~~~~~~~~~~\n# \n\n\n# %%\n# 2 a. Load from raster file\n# ^^^^^^^^^^^^^^^^^^^^^^^^^^\n# \n#\n# .. admonition:: Coming soon: Importing raster data\n#\n# This feature is not yet available in the current version of GemPy. Probably will be moved to `subsurface` since\n# coupling it with the geological model does not add much value.\n#\n# %%\n\n# This is to make it work in sphinx gallery\n# cwd = os.getcwd()\n# if not 'examples' in cwd:\n# path_dir = os.getcwd() + '/examples/tutorials/ch5_probabilistic_modeling'\n# else:\n# path_dir = cwd\n# \n# fp = path_dir + \"/../../data/input_data/tut-ch1-7/bogota.tif\"\n# \n# # %% \n# geo_model.set_topography(source='gdal', filepath=fp)\n# gp.plot_2d(geo_model, show_topography=True, section_names=['topography'], show_lith=False,\n# show_boundaries=False,\n# kwargs_topography={'cmap': 'gray', 'norm': None}\n# )\n# plt.show()\n\n# %%\n# 2 b. create fun topography\n# ^^^^^^^^^^^^^^^^^^^^^^^^^^\n# \n\n\n# %%\n# If there is no topography file, but you think that your model with\n# topography would look significantly cooler, you can use gempys\n# :meth:`set_topography ` function\n# to generate a random topography based on a fractal grid:\n# \n\n# %%\n# sphinx_gallery_thumbnail_number = 2\n\ngp.set_topography_from_random(grid=geo_model.grid)\ngpv.plot_2d(geo_model, show_topography=True, section_names=['topography'])\n\n# %%\n# It has additional keywords to play around with:\n#\n# * fd: fractal dimension:\n# defaults to 2.0. The higher (try 2.9), the rougher the landscape will\n# be.\n#\n# * d\\_z: height difference:\n# If none, last 20% of the model in z\n# direction.\n#\n# * extent:\n# extent in xy direction. If none,\n# ``geo_model.grid.extent`` is used.\n#\n# * resolution:\n# resolution of the topography array.\n# If none, ``geo_model.grid.resoution`` is used. Increasing the resolution leads to\n# much nicer geological maps!\n#\n# \n\n# %% \ngp.set_topography_from_random(\n grid=geo_model.grid,\n fractal_dimension=1.9,\n d_z=np.array([0, 250]),\n topography_resolution=np.array([200, 200])\n)\n\n# %%\n# Compute model\n# ~~~~~~~~~~~~~\n# \n\n# %% \ngp.compute_model(geo_model)\n\n# %%\n# Visualize:\n# ^^^^^^^^^^\n# \n# Now, the solutions object does also contain the computed geological map.\n# It can be visualized using the 2D and 3D plotting functionality:\n#\n\n# %% \ngpv.plot_2d(geo_model, show_topography=True, section_names=['topography'], show_boundaries=False, show_data=True)\n\n# %% \ngpv.plot_2d(geo_model, show_topography=True, section_names=['section1'])\n\n# %%\ng3d = gpv.plot_3d(\n model=geo_model,\n show_topography=True,\n show_lith=False,\n show_surfaces=False,\n show_results=False,\n ve=5\n)\n\n# %%\n# sphinx_gallery_thumbnail_number = 3\ng3d = gpv.plot_3d(\n model=geo_model,\n show_topography=True,\n show_lith=True,\n show_surfaces=True,\n ve=5\n)\n" }, { "path": "examples/tutorials/ch1_fundamentals/ch1_4_onlap_relations.py", "content": "\"\"\"\n1.4: Unconformity relationships\n===============================\n\n\"\"\"\n# sphinx_gallery_thumbnail_number = 2\n\n# %%\n# Importing gempy\nimport gempy as gp\nimport gempy_viewer as gpv\n\n# Aux imports\nimport numpy as np\nimport os\n\nfrom gempy_engine.config import AvailableBackends\n\nnp.random.seed(1515)\n\n# %%\n# We import a model from an existing folder, representing a subduction\n# zone with onlap relationships. \n\ndata_path = os.path.abspath('../../')\n\ngeo_model: gp.data.GeoModel = gp.create_geomodel(\n project_name='Onlap_relations',\n extent=[-200, 1000, -500, 500, -1000, 0],\n resolution=[50, 50, 50],\n refinement=6,\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=data_path + \"/data/input_data/tut-ch1-4/tut_ch1-4_orientations.csv\",\n path_to_surface_points=data_path + \"/data/input_data/tut-ch1-4/tut_ch1-4_points.csv\",\n )\n)\n\ngp.set_topography_from_random(grid=geo_model.grid, d_z=np.array([-600, -100]))\n\n# %% \ngpv.plot_2d(geo_model)\n\n# %%\n\n# %% \n# Raw structural frame\ngeo_model.structural_frame\n\n# %%\ngeo_model.input_transform.apply_anisotropy(gp.data.GlobalAnisotropy.NONE)\ngp.add_structural_group(\n model=geo_model,\n group_index=0,\n structural_group_name=\"seafloor_series\",\n elements=[geo_model.structural_frame.get_element_by_name(\"seafloor\")],\n structural_relation=gp.data.StackRelationType.ERODE,\n)\n\ngp.add_structural_group(\n model=geo_model,\n group_index=1,\n structural_group_name=\"right_series\",\n elements=[\n geo_model.structural_frame.get_element_by_name(\"rock1\"),\n geo_model.structural_frame.get_element_by_name(\"rock2\"),\n ],\n structural_relation=gp.data.StackRelationType.ONLAP\n)\n\ngp.add_structural_group(\n model=geo_model,\n group_index=2,\n structural_group_name=\"onlap_series\",\n elements=[geo_model.structural_frame.get_element_by_name(\"onlap_surface\")],\n structural_relation=gp.data.StackRelationType.ERODE\n)\n\ngp.add_structural_group(\n model=geo_model,\n group_index=3,\n structural_group_name=\"left_series\",\n elements=[geo_model.structural_frame.get_element_by_name(\"rock3\")],\n structural_relation=gp.data.StackRelationType.BASEMENT\n)\n\ngp.remove_structural_group_by_name(model=geo_model, group_name=\"default_formation\")\n\n# Final structural frame\ngeo_model.structural_frame\n\n# %%\ns = gp.compute_model(\n gempy_model=geo_model,\n engine_config=gp.data.GemPyEngineConfig(\n backend=AvailableBackends.PYTORCH\n )\n)\n\n# %% \ngpv.plot_2d(geo_model, show_data=True)\ngpv.plot_3d(\n model=geo_model,\n show_surfaces=True,\n show_data=True,\n image=True,\n show_topography=True,\n kwargs_plot_structured_grid={'opacity': .2}\n)\n\n# %%\ngpv.plot_2d(\n model=geo_model,\n cell_number=2,\n override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix[0],\n show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None}\n)\n\ngpv.plot_2d(\n model=geo_model,\n cell_number=2,\n override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix[1],\n show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None}\n)\n\ngpv.plot_2d(\n model=geo_model,\n cell_number=2,\n override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix[2],\n show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None}\n)\n\ngpv.plot_2d(\n model=geo_model,\n cell_number=2,\n override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix[3],\n show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None}\n)\n\n# %%\ngpv.plot_2d(\n model=geo_model,\n cell_number=2,\n override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix_squeezed[0],\n show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None}\n)\n\ngpv.plot_2d(\n model=geo_model,\n cell_number=2,\n override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix_squeezed[1],\n show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None}\n)\n\ngpv.plot_2d(\n model=geo_model,\n cell_number=2,\n override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix_squeezed[2],\n show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None}\n)\n\ngpv.plot_2d(\n model=geo_model,\n cell_number=2,\n override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix_squeezed[3],\n show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None}\n)\n" }, { "path": "examples/tutorials/ch1_fundamentals/ch1_5_fault_relations.py", "content": "\"\"\"\n1.5: Fault relations\n====================\n\n\"\"\"\n\n# %%\n# Importing gempy\nimport gempy as gp\nimport gempy_viewer as gpv\n\n# Aux imports\nimport numpy as np\nimport os\n\nnp.random.seed(1515)\n\n# %%\n# We import a model from an existing folder.\n# \n\n# %%\ndata_path = os.path.abspath('../../')\n\ngeo_model: gp.data.GeoModel = gp.create_geomodel(\n project_name='Faults_relations',\n extent=[0, 1000, 0, 1000, -1000, -400],\n resolution=[20, 20, 20],\n refinement=6, # * For this model is better not to use octrees because we want to see what is happening in the scalar fields\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=data_path + \"/data/input_data/tut-ch1-5/tut_ch1-5_orientations.csv\",\n path_to_surface_points=data_path + \"/data/input_data/tut-ch1-5/tut_ch1-5_points.csv\",\n )\n)\n\nprint(geo_model)\n# %%\n# One fault model\n# ---------------\n\n# %% \n# Setting the structural frame\n\nfault1: gp.data.StructuralElement = geo_model.structural_frame.get_element_by_name(\"fault1\")\nfault2: gp.data.StructuralElement = geo_model.structural_frame.get_element_by_name(\"fault2\")\n\n# Remove the faults from the default group\ndefault_group: gp.data.StructuralGroup = geo_model.structural_frame.get_group_by_name(\"default_formation\")\ndefault_group.elements.remove(fault1)\ndefault_group.elements.remove(fault2)\n\n# Add a new group for the fault\ngp.add_structural_group(\n model=geo_model,\n group_index=0,\n structural_group_name=\"fault_series_1\",\n elements=[fault1],\n structural_relation=gp.data.StackRelationType.FAULT,\n fault_relations=gp.data.FaultsRelationSpecialCase.OFFSET_ALL\n)\n\nprint(geo_model.structural_frame)\n\n# %% \n\ngeo_model.input_transform.apply_anisotropy(gp.data.GlobalAnisotropy.NONE)\ngp.compute_model(geo_model)\n# %%\nprint(geo_model.solutions.raw_arrays.block_matrix[0]) # This contains the block values for the fault1\nprint(geo_model.solutions.raw_arrays.block_matrix[1]) # This contains the block values for the formations\n# %%\ngpv.plot_2d(geo_model, show_data=True)\ngpv.plot_3d(geo_model, show_data=True, kwargs_plot_structured_grid={'opacity': .2})\n\n\n# %5\n# Graben example\n# --------------\n\n# %%\ngp.add_structural_group(\n model=geo_model,\n group_index=1,\n structural_group_name=\"fault_series_2\",\n elements=[fault2],\n structural_relation=gp.data.StackRelationType.FAULT,\n fault_relations=gp.data.FaultsRelationSpecialCase.OFFSET_ALL\n)\nprint(geo_model.structural_frame)\n\ngp.compute_model(geo_model)\n\n# %%\ngpv.plot_2d(geo_model, show_data=True)\ngpv.plot_3d(geo_model, show_data=True, image=True, kwargs_plot_structured_grid={'opacity': .2})\n\n# %%\ngpv.plot_2d(geo_model, show_scalar=True, show_lith=False, series_n=0)\ngpv.plot_2d(geo_model, show_scalar=True, show_lith=False, series_n=1)\ngpv.plot_2d(geo_model, show_scalar=True, show_lith=False, series_n=2)\n\n# %%\n# Finite Faults\n# -------------\n\n\n\n# %%\n# Faults relations\n# ----------------\n\n# %%\n# Let's split the formations in two groups\n\ngp.add_structural_group(\n model=geo_model,\n group_index=2,\n structural_group_name=\"series_1\",\n elements=[\n geo_model.structural_frame.get_element_by_name(\"rock4\"),\n geo_model.structural_frame.get_element_by_name(\"rock3\")\n ],\n structural_relation=gp.data.StackRelationType.ERODE\n)\n\ndefault_group.elements.remove(geo_model.structural_frame.get_element_by_name(\"rock4\"))\ndefault_group.elements.remove(geo_model.structural_frame.get_element_by_name(\"rock3\"))\n\ngp.set_fault_relation(\n frame=geo_model.structural_frame,\n rel_matrix=np.array([\n [0, 1, 1, 1],\n [0, 0, 0, 1],\n [0, 0, 0, 0],\n [0, 0, 0, 0]\n ]\n )\n)\nprint(geo_model.structural_frame)\n\n# %%\ngp.compute_model(geo_model)\n\n# %%\ngpv.plot_2d(geo_model, show_data=True)\ngpv.plot_3d(geo_model, show_data=True, image=False, kwargs_plot_structured_grid={'opacity': .2})\n" }, { "path": "examples/tutorials/ch1_fundamentals/ch1_6_2d_visualization.py", "content": "\"\"\"\n1.6: 2D Visualization.\n======================\n\"\"\"\n\n# %%\n\nimport os\n\n# Importing auxiliary libraries\nimport numpy as np\n\n# Importing GemPy\nimport gempy as gp\nimport gempy_viewer as gpv\n\n# sphinx_gallery_thumbnail_number = -1\n\nnp.random.seed(1515)\n\n# %%\n# Model interpolation\n# ~~~~~~~~~~~~~~~~~~~\n# \n\n# %% \n# Data Preparation\ndata_path = os.path.abspath('../../')\n\ngeo_data: gp.data.GeoModel = gp.create_geomodel(\n project_name='viz_2d',\n extent=[0, 1000, 0, 1000, 0, 1000],\n resolution=[10, 10, 10],\n refinement=4,\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=data_path + \"/data/input_data/jan_models/model5_orientations.csv\",\n path_to_surface_points=data_path + \"/data/input_data/jan_models/model5_surface_points.csv\",\n )\n)\n\ngp.set_topography_from_random(grid=geo_data.grid, d_z=np.array([500, 1000]))\n\n# %% \ngpv.plot_2d(geo_data)\n\n# %% \nsection_dict = {'section1': ([0, 0], [1000, 1000], [100, 80]),\n 'section2': ([800, 0], [800, 1000], [150, 100]),\n 'section3': ([50, 200], [100, 500], [200, 150])}\n\n# %% \ngp.set_section_grid(geo_data.grid, section_dict)\ngpv.plot_section_traces(geo_data)\n\n# %% \ngeo_data.grid.sections\n\n# %% \n\n# %% \ngp.map_stack_to_surfaces(\n gempy_model=geo_data,\n mapping_object={\n \"Fault_Series\": 'fault',\n \"Strat_Series\": ('rock2', 'rock1')\n }\n)\n\ngp.set_is_fault(\n frame=geo_data.structural_frame,\n fault_groups=['Fault_Series']\n)\n\n# %% \ngeo_data.grid.active_grids\n\n# %% \ngp.compute_model(geo_data)\n\n# %% \n# new plotting api\ngpv.plot_2d(geo_data, section_names=['section1'])\n\n# %%\n# Plot API\n# --------\n# \n\n\n# %%\n# If nothing is passed, a Plot2D object is created and therefore you are\n# in the same situation as above:\n# \n\n# %% \np3 = gpv.plot_2d(geo_data)\n\n# %%\n# Alternatively you can pass section\\_names, cell\\_numbers + direction or\n# any combination of the above:\n# \n\n# %% \ngpv.plot_2d(geo_data, section_names=['topography'])\n\n# %% \ngpv.plot_2d(geo_data, section_names=['section1'])\n\n# %% \ngpv.plot_2d(geo_data, section_names=['section1', 'section2'])\n\n# %% \ngpv.plot_2d(geo_data, figsize=(15, 15), section_names=['section1', 'section2', 'topography'], cell_number='mid')\n" }, { "path": "examples/tutorials/ch1_fundamentals/ch1_7_3d_visualization.py", "content": "\"\"\"\n1.7: 3-D Visualization\n======================\n\n\"\"\"\n\n# %% \n# Importing GemPy\nimport gempy as gp\nimport gempy_viewer as gpv\nfrom gempy import generate_example_model\nfrom gempy.core.data.enumerators import ExampleModel\nimport dotenv\n\ndotenv.load_dotenv()\n\n# sphinx_gallery_thumbnail_number = -1\n\n# %%\n# Loading an example geomodel\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# \n\n# %%\n\n\ngeo_model = generate_example_model(ExampleModel.GRABEN)\n\ngp.compute_model(geo_model)\n\n# %%\n# Basic plotting API\n# ------------------\n# \n\n\n# %%\n# Data plot\n# ~~~~~~~~~\n# \n\n# %% \ngpv.plot_3d(\n model=geo_model,\n show_surfaces=False,\n show_data=True,\n show_lith=False,\n image=False\n)\n\n# %%\n# Geomodel plot\n# ~~~~~~~~~~~~~\n# \n\n# %% \ngpv.plot_3d(geo_model, image=False)\n\n# %%\n# LiquidEarth Integration\n# ~~~~~~~~~~~~~~~~~~~~~~~\n# Beyond the classical plotting capabilities introduced in GemPy v3, users can now also upload models to LiquidEarth. \n# `LiquidEarth `_ is a collaborative platform designed for 3D visualization,\n# developed by many of the main `gempy` maintainers, with a strong focus on collaboration and sharing. \n# This makes it an excellent tool for sharing your models with others and viewing them across different platforms.\n# To upload a model to LiquidEarth, you must have an account and a user token. Once your model is uploaded, \n# you can easily share the link with anyone.\n\n# %%\nlink = gpv.plot_to_liquid_earth(\n geo_model=geo_model,\n space_name=\"[PUBLIC] GemPy Tutorial 1.7: 3-D Visualization\",\n file_name=\"gempy_model\",\n user_token=None, # If None, it will try to grab it from the environment\n grab_link=True,\n make_new_space=False\n)\n\nprint(f\"Generated Link: {link}\")\n\n# %%\n# Now we can use `this link `_ to visualize the model in Liquid Earth.\n\n# %%\n# .. image:: /_static/gp_model_in_le.png\n" }, { "path": "examples/tutorials/ch2-Geophysics/README.rst", "content": "Chapter 2: Forward Geophysics\n-----------------------------\n\nThese 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!" }, { "path": "examples/tutorials/ch2-Geophysics/ch2_1_gravity.py", "content": "\"\"\"\n2.1 Forward Gravity: Simple example\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n\"\"\"\n\n# %%\n# Importing gempy\nimport gempy as gp\nimport gempy_viewer as gpv\n\n# Aux imports\nimport numpy as np\nimport pandas as pd\nimport os\nimport matplotlib.pyplot as plt\n\n\nnp.random.seed(1515)\npd.set_option('display.precision', 2)\n\n# %%\ndata_path = os.path.abspath('../../data/input_data/tut_SandStone')\n\n# Importing the data from csv\n\ngeo_model: gp.data.GeoModel = gp.create_geomodel(\n project_name='Greenstone',\n extent=[696000, 747000, 6863000, 6930000, -20000, 200], # * Here we define the extent of the model\n refinement=5, # * Here we define the number of octree levels. If octree levels are defined, the resolution is ignored.\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=data_path + \"/SandStone_Foliations.csv\",\n path_to_surface_points=data_path + \"/SandStone_Points.csv\",\n hash_surface_points=None,\n hash_orientations=None\n )\n)\n\n# %% \ngp.map_stack_to_surfaces(\n gempy_model=geo_model,\n mapping_object={\n \"EarlyGranite_Series\": 'EarlyGranite',\n \"BIF_Series\": ('SimpleMafic2', 'SimpleBIF'),\n \"SimpleMafic_Series\": 'SimpleMafic1', 'Basement': 'basement'\n }\n)\n\n# %% \ngpv.plot_2d(geo_model)\n\n# %%\n\n# %%\n# Creating grid\n# ~~~~~~~~~~~~~\n# \n\n# %%\n# First we need to define the location of the devices. For this example we\n# can make a map:\n# \n\n# %% \ngrav_res = 20\nX = np.linspace(7.050000e+05, 747000, grav_res)\nY = np.linspace(6863000, 6925000, grav_res)\nZ = 300\nxyz = np.meshgrid(X, Y, Z)\nxy_ravel = np.vstack(list(map(np.ravel, xyz))).T\nxy_ravel\n\n# %%\n# We can see the location of the devices relative to the model data:\n# \n\n# %%\n\ngpv.plot_2d(geo_model, direction='z', show=False)\nplt.scatter(xy_ravel[:, 0], xy_ravel[:, 1], s=1)\nplt.show()\n\n# %%\n# Now we need to create the grid centered on the devices (see:\n# https://github.com/cgre-aachen/gempy/blob/master/notebooks/tutorials/ch1-3-Grids.ipynb)\n# \n\n# %% \n# geo_model.set_centered_grid(xy_ravel, resolution=[10, 10, 15], radius=5000)\n\ngp.set_centered_grid(\n grid=geo_model.grid,\n centers=xy_ravel,\n resolution=np.array([10, 10, 15]),\n radius=np.array([5000, 5000, 5000])\n)\n\n# %% \ngeo_model.grid.centered_grid.kernel_grid_centers\n\n# %%\n# Now we need to compute the component tz (see\n# https://github.com/cgre-achen/gempy/blob/master/notebooks/tutorials/ch2-2-Cell_selection.ipynb)\n# \n\n# %%\n\ngravity_gradient = gp.calculate_gravity_gradient(geo_model.grid.centered_grid)\ngravity_gradient\n\n# %%\ngeo_model.geophysics_input = gp.data.GeophysicsInput(\n tz=gravity_gradient,\n densities=np.array([2.61, 2.92, 3.1, 2.92, 2.61]),\n)\n\n\n# %%\n# Once we have created a gravity interpolator we can call it from compute\n# model as follows:\n# \n\n# %% \ngeo_model.interpolation_options.mesh_extraction = False\nsol = gp.compute_model(\n gempy_model=geo_model,\n engine_config=gp.data.GemPyEngineConfig(\n backend=gp.data.AvailableBackends.numpy,\n dtype='float32'\n )\n)\n\ngrav = sol.gravity\n\n\n# %% \ngpv.plot_2d(geo_model, cell_number=[-1], direction=['z'], show_data=False)\n\n# %% \ngpv.plot_2d(geo_model, cell_number=['mid'], direction='x')\n\n# %% \ngpv.plot_2d(geo_model, direction=['z'], height=7, show_results=False, show_data=True, show=False)\nplt.scatter(xy_ravel[:, 0], xy_ravel[:, 1], s=1)\nplt.imshow(sol.gravity.reshape(grav_res, grav_res),\n extent=(xy_ravel[:, 0].min() + (xy_ravel[0, 0] - xy_ravel[1, 0]) / 2,\n xy_ravel[:, 0].max() - (xy_ravel[0, 0] - xy_ravel[1, 0]) / 2,\n xy_ravel[:, 1].min() + (xy_ravel[0, 1] - xy_ravel[30, 1]) / 2,\n xy_ravel[:, 1].max() - (xy_ravel[0, 1] - xy_ravel[30, 1]) / 2),\n cmap='viridis_r', origin='lower')\nplt.show()\n\n# %%\n# Plotting lithologies\n# ^^^^^^^^^^^^^^^^^^^^\n# \n# If we want to compute the lithologies we will need to create a normal\n# interpolator object as seen in the Chapter 1 of the tutorials\n# \n\n\n# %%\n# Now we can plot all together (change the alpha parameter to see the\n# gravity overlying):\n# \n\n# %%\ngpv.plot_2d(geo_model, cell_number=[-1], direction=['z'], show=False,\n kwargs_regular_grid={'alpha': .5})\n\nplt.scatter(xy_ravel[:, 0], xy_ravel[:, 1], s=1)\nplt.imshow(grav.reshape(grav_res, grav_res),\n extent=(xy_ravel[:, 0].min() + (xy_ravel[0, 0] - xy_ravel[1, 0]) / 2,\n xy_ravel[:, 0].max() - (xy_ravel[0, 0] - xy_ravel[1, 0]) / 2,\n xy_ravel[:, 1].min() + (xy_ravel[0, 1] - xy_ravel[30, 1]) / 2,\n xy_ravel[:, 1].max() - (xy_ravel[0, 1] - xy_ravel[30, 1]) / 2),\n cmap='viridis_r', origin='lower', alpha=.8)\ncbar = plt.colorbar()\ncbar.set_label(r'$\\mu$gal')\nplt.show()\n\n# sphinx_gallery_thumbnail_number = -2\n" }, { "path": "examples/tutorials/ch2-Geophysics/ch2_2_cell_selection.py", "content": "\"\"\"\n2.2: Centered Grid.\n^^^^^^^^^^^^^^^^^^^\n\n\"\"\"\n\n# %%\n# \n# Concept of a measurement-centered grid\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n#\n# Geophysics Preprocessing builds on the centered grid\n# (https://github.com/cgre-aachen/gempy/blob/master/notebooks/tutorials/ch1-3-Grids.ipynb)\n# to precompute the constant part of forward physical computations as for\n# example gravity:\n\n# .. math::\n\n# 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}|^{\n# z_2}_{z_1}\n\n\n# where we can compress the grid dependent terms as\n\n# .. math::\n\n# 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}\n\n# By doing this decomposition an keeping the grid constant we can compute\n# the forward gravity by simply operate:\n\n# .. math::\n\n# F_z = G_{\\\\rho} \\cdot t_z\n\n\n# %%\n\n# Importing gempy\nimport gempy as gp\n\n# Aux imports\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nnp.random.seed(1515)\npd.set_option('display.precision', 2)\n\n# %% \nfrom gempy_engine.core.data.centered_grid import CenteredGrid\ncentered_grid = CenteredGrid(\n centers=np.array([0,0,0]),\n resolution=[10, 10, 20],\n radius=100\n)\n# %%\n# ``create_irregular_grid_kernel`` will create a constant kernel around\n# the point 0,0,0. This kernel will be what we use for each device.\n# \n\n# %% \ncentered_grid.kernel_grid_centers\n\n# %%\n# :math:`t_z` is only dependent on distance and therefore we can use the\n# kerenel created on the previous cell\n# \n\n# %% \ngravity_gradient = gp.calculate_gravity_gradient(centered_grid)\ngravity_gradient\n\n# %%\n# To compute tz we also need the edges of each voxel. The distance to the\n# edges are stored on ``kernel_dxyz_left`` and ``kernel_dxyz_right``. We\n# can plot all the data as follows:\n# \n\n# %% \na, b, c = centered_grid.kernel_grid_centers, centered_grid.left_voxel_edges, centered_grid.right_voxel_edges\ntz = gravity_gradient\n\n# %% \nfig = plt.figure(figsize=(13, 7))\nplt.quiver(a[:, 0].reshape(11, 11, 21)[5, :, :].ravel(),\n a[:, 2].reshape(11, 11, 21)[:, 5, :].ravel(),\n np.zeros(231),\n tz.reshape(11, 11, 21)[5, :, :].ravel(), label='$t_z$', alpha=.3\n )\n\nplt.plot(a[:, 0].reshape(11, 11, 21)[5, :, :].ravel(),\n a[:, 2].reshape(11, 11, 21)[:, 5, :].ravel(), 'o', alpha=.3, label='Centers')\n\nplt.plot(a[:, 0].reshape(11, 11, 21)[5, :, :].ravel() - b[:, 0].reshape(11, 11, 21)[5, :, :].ravel(),\n a[:, 2].reshape(11, 11, 21)[:, 5, :].ravel(), '.', alpha=.3, label='Lefts')\n\nplt.plot(a[:, 0].reshape(11, 11, 21)[5, :, :].ravel(),\n a[:, 2].reshape(11, 11, 21)[:, 5, :].ravel() - b[:, 2].reshape(11, 11, 21)[:, 5, :].ravel(), '.', alpha=.6,\n label='Ups')\n\nplt.plot(a[:, 0].reshape(11, 11, 21)[5, :, :].ravel() + c[:, 0].reshape(11, 11, 21)[5, :, :].ravel(),\n a[:, 2].reshape(11, 11, 21)[:, 5, :].ravel(), '.', alpha=.3, label='Rights')\n\nplt.plot(a[:, 0].reshape(11, 11, 21)[5, :, :].ravel(),\n a[:, 2].reshape(11, 11, 21)[:, 5, :].ravel() + c[:, 2].reshape(11, 11, 21)[5, :, :].ravel(), '.', alpha=.3,\n label='Downs')\n\nplt.xlim(-200, 200)\nplt.ylim(-200, 0)\nplt.legend()\nplt.show()\n\n# %%\n# Just the quiver:\n# \n\n# %%\nfig = plt.figure(figsize=(13, 7))\nplt.quiver(a[:, 0].reshape(11, 11, 21)[5, :, :].ravel(),\n a[:, 2].reshape(11, 11, 21)[:, 5, :].ravel(),\n np.zeros(231),\n tz.reshape(11, 11, 21)[5, :, :].ravel()\n )\nplt.show()\n\n# %%\n# Remember this is happening always in 3D:\n# \n\n# %%\nfig = plt.figure()\nax = fig.add_subplot(111, projection='3d')\n\nax.scatter(a[:, 0], a[:, 1], a[:, 2], c=tz)\n\nax.set_xlabel('X Label')\nax.set_ylabel('Y Label')\nax.set_zlabel('Z Label')\nplt.show()" }, { "path": "examples/tutorials/ch3-Interpolations/README.rst", "content": "Chapter 3: Domain Kriging\n-------------------------\n\nGemPy uses Kriging to interpolate the scalar field to construct the\nstructural model. However to populate petrophysical data within each domain\nwe also provide functionality to interpolate data in 3D." }, { "path": "examples/tutorials/ch3-Interpolations/ch3_1_kriging_interpolation_and_simulation.py", "content": "\"\"\"\n3.1: Simple example of kriging in gempy\n=======================================\n\n\"\"\"\n\n# %%\n# In this notebook it will be shown how to create a kriged or simulated\n# field in a simple geological model in gempy. We start by creating a\n# simple model with three horizontally layered units, as shown in the\n# gempy examples.\n# \n\n# %%\n# Importing GemPy\nimport gempy as gp\nimport gempy_viewer as gpv\n\n# Importing auxiliary libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\n\n# new for this\nfrom gempy_plugins.kriging import kriging\n\nnp.random.seed(5555)\n\n# %%\n# Creating the model by importing the input data and displaying it:\n# \n\n# %% \ndata_path = os.path.abspath('../../')\n\ngeo_data: gp.data.GeoModel = gp.create_geomodel(\n project_name='kriging',\n extent=[0, 1000, 0, 50, 0, 1000],\n resolution=[50, 10, 50],\n refinement=1,\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=data_path + \"/data/input_data/jan_models/model1_orientations.csv\",\n path_to_surface_points=data_path + \"/data/input_data/jan_models/model1_surface_points.csv\",\n )\n)\n\n# %%\n# Setting and ordering the units and series:\n# \n\n# %% \ngp.map_stack_to_surfaces(\n gempy_model=geo_data,\n mapping_object={\n \"Strat_Series\": ('rock2', 'rock1'),\n \"Basement_Series\": ('basement')\n }\n)\n\n# %%\n# Calculating the model:\n# \n\n# %% \n# no mesh computed as basically 2D model\nsol = gp.compute_model(geo_data)\n\n# %%\n# So here is the very simple, basically 2D model that we created:\n# \n\n# %% \ngpv.plot_2d(geo_data, cell_number=0, show_data=False)\n\n# %%\n# 1) Creating domain\n# ------------------\n# \n# Let us assume we have a couple of measurements in a domain of interest\n# within our model. In our case the unit of interest is the central rock\n# layer (rock1). In the kriging module we can define the domain by\n# handing over a number of surfaces by id - in this case the id of rock1\n# is 2. In addition we define four input data points in cond_data, each\n# defined by x,y,z coordinate and a measurement value.\n# \n\n# %% \n# conditioning data (data measured at locations)\ncond_data = np.array([[100, .5, 500, 2], [900, .5, 500, 1],\n [500, .5, 550, 1], [300, .5, 400, 5]])\n\n# %% \n# creating a domain object from the gempy solution, a defined domain conditioning data\ndomain = kriging.Domain(\n model_solutions=sol,\n transform=geo_data.input_transform,\n domain=[2],\n data=cond_data\n)\n\n# %%\n# 2) Creating a variogram model\n# -----------------------------\n# \n\n# %% \nvariogram_model = kriging.VariogramModel(\n theoretical_model='exponential',\n range_=200,\n sill=np.var(cond_data[:, 3])\n)\n\n# %% \nvariogram_model.plot(type_='both', show_parameters=True)\nplt.show()\n\n# %%\n# 3) Kriging interpolation\n# ------------------------\n# \n\n\n# %%\n# In the following we define an object called kriging_model and set all\n# input parameters. Finally we generate the kriged field.\n# \n\n# %% \nkriging_solution = kriging.create_kriged_field(domain, variogram_model)\n\n# %%\n# The result of our calculation is saved in the following dataframe,\n# containing an estimated value and the kriging variance for each point in\n# the grid:\n# \n\n# %% \nkriging_solution.results_df.head()\n\n\n# %%\n# It is also possible to plot the results in cross section similar to the\n# way gempy models are plotted.\n# \n\n\n# %% \nfrom gempy_viewer.modules.plot_2d.visualization_2d import Plot2D\n\nplot_2d: Plot2D = gpv.plot_2d(\n model=geo_data,\n cell_number=0,\n show_data=False,\n show=False,\n kwargs_lithology={ 'alpha': 0.5 }\n)\nkriging.plot_kriging_results(\n geo_data=geo_data,\n kriging_solution=kriging_solution,\n plot_2d=plot_2d,\n title='Kriging interpolation: Estimated values',\n result_column=['estimated value']\n)\n\n# %%\nplot_2d_both = gpv.plot_2d(\n model=geo_data,\n cell_number=[0, 0],\n show_data=False,\n show=False,\n kwargs_lithology={ 'alpha': 0.5 }\n)\n\nkriging.plot_kriging_results(\n geo_data=geo_data,\n kriging_solution=kriging_solution,\n plot_2d=plot_2d_both,\n title='Kriging interpolation: Estimated values',\n result_column=['estimated value', 'estimation variance']\n)\n\n# %%\n# 4) Simulated field\n# ------------------\n# \n# Based on the same objects (domain and varigoram model) also a simulated\n# field (stationary Gaussian Field) can be generated. A Sequential\n# Gaussian Simulation approach is applied in this module:\n# \n\n# %% \nsolution_sim = kriging.create_gaussian_field(domain, variogram_model)\n\n# %% \nsolution_sim.results_df.head()\n\n# %% \nsolution_sim.results_df['estimated value']\n\n# %%\nplot_2d: Plot2D = gpv.plot_2d(\n model=geo_data,\n cell_number=0,\n show_data=False,\n show=False,\n kwargs_lithology={ 'alpha': 0.5 }\n)\nkriging.plot_kriging_results(\n geo_data=geo_data,\n kriging_solution=solution_sim,\n plot_2d=plot_2d,\n title='Kriging interpolation: Estimated values',\n result_column=['estimated value']\n)\n\n# %%\nplot_2d_both = gpv.plot_2d(\n model=geo_data,\n cell_number=[0, 0],\n show_data=False,\n show=False,\n kwargs_lithology={ 'alpha': 0.5 }\n)\n\nkriging.plot_kriging_results(\n geo_data=geo_data,\n kriging_solution=solution_sim,\n plot_2d=plot_2d_both,\n title='Kriging interpolation: Estimated values',\n result_column=['estimated value', 'estimation variance']\n)\n# sphinx_gallery_thumbnail_number = 3\n" }, { "path": "examples/tutorials/ch4-Topology/README.rst", "content": "Chapter 4: Topological analysis\n-------------------------------\n\nOne of the reasons to perform structural modeling is to analyse the topology\nof the system, i.e. the connectivity between layers. GemPy provide built-in\nfunctionality to calculate and analyse this aspect of each geological modeling" }, { "path": "examples/tutorials/ch4-Topology/__init__.py", "content": "" }, { "path": "examples/tutorials/ch4-Topology/ch4-1-Topology.py", "content": "\"\"\"\nChapter 4: Analyzing Geomodel Topology\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n\"\"\"\nimport gempy as gp\nimport gempy_viewer as gpv\nfrom gempy_viewer.modules.plot_2d.visualization_2d import Plot2D\nfrom gempy_plugins.topology_analysis import topology as tp\n\nimport os\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n\n# %%\n# Load example Model\n# ^^^^^^^^^^^^^^^^^^\n# \n# First let's set up a very simple example model. For that we initialize\n# the geo_data object with the correct model extent and the resolution we\n# like. Then we load our data points from csv files and set the series and\n# order the formations (stratigraphic pile).\n# \n\n# %% \ndata_path = os.path.abspath('../../')\n\ngeo_model: gp.data.GeoModel = gp.create_geomodel(\n project_name='Model_Tutorial6',\n extent= [0, 3000, 0, 20, 0, 2000],\n resolution=[50, 10, 67],\n refinement=1, # * For this model is better not to use octrees because we want to see what is happening in the scalar fields\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=data_path + \"/data/input_data/tut_chapter6/ch6_data_fol.csv\",\n path_to_surface_points=data_path + \"/data/input_data/tut_chapter6/ch6_data_interf.csv\",\n )\n)\n\ngp.map_stack_to_surfaces(\n gempy_model=geo_model,\n mapping_object=\n {\n \"fault\": \"Fault\",\n \"Rest\": ('Layer 2', 'Layer 3', 'Layer 4', 'Layer 5')\n }\n)\n\ngp.set_is_fault(geo_model, ['fault'])\n\ngeo_model.interpolation_options.mesh_extraction = False\nsol = gp.compute_model(geo_model)\n\n# %% \ngpv.plot_2d(geo_model, cell_number=[5])\n\n\n# %%\n# Analyzing Topology\n# ^^^^^^^^^^^^^^^^^^\n# \n# GemPy sports in-built functionality to analyze the topology of its\n# models. All we need for this is our geo_data object, lithology block and\n# the fault block. We input those into *gp.topology_compute* and get\n# several useful outputs:\n# \n# - an adjacency graph **G**, representing the topological relationships\n# of the model\n# - the **centroids** of the all the unique topological regions in the\n# model (x,y,z coordinates of their center)\n# - a list of all the unique labels (labels_unique)\n# - two look-up-tables from the lithology id's to the node labels, and\n# vice versa\n# \n\n# %% \nedges, centroids = tp.compute_topology(geo_model)\n\n\n# %%\n# The first output of the topology function is the ``set`` of edges\n# representing topology relationships between unique geobodies of the\n# block model. An edge is represented by a ``tuple`` of two ``int``\n# geobody (or node) labels:\n# \n\n# %% \nedges\n\n\n# %%\n# The second output is the centroids ``dict``, mapping the unique geobody\n# id's (graph node id's) to the geobody centroid position in grid\n# coordinates:\n# \n\n# %% \ncentroids\n\n\n# %%\n# After computing the model topology, we can overlay the topology graph\n# over a model section:\n# \n\n\n# %%\n# Visualizing topology\n# ~~~~~~~~~~~~~~~~~~~~\n# \n# 2-D Visualization of the Topology Graph\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# \n\n\n# %% \ngpv.plot_topology(\n regular_grid=geo_model.grid.regular_grid,\n edges=edges,\n centroids=centroids\n)\n\n# %% \nplot_2d: Plot2D = gpv.plot_2d(geo_model, cell_number=[5], show=False)\ngpv.plot_topology(\n regular_grid=geo_model.grid.regular_grid,\n edges=edges,\n centroids=centroids,\n ax=plot_2d.axes[0]\n)\n\n# %%\n# Adjacency Matrix\n# ~~~~~~~~~~~~~~~~\n# \n# Another way to encode and visualize the geomodel topology is using an\n# adjacency graph:\n# \n\n# %% \nM = tp.get_adjacency_matrix(geo_model, edges, centroids)\nprint(M)\n\n# %% \ntp.plot_adjacency_matrix(geo_model, M)\n\n\n# %%\n# Look-up tables\n# ~~~~~~~~~~~~~~\n# \n\n\n# %%\n# The ``topology`` asset provides several look-up tables to work with the\n# unique geobody topology id's.\n# \n# Mapping node id's back to lithology / surface id's:\n# \n\n# %% \nlith_lot = tp.get_lot_node_to_lith_id(geo_model, centroids)\nlith_lot\n\n\n# %%\n# Figuring out which nodes are in which fault block:\n# \n\n# %% \nfault_lot = tp.get_lot_node_to_fault_block(geo_model, centroids)\nfault_lot\n\n\n# %%\n# We can also easily map the lithology id to the corresponding topology\n# id's:\n# \n\n# %% \ntp.get_lot_lith_to_node_id(lith_lot)\n\n\n# %%\n# Detailed node labeling\n# ~~~~~~~~~~~~~~~~~~~~~~\n# \n\n# %%\n# sphinx_gallery_thumbnail_number = 4\ndedges, dcentroids = tp.get_detailed_labels(geo_model, edges, centroids)\n# %% \nplot_2d: Plot2D = gpv.plot_2d(geo_model, cell_number=[5], show=False)\ngpv.plot_topology(\n regular_grid=geo_model.grid.regular_grid,\n edges=dedges,\n centroids=dcentroids,\n ax=plot_2d.axes[0]\n)\n\n# %% \ndedges\n\n# %% \ndcentroids\n\n\n# %%\n# Checking adjacency\n# ~~~~~~~~~~~~~~~~~~\n# \n\n\n# %%\n# So lets say we want to check if the purple layer (id 5) is connected\n# across the fault to the yellow layer (id 3). For this we can make easy\n# use of the detailed labeling and the ``check_adjacency`` function:\n# \n\n# %% \ntp.check_adjacency(dedges, \"5_1\", \"3_0\")\n\n\n# %%\n# We can also check all geobodies that are adjacent to the purple layer\n# (id 5) on the left side of the fault (fault id 1):\n# \n\n# %% \ntp.get_adjacencies(dedges, \"5_1\")\n\n# %% \n" }, { "path": "examples/tutorials/z_other_tutorials/README.rst", "content": "Video tutorials\n---------------\n\nCompilation of tutorials for videos on the `gempy` YouTube channel https://www.youtube.com/@GemPy3D." }, { "path": "examples/tutorials/z_other_tutorials/__init__.py", "content": "" }, { "path": "examples/tutorials/z_other_tutorials/json_io/01_surface_points_io.py", "content": "\"\"\"\nTutorial for JSON I/O operations in GemPy - Surface Points\n=======================================================\n\nThis tutorial demonstrates how to save and load surface points data using JSON files in GemPy.\n\"\"\"\n\nimport json\nimport numpy as np\nimport gempy as gp\nfrom gempy.modules.json_io import JsonIO\n\n# Create a sample surface points dataset\n# ------------------------------------\n\n# Create some sample surface points\nx = np.array([0, 1, 2, 3, 4])\ny = np.array([0, 1, 2, 3, 4])\nz = np.array([0, 1, 2, 3, 4])\nids = np.array([0, 0, 1, 1, 2]) # Three different surfaces\nnugget = np.array([0.00002, 0.00002, 0.00002, 0.00002, 0.00002])\n\n# Create name to id mapping\nname_id_map = {f\"surface_{id}\": id for id in np.unique(ids)}\n\n# Create a SurfacePointsTable\nsurface_points = gp.data.SurfacePointsTable.from_arrays(\n x=x,\n y=y,\n z=z,\n names=[f\"surface_{id}\" for id in ids],\n nugget=nugget,\n name_id_map=name_id_map\n)\n\n# Create a JSON file with the surface points data\n# ---------------------------------------------\n\n# Create the JSON structure\njson_data = {\n \"metadata\": {\n \"name\": \"sample_model\",\n \"creation_date\": \"2024-03-19\",\n \"last_modification_date\": \"2024-03-19\",\n \"owner\": \"tutorial\"\n },\n \"surface_points\": [\n {\n \"x\": float(x[i]),\n \"y\": float(y[i]),\n \"z\": float(z[i]),\n \"id\": int(ids[i]),\n \"nugget\": float(nugget[i])\n }\n for i in range(len(x))\n ],\n \"orientations\": [],\n \"faults\": [],\n \"series\": [],\n \"grid_settings\": {\n \"regular_grid_resolution\": [10, 10, 10],\n \"regular_grid_extent\": [0, 4, 0, 4, 0, 4],\n \"octree_levels\": None\n },\n \"interpolation_options\": {}\n}\n\n# Save the JSON file\nwith open(\"sample_surface_points.json\", \"w\") as f:\n json.dump(json_data, f, indent=4)\n\n# Load the surface points from JSON\n# -------------------------------\n\n# Load the model from JSON\nloaded_surface_points = JsonIO._load_surface_points(json_data[\"surface_points\"])\n\n# Verify the loaded data\nprint(\"\\nOriginal surface points:\")\nprint(surface_points)\nprint(\"\\nLoaded surface points:\")\nprint(loaded_surface_points)\n\n# Verify the data matches\nprint(\"\\nVerifying data matches:\")\nprint(f\"X coordinates match: {np.allclose(surface_points.xyz[:, 0], loaded_surface_points.xyz[:, 0])}\")\nprint(f\"Y coordinates match: {np.allclose(surface_points.xyz[:, 1], loaded_surface_points.xyz[:, 1])}\")\nprint(f\"Z coordinates match: {np.allclose(surface_points.xyz[:, 2], loaded_surface_points.xyz[:, 2])}\")\nprint(f\"IDs match: {np.array_equal(surface_points.ids, loaded_surface_points.ids)}\")\nprint(f\"Nugget values match: {np.allclose(surface_points.nugget, loaded_surface_points.nugget)}\")\n\n# Print the name_id_maps to compare\nprint(\"\\nName to ID mappings:\")\nprint(f\"Original: {surface_points.name_id_map}\")\nprint(f\"Loaded: {loaded_surface_points.name_id_map}\") " }, { "path": "examples/tutorials/z_other_tutorials/json_io/02_horizontal_stratigraphic.py", "content": "\"\"\"\nTutorial: Loading a Horizontal Stratigraphic Model using JSON I/O\n===============================================================\n\nThis tutorial demonstrates how to load a horizontal stratigraphic model using GemPy's JSON I/O functionality.\nThe model consists of two horizontal layers (rock1 and rock2) with surface points and orientations.\n\"\"\"\n\n# %%\n# Import necessary libraries\nimport matplotlib\nmatplotlib.use('Agg') # Use non-interactive backend\nimport gempy as gp\nimport gempy_viewer as gpv\nimport numpy as np\nimport json\nfrom pathlib import Path\nimport matplotlib.pyplot as plt\nfrom datetime import datetime\n\n# %%\n# Define the model data\nmodel_data = {\n \"metadata\": {\n \"name\": \"Horizontal Stratigraphic Model\",\n \"creation_date\": \"2024-03-24\",\n \"last_modification_date\": datetime.now().strftime(\"%Y-%m-%d\"),\n \"owner\": \"GemPy Team\"\n },\n \"surface_points\": [\n {\"x\": 0.0, \"y\": 0.0, \"z\": 0.0, \"id\": 1, \"nugget\": 0.0},\n {\"x\": 1.0, \"y\": 0.0, \"z\": 0.0, \"id\": 1, \"nugget\": 0.0},\n {\"x\": 0.0, \"y\": 1.0, \"z\": 0.0, \"id\": 1, \"nugget\": 0.0},\n {\"x\": 1.0, \"y\": 1.0, \"z\": 0.0, \"id\": 1, \"nugget\": 0.0},\n {\"x\": 0.0, \"y\": 0.0, \"z\": 1.0, \"id\": 2, \"nugget\": 0.0},\n {\"x\": 1.0, \"y\": 0.0, \"z\": 1.0, \"id\": 2, \"nugget\": 0.0},\n {\"x\": 0.0, \"y\": 1.0, \"z\": 1.0, \"id\": 2, \"nugget\": 0.0},\n {\"x\": 1.0, \"y\": 1.0, \"z\": 1.0, \"id\": 2, \"nugget\": 0.0}\n ],\n \"orientations\": [\n {\"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},\n {\"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}\n ],\n \"series\": [\n {\n \"name\": \"series1\",\n \"surfaces\": [\"layer1\", \"layer2\"],\n \"structural_relation\": \"ERODE\",\n \"colors\": [\"#ff0000\", \"#00ff00\"]\n }\n ],\n \"grid_settings\": {\n \"regular_grid_resolution\": [10, 10, 10],\n \"regular_grid_extent\": [0.0, 1.0, 0.0, 1.0, 0.0, 1.0],\n \"octree_levels\": None\n },\n \"interpolation_options\": {\n \"kernel_options\": {\n \"range\": 1.7,\n \"c_o\": 10\n },\n \"mesh_extraction\": True,\n \"number_octree_levels\": 1\n },\n \"fault_relations\": None,\n \"id_name_mapping\": {\n \"name_to_id\": {\n \"layer1\": 1,\n \"layer2\": 2\n }\n }\n}\n\n# %%\n# Save the model data to a JSON file\ntutorial_dir = Path(__file__).parent\njson_file = tutorial_dir / \"horizontal_stratigraphic.json\"\nwith open(json_file, \"w\") as f:\n json.dump(model_data, f, indent=4)\n\n# %%\n# Load the model from JSON\nmodel = gp.modules.json_io.JsonIO.load_model_from_json(str(json_file))\n\n# %%\n# Compute the geological model\ngp.compute_model(model)\n\n# %%\n# Plot the model\n# Plot the initial geological model in the y direction without results\nfig, ax = plt.subplots(figsize=(10, 6))\ngpv.plot_2d(model, direction=['y'], show_results=False, ax=ax)\nplt.title(\"Initial Geological Model (y direction)\")\nplt.savefig('initial_model_y.png')\nplt.close()\n\n# Plot the result of the model in the x and y direction with data and without boundaries\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\ngpv.plot_2d(model, direction=['x'], show_data=True, show_boundaries=False, ax=ax1)\nax1.set_title(\"Model with Data (x direction)\")\ngpv.plot_2d(model, direction=['y'], show_data=True, show_boundaries=False, ax=ax2)\nax2.set_title(\"Model with Data (y direction)\")\nplt.tight_layout()\nplt.savefig('model_with_data.png')\nplt.close() " }, { "path": "examples/tutorials/z_other_tutorials/json_io/03_multiple_series_faults.py", "content": "\"\"\"\nTutorial: Loading a Model with Multiple Series and Faults using JSON I/O\n=====================================================================\n\nThis tutorial demonstrates how to load a geological model with multiple series and faults using GemPy's JSON I/O functionality.\nThe model consists of two layers (rock1, rock2) and a fault that offsets them.\n\"\"\"\n\n# %%\n# Import necessary libraries\nimport matplotlib\n# matplotlib.use('Agg') # Use non-interactive backend\nimport gempy as gp\nimport gempy_viewer as gpv\nimport numpy as np\nimport json\nfrom pathlib import Path\nimport matplotlib.pyplot as plt\nfrom gempy_engine.core.data.stack_relation_type import StackRelationType\nfrom gempy.modules.json_io.json_operations import JsonIO # Updated import path\nfrom datetime import datetime\n\n# %%\n# Define the model data\nmodel_data = {\n \"metadata\": {\n \"name\": \"Multiple Series and Faults Model\",\n \"creation_date\": \"2024-03-24\", # Fixed creation date\n \"last_modification_date\": datetime.now().strftime(\"%Y-%m-%d\"), # Current date only\n \"owner\": \"GemPy Team\"\n },\n \"surface_points\": [\n # rock1 points\n {\"x\": 0, \"y\": 200, \"z\": 600, \"id\": 0, \"nugget\": 0.00002},\n {\"x\": 0, \"y\": 500, \"z\": 600, \"id\": 0, \"nugget\": 0.00002},\n {\"x\": 0, \"y\": 800, \"z\": 600, \"id\": 0, \"nugget\": 0.00002},\n {\"x\": 200, \"y\": 200, \"z\": 600, \"id\": 0, \"nugget\": 0.00002},\n {\"x\": 200, \"y\": 500, \"z\": 600, \"id\": 0, \"nugget\": 0.00002},\n {\"x\": 200, \"y\": 800, \"z\": 600, \"id\": 0, \"nugget\": 0.00002},\n {\"x\": 800, \"y\": 200, \"z\": 200, \"id\": 0, \"nugget\": 0.00002},\n {\"x\": 800, \"y\": 500, \"z\": 200, \"id\": 0, \"nugget\": 0.00002},\n {\"x\": 800, \"y\": 800, \"z\": 200, \"id\": 0, \"nugget\": 0.00002},\n {\"x\": 1000, \"y\": 200, \"z\": 200, \"id\": 0, \"nugget\": 0.00002},\n {\"x\": 1000, \"y\": 500, \"z\": 200, \"id\": 0, \"nugget\": 0.00002},\n {\"x\": 1000, \"y\": 800, \"z\": 200, \"id\": 0, \"nugget\": 0.00002},\n # rock2 points\n {\"x\": 0, \"y\": 200, \"z\": 800, \"id\": 1, \"nugget\": 0.00002},\n {\"x\": 0, \"y\": 800, \"z\": 800, \"id\": 1, \"nugget\": 0.00002},\n {\"x\": 200, \"y\": 200, \"z\": 800, \"id\": 1, \"nugget\": 0.00002},\n {\"x\": 200, \"y\": 800, \"z\": 800, \"id\": 1, \"nugget\": 0.00002},\n {\"x\": 800, \"y\": 200, \"z\": 400, \"id\": 1, \"nugget\": 0.00002},\n {\"x\": 800, \"y\": 800, \"z\": 400, \"id\": 1, \"nugget\": 0.00002},\n {\"x\": 1000, \"y\": 200, \"z\": 400, \"id\": 1, \"nugget\": 0.00002},\n {\"x\": 1000, \"y\": 800, \"z\": 400, \"id\": 1, \"nugget\": 0.00002},\n # fault points\n {\"x\": 500, \"y\": 500, \"z\": 500, \"id\": 2, \"nugget\": 0.00002},\n {\"x\": 450, \"y\": 500, \"z\": 600, \"id\": 2, \"nugget\": 0.00002},\n {\"x\": 500, \"y\": 200, \"z\": 500, \"id\": 2, \"nugget\": 0.00002},\n {\"x\": 450, \"y\": 200, \"z\": 600, \"id\": 2, \"nugget\": 0.00002},\n {\"x\": 500, \"y\": 800, \"z\": 500, \"id\": 2, \"nugget\": 0.00002},\n {\"x\": 450, \"y\": 800, \"z\": 600, \"id\": 2, \"nugget\": 0.00002}\n ],\n \"orientations\": [\n # rock2 orientations\n {\"x\": 100, \"y\": 500, \"z\": 800, \"G_x\": 0, \"G_y\": 0, \"G_z\": 1, \"id\": 1, \"nugget\": 0.00002, \"polarity\": 1},\n {\"x\": 900, \"y\": 500, \"z\": 400, \"G_x\": 0, \"G_y\": 0, \"G_z\": 1, \"id\": 1, \"nugget\": 0.00002, \"polarity\": 1},\n # rock1 orientations\n {\"x\": 100, \"y\": 500, \"z\": 600, \"G_x\": 0, \"G_y\": 0, \"G_z\": 1, \"id\": 0, \"nugget\": 0.00002, \"polarity\": 1},\n {\"x\": 900, \"y\": 500, \"z\": 200, \"G_x\": 0, \"G_y\": 0, \"G_z\": 1, \"id\": 0, \"nugget\": 0.00002, \"polarity\": 1},\n # fault orientation (60-degree dip)\n {\"x\": 500, \"y\": 500, \"z\": 500, \"G_x\": 0.866, \"G_y\": 0, \"G_z\": 0.5, \"id\": 2, \"nugget\": 0.00002, \"polarity\": 1}\n ],\n \"series\": [\n {\n \"name\": \"Fault_Series\",\n \"surfaces\": [\"fault\"],\n \"structural_relation\": \"FAULT\",\n \"colors\": [\"#ffbe00\"]\n },\n {\n \"name\": \"Strat_Series\",\n \"surfaces\": [\"rock2\", \"rock1\"],\n \"structural_relation\": \"ERODE\",\n \"colors\": [\"#015482\", \"#9f0052\"]\n }\n ],\n \"grid_settings\": {\n \"regular_grid_resolution\": [50, 50, 50],\n \"regular_grid_extent\": [0, 1000, 0, 1000, 0, 1000],\n \"octree_levels\": None\n },\n \"interpolation_options\": {\n \"kernel_options\": {\n \"range\": 1.7,\n \"c_o\": 10\n },\n \"mesh_extraction\": True,\n \"number_octree_levels\": 1\n },\n \"fault_relations\": [[0, 1], [0, 0]], # Fault series affects Strat_Series\n \"id_name_mapping\": {\n \"name_to_id\": {\n \"rock1\": 0,\n \"rock2\": 1,\n \"fault\": 2\n }\n }\n}\n\n# %%\n# Save the model data to a JSON file\ntutorial_dir = Path(__file__).parent\njson_file = tutorial_dir / \"multiple_series_faults.json\"\nwith open(json_file, \"w\") as f:\n json.dump(model_data, f, indent=4)\n\n# %%\n# Load the model from JSON\nmodel = JsonIO.load_model_from_json(str(json_file))\n\n# Print metadata to verify it's properly loaded\nprint(\"\\nModel Metadata:\")\nprint(f\"Name: {model.meta.name}\")\nprint(f\"Creation Date: {model.meta.creation_date}\")\nprint(f\"Last Modification Date: {model.meta.last_modification_date}\")\nprint(f\"Owner: {model.meta.owner}\")\n\n# Print structural groups\nprint(\"\\nStructural Groups:\")\nprint(model.structural_frame.structural_groups)\n\n# %%\n# Compute the geological model\ngp.compute_model(model)\n\n# Print scalar field values to verify they are calculated\nprint(\"\\nScalar Field Values (Fault Series):\")\nprint(f\"Shape: {model.solutions.raw_arrays.scalar_field_matrix.shape}\")\nprint(f\"Min value: {model.solutions.raw_arrays.scalar_field_matrix[0].min()}\")\nprint(f\"Max value: {model.solutions.raw_arrays.scalar_field_matrix[0].max()}\")\nprint(f\"Mean value: {model.solutions.raw_arrays.scalar_field_matrix[0].mean()}\")\n\n# %%\n# Save the computed model to a new JSON file\ncomputed_json_file = tutorial_dir / \"multiple_series_faults_computed.json\"\nJsonIO.save_model_to_json(model, str(computed_json_file))\nprint(f\"\\nSaved computed model to: {computed_json_file}\")\n\n# %%\n# Load the computed model back to verify metadata is preserved\nreloaded_model = JsonIO.load_model_from_json(str(computed_json_file))\nprint(\"\\nReloaded Model Metadata:\")\nprint(f\"Name: {reloaded_model.meta.name}\")\nprint(f\"Creation Date: {reloaded_model.meta.creation_date}\")\nprint(f\"Last Modification Date: {reloaded_model.meta.last_modification_date}\")\nprint(f\"Owner: {reloaded_model.meta.owner}\")\n\n# %%\n# Create plots with proper configuration\n# Plot 1: Cross-section in Y direction (XZ plane)\nfig = plt.figure(figsize=(10, 8))\nax = fig.add_subplot(111)\ngpv.plot_2d(\n model,\n cell_number=25, # Middle of the model\n direction='y',\n show_data=True,\n show_boundaries=True,\n show_results=True,\n ax=ax\n)\nplt.title(\"Geological Model - Y Direction (XZ plane)\")\nplt.savefig('model_y_direction.png', dpi=300, bbox_inches='tight')\nplt.close()\n\n# Plot 2: Cross-section in X direction (YZ plane)\nfig = plt.figure(figsize=(10, 8))\nax = fig.add_subplot(111)\ngpv.plot_2d(\n model,\n cell_number=25, # Middle of the model\n direction='x',\n show_data=True,\n show_boundaries=True,\n show_results=True,\n ax=ax\n)\nplt.title(\"Geological Model - X Direction (YZ plane)\")\nplt.savefig('model_x_direction.png', dpi=300, bbox_inches='tight')\nplt.close()\n\n# Plot 3: Scalar field of the fault\nplt.figure(figsize=(10, 8))\nax = plt.gca()\n\n# Get scalar field values and reshape to grid\nscalar_field = model.solutions.raw_arrays.scalar_field_matrix[0].reshape(50, 50, 50)\n\n# Plot middle slice in Y direction\nmiddle_slice = scalar_field[:, 25, :]\nim = ax.imshow(middle_slice.T, \n extent=[0, 1000, 0, 1000],\n origin='lower',\n cmap='RdBu',\n aspect='equal')\n\n# Add colorbar\nplt.colorbar(im, ax=ax, label='Scalar Field Value')\n\n# Plot surface points\nfault_element = model.structural_frame.get_element_by_name(\"fault\")\nif fault_element and fault_element.surface_points is not None:\n fault_points_coords = fault_element.surface_points.xyz\n\n # Filter points near the slice (Y around 500)\n mask = np.abs(fault_points_coords[:, 1] - 500) < 100\n filtered_points = fault_points_coords[mask]\n\n if len(filtered_points) > 0:\n ax.scatter(filtered_points[:, 0], filtered_points[:, 2], \n c='red', s=50, label='Surface Points')\n ax.legend()\n\nax.set_xlabel('X')\nax.set_ylabel('Z')\nax.set_title('Fault Scalar Field - Y Direction (Middle Slice)')\n\n# Save plot\nplt.savefig('fault_scalar_field.png', dpi=300, bbox_inches='tight')\nplt.close()\n\nprint(\"\\nPlot saved as fault_scalar_field.png\")\n\n# Plot 4: 3D visualization (optional)\nPLOT_3D = False # Set to True to enable 3D plotting\n\nif PLOT_3D:\n try:\n import pyvista as pv\n p = pv.Plotter(notebook=False, off_screen=True)\n gpv.plot_3d(\n model,\n show_data=True,\n show_surfaces=True,\n show_boundaries=True,\n plotter=p\n )\n p.screenshot('model_3d.png', transparent_background=False)\n p.close()\n except Exception as e:\n print(f\"Could not create 3D plot: {e}\")\n# %%\n" }, { "path": "examples/tutorials/z_other_tutorials/json_io/04_combination_model.py", "content": "# %%\n\"\"\"\nCombination Model with JSON I/O\n==============================\n\nThis example demonstrates how to create a model combining faults and unconformities using GemPy's JSON I/O functionality.\nThe model is based on the g07_combination.py example, featuring a folded domain with an unconformity and a fault.\n\nPart 1: Create and save the initial model structure\nPart 2: Load the model, compute it, and visualize results\n\"\"\"\n\nimport gempy as gp\nimport gempy_viewer as gpv\nimport numpy as np\nimport os\nfrom gempy_engine.core.data.stack_relation_type import StackRelationType\nfrom gempy_engine.config import AvailableBackends\nfrom gempy.modules.json_io.json_operations import JsonIO\n\n# %% \n# Part 1: Create and save the initial model structure\n# -------------------------------------------------\n\n# Define paths\ndata_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'\npath_to_data = data_path + \"/data/input_data/jan_models/\"\njson_file = \"combination_model.json\"\ncomputed_json_file = \"combination_model_computed.json\"\n\n# Create the model with data import\nmodel = gp.create_geomodel(\n project_name='Combination Model',\n extent=[0, 2500, 0, 1000, 0, 1000],\n resolution=[20, 20, 20],\n refinement=6,\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=path_to_data + \"model7_orientations.csv\",\n path_to_surface_points=path_to_data + \"model7_surface_points.csv\"\n )\n)\n\n# Set metadata\nmodel.meta.creation_date = \"2024-03-24\"\nmodel.meta.last_modification_date = \"2024-03-24\"\n\n# Map geological series to surfaces\ngp.map_stack_to_surfaces(\n gempy_model=model,\n mapping_object={\n \"Fault_Series\": ('fault',),\n \"Strat_Series1\": ('rock3',),\n \"Strat_Series2\": ('rock2', 'rock1'),\n }\n)\n\n# Set structural relations\nmodel.structural_frame.structural_groups[0].structural_relation = StackRelationType.FAULT\nmodel.structural_frame.fault_relations = np.array([\n [0, 1, 1],\n [0, 0, 0],\n [0, 0, 0]\n])\n\n# Set colors for visualization\nmodel.structural_frame.get_element_by_name(\"fault\").color = \"#015482\"\nmodel.structural_frame.get_element_by_name(\"rock3\").color = \"#9f0052\"\nmodel.structural_frame.get_element_by_name(\"rock2\").color = \"#ffbe00\"\nmodel.structural_frame.get_element_by_name(\"rock1\").color = \"#728f02\"\n\n# Set interpolation options\nmodel.interpolation_options.number_octree_levels_surface = 5\n\n# Save the model data to a JSON file\nJsonIO.save_model_to_json(model, json_file)\nprint(f\"\\nSaved initial model to: {os.path.abspath(json_file)}\")\n\n# %% \n# Part 2: Load the model and compute\n# ---------------------------------\n\nprint(\"\\nLoading model from JSON...\")\nmodel = JsonIO.load_model_from_json(json_file)\n\nprint(\"\\nModel Metadata:\")\nprint(f\"Name: {model.meta.name}\")\nprint(f\"Creation Date: {model.meta.creation_date}\")\n# TODO: This does not update here when running. In 03 you have a current date time setter\nprint(f\"Last Modified: {model.meta.last_modification_date}\")\n\nprint(\"\\nStructural Groups:\")\nprint(model.structural_frame)\n\n# Compute the model\nprint(\"\\nComputing the model...\")\ns = gp.compute_model(\n gempy_model=model,\n engine_config=gp.data.GemPyEngineConfig(\n backend=AvailableBackends.numpy\n )\n)\n\n# Save the computed model\n# TODO: This is identical to the file saved before computation (no results stored)\nJsonIO.save_model_to_json(model, computed_json_file)\nprint(f\"\\nSaved computed model to: {os.path.abspath(computed_json_file)}\")\n\n#%%\n\n# Plot the results\nprint(\"\\nGenerating plots...\")\n\n\n# 2D plots\ngpv.plot_2d(model, direction='y', show_results=False)\ngpv.plot_2d(model, direction='y', show_data=True, show_boundaries=True)\ngpv.plot_2d(model, direction='x', show_data=True)\n\n# Plot the blocks accounting for fault blocks\ngpv.plot_2d(\n model=model,\n override_regular_grid=model.solutions.raw_arrays.litho_faults_block,\n show_data=True, kwargs_lithology={'cmap': 'Set1', 'norm': None}\n)\n\n# 3D plot\ngpv.plot_3d(model)\n\nprint(\"\\nDone! The model has been:\")\nprint(\"1. Created and saved to:\", json_file)\nprint(\"2. Loaded from JSON\")\nprint(\"3. Computed\")\nprint(\"4. Saved with computation results to:\", computed_json_file)\nprint(\"5. Visualized with various plots\") " }, { "path": "examples/tutorials/z_other_tutorials/json_io/05_minimal_json.py", "content": "\"\"\"\nTutorial: Minimal JSON I/O with default metadata, interpolation options, and grid settings\nThis tutorial demonstrates how to use the JSON I/O functionality with minimal input,\nrelying on default metadata values, interpolation options, and grid settings.\n\"\"\"\n\n# %%\nimport numpy as np\nfrom datetime import datetime\n\nimport gempy as gp\nimport gempy_viewer as gpv\nimport json\nimport pyvista as pv\n\nfrom gempy.modules.json_io.json_operations import JsonIO # Updated import path\n\n\n# %%\n# Create the corresponding minimal JSON model\nmodel_data = {\n \"surface_points\": [\n {\n \"x\": 50.0,\n \"y\": 0.0,\n \"z\": -20.0,\n \"id\": 0,\n }\n ],\n \"orientations\": [\n {\n \"x\": 50.0,\n \"y\": 0.0,\n \"z\": -20.0,\n \"G_x\": 1.0,\n \"G_y\": 0.0,\n \"G_z\": 1.0,\n \"id\": 0,\n \"polarity\": 1\n }\n ],\n \"grid_settings\": {\n \"regular_grid_resolution\": [100, 2, 100],\n \"regular_grid_extent\": [0, 150, -10, 10, -100, 0]\n }\n}\n\n# %%\n# Save the minimal model to JSON\nwith open(\"minimal_model.json\", \"w\") as f:\n json.dump(model_data, f, indent=4)\n\n# Load the model from JSON\ngeo_model = JsonIO.load_model_from_json(\"minimal_model.json\")\n\n# Compute the geological model\ngp.compute_model(geo_model)\n\np2d = gpv.plot_2d(geo_model)\n# %%\n\n# Print the model metadata (should use default values)\nprint(\"\\nModel Metadata:\")\nprint(f\"Name: {geo_model.meta.name}\")\nprint(f\"Creation Date: {geo_model.meta.creation_date}\")\nprint(f\"Last Modification Date: {geo_model.meta.last_modification_date}\")\nprint(f\"Owner: {geo_model.meta.owner}\")\n\n# Print the interpolation options (should use default values)\nprint(\"\\nInterpolation Options:\")\nprint(f\"Range: {geo_model.interpolation_options.kernel_options.range}\")\nprint(f\"Mesh Extraction: {geo_model.interpolation_options.mesh_extraction}\")\n\n# Print the grid settings (should use default values)\nprint(\"\\nGrid Settings:\")\nprint(f\"Resolution: {geo_model.grid._dense_grid.resolution}\")\nprint(f\"Extent: {geo_model.grid._dense_grid.extent}\")\n\n# Print the structural groups\nprint(\"\\nStructural Groups:\")\nfor group in geo_model.structural_frame.structural_groups:\n print(group)\n\n# Save the loaded model to verify the metadata, interpolation options, and grid settings are preserved\nJsonIO.save_model_to_json(geo_model, \"minimal_model_loaded.json\")\n\n# %%\n" }, { "path": "examples/tutorials/z_other_tutorials/json_io/05c_minimal_comparison.py", "content": "\"\"\"\nTutorial: Compare minimal GemPy model with JSON representation\nThis tutorial demonstrates how to create a minimal GemPy model and compare it with its JSON representation.\n\"\"\"\n\n# %%\nimport gempy as gp\nimport gempy_viewer as gpv\nimport json\nimport numpy as np\nfrom gempy.modules.json_io.json_operations import JsonIO\n\n# %%\n# Set up the basic model\ngeo_model: gp.data.GeoModel = gp.create_geomodel(\n project_name='Model1',\n extent=[0, 150, -10, 10, -100, 0],\n resolution=[100, 2, 100],\n structural_frame=gp.data.StructuralFrame.initialize_default_structure()\n)\n\n# Add a surface point\ngp.add_surface_points(\n geo_model=geo_model,\n x=[50],\n y=[0],\n z=[-20],\n elements_names=['surface1']\n)\n\n# Add an orientation\ngp.add_orientations(\n geo_model=geo_model,\n x=[50],\n y=[0],\n z=[-68],\n elements_names=['surface1'],\n pole_vector=[[1, 0, 1]]\n)\n\n# Set interpolation options\ngeo_model.interpolation_options.kernel_options.range = 10.\ngeo_model.interpolation_options.kernel_options.c_o = 5.\ngeo_model.interpolation_options.mesh_extraction = True\ngeo_model.interpolation_options.number_octree_levels = 2\n\ngeo_model.update_transform(gp.data.GlobalAnisotropy.NONE)\ngp.compute_model(geo_model, engine_config=gp.data.GemPyEngineConfig())\n\n# %%\n# Plot the model\np2d = gpv.plot_2d(geo_model)\n\n# %%\n# Create the corresponding minimal JSON model\njson_model_data = {\n \"surface_points\": [\n {\n \"x\": 50.0,\n \"y\": 0.0,\n \"z\": -20.0,\n \"id\": 0\n }\n ],\n \"orientations\": [\n {\n \"x\": 50.0,\n \"y\": 0.0,\n \"z\": -68.0,\n \"G_x\": 1.0,\n \"G_y\": 0.0,\n \"G_z\": 1.0,\n \"id\": 0\n }\n ],\n \"grid_settings\": {\n \"regular_grid_resolution\": [100, 2, 100],\n \"regular_grid_extent\": [0, 150, -10, 10, -100, 0]\n },\n \"interpolation_options\": {\n \"kernel_options\": {\n \"range\": 10.0,\n \"c_o\": 5.0\n },\n \"mesh_extraction\": True,\n \"number_octree_levels\": 2\n },\n \"series\": [\n {\n \"name\": \"default_formations\",\n \"surfaces\": [\"surface1\"],\n \"structural_relation\": \"ERODE\"\n }\n ]\n}\n\n# Save the JSON model\nwith open(\"minimal_model.json\", \"w\") as f:\n json.dump(json_model_data, f, indent=4)\n\n# Load the model from JSON\njson_geo_model = JsonIO.load_model_from_json(\"minimal_model.json\")\n\n# Compute the JSON model\ngp.compute_model(json_geo_model, engine_config=gp.data.GemPyEngineConfig())\n\n# %%\n# Compare the models\nprint(\"\\nComparing GemPy and JSON models:\")\nprint(\"\\n1. Surface Points:\")\nprint(\"GemPy model:\")\nprint(geo_model.surface_points_copy)\nprint(\"\\nJSON model:\")\nprint(json_geo_model.surface_points_copy)\n\nprint(\"\\n2. Orientations:\")\nprint(\"GemPy model:\")\nprint(geo_model.orientations_copy)\nprint(\"\\nJSON model:\")\nprint(json_geo_model.orientations_copy)\n\nprint(\"\\n3. Grid Settings:\")\nprint(\"GemPy model:\")\nprint(f\"Resolution: {geo_model.grid._dense_grid.resolution}\")\nprint(f\"Extent: {geo_model.grid._dense_grid.extent}\")\nprint(\"\\nJSON model:\")\nprint(f\"Resolution: {json_geo_model.grid._dense_grid.resolution}\")\nprint(f\"Extent: {json_geo_model.grid._dense_grid.extent}\")\n\nprint(\"\\n4. Interpolation Options:\")\nprint(\"GemPy model:\")\nprint(f\"Range: {geo_model.interpolation_options.kernel_options.range}\")\nprint(f\"C_o: {geo_model.interpolation_options.kernel_options.c_o}\")\nprint(f\"Mesh Extraction: {geo_model.interpolation_options.mesh_extraction}\")\nprint(f\"Number Octree Levels: {geo_model.interpolation_options.number_octree_levels}\")\nprint(\"\\nJSON model:\")\nprint(f\"Range: {json_geo_model.interpolation_options.kernel_options.range}\")\nprint(f\"C_o: {json_geo_model.interpolation_options.kernel_options.c_o}\")\nprint(f\"Mesh Extraction: {json_geo_model.interpolation_options.mesh_extraction}\")\nprint(f\"Number Octree Levels: {json_geo_model.interpolation_options.number_octree_levels}\")\n\nprint(\"\\n5. Structural Groups:\")\nprint(\"GemPy model:\")\nfor group in geo_model.structural_frame.structural_groups:\n print(group)\nprint(\"\\nJSON model:\")\nfor group in json_geo_model.structural_frame.structural_groups:\n print(group)\n\n# %%\n# Plot both models side by side\np2d_gempy = gpv.plot_2d(geo_model, title=\"GemPy Model\")\np2d_json = gpv.plot_2d(json_geo_model, title=\"JSON Model\")\n\n# %% " }, { "path": "examples/tutorials/z_other_tutorials/json_io/horizontal_stratigraphic.json", "content": "{\n \"metadata\": {\n \"name\": \"horizontal_stratigraphic\",\n \"creation_date\": \"2024-03-19\",\n \"last_modification_date\": \"2024-03-19\",\n \"owner\": \"tutorial\"\n },\n \"surface_points\": [\n {\n \"x\": 100.0,\n \"y\": 200.0,\n \"z\": 600.0,\n \"id\": 1,\n \"nugget\": 2e-05\n },\n {\n \"x\": 500.0,\n \"y\": 200.0,\n \"z\": 600.0,\n \"id\": 1,\n \"nugget\": 2e-05\n },\n {\n \"x\": 900.0,\n \"y\": 200.0,\n \"z\": 600.0,\n \"id\": 1,\n \"nugget\": 2e-05\n },\n {\n \"x\": 100.0,\n \"y\": 800.0,\n \"z\": 600.0,\n \"id\": 1,\n \"nugget\": 2e-05\n },\n {\n \"x\": 500.0,\n \"y\": 800.0,\n \"z\": 600.0,\n \"id\": 1,\n \"nugget\": 2e-05\n },\n {\n \"x\": 900.0,\n \"y\": 800.0,\n \"z\": 600.0,\n \"id\": 1,\n \"nugget\": 2e-05\n },\n {\n \"x\": 100.0,\n \"y\": 200.0,\n \"z\": 400.0,\n \"id\": 0,\n \"nugget\": 2e-05\n },\n {\n \"x\": 500.0,\n \"y\": 200.0,\n \"z\": 400.0,\n \"id\": 0,\n \"nugget\": 2e-05\n },\n {\n \"x\": 900.0,\n \"y\": 200.0,\n \"z\": 400.0,\n \"id\": 0,\n \"nugget\": 2e-05\n },\n {\n \"x\": 100.0,\n \"y\": 800.0,\n \"z\": 400.0,\n \"id\": 0,\n \"nugget\": 2e-05\n },\n {\n \"x\": 500.0,\n \"y\": 800.0,\n \"z\": 400.0,\n \"id\": 0,\n \"nugget\": 2e-05\n },\n {\n \"x\": 900.0,\n \"y\": 800.0,\n \"z\": 400.0,\n \"id\": 0,\n \"nugget\": 2e-05\n }\n ],\n \"orientations\": [\n {\n \"x\": 500.0,\n \"y\": 500.0,\n \"z\": 600.0,\n \"G_x\": 0.0,\n \"G_y\": 0.0,\n \"G_z\": 1.0,\n \"id\": 1,\n \"nugget\": 0.01,\n \"polarity\": 1\n },\n {\n \"x\": 500.0,\n \"y\": 500.0,\n \"z\": 400.0,\n \"G_x\": 0.0,\n \"G_y\": 0.0,\n \"G_z\": 1.0,\n \"id\": 0,\n \"nugget\": 0.01,\n \"polarity\": 1\n }\n ],\n \"series\": [\n {\n \"name\": \"Strat_Series\",\n \"surfaces\": [\n \"rock2\",\n \"rock1\"\n ]\n }\n ],\n \"grid_settings\": {\n \"regular_grid_resolution\": [\n 10,\n 10,\n 10\n ],\n \"regular_grid_extent\": [\n 0,\n 1000,\n 0,\n 1000,\n 0,\n 1000\n ],\n \"octree_levels\": null\n },\n \"interpolation_options\": {}\n}" }, { "path": "examples/tutorials/z_other_tutorials/json_io/minimal_model.json", "content": "{\n \"surface_points\": [\n {\n \"x\": 50.0,\n \"y\": 0.0,\n \"z\": -20.0,\n \"id\": 0\n }\n ],\n \"orientations\": [\n {\n \"x\": 50.0,\n \"y\": 0.0,\n \"z\": -68.0,\n \"G_x\": 1.0,\n \"G_y\": 0.0,\n \"G_z\": 1.0,\n \"id\": 0\n }\n ],\n \"grid_settings\": {\n \"regular_grid_resolution\": [\n 100,\n 2,\n 100\n ],\n \"regular_grid_extent\": [\n 0,\n 150,\n -10,\n 10,\n -100,\n 0\n ]\n },\n \"interpolation_options\": {\n \"kernel_options\": {\n \"range\": 10.0,\n \"c_o\": 5.0\n },\n \"mesh_extraction\": true,\n \"number_octree_levels\": 2\n },\n \"series\": [\n {\n \"name\": \"default_formations\",\n \"surfaces\": [\n \"surface1\"\n ],\n \"structural_relation\": \"ERODE\"\n }\n ]\n}" }, { "path": "examples/tutorials/z_other_tutorials/json_io/minimal_model_loaded.json", "content": "{\n \"metadata\": {\n \"name\": \"GemPy Model\",\n \"creation_date\": \"2025-03-28\",\n \"last_modification_date\": \"2025-03-28\",\n \"owner\": \"GemPy Modeller\"\n },\n \"surface_points\": [\n {\n \"x\": 50.0,\n \"y\": 0.0,\n \"z\": -20.0,\n \"id\": 34966260,\n \"nugget\": 0.0\n }\n ],\n \"orientations\": [\n {\n \"x\": 50.0,\n \"y\": 0.0,\n \"z\": -20.0,\n \"G_x\": 1.0,\n \"G_y\": 0.0,\n \"G_z\": 1.0,\n \"id\": 34966260,\n \"nugget\": 0.01,\n \"polarity\": 1\n }\n ],\n \"series\": [\n {\n \"name\": \"Strat_Series\",\n \"surfaces\": [\n \"surface_0\"\n ],\n \"structural_relation\": \"ERODE\",\n \"colors\": [\n \"#015482\"\n ]\n }\n ],\n \"grid_settings\": {\n \"regular_grid_resolution\": [\n 100,\n 2,\n 100\n ],\n \"regular_grid_extent\": [\n 0.0,\n 150.0,\n -10.0,\n 10.0,\n -100.0,\n 0.0\n ],\n \"octree_levels\": null\n },\n \"interpolation_options\": {\n \"kernel_options\": {\n \"range\": 1.7,\n \"c_o\": 10.0\n },\n \"mesh_extraction\": true,\n \"number_octree_levels\": 4\n },\n \"fault_relations\": [\n [\n 0\n ]\n ],\n \"id_name_mapping\": {\n \"name_to_id\": {\n \"surface_0\": 34966260,\n \"basement\": 91927817\n }\n }\n}" }, { "path": "examples/tutorials/z_other_tutorials/json_io/multiple_series_faults.json", "content": "{\n \"metadata\": {\n \"name\": \"multiple_series_faults\",\n \"creation_date\": \"2024-03-19\",\n \"last_modification_date\": \"2024-03-19\",\n \"owner\": \"tutorial\"\n },\n \"surface_points\": [\n {\n \"x\": 0.0,\n \"y\": 200.0,\n \"z\": 600.0,\n \"id\": 2,\n \"nugget\": 2e-05\n },\n {\n \"x\": 0.0,\n \"y\": 500.0,\n \"z\": 600.0,\n \"id\": 2,\n \"nugget\": 2e-05\n },\n {\n \"x\": 0.0,\n \"y\": 800.0,\n \"z\": 600.0,\n \"id\": 2,\n \"nugget\": 2e-05\n },\n {\n \"x\": 200.0,\n \"y\": 200.0,\n \"z\": 600.0,\n \"id\": 2,\n \"nugget\": 2e-05\n },\n {\n \"x\": 200.0,\n \"y\": 500.0,\n \"z\": 600.0,\n \"id\": 2,\n \"nugget\": 2e-05\n },\n {\n \"x\": 200.0,\n \"y\": 800.0,\n \"z\": 600.0,\n \"id\": 2,\n \"nugget\": 2e-05\n },\n {\n \"x\": 800.0,\n \"y\": 200.0,\n \"z\": 200.0,\n \"id\": 2,\n \"nugget\": 2e-05\n },\n {\n \"x\": 800.0,\n \"y\": 500.0,\n \"z\": 200.0,\n \"id\": 2,\n \"nugget\": 2e-05\n },\n {\n \"x\": 800.0,\n \"y\": 800.0,\n \"z\": 200.0,\n \"id\": 2,\n \"nugget\": 2e-05\n },\n {\n \"x\": 1000.0,\n \"y\": 200.0,\n \"z\": 200.0,\n \"id\": 2,\n \"nugget\": 2e-05\n },\n {\n \"x\": 1000.0,\n \"y\": 500.0,\n \"z\": 200.0,\n \"id\": 2,\n \"nugget\": 2e-05\n },\n {\n \"x\": 1000.0,\n \"y\": 800.0,\n \"z\": 200.0,\n \"id\": 2,\n \"nugget\": 2e-05\n },\n {\n \"x\": 0.0,\n \"y\": 200.0,\n \"z\": 800.0,\n \"id\": 1,\n \"nugget\": 2e-05\n },\n {\n \"x\": 0.0,\n \"y\": 800.0,\n \"z\": 800.0,\n \"id\": 1,\n \"nugget\": 2e-05\n },\n {\n \"x\": 200.0,\n \"y\": 200.0,\n \"z\": 800.0,\n \"id\": 1,\n \"nugget\": 2e-05\n },\n {\n \"x\": 200.0,\n \"y\": 800.0,\n \"z\": 800.0,\n \"id\": 1,\n \"nugget\": 2e-05\n },\n {\n \"x\": 800.0,\n \"y\": 200.0,\n \"z\": 400.0,\n \"id\": 1,\n \"nugget\": 2e-05\n },\n {\n \"x\": 800.0,\n \"y\": 800.0,\n \"z\": 400.0,\n \"id\": 1,\n \"nugget\": 2e-05\n },\n {\n \"x\": 1000.0,\n \"y\": 200.0,\n \"z\": 400.0,\n \"id\": 1,\n \"nugget\": 2e-05\n },\n {\n \"x\": 1000.0,\n \"y\": 800.0,\n \"z\": 400.0,\n \"id\": 1,\n \"nugget\": 2e-05\n },\n {\n \"x\": 500.0,\n \"y\": 500.0,\n \"z\": 500.0,\n \"id\": 0,\n \"nugget\": 2e-05\n },\n {\n \"x\": 450.0,\n \"y\": 500.0,\n \"z\": 600.0,\n \"id\": 0,\n \"nugget\": 2e-05\n },\n {\n \"x\": 500.0,\n \"y\": 200.0,\n \"z\": 500.0,\n \"id\": 0,\n \"nugget\": 2e-05\n },\n {\n \"x\": 450.0,\n \"y\": 200.0,\n \"z\": 600.0,\n \"id\": 0,\n \"nugget\": 2e-05\n },\n {\n \"x\": 500.0,\n \"y\": 800.0,\n \"z\": 500.0,\n \"id\": 0,\n \"nugget\": 2e-05\n },\n {\n \"x\": 450.0,\n \"y\": 800.0,\n \"z\": 600.0,\n \"id\": 0,\n \"nugget\": 2e-05\n }\n ],\n \"orientations\": [\n {\n \"x\": 100.0,\n \"y\": 500.0,\n \"z\": 800.0,\n \"G_x\": 0.0,\n \"G_y\": 0.0,\n \"G_z\": 1.0,\n \"id\": 1,\n \"nugget\": 0.01,\n \"polarity\": 1\n },\n {\n \"x\": 100.0,\n \"y\": 500.0,\n \"z\": 600.0,\n \"G_x\": 0.0,\n \"G_y\": 0.0,\n \"G_z\": 1.0,\n \"id\": 2,\n \"nugget\": 0.01,\n \"polarity\": 1\n },\n {\n \"x\": 500.0,\n \"y\": 500.0,\n \"z\": 500.0,\n \"G_x\": 0.8,\n \"G_y\": 0.0,\n \"G_z\": 0.6,\n \"id\": 0,\n \"nugget\": 0.01,\n \"polarity\": 1\n },\n {\n \"x\": 900.0,\n \"y\": 500.0,\n \"z\": 400.0,\n \"G_x\": 0.0,\n \"G_y\": 0.0,\n \"G_z\": 1.0,\n \"id\": 1,\n \"nugget\": 0.01,\n \"polarity\": 1\n },\n {\n \"x\": 900.0,\n \"y\": 500.0,\n \"z\": 200.0,\n \"G_x\": 0.0,\n \"G_y\": 0.0,\n \"G_z\": 1.0,\n \"id\": 2,\n \"nugget\": 0.01,\n \"polarity\": 1\n }\n ],\n \"series\": [\n {\n \"name\": \"Fault_Series\",\n \"surfaces\": [\n \"fault\"\n ],\n \"structural_relation\": \"FAULT\",\n \"color\": \"#015482\"\n },\n {\n \"name\": \"Strat_Series\",\n \"surfaces\": [\n \"rock2\",\n \"rock1\"\n ],\n \"structural_relation\": \"ERODE\",\n \"colors\": [\n \"#ffbe00\",\n \"#9f0052\"\n ]\n }\n ],\n \"grid_settings\": {\n \"regular_grid_resolution\": [\n 90,\n 30,\n 30\n ],\n \"regular_grid_extent\": [\n 0,\n 1000,\n 0,\n 1000,\n 0,\n 1000\n ],\n \"octree_levels\": null\n },\n \"interpolation_options\": {}\n}" }, { "path": "examples/tutorials/z_other_tutorials/json_io/sample_surface_points.json", "content": "{\n \"metadata\": {\n \"name\": \"sample_model\",\n \"creation_date\": \"2024-03-19\",\n \"last_modification_date\": \"2024-03-19\",\n \"owner\": \"tutorial\"\n },\n \"surface_points\": [\n {\n \"x\": 0.0,\n \"y\": 0.0,\n \"z\": 0.0,\n \"id\": 0,\n \"nugget\": 2e-05\n },\n {\n \"x\": 1.0,\n \"y\": 1.0,\n \"z\": 1.0,\n \"id\": 0,\n \"nugget\": 2e-05\n },\n {\n \"x\": 2.0,\n \"y\": 2.0,\n \"z\": 2.0,\n \"id\": 1,\n \"nugget\": 2e-05\n },\n {\n \"x\": 3.0,\n \"y\": 3.0,\n \"z\": 3.0,\n \"id\": 1,\n \"nugget\": 2e-05\n },\n {\n \"x\": 4.0,\n \"y\": 4.0,\n \"z\": 4.0,\n \"id\": 2,\n \"nugget\": 2e-05\n }\n ],\n \"orientations\": [],\n \"faults\": [],\n \"series\": [],\n \"grid_settings\": {\n \"regular_grid_resolution\": [\n 10,\n 10,\n 10\n ],\n \"regular_grid_extent\": [\n 0,\n 4,\n 0,\n 4,\n 0,\n 4\n ],\n \"octree_levels\": null\n },\n \"interpolation_options\": {}\n}" }, { "path": "examples/tutorials/z_other_tutorials/video_tutorial_model_1.py", "content": "\"\"\"\nVideo Tutorial \"code-along\": Modeling step by step\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\"\"\"\n\n\n# %%\n# This tutorial demonstrates step-by-step geological modeling using the `gempy` and `gempy_viewer` libraries.\n# It follows the Video tutorial series available on the `gempy YouTube channel `_.\n\n\n# %%\n# Video tutorial 1: Introduction\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n#\n# The first video is an introduction to GemPy - please view online before starting the tutorial.\n#\n#\n\n# %%\n#.. raw:: html\n#\n# \n\n\n# %%\n# Video tutorial 2: Input data\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n\n# %%\n#.. raw:: html\n#\n# \n\n\n\n# Required imports\nimport gempy as gp\nimport gempy_viewer as gpv\n\n# %%\n\n# Path to input data\ndata_path = \"https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/\"\npath_to_data = data_path + \"/data/input_data/video_tutorials_v3/\"\n\n# %%\n\n# Create instance of geomodel\ngeo_model = gp.create_geomodel(\n project_name = 'tutorial_model',\n extent=[0,2500,0,1000,0,1000],\n resolution=[100,40,40],\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=path_to_data+\"tutorial_model_orientations.csv\",\n path_to_surface_points=path_to_data+\"tutorial_model_surface_points.csv\"\n )\n)\n# %%\n\n# Display a basic cross section of input data\ngpv.plot_2d(geo_model)\n\n# %%\n\n# Manually add a surface point\ngp.add_surface_points(\n geo_model=geo_model,\n x=[2250],\n y=[500],\n z=[750],\n elements_names=['rock1']\n)\n\n# %%\n\n# Show added point in cross section\ngpv.plot_2d(geo_model)\n\n# %%\n# Video tutorial 3: Structural frame\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n\n# %%\n#.. raw:: html\n#\n# \n\n# View structural frame\ngeo_model.structural_frame\n\n# %%\n\n# View structural elements\ngeo_model.structural_frame.structural_elements\n\n# %%\n\n# Define structural groups and age/stratigraphic relationship\ngp.map_stack_to_surfaces(\n gempy_model=geo_model,\n mapping_object={\n \"Strat_Series2\": (\"rock3\"),\n \"Strat_Series1\": (\"rock2\", \"rock1\")\n }\n)\n\n# %%\n# Video tutorial 4: Computation and results\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n\n# %%\n#.. raw:: html\n#\n# \n\n\n# View interpolation options\ngeo_model.interpolation_options\n\n# %%\n\n# Compute a solution for the model\ngp.compute_model(geo_model)\n\n# %%\n\n# Display the result in 2d section\ngpv.plot_2d(geo_model, cell_number=20)\n\n# %%\n\n# Some examples of how to access results\nprint(geo_model.solutions.raw_arrays.lith_block)\nprint(geo_model.grid.dense_grid.values)\n\n# %%\n# Video tutorial 5: 2D visualization\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n\n# %%\n#.. raw:: html\n#\n# \n\n\n# 2d plotting options\ngpv.plot_2d(geo_model, show_value=True, show_lith=False, show_scalar=True, series_n=1, cell_number=25)\n\n# %%\n\n# Create custom section lines\ngp.set_section_grid(\n grid=geo_model.grid,\n section_dict={\n 'section1': ([0, 0], [2500, 1000], [100, 50]),\n 'section2': ([1000, 1000], [1500, 0], [100, 100]),\n }\n)\n\n# %%\n\n# Show custom cross-section traces\ngpv.plot_section_traces(geo_model)\n\n# %%\n\n# Recompute model as a new grid was added\ngp.compute_model(geo_model)\n\n# %%\n\n# Display custom cross-sections\ngpv.plot_2d(geo_model, section_names=['section1', 'section2'], show_data=False)\n\n# %%\n# Video tutorial 6: 3D visualization\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n\n# %%\n#.. raw:: html\n#\n# \n\n\n# Display the result in 3d\ngpv.plot_3d(geo_model, show_lith=True, show_boundaries=True, ve=None)\n\n# %%\n\n# How to access DC meshes\ngeo_model.solutions.dc_meshes[0].dc_data\n\n# transform mesh vertices to original coordinate system\n\nback_transformed_vertices = geo_model.input_transform.apply_inverse(geo_model.solutions.dc_meshes[0].vertices)\n\n# %%\n# Video tutorial 7: Topography\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n\n\n# %%\n#.. raw:: html\n#\n# \n\n\n# Setting a randomly generated topography\nimport numpy as np\n\ngp.set_topography_from_random(\n grid=geo_model.grid,\n fractal_dimension=1.2,\n d_z=np.array([700, 900]),\n topography_resolution=np.array([250, 100])\n)\n\n# %%\n\n# Recompute model as a new grid was added\ngp.compute_model(geo_model)\n\n# %%\n\n# Display a cross-section with topography\ngpv.plot_2d(geo_model, show_topography=True)\n\n# %%\n\n# Displaying a geological map\ngpv.plot_2d(geo_model, show_topography=True, section_names=['topography'], show_boundaries=False, show_data=False)\n\n# %%\n\n# Display the 3d model with topography\ngpv.plot_3d(geo_model, show_lith=True, show_topography=True)\n\n# sphinx_gallery_thumbnail_number = -1\n" }, { "path": "examples/tutorials/z_other_tutorials/video_tutorial_model_2_faults.py", "content": "\"\"\"\nVideo Tutorial \"code-along\": Faults\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\"\"\"\n\n\n# %%\n# This tutorial demonstrates step-by-step how to add faults to our geological models created with `gempy`.\n# It follows the Video tutorial series available on the [gempy YouTube channel](https://www.youtube.com/@GemPy3D).\n# Please follow the first part of the tutorial to learn the basics of modeling with gempy before diving into this tutorial.\n\n\n# %%\n# Video tutorial faults: Introduction\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n#\n# The first video introduces the concept of modeling faults with GemPy - please view online before starting the tutorial.\n#\n# \n\n# %%\n# .. raw:: html\n# \n# \n# \n# \n\n# %%\n\n# Required imports\nimport gempy as gp\nimport gempy_viewer as gpv\nimport numpy as np\n\n\n# %%\n\n# Path to input data\ndata_path = \"https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/\"\npath_to_data = data_path + \"/data/input_data/video_tutorials_v3/\"\n\n\n# %%\n\n# Create instance of geomodel\ngeo_model = gp.create_geomodel(\n project_name = 'tutorial_model_faults',\n extent=[0,3000,0,1000,0,1000],\n resolution=[90,30,30],\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=path_to_data + \"tutorial_model_faults_1_orientations.csv\",\n path_to_surface_points=path_to_data + \"tutorial_model_faults_1_surface_points.csv\"\n )\n)\n\n\n# %%\n\n\n# Display a basic cross section of input data\ngpv.plot_2d(geo_model);\n\n\n# %%\n\n\n# Map geological series to surfaces\ngp.map_stack_to_surfaces(\n gempy_model=geo_model,\n mapping_object={\n \"Fault_Series1\" : ('fault'),\n \"Strat_Series1\": ('rock3'),\n \"Strat_Series2\": ('rock2', 'rock1'),\n }\n)\n\n\n# %%\n\n\n# Define youngest structural group as fault\ngp.set_is_fault(geo_model, [\"Fault_Series1\"])\n\n\n# %%\n\n\n# Compute a solution for the model\ngp.compute_model(geo_model)\n\n\n# %%\n\n\n# Display the result in 2d section\ngpv.plot_2d(geo_model, cell_number=20)\n# gpv.plot_3d(geo_model)\n\n\n# %%\n\n\n# Display the scalar field of the fault in 2d section\ngpv.plot_2d(geo_model, show_scalar=True, show_lith=False, series_n=0)\ngpv.plot_2d(geo_model, show_scalar=True, show_lith=False, series_n=1)\ngpv.plot_2d(geo_model, show_scalar=True, show_lith=False, series_n=2)\n\n\n# %%\n# Video tutorial 9: Fault relations\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n\n# %%\n# .. raw:: html\n# \n# \n# \n# \n\n# ***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. \n\n# %%\n\n\n# Creating a new strucutral element with surface point and orientation data\nnew_element = gp.data.StructuralElement(\n name='fault0',\n color=next(geo_model.structural_frame.color_generator),\n surface_points=gp.data.SurfacePointsTable.from_arrays(\n x=np.array([2750, 2750, 2750]),\n y=np.array([0, 500, 1000]),\n z=np.array([400, 400, 400]),\n names=['fault0']*3\n ),\n orientations=gp.data.OrientationsTable.from_arrays(\n x=np.array([2750]),\n y=np.array([500]),\n z=np.array([400]),\n G_x=np.array([0.8]),\n G_y=np.array([0]),\n G_z=np.array([0.6]),\n names=['fault0']\n )\n)\n\n# Creating a new structural group that contains the new element\ngroup_fault0 = gp.data.StructuralGroup(\n name='Fault_Series0',\n elements=[new_element],\n structural_relation=gp.data.StackRelationType.ERODE,\n)\n\n# Insert the fault group into the structural frame at first position (index 0)\ngeo_model.structural_frame.insert_group(index=0, group=group_fault0);\n\n\n# %%\n\n\n# Define youngest structural group as fault\ngp.set_is_fault(geo_model, [\"Fault_Series0\"])\n\n\n# %%\n\n\n# Add additional information for exisitng elements on other side of fault\ngp.add_surface_points(\n geo_model=geo_model,\n x=[2950, 2950, 2950],\n y=[0, 500, 1000],\n z=[500, 500, 500],\n elements_names=['rock2']*3\n);\n\ngp.add_surface_points(\n geo_model=geo_model,\n x=[2950, 2950, 2950],\n y=[0, 500, 1000],\n z=[350, 350, 350],\n elements_names=['rock1']*3\n);\n\ngp.add_surface_points(\n geo_model=geo_model,\n x=[2950, 2950, 2950],\n y=[0, 500, 1000],\n z=[550, 500, 500],\n elements_names=['rock3']*3\n);\n\n\n# %%\n\n\n# Display input data with the new fault and the additional input data for the other elements\ngpv.plot_2d(geo_model, show_data=True, show_boundaries=False, show_lith=False)\n\n\n# %%\n\n\n# Recompute the model with the new information\ngp.compute_model(geo_model)\n\n\n# %%\n\n\n# Display the new result\ngpv.plot_2d(geo_model)\n\n\n# %%\n\n\n# Switching the order by adding a new group containing rock3 on top\ngp.add_structural_group(\n model=geo_model,\n group_index=0,\n structural_group_name=\"Strat_Series0\",\n elements=[\n geo_model.structural_frame.get_element_by_name(\"rock3\")\n ],\n structural_relation=gp.data.StackRelationType.ERODE\n)\n\n# Removing the old group that contained rock3\ngp.remove_structural_group_by_name(geo_model, group_name=\"Strat_Series1\")\n\n\n# %%\n\n\n# Recompute the model with new order\ngp.compute_model(geo_model)\n\n\n# %%\n\n\n# Display the new result\ngpv.plot_2d(geo_model)\n\n\n# %%\n\n\n# Set fault relations manually\ngp.set_fault_relation(\n frame=geo_model.structural_frame,\n rel_matrix=np.array([\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 0, 1],\n [0, 0, 0, 0]\n ]\n )\n)\n\n\n# %%\n\n\n# Recompute model\ngp.compute_model(geo_model)\n\n\n# %%\n\n\n# Display result\ngpv.plot_2d(geo_model)\n\n\n# %%\n# Video tutorial 10: Fault groups and cross-cutting faults\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n\n# %%\n# .. raw:: html\n# \n# \n# \n# \n\n# %%\n\n\n# Create instance of new geomodel\ngeo_model_cross = gp.create_geomodel(\n project_name = 'tutorial_model_faults_2',\n extent=[0,1000,0,500,0,1000],\n resolution=[50,25,50],\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=path_to_data + \"tutorial_model_faults_2_orientations.csv\",\n path_to_surface_points=path_to_data + \"tutorial_model_faults_2_surface_points.csv\"\n )\n)\n\n\n# %%\n\n\n# Map geological series to surfaces\ngp.map_stack_to_surfaces(\n gempy_model=geo_model_cross,\n mapping_object={\n \"Fault_Series2\" : ('fault3', 'fault2'),\n \"Fault_Series1\" : ('fault1'),\n \"Strat_Series\": ('rock3', 'rock2', 'rock1'),\n }\n)\n\n# Define youngest structural group as fault\ngp.set_is_fault(geo_model_cross, [\"Fault_Series1\", \"Fault_Series2\"])\n\n# Change color of basement for better visibility\ngeo_model_cross.structural_frame.basement_color=\"#F7B529\" \ngeo_model_cross.structural_frame.structural_elements[0].color = '#000000'\ngeo_model_cross.structural_frame.structural_elements[1].color = '#36454F'\ngeo_model_cross.structural_frame.structural_elements[2].color = '#D3D3D3'\n\n\n# %%\n\n\ngeo_model_cross.structural_frame\n\n\n# %%\n\n\n# Set fault relations manually\ngp.set_fault_relation(\n frame=geo_model_cross.structural_frame,\n rel_matrix=np.array([\n [0, 1, 1],\n [0, 0, 1],\n [0, 0, 0],\n ]\n )\n)\n\n\n# %%\n\n\n# Display input data on cross section\ngpv.plot_2d(geo_model_cross)\n\n\n# %%\n\n\n# Compute model\ngp.compute_model(geo_model_cross)\n\n\n# %%\n\n\n# Display reusult on cross section\ngpv.plot_2d(geo_model_cross)\n\n\n# sphinx_gallery_thumbnail_number = -1\n\n" }, { "path": "examples/tutorials/z_other_tutorials/video_tutorial_model_3_onlap.py", "content": "\"\"\"\nVideo Tutorial \"code-along\": Onlap relations\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\"\"\"\n\n\n# %%\n# This tutorial demonstrates step-by-step how to incorporate onlap relations to our geological models created with gempy.\n# It follows the Video tutorial series available on the [gempy YouTube channel](https://www.youtube.com/@GemPy3D).\n# Please follow the first and second part of the tutorials to learn the basics of modeling with gempy before diving into this tutorial.\n\n# %%\n# Video tutorial 11: Basic onlap scenario\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n\n\n# %%\n# .. raw:: html\n#\n# \n#\n#\n\n# %%\n\n# Required imports\nimport gempy as gp\nimport gempy_viewer as gpv\nimport numpy as np\n\n# %%\n\n# Path to input data\ndata_path = \"https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/\"\npath_to_data = data_path + \"/data/input_data/video_tutorials_v3/\"\n\n# %%\n\n# Create instance of geomodel\ngeo_model_onlap = gp.create_geomodel(\n project_name = 'tutorial_model_onlap_1',\n extent=[0,2000,0,1000,0,1000],\n resolution=[100,50,50],\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=path_to_data+\"tutorial_model_onlap_1_orientations.csv\",\n path_to_surface_points=path_to_data+\"tutorial_model_onlap_1_surface_points.csv\"\n )\n)\n\n\n# %%\n\n# Map geological series to surfaces\ngp.map_stack_to_surfaces(\n gempy_model=geo_model_onlap,\n mapping_object={\n \"Young_Series\": (\"basin_fill_2\", \"basin_fill_1\"),\n \"Old_Series\": (\"basin_top\", \"basin_bottom\")\n }\n)\n\n# Alternative way of mapping geological series to surfaces\n# gp.map_stack_to_surfaces(\n# gempy_model=geo_model_onlap,\n# mapping_object={\n# \"Young_Series\": (\"basin_fill_2\", \"basin_fill_1\"),\n# \"Onlap_Series\": (\"basin_top\"),\n# \"Old_Series\": (\"basin_bottom\")\n# }\n# )\n\n# %%\n\n# Display a basic cross section of input data\ngpv.plot_2d(geo_model_onlap, show_data=True)\n\n# %%\n\n# Compute a solution for the model\ngp.compute_model(geo_model_onlap)\n\n# %%\n\n# Display the result in 2d section\ngpv.plot_2d(geo_model_onlap, show_boundaries=False)\n\n# %%\n\n# Set the relation of the youngest group to Onlap\nfrom gempy_engine.core.data.stack_relation_type import StackRelationType\ngeo_model_onlap.structural_frame.structural_groups[0].structural_relation = StackRelationType.ONLAP\n\n# %%\n\n# Display updated strucutral frame\ngeo_model_onlap.structural_frame\n\n# %%\n\n# Compute a solution for the model\ngp.compute_model(geo_model_onlap)\n\n# %%\n\n# Display the result in 2d section\ngpv.plot_2d(geo_model_onlap, show_boundaries=False)\n\n# %%\n# Video tutorial 12: Advanced onlap - Subduction zone\n# \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n\n# %%\n# .. raw:: html\n#\n# \n#\n#\n\n# %%\n\n# Create instance of geomodel\ngeo_model_subduction = gp.create_geomodel(\n project_name = 'tutorial_model_onlap_2',\n extent=[0,2000,0,1000,0,1000],\n resolution=[100,50,50],\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=path_to_data+\"tutorial_model_onlap_2_orientations.csv?\",\n path_to_surface_points=path_to_data+\"tutorial_model_onlap_2_surface_points.csv?\"\n )\n)\n\n# %%\n\n# Display a basic cross section of input data\ngpv.plot_2d(geo_model_subduction)\n\n# %%\n\n# Map geological series to surfaces\ngp.map_stack_to_surfaces(\n gempy_model=geo_model_subduction,\n mapping_object={\n \"Top\": (\"continental_top\"),\n \"Continental_Series\": (\"continental_shallow\", \"continental_deep\"),\n \"Oceanic_Series\": (\"oceanic_top\", \"oceanic_bottom\")\n }\n)\n\n# %%\n\n# Set the realtion of the youngest and second youngest group to Onlap\ngeo_model_subduction.structural_frame.structural_groups[0].structural_relation = StackRelationType.ONLAP\ngeo_model_subduction.structural_frame.structural_groups[1].structural_relation = StackRelationType.ONLAP\n\n# %%\n\n# Display updated structural frame\ngeo_model_subduction.structural_frame\n\n# %%\n\n# Create a simple topography using numpy\n\n# Define grid spacing\nspacing = 20\n\n# Generate grid\nx = np.arange(geo_model_subduction.grid.regular_grid.extent[0], geo_model_subduction.grid.regular_grid.extent[1] + spacing, spacing)\ny = np.arange(geo_model_subduction.grid.regular_grid.extent[2], geo_model_subduction.grid.regular_grid.extent[3] + spacing, spacing)\nX, Y = np.meshgrid(x, y)\n\n# Define elevation (z) based on x, creating a simple mountain range\nZ = np.ones_like(X) * 590 # Default elevation\nZ[(X >= 570) & (X < 1000)] = 590 + (200 * (X[(X >= 570) & (X < 1000)] - 600) / 400)\nZ[(X >= 1000) & (X < 1300)] = 810 - (250 * (X[(X >= 1000) & (X < 1300)] - 1000) / 300)\nZ[X >= 1300] = 540\n\n# Flatten the data into (N,3) shape\ntopography_points = np.vstack((X.ravel(), Y.ravel(), Z.ravel())).T\n\n# %%\n\n# Set topography from numpy array\ngp.set_topography_from_arrays(grid=geo_model_subduction.grid, xyz_vertices=topography_points)\n\n# %%\n\n# Compute a solution for the model\ngp.compute_model(geo_model_subduction)\n\n# %%\n\n# Display the result in 2d section\ngpv.plot_2d(geo_model_subduction, show_topography=True, show_boundaries=False)\n\n# %%\n\n# Display 3d plot of final model\ngpv.plot_3d(geo_model_subduction, show_topography=True, image=True)\n\n# sphinx_gallery_thumbnail_number = -1\n\n" }, { "path": "gempy/API/__init__.py", "content": "# Initialization API\nfrom .initialization_API import (\n create_data_legacy,\n create_geomodel,\n structural_elements_from_borehole_set\n)\n\n# Compute API\nfrom .compute_API import (\n compute_model,\n compute_model_at\n)\n\n# Map stack to surfaces API\nfrom .map_stack_to_surfaces_API import map_stack_to_surfaces\n\n# Grid API\nfrom .grid_API import (\n set_active_grid,\n set_custom_grid,\n set_section_grid,\n set_centered_grid,\n set_topography_from_random,\n set_topography_from_file,\n set_topography_from_subsurface_structured_grid,\n set_topography_from_arrays\n)\n\n# Examples generator\nfrom .examples_generator import generate_example_model\n\n# Faults API\nfrom .faults_API import (\n set_fault_relation,\n set_is_fault,\n set_is_finite_fault\n)\n\n# Data manipulation: points\nfrom ..modules.data_manipulation.manipulate_points import (\n add_orientations,\n add_surface_points,\n delete_orientations,\n delete_surface_points,\n modify_orientations,\n modify_surface_points\n)\n\n# Data manipulation: orientations from surface points\nfrom ..modules.data_manipulation.orientations_from_surface_points import (\n create_orientations_from_surface_points_coords\n)\n\n# Data manipulation: structural frame\nfrom ..modules.data_manipulation.manipulate_structural_frame import (\n add_structural_group,\n remove_structural_group_by_index,\n remove_structural_group_by_name,\n remove_element_by_name\n)\n\nfrom ..modules.serialization.save_load import save_model, load_model\n\n# Geophysics\nfrom gempy_engine.modules.geophysics.gravity_gradient import calculate_gravity_gradient\n\n__all__ = [\n 'create_data_legacy', 'create_geomodel', 'structural_elements_from_borehole_set',\n 'compute_model', 'compute_model_at', 'map_stack_to_surfaces',\n 'set_section_grid', 'set_active_grid', 'set_topography_from_random', 'set_topography_from_file', 'set_topography_from_subsurface_structured_grid', 'set_topography_from_arrays',\n 'set_custom_grid', 'set_centered_grid',\n 'generate_example_model', 'set_fault_relation', 'set_is_fault', 'set_is_finite_fault',\n 'add_surface_points', 'add_orientations', 'delete_surface_points', 'delete_orientations',\n 'create_orientations_from_surface_points_coords', 'modify_surface_points', 'modify_orientations',\n 'add_structural_group', 'remove_structural_group_by_index', 'remove_structural_group_by_name', 'remove_element_by_name',\n 'calculate_gravity_gradient',\n 'save_model', 'load_model'\n]\n" }, { "path": "gempy/API/_version.py", "content": "" }, { "path": "gempy/API/compute_API.py", "content": "import dotenv\nimport numpy as np\nimport os\nfrom typing import Optional\n\nimport gempy_engine\nfrom gempy_engine.config import AvailableBackends\nfrom gempy_engine.core.backend_tensor import BackendTensor\nfrom gempy_engine.core.data import Solutions\nfrom .grid_API import set_custom_grid\nfrom ..core.data import StructuralGroup\nfrom ..core.data.gempy_engine_config import GemPyEngineConfig\nfrom ..core.data.geo_model import GeoModel\nfrom ..modules.data_manipulation import interpolation_input_from_structural_frame\nfrom ..modules.optimize_nuggets import nugget_optimizer\n\ndotenv.load_dotenv()\n\n\ndef compute_model(gempy_model: GeoModel, engine_config: Optional[GemPyEngineConfig] = None,\n **kwargs) -> Solutions:\n \"\"\"\n Compute the geological model given the provided GemPy model.\n\n Args:\n gempy_model (GeoModel): The GemPy model to compute.\n engine_config (Optional[GemPyEngineConfig]): Configuration for the computational engine. Defaults to None, in which case a default configuration will be used.\n\n Raises:\n ValueError: If the provided backend in the engine_config is not supported.\n\n Returns:\n Solutions: The computed geological model.\n \"\"\"\n engine_config = engine_config or GemPyEngineConfig(use_gpu=False)\n\n match engine_config.backend:\n case AvailableBackends.numpy | AvailableBackends.PYTORCH:\n\n BackendTensor.change_backend_gempy(\n engine_backend=engine_config.backend,\n use_gpu=engine_config.use_gpu,\n dtype=engine_config.dtype\n )\n\n # TODO: To decide what to do with this.\n interpolation_input = interpolation_input_from_structural_frame(gempy_model)\n gempy_model.taped_interpolation_input = interpolation_input # * This is used for gradient tape\n\n gempy_model.solutions = gempy_engine.compute_model(\n interpolation_input=interpolation_input,\n options=gempy_model.interpolation_options,\n data_descriptor=gempy_model.input_data_descriptor,\n geophysics_input=gempy_model.geophysics_input,\n )\n case _:\n raise ValueError(f'Backend {engine_config} not supported')\n\n if os.getenv(\"VALIDATE_SERIALIZATION\", \"False\") == \"True\" and kwargs.get(\"validate_serialization\", True):\n from ..modules.serialization.save_load import save_model\n import tempfile\n with tempfile.NamedTemporaryFile(mode='w+', delete=True) as tmp:\n save_model(model=gempy_model, path=tmp.name, validate_serialization=True)\n\n return gempy_model.solutions\n\n\ndef compute_model_at(gempy_model: GeoModel, at: np.ndarray,\n engine_config: Optional[GemPyEngineConfig] = None) -> np.ndarray:\n \"\"\"\n Compute the geological model at specific coordinates.\n \n Note: This function sets a custom grid and computes the model so be wary of side effects.\n\n Args:\n gempy_model (GeoModel): The GemPy model to compute.\n at (np.ndarray): The coordinates at which to compute the model.\n engine_config (Optional[GemPyEngineConfig], optional): Configuration for the computational engine. Defaults to None, in which case a default configuration will be used.\n\n Returns:\n np.ndarray: The computed geological model at the specified coordinates.\n \"\"\"\n \n print(\"WARNING: This function sets a custom grid and computes the model so be wary of side effects.\")\n set_custom_grid(\n grid=gempy_model.grid,\n xyz_coord=at,\n reset=True\n )\n \n sol = compute_model(gempy_model, engine_config, validate_serialization=True)\n return sol.raw_arrays.custom\n\n\ndef optimize_nuggets(geo_model: GeoModel, engine_config: GemPyEngineConfig, max_epochs: int = 10,\n convergence_criteria: float = 1e5, only_groups:list[StructuralGroup] | None = None) -> GeoModel:\n \"\"\"\n Optimize the nuggets of the interpolation input of the provided model. \n \"\"\"\n\n if engine_config.backend != AvailableBackends.PYTORCH:\n raise ValueError(f'Only PyTorch backend is supported for optimization. Received {engine_config.backend}')\n \n geo_model = nugget_optimizer(\n target_cond_num=convergence_criteria,\n engine_cfg=engine_config,\n model=geo_model,\n max_epochs=max_epochs,\n only_groups=only_groups\n )\n\n return geo_model\n\ndef optimize_and_compute(geo_model: GeoModel, engine_config: GemPyEngineConfig, max_epochs: int = 10,\n convergence_criteria: float = 1e5):\n \n optimize_nuggets(geo_model, engine_config, max_epochs, convergence_criteria)\n geo_model.solutions = gempy_engine.compute_model(\n interpolation_input=geo_model.taped_interpolation_input,\n options=geo_model.interpolation_options,\n data_descriptor=geo_model.input_data_descriptor,\n geophysics_input=geo_model.geophysics_input,\n )\n return geo_model.solutions\n\n\n" }, { "path": "gempy/API/examples_generator.py", "content": "import os\n\nimport numpy as np\n\nimport gempy as gp\nfrom gempy_engine.core.data.stack_relation_type import StackRelationType\nfrom gempy.core.data.enumerators import ExampleModel\n\n\ndef generate_example_model(example_model: ExampleModel, compute_model: bool = True) -> gp.data.GeoModel:\n match example_model:\n case ExampleModel.HORIZONTAL_STRAT:\n return _generate_horizontal_stratigraphic_model(compute_model)\n case ExampleModel.ANTICLINE:\n return _generate_anticline_model(compute_model)\n case ExampleModel.ONE_FAULT:\n return _generate_one_fault_model(compute_model)\n case ExampleModel.TWO_AND_A_HALF_D:\n return _generate_2_5d_model(compute_model)\n case ExampleModel.COMBINATION:\n return _generate_combination_model(compute_model)\n case ExampleModel.ONE_FAULT_GRAVITY:\n return _generate_one_fault_model_gravity(compute_model)\n case ExampleModel.GRABEN:\n return _generate_graben_model(compute_model)\n case ExampleModel.GREENSTONE:\n return _generate_greenstone_model(compute_model)\n case ExampleModel.FAULT_RELATION:\n return _generate_fault_relation_model(compute_model)\n case _:\n raise NotImplementedError(f\"Example model {example_model} not implemented.\")\n\n\ndef _generate_2_5d_model(compute_model: bool) -> gp.data.GeoModel:\n geo_model: gp.data.GeoModel = gp.create_geomodel(\n project_name='Model1',\n extent=[0, 791, -200, 200, -582, 0],\n resolution=[50, 50, 50],\n refinement=1,\n structural_frame=gp.data.StructuralFrame.initialize_default_structure()\n )\n\n gp.add_surface_points(\n geo_model=geo_model,\n x=[223, 458, 612],\n y=[0.01, 0, 0],\n z=[-94, -197, -14],\n elements_names='surface1'\n )\n\n gp.add_orientations(\n geo_model=geo_model,\n x=[350],\n y=[1],\n z=[-300],\n elements_names=['surface1'],\n pole_vector=[[0, 0, 1]]\n )\n\n geo_model.update_transform(gp.data.GlobalAnisotropy.NONE) # * Remove the auto anisotropy for this 2.5D model\n\n element2 = gp.data.StructuralElement(\n name='surface2',\n color=next(geo_model.structural_frame.color_generator),\n surface_points=gp.data.SurfacePointsTable.from_arrays(\n x=np.array([225, 459]),\n y=np.array([0, 0]),\n z=np.array([-269, -279]),\n names='surface2'\n ),\n orientations=gp.data.OrientationsTable.initialize_empty()\n )\n\n geo_model.structural_frame.structural_groups[0].append_element(element2)\n\n element3 = gp.data.StructuralElement(\n name='surface3',\n color=next(geo_model.structural_frame.color_generator),\n surface_points=gp.data.SurfacePointsTable.from_arrays(\n x=np.array([225, 464, 619]),\n y=np.array([0, 0, 0]),\n z=np.array([-439, -456, -433]),\n names='surface3'\n ),\n orientations=gp.data.OrientationsTable.initialize_empty()\n )\n\n geo_model.structural_frame.structural_groups[0].append_element(element3)\n\n element_fault = gp.data.StructuralElement(\n name='fault1',\n color=next(geo_model.structural_frame.color_generator),\n surface_points=gp.data.SurfacePointsTable.from_arrays(\n x=np.array([550, 650]),\n y=np.array([0, 0]),\n z=np.array([-30, -200]),\n names='fault1'\n ),\n orientations=gp.data.OrientationsTable.from_arrays(\n x=np.array([600]),\n y=np.array([0]),\n z=np.array([-100]),\n G_x=np.array([.3]),\n G_y=np.array([0]),\n G_z=np.array([.3]),\n names='fault1'\n )\n )\n\n group_fault = gp.data.StructuralGroup(\n name='Fault1',\n elements=[element_fault],\n structural_relation=gp.data.StackRelationType.FAULT,\n fault_relations=gp.data.FaultsRelationSpecialCase.OFFSET_ALL\n )\n\n 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\n\n gp.set_topography_from_random(\n grid=geo_model.grid,\n fractal_dimension=1.9,\n d_z=np.array([-150, 0]),\n topography_resolution=np.array([50, 40])\n )\n\n if compute_model:\n gp.compute_model(geo_model)\n\n return geo_model\n\n\ndef _generate_horizontal_stratigraphic_model(compute_model: bool) -> gp.data.GeoModel:\n \"\"\"\n Function to create a geological model of horizontally stacked layers,\n map the geological series to surfaces, and compute the geological model.\n \"\"\"\n # Define the path to data\n data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'\n\n # Create a GeoModel instance\n geo_data = gp.create_geomodel(\n project_name='horizontal',\n extent=[0, 1000, 0, 1000, 0, 1000],\n resolution=[50, 5, 50],\n refinement=3,\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=data_path + \"/data/input_data/jan_models/model1_orientations.csv\",\n path_to_surface_points=data_path + \"/data/input_data/jan_models/model1_surface_points.csv\"\n )\n )\n\n # Map geological series to surfaces\n gp.map_stack_to_surfaces(\n gempy_model=geo_data,\n mapping_object={\"Strat_Series\": ('rock2', 'rock1')}\n )\n\n if compute_model:\n # Compute the geological model\n gp.compute_model(geo_data)\n\n return geo_data\n\n\ndef _generate_anticline_model(compute_model: bool) -> gp.data.GeoModel:\n \"\"\"\n Function to create a geological model of an anticline structure,\n map the geological series to surfaces, and compute the geological model.\n \"\"\"\n # Define the path to data\n data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'\n path_to_data = data_path + \"/data/input_data/jan_models/\"\n\n # Create a GeoModel instance\n geo_data: gp.data.GeoModel = gp.create_geomodel(\n project_name='fold',\n extent=[0, 1000, 0, 1000, 0, 1000],\n refinement=5,\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=path_to_data + \"model2_orientations.csv\",\n path_to_surface_points=path_to_data + \"model2_surface_points.csv\"\n )\n )\n\n # Map geological series to surfaces \n gp.map_stack_to_surfaces(\n gempy_model=geo_data,\n mapping_object={\"Strat_Series\": ('rock2', 'rock1')}\n )\n\n if compute_model:\n # Compute the geological model\n gp.compute_model(geo_data)\n\n return geo_data\n\n\ndef _generate_one_fault_model(compute_model: bool) -> gp.data.GeoModel:\n \"\"\"\n Function to create a simple fault model,\n map the geological series to surfaces, and compute the geological model.\n \"\"\"\n # Define the path to data\n data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'\n path_to_data = data_path + \"/data/input_data/jan_models/\"\n\n # Create a GeoModel instance\n geo_data = gp.create_geomodel(\n project_name='fault',\n extent=[0, 1000, 0, 1000, 0, 1000],\n refinement=6,\n # resolution=[20, 20, 20],\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=path_to_data + \"model5_orientations.csv\",\n path_to_surface_points=path_to_data + \"model5_surface_points.csv\"\n )\n )\n\n # Map geological series to surfaces\n gp.map_stack_to_surfaces(\n gempy_model=geo_data,\n mapping_object={\n \"Fault_Series\": 'fault',\n \"Strat_Series\": ('rock2', 'rock1')\n }\n )\n\n # Define fault groups\n geo_data.structural_frame.structural_groups[0].structural_relation = StackRelationType.FAULT\n geo_data.structural_frame.fault_relations = np.array([[0, 1],\n [0, 0]])\n gp.set_is_fault(\n frame=geo_data,\n fault_groups=['Fault_Series']\n )\n\n if compute_model:\n # Compute the geological model\n gp.compute_model(geo_data)\n\n return geo_data\n\n\ndef _generate_combination_model(compute_model: bool) -> gp.data.GeoModel:\n \"\"\"\n Function to create a model with a folded domain featuring an unconformity and a fault,\n map the geological series to surfaces, and compute the geological model.\n \"\"\"\n # Define the path to data\n data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'\n path_to_data = data_path + \"/data/input_data/jan_models/\"\n\n # Create a GeoModel instance\n geo_data = gp.create_geomodel(\n project_name='combination',\n extent=[0, 2500, 0, 1000, 0, 1000],\n refinement=4,\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=path_to_data + \"model7_orientations.csv\",\n path_to_surface_points=path_to_data + \"model7_surface_points.csv\"\n ),\n legacy_octree_init=False\n )\n geo_data.interpolation_options.evaluation_options.number_octree_levels_surface = 4\n\n # Map geological series to surfaces\n gp.map_stack_to_surfaces(\n gempy_model=geo_data,\n mapping_object={\n \"Fault_Series\" : ('fault'),\n \"Strat_Series1\": ('rock3'),\n \"Strat_Series2\": ('rock2', 'rock1'),\n }\n )\n\n # Define the structural relation\n geo_data.structural_frame.structural_groups[0].structural_relation = StackRelationType.FAULT\n geo_data.structural_frame.fault_relations = np.array(\n [[0, 1, 1],\n [0, 0, 0],\n [0, 0, 0]]\n )\n\n # Compute the geological model\n if compute_model:\n gp.compute_model(\n gempy_model=geo_data,\n engine_config=gp.data.GemPyEngineConfig(\n backend=gp.data.AvailableBackends.numpy\n )\n )\n\n return geo_data\n\n\ndef _generate_one_fault_model_gravity(compute_model):\n from gempy_engine.core.backend_tensor import BackendTensor\n from gempy.optional_dependencies import require_pandas\n pd = require_pandas()\n\n resolution = [150, 10, 150]\n extent = [0, 200, -100, 100, -100, 0]\n\n # %%\n # Configure GemPy for geological modeling with PyTorch backend\n BackendTensor.change_backend_gempy(engine_backend=gp.data.AvailableBackends.PYTORCH, dtype=\"float64\")\n\n geo_model: gp.data.GeoModel = gp.create_geomodel(\n project_name='Fault model',\n extent=extent,\n resolution=resolution,\n structural_frame=gp.data.StructuralFrame.initialize_default_structure()\n )\n\n interpolation_options = geo_model.interpolation_options\n interpolation_options.mesh_extraction = False\n interpolation_options.kernel_options.range = .7\n interpolation_options.kernel_options.c_o = 3\n interpolation_options.kernel_options.compute_condition_number = True\n\n gp.add_surface_points(\n geo_model=geo_model,\n x=[40, 60, 120, 140],\n y=[0, 0, 0, 0],\n z=[-50, -50, -60, -60],\n elements_names=['surface1', 'surface1', 'surface1', 'surface1']\n )\n\n gp.add_orientations(\n geo_model=geo_model,\n x=[130],\n y=[0],\n z=[-50],\n elements_names=['surface1'],\n pole_vector=[[0, 0, 1.]]\n )\n\n # Define second element\n element2 = gp.data.StructuralElement(\n name='surface2',\n color=next(geo_model.structural_frame.color_generator),\n surface_points=gp.data.SurfacePointsTable.from_arrays(\n x=np.array([120]),\n y=np.array([0]),\n z=np.array([-40]),\n names='surface2'\n ),\n orientations=gp.data.OrientationsTable.initialize_empty()\n )\n\n # Add second element to structural frame\n geo_model.structural_frame.structural_groups[0].append_element(element2)\n\n # add fault\n # Calculate orientation from point values\n fault_point_1 = (80, -20)\n fault_point_2 = (110, -80)\n\n # calculate angle\n angle = np.arctan((fault_point_2[0] - fault_point_1[0]) / (fault_point_2[1] - fault_point_1[1]))\n\n x = np.cos(angle)\n z = - np.sin(angle)\n\n element_fault = gp.data.StructuralElement(\n name='fault1',\n color=next(geo_model.structural_frame.color_generator),\n surface_points=gp.data.SurfacePointsTable.from_arrays(\n x=np.array([fault_point_1[0], fault_point_2[0]]),\n y=np.array([0, 0]),\n z=np.array([fault_point_1[1], fault_point_2[1]]),\n names='fault1'\n ),\n orientations=gp.data.OrientationsTable.from_arrays(\n x=np.array([fault_point_1[0]]),\n y=np.array([0]),\n z=np.array([fault_point_1[1]]),\n G_x=np.array([x]),\n G_y=np.array([0]),\n G_z=np.array([z]),\n names='fault1'\n )\n )\n\n group_fault = gp.data.StructuralGroup(\n name='Fault1',\n elements=[element_fault],\n structural_relation=gp.data.StackRelationType.FAULT,\n fault_relations=gp.data.FaultsRelationSpecialCase.OFFSET_ALL\n )\n\n # Insert the fault group into the structural frame:\n geo_model.structural_frame.insert_group(0, group_fault)\n # %% md\n ## Compute model\n # %%\n geo_model.update_transform(gp.data.GlobalAnisotropy.NONE)\n\n interesting_columns = pd.DataFrame()\n # x_vals = np.arange(20, 191, 10)\n\n x_vals = np.linspace(20, 191, 6)\n interesting_columns['X'] = x_vals\n interesting_columns['Y'] = np.zeros_like(x_vals)\n\n # Configuring the data correctly is key for accurate gravity calculations.\n device_location = interesting_columns[['X', 'Y']]\n device_location['Z'] = 0 # Add a Z-coordinate\n\n # Set up a centered grid for geophysical calculations\n # This grid will be used for gravity gradient calculations.\n gp.set_centered_grid(\n grid=geo_model.grid,\n centers=device_location,\n resolution=np.array([75 / 3, 5, 150 / 3]),\n radius=np.array([150, 10, 300])\n )\n\n # Calculate the gravity gradient using GemPy\n # Gravity gradient data is critical for geophysical modeling and inversion.\n gravity_gradient = gp.calculate_gravity_gradient(geo_model.grid.centered_grid)\n\n densities_tensor = BackendTensor.t.array([2., 2., 3., 2.])\n densities_tensor.requires_grad = True\n\n # Set geophysics input for the GemPy model\n # Configuring this input is crucial for the forward gravity calculation.\n geo_model.geophysics_input = gp.data.GeophysicsInput(\n tz=BackendTensor.t.array(gravity_gradient),\n densities=densities_tensor\n )\n\n # %%\n # Compute the geological model with geophysical data\n # This computation integrates the geological model with gravity data.\n if compute_model:\n sol = gp.compute_model(\n gempy_model=geo_model,\n engine_config=gp.data.GemPyEngineConfig(\n backend=gp.data.AvailableBackends.PYTORCH,\n dtype='float32',\n use_gpu=True\n )\n )\n grav = - sol.gravity\n grav[0].backward()\n\n return geo_model\n\n\ndef _generate_graben_model(compute_model: bool) -> gp.data.GeoModel:\n # Data path is in root/examples/data\n # script_dir = os.path.dirname(os.path.abspath(__file__))\n # data_path = os.path.join(script_dir, '.../examples/')\n # n_model = 7\n # https: // github.com / gempy - project / gempy / blob / 279\n # bbe904283e16320c54d868fe74be873177cca / examples / data / input_data / lisa_models / interfaces7.csv\n # csv_ = data_path + \"/data/input_data/lisa_models/foliations\" + n_model + \".csv\"\n\n data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'\n path_to_data = data_path + \"/data/input_data/lisa_models/\"\n\n geo_data: gp.data.GeoModel = gp.create_geomodel(\n project_name=\"Graben\",\n extent=[0, 2000, 0, 2000, 0, 1600],\n resolution=[50, 50, 50],\n refinement=6, # * For this model is better not to use octrees because we want to see what is happening in the scalar fields\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=path_to_data + \"foliations7.csv\",\n path_to_surface_points=path_to_data + \"interfaces7.csv\"\n )\n )\n\n gp.map_stack_to_surfaces(\n gempy_model=geo_data,\n mapping_object={\n \"Fault_1\" : 'Fault_1', \"Fault_2\": 'Fault_2',\n \"Strat_Series\": ('Sandstone', 'Siltstone', 'Shale', 'Sandstone_2', 'Schist', 'Gneiss')\n },\n )\n\n gp.set_is_fault(geo_data, ['Fault_1', 'Fault_2'])\n if compute_model:\n sol = gp.compute_model(gempy_model=geo_data)\n\n return geo_data\n\n\ndef _generate_greenstone_model(compute_model: bool) -> gp.data.GeoModel:\n test_dir = os.path.dirname(os.path.abspath(__file__))\n\n # Build the path relative to the test file location\n path = os.path.join(test_dir, '..', '..', 'examples', 'data', 'gempy_models', 'Greenstone.gempy')\n with open(path, 'rb') as f:\n binary_file = f.read()\n\n from gempy.modules.serialization.save_load import _load_model_from_bytes\n geo_model: gp.data.GeoModel = _load_model_from_bytes(binary_file)\n\n if compute_model:\n sol = gp.compute_model(\n gempy_model=geo_model,\n engine_config=gp.data.GemPyEngineConfig(\n backend=gp.data.AvailableBackends.numpy,\n dtype='float32'\n )\n )\n return geo_model\n\n\ndef _generate_fault_relation_model(compute_model: bool) -> gp.data.GeoModel:\n # Path to input data\n test_dir = os.path.dirname(os.path.abspath(__file__))\n data_path = os.path.join(test_dir, '..', '..', 'examples', 'data', 'input_data', 'tests')\n # data_path = os.path.join(test_dir, \"..\\\\..\\\\examples\\\\data\\\\input_data\\\\tests\\\\\")\n\n # Create instance of new geomodel\n geo_model = gp.create_geomodel(\n project_name='fault_reations_test',\n extent=[0, 1000, 0, 1000, -1000, -400],\n resolution=[20, 20, 20],\n refinement=4,\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=data_path + \"/fault_relations_test_ori.csv\",\n path_to_surface_points=data_path + \"/fault_relations_test_surf.csv\"\n )\n )\n # Map geological series to surfaces\n gp.map_stack_to_surfaces(\n gempy_model=geo_model,\n mapping_object={\n \"fault_series_1\": ('fault1'),\n \"fault_series_2\": ('fault2'),\n \"series_1\" : ('rock4', 'rock3'),\n \"default_series\": ('rock2', 'rock1')\n }\n )\n\n gp.set_is_fault(geo_model, [\"fault_series_1\", \"fault_series_2\"])\n\n gp.set_fault_relation(\n frame=geo_model.structural_frame,\n rel_matrix=np.array([\n [0, 1, 1, 1],\n [0, 0, 0, 1],\n [0, 0, 0, 0],\n [0, 0, 0, 0]\n ]\n )\n )\n geo_model.input_transform.apply_anisotropy(gp.data.GlobalAnisotropy.NONE)\n if compute_model:\n sol = gp.compute_model(\n gempy_model=geo_model,\n engine_config=gp.data.GemPyEngineConfig(\n backend=gp.data.AvailableBackends.numpy,\n dtype='float32'\n ),\n validate_serialization=False\n )\n return geo_model\n" }, { "path": "gempy/API/faults_API.py", "content": "from typing import Union\nimport numpy as np\n\nfrom gempy_engine.core.data.stack_relation_type import StackRelationType\nfrom gempy.core.data import GeoModel, StructuralFrame\nfrom gempy.core.data.structural_group import StructuralGroup, FaultsRelationSpecialCase\n\n\ndef set_is_fault(frame: Union[GeoModel, StructuralFrame], fault_groups: Union[list[str], list[StructuralGroup]],\n faults_relation_type: FaultsRelationSpecialCase = FaultsRelationSpecialCase.OFFSET_FORMATIONS,\n change_color: bool = True) -> StructuralFrame:\n \"\"\"\n Sets given groups as fault in the structural frame of the GeoModel. It can optionally change the color of these groups.\n\n Args:\n frame (Union[GeoModel, StructuralFrame]): GeoModel or its StructuralFrame to be modified.\n fault_groups (Union[list[str], list[StructuralGroup]]): Groups to be set as faults.\n faults_relation_type (FaultsRelationSpecialCase, optional): Faults relation type to be set. Defaults to FaultsRelationSpecialCase.OFFSET_FORMATIONS.\n change_color (bool, optional): If True, changes the color of the fault groups. Defaults to True.\n\n Returns:\n StructuralFrame: The updated StructuralFrame object.\n \"\"\"\n if isinstance(frame, GeoModel):\n frame = frame.structural_frame\n\n frame = _find_and_set_fields(\n frame=frame,\n fault_groups=fault_groups,\n faults_relation_type=faults_relation_type,\n stack_relation_type=StackRelationType.FAULT,\n change_color=change_color\n )\n\n # * TODO: Set the fault colors\n\n return frame\n\n\ndef unset_is_fault(frame: Union[GeoModel, StructuralFrame], fault_groups: Union[list[str], list[StructuralGroup]]) -> StructuralFrame:\n \"\"\"\n Unsets given groups as fault in the structural frame of the GeoModel.\n\n Args:\n frame (Union[GeoModel, StructuralFrame]): GeoModel or its StructuralFrame to be modified.\n fault_groups (Union[list[str], list[StructuralGroup]]): Groups to be unset as faults.\n\n Returns:\n StructuralFrame: The updated StructuralFrame object.\n \"\"\"\n if isinstance(frame, GeoModel):\n frame = frame.structural_frame\n\n frame = _find_and_set_fields(\n frame=frame,\n fault_groups=fault_groups,\n faults_relation_type=FaultsRelationSpecialCase.OFFSET_NONE,\n stack_relation_type=StackRelationType.ERODE,\n change_color=False\n )\n\n return frame\n\n\ndef set_fault_relation(frame: Union[GeoModel, StructuralFrame], rel_matrix: np.ndarray) -> StructuralFrame:\n \"\"\"\n Sets the fault relations in the structural frame of the GeoModel.\n\n Args:\n frame (Union[GeoModel, StructuralFrame]): GeoModel or its StructuralFrame to be modified.\n rel_matrix (np.ndarray): Fault relation matrix to be set.\n\n Returns:\n StructuralFrame: The updated StructuralFrame object.\n \"\"\"\n\n if isinstance(frame, GeoModel):\n frame = frame.structural_frame\n\n frame.fault_relations = rel_matrix\n return frame\n\n\ndef set_is_finite_fault(self, series_fault=None, toggle: bool = True):\n \"\"\"\"\"\"\n raise NotImplementedError\n s = self._faults.set_is_finite_fault(series_fault,\n toggle) # change df in Fault obj\n # change shared aesara variable for infinite factor\n self._interpolator.set_aesara_shared_is_finite()\n return s\n\n\n# TODO: Move to a faults module\ndef _find_and_set_fields(frame: StructuralFrame, fault_groups: list[StructuralGroup],\n faults_relation_type: FaultsRelationSpecialCase, stack_relation_type: StackRelationType,\n change_color: bool) -> StructuralFrame:\n for index, group in enumerate(fault_groups):\n if isinstance(group, str):\n group = next((g for g in frame.structural_groups if g.name == group), None)\n if isinstance(group, StructuralGroup):\n group.structural_relation = stack_relation_type\n group.fault_relations = faults_relation_type # * Set the default fault relations\n if change_color:\n for element in group.elements:\n element.color = '#527682'\n else:\n raise ValueError(f\"Could not find group '{group}' in structural frame.\")\n return frame\n" }, { "path": "gempy/API/gp2_gp3_compatibility/__init__.py", "content": "" }, { "path": "gempy/API/gp2_gp3_compatibility/gp3_to_gp2_input.py", "content": "from gempy.core.data import GeoModel\nfrom gempy.core.data.orientations import OrientationsTable\nfrom gempy.core.data.surface_points import SurfacePointsTable\nfrom gempy.optional_dependencies import require_gempy_legacy\n\n\ndef gempy3_to_gempy2(geo_model: GeoModel) -> \"gempy_legacy.Project\":\n gl = require_gempy_legacy()\n legacy_model: \"gempy_legacy.Project\" = gl.create_model(project_name=geo_model.meta.name)\n\n surface_points: SurfacePointsTable = geo_model.structural_frame.surface_points\n surface_points_df = surface_points.df # This is a property\n surface_points_df['surfaces'] = surface_points_df['id'].map(geo_model.structural_frame.element_id_name_map)\n \n orientations: OrientationsTable = geo_model.structural_frame.orientations\n orientations_df = orientations.df\n orientations_df['surfaces'] = orientations_df['id'].map(geo_model.structural_frame.element_id_name_map)\n \n # * Set data\n gl.init_data(\n geo_model=legacy_model,\n extent=geo_model.grid.regular_grid.extent,\n resolution=geo_model.grid.regular_grid.resolution,\n surface_points_df=surface_points_df,\n orientations_df=orientations_df\n )\n\n\n # # * Map StructuralFrame\n mapper: dict[str, list[str]] = geo_model.structural_frame.groups_to_mapper\n gl.map_stack_to_surfaces(\n geo_model=legacy_model,\n mapping_object=mapper\n )\n \n legacy_model.add_surfaces(\"basement\")\n\n \n return legacy_model\n " }, { "path": "gempy/API/gp2_gp3_compatibility/gp3_to_gp2_output.py", "content": "import numpy as np\n\nfrom gempy import Project, Solution\nfrom gempy_engine.core.data.dual_contouring_mesh import DualContouringMesh\nfrom gempy_engine.core.data.octree_level import OctreeLevel\nfrom gempy_engine.core.data.solutions import Solutions\nfrom gempy_engine.modules.octrees_topology.octrees_topology_interface import get_regular_grid_value_for_level\n\n\ndef set_gp3_solutions_to_gp2_solution(gp3_solution: Solutions, geo_model: Project) -> Solution:\n octree_lvl = -1\n\n octree_output: OctreeLevel = gp3_solution.octrees_output[octree_lvl]\n \n regular_grid_scalar = get_regular_grid_value_for_level(gp3_solution.octrees_output).astype(\"int8\")\n\n _set_block_matrix(geo_model, octree_output)\n _set_lith_block(geo_model, octree_output)\n _set_scalar_field(geo_model, octree_output)\n\n _set_scalar_field_at_surface_points(geo_model, octree_output)\n \n meshes: list[DualContouringMesh] = gp3_solution.dc_meshes\n\n geo_model.set_surface_order_from_solution()\n \n if meshes: _set_surfaces_meshes(geo_model, meshes)\n \n return geo_model.solutions\n\n\ndef _set_surfaces_meshes(geo_model: Project, meshes: list[DualContouringMesh]) -> Project:\n \n geo_model.solutions.vertices = [mesh.vertices for mesh in meshes]\n geo_model.solutions.edges = [mesh.edges for mesh in meshes]\n \n rescaling_factor: float = geo_model._additional_data.rescaling_data.df.loc['values', 'rescaling factor']\n shift: np.array = geo_model._additional_data.rescaling_data.df.loc['values', 'centers']\n \n surfaces_df = geo_model.solutions.surfaces.df\n idx_of_vertices = surfaces_df.columns.get_loc('vertices')\n idx_of_edges = surfaces_df.columns.get_loc('edges')\n\n # flip mesh 4 and 5\n # meshed_flipped = [ meshes[0], meshes[1], meshes[2], meshes[4], meshes[3]] bug: meshes are not in the right order yet\n in_ = meshes\n \n for i in range(0, len(meshes)): \n surfaces_df.iloc[i, idx_of_vertices] = [(in_[i].vertices - 0.5001) * rescaling_factor + shift] # ! remember the 0.5001\n surfaces_df.iloc[i, idx_of_edges] = [in_[i].edges]\n \n return geo_model\n\n\ndef _set_block_matrix(geo_model: Project, octree_output: OctreeLevel) -> Project:\n temp_list = []\n for i in range(octree_output.number_of_outputs):\n temp_list.append(octree_output.outputs[i].values_block)\n\n block_matrix_stacked = np.vstack(temp_list)\n geo_model.solutions.block_matrix = block_matrix_stacked\n return geo_model\n\n\ndef _set_scalar_field(geo_model: Project, octree_output: OctreeLevel) -> Project:\n temp_list = []\n for i in range(octree_output.number_of_outputs):\n temp_list.append(octree_output.outputs[i].scalar_fields.exported_fields.scalar_field)\n\n scalar_field_stacked = np.vstack(temp_list)\n geo_model.solutions.scalar_field_matrix = scalar_field_stacked\n return geo_model\n\n\ndef _set_scalar_field_at_surface_points(geo_model: Project, octree_output: OctreeLevel) -> Project:\n temp_list = []\n for i in range(octree_output.number_of_outputs):\n temp_list.append(octree_output.outputs[i].scalar_fields.exported_fields.scalar_field_at_surface_points)\n \n geo_model.solutions.scalar_field_at_surface_points = temp_list\n return geo_model\n\n\ndef _set_lith_block(geo_model: Project, octree_output: OctreeLevel) -> Project:\n block = octree_output.last_output_center.ids_block\n block[block == 0] = block.max() + 1\n geo_model.solutions.lith_block = block\n return geo_model\n" }, { "path": "gempy/API/grid_API.py", "content": "from typing import Union, Sequence\n\nimport numpy as np\n\nfrom ..core.data import Grid\nfrom ..core.data.grid_modules import CustomGrid, Sections\nfrom ..core.data.grid_modules.topography import Topography\nfrom ..modules.grids.create_topography import create_random_topography\nfrom ..optional_dependencies import require_subsurface\n\n\ndef set_section_grid(grid: Grid, section_dict: dict):\n if grid.sections is None:\n grid.sections = Sections(\n z_ext=grid.regular_grid.extent[4:],\n section_dict=section_dict\n )\n else:\n grid.sections.set_sections(section_dict,\n regular_grid=grid.regular_grid)\n\n set_active_grid(grid, [Grid.GridTypes.SECTIONS])\n return grid.sections\n\n\ndef set_topography_from_random(grid: Grid, fractal_dimension: float = 2.0, d_z: Union[Sequence, None] = None,\n topography_resolution: Union[Sequence, None] = None):\n \"\"\"\n Sets the topography of the grid using a randomly generated topography.\n\n Args:\n grid (Grid): The grid object on which to set the topography.\n fractal_dimension (float, optional): The fractal dimension of the random topography. Defaults to 2.0.\n d_z (Union[Sequence, None], optional): The sequence of elevation increments for the random topography.\n If None, a default sequence will be used. Defaults to None.\n topography_resolution (Union[Sequence, None], optional): The resolution of the random topography.\n If None, the resolution of the grid's regular grid will be used. Defaults to None.\n\n Returns:\n The topography object that was set on the grid.\n\n Example:\n >>> grid = Grid()\n >>> set_topography_from_random(grid, fractal_dimension=1.5, d_z=[0.1, 0.2, 0.3], topography_resolution=[10, 10])\n\n Note:\n If topography_resolution is None, the resolution of the grid's regular grid will be used.\n If d_z is None, a default sequence of elevation increments will be used.\n \"\"\"\n\n if topography_resolution is None:\n topography_resolution = grid.regular_grid.resolution\n\n random_topography: np.ndarray = create_random_topography(\n extent=grid.regular_grid.extent,\n resolution=topography_resolution,\n dz=d_z,\n fractal_dimension=fractal_dimension\n )\n\n grid.topography = Topography(\n _regular_grid=grid.regular_grid,\n values_2d=random_topography\n )\n\n set_active_grid(grid, [Grid.GridTypes.TOPOGRAPHY])\n return grid.topography\n\n\ndef set_topography_from_subsurface_structured_grid(grid: Grid, struct: \"subsurface.StructuredData\"):\n grid.topography = Topography.from_subsurface_structured_data(struct, grid.regular_grid)\n set_active_grid(grid, [Grid.GridTypes.TOPOGRAPHY])\n return grid.topography\n\n\ndef set_topography_from_arrays(grid: Grid, xyz_vertices: np.ndarray):\n grid.topography = Topography.from_unstructured_mesh(grid.regular_grid, xyz_vertices)\n set_active_grid(grid, [Grid.GridTypes.TOPOGRAPHY])\n return grid.topography\n\n\ndef set_topography_from_file(grid: Grid, filepath: str, crop_to_extent: Union[Sequence, None] = None):\n ss = require_subsurface()\n struct: ss.StructuredData = ss.modules.reader.read_structured_topography(\n path=filepath,\n crop_to_extent=crop_to_extent\n )\n return set_topography_from_subsurface_structured_grid(grid, struct)\n\n\ndef set_custom_grid(grid: Grid, xyz_coord: np.ndarray, reset: bool = False):\n custom_grid = CustomGrid(values=xyz_coord)\n grid.custom_grid = custom_grid\n\n set_active_grid(grid, grid_type=[Grid.GridTypes.CUSTOM], reset=reset)\n return grid.custom_grid\n\n\ndef set_centered_grid(grid: Grid, centers: np.ndarray, resolution: Sequence[float], radius: Union[float, Sequence[float]]):\n from gempy_engine.core.data.centered_grid import CenteredGrid\n centered_grid = CenteredGrid(\n centers=centers,\n resolution=resolution,\n radius=radius\n )\n grid.centered_grid = centered_grid\n set_active_grid(grid, [Grid.GridTypes.CENTERED])\n return grid.centered_grid\n\n\ndef set_topography_from_gdal():\n raise NotImplementedError(\"This is not implemented yet\")\n\n\ndef set_topography_from_array():\n raise NotImplementedError(\"This is not implemented yet\")\n\n\ndef set_active_grid(grid: Grid, grid_type: list[Grid.GridTypes], reset: bool = False):\n if reset is True:\n grid.active_grids = Grid.GridTypes.NONE\n for grid_type in grid_type:\n grid.active_grids |= grid_type\n\n grid._update_values()\n \n print(f'Active grids: {grid.active_grids}')\n return grid\n" }, { "path": "gempy/API/implicit_functions.py", "content": "from ..modules.custom_implicit_functions.ellipsoid_implicit_function import ellipsoid_3d_factory\n\n__all__ = ['ellipsoid_3d_factory']" }, { "path": "gempy/API/initialization_API.py", "content": "import warnings\nfrom typing import Union, Hashable, Literal\n\nimport numpy as np\nfrom numpy import ndarray\n\nfrom gempy.API.io_API import read_surface_points, read_orientations\nfrom gempy_engine.core.data import InterpolationOptions\nfrom ..core.data import StructuralElement\nfrom ..core.data.geo_model import GeoModel\nfrom ..core.data.grid import Grid\nfrom ..core.data.importer_helper import ImporterHelper\nfrom ..core.data.options import InterpolationOptionsType\nfrom ..core.data.orientations import OrientationsTable\nfrom ..core.data.structural_frame import StructuralFrame\nfrom ..core.data.surface_points import SurfacePointsTable\nfrom ..optional_dependencies import require_pooch\nfrom ..optional_dependencies import require_subsurface\n\n\ndef create_geomodel(\n *,\n project_name: str = 'default_project',\n extent: Union[list, ndarray] = None,\n resolution: Union[list, ndarray] = None,\n refinement: int = 1,\n structural_frame: StructuralFrame = None,\n importer_helper: ImporterHelper = None,\n intpolation_options_tye: InterpolationOptionsType = InterpolationOptionsType.OCTREE,\n **kwargs\n) -> GeoModel: # ? Do I need to pass pandas read kwargs?\n \"\"\"\n Creates a geological model based on input parameters, spatial configuration, and interpolation options.\n\n This function initializes a geological model by defining the grid (dense grid or\n octree-based), selecting interpolation options based on the desired type, and \n configuring the structural frame using either a provided structural frame or an \n importer helper instance. The geological model is tailored for specific project-based \n requirements and can be either dense or with variable resolution depending on the \n refinement level.\n\n Args:\n project_name (str): Name of the geological model project. Defaults to 'default_project'.\n extent (list, ndarray): Spatial extent of the geological model in the form of\n [min_x, max_x, min_y, max_y, min_z, max_z].\n resolution (list, ndarray): Resolution of the model grid in the form [x_res, y_res, z_res].\n If not provided, the function will default to octree initialization.\n refinement (int): Refinement level for the octree grid. Ignored if resolution is provided.\n Defaults to 1.\n structural_frame (StructuralFrame): Pre-configured instance of StructuralFrame\n for the geological model. If not provided, an importer_helper must be supplied.\n importer_helper (ImporterHelper): Helper object for initializing a structural frame if none\n is explicitly provided.\n intpolation_options_tye (InterpolationOptionsType): Enum representing the desired type of \n interpolation options. Defaults to InterpolationOptionsType.OCTREE.\n\n Returns:\n GeoModel: An initialized geological model with specified spatial configuration \n and interpolation properties.\n\n Raises:\n ValueError: If neither structural_frame nor importer_helper is provided.\n ValueError: If the interpolation options type is unrecognized.\n \"\"\"\n \n # init resolutions well\n if resolution is None:\n grid: Grid = Grid.init_octree_grid(\n extent=extent,\n octree_levels=refinement,\n legacy=kwargs.get('legacy_octree_init', False)\n )\n else:\n grid: Grid = Grid.init_dense_grid(\n extent=extent,\n resolution=resolution\n )\n\n match intpolation_options_tye:\n case InterpolationOptionsType.DENSE_GRID:\n interpolation_options: InterpolationOptions = InterpolationOptions.init_dense_grid_options()\n case InterpolationOptionsType.OCTREE:\n interpolation_options: InterpolationOptions = InterpolationOptions.init_octree_options(refinement=refinement)\n case _:\n raise ValueError(f\"Interpolation options type {intpolation_options_tye} not recognized. Use InterpolationOptionsType.DENSE_GRID or InterpolationOptionsType.OCTREE.\")\n\n match (structural_frame, importer_helper):\n case (None, None):\n # ? For now my gut feeling is that is better to pass the structural frame explicitly\n raise ValueError(\"Either structural_frame or importer_helper must be provided. You can use StructuralFrame.initialize_default_structure() to create a default structural frame.\")\n structural_frame = StructuralFrame.initialize_default_structure()\n case (None, _):\n structural_frame = _initialize_structural_frame(importer_helper)\n case _:\n pass\n\n geo_model: GeoModel = GeoModel.from_args(\n name=project_name,\n structural_frame=structural_frame,\n grid=grid,\n interpolation_options=interpolation_options\n )\n\n return geo_model\n\n\ndef structural_elements_from_borehole_set(\n borehole_set: \"subsurface.core.geological_formats.BoreholeSet\",\n elements_dict: dict,\n group_by: Literal['component lith', 'lith_ids'] = 'lith_ids'\n) -> list[StructuralElement]:\n \"\"\"Creates a list of StructuralElements from a BoreholeSet.\n\n Args:\n borehole_set (subsurface.core.geological_formats.BoreholeSet): The BoreholeSet object containing the boreholes.\n elements_dict (dict): A dictionary containing the properties of the structural elements to be created.\n\n Returns:\n list[StructuralElement]: A list of StructuralElement objects created from the borehole set.\n\n Raises:\n ValueError: If a top lithology ID specified in `elements_dict` is not found in the borehole set.\n\n \"\"\"\n\n ss = require_subsurface()\n borehole_set: ss.core.geological_formats.BoreholeSet\n\n elements = []\n component_lith: dict[Hashable, np.ndarray] = borehole_set.get_bottom_coords_for_each_lith(group_by=group_by)\n\n for name, properties in elements_dict.items():\n top_coordinates = component_lith.get(properties['id'])\n if top_coordinates is None:\n raise ValueError(f\"Top lithology {properties['id']} not found in borehole set.\")\n\n element = StructuralElement(\n name=name,\n id=properties['id'],\n color=properties['color'],\n surface_points=SurfacePointsTable.from_arrays(\n x=top_coordinates[:, 0],\n y=top_coordinates[:, 1],\n z=top_coordinates[:, 2],\n names=[name],\n name_id_map={name: properties['id']}\n ),\n orientations=OrientationsTable(np.zeros(0, dtype=OrientationsTable.dt))\n )\n elements.append(element)\n # Reverse the list to have the oldest rocks at the bottom\n\n return elements\n\n\ndef create_data_legacy(\n *,\n project_name: str = 'default_project',\n extent: Union[list, ndarray] = None,\n resolution: Union[list, ndarray] = None,\n path_i: str = None,\n path_o: str = None) -> GeoModel: # ? Do I need to pass pandas read kwargs?\n\n warnings.warn(\"This method is deprecated. Use create_geomodel instead.\", DeprecationWarning)\n return create_geomodel(\n project_name=project_name,\n extent=extent,\n resolution=resolution,\n importer_helper=ImporterHelper(\n path_to_surface_points=path_i,\n path_to_orientations=path_o\n )\n )\n\n\ndef _initialize_structural_frame(importer_helper: ImporterHelper) -> StructuralFrame:\n surface_points, orientations = _read_input_points(importer_helper)\n\n return StructuralFrame.from_data_tables(surface_points, orientations)\n\n\ndef _read_input_points(importer_helper: ImporterHelper) -> (SurfacePointsTable, OrientationsTable):\n orientations_file, surface_points_file = _fetch_data_with_pooch(\n orientations_hash=importer_helper.hash_orientations,\n orientations_path=importer_helper.path_to_orientations,\n surface_points_hash=importer_helper.hash_surface_points,\n surface_points_path=importer_helper.path_to_surface_points\n )\n\n surface_points: SurfacePointsTable = read_surface_points(\n path=surface_points_file,\n coord_x_name=importer_helper.coord_x_name,\n coord_y_name=importer_helper.coord_y_name,\n coord_z_name=importer_helper.coord_z_name,\n surface_name=importer_helper.surface_name,\n pandas_kwargs=importer_helper.pandas_reader_kwargs\n )\n\n orientations: OrientationsTable = read_orientations(\n path=orientations_file,\n coord_x_name=importer_helper.coord_x_name,\n coord_y_name=importer_helper.coord_y_name,\n coord_z_name=importer_helper.coord_z_name,\n surface_name=importer_helper.surface_name,\n gx_name=importer_helper.gx_name,\n gy_name=importer_helper.gy_name,\n gz_name=importer_helper.gz_name,\n pandas_kwargs=importer_helper.pandas_reader_kwargs,\n name_id_map=surface_points.name_id_map\n )\n\n return surface_points, orientations\n\n\ndef _fetch_data_with_pooch(orientations_hash, orientations_path, surface_points_hash, surface_points_path):\n def is_url(url):\n from urllib.parse import urlparse\n try:\n result = urlparse(url)\n return all([result.scheme, result.netloc])\n except ValueError:\n return False\n\n pooch = require_pooch() if is_url(surface_points_path) or is_url(orientations_path) else None\n # * Fetch or define path for surface points\n if is_url(surface_points_path):\n surface_points_file = pooch.retrieve(\n url=surface_points_path,\n known_hash=surface_points_hash\n )\n print(\"Surface points hash: \", pooch.file_hash(surface_points_file))\n else:\n surface_points_file = surface_points_path\n # * Fetch or define path for orientations\n if is_url(orientations_path):\n orientations_file = pooch.retrieve(\n url=orientations_path,\n known_hash=orientations_hash\n )\n print(\"Orientations hash: \", pooch.file_hash(orientations_file))\n else:\n orientations_file = orientations_path\n return orientations_file, surface_points_file\n" }, { "path": "gempy/API/io_API.py", "content": "from typing import Optional\n\nimport numpy as np\n\nfrom gempy.core.data.orientations import OrientationsTable\nfrom gempy.core.data.surface_points import SurfacePointsTable\nfrom gempy.optional_dependencies import require_pandas\n\n\ndef read_surface_points(path: str,\n coord_x_name=\"X\",\n coord_y_name=\"Y\",\n coord_z_name=\"Z\",\n surface_name=\"formation\",\n name_id_map: Optional[dict[str, int]] = None,\n pandas_kwargs: dict = None) -> SurfacePointsTable:\n \n pandas_kwargs = pandas_kwargs or {}\n if 'sep' not in pandas_kwargs:\n pandas_kwargs['sep'] = ','\n\n pd = require_pandas()\n csv = pd.read_csv(path, **pandas_kwargs)\n csv = _standardize(csv)\n\n surface_points: SurfacePointsTable = SurfacePointsTable.from_arrays(\n x=csv[coord_x_name].values,\n y=csv[coord_y_name].values,\n z=csv[coord_z_name].values,\n names=csv[surface_name].values, # TODO: This we will have to map it with StructuralFrame\n name_id_map=name_id_map\n )\n\n return surface_points\n\n\ndef read_orientations(\n path: str,\n coord_x_name=\"X\",\n coord_y_name=\"Y\",\n coord_z_name=\"Z\",\n gx_name=\"G_x\",\n gy_name=\"G_y\",\n gz_name=\"G_z\",\n surface_name=\"formation\",\n name_id_map: Optional[dict[str, int]] = None,\n pandas_kwargs: dict = None\n ) -> OrientationsTable:\n \n pandas_kwargs = pandas_kwargs or {}\n if 'sep' not in pandas_kwargs:\n pandas_kwargs['sep'] = ','\n \n pd = require_pandas()\n csv = pd.read_csv(path, **pandas_kwargs)\n csv_standardized = _standardize(csv)\n csv_with_gradient = _add_gradient_columns(csv_standardized)\n\n orientations: OrientationsTable = OrientationsTable.from_arrays(\n x=csv_with_gradient[coord_x_name].values,\n y=csv_with_gradient[coord_y_name].values,\n z=csv_with_gradient[coord_z_name].values,\n G_x=csv_with_gradient[gx_name].values,\n G_y=csv_with_gradient[gy_name].values,\n G_z=csv_with_gradient[gz_name].values,\n names=csv_with_gradient[surface_name].values, # TODO: This we will have to map it with StructuralFrame\n name_id_map=name_id_map\n )\n\n return orientations\n\n\nCOLUMN_NAME_MAPPING = {\n \"X\" : [\"X\", \"x\"],\n \"Y\" : [\"Y\", \"y\"],\n \"Z\" : [\"Z\", \"z\"],\n \"azimuth\" : [\"azimuth\", \"Azimuth\"],\n \"dip\" : [\"dip\", \"Dip\"],\n \"polarity\" : [\"polarity\", \"Polarity\"],\n \"formation\": [\"formation\", \"Formation\", \"surface\"],\n \"G_x\" : [\"G_x\", \"gradient_x\"],\n \"G_y\" : [\"G_y\", \"gradient_y\"],\n \"G_z\" : [\"G_z\", \"gradient_z\"],\n}\n\n\ndef _standardize(df: 'pandas.DataFrame'):\n for column in df.columns:\n for standard_name, possible_names in COLUMN_NAME_MAPPING.items():\n if column in possible_names:\n df.rename(columns={column: standard_name}, inplace=True)\n\n return df\n\n\ndef _add_gradient_columns(df):\n if \"azimuth\" in df.columns and \"dip\" in df.columns and \"polarity\" in df.columns:\n # Convert azimuth, dip, polarity to gradient\n df['G_x'] = np.sin(np.deg2rad(df['dip'])) * np.sin(np.deg2rad(df['azimuth'])) * df['polarity']\n df['G_y'] = np.sin(np.deg2rad(df['dip'])) * np.cos(np.deg2rad(df['azimuth'])) * df['polarity']\n df['G_z'] = np.cos(np.deg2rad(df['dip'])) * df['polarity']\n\n return df\n" }, { "path": "gempy/API/map_stack_to_surfaces_API.py", "content": "from typing import Union\n\nfrom gempy_engine.core.data.stack_relation_type import StackRelationType\nfrom ..core.data.geo_model import GeoModel\nfrom ..core.data.structural_frame import StructuralFrame\nfrom ..core.data.structural_group import StructuralGroup\n\n\ndef map_stack_to_surfaces(gempy_model: GeoModel, mapping_object: Union[dict[str, list[str]] | dict[str, tuple]],\n set_series: bool = True, remove_unused_series=True, series_data: list = None) -> StructuralFrame:\n \"\"\"\n Map stack (series) to surfaces by reorganizing elements between groups in a GeoModel's structural frame.\n\n This function reorganizes structural elements (surfaces) based on a mapping object \n and updates the structural frame of the GeoModel. It can also create new series \n and remove unused ones.\n\n Args:\n gempy_model (GeoModel): The GeoModel object whose structural frame is to be modified.\n mapping_object (Union[dict[str, list[str]] | dict[str, tuple]]): Dictionary mapping group names to element names.\n set_series (bool, optional): If True, creates new series for groups not present in the GeoModel. Defaults to True.\n remove_unused_series (bool, optional): If True, removes groups without any elements. Defaults to True.\n series_data (list, optional): List of series data from JSON containing structural relations. Defaults to None.\n\n Returns:\n StructuralFrame: The updated StructuralFrame object.\n \"\"\"\n structural_groups: list[StructuralGroup] = gempy_model.structural_frame.structural_groups\n\n for index, (group_name, elements) in enumerate(mapping_object.items()):\n # region Create new series if needed\n group_already_exists = any(group.name == group_name for group in structural_groups)\n if set_series and not group_already_exists:\n # Get structural relation from series_data if available\n structural_relation = StackRelationType.ERODE # Default value\n if series_data:\n for series in series_data:\n if series['name'] == group_name:\n structural_relation = StackRelationType[series['structural_relation']]\n break\n\n new_group = StructuralGroup(\n name=group_name,\n elements=[],\n structural_relation=structural_relation\n )\n structural_groups.insert(index, new_group)\n # endregion\n\n # Make sure elements is a list or tuple\n if isinstance(elements, str):\n elements = [elements]\n \n for element_name in elements:\n # Here we need to find out the current group of the element.\n # This can be done by looking up the element in each group.\n from_group_name = None\n for group in structural_groups:\n if any(element.name == element_name for element in group.elements):\n from_group_name = group.name\n break\n\n # If we've found the group, we can proceed with moving the element.\n if from_group_name:\n _move_element(\n structural_groups=structural_groups,\n element_name=element_name,\n from_group_name=from_group_name,\n to_group_name=group_name\n )\n else:\n print(f\"Could not find element '{element_name}' in any group.\")\n\n # Remove empty groups\n if remove_unused_series:\n structural_groups = [group for group in structural_groups if group.elements]\n\n gempy_model.structural_frame.structural_groups = structural_groups\n return gempy_model.structural_frame\n\n\ndef _move_element(structural_groups: list[StructuralGroup], element_name: str, from_group_name: str, to_group_name: str):\n # Find the source and destination groups\n from_group = next((group for group in structural_groups if group.name == from_group_name), None)\n to_group = next((group for group in structural_groups if group.name == to_group_name), None)\n\n # Check if both groups exist\n if from_group is None or to_group is None:\n raise ValueError(\"One or both group names are not found.\")\n\n # Find the element in the source group\n element = next((element for element in from_group.elements if element.name == element_name), None)\n\n # Check if element exists\n if element is None:\n raise ValueError(f\"Element '{element_name}' not found in group '{from_group_name}'.\")\n\n # Remove the element from the source group and add it to the destination group\n from_group.elements.remove(element)\n to_group.elements.append(element)\n" }, { "path": "gempy/__init__.py", "content": "\"\"\"\nModule initialisation for GemPy\nCreated on 06.19.2023\n\n@author: Miguel de la Varga\n\"\"\"\nimport sys\nfrom . import config\n\n# =================== CORE ===================\nfrom .core import data\nfrom .core.color_generator import ColorsGenerator\n\n# =================== API ===================\n\nfrom .API import *\nfrom .API import implicit_functions\n\n# * Assert at least pyton 3.10\nassert sys.version_info[0] >= 3 and sys.version_info[1] >= 10, \"GemPy requires Python 3.10 or higher\"\n\nfrom ._version import __version__\n\nif __name__ == '__main__':\n pass\n" }, { "path": "gempy/_version.py", "content": "# file generated by setuptools_scm\n# don't change, don't track in version control\nTYPE_CHECKING = False\nif TYPE_CHECKING:\n from typing import Tuple, Union\n VERSION_TUPLE = Tuple[Union[int, str], ...]\nelse:\n VERSION_TUPLE = object\n\nversion: str\n__version__: str\n__version_tuple__: VERSION_TUPLE\nversion_tuple: VERSION_TUPLE\n\n__version__ = version = '2024.2.0.3.dev0+gf344a731.d20240626'\n__version_tuple__ = version_tuple = (2024, 2, 0, 3, 'dev0', 'gf344a731.d20240626')\n" }, { "path": "gempy/config.py", "content": "import os\n\nfrom dotenv import load_dotenv\n\n# Define the paths for the .env files\n\nscript_dir = os.path.dirname(os.path.abspath(__file__))\n\ndotenv_path = os.path.join(script_dir, '../.env')\ndotenv_gempy_engine_path = os.path.expanduser('~/.env')\n\n# Check if the .env files exist and prioritize the local .env file\nif os.path.exists(dotenv_path):\n load_dotenv(dotenv_path)\nelif os.path.exists(dotenv_gempy_engine_path):\n load_dotenv(dotenv_gempy_engine_path)\nelse:\n load_dotenv()\n" }, { "path": "gempy/core/__init__.py", "content": "if __name__ == '__main__':\n pass\n" }, { "path": "gempy/core/color_generator.py", "content": "from dataclasses import dataclass\nfrom functools import cached_property\nfrom typing import Optional\n\nimport numpy as np\nfrom pydantic import Field\n\n\n@dataclass\nclass ColorsGenerator:\n \"\"\"\n Object that handles the color management.\n \"\"\"\n\n _gempy_default_colors = [\n '#015482', '#9f0052', '#ffbe00', '#728f02', '#443988',\n '#ff3f20', '#5DA629', '#b271d0', '#72e54a', '#583bd1',\n '#d0e63d', '#b949e2', '#95ce4b', '#6d2b9f', '#60eb91',\n '#d746be', '#52a22e', '#5e63d8', '#e5c339', '#371970',\n '#d3dc76', '#4d478e', '#43b665', '#d14897', '#59e5b8',\n '#e5421d', '#62dedb', '#df344e', '#9ce4a9', '#d94077',\n '#99c573', '#842f74', '#578131', '#708de7', '#df872f',\n '#5a73b1', '#ab912b', '#321f4d', '#e4bd7c', '#142932',\n '#cd4f30', '#69aedd', '#892a23', '#aad6de', '#5c1a34',\n '#cfddb4', '#381d29', '#5da37c', '#d8676e', '#52a2a3',\n '#9b405c', '#346542', '#de91c9', '#555719', '#bbaed6',\n '#945624', '#517c91', '#de8a68', '#3c4b64', '#9d8a4d',\n '#825f7e', '#2c3821', '#ddadaa', '#5e3524', '#a3a68e',\n '#a2706b', '#686d56'\n ] # source: https://medialab.github.io/iwanthue/\n\n hex_colors: list[str] = Field(\n default=_gempy_default_colors,\n exclude=True\n )\n _index: int = 0\n\n def __post_init__(self):\n self.regenerate_color_palette()\n\n @staticmethod\n def _random_hexcolor() -> str:\n \"\"\"Generates a random hex color string.\"\"\"\n return f\"#{np.random.randint(0, 0xffffff):06x}\"\n\n def regenerate_color_palette(self, seaborn_palettes: Optional[list[str]] = None):\n try:\n import seaborn as sns\n seaborn_installed = True\n except ImportError:\n seaborn_installed = False\n\n if seaborn_palettes and seaborn_installed:\n hex_colors = []\n for palette in seaborn_palettes: # for each palette\n hex_colors += sns.color_palette(palette).as_hex() # get all colors in palette and add to list\n\n elif seaborn_palettes and not seaborn_installed:\n raise ImportError(\"Seaborn is not installed. Please install it to use color palettes.\")\n else:\n hex_colors = list(self._gempy_default_colors)\n\n self.hex_colors = hex_colors\n\n def __iter__(self) -> 'ColorsGenerator':\n \"\"\"Returns the object itself as an iterator.\"\"\"\n return self\n\n def __next__(self) -> str:\n \"\"\"Generator that yields the next color.\"\"\"\n color = self.up_next()\n self._index += 1\n del self._next_color\n return color\n\n def up_next(self) -> str:\n \"\"\"Returns the next color without incrementing the index.\"\"\"\n return self._next_color\n\n @cached_property\n def _next_color(self) -> str:\n if self._index < len(self.hex_colors):\n return self.hex_colors[self._index]\n\n return self._random_hexcolor()\n" }, { "path": "gempy/core/data/DEP_xsolution.py", "content": "from typing import Union\n\nfrom gempy.core.data_modules.stack import Stack\nfrom gempy import Surfaces, Grid\n\ntry:\n import xarray as xr\n import subsurface\n from subsurface.structs.base_structures.common_data_utils import to_netcdf\nexcept:\n print(\"Not subsurface compatibility available\")\n\n\n\nimport numpy as np\nimport pandas as pd\n\n\nclass XSolution(object):\n \"\"\"This class stores the output of the interpolation and the necessary objects\n to visualize and manipulate this data using xarray as backend.\n\n Depending on the activated grid (see :class:`Grid`) a different number of\n properties are returned returned:\n\n Args:\n grid (Grid): [s0]\n surfaces (Surfaces): [s1]\n series (Series): [s2]\n\n Attributes:\n grid (Grid)\n surfaces (Surfaces)\n series (Series)\n weights_vector (numpy.array): [s3]\n scalar_field_at_surface_points (numpy.array): [s4]\n block_at_surface_points (numpy.array): [s5]\n mask_at_surface_points (numpy.array): [s6]\n values_at_surface_points (numpy.array): [s7]\n lith_block (numpy.array): [s8]\n scalar_field_matrix (numpy.array): [s9]\n block_matrix (numpy.array): [s10]\n mask_matrix (numpy.array): [s11]\n mask_matrix_pad (numpy.array): mask matrix padded 2 block in order to guarantee that the layers intersect each\n other after marching cubes\n values_matrix (numpy.array): [s12]\n vertices (list[numpy.array]): [s13]\n edges (list[numpy.array]): [s14]\n geological_map (numpy.array): [s15]\n\n \"\"\"\n\n def __init__(self, grid: Grid,\n surfaces: Surfaces = None,\n stack: Stack = None,\n ):\n\n self.grid = grid\n self.stack = stack\n self.surfaces = surfaces # Used to store ver/sim there\n\n # Define xarrays\n self.weights_vector = None\n self.at_surface_points = None\n self.s_regular_grid = None # xr.Dataset()\n self.s_custom_grid = None # xr.Dataset()\n self.s_topography = None # xr.Dataset()\n self.s_at_surface_points = None # xr.Dataset()\n self.s_sections = dict()\n self.meshes = None # xr.Dataset()\n\n def set_meshes(self, surfaces: Surfaces = None):\n \"\"\"Create xarray from the Surfaces object. In GemPy Engine we will set\n them directly.\n \"\"\"\n\n surf_ver_sim = surfaces.df[['id', 'vertices', 'edges']]\n vertex = []\n simplex = []\n ids = []\n last_idx = 0\n self.extract_each_surface_representations(ids, last_idx, simplex,\n surf_ver_sim, vertex)\n\n vertex_array = np.concatenate(vertex)\n simplex_array = np.concatenate(simplex)\n ids_array = np.concatenate(ids)\n\n self.meshes = subsurface.UnstructuredData.from_array(\n vertex=vertex_array,\n cells=simplex_array,\n attributes=pd.DataFrame(ids_array, columns=['id'])\n )\n\n return self.meshes\n\n @staticmethod\n def extract_each_surface_representations(ids, last_idx, simplex,\n surf_ver_sim, vertex):\n for index, row in surf_ver_sim.iterrows():\n v_ = row['vertices']\n e_ = row['edges'] + last_idx\n if v_ is not np.nan and e_ is not np.nan:\n i_ = np.ones(e_.shape[0]) * row['id']\n vertex.append(v_)\n simplex.append(e_)\n ids.append(i_)\n last_idx = e_[-20:].max() + 1\n\n def set_values(self,\n values: list,\n active_features=None,\n surf_properties=None,\n ):\n \"\"\" At this stage we should split values into the different grids\n\n Args:\n values:\n\n Returns:\n\n \"\"\"\n\n # Get an array with all the indices for each grid\n l = self.grid.length\n\n coords_base, xyz = self.prepare_common_args(active_features,\n surf_properties)\n self.weights_vector = values[3]\n\n if self.grid.active_grids_bool[0]:\n self.set_values_to_regular_grid_(values, l[0], l[1], coords_base.copy())\n if self.grid.active_grids_bool[1]:\n self.set_values_to_custom_grid(values, l[1], l[2], coords_base.copy(),\n xyz=xyz)\n if self.grid.active_grids_bool[2]:\n self.set_values_to_topography(values, l[2], l[3], coords_base.copy())\n if self.grid.active_grids_bool[3]:\n self.set_values_to_sections(values, l[3], l[4], coords_base.copy())\n if self.grid.active_grids_bool[4]:\n self.set_values_to_centered()\n\n # TODO: Add xyz from surface points\n self.set_values_to_surface_points_(values, l[-1], coords_base, xyz=None)\n\n def prepare_common_args(self, active_features, surf_properties):\n if active_features is None and self.stack is not None:\n active_features = self.stack.df.groupby('isActive').get_group(True).index\n if surf_properties is None and self.surfaces is not None:\n surf_properties = self.surfaces.properties_val\n coords_base = dict()\n if active_features is not None:\n coords_base['Features'] = active_features.to_list()\n if surf_properties is not None:\n coords_base['Properties'] = surf_properties.to_list()\n if self.grid.custom_grid is not None:\n xyz = self.grid.custom_grid.values\n else:\n xyz = None\n return coords_base, xyz\n\n def set_values_to_centered(self):\n return\n\n def set_values_to_surface_points_(self, values, l0, coords_base, xyz=None):\n coords = coords_base\n l1 = values[0].shape[-1]\n arrays = self.create_unstruct_xarray(values, l0, l1, xyz)\n\n self.s_at_surface_points = xr.Dataset(\n data_vars=arrays,\n coords=coords\n )\n return self.s_at_surface_points\n\n @staticmethod\n def create_struc_xarrays(values, l0, l1, res: Union[list, np.ndarray]):\n arrays = dict()\n\n n_dim = len(res)\n xyz = ['X', 'Y', 'Z'][:n_dim]\n if values[0] is not None:\n # This encompass lith_block and values matrix\n property_matrix = xr.DataArray(\n data=values[0][:, l0:l1].reshape(-1, *res),\n dims=['Properties', *xyz],\n )\n arrays['property_matrix'] = property_matrix\n\n if values[1] is not None:\n # This is the block matrix\n i, j, _ = values[1].shape\n block_matrix = xr.DataArray(\n data=values[1][:, :, l0:l1].reshape(i, j, *res),\n dims=['Features', 'Properties', *xyz],\n )\n arrays['block_matrix'] = block_matrix\n\n # Fault block?\n\n if values[4] is not None:\n # Scalar field matrix\n scalar_matrix = xr.DataArray(\n data=values[4][:, l0:l1].reshape(-1, *res),\n dims=['Features', *xyz],\n )\n arrays['scalar_field_matrix'] = scalar_matrix\n\n if values[6] is not None:\n # Mask matrix\n mask_matrix = xr.DataArray(\n data=values[6][:, l0:l1].reshape(-1, *res),\n dims=['Features', *xyz],\n )\n arrays['mask_matrix'] = mask_matrix\n\n if values[7] is not None:\n # Fault mask matrix\n fault_mask = xr.DataArray(\n data=values[7][:, l0:l1].reshape(-1, *res),\n dims=['Features', *xyz],\n )\n arrays['fault_mask'] = fault_mask\n\n return arrays\n\n @staticmethod\n def create_unstruct_xarray(values, l0, l1, xyz):\n arrays = dict()\n if xyz is not None:\n cartesian_matrix = xr.DataArray(\n data=xyz,\n dims=['Point', 'XYZ'],\n coords={'XYZ': ['X', 'Y', 'Z']}\n )\n arrays['cartesian_matrix'] = cartesian_matrix\n\n if values[0] is not None:\n # Values and lith block\n property_v3 = xr.DataArray(\n data=values[0][:, l0:l1],\n dims=['cell_attr', 'cell'],\n )\n\n arrays['property'] = property_v3\n\n if values[1] is not None:\n # block\n block_v3 = xr.DataArray(\n data=values[1][:, :, l0:l1],\n dims=['Features', 'cell_attr', 'cell'],\n )\n\n arrays['block'] = block_v3\n\n if values[4] is not None:\n # Scalar field\n scalar_field_v3 = xr.DataArray(\n data=values[4][:, l0:l1],\n dims=['Features', 'cell'],\n )\n arrays['scalar_field'] = scalar_field_v3\n\n if values[6] is not None:\n # Scalar field\n mask_v3 = xr.DataArray(\n data=values[6][:, l0:l1],\n dims=['Features', 'cell'],\n )\n arrays['mask'] = mask_v3\n\n return arrays\n\n def set_values_to_custom_grid(self, values: list, l0, l1,\n coords_base: dict, xyz=None):\n\n coords = coords_base\n arrays = self.create_unstruct_xarray(values, l0, l1, xyz=None)\n\n self.s_custom_grid = subsurface.UnstructuredData.from_array(\n vertex=xyz,\n cells=\"points\",\n cells_attr=arrays,\n coords=coords,\n default_cells_attr_name=\"block\"\n )\n\n return self.s_custom_grid\n\n def set_values_to_regular_grid_(self, values: list, l0, l1,\n coords_base: dict):\n\n coords = self.add_cartesian_coords(coords_base)\n\n arrays = self.create_struc_xarrays(values, l0, l1,\n self.grid.regular_grid.resolution)\n\n self.s_regular_grid = subsurface.StructuredData.from_dict(data_dict=arrays, coords=coords)\n\n def add_cartesian_coords(self, coords_base):\n coords = coords_base\n coords['X'] = self.grid.regular_grid.x\n coords['Y'] = self.grid.regular_grid.y\n coords['Z'] = self.grid.regular_grid.z\n return coords\n\n def set_values_to_topography(self,\n values: list,\n l0, l1,\n coords_base):\n coords = coords_base\n coords['X'] = self.grid.topography.x\n coords['Y'] = self.grid.topography.y\n resolution = self.grid.topography.resolution\n arrays = self.create_struc_xarrays(values, l0, l1, resolution)\n\n self.s_topography = subsurface.StructuredData.from_dict(data_dict=arrays, coords=coords)\n return self.s_topography\n\n def set_values_to_sections(self,\n values: list,\n l0, l1,\n coords_base):\n coords = coords_base\n sections = self.grid.sections\n\n for e, axis_coord in enumerate(sections.generate_axis_coord()):\n resolution = sections.resolution[e]\n l0_s = sections.length[e]\n l1_s = sections.length[e + 1]\n name, xy = axis_coord\n coords['X'] = xy[:, 0]\n coords['Y'] = xy[:, 1]\n\n arrays = self.create_struc_xarrays(values, l0 + l0_s, l0 + l1_s, resolution)\n\n self.s_sections[name] = subsurface.StructuredData.from_dict(data_dict=arrays, coords=coords)\n return self.s_sections\n\n @property\n def data_structures(self):\n # TODO: Add sections\n args = [self.s_regular_grid, self.s_custom_grid, self.s_topography, self.meshes]\n names = ['regular_grid', 'custom_grid', 'topography', 'meshes']\n return zip(args, names)\n\n def to_netcdf(self, path, name, **kwargs):\n for a, n in self.data_structures:\n if a is not None:\n to_netcdf(a, f'{path}/{name}_{n}.nc', **kwargs)\n" }, { "path": "gempy/core/data/__init__.py", "content": "from .geo_model import GeoModel\nfrom .structural_frame import StructuralFrame\nfrom .structural_group import StructuralGroup\nfrom .structural_element import StructuralElement\nfrom .orientations import OrientationsTable\nfrom .surface_points import SurfacePointsTable\nfrom .grid import Grid, Topography\nfrom .importer_helper import ImporterHelper\nfrom .gempy_engine_config import GemPyEngineConfig\nfrom .structural_group import FaultsRelationSpecialCase\nfrom ..color_generator import ColorsGenerator\n\nfrom gempy_engine.core.data.interpolation_input import InterpolationInput\nfrom gempy_engine.core.data.stack_relation_type import StackRelationType\nfrom gempy_engine.core.data.options import InterpolationOptions\nfrom gempy_engine.core.data.solutions import Solutions\nfrom gempy_engine.core.data.raw_arrays_solution import RawArraysSolution\nfrom gempy_engine.core.data.transforms import GlobalAnisotropy, Transform\nfrom gempy_engine.core.data.kernel_classes.faults import FaultsData, FiniteFaultData\nfrom gempy_engine.config import AvailableBackends\nfrom gempy_engine.core.data.geophysics_input import GeophysicsInput\n\n\n__all__ = [\n # From gempy\n 'GeoModel', 'StructuralFrame', 'StructuralGroup', 'StructuralElement', 'OrientationsTable', 'SurfacePointsTable',\n 'Grid', 'Topography',\n 'ImporterHelper', 'GemPyEngineConfig', 'FaultsRelationSpecialCase', 'ColorsGenerator',\n # From gempy engine\n 'InterpolationInput','StackRelationType', 'InterpolationOptions', 'Solutions', 'RawArraysSolution', 'GlobalAnisotropy', 'Transform',\n 'FaultsData', 'FiniteFaultData', 'AvailableBackends', 'GeophysicsInput'\n]" }, { "path": "gempy/core/data/_data_points_helpers.py", "content": "import hashlib\nimport sys\n\nfrom typing import Sequence\n\nimport numpy as np\n\n\ndef structural_element_hasher(i: int, name: str, hash_length: int = 8) -> int:\n # Get the last 'hash_length' digits from the hash\n name_hash = int(hashlib.md5(name.encode('utf-8')).hexdigest(), 16) % (10 ** hash_length)\n return i * (10 ** hash_length) + name_hash\n\n\ndef generate_ids_from_names(name_id_map, names, x):\n name_id_map = name_id_map or {name: structural_element_hasher(i, name) for i, name in enumerate(np.unique(names))}\n if isinstance(names, str):\n ids = np.array([name_id_map[names]] * len(x))\n elif isinstance(names, Sequence) or isinstance(names, np.ndarray):\n ids = np.array([name_id_map[name] for name in names])\n elif type(names).__module__.startswith('pandas') and type(names).__name__ == 'StringArray':\n ids = np.array([name_id_map[name] for name in names])\n else:\n raise TypeError(f\"Names should be a string or a NumPy array, not {type(names)}\")\n return ids, name_id_map\n" }, { "path": "gempy/core/data/core_utils.py", "content": "import numpy as np\n\nfrom gempy.optional_dependencies import require_scipy\n\n\ndef calculate_line_coordinates_2points(p1, p2, res):\n if isinstance(p1, list) or isinstance(p1, tuple):\n p1 = np.array(p1)\n if isinstance(p2, list) or isinstance(p2, tuple):\n p2 = np.array(p2)\n v = p2 - p1 # vector pointing from p1 to p2\n u = v / np.linalg.norm(v) # normalize it\n distance = distance_2_points(p1, p2)\n steps = np.linspace(0, distance, res)\n values = p1.reshape(2, 1) + u.reshape(2, 1) * steps.ravel()\n return values.T\n\n\ndef distance_2_points(p1, p2):\n return np.sqrt(np.diff((p1[0], p2[0])) ** 2 + np.diff((p1[1], p2[1])) ** 2)\n\n\ndef interpolate_zvals_at_xy(xy, topography, method='DEP'):\n \"\"\"\n Interpolates DEM values on a defined section.\n\n Args:\n xy (np.ndarray): Array of shape (n, 2) containing x (EW) and y (NS) coordinates of the profile.\n topography (Topography): An instance of Topography containing the DEM data.\n\n Returns:\n np.ndarray: z values, i.e., topography along the profile.\n \"\"\"\n scipy = require_scipy()\n xj = topography.values_2d[:, 0, 0]\n yj = topography.values_2d[0, :, 1]\n zj = topography.values_2d[:, :, 2]\n\n spline = scipy.interpolate.RectBivariateSpline(xj, yj, zj)\n zi = spline.ev(xy[:, 0], xy[:, 1])\n \n return zi\n" }, { "path": "gempy/core/data/encoders/__init__.py", "content": "" }, { "path": "gempy/core/data/encoders/binary_encoder.py", "content": "import numpy as np\n\nfrom ..surface_points import SurfacePointsTable\nfrom ..orientations import OrientationsTable\n\n\ndef deserialize_input_data_tables(binary_array: bytes, name_id_map: dict, \n sp_binary_length_: int, ori_binary_length_: int) -> tuple[OrientationsTable, SurfacePointsTable]:\n \"\"\"\n Deserializes binary data into two tables: OrientationsTable and SurfacePointsTable.\n\n This function takes a binary array, a mapping of names to IDs, and lengths for\n specific parts of the binary data to extract and deserialize two distinct data\n tables: OrientationsTable and SurfacePointsTable. It uses the provided lengths\n to split the binary data accordingly and reconstructs the table contents from\n their respective binary representations.\n\n Args:\n binary_array (bytes): A bytes array containing the serialized data for\n both the OrientationsTable and SurfacePointsTable.\n name_id_map (dict): A dictionary mapping names to IDs which is used to\n help reconstruct the table objects.\n sp_binary_length_ (int): The length of the binary segment corresponding\n to the SurfacePointsTable data.\n ori_binary_length_ (int): The length of the binary segment corresponding\n to the OrientationsTable data.\n\n Returns:\n tuple[OrientationsTable, SurfacePointsTable]: A tuple containing two table\n objects: first the OrientationsTable, and second the SurfacePointsTable.\n \"\"\"\n sp_binary = binary_array[:sp_binary_length_]\n ori_binary = binary_array[sp_binary_length_:sp_binary_length_+ori_binary_length_]\n # Reconstruct arrays\n sp_data: np.ndarray = np.frombuffer(sp_binary, dtype=SurfacePointsTable.dt)\n ori_data: np.ndarray = np.frombuffer(ori_binary, dtype=OrientationsTable.dt)\n surface_points_table = SurfacePointsTable(data=sp_data, name_id_map=name_id_map)\n orientations_table = OrientationsTable(data=ori_data, name_id_map=name_id_map)\n return orientations_table, surface_points_table\n\n\ndef deserialize_grid(binary_array:bytes, custom_grid_length: int, topography_length: int) -> tuple[np.ndarray, np.ndarray]:\n \"\"\"\n Deserialize binary grid data into two numpy arrays.\n\n This function takes a binary array representing a grid and splits it into two separate\n numpy arrays: one for the custom grid and one for the topography. The binary array is\n segmented based on the provided lengths for the custom grid and topography.\n\n Args:\n binary_array: The binary data representing the combined custom grid and topography data.\n custom_grid_length: The length of the custom grid data segment in bytes.\n topography_length: The length of the topography data segment in bytes.\n\n Returns:\n A tuple where the first element is a numpy array representing the custom grid, and\n the second element is a numpy array representing the topography data.\n\n Raises:\n ValueError: If input lengths do not match the specified boundaries or binary data.\n \"\"\"\n\n \n\n custom_grid_binary = binary_array[:custom_grid_length]\n topography_binary = binary_array[custom_grid_length:custom_grid_length + topography_length]\n custom_grid = np.frombuffer(custom_grid_binary, dtype=np.float64)\n topography = np.frombuffer(topography_binary)\n \n \n return custom_grid, topography\n" }, { "path": "gempy/core/data/encoders/converters.py", "content": "from typing import Annotated\n\nfrom contextlib import contextmanager\n\nfrom contextvars import ContextVar\n\nimport numpy as np\nfrom pydantic import BeforeValidator\n\n\ndef convert_to_arrays(values, keys):\n for key in keys:\n if key in values and not isinstance(values[key], np.ndarray):\n values[key] = np.array(values[key])\n return values\n\n\ndef validate_numpy_array(v):\n return np.array(v) if v is not None else None\n\n\nshort_array_type = Annotated[np.ndarray, (BeforeValidator(lambda v: np.array(v) if v is not None else None))]\n\n\ndef instantiate_if_necessary(data: dict, key: str, type: type) -> None:\n \"\"\"\n Creates instances of the specified type for a dictionary key if the key exists and its\n current type does not match the specified type. This function modifies the dictionary\n in place by converting the value associated with the key into an instance of the given\n type.\n\n This is typically used when a dictionary contains data that needs to be represented as\n objects of a specific class type.\n\n Args:\n data (dict): The dictionary containing the key-value pair to inspect and possibly\n convert.\n key (str): The key in the dictionary whose value should be inspected and converted\n if necessary.\n type (type): The type to which the value of `key` should be converted, if it is not\n already an instance of the type.\n\n Returns:\n None\n \"\"\"\n if key in data and not isinstance(data[key], type):\n data[key] = type(**data[key])\n\nnumpy_array_short_validator = BeforeValidator(validate_numpy_array)\n\n# First, create a context variable\nloading_model_context = ContextVar('loading_model_context', default={})\n\n@contextmanager\ndef loading_model_from_binary(input_binary: bytes, grid_binary: bytes):\n token = loading_model_context.set({\n 'input_binary': input_binary,\n 'grid_binary': grid_binary\n })\n try:\n yield\n finally:\n loading_model_context.reset(token)\n\n" }, { "path": "gempy/core/data/encoders/json_geomodel_encoder.py", "content": "import numpy as np\n\n\ndef encode_numpy_array(array: np.ndarray):\n # Check length \n if array.size > 10:\n return []\n return array.tolist()\n" }, { "path": "gempy/core/data/enumerators.py", "content": "from enum import Enum, auto\n\n\nclass ExampleModel(Enum):\n TWO_AND_A_HALF_D = auto()\n HORIZONTAL_STRAT = auto()\n ANTICLINE = auto()\n ONE_FAULT = auto()\n COMBINATION = auto()\n ONE_FAULT_GRAVITY = auto()\n GRABEN = auto()\n GREENSTONE = auto()\n FAULT_RELATION = auto()\n" }, { "path": "gempy/core/data/gempy_engine_config.py", "content": "from dataclasses import dataclass\nfrom typing import Optional\n\nfrom gempy_engine import config\nfrom gempy_engine.config import AvailableBackends\n\n\n@dataclass\nclass GemPyEngineConfig:\n backend: AvailableBackends = config.DEFAULT_BACKEND # ? This can be grabbed from gempy.config file?\n use_gpu: bool = False\n dtype: Optional[str] = None #: The data type used in the engine. If None, the default data type of the backend is used.\n " }, { "path": "gempy/core/data/geo_model.py", "content": "import datetime\nimport numpy as np\nimport pprint\nimport warnings\nfrom dataclasses import dataclass\nfrom pydantic import BaseModel, ConfigDict, Field, PrivateAttr, computed_field, model_validator, ValidationError\nfrom pydantic.functional_validators import ModelWrapValidatorHandler\nfrom typing import Sequence, Optional, Union\n\nfrom gempy_engine.core.data import InterpolationOptions\nfrom gempy_engine.core.data import Solutions\nfrom gempy_engine.core.data.engine_grid import EngineGrid\nfrom gempy_engine.core.data.geophysics_input import GeophysicsInput\nfrom gempy_engine.core.data.input_data_descriptor import InputDataDescriptor\nfrom gempy_engine.core.data.interpolation_input import InterpolationInput\nfrom gempy_engine.core.data.raw_arrays_solution import RawArraysSolution\nfrom gempy_engine.core.data.transforms import Transform, GlobalAnisotropy\nfrom .encoders.converters import instantiate_if_necessary\nfrom .encoders.json_geomodel_encoder import encode_numpy_array\nfrom .grid import Grid\nfrom .orientations import OrientationsTable\nfrom .structural_frame import StructuralFrame\nfrom .surface_points import SurfacePointsTable\nfrom ...modules.data_manipulation import interpolation_input_from_structural_frame\n\n\n\"\"\"\nTODO:\n - [ ] StructuralFrame will all input points chunked on Elements. Here I will need a property to put all\n together to feed to InterpolationInput\n\n\"\"\"\n\n\n@dataclass\nclass GeoModelMeta:\n \"\"\"\n Container for metadata associated with a GeoModel.\n\n Attributes:\n name (str): Name of the geological model.\n creation_date (str): Date of creation of the model.\n last_modification_date (str): Last modification date of the model.\n owner (str): Owner of the geological model.\n \"\"\"\n\n name: str\n creation_date: str\n last_modification_date: str | None\n owner: str | None\n\n\nclass GeoModel(BaseModel):\n \"\"\"\n Class representing a geological model.\n\n \"\"\"\n\n meta: GeoModelMeta = Field(exclude=False) #: Meta-information about the geological model, like its name, creation and modification dates, and owner.\n structural_frame: StructuralFrame = Field(exclude=False) #: The structural information of the geological model.\n grid: Grid = Field(exclude=False, default=None) #: The general grid used in the geological model.\n\n # region GemPy engine data types\n _interpolation_options: InterpolationOptions #: The interpolation options provided by the user.\n\n geophysics_input: GeophysicsInput | None = Field(default=None, exclude=False) #: The geophysics input of the geological model.\n input_transform: Transform = Field(default=None, exclude=False) #: The transformation used in the geological model for input points.\n\n interpolation_grid: EngineGrid = Field(default=None, exclude=True) #: ptional grid used for interpolation. Can be seen as a cache field.\n _interpolationInput: InterpolationInput = PrivateAttr(default=None) #: Input data for interpolation. Fed by the structural frame and can be seen as a cache field.\n _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.\n\n # endregion\n _solutions: Solutions = PrivateAttr(init=False, default=None) #: The computed solutions of the geological model. \n _taped_interpolation_input: InterpolationInput | None = PrivateAttr(default=None)\n\n def __repr__(self):\n # TODO: Improve this\n return pprint.pformat(self.__dict__)\n\n # region Properties\n\n @computed_field(alias=\"_interpolation_options\")\n @property\n def interpolation_options(self) -> InterpolationOptions:\n self._infer_dense_grid_solution()\n self._interpolation_options.cache_model_name = self.meta.name\n return self._interpolation_options\n\n @interpolation_options.setter\n def interpolation_options(self, value):\n self._interpolation_options = value\n\n @property\n def solutions(self) -> Solutions:\n return self._solutions\n\n @solutions.setter\n def solutions(self, value: Solutions):\n # * This is set from the gempy engine\n\n self._solutions = value\n\n # * Set solutions per element\n for e, element in enumerate(self.structural_frame.structural_elements[:-1]): # * Ignore basement\n element.scalar_field_at_interface = value.scalar_field_at_surface_points[e]\n\n if self._solutions.dc_meshes is None:\n continue\n dc_mesh = self._solutions.dc_meshes[e]\n if dc_mesh is None:\n continue\n\n # TODO: These meshes are in the order of the scalar field \n world_coord_vertices = self.input_transform.apply_inverse(dc_mesh.vertices)\n world_coord_vertices = self.grid.transform.apply_inverse_with_cached_pivot(world_coord_vertices)\n\n element.vertices = world_coord_vertices\n element.edges = (dc_mesh.edges if dc_mesh is not None else None)\n\n # * Reordering the elements according to the scalar field\n for e, order_per_structural_group in enumerate(self._solutions._ordered_elements):\n elements = self.structural_frame.structural_groups[e].elements\n reordered_elements = [elements[i] for i in order_per_structural_group]\n self.structural_frame.structural_groups[e].elements = reordered_elements\n\n @property\n def surface_points_copy(self):\n \"\"\"This is a copy! Returns a SurfacePointsTable for all surface points across the structural elements\"\"\"\n surface_points_table = self.structural_frame.surface_points_copy\n return surface_points_table\n\n @property\n def surface_points_copy_transformed(self) -> SurfacePointsTable:\n og_sp = self.surface_points_copy\n\n og_sp.xyz_view = self.grid.transform.apply_with_cached_pivot(og_sp.xyz)\n og_sp.xyz_view = self.input_transform.apply(og_sp.xyz)\n return og_sp\n\n @property\n def surface_points(self):\n raise AttributeError(\"This property can only be set, not read. You can access the copy with `surface_points_copy` or\"\n \"the original on the individual structural elements.\")\n\n @surface_points.setter\n def surface_points(self, value):\n self.structural_frame.surface_points = value\n\n @property\n def orientations_copy(self) -> OrientationsTable:\n \"\"\"This is a copy! Returns a OrientationsTable for all orientations across the structural elements\"\"\"\n orientations_table = self.structural_frame.orientations_copy\n return orientations_table\n\n @property\n def orientations_copy_transformed(self) -> OrientationsTable:\n # ! This is not done\n og_or = self.orientations_copy\n total_transform: Transform = self.input_transform + self.grid.transform\n\n og_or.xyz_view = self.grid.transform.apply_with_cached_pivot(og_or.xyz)\n og_or.xyz_view = self.input_transform.apply(og_or.xyz)\n\n og_or.grads_view = total_transform.transform_gradient(og_or.grads)\n return og_or\n\n @property\n def regular_grid_coordinates(self) -> np.ndarray:\n return self.grid.regular_grid.get_values_vtk_format(orthogonal=False)\n\n @property\n def regular_grid_coordinates_transformed(self) -> np.ndarray:\n values_transformed = self.grid.regular_grid.get_values_vtk_format(orthogonal=True)\n values_transformed = self.input_transform.apply(values_transformed)\n return values_transformed\n\n @property\n def orientations(self) -> OrientationsTable:\n raise AttributeError(\"This property can only be set, not read. You can access the copy with `orientations_copy` or\"\n \"the original on the individual structural elements.\")\n\n @orientations.setter\n def orientations(self, value):\n self.structural_frame.orientations = value\n\n @property\n def project_bounds(self) -> np.ndarray:\n return self.grid.bounding_box\n\n @property\n def extent_transformed_transformed_by_input(self) -> np.ndarray:\n transformed = self.input_transform.apply(self.project_bounds) # ! grid already has the grid transform applied\n new_extents = np.array([transformed[:, 0].min(), transformed[:, 0].max(),\n transformed[:, 1].min(), transformed[:, 1].max(),\n transformed[:, 2].min(), transformed[:, 2].max()])\n return new_extents\n\n @property\n def interpolation_input_copy(self):\n warnings.warn(\"This property is deprecated. Use directly \"\n \"`interpolation_input_from_structural_frame` instead.\", DeprecationWarning)\n\n if self.structural_frame.is_dirty is False:\n return self._interpolationInput\n\n self._interpolationInput = interpolation_input_from_structural_frame(\n geo_model=self\n )\n\n return self._interpolationInput\n\n @property\n def input_data_descriptor(self) -> InputDataDescriptor:\n # TODO: This should have the exact same dirty logic as interpolation_input\n return self.structural_frame.input_data_descriptor\n \n @property\n def taped_interpolation_input(self) -> InterpolationInput:\n return self._taped_interpolation_input\n \n @taped_interpolation_input.setter\n def taped_interpolation_input(self, value: InterpolationInput):\n self._taped_interpolation_input = value\n\n # endregion\n # region Constructors\n @classmethod\n def from_args(cls, name: str, structural_frame: StructuralFrame, grid: Grid, interpolation_options: InterpolationOptions):\n # TODO: Fill the arguments properly\n meta = GeoModelMeta(\n name=name,\n creation_date=datetime.datetime.now().isoformat(),\n last_modification_date=None,\n owner=None\n )\n\n structural_frame = structural_frame # ? This could be Optional\n\n grid = grid\n _interpolation_options = interpolation_options\n input_transform = Transform.from_input_points(\n surface_points=structural_frame.surface_points_copy,\n orientations=structural_frame.orientations_copy\n )\n\n model = GeoModel(\n meta=meta,\n structural_frame=structural_frame,\n grid=grid,\n input_transform=input_transform,\n _interpolation_options=_interpolation_options\n )\n\n return model\n\n # endregion\n\n # region Methods\n def update_transform(self, auto_anisotropy: GlobalAnisotropy = GlobalAnisotropy.NONE, anisotropy_limit: Optional[np.ndarray] = None):\n \"\"\"Update the transformation of the geological model.\n\n This function updates the transformation of the geological model using the provided surface points and orientations.\n It also applies anisotropy based on the specified type and limit.\n\n Args:\n auto_anisotropy (GlobalAnisotropy): The type of anisotropy to apply. Defaults to GlobalAnisotropy.NONE.\n anisotropy_limit (Optional[np.ndarray]): Anisotropy limit values. If None, no limit is applied.\n\n \"\"\"\n\n self.input_transform = Transform.from_input_points(\n surface_points=self.surface_points_copy,\n orientations=self.orientations_copy\n )\n\n self.input_transform.apply_anisotropy(anisotropy_type=auto_anisotropy, anisotropy_limit=anisotropy_limit)\n\n def add_surface_points(self, X: Sequence[float], Y: Sequence[float], Z: Sequence[float],\n surface: Sequence[str], nugget: Optional[Sequence[float]] = None) -> None:\n raise NotImplementedError(\"This method is deprecated. Use `gp.add_surface_points` instead\")\n\n # endregion\n\n # region Pydantic serialization\n\n model_config = ConfigDict(\n arbitrary_types_allowed=True,\n use_enum_values=False,\n json_encoders={\n np.ndarray: encode_numpy_array,\n }\n )\n\n @model_validator(mode='wrap')\n @classmethod\n def deserialize_properties(cls, data: Union[\"GeoModel\", dict], constructor: ModelWrapValidatorHandler[\"GeoModel\"]) -> \"GeoModel\":\n match data:\n case GeoModel(): \n return data\n case dict(): # \n instance: GeoModel = constructor(data)\n instantiate_if_necessary(\n data=data,\n key=\"_interpolation_options\",\n type=InterpolationOptions\n )\n instance._interpolation_options = data.get(\"_interpolation_options\")\n \n # * Reset geophysics if necessary\n centered_grid = instance.grid.centered_grid\n if centered_grid is not None and instance.geophysics_input is not None:\n from gempy_engine.modules.geophysics.gravity_gradient import calculate_gravity_gradient\n instance.geophysics_input.tz = calculate_gravity_gradient(centered_grid)\n \n return instance\n case _:\n raise ValidationError\n\n # endregion\n\n def _infer_dense_grid_solution(self):\n octrees_set: bool = Grid.GridTypes.OCTREE in self.grid.active_grids\n dense_set: bool = Grid.GridTypes.DENSE in self.grid.active_grids\n # Create a tuple representing the conditions\n match (octrees_set, dense_set):\n case (True, False):\n self._interpolation_options.block_solutions_type = RawArraysSolution.BlockSolutionType.OCTREE\n case (True, True):\n warnings.warn(\"Both octree levels and resolution are set. The default grid for the `raw_array_solution`\"\n \"and plots will be the dense regular grid. To use octrees instead, set resolution to None in the \"\n \"regular grid.\")\n self._interpolation_options.block_solutions_type = RawArraysSolution.BlockSolutionType.DENSE_GRID\n case (False, True):\n self._interpolation_options.block_solutions_type = RawArraysSolution.BlockSolutionType.DENSE_GRID\n case (False, False):\n self._interpolation_options.block_solutions_type = RawArraysSolution.BlockSolutionType.NONE\n" }, { "path": "gempy/core/data/grid.py", "content": "import dataclasses\nimport enum\nimport numpy as np\nfrom pydantic import Field, model_validator, computed_field, ValidationError\nfrom pydantic.functional_validators import ModelWrapValidatorHandler\nfrom typing import Optional, Annotated, Union\n\nfrom gempy_engine.core.data.centered_grid import CenteredGrid\nfrom gempy_engine.core.data.options import EvaluationOptions\nfrom gempy_engine.core.data.transforms import Transform\nfrom .encoders.binary_encoder import deserialize_grid\nfrom .encoders.converters import loading_model_context\nfrom .grid_modules import RegularGrid, CustomGrid, Sections\nfrom .grid_modules.topography import Topography\n\n\n@dataclasses.dataclass(init=True)\nclass Grid:\n class GridTypes(enum.Flag):\n OCTREE = 2 ** 0\n DENSE = 2 ** 1\n CUSTOM = 2 ** 2\n TOPOGRAPHY = 2 ** 3\n SECTIONS = 2 ** 4\n CENTERED = 2 ** 5\n NONE = 2 ** 10\n\n # ? What should we do with the extent?\n\n values: Annotated[np.ndarray, Field(exclude=True)] = dataclasses.field(default_factory=lambda: np.empty((0, 3)))\n length: Annotated[np.ndarray, Field(exclude=True)] = dataclasses.field(default_factory=lambda: np.empty(0))\n\n _octree_grid: Optional[RegularGrid] = None\n _dense_grid: Optional[RegularGrid] = None\n _custom_grid: Optional[CustomGrid] = None\n _topography: Optional[Topography] = None\n _sections: Optional[Sections] = None\n _centered_grid: Optional[CenteredGrid] = None\n\n _active_grids = GridTypes.NONE\n _transform: Optional[Transform] = None\n\n _octree_levels: int = -1\n\n def __init__(self, extent=None, resolution=None):\n\n self.values = np.empty((0, 3))\n self.length = np.empty(0)\n\n # Init basic grid empty\n if extent is not None and resolution is not None:\n self.dense_grid = RegularGrid(extent, resolution)\n\n def __setattr__(self, name, value):\n if name == 'active_grids':\n self._active_grids = value\n self._update_values()\n else:\n super().__setattr__(name, value)\n \n @model_validator(mode='wrap')\n @classmethod\n def deserialize_properties(cls, data: Union[\"Grid\", dict], constructor: ModelWrapValidatorHandler[\"Grid\"]) -> \"Grid\":\n try:\n match data:\n case Grid():\n return data\n case dict():\n grid: Grid = constructor(data)\n grid._active_grids = Grid.GridTypes(data[\"active_grids\"])\n # TODO: Digest binary data\n\n metadata = data.get('binary_meta_data', {})\n context = loading_model_context.get()\n\n if 'grid_binary' in context:\n custom_grid_vals, topography_vals = deserialize_grid(\n binary_array=context['grid_binary'],\n custom_grid_length=metadata[\"custom_grid_binary_length\"],\n topography_length=metadata[\"topography_binary_length\"]\n )\n\n if grid.custom_grid is not None:\n grid.custom_grid.values = custom_grid_vals.reshape(-1, 3)\n\n if grid.topography is not None:\n grid.topography.set_values2d(values=topography_vals)\n\n if grid.octree_grid is not None:\n # * Update the octree grid values. In the future we should also serialize them.\n grid.octree_grid.set_regular_grid(\n extent=grid.octree_grid.extent,\n resolution=grid.octree_grid.resolution,\n transform=grid.octree_grid.transform\n )\n\n grid._update_values()\n return grid\n case _:\n raise ValidationError\n except ValidationError:\n raise\n\n @property\n def grid_binary(self):\n custom_grid_bytes = self._custom_grid.values.astype(\"float64\").tobytes() if self._custom_grid else b''\n topography_bytes = self._topography.values.astype(\"float64\").tobytes() if self._topography else b''\n return custom_grid_bytes + topography_bytes\n\n @computed_field\n def binary_meta_data(self) -> dict:\n return {\n 'custom_grid_binary_length': len(self._custom_grid.values.astype(\"float64\").tobytes()) if self._custom_grid else 0,\n 'topography_binary_length' : len(self._topography.values.astype(\"float64\").tobytes()) if self._topography else 0,\n }\n\n @computed_field(alias=\"active_grids\")\n @property\n def active_grids(self) -> GridTypes:\n return self._active_grids\n\n @active_grids.setter\n def active_grids(self, value: GridTypes):\n self._active_grids = value\n self._update_values()\n\n @classmethod\n def init_octree_grid(cls, extent, octree_levels, legacy: bool = False):\n grid = cls()\n\n if legacy:\n base_resolution = (np.array([2, 2, 2]))\n else:\n lengths = np.array([\n extent[1] - extent[0], # x\n extent[3] - extent[2], # y\n extent[5] - extent[4] # z\n ])\n\n min_length = np.min(lengths)\n base_resolution = np.round(lengths / min_length).astype(int) * 2\n\n grid._octree_grid = RegularGrid.octree_init(\n extent=extent,\n octree_levels=octree_levels,\n base_resolution=base_resolution\n )\n\n grid.active_grids |= grid.GridTypes.OCTREE\n grid._update_values()\n return grid\n\n @classmethod\n def init_dense_grid(cls, extent, resolution):\n return cls(extent, resolution)\n\n def __str__(self):\n active_grid_types_str = [g_type for g_type in self.GridTypes if self.active_grids & g_type]\n\n grid_summary = [f\"{g_type} (active: {getattr(self, g_type + '_grid_active')}): {len(getattr(self, g_type + '_grid').values)} points\"\n for g_type in active_grid_types_str]\n grid_summary_str = \"\\n\".join(grid_summary)\n return f\"Grid Object:\\n{grid_summary_str}\"\n\n @property\n def transform(self) -> Transform:\n if self.dense_grid is not None:\n return self.dense_grid.transform\n elif self.octree_grid is not None:\n return self.octree_grid.transform\n else:\n return Transform.init_neutral()\n\n @transform.setter\n def transform(self, value: Transform):\n self._transform = value\n\n @property\n def extent(self):\n if self.dense_grid is not None:\n return self.dense_grid.extent\n elif self.octree_grid is not None:\n return self.octree_grid.extent\n else:\n raise AttributeError('Extent is not defined')\n\n @property\n def corner_min(self):\n return self.extent[::2]\n\n @property\n def corner_max(self):\n return self.extent[1::2]\n\n @property\n def bounding_box(self):\n extents = self.extent\n # Define 3D points of the bounding box corners based on extents\n bounding_box_points = np.array([[extents[0], extents[2], extents[4]], # min x, min y, min z\n [extents[0], extents[2], extents[5]], # min x, min y, max z\n [extents[0], extents[3], extents[4]], # min x, max y, min z\n [extents[0], extents[3], extents[5]], # min x, max y, max z\n [extents[1], extents[2], extents[4]], # max x, min y, min z\n [extents[1], extents[2], extents[5]], # max x, min y, max z\n [extents[1], extents[3], extents[4]], # max x, max y, min z\n [extents[1], extents[3], extents[5]]]) # max x, max y, max z\n return bounding_box_points\n\n @property\n def dense_grid(self) -> RegularGrid:\n return self._dense_grid\n\n @dense_grid.setter\n def dense_grid(self, value):\n self._dense_grid = value\n self.active_grids |= self.GridTypes.DENSE\n self._update_values()\n\n @property\n def octree_grid(self):\n return self._octree_grid\n\n @octree_grid.setter\n def octree_grid(self, value):\n raise AttributeError('Octree grid is not allowed to be set directly. Use init_octree_grid instead')\n\n def set_octree_grid(self, regular_grid: RegularGrid):\n self._octree_grid = regular_grid\n self.active_grids |= self.GridTypes.OCTREE\n self._update_values()\n\n def set_octree_grid_by_levels(self, octree_levels: int, evaluation_options: EvaluationOptions, extent: Optional[np.ndarray] = None):\n if extent is None:\n extent = self.extent\n\n self._octree_grid = RegularGrid(\n extent=extent,\n resolution=np.array([2 ** octree_levels] * 3),\n )\n evaluation_options.number_octree_levels = octree_levels\n self.active_grids |= self.GridTypes.OCTREE\n self._update_values()\n\n @property\n def octree_levels(self):\n return self._octree_levels\n\n @octree_levels.setter\n def octree_levels(self, value):\n raise AttributeError('Octree levels are not allowed to be set directly. Use set_octree_grid instead')\n\n @property\n def custom_grid(self):\n return self._custom_grid\n\n @custom_grid.setter\n def custom_grid(self, value):\n self._custom_grid = value\n self.active_grids |= self.GridTypes.CUSTOM\n self._update_values()\n\n @property\n def topography(self):\n return self._topography\n\n @topography.setter\n def topography(self, value):\n self._topography = value\n self.active_grids |= self.GridTypes.TOPOGRAPHY\n self._update_values()\n\n @property\n def sections(self):\n return self._sections\n\n @sections.setter\n def sections(self, value):\n self._sections = value\n self.active_grids |= self.GridTypes.SECTIONS\n self._update_values()\n\n @property\n def centered_grid(self):\n return self._centered_grid\n\n @centered_grid.setter\n def centered_grid(self, value):\n self._centered_grid = value\n self.active_grids |= self.GridTypes.CENTERED\n self._update_values()\n\n @property\n def regular_grid(self):\n dense_grid_exists_and_active = self.dense_grid is not None and self.GridTypes.DENSE in self.active_grids\n octree_grid_exists_and_active = self.octree_grid is not None and self.GridTypes.OCTREE in self.active_grids\n\n if dense_grid_exists_and_active and octree_grid_exists_and_active:\n raise AttributeError('Both dense_grid and octree_grid are active. This is not possible.')\n elif dense_grid_exists_and_active:\n return self.dense_grid\n elif octree_grid_exists_and_active:\n return self.octree_grid\n else:\n return None\n\n # noinspection t\n def _update_values(self):\n values = []\n\n if self.GridTypes.OCTREE in self.active_grids:\n if self.octree_grid is None: raise AttributeError('Octree grid is active but not defined')\n values.append(self.octree_grid.values)\n if self.GridTypes.DENSE in self.active_grids:\n if self.dense_grid is None: raise AttributeError('Dense grid is active but not defined')\n values.append(self.dense_grid.values)\n if self.GridTypes.CUSTOM in self.active_grids:\n if self.custom_grid is None: raise AttributeError('Custom grid is active but not defined')\n values.append(self.custom_grid.values)\n if self.GridTypes.TOPOGRAPHY in self.active_grids:\n if self.topography is None: raise AttributeError('Topography grid is active but not defined')\n values.append(self.topography.values)\n if self.GridTypes.SECTIONS in self.active_grids:\n if self.sections is None: raise AttributeError('Sections grid is active but not defined')\n values.append(self.sections.values)\n if self.GridTypes.CENTERED in self.active_grids:\n if self.centered_grid is None: raise AttributeError('Centered grid is active but not defined')\n values.append(self.centered_grid.values)\n\n # make sure values is not empty\n if len(values) == 0:\n return self.values\n\n self.values = np.concatenate(values)\n\n return self.values\n\n def get_section_args(self, section_name: str):\n # TODO: This method should be part of the sections\n # assert type(section_name) is str, 'Only one section type can be retrieved'\n l0, l1 = self.get_grid_args('sections')\n where = np.where(self.sections.names == section_name)[0][0]\n return l0 + self.sections.length[where], l0 + self.sections.length[where + 1]\n" }, { "path": "gempy/core/data/grid_modules/__init__.py", "content": "from .regular_grid import RegularGrid\nfrom .custom_grid import CustomGrid\nfrom .sections_grid import Sections\nfrom .topography import Topography\n" }, { "path": "gempy/core/data/grid_modules/custom_grid.py", "content": "import dataclasses\nimport numpy as np\nfrom pydantic import Field\n\n\n@dataclasses.dataclass\nclass CustomGrid:\n \"\"\"Object that contains arbitrary XYZ coordinates.\n\n Args:\n xyx_coords (numpy.ndarray like): XYZ (in columns) of the desired coordinates\n\n Attributes:\n values (np.ndarray): XYZ coordinates\n \"\"\"\n\n values: np.ndarray = Field(\n exclude=True, \n default_factory=lambda: np.zeros((0, 3)),\n repr=False\n )\n \n \n def __post_init__(self):\n custom_grid = np.atleast_2d(self.values)\n assert type(custom_grid) is np.ndarray and custom_grid.shape[1] == 3, \\\n 'The shape of new grid must be (n,3) where n is the number of' \\\n ' points of the grid'\n\n \n @property\n def length(self):\n return self.values.shape[0]\n" }, { "path": "gempy/core/data/grid_modules/diamond_square.py", "content": "\"\"\"Implementation of Diamond-Square algorithm\n\nThis algorithm is often used for random topography generation, following Fournier et al., 1982\nsee https://en.wikipedia.org/wiki/Diamond-square_algorithm.\n\nFournier, Alain; Fussell, Don; Carpenter, Loren (June 1982). \"Computer rendering of stochastic models\".\nCommunications of the ACM. 25 (6): 371–384.\n\nHere the description from the wikipedia page:\n\n+++ begin Wikipedia +++\n\nThe diamond-square algorithm begins with a 2D square array of width and\nheight 2n + 1. The four corner points of the array must first be set to initial values. The diamond and square steps\nare then performed alternately until all array values have been set.\n\nThe diamond step: For each square in the array, set the midpoint of that square to be the average of the four corner\npoints plus a random value.\n\nThe square step: For each diamond in the array, set the midpoint of that diamond to be the average of the four corner\npoints plus a random value.\n\nAt each iteration, the magnitude of the random value should be reduced.\n\nDuring the square steps, points located on the edges of the array will have only three adjacent values set rather\nthan four. There are a number of ways to handle this complication - the simplest being to take the average of just\nthe three adjacent values. Another option is to 'wrap around', taking the fourth value from the other side of the\narray. When used with consistent initial corner values this method also allows generated fractals to be stitched\ntogether without discontinuities.\n\n+++ end Wikipedia +++\n\nThe implementations (in Python) I could find online were either difficult to understand or had many case\nselections, especially at edges. Here is a fully vectorized implementation, using padding at edges, to avoid\nall these case selections for a more straight-forward implementation (I hope).\n\nThis implementation is also adjusted to work on non-square start matrices, and on reduced hierarchies with\nmore initial (internal) points.\n\nCreated on 10.04.2020\n\n@author: Florian Wellmann\n\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nclass DiaomondSquare(object):\n\n def __init__(self, size: tuple = (16, 16), roughness: float = 0.5, z_min: float = 0, z_max:\n float = 1, r_type: chr = 'default', **kwds):\n \"\"\"Implementation of vectorized Diaomnd-Square algorithm for random topography generation\n\n Args:\n size (int, int): shape of grid to interpolate; note: the standard diamond-square algorithm\n operates on a square grid with side length 2**n+1. This implementation is adjusted to non-square\n grids with (2**n+1, 2**m+1). If the input size (int, int) is not matching to the ideal dimension,\n the next bigger size is taken and the grid finally cut (lower left corner is kept);\n roughness: roughness parameter, [0,1]: 0: deterministic interpolation, 1: very rough and bumpy (in standard\n randomisation method - other options available in self.random_func())\n z_min: minimum height of surface\n z_max: maximum height of surface\n seed: seed for random function to enable reproducibility and testing\n r_type: 'basic', 'level_scale' (See below for details)\n \n Options for the randomization function (self.r_type) are:\n - 'default': standard reduction\n - 'level_scale' : uniform distribution between z_min, z_max, scaled by (level + 1)\n\n \"\"\"\n self.size = size\n # Create mesh with optimal size (2**n+1, 2**m+1)\n # If the input size `self.size` does not match to these dimensions, then the next larger suitable\n # size is chosen.\n self.n = np.ceil(np.log2(self.size[0] - 1)).astype('int8')\n self.m = np.ceil(np.log2(self.size[1] - 1)).astype('int8')\n self.grid = np.zeros((2 ** self.n + 1, 2 ** self.m + 1))\n self.roughness = roughness\n self.z_min = z_min\n self.z_max = z_max\n if 'seed' in kwds:\n np.random.seed(kwds['seed'])\n self.base_rand_range = 1\n self.r_type = r_type\n\n def random_func(self, i, level_shape):\n \"\"\"Define random function to adjust range for subsequent steps\n\n The random function type is defined on class level (self.r_type)\n \"\"\"\n\n if self.r_type == 'default':\n if self.roughness > 0:\n rand_range = self.base_rand_range * 2 ** (- (1 - self.roughness) * i)\n return np.random.uniform(-rand_range, rand_range, level_shape)\n else:\n return 0.\n\n if self.r_type == 'long_range':\n if self.roughness > 0:\n rand_range = self.base_rand_range * 16 ** (- (1 - self.roughness) * i)\n return np.random.uniform(-rand_range, rand_range, level_shape)\n else:\n return 0.\n\n elif self.r_type == 'level_scale':\n if self.roughness > 0:\n return np.random.uniform(self.z_min, self.z_max,\n level_shape) / (1 + i)\n else:\n return 0.\n\n elif self.r_type == 'deterministic':\n # No randomization at all - deterministic interpolation result\n return 0.\n\n else:\n raise NotImplementedError(\"Random function type %s not implemented\" % self.r_type)\n\n def random_initialization(self, level='highest', verbose: bool=False):\n \"\"\"Initialize cells on speicifc hierarchy with random values\n\n Args:\n level = 'hightest', int : hierarchy level for interpolation (default: highest)\n verbose: verbose output (default: False)\n\n With highest hierarchy, we refer here to the largest diamond-square step, i.e. the corner points\n for a square grid; Or, more formally: the diamond points for `min(self.n, self.m)`\n \"\"\"\n if level == 'highest':\n m_pow_max = min(self.n, self.m)\n else:\n m_pow_max = level\n\n step_size = int(2 ** m_pow_max)\n if verbose:\n print(\"Initialize on step size %d\" % step_size)\n\n level_shape = self.grid[::step_size, ::step_size].shape\n\n self.grid[::step_size, ::step_size] = np.random.uniform(0, 1, level_shape) # self.random_func(0, level_shape)\n\n def interpolate(self, level='highest'):\n \"\"\"Perform diamond-square interpolation\n\n Args:\n level = 'hightest', int : hierarchy level for interpolation (default: highest)\n\n This step follows the conventional procedure:\n Iterate over hierarchies and repeat:\n 2) Perform Diamond interpolation step\n 3) Perform Square interpolation step\n 4) Reduce roughness factor\n \"\"\"\n if level == 'highest':\n # determine highest hierarchy level (determined by shorter rectangle side)\n m_pow_max = min(self.n, self.m)\n else:\n m_pow_max = level\n\n for i, m_pow in enumerate(np.arange(m_pow_max)[::-1]):\n self.perform_diamond_step(i, m_pow)\n self.perform_square_step(i, m_pow)\n\n def reset_grid(self):\n \"\"\"Reset grid back to zero values\"\"\"\n self.grid[:, :] = 0\n\n def perform_diamond_step(self, i: int, m_pow: int):\n \"\"\"Perform one diamond interpolation step on hierarchy m_pow\n\n Note: for more details on the vectorized selection, see self.get_selection_diamond()\n \"\"\"\n step_size = int(2 ** m_pow)\n\n # Diamond step\n # ----------------\n\n # get shape of this step\n level_shape = self.grid[step_size::2 * step_size, step_size::2 * step_size].shape\n\n self.grid[step_size::2 * step_size, step_size::2 * step_size] = \\\n (self.grid[:-2 * step_size:2 * step_size, :-2 * step_size:2 * step_size] +\n self.grid[:-2 * step_size:2 * step_size, 2 * step_size::2 * step_size] +\n self.grid[2 * step_size::2 * step_size, :-2 * step_size:2 * step_size] +\n self.grid[2 * step_size::2 * step_size, 2 * step_size::2 * step_size]) / \\\n 4. + \\\n self.random_func(i, level_shape)\n\n # np.random.uniform(-rand_range, rand_range, level_shape)\n\n # self.d * 2**()\n # self.roughness ** i * (np.random.random(step_shape) - 0.5)\n\n # (np.random.random(step_shape) - 0.5) * i * self.roughness\n\n def perform_square_step(self, i: int, m_pow: int):\n \"\"\"Perform one square interpolation step on hierarchy m_pow\n\n Note: for more details on the vectorized selection, see self.get_selection_square()\n \"\"\"\n step_size = int(2 ** m_pow)\n\n # pad cells with zero value\n z_pad = np.pad(self.grid, step_size, mode='constant')\n\n # also create a grid for division to divide only by 3 on borders\n grid_div = np.ones_like(self.grid[1:-1, 1:-1]) * 4.\n grid_div = np.pad(grid_div, step_size + 1, mode='constant', constant_values=3.)\n\n # Checkerboard odd\n # ----------------\n\n # get shape of this step\n level_shape = grid_div[step_size::2 * step_size, 2 * step_size:-2 * step_size:2 * step_size].shape\n\n z_pad[step_size::2 * step_size, 2 * step_size:-2 * step_size:2 * step_size] = \\\n (z_pad[step_size::2 * step_size, step_size:-2 * step_size:2 * step_size] +\n z_pad[step_size::2 * step_size, 3 * step_size:-step_size:2 * step_size] +\n z_pad[:-step_size:2 * step_size, 2 * step_size:-2 * step_size:2 * step_size] +\n z_pad[2 * step_size::2 * step_size, 2 * step_size:-2 * step_size:2 * step_size]) / \\\n grid_div[step_size::2 * step_size, 2 * step_size:-2 * step_size:2 * step_size] + \\\n self.random_func(i, level_shape)\n\n # np.random.uniform(-rand_range, rand_range, level_shape)\n\n # self.roughness ** i * (np.random.random(step_shape) - 0.5)\n\n # (np.random.random(step_shape) - 0.5) * i * self.roughness\n\n # Checkerboard even\n # -----------------\n\n # get shape of this step\n level_shape = z_pad[2 * step_size:-2 * step_size:2 * step_size, step_size:-step_size:2 * step_size].shape\n\n # check-even, values to interpolate:\n z_pad[2 * step_size:-2 * step_size:2 * step_size, step_size:-step_size:2 * step_size] = \\\n (z_pad[2 * step_size:-2 * step_size:2 * step_size, :-2 * step_size:2 * step_size] +\n z_pad[2 * step_size:-2 * step_size:2 * step_size, 2 * step_size::2 * step_size] +\n z_pad[step_size:-2 * step_size:2 * step_size, step_size:-step_size:2 * step_size] +\n z_pad[3 * step_size::2 * step_size, step_size:-step_size:2 * step_size]) / \\\n grid_div[2 * step_size:-2 * step_size:2 * step_size, step_size:-step_size:2 * step_size] + \\\n self.random_func(i, level_shape)\n\n # np.random.uniform(-rand_range, rand_range, level_shape)\n\n # self.roughness ** i * (np.random.random(step_shape) - 0.5)\n\n # (np.random.random(step_shape) - 0.5) * i * self.roughness\n\n # assign results back to self.grid\n self.grid = z_pad[step_size:-step_size, step_size:-step_size]\n\n def get_selection_diamond(self, m_pow: int):\n \"\"\"get selected points for diamond step on grid z on hierarchy m\n\n This method is mostly implemented for testing and visualization purposes.\n \"\"\"\n\n step_size = int(2 ** m_pow)\n\n z = np.zeros_like(self.grid, dtype='int8')\n\n # points to interpolate\n z[step_size::2 * step_size, step_size::2 * step_size] = 1\n\n # top left\n z[:-2 * step_size:2 * step_size, :-2 * step_size:2 * step_size] = 2\n\n # top right\n z[:-2 * step_size:2 * step_size, 2 * step_size::2 * step_size] = 2\n\n # bottom left\n z[2 * step_size::2 * step_size, :-2 * step_size:2 * step_size] = 2\n\n # bottom right\n z[2 * step_size::2 * step_size, 2 * step_size::2 * step_size] = 2\n\n return z\n\n def get_selection_square(self, m_pow: int):\n \"\"\"Plot selected points for square step on grid z on hierarchy m\n\n This method is mostly implemented for testing and visualization purposes.\n \"\"\"\n z = np.zeros_like(self.grid, dtype='int8')\n step_size = int(2 ** m_pow)\n\n # pad cells with zero value\n z_pad = np.pad(z, step_size, mode='constant')\n\n # Checkerboard odd\n # ----------------\n\n # check-odd, values to interpolate:\n z_pad[step_size::2 * step_size, 2 * step_size:-2 * step_size:2 * step_size] = 1\n\n # check-odd, left\n z_pad[step_size::2 * step_size, step_size:-2 * step_size:2 * step_size] = 2\n\n # check-odd, right\n z_pad[step_size::2 * step_size, 3 * step_size:-step_size:2 * step_size] = 2\n\n # check-odd, top\n z_pad[:-step_size:2 * step_size, 2 * step_size:-2 * step_size:2 * step_size] = 2\n\n # check-odd, bottom\n z_pad[2 * step_size::2 * step_size, 2 * step_size:-2 * step_size:2 * step_size] = 2\n\n # Checkerboard even\n # -----------------\n\n # check-even, values to interpolate:\n z_pad[2 * step_size:-2 * step_size:2 * step_size, step_size:-step_size:2 * step_size] = 1\n\n # check-even, left:\n z_pad[2 * step_size:-2 * step_size:2 * step_size, :-2 * step_size:2 * step_size] = 2\n\n # check-even, right:\n z_pad[2 * step_size:-2 * step_size:2 * step_size, 2 * step_size::2 * step_size] = 2\n\n # check-even, top:\n z_pad[step_size:-2 * step_size:2 * step_size, step_size:-step_size:2 * step_size] = 2\n\n # check-even, bottom:\n z_pad[3 * step_size::2 * step_size, step_size:-step_size:2 * step_size] = 2\n\n return z_pad\n\n def plot_diamond_and_square(self, pad=False):\n \"\"\"Plot selected points for diamond and square step for all hierarchies side by side\"\"\"\n\n m_pow_max = min(self.n, self.m)\n\n shape_ratio = self.n / self.m\n\n f, axes = plt.subplots(2, m_pow_max, figsize=(12, 12 * shape_ratio / m_pow_max * 2))\n\n for i, m_pow in enumerate(np.arange(m_pow_max)[::-1]):\n m = 2 ** m_pow\n # z_zero = np.zeros_like(self.grid)\n z_diamond = self.get_selection_diamond(m_pow)\n z_square = self.get_selection_square(m_pow)\n if pad:\n z_pad = np.pad(z_diamond, m, mode='constant')\n axes[0, i].imshow(z_pad, cmap='viridis', vmin=0, vmax=2)\n axes[1, i].imshow(z_square, cmap='viridis', vmin=0, vmax=2)\n else:\n axes[0, i].imshow(z_diamond, cmap='viridis', vmin=0, vmax=2)\n axes[1, i].imshow(z_square[m:-m, m:-m], cmap='viridis', vmin=0, vmax=2)\n" }, { "path": "gempy/core/data/grid_modules/regular_grid.py", "content": "import dataclasses\nfrom pydantic import model_validator, Field\n\nfrom typing import Optional, Sequence, Annotated\n\nimport numpy as np\n\nfrom ..encoders.converters import numpy_array_short_validator\nfrom .... import optional_dependencies\nfrom gempy_engine.core.data.transforms import Transform\n\n\n@dataclasses.dataclass\nclass RegularGrid:\n \"\"\"\n Class with the methods and properties to manage 3D regular grids where the model will be interpolated.\n\n \"\"\"\n resolution: Annotated[np.ndarray, numpy_array_short_validator] = dataclasses.field(default_factory=lambda: np.ones((0, 3), dtype='int64'))\n 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\n values: Annotated[np.ndarray, Field(exclude=True)] = dataclasses.field(default_factory=lambda: np.zeros((0, 3)))\n mask_topo: Annotated[np.ndarray, Field(exclude=True)] = dataclasses.field(default_factory=lambda: np.zeros((0, 3), dtype=bool))\n _transform: Transform | None = None #: If a transform exists, it will be applied to the grid\n _base_resolution: Annotated[np.ndarray, numpy_array_short_validator] = dataclasses.field(default_factory=lambda: np.array([2,2,2], dtype='int64'))\n\n def __init__(self, extent: np.ndarray, resolution: np.ndarray, transform: Optional[Transform] = None):\n self.resolution = np.ones((0, 3), dtype='int64')\n self.extent = np.zeros(6, dtype='float64')\n self.values = np.zeros((0, 3))\n self.mask_topo = np.zeros((0, 3), dtype=bool)\n self._base_resolution = np.array([2,2,2])\n\n self.set_regular_grid(extent, resolution, transform)\n\n @classmethod\n def octree_init(cls, extent: np.ndarray, octree_levels: int, base_resolution: np.ndarray, transform: Optional[Transform] = None):\n grid = cls(\n extent=extent,\n resolution=base_resolution * 2 ** (octree_levels - 1),\n transform=transform\n )\n grid._base_resolution = base_resolution\n return grid\n\n @property\n def base_resolution(self):\n return self._base_resolution\n\n @base_resolution.setter\n def base_resolution(self, value):\n self._base_resolution = value\n\n @model_validator(mode=\"after\")\n def _validate_regular_grid(self):\n self._create_regular_grid_3d()\n return self\n\n def _create_regular_grid_3d(self):\n coords = self.x_coord, self.y_coord, self.z_coord\n\n g = np.meshgrid(*coords, indexing=\"ij\")\n values = np.vstack(tuple(map(np.ravel, g))).T.astype(\"float64\")\n\n # Transform the values\n if self.transform is not None:\n self.values = self.transform.apply_inverse_with_pivot(\n points=values,\n pivot=np.array([self.extent[0], self.extent[2], self.extent[4]])\n )\n else:\n self.values = values\n\n def set_regular_grid(self, extent: Sequence[float], resolution: Sequence[int], transform: Optional[Transform] = None):\n \"\"\"\n Set a regular grid into the values parameters for further computations\n Args:\n extent (list, np.ndarry): [x_min, x_max, y_min, y_max, z_min, z_max]\n resolution (list, np.ndarray): [nx, ny, nz]\n \"\"\"\n # * Check extent and resolution are not the same\n extent_equal = np.array_equal(extent, self.extent)\n resolution_equal = np.array_equal(resolution, self.resolution)\n\n if extent_equal and resolution_equal:\n return self.values\n\n self.extent = np.asarray(extent, dtype='float64')\n self.resolution = np.asarray(resolution)\n self.transform = transform\n self._create_regular_grid_3d()\n\n @property\n def transform(self) -> Transform:\n if self._transform is None:\n return Transform.init_neutral()\n return self._transform\n\n @transform.setter\n def transform(self, value: Transform):\n self._transform = value\n\n @classmethod\n def from_corners_box(cls, pivot: tuple, point_x_axis: tuple, distance_point3: float,\n zmin: float, zmax: float, resolution: np.ndarray, plot: bool = True):\n \"\"\"Always rotate around the z axis towards the positive x axis. \n The distance_point3 is the distance from the pivot to the point3.\"\"\"\n\n def _calculate_rotated_box_val(v1, v2):\n # Check if the points are collinear\n # noinspection PyUnreachableCode\n cross = np.cross(v1[:2], v2[:2])\n if np.isclose(cross, 0):\n raise ValueError(\"The points are collinear\")\n # Check if v1 and v2 are perpendicular\n if not np.isclose(np.dot(v1, v2), 0, atol=1e-4):\n # Compute the closest valid p3\n v1_norm = v1 / np.linalg.norm(v1)\n v2_projected = v2 - np.dot(v2, v1_norm) * v1_norm\n p3_corrected = np.array([x1, y1, 0]) + v2_projected\n x3, y3 = p3_corrected[:2]\n raise ValueError(f\"The provided points are not perpendicular. Suggested corrected point3: ({x3}, {y3})\")\n # Calculate the orthonormal basis\n v1_norm = v1 / np.linalg.norm(v1)\n v2_norm = v2 / np.linalg.norm(v2)\n # noinspection PyUnreachableCode\n # v3_norm = np.cross(v1_norm, v2_norm)\n v3_norm = np.array([0, 0, 1]) # ! We assume rotation is always around z axis\n return v1_norm, v2_norm, v3_norm\n\n # Define the coordinates of the three points\n x1, y1 = pivot\n x2, y2 = point_x_axis\n\n dx, dy = point_x_axis[0] - pivot[0], point_x_axis[1] - pivot[1]\n x3, y3 = (pivot[0] - dy * distance_point3 / np.hypot(dx, dy), pivot[1] + dx * distance_point3 / np.hypot(dx, dy))\n\n # Calculate the vectors along the sides of the rectangle\n v1 = np.array([x2 - x1, y2 - y1, 0])\n v2 = np.array([x3 - x1, y3 - y1, 0])\n\n v1_norm, v2_norm, v3_norm = _calculate_rotated_box_val(v1, v2)\n\n # Create the rotation matrix\n rotation_matrix = np.array([\n [v1_norm[0], v2_norm[0], v3_norm[0], 0],\n [v1_norm[1], v2_norm[1], v3_norm[1], 0],\n [v1_norm[2], v2_norm[2], v3_norm[2], 0],\n [0, 0, 0, 1]\n ])\n\n inverted_rotation_matrix = np.linalg.inv(rotation_matrix)\n # Calculate the extents in the new coordinate system\n extent_x = np.linalg.norm(v1)\n extent_y = np.linalg.norm(v2)\n\n # We transform the origin point1 to the new coordinates\n origin_ = [x1, y1, zmin]\n\n xmin, ymin, zmin = origin_\n xmax, ymax, zmax = xmin + extent_x, ymin + extent_y, zmin + zmax - zmin\n\n extent = np.array([xmin, xmax, ymin, ymax, zmin, zmax], dtype='float64')\n\n transform = Transform.from_matrix(inverted_rotation_matrix)\n transform.cached_pivot = origin_\n grid = cls(extent=extent, resolution=resolution, transform=transform)\n\n if plot:\n cls.plot_rotation(\n regular_grid=grid,\n pivot=pivot,\n point_x_axis=point_x_axis,\n point_y_axis=(x3, y3)\n )\n return grid\n\n @property\n def bounding_box(self) -> np.ndarray:\n extents = self.extent\n # Define 3D points of the bounding box corners based on extents\n bounding_box_points = np.array([[extents[0], extents[2], extents[4]], # min x, min y, min z\n [extents[0], extents[2], extents[5]], # min x, min y, max z\n [extents[0], extents[3], extents[4]], # min x, max y, min z\n [extents[0], extents[3], extents[5]], # min x, max y, max z\n [extents[1], extents[2], extents[4]], # max x, min y, min z\n [extents[1], extents[2], extents[5]], # max x, min y, max z\n [extents[1], extents[3], extents[4]], # max x, max y, min z\n [extents[1], extents[3], extents[5]]]) # max x, max y, max z\n return bounding_box_points\n\n @property\n def x_coord(self):\n return np.linspace(self.extent[0] + self.dx / 2, self.extent[1] - self.dx / 2, self.resolution[0], dtype=\"float64\")\n\n @property\n def y_coord(self):\n return np.linspace(self.extent[2] + self.dy / 2, self.extent[3] - self.dy / 2, self.resolution[1], dtype=\"float64\")\n\n @property\n def z_coord(self):\n return np.linspace(self.extent[4] + self.dz / 2, self.extent[5] - self.dz / 2, self.resolution[2], dtype=\"float64\")\n\n @property\n def dx_dy_dz(self):\n dx = (self.extent[1] - self.extent[0]) / self.resolution[0]\n dy = (self.extent[3] - self.extent[2]) / self.resolution[1]\n dz = (self.extent[5] - self.extent[4]) / self.resolution[2]\n return dx, dy, dz\n\n @property\n def dx(self):\n return (self.extent[1] - self.extent[0]) / self.resolution[0]\n\n @property\n def dy(self):\n return (self.extent[3] - self.extent[2]) / self.resolution[1]\n\n @property\n def dz(self):\n return (self.extent[5] - self.extent[4]) / self.resolution[2]\n\n @property\n def values_vtk_format(self) -> np.ndarray:\n return self.get_values_vtk_format()\n\n def get_values_vtk_format(self, orthogonal: bool = False) -> np.ndarray:\n extent = self.extent\n resolution = self.resolution + 1\n\n x = np.linspace(extent[0], extent[1], resolution[0], dtype=\"float64\")\n y = np.linspace(extent[2], extent[3], resolution[1], dtype=\"float64\")\n z = np.linspace(extent[4], extent[5], resolution[2], dtype=\"float64\")\n xv, yv, zv = np.meshgrid(x, y, z, indexing=\"ij\")\n g = np.vstack((xv.ravel(), yv.ravel(), zv.ravel())).T\n\n # Transform the values\n if self.transform is not None and orthogonal is False:\n g = self.transform.apply_inverse_with_pivot(\n points=g,\n pivot=np.array([self.extent[0], self.extent[2], self.extent[4]])\n )\n\n return g\n\n @staticmethod\n def plot_rotation(regular_grid, pivot, point_x_axis, point_y_axis):\n plt = optional_dependencies.require_matplotlib()\n x1, y1 = pivot\n x2, y2 = point_x_axis\n x3, y3 = point_y_axis\n\n # Plot the original corners\n plt.scatter([x1, x2, x3], [y1, y2, y3], c='r')\n # Plot lines with distance labels\n plt.plot([x1, x2], [y1, y2], 'r')\n plt.text((x1 + x2) / 2, (y1 + y2) / 2, f'{np.linalg.norm([x1 - x2, y1 - y2]):.2f}', color='r')\n plt.plot([x1, x3], [y1, y3], 'r')\n plt.text((x1 + x3) / 2, (y1 + y3) / 2, f'{np.linalg.norm([x1 - x3, y1 - y3]):.2f}', color='r')\n # Plot the transformed corners\n transformed_extent = regular_grid.extent\n bounding_box = regular_grid.bounding_box\n plt.scatter(bounding_box[:, 0], bounding_box[:, 1], c='b')\n # Plot lines with distance labels\n plt.plot([bounding_box[2, 0], bounding_box[0, 0]], [bounding_box[2, 1], bounding_box[0, 1]], 'b')\n 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')\n plt.plot([bounding_box[0, 0], bounding_box[4, 0]], [bounding_box[0, 1], bounding_box[4, 1]], 'b')\n 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')\n # Plot the values of the grid\n values = regular_grid.values\n plt.scatter(values[:, 0], values[:, 1], c='g')\n plt.show()\n" }, { "path": "gempy/core/data/grid_modules/sections_grid.py", "content": "from __future__ import annotations # Python 3.7+ only\nimport dataclasses\nimport numpy as np\nfrom pydantic import Field\nfrom typing import Tuple, Dict, List, Optional, Any\n\nfrom ..core_utils import calculate_line_coordinates_2points\nfrom ..encoders.converters import short_array_type\nfrom ....optional_dependencies import require_pandas\n\n\n\n@dataclasses.dataclass\nclass Sections:\n \"\"\"\n Object that creates a grid of cross sections between two points.\n\n Args:\n regular_grid: Model.grid.regular_grid\n section_dict: {'section name': ([p1_x, p1_y], [p2_x, p2_y], [xyres, zres])}\n \"\"\"\n\n \"\"\"\n Pydantic v2 model of your original Sections class.\n All computed fields are initialized with model_validator.\n \"\"\"\n\n # user‐provided inputs\n \n z_ext: Tuple[float, float] | short_array_type\n section_dict: Dict[\n str,\n Tuple[\n Tuple[float, float], # start\n Tuple[float, float], # stop\n Tuple[int, int] # resolution\n ]\n ]\n\n # computed/internal (will be serialized too unless excluded)\n names: short_array_type = Field(default=np.array([]), exclude=True)\n points: List[List[Tuple[float, float]]] = Field(default_factory=list, exclude=True)\n resolution: List[Tuple[int, int]] = Field(default_factory=list, exclude=True)\n length: np.ndarray = Field(default_factory=lambda: np.array([0]), exclude=True)\n dist: np.ndarray = Field(default_factory=lambda: np.array([]), exclude=True)\n df: Optional[Any] = Field(default=None, exclude=True)\n values: np.ndarray = Field(default_factory=lambda: np.empty((0, 3)), exclude=True)\n extent: Optional[np.ndarray] = Field(default=None, exclude=True)\n\n def __post_init__(self):\n self.initialize_computations()\n pass\n\n def initialize_computations(self):\n # copy names\n self.names = np.array(list(self.section_dict.keys()))\n\n # build points/resolution/length\n self._get_section_params()\n # compute distances\n self._calculate_all_distances()\n # re-build DataFrame\n pd = require_pandas()\n df = pd.DataFrame.from_dict(\n data=self.section_dict,\n orient=\"index\",\n columns=[\"start\", \"stop\", \"resolution\"],\n )\n df[\"dist\"] = self.dist\n self.df = df\n\n # compute the XYZ grid\n self._compute_section_coordinates()\n\n def _repr_html_(self):\n return self.df.to_html()\n\n def __repr__(self):\n return self.df.to_string()\n\n def show(self):\n pass\n\n def set_sections(self, section_dict, regular_grid=None, z_ext=None):\n pd = require_pandas()\n self.section_dict = section_dict\n if regular_grid is not None:\n self.z_ext = regular_grid.extent[4:]\n \n self.initialize_computations()\n\n def _get_section_params(self):\n self.points = []\n self.resolution = []\n self.length = [0]\n\n for i, section in enumerate(self.names):\n points = [self.section_dict[section][0], self.section_dict[section][1]]\n assert points[0] != points[\n 1], 'The start and end points of the section must not be identical.'\n\n self.points.append(points)\n self.resolution.append(self.section_dict[section][2])\n self.length = np.append(self.length, self.section_dict[section][2][0] *\n self.section_dict[section][2][1])\n self.length = np.array(self.length).cumsum()\n\n def _calculate_all_distances(self):\n self.coordinates = np.array(self.points).ravel().reshape(-1,\n 4) # axis are x1,y1,x2,y2\n self.dist = np.sqrt(np.diff(self.coordinates[:, [0, 2]]) ** 2 + np.diff(\n self.coordinates[:, [1, 3]]) ** 2)\n\n def _compute_section_coordinates(self):\n for i in range(len(self.names)):\n xy = calculate_line_coordinates_2points(self.coordinates[i, :2],\n self.coordinates[i, 2:],\n self.resolution[i][0])\n zaxis = np.linspace(self.z_ext[0], self.z_ext[1], self.resolution[i][1],\n dtype=\"float64\")\n X, Z = np.meshgrid(xy[:, 0], zaxis, indexing='ij')\n Y, _ = np.meshgrid(xy[:, 1], zaxis, indexing='ij')\n xyz = np.vstack((X.flatten(), Y.flatten(), Z.flatten())).T\n if i == 0:\n self.values = xyz\n else:\n self.values = np.vstack((self.values, xyz))\n\n def generate_axis_coord(self):\n for i, name in enumerate(self.names):\n xy = calculate_line_coordinates_2points(\n self.coordinates[i, :2],\n self.coordinates[i, 2:],\n self.resolution[i][0]\n )\n yield name, xy\n\n def get_section_args(self, section_name: str):\n where = np.where(self.names == section_name)[0][0]\n return self.length[where], self.length[where + 1]\n\n def get_section_grid(self, section_name: str):\n l0, l1 = self.get_section_args(section_name)\n return self.values[l0:l1]\n" }, { "path": "gempy/core/data/grid_modules/topography.py", "content": "import dataclasses\n\nimport warnings\nfrom pydantic import Field\nfrom typing import Optional, Tuple, Annotated\n\nimport numpy as np\n\nfrom .regular_grid import RegularGrid\n\nfrom ....optional_dependencies import require_skimage, require_scipy\nfrom dataclasses import field, dataclass\nfrom ..encoders.converters import short_array_type\n\n\n@dataclass\nclass Topography:\n\n \"\"\"\n Object to include topography in the model.\n Notes:\n This always assumes that the topography we pass fits perfectly the extent.\n \"\"\"\n\n _regular_grid: RegularGrid\n values_2d: Annotated[np.ndarray, Field(exclude=True)] = field(default_factory=lambda: np.zeros((0, 0, 3)))\n source: Optional[str] = None\n\n # Fields managed internally\n values: short_array_type = field(init=False, default_factory=lambda: np.zeros((0, 3)))\n resolution: Tuple[int, int] = Field(init=True, default=(0, 0))\n raster_shape: Tuple[int, ...] = field(init=False, default_factory=tuple)\n _mask_topo: Optional[np.ndarray] = field(init=False, default=None, repr=False)\n _x: Optional[np.ndarray] = field(init=False, default=None, repr=False)\n _y: Optional[np.ndarray] = field(init=False, default=None, repr=False)\n\n def __post_init__(self):\n # if a non-empty array was provided, initialize the flattened values\n if self.values_2d.size:\n self.set_values(self.values_2d)\n\n\n @classmethod\n def from_subsurface_structured_data(cls, structured_data: 'subsurface.StructuredData', regular_grid: RegularGrid):\n \"\"\"Creates a topography object from a subsurface structured data object\n\n Args:\n structured_data (subsurface.StructuredData): Structured data object\n\n Returns:\n :class:`gempy.core.grid_modules.topography.Topography`\n\n \"\"\"\n\n # Generate meshgrid for x and y coordinates\n ds = structured_data.data\n x_coordinates = ds['x']\n y_coordinates = ds['y']\n height_values = ds['topography']\n\n return cls.from_arrays(regular_grid, x_coordinates, y_coordinates, height_values)\n\n @classmethod\n def from_unstructured_mesh(cls, regular_grid, xyz_vertices):\n \"\"\"Creates a topography object from an unstructured mesh of XYZ vertices.\n\n Args:\n regular_grid (RegularGrid): The regular grid object.\n xyz_vertices (numpy.ndarray): Array of XYZ vertices of the unstructured mesh.\n\n Returns:\n :class:`gempy.core.grid_modules.topography.Topography`\n \"\"\"\n # Perform Delaunay triangulation on the vertices\n\n # Generate the regular grid points\n from scipy.interpolate import griddata\n \n x_regular, y_regular = np.meshgrid(\n np.linspace(regular_grid.extent[0], regular_grid.extent[1], regular_grid.resolution[0]),\n np.linspace(regular_grid.extent[2], regular_grid.extent[3], regular_grid.resolution[1]),\n indexing='ij'\n )\n\n # Interpolate the z-values onto the regular grid\n z_regular = griddata(\n points=xyz_vertices[:, :2],\n values=xyz_vertices[:, 2],\n xi=(x_regular, y_regular),\n method='nearest',\n fill_value=np.nan # You can choose a different fill value or method\n )\n\n # Reshape the grid for compatibility with existing structure\n values_2d = np.stack((x_regular, y_regular, z_regular), axis=-1)\n\n return cls(_regular_grid=regular_grid, values_2d=values_2d)\n \n \n @classmethod\n def from_arrays(cls, regular_grid, x_coordinates, y_coordinates, height_values,):\n x_vals, y_vals = np.meshgrid(x_coordinates, y_coordinates, indexing='ij')\n # Reshape arrays for stacking\n x_vals = x_vals[:, :, np.newaxis] # shape (73, 34, 1)\n y_vals = y_vals[:, :, np.newaxis] # shape (73, 34, 1)\n topography_vals = height_values.values[:, :, np.newaxis] # shape (73, 34, 1)\n # Stack along the last dimension\n result = np.concatenate([x_vals, y_vals, topography_vals], axis=2) # shape (73, 34, 3)\n return cls(_regular_grid=regular_grid, values_2d=result)\n\n @property\n def extent(self):\n return self._regular_grid.extent\n\n @property\n def regular_grid_resolution(self):\n return self._regular_grid.resolution\n\n @property\n def x(self):\n if self._x is not None:\n return self._x\n else:\n val = self.values[:, 0].copy()\n val.sort()\n return np.unique(val)\n\n @property\n def y(self):\n if self._y is not None:\n return self._y\n else:\n val = self.values[:, 1].copy()\n val.sort()\n return np.unique(val)\n\n def set_values(self, values_2d: np.ndarray):\n \"\"\"General method to set topography\n\n Args:\n values_2d (numpy.ndarray[float,float, 3]): array with the XYZ values\n in 2D\n\n Returns:\n :class:`gempy.core.grid_modules.topography.Topography`\n\n\n \"\"\"\n # Original topography data\n self.values_2d = values_2d\n self.resolution = values_2d.shape[:2]\n\n # n,3 array\n self.values = values_2d.reshape((-1, 3), order='C')\n return self\n\n def set_values2d(self, values: np.ndarray) -> \"Topography\":\n \"\"\"\n Reconstruct the 2D topography (shape = resolution + [3]) from\n a flat Nx3 array (or from self.values if none is provided).\n \"\"\"\n # default to the already-flattened XYZ array\n \n # compute expected size\n nx, ny = self.resolution\n expected = nx * ny * 3\n if values.size != expected:\n raise ValueError(\n f\"Cannot reshape array of size {values.size} into shape {(nx, ny, 3)}.\"\n )\n\n # reshape in C-order to (nx, ny, 3)\n self.set_values(\n values_2d=values.reshape(nx, ny, 3, order=\"C\")\n )\n\n # invalidate any cached mask\n self._mask_topo = None\n return self\n\n def set_values2d_(self, values: np.ndarray):\n resolution = (60, 60)\n self.values_2d = values.reshape(*resolution, 3)\n\n @property\n def topography_mask(self):\n \"\"\"This method takes a topography grid of the same extent as the regular\n grid and creates a mask of voxels\n\n \"\"\"\n\n # * Check if the topography is the same as the cached one and if so, return the cached mask\n if self._mask_topo is not None:\n return self._mask_topo\n\n # interpolate topography values to the regular grid\n skimage = require_skimage()\n regular_grid_topo = skimage.transform.resize(\n image=self.values_2d,\n output_shape=(self.regular_grid_resolution[0], self.regular_grid_resolution[1]),\n mode='constant',\n anti_aliasing=False,\n preserve_range=True\n )\n\n # Adjust the topography to be lower by half a voxel height\n # Assumes your voxel heights are uniform and can be calculated as the total height divided by resolution\n voxel_height = self._regular_grid.dz * 2\n regular_grid_topo = regular_grid_topo - voxel_height\n\n # Reshape the Z values of the regular grid to 3d\n values_3d = self._regular_grid.values[:, 2].reshape(self.regular_grid_resolution)\n if regular_grid_topo.ndim == 3:\n regular_grid_topo_z = regular_grid_topo[:, :, [2]]\n elif regular_grid_topo.ndim == 2:\n regular_grid_topo_z = regular_grid_topo\n else:\n raise ValueError()\n mask = np.greater(values_3d[:, :, :], regular_grid_topo_z)\n\n self._mask_topo = mask\n return self._mask_topo\n\n def resize_topo(self):\n skimage = require_skimage()\n regular_grid_topo = skimage.transform.resize(\n self.values_2d,\n (self.regular_grid_resolution[0], self.regular_grid_resolution[1]),\n mode='constant',\n anti_aliasing=False, preserve_range=True)\n\n return regular_grid_topo\n\n def load_random_hills(self, **kwargs):\n warnings.warn('This function is deprecated. Use load_from_random instead', DeprecationWarning)\n if 'extent' in kwargs:\n self.extent = kwargs.pop('extent')\n\n if 'resolution' in kwargs:\n self.regular_grid_resolution = kwargs.pop('resolution')\n\n dem = _LoadDEMArtificial(extent=self.extent,\n resolution=self.regular_grid_resolution, **kwargs)\n\n self._x, self._y = dem.x, dem.y\n self.set_values(dem.get_values())\n\n def save(self, path):\n np.save(path, self.values_2d)\n\n def load(self, path):\n self.set_values(np.load(path))\n self._x, self._y = None, None\n return self.values\n\n def load_from_saved(self, *args, **kwargs):\n self.load(*args, **kwargs)\n\n\nclass _LoadDEMArtificial: # * Cannot think of a good reason to be a class\n\n def __init__(self, grid=None, fd=2.0, extent=None, resolution=None, d_z=None):\n \"\"\"Class to create a random topography based on a fractal grid algorithm.\n\n Args:\n fd: fractal dimension, defaults to 2.0\n d_z: maximum height difference. If none, last 20% of the model in z direction\n extent: extent in xy direction. If none, geo_model.grid.extent\n resolution: desired resolution of the topography array. If none, geo_model.grid.resolution\n \"\"\"\n self.values_2d = np.array([])\n self.resolution = grid.resolution[:2] if resolution is None else resolution\n\n assert all(np.asarray(self.resolution) >= 2), 'The regular grid needs to be at least of size 2 on all directions.'\n self.extent = grid.extent if extent is None else extent\n\n if d_z is None:\n self.d_z = np.array([self.extent[5] - (self.extent[5] - self.extent[4]) * 1 / 5, self.extent[5]])\n print(self.d_z)\n else:\n self.d_z = d_z\n\n topo = self.fractalGrid(fd, n=self.resolution.max())\n topo = np.interp(topo, (topo.min(), topo.max()), self.d_z)\n\n self.dem_zval = topo[:self.resolution[0], :self.resolution[1]] # crop fractal grid with resolution\n self.create_topo_array()\n\n @staticmethod\n def fractalGrid(fd, n=256):\n \"\"\"\n Modified after https://github.com/samthiele/pycompass/blob/master/examples/3_Synthetic%20Examples.ipynb\n\n Generate isotropic fractal surface image using\n spectral synthesis method [1, p.]\n References:\n 1. Yuval Fisher, Michael McGuire,\n The Science of Fractal Images, 1988\n\n (cf. http://shortrecipes.blogspot.com.au/2008/11/python-isotropic-fractal-surface.html)\n **Arguments**:\n -fd = the fractal dimension\n -N = the size of the fractal surface/image\n\n \"\"\"\n h = 1 - (fd - 2)\n # X = np.zeros((N, N), complex)\n a = np.zeros((n, n), complex)\n powerr = -(h + 1.0) / 2.0\n\n for i in range(int(n / 2) + 1):\n for j in range(int(n / 2) + 1):\n phase = 2 * np.pi * np.random.rand()\n\n if i != 0 or j != 0:\n rad = (i * i + j * j) ** powerr * np.random.normal()\n else:\n rad = 0.0\n\n a[i, j] = complex(rad * np.cos(phase), rad * np.sin(phase))\n\n if i == 0:\n i0 = 0\n else:\n i0 = n - i\n\n if j == 0:\n j0 = 0\n else:\n j0 = n - j\n\n a[i0, j0] = complex(rad * np.cos(phase), -rad * np.sin(phase))\n\n a.imag[int(n / 2)][0] = 0.0\n a.imag[0, int(n / 2)] = 0.0\n a.imag[int(n / 2)][int(n / 2)] = 0.0\n\n for i in range(1, int(n / 2)):\n for j in range(1, int(n / 2)):\n phase = 2 * np.pi * np.random.rand()\n rad = (i * i + j * j) ** powerr * np.random.normal()\n a[i, n - j] = complex(rad * np.cos(phase), rad * np.sin(phase))\n a[n - i, j] = complex(rad * np.cos(phase), -rad * np.sin(phase))\n\n scipy = require_scipy()\n itemp = scipy.fftpack.ifft2(a)\n itemp = itemp - itemp.min()\n\n return itemp.real / itemp.real.max()\n\n def create_topo_array(self):\n \"\"\"for masking the lith block\"\"\"\n x = np.linspace(self.extent[0], self.extent[1], self.resolution[0])\n y = np.linspace(self.extent[2], self.extent[3], self.resolution[1])\n self.x = x\n self.y = y\n xx, yy = np.meshgrid(x, y, indexing='ij')\n self.values_2d = np.dstack([xx, yy, self.dem_zval])\n\n def get_values(self):\n return self.values_2d" }, { "path": "gempy/core/data/importer_helper.py", "content": "from dataclasses import dataclass\nfrom typing import Optional\n\n\n@dataclass\nclass ImporterHelper:\n path_to_surface_points: Optional[str] \n path_to_orientations: Optional[str]\n \n hash_surface_points: Optional[str] = None\n hash_orientations: Optional[str] = None\n \n coord_x_name: Optional[str] = \"X\"\n coord_y_name: Optional[str] = \"Y\"\n coord_z_name: Optional[str] = \"Z\"\n surface_name: Optional[str] = \"formation\"\n \n gx_name: Optional[str] = \"G_x\"\n gy_name: Optional[str] = \"G_y\"\n gz_name: Optional[str] = \"G_z\"\n \n pandas_reader_kwargs: Optional[dict] = None\n \n \n # ? Add here pandas reader options" }, { "path": "gempy/core/data/options.py", "content": "import enum\nclass InterpolationOptionsType(enum.Enum):\n \n DENSE_GRID = enum.auto()\n OCTREE = enum.auto()\n" }, { "path": "gempy/core/data/orientations.py", "content": "from dataclasses import dataclass\nfrom typing import Optional, Sequence, Union\n\nimport numpy as np\nfrom gempy_engine.core.data.transforms import Transform\nfrom pydantic import Field\n\nfrom ...optional_dependencies import require_pandas\nfrom ._data_points_helpers import generate_ids_from_names\n\nDEFAULT_ORI_NUGGET = 0.01\n\n\n# ? Maybe we should merge this with the SurfacePoints class from gempy_engine\n\n\n@dataclass(init=True)\nclass OrientationsTable:\n \"\"\"\n A dataclass to represent a table of orientations in a geological model.\n \n \"\"\"\n\n 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.\n data: np.ndarray = Field(\n default=np.zeros(0, dtype=dt),\n exclude=True,\n description=\"A structured NumPy array holding the X, Y, Z coordinates, gradients G_x, G_y, G_z, id, and nugget of each orientation.\",\n ) #: A structured NumPy array holding the X, Y, Z coordinates, id, and nugget of each surface point.\n name_id_map: Optional[dict[str, int]] = None #: A mapping between orientation names and ids.\n\n\n _model_transform: Optional[Transform] = None\n\n def __post_init__(self):\n # Check if the data array has the correct data type\n if self.data.dtype != OrientationsTable.dt:\n raise ValueError(f\"Data array must have the following data type: {OrientationsTable.dt}\")\n\n @classmethod\n def from_arrays(cls, x: np.ndarray, y: np.ndarray, z: np.ndarray,\n G_x: np.ndarray, G_y: np.ndarray, G_z: np.ndarray,\n names: Union[Sequence | str], nugget: Optional[np.ndarray] = None,\n name_id_map: Optional[dict[str, int]] = None) -> 'OrientationsTable':\n \"\"\"Create an OrientationsTable from arrays.\n\n Args:\n x (np.ndarray): Array of x-coordinates.\n y (np.ndarray): Array of y-coordinates.\n z (np.ndarray): Array of z-coordinates.\n G_x (np.ndarray): Array of x-components of the gradients.\n G_y (np.ndarray): Array of y-components of the gradients.\n G_z (np.ndarray): Array of z-components of the gradients.\n names (Union[Sequence[str], str]): Sequence of names corresponding to each orientation.\n nugget (Optional[np.ndarray]): Array of nugget values. If None, defaults are used.\n name_id_map (Optional[dict[str, int]]): Mapping between names and ids.\n\n Returns:\n OrientationsTable: The created OrientationsTable.\n \"\"\"\n\n data, name_id_map = cls._data_from_arrays(x, y, z, G_x, G_y, G_z, names, nugget, name_id_map)\n return cls(data, name_id_map)\n\n\n @classmethod\n 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]]:\n if nugget is None:\n nugget = np.zeros_like(x) + DEFAULT_ORI_NUGGET\n\n if name_id_map is None:\n ids, name_id_map = generate_ids_from_names(name_id_map, names, x)\n else:\n ids = np.array([name_id_map[name] for name in names])\n data = np.zeros(len(x), dtype=OrientationsTable.dt)\n 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\n return data, name_id_map\n\n @classmethod\n def initialize_empty(cls) -> 'OrientationsTable':\n return cls(np.zeros(0, dtype=OrientationsTable.dt))\n\n @property\n def xyz(self) -> np.ndarray:\n \"\"\"Get the XYZ coordinates.\n\n Returns:\n np.ndarray: The XYZ coordinates.\n \"\"\"\n return np.array([self.data['X'], self.data['Y'], self.data['Z']]).T\n\n @property\n def xyz_view(self) -> np.ndarray:\n \"\"\"Get a view of the XYZ coordinates.\n\n Returns:\n np.ndarray: A view of the XYZ coordinates.\n \"\"\"\n return self.data[['X', 'Y', 'Z']]\n\n @xyz_view.setter\n def xyz_view(self, value: np.ndarray):\n \"\"\"Set the XYZ coordinates.\n\n Args:\n value (np.ndarray): The new XYZ coordinates.\n \"\"\"\n self.data['X'], self.data['Y'], self.data['Z'] = value.T\n\n @property\n def grads(self) -> np.ndarray:\n \"\"\"Get the gradient components.\n\n Returns:\n np.ndarray: The gradient components.\n \"\"\"\n return np.array([self.data['G_x'], self.data['G_y'], self.data['G_z']]).T\n\n @property\n def grads_view(self) -> np.ndarray:\n \"\"\"Get a view of the gradient components.\n\n Returns:\n np.ndarray: A view of the gradient components.\n \"\"\"\n return self.data[['G_x', 'G_y', 'G_z']]\n\n @grads_view.setter\n def grads_view(self, value: np.ndarray):\n \"\"\"Set the gradient components.\n\n Args:\n value (np.ndarray): The new gradient components.\n \"\"\"\n self.data['G_x'], self.data['G_y'], self.data['G_z'] = value.T\n\n @property\n def nugget(self) -> np.ndarray:\n \"\"\"Get the nugget values.\n\n Returns:\n np.ndarray: The nugget values.\n \"\"\"\n return self.data['nugget']\n\n @property\n def ids(self) -> np.ndarray:\n \"\"\"Get the IDs.\n\n Returns:\n np.ndarray: The IDs.\n \"\"\"\n return self.data['id']\n\n def get_orientations_by_name(self, name: str) -> 'OrientationsTable':\n \"\"\"Get orientations by name.\n\n Args:\n name (str): The name of the orientations.\n\n Returns:\n OrientationsTable: The orientations corresponding to the given name.\n \"\"\"\n return self.get_orientations_by_id(self.name_id_map[name])\n\n def get_orientations_by_id(self, id: int) -> 'OrientationsTable':\n \"\"\"Get orientations by ID.\n\n Args:\n id (int): The ID of the orientations.\n\n Returns:\n OrientationsTable: The orientations corresponding to the given ID.\n \"\"\"\n return OrientationsTable(self.data[self.data['id'] == id], self.name_id_map)\n\n def get_orientations_by_id_groups(self) -> list['OrientationsTable']:\n \"\"\"Get orientations grouped by ID.\n\n Returns:\n list[OrientationsTable]: A list of OrientationsTable objects, each corresponding to a unique ID.\n \"\"\"\n ids = np.unique(self.data['id'])\n return [self.get_orientations_by_id(id) for id in ids]\n\n @classmethod\n def fill_missing_orientations_groups(cls, orientations_groups: list['OrientationsTable'],\n surface_points_groups: list['SurfacePointsTable']) -> list['OrientationsTable']:\n \"\"\"\n Fill Missing Orientations Groups\n\n Fills in missing orientations in a list of orientations groups based on a list of surface points.\n\n Args:\n\n \"\"\"\n # region Deal with elements without orientations\n if len(surface_points_groups) > len(orientations_groups):\n # Check the ids of the surface points and find the missing ones\n surface_points_ids = [surface_points_group.id for surface_points_group in surface_points_groups]\n orientations_ids = [orientations_group.id for orientations_group in orientations_groups]\n\n missing_ids = list(set(surface_points_ids) - set(orientations_ids))\n\n empty_orientations = [cls(data=np.zeros(0, dtype=cls.dt)) for id in missing_ids] # Create empty orientations\n\n for empty_orientation, id in zip(empty_orientations, missing_ids): # Insert the empty orientations in the right position\n orientations_groups.insert(id, empty_orientation)\n # endregion\n\n return orientations_groups\n\n @classmethod\n def empty_orientation(cls, id: int) -> 'OrientationsTable':\n zeros = np.zeros(0, dtype=cls.dt)\n zeros['id'] = id\n return cls(data=zeros, name_id_map={})\n\n @property\n def id(self) -> int:\n # Check id is the same in the whole column and return it or throw an error\n ids = np.unique(self.data['id'])\n if len(ids) > 1:\n raise ValueError(f\"OrientationsTable contains more than one id: {ids}\")\n if len(ids) == 0:\n raise ValueError(f\"OrientationsTable contains no ids\")\n return ids[0]\n\n @property\n def model_transform(self) -> Transform:\n if self._model_transform is None:\n 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.\")\n return self._model_transform\n\n @model_transform.setter\n def model_transform(self, value: Transform):\n self._model_transform = value\n\n @property\n def df(self) -> 'pd.DataFrame':\n pd = require_pandas()\n return pd.DataFrame(self.data)\n\n def __str__(self):\n return \"\\n\" + np.array2string(self.data, precision=2, separator=',', suppress_small=True)\n\n def __repr__(self):\n return f\"OrientationsTable(data=\\n{np.array2string(self.data, precision=2, separator=',', suppress_small=True)},\\nname_id_map={self.name_id_map})\"\n\n def _repr_html_(self):\n rows_to_display = 10 # Define the number of rows to display from beginning and end\n html = \"\"\n html += \"\"\n if len(self.data) > 2 * rows_to_display:\n for point in self.data[:rows_to_display]:\n html += \"\".format(*point)\n html += \"\"\n for point in self.data[-rows_to_display:]:\n html += \"\".format(*point)\n else:\n for point in self.data:\n html += \"\".format(*point)\n 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}
\"\n return html\n\n def __len__(self):\n return len(self.data)\n" }, { "path": "gempy/core/data/structural_element.py", "content": "import re\nfrom dataclasses import dataclass, field\nfrom pydantic import Field\nfrom typing import Optional\n\nimport numpy as np\n\nfrom .orientations import OrientationsTable\nfrom .surface_points import SurfacePointsTable\n\n\"\"\"\nTODO:\n - [ ] Add repr and _repr_html_ methods. Legacy representations depended on pandas, which is optional now\n\n\"\"\"\n\n\n@dataclass\nclass StructuralElement:\n \"\"\"\n Class that represents a structural element in a geological model.\n \n \"\"\"\n \n name: str #: The name of the structural element.\n is_active: bool #: The active state of the structural element.\n _color: str #: The color of the structural element in hexadecimal format.\n surface_points: SurfacePointsTable #: The points on the surface of the structural element.\n orientations: OrientationsTable #: The orientations of the structural element.\n\n # Output\n # ? Should we extract this to a separate class?\n vertices: np.ndarray | None = Field(default=None, exclude=True) #: The vertices of the element in 3D space.\n edges: np.ndarray | None = Field(default=None, exclude=True) #: The edges of the element in 3D space.\n scalar_field_at_interface: float | None = None #: The scalar field value for the element.\n\n _id: int = -1\n \n def __init__(self, name: str, surface_points: SurfacePointsTable, orientations: OrientationsTable,\n id: Optional[int] = -1, is_active: Optional[bool] = True, color: Optional[str] = None):\n self.name = name\n \n self.surface_points = surface_points\n self.orientations = orientations\n \n self.is_active = is_active\n self.color = color\n \n self._id = id \n\n @property\n def id(self):\n if self._id == -1:\n from gempy.core.data._data_points_helpers import structural_element_hasher\n return structural_element_hasher(0, self.name)\n return self._id\n\n def __repr__(self):\n r, g, b = int(self._color[1:3], 16), int(self._color[3:5], 16), int(self._color[5:7], 16)\n colored_color = f'\\033[38;2;{r};{g};{b}m' + self._color + '\\033[0m'\n return f\"Element(\\n\\tname={self.name},\\n\\tcolor={colored_color},\\n\\tis_active={self.is_active}\\n)\"\n\n def _repr_html_(self):\n html = f\"\"\"\n \n \n \n
StructuralElement:
Name:{self.name}
\n \"\"\"\n return html\n\n def _repr_html_2(self):\n html = f\"\"\"
\n    StructuralElement:\n      Name: {self.name}\n      Color: 
\n      Is Active: {'Yes' if self.is_active else 'No'}\n
\"\"\"\n return html\n\n @property\n def number_of_points(self) -> int:\n return len(self.surface_points)\n \n @property\n def number_of_orientations(self) -> int:\n return len(self.orientations)\n\n @property\n def color(self):\n return self._color\n\n @color.setter\n def color(self, value):\n if not isinstance(value, str) or not re.match(\"^#([A-Fa-f0-9]{6}|[A-Fa-f0-9]{3})$\", value):\n raise ValueError(f\"Invalid color: {value}\")\n self._color = value\n\n @property\n def is_basement(self):\n # ? Not sure if this will be necessary\n raise NotImplementedError\n\n @property\n def has_data(self):\n raise NotImplementedError\n\n @property\n def index(self):\n raise NotImplementedError\n\n @property\n def structural_group(self):\n raise NotImplementedError\n" }, { "path": "gempy/core/data/structural_frame.py", "content": "import hashlib\n\nimport numpy as np\nimport warnings\nfrom dataclasses import dataclass\nfrom pydantic import model_validator, computed_field, ValidationError, Field\nfrom pydantic.functional_validators import ModelWrapValidatorHandler\nfrom typing import Generator, Union\n\nfrom gempy_engine.core.data.input_data_descriptor import InputDataDescriptor\nfrom gempy_engine.core.data.kernel_classes.faults import FaultsData\nfrom gempy_engine.core.data.stack_relation_type import StackRelationType\n\nfrom .encoders.binary_encoder import deserialize_input_data_tables\nfrom .encoders.converters import loading_model_context\nfrom .orientations import OrientationsTable\nfrom .structural_element import StructuralElement\nfrom .structural_group import StructuralGroup, FaultsRelationSpecialCase\nfrom .surface_points import SurfacePointsTable\nfrom ..color_generator import ColorsGenerator\n\n\n@dataclass\nclass StructuralFrame:\n \"\"\"\n Represents a structural frame, which is a collection of structural groups that constitute a geological model.\n\n Attributes:\n structural_groups (list[StructuralGroup]): List of structural groups that constitute the geological model.\n color_generator (ColorsGenerator): Instance of ColorsGenerator used for assigning distinct colors to different structural elements.\n is_dirty (bool): Boolean flag indicating if the structural frame has been modified.\n \"\"\"\n\n structural_groups: list[StructuralGroup]\n color_generator: ColorsGenerator = Field(default_factory=ColorsGenerator)\n basement_color: str = None\n # ? Should I create some sort of structural options class? For example, the masking descriptor and faults relations pointer\n is_dirty: bool = True\n\n # region Constructor\n # \n def __init__(self, structural_groups: list[StructuralGroup], color_gen: ColorsGenerator):\n self.structural_groups = structural_groups # ? This maybe could be optional\n self.color_generator = color_gen\n \n def __post_init__(self):\n pass\n\n @classmethod\n def from_data_tables(cls, surface_points: SurfacePointsTable, orientations: OrientationsTable):\n surface_points_groups: list[SurfacePointsTable] = surface_points.get_surface_points_by_id_groups()\n colors_generator = ColorsGenerator()\n\n structural_elements = []\n for i in range(len(surface_points_groups)):\n id_ = surface_points_groups[i].id\n orientation_i = orientations.get_orientations_by_id(id_)\n if len(orientation_i) == 0:\n orientation_i = OrientationsTable.empty_orientation(id_)\n\n structural_element: StructuralElement = StructuralElement(\n name=surface_points.id_to_name(i),\n id=id_,\n surface_points=surface_points_groups[i],\n orientations=orientation_i,\n color=next(colors_generator)\n )\n\n structural_elements.append(structural_element)\n # * Structural groups definitions\n default_formation: StructuralGroup = StructuralGroup(\n name=\"default_formation\",\n elements=structural_elements,\n structural_relation=StackRelationType.ERODE\n )\n # ? Should I move this to the constructor?\n structural_frame: StructuralFrame = cls(\n structural_groups=[default_formation],\n color_gen=colors_generator\n )\n\n return structural_frame\n\n @classmethod\n def initialize_default_structure(cls) -> 'StructuralFrame':\n \"\"\"\n Initialize the default structure.\n\n This method is used to initialize the default structure for a `StructuralFrame` object.\n\n Args:\n None\n\n Returns:\n 'StructuralFrame': A `StructuralFrame` object representing the default structure.\n\n Example:\n structural_frame = initialize_default_structure()\n \"\"\"\n color_gen = ColorsGenerator()\n\n structural_group = StructuralGroup(\n name=\"default_formations\",\n elements=[\n StructuralElement(\n name=\"surface1\",\n surface_points=SurfacePointsTable.initialize_empty(),\n orientations=OrientationsTable.initialize_empty(),\n color=next(color_gen)\n )\n ],\n structural_relation=StackRelationType.ERODE\n )\n\n structural_frame = cls(\n structural_groups=[structural_group],\n color_gen=color_gen\n )\n\n return structural_frame\n\n # endregion\n\n # region Methods\n def get_element_by_name(self, element_name: str) -> StructuralElement:\n elements: Generator = (group.get_element_by_name(element_name) for group in self.structural_groups)\n valid_elements: Generator = (element for element in elements if element is not None)\n element = next(valid_elements, None)\n if element is None:\n raise ValueError(f\"Element with name {element_name} not found in the structural frame.\")\n return element\n\n def get_group_by_name(self, group_name: str) -> StructuralGroup:\n groups: Generator = (group for group in self.structural_groups if group.name == group_name)\n group = next(groups, None)\n if group is None:\n raise ValueError(f\"Group with name {group_name} not found in the structural frame.\")\n return group\n\n def get_group_by_element(self, element: StructuralElement) -> StructuralGroup:\n groups: Generator = (group for group in self.structural_groups if element in group.elements)\n group = next(groups, None)\n if group is None:\n raise ValueError(f\"Element {element.name} not found in any group in the structural frame.\")\n return group\n\n def append_group(self, group: StructuralGroup):\n self.structural_groups.append(group)\n\n def insert_group(self, index: int, group: StructuralGroup):\n self.structural_groups.insert(index, group)\n\n def __repr__(self):\n structural_groups_repr = ',\\n'.join([repr(g) for g in self.structural_groups])\n fault_relations_str = np.array2string(self.fault_relations, precision=2, separator=', ', suppress_small=True) if self.fault_relations is not None else 'None'\n return (f\"StructuralFrame(\\n\"\n f\"\\tstructural_groups=[\\n{structural_groups_repr}\\n],\\n\"\n f\"\\tfault_relations=\\n{fault_relations_str},\\n\"\n )\n\n def _repr_html_(self):\n structural_groups_html = '
'.join([g._repr_html_() for g in self.structural_groups])\n if self.fault_relations is not None:\n # Define the colors for True and False values\n true_color = '#527682'\n false_color = '#FFB6C1'\n\n table_headers = '' + ''.join('{}'.format((g.name[:10] + '...') if len(g.name) > 10 else g.name) for g in self.structural_groups)\n 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))\n fault_relations_str = '{}{}
'.format(table_headers, table_rows)\n else:\n fault_relations_str = 'None'\n\n # Define the legend\n legend = f\"\"\"\n \n \n \n \n \n
True
False
\n \"\"\"\n\n html = f\"\"\"\n \n \n \n \n
Structural Groups:{structural_groups_html}
Fault Relations:{fault_relations_str}
{legend}
\n \"\"\"\n return html\n\n # endregion\n\n # region Properties\n @property\n def structural_elements(self) -> list[StructuralElement]:\n \"\"\"Returns a list of all structural elements across the structural groups.\"\"\"\n elements = []\n for group in self.structural_groups:\n elements.extend(group.elements)\n elements.append(self._basement_element)\n return elements\n\n @property\n def n_elements(self) -> int:\n \"\"\"Returns the total number of elements in the structural frame.\"\"\"\n return len(self.structural_elements)\n\n\n @property\n def _basement_element(self) -> StructuralElement:\n \"\"\"Returns the basement structural element with a unique color.\"\"\"\n\n def _get_unique_basement_color(color_generator: ColorsGenerator, used_colors: list[str]) -> str:\n color = next(color_generator)\n if color in used_colors:\n return _get_unique_basement_color(color_generator, used_colors)\n return color\n\n elements = []\n for group in self.structural_groups:\n elements.extend(group.elements)\n\n used_colors = [element.color for element in elements]\n\n if self.basement_color is None or self.basement_color in used_colors:\n self.basement_color = _get_unique_basement_color(\n color_generator=self.color_generator, \n used_colors=used_colors\n )\n\n basement = StructuralElement(\n name=\"basement\",\n surface_points=SurfacePointsTable(data=np.zeros(0, dtype=SurfacePointsTable.dt)),\n orientations=OrientationsTable(data=np.zeros(0, dtype=OrientationsTable.dt)),\n color=self.basement_color\n )\n\n return basement\n\n # ? Should I move this property to StructuralGroup?\n @property\n def fault_relations(self) -> np.ndarray:\n \"\"\"Returns a array describing the fault relations between the structural groups.\"\"\"\n # Initialize an empty boolean array with dimensions len(structural_groups) x len(structural_groups)\n\n fault_relations = np.zeros((len(self.structural_groups), len(self.structural_groups)), dtype=bool)\n\n # We assume that the list is ordered from older to younger\n # Iterate over the list of structural_groups\n for i, group in enumerate(self.structural_groups):\n match (group.structural_relation, group.fault_relations):\n case (StackRelationType.FAULT, FaultsRelationSpecialCase.OFFSET_ALL): # It affects all younger groups\n fault_relations[i, i + 1:] = True\n case (StackRelationType.FAULT, FaultsRelationSpecialCase.OFFSET_NONE): # It affects no groups\n pass\n case (StackRelationType.FAULT, FaultsRelationSpecialCase.OFFSET_FORMATIONS): # It affects all younger groups that are formations\n do_offset = []\n for group_internal in self.structural_groups[i + 1:]:\n do_offset.append(group_internal.structural_relation != StackRelationType.FAULT)\n fault_relations[i, i + 1:] = do_offset\n case (StackRelationType.FAULT, list(fault_groups)) if fault_groups: # It affects only the specified groups\n for fault_group in fault_groups:\n j = self.structural_groups.index(fault_group)\n if j <= i: # Only consider groups that are \n raise ValueError(f\"Fault {group.name} cannot affect older fault {fault_group.name}\")\n fault_relations[i, j] = True\n case (StackRelationType.FAULT, _):\n raise ValueError(f\"Fault {group.name} has an invalid fault relation\")\n case _:\n pass # If not a fault or fault relation is not specified, do nothing\n return fault_relations\n\n @fault_relations.setter\n def fault_relations(self, matrix: np.ndarray):\n \"\"\"Sets the fault relations between structural groups using the provided matrix.\"\"\"\n assert matrix.shape == (len(self.structural_groups), len(self.structural_groups))\n\n # Iterate over each StructuralGroup\n for i, group in enumerate(self.structural_groups):\n\n affected_groups = matrix[i, :] # * If the group is a fault\n # If all younger groups are affected\n all_younger_groups_affected = np.all(affected_groups[i + 1:])\n any_younger_groups_affected = np.any(affected_groups[i + 1:])\n\n if all_younger_groups_affected:\n group.fault_relations = FaultsRelationSpecialCase.OFFSET_ALL\n elif not any_younger_groups_affected:\n group.fault_relations = FaultsRelationSpecialCase.OFFSET_NONE\n else: # * A specific set of groups are affected\n group.fault_relations = [g for j, g in enumerate(self.structural_groups) if affected_groups[j]]\n\n @property\n def group_is_fault(self) -> list[bool]:\n \"\"\"Returns a list of booleans indicating if each structural element is a fault.\"\"\"\n return [group.is_fault for group in self.structural_groups]\n\n @property\n def group_is_lithology(self) -> list[bool]:\n \"\"\"Returns a list of booleans indicating if each structural element is a lithology.\"\"\"\n return [group.is_lithology for group in self.structural_groups]\n\n @property\n def input_data_descriptor(self):\n \"\"\"Returns a descriptor for the input data, detailing the relations and faults between groups.\"\"\"\n # TODO: This should have the exact same dirty logic as interpolation_input\n\n self._validate_faults_relations()\n return InputDataDescriptor.from_structural_frame(\n structural_frame=self,\n making_descriptor=self.groups_structural_relation,\n faults_relations=self.fault_relations,\n faults_input_data=self.faults_input_data\n\n )\n\n @property\n def faults_input_data(self):\n \"\"\"Returns a descriptor for the input data, detailing the relations and faults between groups.\"\"\"\n faults_input_data: list[FaultsData] = [group.faults_input_data for group in self.structural_groups]\n return faults_input_data\n\n @property\n def groups_structural_relation(self) -> list[StackRelationType]:\n \"\"\"Returns a list of the structural relations for each group.\"\"\"\n groups_ = [group.structural_relation for group in self.structural_groups]\n groups_[-1] = StackRelationType.BASEMENT\n return groups_\n\n @property\n def number_of_points_per_element(self) -> np.ndarray:\n \"\"\"Returns an array with the number of points for each structural element.\"\"\"\n return np.array([element.number_of_points for element in self.structural_elements])\n\n @property\n def number_of_points_per_group(self) -> np.ndarray:\n \"\"\"Returns an array with the number of points for each structural group.\"\"\"\n return np.array([group.number_of_points for group in self.structural_groups])\n\n @property\n def number_of_orientations_per_group(self) -> np.ndarray:\n \"\"\"Returns an array with the number of orientations for each structural group.\"\"\"\n return np.array([group.number_of_orientations for group in self.structural_groups])\n\n @property\n def number_of_elements_per_group(self) -> np.ndarray:\n \"\"\"Returns an array with the number of elements for each structural group.\"\"\"\n return np.array([group.number_of_elements for group in self.structural_groups])\n\n @property\n def surfaces(self) -> list[StructuralElement]:\n \"\"\"Returns a list of all surfaces in the structural elements.\"\"\"\n return self.structural_elements\n\n @property\n def number_of_elements(self) -> int:\n \"\"\"Returns the total number of elements in the structural frame.\"\"\"\n return len(self.structural_elements)\n\n @property\n def elements_names(self) -> list[str]:\n \"\"\"Returns a list of names of all structural elements.\"\"\"\n return [element.name for element in self.structural_elements]\n\n @property\n def elements_ids(self) -> np.ndarray:\n \"\"\"Returns an array of IDs for all structural elements.\"\"\"\n return np.arange(len(self.structural_elements)) + 1\n\n @property\n def surface_points_copy(self) -> SurfacePointsTable:\n \"\"\"Returns a SurfacePointsTable for all surface points across the structural elements. This is a copy!\"\"\"\n all_data: np.ndarray = np.concatenate([element.surface_points.data for element in self.structural_elements])\n return SurfacePointsTable(data=all_data, name_id_map=self.element_name_id_map)\n\n @property\n def surface_points(self):\n raise AttributeError(\"This property can only be set, not read. You can access the copy with `surface_points_copy` or\"\n \"the original on the individual structural elements.\")\n\n @surface_points.setter\n def surface_points(self, modified_surface_points: SurfacePointsTable) -> None:\n \"\"\"Distributes the modified surface points back to the structural elements.\"\"\"\n for element in self.structural_elements:\n element.surface_points.data = modified_surface_points.get_surface_points_by_id(element.id).data\n\n @property\n def orientations_copy(self) -> OrientationsTable:\n \"\"\"Returns an OrientationsTable for all orientations across the structural elements.\"\"\"\n all_data: np.ndarray = np.concatenate([element.orientations.data for element in self.structural_elements])\n return OrientationsTable(data=all_data)\n\n @property\n def orientations(self) -> OrientationsTable:\n raise AttributeError(\"This property can only be set, not read. You can access the copy with `orientations_copy` or\"\n \"the original on the individual structural elements.\")\n\n @orientations.setter\n def orientations(self, modified_orientations: OrientationsTable) -> None:\n \"\"\"Distributes the modified orientations back to the structural elements.\"\"\"\n for element in self.structural_elements:\n element.orientations.data = modified_orientations.get_orientations_by_id(element.id).data\n \n @property\n def input_tables_binary(self):\n return self.surface_points_copy.data.tobytes() + self.orientations_copy.data.tobytes()\n\n @property\n def element_id_name_map(self) -> dict[int, str]:\n \"\"\"Returns a dictionary mapping element IDs to names.\"\"\"\n return {element.id: element.name for i, element in enumerate(self.structural_elements)}\n\n @property\n def element_name_id_map(self) -> dict[str, int]:\n \"\"\"Returns a dictionary mapping element names to IDs.\"\"\"\n return {element.name: element.id for i, element in enumerate(self.structural_elements)}\n\n @property\n def elements_colors(self) -> list[str]:\n \"\"\"Returns a list of colors assigned to each structural element. Used in matplotlib\"\"\"\n # reversed\n return [element.color for element in self.structural_elements][::-1]\n\n @property\n def elements_colors_volumes(self) -> list[str]:\n \"\"\"Returns a list of colors assigned to each structural element for volume representation. Used in pyvista\"\"\"\n return self.elements_colors\n\n @property\n def elements_colors_contacts(self) -> list[str]:\n \"\"\"Returns a list of colors assigned to each structural element for contact representation. Used in many places\"\"\"\n points_ = [element.color for element in self.structural_elements if len(element.surface_points) > 0]\n return points_\n\n @property\n def elements_colors_orientations(self) -> list[str]:\n \"\"\"Returns a list of colors assigned to each structural element for orientation representation. Used to paint\n orientations in pyvista\n \"\"\"\n orientations_ = [element.color for element in self.structural_elements if len(element.orientations) > 0]\n return orientations_\n\n @property\n def surface_points_colors_per_item(self) -> list[str]:\n \"\"\"Returns a list of colors assigned to each surface point across structural elements. Used in matplotlib\"\"\"\n surface_points_colors = [element.color for element in self.structural_elements for _ in range(element.number_of_points)]\n return surface_points_colors\n\n @property\n def orientations_colors_per_item(self) -> list[str]:\n \"\"\"Returns a list of colors assigned to each orientation across structural elements. Used in matplotlib\"\"\"\n orientations_colors = [element.color for element in self.structural_elements for _ in range(element.number_of_orientations)]\n return orientations_colors\n\n @property\n def groups_to_mapper(self) -> dict[str, list[str]]:\n \"\"\"Returns a dictionary mapping each structural group to its corresponding elements.\"\"\"\n result_dict = {}\n for group in self.structural_groups:\n element_names = [element.name for element in group.elements]\n result_dict[group.name] = element_names\n return result_dict\n\n # region Depends on Pandas\n @property\n def surfaces_df(self) -> 'pd.DataFrame':\n \"\"\"Returns a DataFrame representation of all surfaces across structural elements.\"\"\"\n # TODO: Loop every structural element. Each element should be a row in the dataframe\n # TODO: The columns have to be ['element, 'group', 'color']\n\n raise NotImplementedError\n\n # endregion\n\n # endregion\n # region Pydantic\n\n @model_validator(mode=\"wrap\")\n @classmethod\n def deserialize_binary(cls, data: Union[\"StructuralFrame\", dict], constructor: ModelWrapValidatorHandler[\"StructuralFrame\"]) -> \"StructuralFrame\":\n match data:\n case StructuralFrame():\n return data\n case dict():\n instance: StructuralFrame = constructor(data)\n metadata = data.get('binary_meta_data', {})\n context = loading_model_context.get()\n\n if 'input_binary' not in context:\n return instance\n\n instance.orientations, instance.surface_points = deserialize_input_data_tables(\n binary_array=context['input_binary'],\n name_id_map=instance.surface_points_copy.name_id_map,\n sp_binary_length_=metadata[\"sp_binary_length\"],\n ori_binary_length_=metadata[\"ori_binary_length\"]\n )\n\n return instance\n case _:\n raise ValidationError(f\"Invalid data type for StructuralFrame: {type(data)}\")\n\n # Access the context variable to get injected data\n\n @computed_field\n def binary_meta_data(self) -> dict:\n return {\n 'sp_binary_length': len(self.surface_points_copy.data.tobytes()),\n 'ori_binary_length': len(self.orientations_copy.data.tobytes()) ,\n }\n\n # endregion\n\n def _validate_faults_relations(self):\n \"\"\"Check that if there are any StackRelationType.FAULT in the structural groups the fault relation matrix is\n given and shape is the right one, i.e. a square matrix of size equals to len(groups)\"\"\"\n\n if any([group.structural_relation == StackRelationType.FAULT for group in self.structural_groups]):\n if self.fault_relations is None:\n raise ValueError(\"The fault relations matrix is not given\")\n if self.fault_relations.shape != (len(self.structural_groups), len(self.structural_groups)):\n raise ValueError(\"The fault relations matrix is not the right shape\")\n" }, { "path": "gempy/core/data/structural_group.py", "content": "import pprint\nfrom abc import ABC\nfrom dataclasses import dataclass, field\nfrom enum import Enum, auto\nfrom typing import Optional, Union, Generator\n\nfrom gempy_engine.core.data.kernel_classes.faults import FaultsData\nfrom gempy_engine.core.data.raw_arrays_solution import RawArraysSolution\nfrom gempy_engine.core.data.stack_relation_type import StackRelationType\nfrom gempy.core.data.structural_element import StructuralElement\n\n\nclass FaultsRelationSpecialCase(Enum):\n OFFSET_FORMATIONS = auto()\n OFFSET_NONE = auto()\n OFFSET_ALL = auto()\n \n \n@dataclass\nclass StructuralGroup(ABC):\n \"\"\"\n An abstract base class that represents a structural group within a geological model.\n \n \"\"\"\n name: str #: The name of the structural group.\n \n elements: list[StructuralElement] = field(repr=False) #: A list of structural elements within the group.\n structural_relation: StackRelationType #: The type of relation between the structural elements in the group.\n\n #: Relations with other groups in terms of faults.\n fault_relations: Optional[Union[list[\"StructuralGroup\"], FaultsRelationSpecialCase]] = field(default=None, repr=False)\n faults_input_data: Optional[FaultsData] = field(default=None, repr=False)\n \n solution: Optional[RawArraysSolution] = field(init=False, default=None, repr=False) #: Solution related to this group from geological computations.\n \n \n def __post_init__(self):\n if not isinstance(self.elements, list):\n raise TypeError(\"elements must be a list of StructuralElement objects.\")\n for e in self.elements:\n if not isinstance(e, StructuralElement):\n raise TypeError(\"elements must be a list of StructuralElement objects.\")\n \n def __repr__(self):\n elements_repr = ',\\n'.join([repr(e) for e in self.elements])\n return f\"StructuralGroup(\\n\" \\\n f\"\\tname={self.name},\\n\" \\\n f\"\\tstructural_relation={self.structural_relation},\\n\" \\\n f\"\\telements=[\\n{elements_repr}\\n]\\n)\"\n\n def _repr_html_(self):\n elements_html = '
'.join([e._repr_html_() for e in self.elements])\n html = f\"\"\"\n \n \n \n \n \n
StructuralGroup:
Name:{self.name}
Structural Relation:{self.structural_relation}
Elements:{elements_html}
\n \"\"\"\n return html\n \n def append_element(self, element: StructuralElement):\n self.elements.append(element)\n \n def remove_element(self, element: StructuralElement):\n self.elements.remove(element)\n\n @property\n def id(self):\n raise NotImplementedError\n \n @property\n def is_fault(self)-> bool:\n return self.structural_relation == StackRelationType.FAULT\n \n @property\n def is_lithology(self)-> bool:\n return self.structural_relation == StackRelationType.ERODE or self.structural_relation == StackRelationType.ONLAP\n \n @property\n def number_of_points(self) -> int:\n return sum([element.number_of_points for element in self.elements])\n \n @property\n def number_of_orientations(self) -> int:\n return sum([element.number_of_orientations for element in self.elements])\n \n @property\n def number_of_elements(self) -> int:\n return len(self.elements)\n \n def get_element_by_name(self, element_name: str) -> StructuralElement | None:\n matched_elements: Generator = (element for element in self.elements if element.name == element_name)\n return next(matched_elements, None)\n\n\n# ? I think these two subclasses are not necessary\n@dataclass\nclass Stack(StructuralGroup): \n def __int__(self, name: str, elements: list[StructuralElement]):\n super().__init__(name, elements)\n \n def __repr__(self):\n return pprint.pformat(self.__dict__)\n\n\n@dataclass\nclass Fault(StructuralGroup): \n pass\n" }, { "path": "gempy/core/data/surface_points.py", "content": "from dataclasses import dataclass\nfrom typing import Optional, Union, Sequence\n\nimport numpy as np\nfrom gempy_engine.core.data.transforms import Transform\nfrom pydantic import Field\n\nfrom ._data_points_helpers import generate_ids_from_names\nfrom ...optional_dependencies import require_pandas\n\nDEFAULT_SP_NUGGET = 0.00002\n\n\n# ? Maybe we should merge this with the SurfacePoints class from gempy_engine\n# ? It does not seem a good a idea because gempy_engine.SurfacePoints is too terse\n\n\n# ! ids are not used apparently\n@dataclass(init=True)\nclass SurfacePointsTable:\n \"\"\"\n A dataclass to represent a table of surface points in a geological model.\n \n \"\"\"\n dt = np.dtype([('X', 'f8'), ('Y', 'f8'), ('Z', 'f8'), ('id', 'i4'), ('nugget', 'f8')]) #: The custom data type for the data array.\n \n data: np.ndarray = Field(\n default=np.zeros(0, dtype=dt),\n exclude=True,\n description=\"A structured NumPy array holding the X, Y, Z coordinates, id, and nugget of each surface point.\"\n ) #: A structured NumPy array holding the X, Y, Z coordinates, id, and nugget of each surface point.\n name_id_map: Optional[dict[str, int]] = None #: A mapping between surface point names and ids.\n _model_transform: Optional[Transform] = None\n\n def __post_init__(self):\n # Check if the data array has the correct data type\n if self.data.dtype != SurfacePointsTable.dt:\n raise ValueError(f\"Data array must have the following data type: {SurfacePointsTable.dt}\")\n\n def __str__(self):\n return \"\\n\" + np.array2string(self.data, precision=2, separator=',', suppress_small=True)\n\n def __repr__(self):\n return f\"SurfacePointsTable(data=\\n{np.array2string(self.data, precision=2, separator=',', suppress_small=True)},\\nname_id_map={self.name_id_map})\"\n\n def _repr_html_(self):\n rows_to_display = 10 # Define the number of rows to display from beginning and end\n html = \"\"\n html += \"\"\n if len(self.data) > 2 * rows_to_display:\n for point in self.data[:rows_to_display]:\n html += \"\".format(*point)\n html += \"\"\n for point in self.data[-rows_to_display:]:\n html += \"\".format(*point)\n else:\n for point in self.data:\n html += \"\".format(*point)\n html += \"
XYZidnugget
{:.2f}{:.2f}{:.2f}{}{:.2f}
...............
{:.2f}{:.2f}{:.2f}{}{:.2f}
{:.2f}{:.2f}{:.2f}{}{:.2f}
\"\n return html\n\n @classmethod\n def from_arrays(cls, x: np.ndarray, y: np.ndarray, z: np.ndarray,\n names: Union[Sequence | str], nugget: Optional[np.ndarray] = None,\n name_id_map: Optional[dict[str, int]] = None\n ) -> 'SurfacePointsTable':\n \"\"\"Create a SurfacePointsTable from arrays of coordinates and names.\n\n Args:\n x (np.ndarray): Array of x-coordinates.\n y (np.ndarray): Array of y-coordinates.\n z (np.ndarray): Array of z-coordinates.\n names (Union[Sequence, str]): Names of the surface points.\n nugget (Optional[np.ndarray]): Nugget values for the surface points.\n name_id_map (Optional[dict[str, int]]): Mapping between surface point names and ids.\n\n Returns:\n SurfacePointsTable: A new instance of SurfacePointsTable.\n \"\"\"\n data, name_id_map = cls._data_from_arrays(x, y, z, names, nugget, name_id_map)\n return cls(data, name_id_map)\n\n\n @classmethod\n def _data_from_arrays(cls, x: np.ndarray, y: np.ndarray, z: np.ndarray,\n names: Union[Sequence | str], nugget: Optional[np.ndarray] = None,\n name_id_map: dict[str, int] = None) -> tuple[np.ndarray, dict[str, int]]:\n if nugget is None:\n nugget = np.zeros_like(x) + DEFAULT_SP_NUGGET\n\n if name_id_map is None:\n ids, name_id_map = generate_ids_from_names(name_id_map, names, x)\n else:\n ids = np.array([name_id_map[name] for name in names])\n\n data = np.zeros(len(x), dtype=SurfacePointsTable.dt)\n data['X'], data['Y'], data['Z'], data['id'], data['nugget'] = x, y, z, ids, nugget\n return data, name_id_map\n\n @classmethod\n def initialize_empty(cls) -> 'SurfacePointsTable':\n \"\"\"Initialize an empty SurfacePointsTable.\n\n Returns:\n SurfacePointsTable: An empty instance of SurfacePointsTable.\n \"\"\"\n return cls(np.zeros(0, dtype=SurfacePointsTable.dt), {})\n\n def id_to_name(self, id: int) -> str:\n \"\"\"Get the name corresponding to a given id.\n\n Args:\n id (int): The id of the surface point.\n\n Returns:\n str: The name of the surface point.\n \"\"\"\n return list(self.name_id_map.keys())[id]\n\n @property\n def xyz(self) -> np.ndarray:\n return np.array([self.data['X'], self.data['Y'], self.data['Z']]).T\n\n @property\n def xyz_view(self) -> np.ndarray:\n return self.data[['X', 'Y', 'Z']]\n\n @xyz_view.setter\n def xyz_view(self, value: np.ndarray):\n self.data['X'], self.data['Y'], self.data['Z'] = value.T\n\n @property\n def nugget(self) -> np.ndarray:\n return self.data['nugget']\n\n @nugget.setter\n def nugget(self, value: np.ndarray):\n self.data['nugget'] = value\n\n @property\n def model_transform(self) -> Transform:\n if self._model_transform is None:\n 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.\")\n return self._model_transform\n\n @model_transform.setter\n def model_transform(self, value: Transform):\n self._model_transform = value\n\n def __len__(self):\n return len(self.data)\n\n def get_surface_points_by_name(self, name: str) -> 'SurfacePointsTable':\n \"\"\"Get a SurfacePointsTable containing points with the specified name.\n\n Args:\n name (str): The name of the surface points.\n\n Returns:\n SurfacePointsTable: A new instance containing the specified surface points.\n \"\"\"\n return self.get_surface_points_by_id(self.name_id_map[name])\n\n def get_surface_points_by_id(self, id: int) -> 'SurfacePointsTable':\n \"\"\"Get a SurfacePointsTable containing points with the specified id.\n\n Args:\n id (int): The id of the surface points.\n\n Returns:\n SurfacePointsTable: A new instance containing the specified surface points.\n \"\"\"\n return SurfacePointsTable(self.data[self.data['id'] == id], self.name_id_map)\n\n def get_surface_points_by_id_groups(self) -> list['SurfacePointsTable']:\n \"\"\"Get a list of SurfacePointsTable objects, each containing points with a unique id.\n\n Returns:\n list[SurfacePointsTable]: A list of SurfacePointsTable objects.\n \"\"\"\n ids = np.unique(self.data['id'])\n return [self.get_surface_points_by_id(id) for id in ids]\n\n @property\n def ids(self) -> np.ndarray:\n return self.data['id']\n\n @property\n def id(self) -> int:\n # Check id is the same in the whole column and return it or throw an error\n ids = np.unique(self.data['id'])\n if len(ids) > 1:\n raise ValueError(f\"OrientationsTable contains more than one id: {ids}\")\n if len(ids) == 0:\n raise ValueError(f\"OrientationsTable contains no ids\")\n return ids[0]\n\n @property\n def df(self) -> 'pd.DataFrame':\n pd = require_pandas()\n return pd.DataFrame(self.data)\n" }, { "path": "gempy/core/engine.rst", "content": "Design document for GemPy Engine 3\n==================================\n\nEarly thoughts about how to redesign the interpolator classes. Some of the\nmain specifications are:\n\n- ?Independent interpolator from the rest so we can use it as each own **microservice** via\n some sort of **sync** function that set up all the constant values\n\n- Compatibility with **TensorFlow** and **Numpy**\n- Outputs of the graph scalable\n- Independence of each scalar field\n - **No global constants**, e.g. each field will have its own range\n - **Scalar field injection**, any recursive value of one field into\n the next one can have an access point.\n- **Octtress** are Native. To deactive it, we just need to set the number of levels to 1\n- Multiple solver (such as **sparse solver** easy to use)\n + cuda solver\n + gradient solver\n + sparse solver\n\n- Interpolator data stored in df and always updated.\n\n- Having several sets of coordinates systems to choose from when we call compute for **anisotropies**\n\nGraph Logic\n~~~~~~~~~~~\n\nTensor super class\n^^^^^^^^^^^^^^^^^^\n\naesara, numpy and tensorflow have a very similar syntax but not 100% exact.\nWe could create a tensor wrapper that makes sure that it returns the right value.\nFor example sin is the same in all 3:\n\n.. code-block:: python\n\n class GemPyTensor:\n def __init__(package='numpy')\n if package=='numpy':\n _tt = np\n elif package=='aesara':\n _tt = aesara.tensor\n\n # Same numpy-aesara easy\n def sin(x):\n return _tt.sin(x)\n\n # Different\n def set_subtensor(x: slice, new_slice)\n if package=='numpy':\n x = new_slice\n return x\n if package == 'aesara':\n return _tt.set_subtensor(x , new_slice)\n\nAdvantages\n----------\n\n- Maintainability: Not having to write 3 different graphs\n- If we use aesara syntax as base for `GemPyTensor` we do not need to rewrite even\n the current graph\n- If a function exist in one package, we can mock it on the others using the correspondent wrappers.\n\nDisadvantages\n-------------\n\n- How do we deal with control flow on run time that is done vastly different\n\n\nData Flow\n~~~~~~~~~\n\n\nScalable Output\n^^^^^^^^^^^^^^^\nIn GemPy 2.1 we moved from having different interpolator objects with different graphs\nto having always one interpolator capable to select how much of the whole graph is going to be\nconstructed at compile time.\n\nAs I see it there some possible paths here:\n\n.. note:: GemPy-server design allows to have several engines up so we could have pretty much anything\n\n1. We construct always the full graph. This will enable to choose in runtime what do we want to compute\n e.g. some gravity, without having to recompile. The cost is memory and as the graph gains in complexity with\n features that may not be used too often it will be a waste.\n\n2. Caching, every time\n\n...\n\n\nIndependent Interpolator\n^^^^^^^^^^^^^^^^^^^^^^^^\n\nTo obtain this we would need to split the interpolator in two classes.\n\n1) One buffer class containing all the data\n2) A graph class\n\nA sync function would feed the graph class\n\n*Which consequences has this for the* ``Model`` *methods?*\n\nThe graph class could be in a different process or could be in the same process.\n\n\n\n\n\n\n" }, { "path": "gempy/modules/__init__.py", "content": "\"\"\"\nGemPy modules package.\n\"\"\"\n\nfrom . import json_io\nfrom . import custom_implicit_functions\nfrom . import data_manipulation\nfrom . import grids\nfrom . import advance_pile\n\n__all__ = [\n 'json_io',\n 'custom_implicit_functions',\n 'data_manipulation',\n 'grids',\n 'advance_pile'\n]\n" }, { "path": "gempy/modules/advance_pile/__init__.py", "content": "" }, { "path": "gempy/modules/advance_pile/strat_definition.py", "content": "" }, { "path": "gempy/modules/custom_implicit_functions/__init__.py", "content": "" }, { "path": "gempy/modules/custom_implicit_functions/ellipsoid_implicit_function.py", "content": "import functools\nimport numpy as np\n\n\ndef ellipsoid_3d_factory(center: np.ndarray, radius: np.ndarray, max_slope: float) -> callable:\n \"\"\"\n Implicit 3D ellipsoid.\n \"\"\"\n\n implicit_ellipsoid = functools.partial(\n ellipsoid_scalar_field,\n center=center,\n radii=radius,\n k_factors=max_slope,\n )\n\n return implicit_ellipsoid\n\n\ndef ellipsoid_scalar_field(xyz, center, radii, k_factors):\n \"\"\"Calculate scalar field value for given coordinates.\n\n Parameters:\n - xyz: numpy array of shape (N, 3), where N is the number of points\n - center: numpy array of shape (3,) representing the center of the ellipsoid\n - radii: numpy array of shape (3,) representing the semiaxes a, b, and c of the ellipsoid\n - k_factors: numpy array of shape (3,) representing the slope factors for x, y, and z directions.\n\n Returns:\n - A numpy array of shape (N,) containing the scalar field values.\n \"\"\"\n displacements = xyz - center\n values = ((displacements[:, 0] / (radii[0] * k_factors[0])) ** 2 +\n (displacements[:, 1] / (radii[1] * k_factors[1])) ** 2 +\n (displacements[:, 2] / (radii[2] * k_factors[2])) ** 2 - 1)\n\n return - sigmoid(values * np.prod(k_factors)) + 1 # multiplying by the product of k_factors to keep the transition sharper\n\n\ndef sigmoid(x):\n \"\"\"Standard sigmoid function.\"\"\"\n return 1 / (1 + np.exp(-x))\n\n\ndef _implicit_3d_ellipsoid_to_slope_(xyz: np.ndarray, center: np.ndarray, radius: np.ndarray,\n max_slope: float = 1000):\n \"\"\"\n Implicit 3D ellipsoid.\n \"\"\"\n # ! Finite faults needs quite a bit of fine tunning once we can compute models in real time\n # ! This function only works for elipses perpendicular to the cartesian axis\n scalar = - np.sum((xyz - center) ** 2.00 / (radius ** 2), axis=1)\n scalar_shifted = scalar - scalar.min()\n\n sigmoid_slope = 10 # ? This probably should be also public\n Z_x = scalar_shifted\n\n drift_0 = 4 # ? Making it a %. It depends on the radius\n scale_0 = max_slope\n scalar_final = scale_0 / (1 + np.exp(-sigmoid_slope * (Z_x - drift_0)))\n return scalar_final\n" }, { "path": "gempy/modules/data_manipulation/__init__.py", "content": "from ._engine_factory import interpolation_input_from_structural_frame" }, { "path": "gempy/modules/data_manipulation/_engine_factory.py", "content": "from typing import Optional\n\nimport numpy as np\n\nfrom ...core.data.grid import Grid\nfrom ...core.data.structural_frame import StructuralFrame\n\nfrom gempy_engine.core.data import SurfacePoints, Orientations\nfrom gempy_engine.core.data import engine_grid\nfrom gempy_engine.core.data.interpolation_input import InterpolationInput\nfrom gempy_engine.core.data.transforms import Transform\n\n\ndef interpolation_input_from_structural_frame(geo_model: \"gempy.data.GeoModel\") -> InterpolationInput:\n import gempy # ! This is important for type safety\n geo_model: gempy.data.GeoModel = geo_model\n\n _legacy_factor = 0\n\n structural_frame: StructuralFrame = geo_model.structural_frame\n input_transform: Transform = geo_model.input_transform\n grid: Grid = geo_model.grid\n\n total_transform: Transform = input_transform + grid.transform\n\n surface_points_copy_transformed = geo_model.surface_points_copy_transformed\n surface_points: SurfacePoints = SurfacePoints(\n sp_coords=geo_model.surface_points_copy_transformed.xyz,\n nugget_effect_scalar=surface_points_copy_transformed.nugget\n )\n\n orientations_copy_transformed = geo_model.orientations_copy_transformed\n orientations: Orientations = Orientations(\n dip_positions=orientations_copy_transformed.xyz,\n dip_gradients=orientations_copy_transformed.grads,\n nugget_effect_grad=orientations_copy_transformed.nugget\n )\n\n grid: engine_grid.EngineGrid = _apply_input_transform_to_grids(\n grid=grid,\n input_transform=input_transform,\n extent_transformed=geo_model.extent_transformed_transformed_by_input\n )\n\n weights = []\n if geo_model.solutions is not None:\n for stack_sol in geo_model.solutions.root_output.outputs:\n weights.append(stack_sol.weights)\n\n interpolation_input: InterpolationInput = InterpolationInput(\n surface_points=surface_points,\n orientations=orientations,\n grid=grid,\n unit_values=structural_frame.elements_ids, # TODO: Here we will need to pass densities etc.\n weights=weights\n )\n\n return interpolation_input\n\n\ndef _apply_input_transform_to_grids(grid: Grid, input_transform: Transform, extent_transformed: np.ndarray) -> engine_grid.EngineGrid:\n new_extents = extent_transformed\n # Initialize all variables to None\n octree_grid: Optional[engine_grid.RegularGrid] = None\n regular_grid: Optional[engine_grid.RegularGrid] = None\n custom_values: Optional[engine_grid.GenericGrid] = None\n topography_values: Optional[engine_grid.GenericGrid] = None\n section_values: Optional[engine_grid.GenericGrid] = None\n centered_grid: Optional[engine_grid.CenteredGrid] = None\n\n if grid.GridTypes.DENSE in grid.active_grids:\n regular_grid = engine_grid.RegularGrid(\n orthogonal_extent=new_extents,\n regular_grid_shape=grid.dense_grid.resolution,\n )\n if grid.GridTypes.CUSTOM in grid.active_grids and grid.custom_grid is not None:\n custom_values = engine_grid.GenericGrid(values=input_transform.apply(grid.custom_grid.values))\n if grid.GridTypes.TOPOGRAPHY in grid.active_grids and grid.topography is not None:\n topography_values = engine_grid.GenericGrid(values=input_transform.apply(grid.topography.values))\n if grid.GridTypes.SECTIONS in grid.active_grids and grid.sections is not None:\n section_values = engine_grid.GenericGrid(values=input_transform.apply(grid.sections.values))\n if grid.GridTypes.CENTERED in grid.active_grids and grid.centered_grid is not None:\n centered_grid = engine_grid.CenteredGrid(\n centers=input_transform.apply(grid.centered_grid.centers),\n radius=input_transform.scale_points(np.atleast_2d(grid.centered_grid.radius))[0],\n resolution=grid.centered_grid.resolution\n )\n if grid.octree_grid is not None:\n base_grid_resolution = grid.octree_grid.base_resolution\n else:\n base_grid_resolution = np.array([2, 2, 2])\n\n octree_grid = engine_grid.RegularGrid(\n orthogonal_extent=new_extents,\n regular_grid_shape=base_grid_resolution\n )\n grid: engine_grid.EngineGrid = engine_grid.EngineGrid( # * Here we convert the GemPy grid to the\n octree_grid=octree_grid, # BUG: Adapt the engine to deal with this\n dense_grid=regular_grid,\n topography=topography_values,\n sections=section_values,\n custom_grid=custom_values,\n geophysics_grid=centered_grid\n )\n return grid\n" }, { "path": "gempy/modules/data_manipulation/manipulate_points.py", "content": "from typing import Sequence, Optional, Union\n\nimport numpy as np\n\nfrom ...core.data import GeoModel, StructuralFrame, SurfacePointsTable, StructuralElement, OrientationsTable\nfrom ...core.data.orientations import DEFAULT_ORI_NUGGET\nfrom ...core.data.surface_points import DEFAULT_SP_NUGGET\n\n\ndef add_surface_points(\n geo_model: GeoModel,\n x: Sequence[float],\n y: Sequence[float],\n z: Sequence[float],\n elements_names: Sequence[str],\n nugget: Optional[Sequence[float]] = None\n) -> StructuralFrame:\n \"\"\"Add surface points to the geological model.\n\n This function adds surface points to the specified geological elements in the model.\n The points are grouped by element names, and optional nugget values can be specified\n for each point.\n\n Args:\n geo_model (GeoModel): The geological model to which the surface points will be added.\n x (Sequence[float]): Sequence of x-coordinates for the surface points.\n y (Sequence[float]): Sequence of y-coordinates for the surface points.\n z (Sequence[float]): Sequence of z-coordinates for the surface points.\n elements_names (Sequence[str]): Sequence of element names corresponding to each surface point.\n nugget (Optional[Sequence[float]]): Sequence of nugget values for each surface point. If not provided,\n a default value will be used for all points.\n\n Returns:\n StructuralFrame: The updated structural frame of the geological model.\n\n Raises:\n ValueError: If the length of the nugget sequence does not match the lengths of the other input sequences.\n \"\"\"\n elements_names = _validate_args(elements_names, x, y, z)\n\n # If nugget is not provided, create a Sequence filled with the default value\n if nugget is None:\n nugget = [DEFAULT_SP_NUGGET] * len(x)\n\n # Ensure nugget also has the same length as the other Sequences\n if len(nugget) != len(x):\n raise ValueError(\"The length of the nugget Sequence does not match the lengths of other input Sequences.\")\n\n # * Split the sequences according to the elements_names\n x = np.array(x)\n y = np.array(y)\n z = np.array(z)\n elements_names = np.array(elements_names)\n nugget = np.array(nugget)\n\n unique_names = np.unique(elements_names)\n grouped_data = {}\n\n for name in unique_names:\n mask = (elements_names == name)\n grouped_data[name] = {\n 'x' : x[mask],\n 'y' : y[mask],\n 'z' : z[mask],\n 'nugget': nugget[mask]\n }\n\n # * Loop per element_name\n for element_name, data in grouped_data.items():\n formatted_data, _ = SurfacePointsTable._data_from_arrays(\n x=data['x'],\n y=data['y'],\n z=data['z'],\n names=[element_name] * len(data['x']),\n nugget=data['nugget'],\n name_id_map=None\n )\n\n element: StructuralElement = geo_model.structural_frame.get_element_by_name(element_name)\n element.surface_points.data = np.concatenate([\n element.surface_points.data,\n formatted_data\n ])\n\n return geo_model.structural_frame\n\n\ndef delete_surface_points():\n raise NotImplementedError\n\n\ndef add_orientations(geo_model: GeoModel,\n x: Sequence[float],\n y: Sequence[float],\n z: Sequence[float],\n elements_names: Sequence[str],\n pole_vector: Optional[Union[Sequence[np.ndarray], np.ndarray]] = None,\n orientation: Optional[Union[Sequence[np.ndarray], np.ndarray]] = None,\n nugget: Optional[Sequence[float]] = None,\n name_id_map: Optional[dict[str, int]] = None #: A mapping between orientation names and ids.\n) -> StructuralFrame:\n \"\"\"Add orientation data to the geological model.\n\n This function adds orientation data to the specified geological elements in the model.\n The orientation can be provided directly as pole vectors or as orientation angles (azimuth, dip, polarity).\n Optional nugget values can also be specified for each orientation point.\n\n Args:\n geo_model (GeoModel): The geological model to which the orientations will be added.\n x (Sequence[float]): Sequence of x-coordinates for the orientation points.\n y (Sequence[float]): Sequence of y-coordinates for the orientation points.\n z (Sequence[float]): Sequence of z-coordinates for the orientation points.\n elements_names (Sequence[str]): Sequence of element names corresponding to each orientation point.\n 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).\n 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).\n nugget (Optional[Sequence[float]]): Sequence of nugget values for each orientation point. If not provided,\n a default value will be used for all points.\n\n Returns:\n StructuralFrame: The updated structural frame of the geological model.\n\n Raises:\n ValueError: If neither pole_vector nor orientation is provided, or if the length of the nugget sequence\n does not match the lengths of the other input sequences.\n \"\"\"\n if pole_vector is None and orientation is None:\n raise ValueError(\"Either pole_vector or orientation must be provided.\")\n\n if orientation is not None:\n orientation = np.array(orientation, ndmin=2)\n pole_vector = convert_orientation_to_pole_vector(\n azimuth=orientation[:, 0],\n dip=orientation[:, 1],\n polarity=orientation[:, 2]\n )\n else:\n pole_vector = np.array(pole_vector, ndmin=2)\n\n elements_names = _validate_args(elements_names, x, y, z, pole_vector)\n\n if nugget is None: # If nugget is not provided, create a Sequence filled with the default value\n nugget = [DEFAULT_ORI_NUGGET] * len(x)\n\n # Ensure nugget also has the same length as the other Sequences\n if len(nugget) != len(x):\n raise ValueError(\"The length of the nugget Sequence does not match the lengths of other input Sequences.\")\n\n # * Split the sequences according to the elements_names\n x = np.array(x)\n y = np.array(y)\n z = np.array(z)\n elements_names = np.array(elements_names)\n pole_vector = np.array(pole_vector)\n nugget = np.array(nugget)\n\n unique_names = np.unique(elements_names)\n grouped_data = {}\n\n for name in unique_names:\n mask = (elements_names == name)\n grouped_data[name] = {\n 'x' : x[mask],\n 'y' : y[mask],\n 'z' : z[mask],\n 'pole_vector': pole_vector[mask],\n 'nugget' : nugget[mask]\n }\n\n # * Loop per element_name\n for element_name, data in grouped_data.items():\n formatted_data, _ = OrientationsTable._data_from_arrays(\n x=data['x'],\n y=data['y'],\n z=data['z'],\n G_x=data['pole_vector'][..., 0],\n G_y=data['pole_vector'][..., 1],\n G_z=data['pole_vector'][..., 2],\n names=[element_name] * len(data['x']),\n nugget=data['nugget'],\n name_id_map=name_id_map\n )\n\n element: StructuralElement = geo_model.structural_frame.get_element_by_name(element_name)\n element.orientations.data = np.concatenate([\n element.orientations.data,\n formatted_data\n ])\n\n return geo_model.structural_frame\n\n\ndef modify_orientations(geo_model: GeoModel, slice: Optional[Union[int, slice]] = None,\n **orientation_field: Union[float, np.ndarray]) -> StructuralFrame:\n \"\"\"\n Modifies specified fields of all orientations in the structural frame. The keys of the orientation_field \n dictionary should match the field names in the orientations (e.g., \"X\", \"Y\", \"Z\", \"G_x\", \"G_y\", \"G_z\", \"nugget\").\n \n Args:\n geo_model (GeoModel): The GeoModel instance to modify.\n slice (Optional[Union[int, slice]]): The slice of orientations to modify. If None, all orientations will be modified.\n \n Keyword Args:\n X (Union[float, np.ndarray]): X coordinates of the orientations.\n Y (Union[float, np.ndarray]): Y coordinates of the orientations.\n Z (Union[float, np.ndarray]): Z coordinates of the orientations.\n azimuth (Union[float, np.ndarray]): Azimuth angles of the orientations.\n dip (Union[float, np.ndarray]): Dip angles of the orientations.\n polarity (Union[float, np.ndarray]): Polarity values of the orientations.\n G_x (Union[float, np.ndarray]): X component of the gradient vector.\n G_y (Union[float, np.ndarray]): Y component of the gradient vector.\n G_z (Union[float, np.ndarray]): Z component of the gradient vector.\n nugget (Union[float, np.ndarray]): Nugget value of the orientations.\n \n Returns:\n StructuralFrame: The modified structural frame.\n \"\"\"\n\n orientations = geo_model.structural_frame.orientations_copy\n\n # If no slice is provided, target all rows; else, target specified slice\n target_rows = slice if slice is not None else np.s_[:]\n\n # Extract provided orientation fields without modifying the dictionary\n azimuth = orientation_field.pop('azimuth', None)\n dip = orientation_field.pop('dip', None)\n polarity = orientation_field.pop('polarity', None)\n\n # Update all the other fields\n for key, value in orientation_field.items():\n if isinstance(value, np.ndarray) and len(value) != len(orientations.data[target_rows]):\n raise ValueError(f\"Length mismatch: Expected size {len(orientations.data[target_rows])} for field {key}, but got {len(value)}.\")\n orientations.data[key][target_rows] = value\n\n # Check if azimuth, dip, or polarity are provided\n any_polar_fields = azimuth is not None or dip is not None or polarity is not None\n all_polar_fields = azimuth is not None and dip is not None and polarity is not None\n\n match (any_polar_fields, all_polar_fields):\n case (True, True):\n # All polar fields provided, convert to gradients\n gx, gy, gz = convert_orientation_to_pole_vector(np.asarray(azimuth), np.asarray(dip), np.asarray(polarity))\n orientations.data['G_x'][target_rows] = gx\n orientations.data['G_y'][target_rows] = gy\n orientations.data['G_z'][target_rows] = gz\n\n case (True, False):\n # Some polar fields missing, compute missing fields from gradients\n prev_azimuth, prev_dip, prev_polarity = compute_adp_from_gradients(\n orientations.data['G_x'],\n orientations.data['G_y'],\n orientations.data['G_z']\n )\n azimuth = np.asarray(azimuth) if azimuth is not None else prev_azimuth\n dip = np.asarray(dip) if dip is not None else prev_dip\n polarity = np.asarray(polarity) if polarity is not None else prev_polarity\n\n gradients = convert_orientation_to_pole_vector(azimuth, dip, polarity)\n orientations.data['G_x'][target_rows] = gradients[:, 0]\n orientations.data['G_y'][target_rows] = gradients[:, 1]\n orientations.data['G_z'][target_rows] = gradients[:, 2]\n\n case (_, _):\n pass\n\n geo_model.orientations = orientations\n return geo_model.structural_frame\n\n\ndef modify_surface_points(geo_model: GeoModel,\n slice: Optional[Union[int, slice]] = None,\n elements_names: Optional[Sequence[str]] = None,\n **surface_points_field: Union[float, np.ndarray]) -> StructuralFrame:\n \"\"\"\n Modifies specified fields of all surface points in the structural frame. The keys of the surface_points_field \n dictionary should match the field names in the surface points (e.g., \"X\", \"Y\", \"Z\", \"nugget\").\n \n Args:\n geo_model (GeoModel): The GeoModel instance to modify.\n slice (Optional[Union[int, slice]]): The slice of surface points to modify. If None, all surface points will be modified.\n \n Keyword Args:\n X (Union[float, np.ndarray]): X coordinates of the surface points.\n Y (Union[float, np.ndarray]): Y coordinates of the surface points.\n Z (Union[float, np.ndarray]): Z coordinates of the surface points.\n nugget (Union[float, np.ndarray]): Nugget value of the surface points.\n \n Returns:\n StructuralFrame: The modified structural frame.\n \"\"\"\n if elements_names is not None and slice is not None:\n raise ValueError(\"Cannot provide both elements_names and slice.\")\n\n surface_points = geo_model.structural_frame.surface_points_copy\n\n if elements_names is not None:\n ids = [surface_points.name_id_map[element] for element in elements_names]\n slice = np.s_[np.isin(surface_points.data['id'], ids)]\n\n # If no slice is provided, target all rows; else, target specified slice\n target_rows = slice if slice is not None else np.s_[:]\n\n for key, value in surface_points_field.items():\n if isinstance(value, np.ndarray) and len(value) != len(surface_points.data[target_rows]):\n raise ValueError(f\"Length mismatch: Expected size {len(surface_points.data[target_rows])} for field {key}, but got {len(value)}.\")\n\n surface_points.data[key][target_rows] = value\n\n geo_model.surface_points = surface_points\n return geo_model.structural_frame\n\n\ndef delete_orientations():\n raise NotImplementedError\n\n\ndef convert_orientation_to_pole_vector(azimuth: Sequence[float], dip: Sequence[float], polarity: Sequence[float]) -> Sequence[np.ndarray]:\n # Convert sequences to numpy arrays for vectorized operations\n azimuth = np.array(azimuth)\n dip = np.array(dip)\n polarity = np.array(polarity)\n\n # Calculate gradient components\n G_x = np.sin(np.deg2rad(dip)) * np.sin(np.deg2rad(azimuth)) * polarity\n G_y = np.sin(np.deg2rad(dip)) * np.cos(np.deg2rad(azimuth)) * polarity\n G_z = np.cos(np.deg2rad(dip)) * polarity\n\n # Combine gradient components into an array of vectors\n gradients = np.vstack([G_x, G_y, G_z]).T\n\n return gradients\n\n\ndef compute_adp_from_gradients(G_x: np.ndarray, G_y: np.ndarray, G_z: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:\n # Calculate polarity (here assumed to be 1 for all, but you can adapt if needed)\n polarity = np.ones_like(G_x)\n\n # Calculate dip\n dip = np.rad2deg(np.nan_to_num(np.arccos(G_z / polarity)))\n\n # Calculate azimuth\n azimuth = np.rad2deg(np.nan_to_num(np.arctan2(G_x / polarity, G_y / polarity)))\n\n # Shift values from [-pi, 0] to [pi,2*pi]\n azimuth[azimuth < 0] += 360\n\n # Adjust azimuth where dip is nearly zero, because if dip is zero, azimuth is undefined\n azimuth[dip < 0.001] = 0\n\n return azimuth, dip, polarity\n\n\ndef _validate_args(elements_names, *args):\n if isinstance(elements_names, str):\n elements_names = np.array([elements_names] * len(args[0]))\n elif isinstance(elements_names, Sequence) or isinstance(elements_names, np.ndarray):\n pass\n else:\n raise TypeError(f\"Names should be a string or a NumPy array, not {type(elements_names)}\")\n # Ensure all provided Sequences have the same length\n lengths = {len(arg) for arg in args}\n if len(lengths) != 1:\n raise ValueError(\"All input Sequences must have the same length.\")\n return elements_names\n" }, { "path": "gempy/modules/data_manipulation/manipulate_structural_frame.py", "content": "from gempy_engine.core.data.stack_relation_type import StackRelationType\nfrom gempy.core.data import StructuralGroup, GeoModel, FaultsRelationSpecialCase, StructuralElement, StructuralFrame\n\n\ndef add_structural_group(\n model: GeoModel, group_index: int, structural_group_name: str, elements: list[StructuralElement],\n structural_relation: StackRelationType, fault_relations: FaultsRelationSpecialCase = FaultsRelationSpecialCase.OFFSET_ALL) -> StructuralFrame:\n \n # Check elements are a Sequence\n if not isinstance(elements, list):\n raise TypeError(\"elements must be a list of StructuralElement objects.\")\n \n new_group = StructuralGroup(\n name=structural_group_name,\n elements=elements,\n structural_relation=structural_relation,\n fault_relations=fault_relations\n )\n\n # Insert the fault group into the structural frame:\n model.structural_frame.insert_group(group_index, new_group)\n return model.structural_frame\n\n\ndef remove_structural_group_by_index(model: GeoModel, group_index: int) -> StructuralFrame:\n model.structural_frame.structural_groups.pop(group_index)\n return model.structural_frame\n\n\ndef remove_structural_group_by_name(model: GeoModel, group_name: str) -> StructuralFrame:\n group = model.structural_frame.get_group_by_name(group_name)\n group_index = model.structural_frame.structural_groups.index(group)\n model.structural_frame.structural_groups.pop(group_index)\n return model.structural_frame\n \n\ndef remove_element_by_name(model: GeoModel, element_name: str) -> StructuralFrame:\n element = model.structural_frame.get_element_by_name(element_name)\n element_group: StructuralGroup = model.structural_frame.get_group_by_element(element)\n element_group.remove_element(element)\n return model.structural_frame \n" }, { "path": "gempy/modules/data_manipulation/orientations_from_surface_points.py", "content": "from typing import Optional\n\nimport numpy as np\nfrom numpy.linalg import svd\n\nfrom gempy.core.data import SurfacePointsTable, OrientationsTable\n\n\ndef create_orientations_from_surface_points_coords(xyz_coords: np.ndarray,\n subset: Optional[np.ndarray] = None,\n element_name: Optional[str] = \"Generated\") -> OrientationsTable:\n # Initialize the arrays\n center = np.empty((len(subset) if subset is not None else 1, 3))\n normal = np.empty((len(subset) if subset is not None else 1, 3))\n\n if subset is None:\n center[0], normal[0] = _plane_fit(xyz_coords)\n else:\n for idx, i in enumerate(subset):\n center[idx], normal[idx] = _plane_fit(xyz_coords[i])\n\n orientations = OrientationsTable.from_arrays(\n x=center[:, 0],\n y=center[:, 1],\n z=center[:, 2],\n G_x=normal[:, 0],\n G_y=normal[:, 1],\n G_z=normal[:, 2],\n names=[element_name]\n )\n return orientations\n\n\ndef _plane_fit(point_list):\n \"\"\"\n Fit plane to points in PointSet\n Fit an d-dimensional plane to the points in a point set.\n adjusted from: http://stackoverflow.com/questions/12299540/plane-fitting-to-4-or-more-xyz-points\n\n Args:\n point_list (array_like): array of points XYZ\n\n Returns:\n Return a point, p, on the plane (the point-cloud centroid),\n and the normal, n.\n \"\"\"\n\n points = point_list.T\n\n points = np.reshape(points, (np.shape(points)[0], -1)) # Collapse trialing dimensions\n assert points.shape[0] <= points.shape[1], \"There are only {} points in {} dimensions.\".format(points.shape[1],\n points.shape[0])\n ctr = points.mean(axis=1)\n x = points - ctr[:, np.newaxis]\n M = np.dot(x, x.T) # Could also use np.cov(x) here.\n\n # ctr = Point(x=ctr[0], y=ctr[1], z=ctr[2], type='utm', zone=self.points[0].zone)\n normal = svd(M)[0][:, -1]\n # return ctr, svd(M)[0][:, -1]\n if normal[2] < 0:\n normal = - normal\n\n return ctr, normal\n" }, { "path": "gempy/modules/grids/__init__.py", "content": "" }, { "path": "gempy/modules/grids/create_topography.py", "content": "\"\"\"\nThis file is part of gempy.\n\nCreated on 16.04.2019\n\n@author: Elisa Heim\n\"\"\"\nfrom typing import Optional\n\nimport numpy as np\n\nfrom ...core.data.grid_modules.topography import _LoadDEMArtificial\n\n\ndef create_random_topography(extent: np.array, resolution: np.array, dz: Optional[np.array] = None,\n fractal_dimension: Optional[float] = 2.0) -> np.array:\n dem = _LoadDEMArtificial(\n extent=extent,\n resolution=resolution,\n d_z=dz,\n fd=fractal_dimension\n )\n\n return dem.get_values()\n\n\n\n" }, { "path": "gempy/modules/json_io/__init__.py", "content": "\"\"\"\nJSON I/O module for GemPy.\nThis module provides functionality to load and save GemPy models to/from JSON files.\n\"\"\"\n\nfrom .json_operations import JsonIO\n\n__all__ = ['JsonIO'] " }, { "path": "gempy/modules/json_io/json_operations.py", "content": "\"\"\"\nModule for JSON I/O operations in GemPy.\nThis module provides functionality to load and save GemPy models to/from JSON files.\n\"\"\"\n\nimport json\nfrom typing import Dict, Any, Optional, List\nimport numpy as np\nfrom datetime import datetime\n\nfrom .schema import SurfacePoint, Orientation, GemPyModelJson, IdNameMapping\nfrom gempy_engine.core.data.stack_relation_type import StackRelationType\n\n\nclass JsonIO:\n \"\"\"Class for handling JSON I/O operations for GemPy models.\"\"\"\n \n @staticmethod\n def _numpy_to_list(obj):\n \"\"\"Convert numpy arrays to lists for JSON serialization.\"\"\"\n if isinstance(obj, np.ndarray):\n return obj.tolist()\n elif isinstance(obj, (np.integer, np.int32, np.int64)):\n return int(obj)\n elif isinstance(obj, (np.floating, np.float32, np.float64)):\n return float(obj)\n return obj\n\n @staticmethod\n def _create_id_to_name(name_to_id: Dict[str, int]) -> Dict[int, str]:\n \"\"\"Create an id_to_name mapping from a name_to_id mapping.\"\"\"\n return {id: name for name, id in name_to_id.items()}\n\n @staticmethod\n def _calculate_default_range(grid_extent: List[float]) -> float:\n \"\"\"Calculate the default range based on the model extent (room diagonal).\"\"\"\n # Extract min and max coordinates\n x_min, x_max = grid_extent[0], grid_extent[1]\n y_min, y_max = grid_extent[2], grid_extent[3]\n z_min, z_max = grid_extent[4], grid_extent[5]\n \n # Calculate the room diagonal (Euclidean distance)\n return np.sqrt((x_max - x_min)**2 + (y_max - y_min)**2 + (z_max - z_min)**2)\n\n @staticmethod\n def _calculate_default_grid_settings(surface_points: List[SurfacePoint], orientations: List[Orientation]) -> Dict[str, Any]:\n \"\"\"Calculate default grid settings based on data points.\n \n Args:\n surface_points: List of surface points\n orientations: List of orientations\n \n Returns:\n Dict containing grid settings with default values\n \"\"\"\n # Collect all x, y, z coordinates\n all_x = [sp['x'] for sp in surface_points] + [ori['x'] for ori in orientations]\n all_y = [sp['y'] for sp in surface_points] + [ori['y'] for ori in orientations]\n all_z = [sp['z'] for sp in surface_points] + [ori['z'] for ori in orientations]\n \n # Calculate extents\n x_min, x_max = min(all_x), max(all_x)\n y_min, y_max = min(all_y), max(all_y)\n z_min, z_max = min(all_z), max(all_z)\n \n # Calculate ranges\n x_range = x_max - x_min\n y_range = y_max - y_min\n z_range = z_max - z_min\n \n # Add 10% padding to each dimension\n x_padding = x_range * 0.1\n y_padding = y_range * 0.1\n z_padding = z_range * 0.1\n \n return {\n \"regular_grid_resolution\": [20, 20, 20],\n \"regular_grid_extent\": [\n x_min - x_padding, x_max + x_padding,\n y_min - y_padding, y_max + y_padding,\n z_min - z_padding, z_max + z_padding\n ]\n }\n\n @staticmethod\n def load_model_from_json(file_path: str):\n \"\"\"\n Load a GemPy model from a JSON file.\n \n Args:\n file_path (str): Path to the JSON file\n \n Returns:\n GeoModel: A new GemPy model instance\n \"\"\"\n # Import here to avoid circular imports\n from gempy.core.data.geo_model import GeoModel, GeoModelMeta\n from gempy.core.data.grid import Grid\n from gempy.core.data.structural_frame import StructuralFrame\n from gempy_engine.core.data import InterpolationOptions\n from gempy.API.map_stack_to_surfaces_API import map_stack_to_surfaces\n \n with open(file_path, 'r') as f:\n data = json.load(f)\n \n # Validate the JSON data against our schema\n if not JsonIO._validate_json_schema(data):\n raise ValueError(\"Invalid JSON schema\")\n \n # Get unique surface IDs from surface points and orientations\n surface_ids = set(sp['id'] for sp in data['surface_points'])\n surface_ids.update(ori['id'] for ori in data['orientations'])\n \n # Create default series if not provided\n if 'series' not in data:\n data['series'] = [{\n 'name': 'Strat_Series',\n 'surfaces': [f'surface_{id}' for id in sorted(surface_ids)],\n 'structural_relation': 'ERODE'\n }]\n \n # Get surface names from series data\n surface_names = []\n for series in data['series']:\n surface_names.extend(series['surfaces'])\n \n # Create ID to name mapping if not provided\n if 'id_name_mapping' in data:\n id_to_name = JsonIO._create_id_to_name(data['id_name_mapping']['name_to_id'])\n else:\n # Create mapping from series data\n id_to_name = {i: name for i, name in enumerate(surface_names)}\n \n # Create surface points table\n surface_points_table = JsonIO._load_surface_points(data['surface_points'], id_to_name)\n \n # Create orientations table\n orientations_table = JsonIO._load_orientations(data['orientations'], id_to_name)\n \n # Create structural frame\n structural_frame = StructuralFrame.from_data_tables(surface_points_table, orientations_table)\n \n # Get grid settings with defaults if not provided\n grid_settings = data.get('grid_settings', JsonIO._calculate_default_grid_settings(data['surface_points'], data['orientations']))\n \n # Create grid\n grid = Grid(\n resolution=grid_settings['regular_grid_resolution'],\n extent=grid_settings['regular_grid_extent']\n )\n \n # Calculate default range based on model extent\n # default_range = JsonIO._calculate_default_range(grid_settings['regular_grid_extent'])\n # set as fixed value\n default_range = 1.7 # Match standard GemPy default\n \n # Create interpolation options with defaults if not provided\n interpolation_options = InterpolationOptions.from_args(\n range=data.get('interpolation_options', {}).get('kernel_options', {}).get('range', default_range),\n c_o=data.get('interpolation_options', {}).get('kernel_options', {}).get('c_o', 10),\n mesh_extraction=data.get('interpolation_options', {}).get('mesh_extraction', True),\n number_octree_levels=data.get('interpolation_options', {}).get('number_octree_levels', 1)\n )\n \n # Create GeoModel with default metadata if not provided\n current_date = datetime.now().strftime(\"%Y-%m-%d\")\n model_name = data.get('metadata', {}).get('name', \"GemPy Model\")\n \n model = GeoModel.from_args(\n name=model_name,\n structural_frame=structural_frame,\n grid=grid,\n interpolation_options=interpolation_options\n )\n \n # Set the metadata with proper dates and defaults\n metadata = data.get('metadata', {})\n model_meta = GeoModelMeta(\n name=metadata.get('name', model.meta.name), # Use model's name if available\n creation_date=metadata.get('creation_date', current_date), # Set current date as default\n last_modification_date=metadata.get('last_modification_date', current_date), # Set current date as default\n owner=metadata.get('owner', \"GemPy Modeller\") # Set default owner\n )\n model.meta = model_meta\n \n # Map series to surfaces with structural relations\n mapping_object = {series['name']: series['surfaces'] for series in data['series']}\n # Ensure each series has structural_relation set to ERODE by default\n series_data = []\n for series in data['series']:\n series_copy = series.copy()\n if 'structural_relation' not in series_copy:\n series_copy['structural_relation'] = 'ERODE'\n series_data.append(series_copy)\n map_stack_to_surfaces(model, mapping_object, series_data=series_data)\n \n # Set fault relations after structural groups are set up\n if 'fault_relations' in data and data['fault_relations'] is not None:\n fault_relations = np.array(data['fault_relations'])\n if fault_relations.shape == (len(model.structural_frame.structural_groups), len(model.structural_frame.structural_groups)):\n model.structural_frame.fault_relations = fault_relations\n \n # Set colors for each element\n for series in data['series']:\n if 'colors' in series:\n for surface_name, color in zip(series['surfaces'], series['colors']):\n element = model.structural_frame.get_element_by_name(surface_name)\n if element is not None:\n element.color = color\n \n return model\n \n @staticmethod\n def _load_surface_points(surface_points_data: List[SurfacePoint], id_to_name: Optional[Dict[int, str]] = None):\n \"\"\"\n Load surface points from JSON data.\n \n Args:\n surface_points_data (List[SurfacePoint]): List of surface point dictionaries\n id_to_name (Optional[Dict[int, str]]): Optional mapping from surface IDs to names\n \n Returns:\n SurfacePointsTable: A new SurfacePointsTable instance\n \n Raises:\n ValueError: If the data is invalid or missing required fields\n \"\"\"\n # Import here to avoid circular imports\n from gempy.core.data.surface_points import SurfacePointsTable\n \n # Validate data structure\n required_fields = {'x', 'y', 'z', 'nugget', 'id'}\n for i, sp in enumerate(surface_points_data):\n missing_fields = required_fields - set(sp.keys())\n if missing_fields:\n raise ValueError(f\"Missing required fields in surface point {i}: {missing_fields}\")\n \n # Validate data types\n if not all(isinstance(sp[field], (int, float)) for field in ['x', 'y', 'z', 'nugget']):\n raise ValueError(f\"Invalid data type in surface point {i}. All coordinates and nugget must be numeric.\")\n if not isinstance(sp['id'], int):\n raise ValueError(f\"Invalid data type in surface point {i}. ID must be an integer.\")\n \n # Extract coordinates and other data\n x = np.array([sp['x'] for sp in surface_points_data])\n y = np.array([sp['y'] for sp in surface_points_data])\n z = np.array([sp['z'] for sp in surface_points_data])\n nugget = np.array([sp['nugget'] for sp in surface_points_data])\n \n # Handle names based on whether id_to_name mapping is provided\n if id_to_name is not None:\n names = [id_to_name.get(sp['id'], f\"surface_{sp['id']}\") for sp in surface_points_data]\n else:\n # If no mapping provided, use surface IDs as names\n names = [f\"surface_{sp['id']}\" for sp in surface_points_data]\n \n # Create SurfacePointsTable\n return SurfacePointsTable.from_arrays(\n x=x,\n y=y,\n z=z,\n names=names,\n nugget=nugget\n )\n\n @staticmethod\n def _load_orientations(orientations_data: List[Orientation], id_to_name: Optional[Dict[int, str]] = None):\n \"\"\"\n Load orientations from JSON data.\n \n Args:\n orientations_data (List[Orientation]): List of orientation dictionaries\n id_to_name (Optional[Dict[int, str]]): Optional mapping from surface IDs to names\n \n Returns:\n OrientationsTable: A new OrientationsTable instance\n \n Raises:\n ValueError: If the data is invalid or missing required fields\n \"\"\"\n # Import here to avoid circular imports\n from gempy.core.data.orientations import OrientationsTable\n \n # Validate data structure\n required_fields = {'x', 'y', 'z', 'G_x', 'G_y', 'G_z', 'nugget', 'polarity', 'id'}\n for i, ori in enumerate(orientations_data):\n missing_fields = required_fields - set(ori.keys())\n if missing_fields:\n raise ValueError(f\"Missing required fields in orientation {i}: {missing_fields}\")\n \n # Validate data types\n if not all(isinstance(ori[field], (int, float)) for field in ['x', 'y', 'z', 'G_x', 'G_y', 'G_z', 'nugget']):\n raise ValueError(f\"Invalid data type in orientation {i}. All coordinates, gradients, and nugget must be numeric.\")\n if not isinstance(ori.get('polarity', 1), int) or ori.get('polarity', 1) not in {-1, 1}:\n raise ValueError(f\"Invalid polarity in orientation {i}. Must be 1 (normal) or -1 (reverse).\")\n if not isinstance(ori['id'], int):\n raise ValueError(f\"Invalid data type in orientation {i}. ID must be an integer.\")\n \n # Extract coordinates and other data\n x = np.array([ori['x'] for ori in orientations_data])\n y = np.array([ori['y'] for ori in orientations_data])\n z = np.array([ori['z'] for ori in orientations_data])\n G_x = np.array([ori['G_x'] for ori in orientations_data])\n G_y = np.array([ori['G_y'] for ori in orientations_data])\n G_z = np.array([ori['G_z'] for ori in orientations_data])\n nugget = np.array([ori['nugget'] for ori in orientations_data])\n \n # Handle names based on whether id_to_name mapping is provided\n if id_to_name is not None:\n names = [id_to_name.get(ori['id'], f\"surface_{ori['id']}\") for ori in orientations_data]\n else:\n # If no mapping provided, use surface IDs as names\n names = [f\"surface_{ori['id']}\" for ori in orientations_data]\n \n # Apply polarity to gradients\n for i, ori in enumerate(orientations_data):\n if ori.get('polarity', 1) == -1:\n G_x[i] *= -1\n G_y[i] *= -1\n G_z[i] *= -1\n \n # Create OrientationsTable\n return OrientationsTable.from_arrays(\n x=x,\n y=y,\n z=z,\n G_x=G_x,\n G_y=G_y,\n G_z=G_z,\n names=names,\n nugget=nugget\n )\n \n @staticmethod\n def save_model_to_json(model, file_path: str) -> None:\n \"\"\"\n Save a GemPy model to a JSON file.\n \n Args:\n model: The GemPy model to save\n file_path (str): Path where to save the JSON file\n \"\"\"\n # Get current date for default values\n current_date = datetime.now().strftime(\"%Y-%m-%d\")\n\n # Create JSON structure with metadata handling\n json_data = {\n \"metadata\": {\n \"name\": model.meta.name if model.meta.name is not None else \"GemPy Model\",\n \"creation_date\": model.meta.creation_date if model.meta.creation_date is not None else current_date,\n \"last_modification_date\": model.meta.last_modification_date if model.meta.last_modification_date is not None else current_date,\n \"owner\": model.meta.owner if model.meta.owner is not None else \"GemPy Modeller\"\n },\n \"surface_points\": [],\n \"orientations\": [],\n \"series\": [],\n \"grid_settings\": {\n \"regular_grid_resolution\": [int(x) for x in model.grid._dense_grid.resolution],\n \"regular_grid_extent\": [float(x) for x in model.grid._dense_grid.extent],\n \"octree_levels\": None # TODO: Add octree levels if needed\n },\n \"interpolation_options\": {\n \"kernel_options\": {\n \"range\": float(model.interpolation_options.kernel_options.range),\n \"c_o\": float(model.interpolation_options.kernel_options.c_o)\n },\n \"mesh_extraction\": bool(model.interpolation_options.mesh_extraction),\n \"number_octree_levels\": int(model.interpolation_options.number_octree_levels)\n },\n \"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,\n \"id_name_mapping\": {\n \"name_to_id\": {k: int(v) for k, v in model.structural_frame.element_name_id_map.items()}\n }\n }\n \n # Get series and surface information\n for group in model.structural_frame.structural_groups:\n series_entry = {\n \"name\": str(group.name),\n \"surfaces\": [str(element.name) for element in group.elements],\n \"structural_relation\": str(group.structural_relation.name),\n \"colors\": [str(element.color) for element in group.elements]\n }\n json_data[\"series\"].append(series_entry)\n \n # Get surface points\n surface_points_table = model.surface_points_copy\n xyz_values = surface_points_table.xyz\n ids = surface_points_table.ids\n nugget_values = surface_points_table.nugget\n \n for i in range(len(xyz_values)):\n point = {\n \"x\": float(xyz_values[i, 0]),\n \"y\": float(xyz_values[i, 1]),\n \"z\": float(xyz_values[i, 2]),\n \"id\": int(ids[i]),\n \"nugget\": float(nugget_values[i])\n }\n json_data[\"surface_points\"].append(point)\n \n # Get orientations\n orientations_table = model.orientations_copy\n ori_xyz_values = orientations_table.xyz\n ori_grads_values = orientations_table.grads\n ori_ids = orientations_table.ids\n ori_nugget_values = orientations_table.nugget\n \n for i in range(len(ori_xyz_values)):\n orientation = {\n \"x\": float(ori_xyz_values[i, 0]),\n \"y\": float(ori_xyz_values[i, 1]),\n \"z\": float(ori_xyz_values[i, 2]),\n \"G_x\": float(ori_grads_values[i, 0]),\n \"G_y\": float(ori_grads_values[i, 1]),\n \"G_z\": float(ori_grads_values[i, 2]),\n \"id\": int(ori_ids[i]),\n \"nugget\": float(ori_nugget_values[i]),\n \"polarity\": 1 # Default value, update if available\n }\n json_data[\"orientations\"].append(orientation)\n \n # Save to file\n with open(file_path, 'w') as f:\n json.dump(json_data, f, indent=4)\n \n @staticmethod\n def _validate_json_schema(data: Dict) -> None:\n \"\"\"Validate the JSON schema and set default values.\"\"\"\n # Required top-level keys\n required_keys = ['surface_points', 'orientations', 'grid_settings']\n for key in required_keys:\n if key not in data:\n raise ValueError(f\"Missing required key: {key}\")\n\n # Validate surface points\n for point in data['surface_points']:\n required_point_keys = ['x', 'y', 'z']\n for key in required_point_keys:\n if key not in point:\n raise ValueError(f\"Missing required key in surface point: {key}\")\n # Set default nugget if not provided\n if 'nugget' not in point:\n point['nugget'] = 0.0 # Default nugget for surface points\n # Set default id if not provided\n if 'id' not in point:\n point['id'] = 0 # Default id\n\n # Validate orientations\n for orientation in data['orientations']:\n required_orientation_keys = ['x', 'y', 'z', 'G_x', 'G_y', 'G_z']\n for key in required_orientation_keys:\n if key not in orientation:\n raise ValueError(f\"Missing required key in orientation: {key}\")\n # Set default nugget if not provided\n if 'nugget' not in orientation:\n orientation['nugget'] = 0.01 # Default nugget for orientations\n # Set default polarity if not provided\n if 'polarity' not in orientation:\n orientation['polarity'] = 1\n # Set default id if not provided\n if 'id' not in orientation:\n orientation['id'] = 0 # Default id\n\n # Validate grid settings\n grid_settings = data['grid_settings']\n required_grid_keys = ['regular_grid_resolution', 'regular_grid_extent']\n for key in required_grid_keys:\n if key not in grid_settings:\n raise ValueError(f\"Missing required key in grid_settings: {key}\")\n\n # Validate series if provided\n if 'series' in data:\n for series in data['series']:\n required_series_keys = ['name', 'surfaces']\n for key in required_series_keys:\n if key not in series:\n raise ValueError(f\"Missing required key in series: {key}\")\n # Set default structural relation if not provided\n if 'structural_relation' not in series:\n series['structural_relation'] = \"ERODE\"\n # Validate colors if provided\n if 'colors' in series:\n if not isinstance(series['colors'], list):\n raise ValueError(\"Colors must be a list\")\n if not all(isinstance(color, str) and color.startswith('#') for color in series['colors']):\n raise ValueError(\"Colors must be hex color codes starting with #\")\n \n # Validate interpolation options if present\n if 'interpolation_options' in data:\n if not isinstance(data['interpolation_options'], dict):\n raise ValueError(\"Interpolation options must be a dictionary\")\n if 'kernel_options' in data['interpolation_options']:\n kernel_options = data['interpolation_options']['kernel_options']\n if not isinstance(kernel_options, dict):\n raise ValueError(\"Kernel options must be a dictionary\")\n if 'range' in kernel_options and not isinstance(kernel_options['range'], (int, float)):\n raise ValueError(\"Kernel range must be a number\")\n if 'c_o' in kernel_options and not isinstance(kernel_options['c_o'], (int, float)):\n raise ValueError(\"Kernel c_o must be a number\")\n if 'mesh_extraction' in data['interpolation_options'] and not isinstance(data['interpolation_options']['mesh_extraction'], bool):\n raise ValueError(\"Mesh extraction must be a boolean\")\n if 'number_octree_levels' in data['interpolation_options'] and not isinstance(data['interpolation_options']['number_octree_levels'], int):\n raise ValueError(\"Number of octree levels must be an integer\")\n \n # Validate and set default metadata\n if 'metadata' not in data:\n data['metadata'] = {}\n\n metadata = data['metadata']\n if not isinstance(metadata, dict):\n raise ValueError(\"Metadata must be a dictionary\")\n\n # Set default values for metadata\n current_date = datetime.now().strftime(\"%Y-%m-%d\")\n if 'name' not in metadata or metadata['name'] is None:\n metadata['name'] = \"GemPy Model\"\n elif not isinstance(metadata['name'], str):\n raise ValueError(\"Metadata name must be a string\")\n\n if 'creation_date' not in metadata or metadata['creation_date'] is None:\n metadata['creation_date'] = current_date\n elif not isinstance(metadata['creation_date'], str):\n raise ValueError(\"Metadata creation_date must be a string\")\n\n if 'last_modification_date' not in metadata or metadata['last_modification_date'] is None:\n metadata['last_modification_date'] = current_date\n elif not isinstance(metadata['last_modification_date'], str):\n raise ValueError(\"Metadata last_modification_date must be a string\")\n\n if 'owner' not in metadata or metadata['owner'] is None:\n metadata['owner'] = \"GemPy Modeller\"\n elif not isinstance(metadata['owner'], str):\n raise ValueError(\"Metadata owner must be a string\")\n\n return True " }, { "path": "gempy/modules/json_io/schema.py", "content": "\"\"\"\nSchema definitions for JSON I/O operations in GemPy.\nThis module defines the expected structure of JSON files for loading and saving GemPy models.\n\"\"\"\n\nfrom typing import TypedDict, List, Dict, Any, Optional, Union, Sequence\n\ntry:\n from typing import NotRequired\nexcept ImportError:\n from typing_extensions import NotRequired # Fallback for older Python versions\n\n\nclass SurfacePoint(TypedDict):\n x: float\n y: float\n z: float\n id: int\n nugget: float\n\nclass Orientation(TypedDict):\n x: float\n y: float\n z: float\n G_x: float # X component of the gradient\n G_y: float # Y component of the gradient\n G_z: float # Z component of the gradient\n id: int\n nugget: float\n polarity: int # 1 for normal, -1 for reverse\n\nclass Surface(TypedDict, total=False):\n name: str # Required\n id: NotRequired[int] # Optional, will be auto-generated if not provided\n color: NotRequired[Optional[str]] # Optional hex color code\n vertices: NotRequired[Optional[List[List[float]]]] # Optional list of [x, y, z] coordinates\n\nclass Fault(TypedDict, total=False):\n name: str # Required\n id: NotRequired[int] # Optional, will be auto-generated\n is_active: NotRequired[bool] # Optional, defaults to True\n surface: Surface\n\nclass Series(TypedDict, total=False):\n name: str # Required\n surfaces: Union[List[str], List[Surface]] # Required, can be list of names or Surface objects\n id: NotRequired[int] # Optional, will be auto-generated\n is_active: NotRequired[bool] # Optional, defaults to True\n is_fault: NotRequired[bool] # Optional, defaults to False\n order_series: NotRequired[int] # Optional, will be auto-generated\n faults: NotRequired[List[Fault]] # Optional, defaults to empty list\n structural_relation: NotRequired[str] # Optional, defaults to \"ONLAP\"\n colors: NotRequired[Optional[List[str]]] # Optional\n\nclass GridSettings(TypedDict, total=False):\n regular_grid_resolution: NotRequired[List[int]] # Optional, defaults to [10, 10, 10]\n regular_grid_extent: NotRequired[List[float]] # Optional, auto-calculated from data\n octree_levels: NotRequired[Optional[int]] # Optional\n\nclass ModelMetadata(TypedDict, total=False):\n name: NotRequired[str] # Optional, defaults to \"GemPy Model\"\n creation_date: NotRequired[str] # Optional, defaults to current date\n last_modification_date: NotRequired[str] # Optional, defaults to current date\n owner: NotRequired[Optional[str]] # Optional, defaults to \"GemPy Team\"\n\nclass IdNameMapping(TypedDict, total=False):\n name_to_id: Dict[str, int] # Required if id_name_mapping is provided\n\nclass GemPyModelJson(TypedDict):\n surface_points: List[SurfacePoint] # Required\n orientations: List[Orientation] # Required\n series: List[Series] # Required but with minimal required fields\n metadata: NotRequired[ModelMetadata] # Optional\n grid_settings: NotRequired[Optional[GridSettings]] # Optional\n interpolation_options: NotRequired[Optional[Dict[str, Any]]] # Optional\n id_name_mapping: NotRequired[IdNameMapping] # Optional " }, { "path": "gempy/modules/mesh_extranction/__init__.py", "content": "" }, { "path": "gempy/modules/mesh_extranction/marching_cubes.py", "content": "import os\nimport numpy as np\nfrom typing import Optional\nfrom skimage import measure\nfrom gempy_engine.core.data.interp_output import InterpOutput\nfrom gempy_engine.core.data.raw_arrays_solution import RawArraysSolution\n\nfrom gempy.core.data import GeoModel, StructuralElement, StructuralGroup\nfrom gempy.core.data.grid_modules import RegularGrid\n\n\ndef set_meshes_with_marching_cubes(model: GeoModel) -> None:\n \"\"\"Extract meshes for all structural elements using the marching cubes algorithm.\n \n Parameters\n ----------\n model : GeoModel\n The geological model containing solutions and structural elements.\n \n Raises\n ------\n ValueError\n If the model solutions do not contain dense grid data.\n \"\"\"\n # Verify that solutions contain dense grid data\n solution_not_having_dense: bool = model.solutions.block_solution_type != RawArraysSolution.BlockSolutionType.DENSE_GRID\n if model.solutions is None or solution_not_having_dense:\n raise ValueError(\"Model solutions must contain dense grid data for mesh extraction.\")\n\n regular_grid: RegularGrid = model.grid.regular_grid\n structural_groups: list[StructuralGroup] = model.structural_frame.structural_groups\n\n if not model.solutions.octrees_output or not model.solutions.octrees_output[0].outputs:\n raise ValueError(\"No interpolation outputs available for mesh extraction.\")\n\n output_lvl0: list[InterpOutput] = model.solutions.octrees_output[0].outputs\n\n for e, structural_group in enumerate(structural_groups):\n if e >= len(output_lvl0):\n continue\n\n output_group: InterpOutput = output_lvl0[e]\n scalar_field_matrix = output_group.exported_fields_dense_grid.scalar_field\n if structural_group.is_fault is False:\n slice_: slice = output_group.grid.dense_grid_slice\n mask = output_group.combined_scalar_field.squeezed_mask_array[slice_]\n else:\n mask = np.ones_like(scalar_field_matrix, dtype=bool)\n\n for element in structural_group.elements:\n extract_mesh_for_element(\n structural_element=element,\n regular_grid=regular_grid,\n scalar_field=scalar_field_matrix,\n mask=mask\n )\n\n\ndef extract_mesh_for_element(structural_element: StructuralElement,\n regular_grid: RegularGrid,\n scalar_field: np.ndarray,\n mask: Optional[np.ndarray] = None) -> None:\n \"\"\"Extract a mesh for a single structural element using marching cubes.\n \n Parameters\n ----------\n structural_element : StructuralElement\n The structural element for which to extract a mesh.\n regular_grid : RegularGrid\n The regular grid defining the spatial discretization.\n scalar_field : np.ndarray\n The scalar field used for isosurface extraction.\n mask : np.ndarray, optional\n Optional mask to restrict the mesh extraction to specific regions.\n \"\"\"\n if type(scalar_field).__module__ == 'torch':\n import torch\n scalar_field = scalar_field.detach().numpy()\n if type(mask).__module__ == \"torch\":\n import torch\n mask = torch.to_numpy(mask)\n\n\n # Extract mesh using marching cubes\n verts, faces, _, _ = measure.marching_cubes(\n volume=scalar_field.reshape(regular_grid.resolution),\n level=structural_element.scalar_field_at_interface,\n spacing=(regular_grid.dx, regular_grid.dy, regular_grid.dz),\n mask=mask.reshape(regular_grid.resolution) if mask is not None else None,\n allow_degenerate=False,\n method=\"lewiner\"\n )\n\n # Adjust vertices to correct coordinates in the model's extent\n verts = (verts + [regular_grid.extent[0],\n regular_grid.extent[2],\n regular_grid.extent[4]])\n\n # Store mesh in the structural element\n structural_element.vertices = verts\n structural_element.edges = faces\n" }, { "path": "gempy/modules/optimize_nuggets/__init__.py", "content": "from ._optimizer import nugget_optimizer" }, { "path": "gempy/modules/optimize_nuggets/_ops.py", "content": "import gempy_engine\nfrom gempy.optional_dependencies import require_torch\nfrom gempy_engine.core.data.continue_epoch import ContinueEpoch\n\n\ndef run_optimization(lr, max_epochs, min_impr, model, nugget, patience, target_cond_num):\n torch = require_torch()\n opt = torch.optim.Adam(\n params=[\n nugget,\n ],\n lr=lr\n )\n prev_cond = float('inf')\n for epoch in range(max_epochs):\n opt.zero_grad()\n try:\n gempy_engine.compute_model(\n interpolation_input=model.taped_interpolation_input,\n options=model.interpolation_options,\n data_descriptor=model.input_data_descriptor,\n geophysics_input=model.geophysics_input,\n )\n except ContinueEpoch:\n if True:\n # Keep only top 10% gradients\n _gradient_masking(nugget, focus=0.01)\n else:\n if epoch % 1 == 0:\n mask_sp = _mask_iqr(nugget.grad.abs().view(-1), multiplier=3)\n print(f\"Outliers sp: {torch.nonzero(mask_sp)}\")\n _apply_outlier_gradients(tensor=nugget, mask=mask_sp)\n\n # Step & clamp safely\n opt.step() \n with torch.no_grad():\n nugget.clamp_(min=1e-7)\n\n # Evaluate condition number\n cur_cond = model.interpolation_options.kernel_options.condition_number\n print(f\"[Epoch {epoch}] cond. num. = {cur_cond:.2e}\")\n\n if _has_converged(cur_cond, prev_cond, target_cond_num, epoch, min_impr, patience):\n break\n prev_cond = cur_cond\n\n # Condition number to numpy\n model.interpolation_options.kernel_options.condition_number = model.interpolation_options.kernel_options.condition_number.detach().numpy()\n return nugget\n\n\ndef _mask_iqr(grads, multiplier: float = 1.5) -> \"torch.BoolTensor\":\n q1, q3 = grads.quantile(0.25), grads.quantile(0.75)\n thresh = q3 + multiplier * (q3 - q1)\n return grads > thresh\n\ndef _apply_outlier_gradients(\n tensor: \"torch.Tensor\",\n mask: \"torch.BoolTensor\",\n amplification: float = 1.0,\n):\n # wrap in no_grad if you prefer, but .grad modifications are fine\n tensor.grad.view(-1)[mask] *= amplification\n tensor.grad.view(-1)[~mask] = 0\n\n\n\ndef _gradient_masking(nugget, focus=0.01):\n \"\"\"Old way of avoiding exploding gradients.\"\"\"\n torch = require_torch()\n grads = nugget.grad.abs()\n k = int(grads.numel() * focus)\n top_vals, top_idx = torch.topk(grads, k, largest=True)\n mask = torch.zeros_like(grads)\n mask[top_idx] = 1\n nugget.grad.mul_(mask)\n\n\ndef _has_converged(\n current: float,\n previous: float,\n target: float = 1e5,\n epoch: int = 0,\n min_improvement: float = 0.01,\n patience: int = 10,\n) -> bool:\n if current < target:\n return True\n if epoch > patience:\n # relative improvement\n rel_impr = abs(current - previous) / max(previous, 1e-8)\n return rel_impr < min_improvement\n return False\n\n" }, { "path": "gempy/modules/optimize_nuggets/_optimizer.py", "content": "from gempy_engine.core.backend_tensor import BackendTensor\nfrom gempy_engine.core.data.interpolation_input import InterpolationInput\nfrom ._ops import run_optimization\nfrom ..data_manipulation.manipulate_points import modify_surface_points\nfrom ...core.data import GeoModel, GemPyEngineConfig, StructuralGroup\nfrom ...modules.data_manipulation import interpolation_input_from_structural_frame\n\n\ndef nugget_optimizer(\n target_cond_num: float,\n engine_cfg: GemPyEngineConfig,\n model: GeoModel,\n max_epochs: int,\n only_groups: list[StructuralGroup] | None = None,\n lr: float = .01,\n patience: int = 10,\n min_impr: float = 0.01,\n) -> GeoModel:\n \"\"\"\n Optimize the nugget effect scalar to achieve a target condition number.\n Returns the final nugget effect value.\n \"\"\"\n # 1) Backend setup (ideally done once, not every call)\n BackendTensor.change_backend_gempy(\n engine_backend=engine_cfg.backend,\n use_gpu=engine_cfg.use_gpu,\n dtype=engine_cfg.dtype,\n grads=True\n )\n\n # 2) Prepare data\n\n all_groups: list[StructuralGroup] = model.structural_frame.structural_groups\n if only_groups is not None:\n groups_to_optimize = only_groups\n else:\n groups_to_optimize: list[StructuralGroup] = all_groups\n \n for group in groups_to_optimize:\n model.structural_frame.structural_groups = [group]\n \n interp_in: InterpolationInput = interpolation_input_from_structural_frame(model)\n model.taped_interpolation_input = interp_in\n nugget: \"torch.Tensor\" = interp_in.surface_points.nugget_effect_scalar\n nugget.requires_grad_(True)\n\n model.interpolation_options.kernel_options.optimizing_condition_number = True\n\n nuggets = run_optimization(lr, max_epochs, min_impr, model, nugget, patience, target_cond_num)\n \n modify_surface_points(\n geo_model=model,\n nugget=nuggets.detach().numpy()\n )\n\n model.interpolation_options.kernel_options.optimizing_condition_number = False\n \n model.structural_frame.structural_groups = all_groups\n \n return model\n\n\n" }, { "path": "gempy/modules/serialization/__init__.py", "content": "" }, { "path": "gempy/modules/serialization/save_load.py", "content": "import re\n\nimport warnings\n\nfrom ...core.data import GeoModel\nfrom ...core.data.encoders.converters import loading_model_from_binary\nfrom ...optional_dependencies import require_zlib\nimport pathlib\nimport os\nimport io\nimport zipfile\n\n\ndef save_model(model: GeoModel, path: str | None = None, validate_serialization: bool = True):\n \"\"\"\n Save a GeoModel to a file with proper extension validation.\n \n Parameters:\n -----------\n model : GeoModel\n The geological model to save\n path : str\n The file path where to save the model\n \n Raises:\n -------\n ValueError\n If the file has an extension other than .gempy\n \"\"\"\n\n # Warning about preview\n warnings.warn(\"This function is still in development. It may not work as expected.\")\n\n # Define the valid extension for gempy models\n VALID_EXTENSION = \".gempy\"\n if path is None:\n path = model.meta.name + VALID_EXTENSION\n\n # Check if path has an extension\n path_obj = pathlib.Path(path)\n if path_obj.suffix:\n # If extension exists but is not valid, raise error\n if path_obj.suffix.lower() != VALID_EXTENSION:\n raise ValueError(f\"Invalid file extension: {path_obj.suffix}. Expected: {VALID_EXTENSION}\")\n else:\n # If no extension, add the valid extension\n path = str(path_obj) + VALID_EXTENSION\n\n binary_file = model_to_bytes(model)\n\n if validate_serialization:\n model_deserialized = _load_model_from_bytes(binary_file)\n _validate_serialization(model, model_deserialized)\n\n # Create directory if it doesn't exist\n directory = os.path.dirname(path)\n if directory and not os.path.exists(directory):\n os.makedirs(directory)\n\n with open(path, 'wb') as f:\n f.write(binary_file)\n\n return path # Return the actual path used (helpful if extension was added)\n\n\ndef model_to_binary(model: GeoModel) -> bytes:\n\n # Compress the binary data\n zlib = require_zlib()\n compressed_binary_input = zlib.compress(model.structural_frame.input_tables_binary)\n compressed_binary_grid = zlib.compress(model.grid.grid_binary)\n\n compressed_binary_grid = zlib.compress(model.grid.grid_binary, level=6)\n\n import hashlib\n print(\"len raw bytes:\", len(model.grid.grid_binary))\n \n print(\"raw bytes hash:\", hashlib.sha256(model.grid.grid_binary).hexdigest())\n print(\"compressed length:\", len(compressed_binary_grid))\n print(\"zlib version:\", zlib.ZLIB_VERSION)\n\n # * Add here the serialization meta parameters like: len_bytes\n model.structural_frame._input_binary_size = len(compressed_binary_input)\n model.grid._grid_binary_size = len(compressed_binary_grid)\n \n model_json = model.model_dump_json(by_alias=True, indent=4)\n binary_file = _to_binary(\n header_json=model_json,\n body_input=compressed_binary_input,\n body_grid=compressed_binary_grid\n )\n return binary_file\n\n\ndef load_model(path: str) -> GeoModel:\n \"\"\"\n Load a GeoModel from a file with extension validation.\n \n Parameters:\n -----------\n path : str\n Path to the gempy model file\n \n Returns:\n --------\n GeoModel\n The loaded geological model\n \n Raises:\n -------\n ValueError\n If the file doesn't have the proper .gempy extension\n FileNotFoundError\n If the file doesn't exist\n \"\"\"\n\n # Warning about preview\n warnings.warn(\"This function is still in development. It may not work as expected.\")\n\n VALID_EXTENSION = \".gempy\"\n\n # Check if path has the valid extension\n path_obj = pathlib.Path(path)\n if not path_obj.suffix or path_obj.suffix.lower() != VALID_EXTENSION:\n raise ValueError(f\"Invalid file extension: {path_obj.suffix}. Expected: {VALID_EXTENSION}\")\n\n # Check if file exists\n if not os.path.exists(path):\n raise FileNotFoundError(f\"File not found: {path}\")\n\n with open(path, 'rb') as f:\n binary_file = f.read()\n\n return _load_model_from_bytes(binary_file)\n\n\ndef model_to_bytes(model: GeoModel) -> bytes:\n # 1) Make a fully deterministic JSON header\n # header_dict = model.model_dump(by_alias=True)\n # header_json = json.dumps(\n # header_dict,\n # sort_keys=True, # always sort object keys\n # separators=(\",\", \":\"), # no extra whitespace\n # ).encode(\"utf-8\")\n \n header_json = model.model_dump_json(by_alias=True, indent=4)\n\n # 2) Raw binary chunks (no additional zlib.compress here)\n input_raw = model.structural_frame.input_tables_binary\n grid_raw = model.grid.grid_binary\n\n # 3) Pack into a ZIP archive in a fixed order:\n \n buf = io.BytesIO()\n with zipfile.ZipFile(\n buf, mode=\"w\",\n compression=zipfile.ZIP_DEFLATED,\n compresslevel=6\n ) as zf:\n # Force a fixed timestamp (1980-01-01) so the file headers don't vary\n def make_info(name):\n zi = zipfile.ZipInfo(name, date_time=(1980,1,1,0,0,0))\n zi.external_attr = 0 # clear OS-specific file permissions\n return zi\n\n zf.writestr(make_info(\"header.json\"), header_json)\n zf.writestr(make_info(\"input.bin\"), input_raw)\n zf.writestr(make_info(\"grid.bin\"), grid_raw)\n\n return buf.getvalue()\n\ndef _load_model_from_bytes(data: bytes) -> GeoModel:\n from ...core.data.encoders.converters import loading_model_from_binary\n\n buf = io.BytesIO(data)\n with zipfile.ZipFile(buf, \"r\") as zf:\n header_json = zf.read(\"header.json\").decode(\"utf-8\")\n # header = json.loads(header_json)\n input_raw = zf.read(\"input.bin\")\n grid_raw = zf.read(\"grid.bin\")\n\n # If you want to validate or decompress further, do it here…\n with loading_model_from_binary(\n input_binary=input_raw,\n grid_binary= grid_raw\n ):\n model = GeoModel.model_validate_json(header_json)\n\n return model\n\ndef _deserialize_binary_file(binary_file):\n import json\n # Get header length from first 4 bytes\n header_length = int.from_bytes(binary_file[:4], byteorder='little')\n # Split header and body\n header_json= binary_file[4:4 + header_length].decode('utf-8')\n header = json.loads(header_json)\n input_metadata = header[\"structural_frame\"][\"binary_meta_data\"]\n input_size = input_metadata[\"input_binary_size\"]\n \n grid_metadata = header[\"grid\"][\"binary_meta_data\"]\n grid_size = grid_metadata[\"grid_binary_size\"]\n \n input_binary = binary_file[4 + header_length: 4 + header_length + input_size]\n all_sections_length = 4 + header_length + input_size + grid_size\n if all_sections_length != len(binary_file):\n raise ValueError(\"Binary file is corrupted\")\n \n grid_binary = binary_file[4 + header_length + input_size: all_sections_length]\n zlib = require_zlib()\n \n with loading_model_from_binary(\n input_binary=(zlib.decompress(input_binary)),\n grid_binary=(zlib.decompress(grid_binary))\n ):\n model = GeoModel.model_validate_json(header_json)\n return model\n\n\ndef _to_binary(header_json, body_input, body_grid) -> bytes:\n header_json_bytes = header_json.encode('utf-8')\n header_json_length = len(header_json_bytes)\n header_json_length_bytes = header_json_length.to_bytes(4, byteorder='little')\n file = header_json_length_bytes + header_json_bytes + body_input + body_grid\n return file\n\n\ndef _validate_serialization(original_model, model_deserialized):\n a = hash(original_model.structural_frame.surface_points_copy.data.tobytes())\n b = hash(model_deserialized.structural_frame.surface_points_copy.data.tobytes())\n o_a = hash(original_model.structural_frame.orientations_copy.data.tobytes())\n o_b = hash(model_deserialized.structural_frame.orientations_copy.data.tobytes())\n assert a == b, \"Hashes for surface points are not equal\"\n assert o_a == o_b, \"Hashes for orientations are not equal\"\n original_model___str__ = re.sub(r'\\s+', ' ', original_model.__str__())\n deserialized___str__ = re.sub(r'\\s+', ' ', model_deserialized.__str__())\n if original_model___str__ != deserialized___str__:\n # Find first char that is not the same\n for i in range(min(len(original_model___str__), len(deserialized___str__))):\n if original_model___str__[i] != deserialized___str__[i]:\n break\n print(f\"First difference at index {i}:\")\n i1 = 10\n print(f\"Original: {original_model___str__[i - i1:i + i1]}\")\n print(f\"Deserialized: {deserialized___str__[i - i1:i + i1]}\")\n\n assert deserialized___str__ == original_model___str__\n\n\n" }, { "path": "gempy/optional_dependencies.py", "content": "def require_pandas():\n try:\n import pandas as pd\n except ImportError:\n raise ImportError(\"The pandas library is required to use this function.\")\n return pd\n\n\ndef require_pooch():\n try:\n import pooch\n except ImportError:\n raise ImportError(\"The pooch library is required to use this function.\")\n return pooch\n\n\ndef require_gempy_legacy():\n try:\n import gempy_legacy\n except ImportError:\n raise ImportError(\"The gempy_legacy library is required to use this function.\")\n return gempy_legacy\n\n\ndef require_gempy_viewer():\n try:\n import gempy_viewer\n except ImportError:\n raise ImportError(\"The gempy_viewer package is required to run this function.\")\n return gempy_viewer\n\ndef require_matplotlib():\n try:\n import matplotlib.pyplot as plt\n except ImportError:\n raise ImportError(\"The matplotlib package is required to run this function.\")\n return plt\n\n\ndef require_skimage():\n try:\n import skimage\n except ImportError:\n raise ImportError(\"The skimage package is required to run this function.\")\n return skimage\n\n\ndef require_scipy():\n try:\n import scipy\n except ImportError:\n raise ImportError(\"The scipy package is required to run this function.\")\n return scipy\n\n\ndef require_subsurface():\n try:\n import subsurface\n except ImportError:\n raise ImportError(\"The subsurface package is required to run this function.\")\n return subsurface\n\ndef require_zlib():\n try:\n import zlib\n except ImportError:\n raise ImportError(\"The zlib package is required to run this function.\")\n return zlib\n\ndef require_torch():\n try:\n import torch\n except ImportError:\n raise ImportError(\"The torch package is required to run this function.\")\n return torch" }, { "path": "requirements/base-requirements.txt", "content": "-r requirements.txt\n\n# This install matplotlib\ngempy_viewer~=2025.1.4\npandas>=2.2.0,<3.0.0\n" }, { "path": "requirements/dev-requirements.txt", "content": "-r optional-requirements.txt\n\npytest\npyvista\nsubsurface-terra\nwellpathpy\ntorch\n\n# Testing\npytest-approvaltests" }, { "path": "requirements/docs_requirements.txt", "content": "-r dev-requirements.txt\n\ntorch\nrasterio\n\nsphinx\nsphinx-gallery\nsphinx_design\nsphinx_automodapi\nliquid-earth-sdk\nwellpathpy\n\nopenpyxl" }, { "path": "requirements/optional-requirements.txt", "content": "-r base-requirements.txt\n\ngempy_plugins\n\n# for data download\npooch \nscipy\nscikit-image" }, { "path": "requirements/requirements.txt", "content": "# This install also numpy\ngempy_engine>=2026.0.1dev0,<2026.1.0\n" }, { "path": "scripts/build_docs.sh", "content": "#!/bin/bash\n\nsource ~/.virtualenvs/gempy_dependencies/bin/activate\n\ncd ../docs || exit\n#make clean\nmake html\ncd - || exit" }, { "path": "scripts/deploy.sh", "content": "#!/usr/bin/env bash\nset -o errexit #abort if any command fails\nme=$(basename \"$0\")\n\nhelp_message=\"\\\nUsage: $me [-c FILE] []\nDeploy generated files to a git branch.\n\nOptions:\n\n -h, --help Show this help information.\n -v, --verbose Increase verbosity. Useful for debugging.\n -e, --allow-empty Allow deployment of an empty directory.\n -m, --message MESSAGE Specify the message used when committing on the\n deploy branch.\n -n, --no-hash Don't append the source commit's hash to the deploy\n commit's message.\n -c, --config-file PATH Override default & environment variables' values\n with those in set in the file at 'PATH'. Must be the\n first option specified.\n\nVariables:\n\n GIT_DEPLOY_DIR Folder path containing the files to deploy.\n GIT_DEPLOY_BRANCH Commit deployable files to this branch.\n GIT_DEPLOY_REPO Push the deploy branch to this repository.\n\nThese variables have default values defined in the script. The defaults can be\noverridden by environment variables. Any environment variables are overridden\nby values set in a '.env' file (if it exists), and in turn by those set in a\nfile specified by the '--config-file' option.\"\n\necho \"docs.gempy.org\" > docs/build/html/CNAME\n\n\nparse_args() {\n\t# Set args from a local environment file.\n\tif [ -e \".env\" ]; then\n\t\tsource .env\n\tfi\n\n\t# Set args from file specified on the command-line.\n\tif [[ $1 = \"-c\" || $1 = \"--config-file\" ]]; then\n\t\tsource \"$2\"\n\t\tshift 2\n\tfi\n\n\t# Parse arg flags\n\t# If something is exposed as an environment variable, set/overwrite it\n\t# here. Otherwise, set/overwrite the internal variable instead.\n\twhile : ; do\n\t\tif [[ $1 = \"-h\" || $1 = \"--help\" ]]; then\n\t\t\techo \"$help_message\"\n\t\t\treturn 0\n\t\telif [[ $1 = \"-v\" || $1 = \"--verbose\" ]]; then\n\t\t\tverbose=true\n\t\t\tshift\n\t\telif [[ $1 = \"-e\" || $1 = \"--allow-empty\" ]]; then\n\t\t\tallow_empty=true\n\t\t\tshift\n\t\telif [[ ( $1 = \"-m\" || $1 = \"--message\" ) && -n $2 ]]; then\n\t\t\tcommit_message=$2\n\t\t\tshift 2\n\t\telif [[ $1 = \"-n\" || $1 = \"--no-hash\" ]]; then\n\t\t\tGIT_DEPLOY_APPEND_HASH=false\n\t\t\tshift\n\t\telse\n\t\t\tbreak\n\t\tfi\n\tdone\n\n\t# Set internal option vars from the environment and arg flags. All internal\n\t# vars should be declared here, with sane defaults if applicable.\n\n\t# Source directory & target branch.\n\tdeploy_directory=${GIT_DEPLOY_DIR:-docs/build/html}\n\tdeploy_branch=${GIT_DEPLOY_BRANCH:-gh-pages}\n\n\t#if no user identity is already set in the current git environment, use this:\n\tdefault_username=${GIT_DEPLOY_USERNAME:-leguark}\n\tdefault_email=${GIT_DEPLOY_EMAIL:-miguel@terranigma-solutions.com}\n\n\t#repository to deploy to. must be readable and writable.\n\trepo=${GIT_DEPLOY_REPO:-origin}\n\n\t#append commit hash to the end of message by default\n\tappend_hash=${GIT_DEPLOY_APPEND_HASH:-true}\n}\n\nmain() {\n\tparse_args \"$@\"\n\n\tenable_expanded_output\n\n\tif ! git diff --exit-code --quiet --cached; then\n\t\techo Aborting due to uncommitted changes in the index >&2\n\t\treturn 1\n\tfi\n\n\tcommit_title=`git log -n 1 --format=\"%s\" HEAD`\n\tcommit_hash=` git log -n 1 --format=\"%H\" HEAD`\n\t\n\t#default commit message uses last title if a custom one is not supplied\n\tif [[ -z $commit_message ]]; then\n\t\tcommit_message=\"publish: $commit_title\"\n\tfi\n\t\n\t#append hash to commit message unless no hash flag was found\n\tif [ $append_hash = true ]; then\n\t\tcommit_message=\"$commit_message\"$'\\n\\n'\"generated from commit $commit_hash\"\n\tfi\n\t\t\n\tprevious_branch=`git rev-parse --abbrev-ref HEAD`\n\n\tif [ ! -d \"$deploy_directory\" ]; then\n\t\techo \"Deploy directory '$deploy_directory' does not exist. Aborting.\" >&2\n\t\treturn 1\n\tfi\n\t\n\t# must use short form of flag in ls for compatibility with OS X and BSD\n\tif [[ -z `ls -A \"$deploy_directory\" 2> /dev/null` && -z $allow_empty ]]; then\n\t\techo \"Deploy directory '$deploy_directory' is empty. Aborting. If you're sure you want to deploy an empty tree, use the --allow-empty / -e flag.\" >&2\n\t\treturn 1\n\tfi\n\n\tif git ls-remote --exit-code $repo \"refs/heads/$deploy_branch\" ; then\n\t\t# deploy_branch exists in $repo; make sure we have the latest version\n\t\t\n\t\tdisable_expanded_output\n\t\tgit fetch --force $repo $deploy_branch:$deploy_branch\n\t\tenable_expanded_output\n\tfi\n\n\t# check if deploy_branch exists locally\n\tif git show-ref --verify --quiet \"refs/heads/$deploy_branch\"\n\tthen incremental_deploy\n\telse initial_deploy\n\tfi\n\n\trestore_head\n}\n\ninitial_deploy() {\n\tgit --work-tree \"$deploy_directory\" checkout --orphan $deploy_branch\n\tgit --work-tree \"$deploy_directory\" add --all\n\tcommit+push\n}\n\nincremental_deploy() {\n\t#make deploy_branch the current branch\n\tgit symbolic-ref HEAD refs/heads/$deploy_branch\n\t#put the previously committed contents of deploy_branch into the index\n\tgit --work-tree \"$deploy_directory\" reset --mixed --quiet\n\tgit --work-tree \"$deploy_directory\" add --all\n\n\tset +o errexit\n\tdiff=$(git --work-tree \"$deploy_directory\" diff --exit-code --quiet HEAD --)$?\n\tset -o errexit\n\tcase $diff in\n\t\t0) echo No changes to files in $deploy_directory. Skipping commit.;;\n\t\t1) commit+push;;\n\t\t*)\n\t\t\techo 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\n\t\t\treturn $diff\n\t\t\t;;\n\tesac\n}\n\ncommit+push() {\n\tset_user_id\n\tgit --work-tree \"$deploy_directory\" commit -m \"$commit_message\"\n\n\tdisable_expanded_output\n\t#--quiet is important here to avoid outputting the repo URL, which may contain a secret token\n\tgit push --quiet $repo $deploy_branch\n\tenable_expanded_output\n}\n\n#echo expanded commands as they are executed (for debugging)\nenable_expanded_output() {\n\tif [ $verbose ]; then\n\t\tset -o xtrace\n\t\tset +o verbose\n\tfi\n}\n\n#this is used to avoid outputting the repo URL, which may contain a secret token\ndisable_expanded_output() {\n\tif [ $verbose ]; then\n\t\tset +o xtrace\n\t\tset -o verbose\n\tfi\n}\n\nset_user_id() {\n\tif [[ -z `git config user.name` ]]; then\n\t\tgit config user.name \"$default_username\"\n\tfi\n\tif [[ -z `git config user.email` ]]; then\n\t\tgit config user.email \"$default_email\"\n\tfi\n}\n\nrestore_head() {\n\tif [[ $previous_branch = \"HEAD\" ]]; then\n\t\t#we weren't on any branch before, so just set HEAD back to the commit it was on\n\t\tgit update-ref --no-deref HEAD $commit_hash $deploy_branch\n\telse\n\t\tgit symbolic-ref HEAD refs/heads/$previous_branch\n\tfi\n\t\n\tgit reset --mixed\n}\n\nfilter() {\n\tsed -e \"s|$repo|\\$repo|g\"\n}\n\nsanitize() {\n\t\"$@\" 2> >(filter 1>&2) | filter\n}\n\n[[ $1 = --source-only ]] || main \"$@\"\n" }, { "path": "scripts/update_doc_elements.sh", "content": "#!/bin/bash \n\n# #!/bin/bash\n\n# sets the working directory to the current directory\ncd './notebooks/tutorials'\n\n# converts all Jupyter Notebook files to basic html\nfor f in *.ipynb; do jupyter nbconvert --to rst --output-dir ../../docs/source/_notebooks $f; done\n\ncd ../../docs\nmake html\n" }, { "path": "setup.cfg", "content": "[metadata]\ndescription-file = README.md" }, { "path": "setup.py", "content": "import os\nfrom os import path\n\nfrom setuptools import setup, find_packages\n\n\ndef read_requirements(file_name, base_path=\"\"):\n # Construct the full path to the requirements file\n full_path = os.path.join(base_path, file_name)\n requirements = []\n with open(full_path, \"r\", encoding=\"utf-8\") as f:\n for line in f:\n # Strip whitespace and ignore comments\n line = line.strip()\n if line.startswith(\"#\") or not line:\n continue\n\n # Handle -r directive\n if line.startswith(\"-r \"):\n referenced_file = line.split()[1] # Extract the file name\n # Recursively read the referenced file, making sure to include the base path\n requirements.extend(read_requirements(referenced_file, base_path=base_path))\n else:\n requirements.append(line)\n\n return requirements\n\n\n\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nsetup(\n name='gempy',\n packages=find_packages(exclude=('test', 'docs', 'examples')),\n install_requires=read_requirements(\"requirements.txt\", \"requirements\"),\n extras_require={\n \"opt\": read_requirements(\"optional-requirements.txt\", \"requirements\"),\n \"base\": read_requirements(\"base-requirements.txt\", \"requirements\"),\n },\n url='https://github.com/cgre-aachen/gempy',\n license='EUPL-1.2',\n author='Miguel de la Varga, Alexander Zimmerman, Elisa Heim, Alexander Schaaf, Fabian Stamm, Florian Wellmann, Jan Niederau, Andrew Annex',\n author_email='gempy@terranigma-solutions.com',\n description='An Open-source, Python-based 3-D structural geological modeling software.',\n long_description=long_description,\n long_description_content_type='text/markdown',\n keywords=['geology', '3-D modeling', 'structural geology', 'uncertainty'],\n classifiers=[\n \"Development Status :: 5 - Production/Stable\",\n \"Intended Audience :: Developers\",\n \"License :: OSI Approved :: European Union Public Licence 1.2 (EUPL 1.2)\",\n \"Operating System :: OS Independent\",\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3.10\",\n \"Programming Language :: Python :: 3.11\",\n \"Programming Language :: Python :: 3.12\",\n ],\n setup_requires=['setuptools_scm'],\n use_scm_version={\n \"root\" : \".\",\n \"relative_to\" : __file__,\n \"write_to\" : path.join(\"gempy\", \"_version.py\"),\n \"fallback_version\": \"3.0.0\"\n },\n)\n" }, { "path": "test/Test Results - pytest_in_test.xml", "content": "\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Setting kriging parameters to their default values.\nCompiling theano function...\nLevel of Optimization: fast_run\nDevice: cpu\nPrecision: float64\nNumber of faults: 0\nCompilation Done!\nKriging values: \n values\nrange 0.0\n$C_o$ 0.0\ndrift equations [3]\nActive grids: ['regular']\nActive grids: ['regular' 'topography']\nSetting kriging parameters to their default values.\nPASSED [ 0%] surface ... color\n2 rock3 ... #ffbe00\n1 rock2 ... #9f0052\n0 rock1 ... #015482\n[3 rows x 4 columns]\n\n \n \n PASSED [ 1%]\n\n \n \n PASSED [ 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/home/miguel/.cache/pooch/b3eaa3b88746691a91bb91f922f3b338-combination\nActive grids: 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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'.\nSHA256 hash of downloaded file: 8f2e02fa778ce50c54bb7afa18ebd9d7063b0deb893062fca81f4827feee68c1\nUse 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.\n\n \n \n \n \n \n \n \n Active grids: ['regular']\nFault colors changed. If you do not like this behavior, set change_color to False.\nPASSED [ 3%]\n\n \n \n \n \n PASSED [ 4%]Active grids: ['regular']\n\n \n \n PASSED [ 5%]Active grids: ['regular']\nFault colors changed. If you do not like this behavior, set change_color to False.\n\n \n \n PASSED [ 5%]Active grids: ['regular']\nFault colors changed. If you do not like this behavior, set change_color to False.\n\n \n \n PASSED [ 6%]Active grids: ['regular']\n\n \n \n \n \n \n \n Setting kriging parameters to their default values.\nCompiling theano function...\nLevel of Optimization: fast_run\nDevice: cpu\nPrecision: float64\nNumber of faults: 0\nCompilation Done!\nKriging values: \n values\nrange 0.0\n$C_o$ 0.0\ndrift equations [3]\nPASSED [ 7%]Active grids: ['regular']\nActive grids: ['regular' 'centered']\nSetting kriging parameters to their default values.\n values\nStructure isLith True\n isFault False\n number faults 0\n number surfaces 2\n number series 1\n number surfaces per series [2]\n len surfaces surface_points [2, 2]\n len series surface_points [4]\n len series orientations [1]\nOptions dtype float64\n output geology\n theano_optimizer fast_compile\n device cpu\n verbosity None\nKriging range 13.266499\n $C_o$ 4.190476\n drift equations [3]\nRescaling rescaling factor 12.0\n centers [6.0, 0.0, 2.51]\n[-1624.17138237]\n\n \n \n \n \n PASSED [ 7%]\n\n \n \n \n \n Setting kriging parameters to their default values.\nCompiling theano function...\nLevel of Optimization: fast_run\nDevice: cpu\nPrecision: float64\nNumber of faults: 0\nCompilation Done!\nKriging values: \n values\nrange 0.0\n$C_o$ 0.0\ndrift equations [3]\nPASSED [ 8%]Active grids: ['regular']\nActive grids: ['regular' 'centered']\nSetting kriging parameters to their default values.\n125\n[473.78364798]\n\n \n \n Active grids: ['centered']\nSetting kriging parameters to their default values.\nPASSED [ 8%]Active grids: ['centered']\n0\n\n \n \n \n \n Active grids: ['regular']\nFault colors changed. If you do not like this behavior, set change_color to False.\nSetting kriging parameters to their default values.\nPASSED [ 9%]\n\n \n \n PASSED [ 10%]\n\n \n \n Active grids: ['regular']\nSetting kriging parameters to their default values.\nPASSED [ 10%]\n\n \n \n PASSED [ 11%]\n\n \n \n \n \n \n \n PASSED [ 12%]Active grids: ['regular']\n\n \n \n SKIPPED (Expensive test. It should be ran manually.) [ 12%]\n\n \n \n SKIPPED (Private data missing from the repo) [ 13%]\n\n \n \n \n \n \n \n PASSED [ 14%]\n\n \n \n \n \n order_series BottomRelation isActive\nfoo3 1 Erosion False\nboo 2 Erosion False\nfoo7 3 Erosion False\nfoo5 4 Erosion False\nfoo20 5 Erosion False\n surface series order_surfaces color id\n0 foo foo20 1 #015482 1\n1 foo2 foo20 2 #9f0052 2\n2 foo5 foo20 3 #ffbe00 3\n3 feeeee foo20 4 #728f02 4\n0 foo\n1 foo2\n2 foo5\n3 feeeee\nName: surface, dtype: object\n surface series order_surfaces color id value_0\n0 foo foo20 1 #015482 1 2\n1 foo2 foo20 2 #9f0052 2 2\n2 foo5 foo20 3 #ffbe00 3 2\n3 feeeee foo20 4 #728f02 4 5\n surface series order_surfaces color id value_0 val_foo val2_foo\n0 foo foo20 1 #015482 1 2 2 2\n1 foo2 foo20 2 #9f0052 2 2 2 2\n2 foo5 foo20 3 #ffbe00 3 2 2 1\n3 feeeee foo20 4 #728f02 4 5 6 8\n surface series order_surfaces color id val2_foo\n0 foo foo20 1 #015482 1 2\n1 foo2 foo20 2 #9f0052 2 2\n2 foo5 foo20 3 #ffbe00 3 1\n3 feeeee foo20 4 #728f02 4 8\n surface series order_surfaces color id val_foo val2_foo\n0 foo foo20 1 #015482 1 2 2\n1 foo2 foo20 2 #9f0052 2 2 2\n2 foo5 foo20 3 #ffbe00 3 2 1\n3 feeeee foo20 4 #728f02 4 6 8\n0 foo20\n1 foo20\n2 foo20\n3 foo20\nName: series, dtype: category\nCategories (5, object): ['foo3', 'boo', 'foo7', 'foo5', 'foo20']\n0 foo\n1 foo2\n2 foo5\n3 feeeee\nName: surface, dtype: object\n surface series order_surfaces color id val_foo val2_foo\n1 foo2 boo 1 #9f0052 1 2 2\n2 foo5 boo 2 #ffbe00 2 2 1\n0 foo foo7 1 #015482 3 2 2\n3 feeeee foo20 1 #728f02 4 6 8\n surface series order_surfaces color id val_foo val2_foo\n1 foo2 boo 1 #9f0052 1 2 2\n2 foo5 boo 2 #ffbe00 2 2 1\n0 foo foo7 1 #015482 3 2 2\n3 feeeee foo20 1 #728f02 4 6 8\n surface series order_surfaces color id val_foo val2_foo\n1 lala boo 1 #9f0052 1 2 2\n2 foo5 boo 2 #ffbe00 2 2 1\n0 foo foo7 1 #015482 3 2 2\n3 feeeee foo20 1 #728f02 4 6 8\n surface series order_surfaces color id val_foo val2_foo\n1 lala boo 1 #9f0052 1 2 2\n2 foo5 boo 2 #ffbe00 2 22 1\n0 foo foo7 1 #015482 3 2 2\n3 feeeee foo20 1 #728f02 4 6 8\nPASSED [ 14%] X Y Z G_x G_y G_z smooth surface\n0 1.0 1.0 1.0 1.000000e+00 0.000000e+00 1.0 0.01 foo\n1 2.0 2.0 2.0 1.000000e-12 1.000000e-12 1.0 0.01 foo\n2 1.0 1.0 1.0 1.000000e+00 0.000000e+00 1.0 0.01 foo\n\n \n \n \n PASSED [ 15%]\n\n \n \n PASSED [ 16%]\n\n \n \n Empty DataFrame\nColumns: [X, Y, Z, smooth, surface]\nIndex: []\n X Y Z smooth surface\n0 0.907111 0.505988 0.391950 0.000002 foo\n1 0.477907 0.556531 0.713103 0.000002 foo5\n2 0.567881 0.533721 0.085224 0.000002 lala\n3 0.474279 0.213689 0.775950 0.000002 foo5\n4 0.135712 0.226703 0.228706 0.000002 lala\n5 0.865928 0.366988 0.052788 0.000002 feeeee\n X Y Z smooth surface\n0 0.907111 0.505988 0.391950 0.000002 foo\n1 0.477907 0.556531 0.713103 0.000002 foo5\n2 0.567881 0.533721 0.085224 0.000002 lala\n3 0.474279 0.213689 0.775950 0.000002 foo5\n4 0.135712 0.226703 0.228706 0.000002 lala\n5 0.865928 0.366988 0.052788 0.000002 feeeee\n X Y Z smooth surface\n0 0.907111 0.505988 0.391950 0.000002 foo\n1 0.477907 0.556531 0.713103 0.000002 foo5\n2 0.567881 0.533721 0.085224 0.000002 lala\n3 0.474279 0.213689 0.775950 0.000002 foo5\n4 0.135712 0.226703 0.228706 0.000002 lala\n5 0.865928 0.366988 0.052788 0.000002 feeeee\n X Y Z smooth surface\n0 0.907111 0.505988 0.391950 0.000002 foo\n1 0.477907 0.556531 0.713103 0.000002 foo5\n2 0.567881 0.533721 0.085224 0.000002 lala\n3 0.474279 0.213689 0.775950 0.000002 foo5\n4 0.135712 0.226703 0.228706 0.000002 lala\n5 0.865928 0.366988 0.052788 0.000002 feeeee\n X Y Z smooth surface\n2 0.567881 0.533721 0.085224 0.000002 lala\n4 0.135712 0.226703 0.228706 0.000002 lala\n1 0.477907 0.556531 0.713103 0.000002 foo5\n3 0.474279 0.213689 0.775950 0.000002 foo5\n0 0.907111 0.505988 0.391950 0.000002 foo\n5 0.865928 0.366988 0.052788 0.000002 feeeee\nPASSED [ 16%]\n\n \n \n Empty DataFrame\nColumns: [X, Y, Z, G_x, G_y, G_z, smooth, surface]\nIndex: []\n X Y Z G_x G_y G_z smooth surface\n0 6.158143 1.074522 7.457793 0.006508 0.860719 0.639610 0.01 foo\n1 1.339547 9.933769 6.031268 0.689821 0.693746 0.314390 0.01 foo5\n2 3.655285 9.722756 3.908049 0.638101 0.231377 0.471284 0.01 lala\n3 5.149455 7.101185 1.878485 0.973958 0.934199 0.797727 0.01 foo5\n4 5.946195 3.441145 3.070265 0.264553 0.578896 0.218209 0.01 lala\n5 0.819608 0.011488 0.608167 0.896632 0.560491 0.377451 0.01 feeeee\n X Y Z G_x G_y G_z smooth surface\n0 4.554007 7.983589 5.890162 0.321616 1.619556 7.214686 0.01 foo\n1 9.349686 5.224257 7.963614 0.572737 2.630836 17.388708 0.01 foo5\n2 3.900684 6.100325 8.972885 0.732125 2.229721 7.274096 0.01 lala\n3 7.580344 5.776509 7.915510 0.001390 0.971052 4.771013 0.01 foo5\n4 9.062750 3.596733 8.679631 0.226881 1.050554 15.519829 0.01 lala\n5 4.979871 4.711573 4.951103 0.968313 3.949245 14.006075 0.01 feeeee\n X Y Z G_x G_y G_z smooth surface\n0 4.554007 7.983589 5.890162 0.321616 1.619556 7.214686 0.01 foo\n1 9.349686 5.224257 7.963614 0.572737 2.630836 17.388708 0.01 foo5\n2 3.900684 6.100325 8.972885 0.732125 2.229721 7.274096 0.01 lala\n3 7.580344 5.776509 7.915510 0.001390 0.971052 4.771013 0.01 foo5\n4 9.062750 3.596733 8.679631 0.226881 1.050554 15.519829 0.01 lala\n5 4.979871 4.711573 4.951103 0.968313 3.949245 14.006075 0.01 feeeee\n X Y Z G_x G_y G_z smooth surface\n0 4.554007 7.983589 5.890162 0.321616 1.619556 7.214686 0.01 foo\n1 9.349686 5.224257 7.963614 0.572737 2.630836 17.388708 0.01 foo5\n2 3.900684 6.100325 8.972885 0.732125 2.229721 7.274096 0.01 lala\n3 7.580344 5.776509 7.915510 0.001390 0.971052 4.771013 0.01 foo5\n4 9.062750 3.596733 8.679631 0.226881 1.050554 15.519829 0.01 lala\n5 4.979871 4.711573 4.951103 0.968313 3.949245 14.006075 0.01 feeeee\n X Y Z G_x G_y G_z smooth surface\n0 4.554007 7.983589 5.890162 0.321616 1.619556 7.214686 0.01 foo\n1 9.349686 5.224257 7.963614 0.572737 2.630836 17.388708 0.01 foo5\n2 3.900684 6.100325 8.972885 0.732125 2.229721 7.274096 0.01 lala\n3 7.580344 5.776509 7.915510 0.001390 0.971052 4.771013 0.01 foo5\n4 9.062750 3.596733 8.679631 0.226881 1.050554 15.519829 0.01 lala\n5 4.979871 4.711573 4.951103 0.968313 3.949245 14.006075 0.01 feeeee\nPASSED [ 17%]\n\n \n \n PASSED [ 17%]\n\n \n \n PASSED [ 18%]\n\n \n \n \n PASSED [ 19%]\n\n \n \n \n \n PASSED [ 19%]The surface passed does not exist in the pandas categories. This may imply thatdoes not exist in the surface object either.\n\n \n \n PASSED [ 20%]\n\n \n \n PASSED [ 21%]\n\n \n \n PASSED [ 21%]\n\n \n \n PASSED [ 22%]\n\n \n \n PASSED [ 23%]\n\n \n \n PASSED [ 23%]\n\n \n \n PASSED [ 24%] X Y Z smooth surface\n0 400.0 300.0 -500.0 0.000001 foo2\n X Y Z smooth surface\n0 400.0 800 -500.0 0.000001 foo2\n\n \n \n PASSED [ 25%]\n\n \n \n PASSED [ 25%]\n\n \n \n PASSED [ 26%]\n\n \n \n PASSED [ 26%]Fault colors changed. If you do not like this behavior, set change_color to False.\nFault colors changed. If you do not like this behavior, set change_color to False.\n['foo2']\n\n \n \n PASSED [ 27%]\n\n \n \n \n \n PASSED [ 28%]Active grids: ['regular']\n\n \n \n PASSED [ 28%]Empty DataFrame\nColumns: [surface, series, order_surfaces, color, id]\nIndex: []\n\n \n \n \n \n PASSED [ 29%]Active grids: ['regular']\n\n \n \n PASSED [ 30%]\n\n \n \n Setting kriging parameters to their default values.\nActive grids: ['regular' 'topography']\nPASSED [ 30%]foo\n\n \n \n PASSED [ 31%]Active grids: ['regular']\nActive grids: ['regular' 'topography']\n\n \n \n PASSED [ 32%]is path /home/miguel/.cache/pooch/0744c4cf13b4426d3eb20cca3d23e6f6-viz_3d\nActive grids: ['regular']\nActive grids: ['regular' 'topography']\n\n 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'.\nSHA256 hash of downloaded file: 37e8a5c5fff03cb38ed12344a585942f91324031f2bfed6f746ac10dbd9ab8be\nUse 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.\n\n \n \n PASSED [ 32%]\n\n Downloading data from 'https://nowhere.zip' to file '/home/miguel/.cache/pooch/0f0b20cbf7607d1a32c3b8d06ce93942-'.\n\n \n \n PASSED [ 33%]is path /home/miguel/.cache/pooch/bdc57aae09be8b8517ff7162a3f12498-Onlap_relations\nActive grids: ['regular']\nActive grids: ['regular' 'topography']\nSetting kriging parameters to their default values.\nCompiling theano function...\nLevel of Optimization: fast_compile\nDevice: cpu\nPrecision: float64\nNumber of faults: 0\nCompilation Done!\nKriging values: \n values\nrange 1854.723699\n$C_o$ 81904.761905\ndrift equations [3, 3, 3, 3, 3]\nnugget grad 0.01\nnugget scalar 0.000001\n\n 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'.\nSHA256 hash of downloaded file: 927968b50ae6ec8d9ff742bca736875bf2c143d993ee7fbc28d514088cd8ba6d\nUse 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.\n\n \n \n PASSED [ 33%]<pooch.core.Pooch object at 0x7f682196d150>\n\n \n \n \n \n PASSED [ 34%]Active grids: ['regular']\n X Y Z smooth surface\n0 0.231023 1.721407 0.356143 0.000002 surface_1\n1 0.020010 0.692064 -0.649647 0.000002 surface_1\n2 0.443810 -1.231450 -0.688408 0.000002 surface_1\n3 0.865262 -0.583194 0.918076 0.000002 surface_2\n4 -0.774628 -0.969160 1.526413 0.000002 surface_2\n X Y Z ... G_z smooth surface\n0 0.812639 -0.845421 0.099480 ... 2.052763 0.01 surface_1\n1 -1.190455 0.212550 -0.682445 ... 0.425689 0.01 surface_1\n2 -0.806670 -1.136213 2.332153 ... -0.725706 0.01 surface_1\n3 -1.933519 -0.346317 0.296644 ... 0.232094 0.01 surface_2\n4 0.679137 -0.091081 0.053065 ... -0.661261 0.01 surface_2\n[5 rows x 8 columns]\nActive grids: ['regular']\n X Y Z smooth surface\n0 0.231023 1.721407 0.356143 0.000002 surface_1\n1 0.020010 0.692064 -0.649647 0.000002 surface_1\n2 0.443810 -1.231450 -0.688408 0.000002 surface_1\n3 0.865262 -0.583194 0.918076 0.000002 surface_2\n4 -0.774628 -0.969160 1.526413 0.000002 surface_2\n X Y Z ... G_z smooth surface\n0 0.812639 -0.845421 0.099480 ... 2.052763 0.01 surface_1\n1 -1.190455 0.212550 -0.682445 ... 0.425689 0.01 surface_1\n2 -0.806670 -1.136213 2.332153 ... -0.725706 0.01 surface_1\n3 -1.933519 -0.346317 0.296644 ... 0.232094 0.01 surface_2\n4 0.679137 -0.091081 0.053065 ... -0.661261 0.01 surface_2\n[5 rows x 8 columns]\n\n \n \n Active grids: ['regular']\nPASSED [ 35%]\n\n \n \n Fault colors changed. If you do not like this behavior, set change_color to False.\nPASSED [ 35%] surface series order_surfaces color id\n4 Main_Fault Fault_Series 1 #527682 1\n0 Shale Strat_Series 1 #015482 2\n1 Sandstone_1 Strat_Series 2 #9f0052 3\n2 Siltstone Strat_Series 3 #ffbe00 4\n3 Sandstone_2 Strat_Series 4 #728f02 5\n5 basement Strat_Series 5 #ff3f20 6\n\n \n \n PASSED [ 36%]Fault colors changed. If you do not like this behavior, set change_color to False.\n['Fault_Series']\nFault colors changed. If you do not like this behavior, set change_color to False.\n[]\n\n \n \n PASSED [ 37%]Setting kriging parameters to their default values.\n\n \n \n PASSED [ 37%]Setting kriging parameters to their default values.\nDirectory already exists, files will be overwritten\n\n \n \n PASSED [ 38%]Setting kriging parameters to their default values.\n[6. 6. 6. ... 2. 2. 2.] values\nStructure isLith True\n isFault True\n number faults 1\n number surfaces 5\n number series 2\n number surfaces per series [1, 4]\n len surfaces surface_points [5, 8, 13, 16, 15]\n len series surface_points [5, 52]\n len series orientations [1, 2]\nOptions dtype float64\n output geology\n theano_optimizer fast_compile\n device cpu\n verbosity None\nKriging range 3464.101615\n $C_o$ 285714.285714\n drift equations [3, 3]\nRescaling rescaling factor 3800.0\n centers [850.0, 1000.0, 900.0]\n[0 3]\n[0 3] drift equations\n range $C_o$ drift equations\nvalues 1 285714.285714 [0, 3]\n[6. 6. 6. ... 2. 2. 2.] values\nStructure isLith True\n isFault True\n number faults 1\n number surfaces 5\n number series 2\n number surfaces per series [1, 4]\n len surfaces surface_points [5, 8, 13, 16, 15]\n len series surface_points [5, 52]\n len series orientations [1, 2]\nOptions dtype float64\n output geology\n theano_optimizer fast_compile\n device cpu\n verbosity None\nKriging range 1\n $C_o$ 285714.285714\n drift equations [0, 3]\nRescaling rescaling factor 3800.0\n centers [850.0, 1000.0, 900.0]\nCompiling theano function...\nLevel of Optimization: fast_compile\nDevice: cpu\nPrecision: float64\nNumber of faults: 1\nCompilation Done!\nKriging values: \n values\nrange 1\n$C_o$ 285714.285714\ndrift equations [0, 3]\n[4.00645492 4.00623068 4.00600644 ... 3.9931591 3.99293487 3.99271063] values\nStructure isLith True\n isFault True\n number faults 1\n number surfaces 5\n number series 2\n number surfaces per series [1, 4]\n len surfaces surface_points [5, 8, 13, 16, 15]\n len series surface_points [5, 52]\n len series orientations [1, 2]\nOptions dtype float64\n output geology\n theano_optimizer fast_compile\n device cpu\n verbosity None\nKriging range 1\n $C_o$ 285714.285714\n drift equations [0, 3]\nRescaling rescaling factor 3800.0\n centers [850.0, 1000.0, 900.0]\n\n \n \n \n \n \n PASSED [ 39%]\n\n \n \n \n \n \n PASSED [ 39%]Active grids: ['regular']\nFault colors changed. If you do not like this behavior, set change_color to False.\nFault colors changed. If you do not like this behavior, set change_color to False.\nSetting kriging parameters to their default values.\n\n \n \n PASSED [ 40%]Active grids: ['regular']\nFault colors changed. If you do not like this behavior, set change_color to False.\nFault colors changed. If you do not like this behavior, set change_color to False.\nSetting kriging parameters to their default values.\n\n \n \n PASSED [ 41%]Active grids: ['regular']\nFault colors changed. If you do not like this behavior, set change_color to False.\n order_series BottomRelation isActive isFault isFinite\nFault1 1 Fault False True False\nCycle1 2 Erosion False False False\n\n \n \n PASSED [ 41%]Active grids: ['regular']\nSetting kriging parameters to their default values.\nFault colors changed. If you do not like this behavior, set change_color to False.\nFault colors changed. If you do not like this behavior, set change_color to False.\n\n \n \n \n \n PASSED [ 42%]Active grids: ['regular']\nSetting kriging parameters to their default values.\n[array([[0.087789 , 0.09072717, 0.7501 ],\n [0.087789 , 0.10739383, 0.7501 ],\n [0.10445567, 0.09072717, 0.7501 ],\n ...,\n [0.90445567, 0.8740605 , 0.7501 ],\n [0.90445567, 0.89072717, 0.7501 ],\n [0.90445567, 0.90739383, 0.7501 ]]), array([[0.087789 , 0.09072717, 0.2501 ],\n [0.087789 , 0.10739383, 0.2501 ],\n [0.10445567, 0.09072717, 0.2501 ],\n ...,\n [0.90445567, 0.8740605 , 0.2501 ],\n [0.90445567, 0.89072717, 0.2501 ],\n [0.90445567, 0.90739383, 0.2501 ]])]\n\n \n \n Active grids: ['regular']\nSetting kriging parameters to their default values.\nPASSED [ 42%]Active grids: ['regular' 'custom']\n\n \n \n PASSED [ 43%][[[False False False ... False False False]\n [False False False ... False False False]\n [False False False ... False False False]\n ...\n [False False False ... False False False]\n [False False False ... False False False]\n [False False False ... False False False]]\n [[False False False ... False False False]\n [False False False ... False False False]\n [False False False ... False False False]\n ...\n [False False False ... False False False]\n [False False False ... False False False]\n [False False False ... False False False]]\n [[False False False ... False False False]\n [False False False ... False False False]\n [False False False ... False False False]\n ...\n [False False False ... False False False]\n [False False False ... False False False]\n [False False False ... False False False]]\n ...\n [[False False False ... False False False]\n [False False False ... False False False]\n [False False False ... False False False]\n ...\n [False False False ... False False False]\n [False False False ... False False False]\n [False False False ... False False False]]\n [[False False False ... False False False]\n [False False False ... False False False]\n [False False False ... False False False]\n ...\n [False False False ... False False False]\n [False False False ... False False False]\n [False False False ... False False False]]\n [[False False False ... False False False]\n [False False False ... False False False]\n [False False False ... False False False]\n ...\n [False False False ... False False False]\n [False False False ... False False False]\n [False False False ... False False False]]]\n[[[ True True True ... False False False]\n [ True True True ... False False False]\n [ True True True ... False False False]\n ...\n [ True True True ... False False False]\n [ True True True ... False False False]\n [ True True True ... False False False]]\n [[ True True True ... False False False]\n [ True True True ... False False False]\n [ True True True ... False False False]\n ...\n [ True True True ... False False False]\n [ True True True ... False False False]\n [ True True True ... False False False]]\n [[ True True True ... False False False]\n [ True True True ... False False False]\n [ True True True ... False False False]\n ...\n [ True True True ... False False False]\n [ True True True ... False False False]\n [ True True True ... False False False]]\n ...\n [[ True True True ... False False False]\n [ True True True ... False False False]\n [ True True True ... False False False]\n ...\n [ True True True ... False False False]\n [ True True True ... False False False]\n [ True True True ... False False False]]\n [[ True True True ... False False False]\n [ True True True ... False False False]\n [ True True True ... False False False]\n ...\n [ True True True ... False False False]\n [ True True True ... False False False]\n [ True True True ... False False False]]\n [[ True True True ... False False False]\n [ True True True ... False False False]\n [ True True True ... False False False]\n ...\n [ True True True ... False False False]\n [ True True True ... False False False]\n [ True True True ... False False False]]]\n[False False False ... True True True]\n\n \n \n \n \n PASSED [ 44%]Active grids: ['regular' 'topography' 'sections']\nActive grids: ['sections']\nSetting kriging parameters to their default values.\n\n \n \n \n \n [-400. 0.]\nFAILED [ 44%]\n\n test/test_core/test_xsol.py:94 (test_xsol)sol_values = [array([[9, 6, 6, ..., 3, 1, 3],\n [9, 8, 0, ..., 2, 2, 8],\n [4, 3, 7, ..., 7, 7, 6]]), array([[[8, 1, 4, ....[27, 23, 22, ..., 22, 20, 23],\n [28, 22, 22, ..., 20, 29, 21],\n [26, 29, 20, ..., 22, 29, 26]]), None, ...]\na_grid = Grid Object. Values: \narray([[ 20. , 20. , -1980. ],\n [ 20. , 20. , -194....4040404],\n [ 1750. , 250. , -20.2020202],\n [ 1750. , 250. , 0. ]])\nstack_eg = order_series BottomRelation isActive isFault isFinite\nfoo3 1 Erosion True False ... 4 Erosion True False False\nfoo20 5 Erosion True False False\nsurface_eg = surface series order_surfaces color id val_foo val2_foo\n0 foo foo20 1 #015482 1 ... foo20 3 #ffbe00 3 2 1\n3 fee foo20 4 #728f02 4 6 8\n def test_xsol(sol_values, a_grid, stack_eg, surface_eg):\n sol = XSolution(a_grid, stack=stack_eg, surfaces=surface_eg)\n> sol.set_values(sol_values)\ntest_core/test_xsol.py:97: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n../gempy/core/xsolution.py:131: in set_values\n self.set_values_to_regular_grid_(values, l[0], l[1], coords_base.copy())\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nself = <gempy.core.xsolution.XSolution object at 0x7f68266ceb30>\nvalues = [array([[9, 6, 6, ..., 3, 1, 3],\n [9, 8, 0, ..., 2, 2, 8],\n [4, 3, 7, ..., 7, 7, 6]]), array([[[8, 1, 4, ....[27, 23, 22, ..., 22, 20, 23],\n [28, 22, 22, ..., 20, 29, 21],\n [26, 29, 20, ..., 22, 29, 26]]), None, ...]\nl0 = 0, l1 = 125000\ncoords_base = {'Features': ['foo3', 'boo', 'foo7', 'foo5', 'foo20'], 'Properties': ['id', 'val_foo', 'val2_foo'], 'X': array([ 20.,...\n 1460., 1500., 1540., 1580., 1620., 1660., 1700., 1740., 1780.,\n 1820., 1860., 1900., 1940., 1980.]), ...}\n def set_values_to_regular_grid_(self, values: list, l0, l1,\n coords_base: dict):\n \n coords = self.add_cartesian_coords(coords_base)\n \n arrays = self.create_struc_xarrays(values, l0, l1,\n self.grid.regular_grid.resolution)\n \n> self.s_regular_grid = subsurface.StructuredData.from_dict(data_dict=arrays, coords=coords)\nE NameError: name 'subsurface' is not defined\n../gempy/core/xsolution.py:297: NameError\n\n \n \n FAILED [ 45%]\n\n test/test_core/test_xsol.py:107 (test_xsol_unstructured)sol_values = [array([[9, 6, 6, ..., 3, 1, 3],\n [9, 8, 0, ..., 2, 2, 8],\n [4, 3, 7, ..., 7, 7, 6]]), array([[[8, 1, 4, ....[27, 23, 22, ..., 22, 20, 23],\n [28, 22, 22, ..., 20, 29, 21],\n [26, 29, 20, ..., 22, 29, 26]]), None, ...]\na_grid = Grid Object. Values: \narray([[ 20. , 20. , -1980. ],\n [ 20. , 20. , -194....4040404],\n [ 1750. , 250. , -20.2020202],\n [ 1750. , 250. , 0. ]])\nstack_eg = order_series BottomRelation isActive isFault isFinite\nfoo3 1 Erosion True False ... 4 Erosion True False False\nfoo20 5 Erosion True False False\nsurface_eg = surface series order_surfaces color id val_foo val2_foo\n0 foo foo20 1 #015482 1 ... foo20 3 #ffbe00 3 2 1\n3 fee foo20 4 #728f02 4 6 8\n def test_xsol_unstructured(sol_values, a_grid, stack_eg, surface_eg):\n sol = XSolution(a_grid, stack=stack_eg, surfaces=surface_eg)\n> sol.set_values(sol_values)\ntest_core/test_xsol.py:110: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n../gempy/core/xsolution.py:131: in set_values\n self.set_values_to_regular_grid_(values, l[0], l[1], coords_base.copy())\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nself = <gempy.core.xsolution.XSolution object at 0x7f68264e6680>\nvalues = [array([[9, 6, 6, ..., 3, 1, 3],\n [9, 8, 0, ..., 2, 2, 8],\n [4, 3, 7, ..., 7, 7, 6]]), array([[[8, 1, 4, ....[27, 23, 22, ..., 22, 20, 23],\n [28, 22, 22, ..., 20, 29, 21],\n [26, 29, 20, ..., 22, 29, 26]]), None, ...]\nl0 = 0, l1 = 125000\ncoords_base = {'Features': ['foo3', 'boo', 'foo7', 'foo5', 'foo20'], 'Properties': ['id', 'val_foo', 'val2_foo'], 'X': array([ 20.,...\n 1460., 1500., 1540., 1580., 1620., 1660., 1700., 1740., 1780.,\n 1820., 1860., 1900., 1940., 1980.]), ...}\n def set_values_to_regular_grid_(self, values: list, l0, l1,\n coords_base: dict):\n \n coords = self.add_cartesian_coords(coords_base)\n \n arrays = self.create_struc_xarrays(values, l0, l1,\n self.grid.regular_grid.resolution)\n \n> self.s_regular_grid = subsurface.StructuredData.from_dict(data_dict=arrays, coords=coords)\nE NameError: name 'subsurface' is not defined\n../gempy/core/xsolution.py:297: NameError\n\n \n \n FAILED [ 46%]\n\n test/test_core/test_xsol.py:113 (test_xsol_structured)sol_values = [array([[9, 6, 6, ..., 3, 1, 3],\n [9, 8, 0, ..., 2, 2, 8],\n [4, 3, 7, ..., 7, 7, 6]]), array([[[8, 1, 4, ....[27, 23, 22, ..., 22, 20, 23],\n [28, 22, 22, ..., 20, 29, 21],\n [26, 29, 20, ..., 22, 29, 26]]), None, ...]\na_grid = Grid Object. Values: \narray([[ 20. , 20. , -1980. ],\n [ 20. , 20. , -194....4040404],\n [ 1750. , 250. , -20.2020202],\n [ 1750. , 250. , 0. ]])\nstack_eg = order_series BottomRelation isActive isFault isFinite\nfoo3 1 Erosion True False ... 4 Erosion True False False\nfoo20 5 Erosion True False False\nsurface_eg = surface series order_surfaces color id val_foo val2_foo\n0 foo foo20 1 #015482 1 ... foo20 3 #ffbe00 3 2 1\n3 fee foo20 4 #728f02 4 6 8\n def test_xsol_structured(sol_values, a_grid, stack_eg, surface_eg):\n sol = XSolution(a_grid, stack=stack_eg, surfaces=surface_eg)\n> sol.set_values(sol_values)\ntest_core/test_xsol.py:116: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n../gempy/core/xsolution.py:131: in set_values\n self.set_values_to_regular_grid_(values, l[0], l[1], coords_base.copy())\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nself = <gempy.core.xsolution.XSolution object at 0x7f68262c4670>\nvalues = [array([[9, 6, 6, ..., 3, 1, 3],\n [9, 8, 0, ..., 2, 2, 8],\n [4, 3, 7, ..., 7, 7, 6]]), array([[[8, 1, 4, ....[27, 23, 22, ..., 22, 20, 23],\n [28, 22, 22, ..., 20, 29, 21],\n [26, 29, 20, ..., 22, 29, 26]]), None, ...]\nl0 = 0, l1 = 125000\ncoords_base = {'Features': ['foo3', 'boo', 'foo7', 'foo5', 'foo20'], 'Properties': ['id', 'val_foo', 'val2_foo'], 'X': array([ 20.,...\n 1460., 1500., 1540., 1580., 1620., 1660., 1700., 1740., 1780.,\n 1820., 1860., 1900., 1940., 1980.]), ...}\n def set_values_to_regular_grid_(self, values: list, l0, l1,\n coords_base: dict):\n \n coords = self.add_cartesian_coords(coords_base)\n \n arrays = self.create_struc_xarrays(values, l0, l1,\n self.grid.regular_grid.resolution)\n \n> self.s_regular_grid = subsurface.StructuredData.from_dict(data_dict=arrays, coords=coords)\nE NameError: name 'subsurface' is not defined\n../gempy/core/xsolution.py:297: NameError\n\n \n \n FAILED [ 46%]\n\n test/test_core/test_xsol.py:119 (test_xsol_to_disk)sol_values = [array([[9, 6, 6, ..., 3, 1, 3],\n [9, 8, 0, ..., 2, 2, 8],\n [4, 3, 7, ..., 7, 7, 6]]), array([[[8, 1, 4, ....[27, 23, 22, ..., 22, 20, 23],\n [28, 22, 22, ..., 20, 29, 21],\n [26, 29, 20, ..., 22, 29, 26]]), None, ...]\na_grid = Grid Object. Values: \narray([[ 20. , 20. , -1980. ],\n [ 20. , 20. , -194....4040404],\n [ 1750. , 250. , -20.2020202],\n [ 1750. , 250. , 0. ]])\nstack_eg = order_series BottomRelation isActive isFault isFinite\nfoo3 1 Erosion True False ... 4 Erosion True False False\nfoo20 5 Erosion True False False\nsurface_eg = surface series order_surfaces color id val_foo val2_foo\n0 foo foo20 1 #015482 1 ... foo20 3 #ffbe00 3 2 1\n3 fee foo20 4 #728f02 4 6 8\ntmpdir = local('/tmp/pytest-of-miguel/pytest-0/test_xsol_to_disk0')\n def test_xsol_to_disk(sol_values, a_grid, stack_eg, surface_eg, tmpdir):\n sol = XSolution(a_grid, stack=stack_eg, surfaces=surface_eg)\n> sol.set_values(sol_values)\ntest_core/test_xsol.py:122: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n../gempy/core/xsolution.py:131: in set_values\n self.set_values_to_regular_grid_(values, l[0], l[1], coords_base.copy())\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nself = <gempy.core.xsolution.XSolution object at 0x7f682641c2e0>\nvalues = [array([[9, 6, 6, ..., 3, 1, 3],\n [9, 8, 0, ..., 2, 2, 8],\n [4, 3, 7, ..., 7, 7, 6]]), array([[[8, 1, 4, ....[27, 23, 22, ..., 22, 20, 23],\n [28, 22, 22, ..., 20, 29, 21],\n [26, 29, 20, ..., 22, 29, 26]]), None, ...]\nl0 = 0, l1 = 125000\ncoords_base = {'Features': ['foo3', 'boo', 'foo7', 'foo5', 'foo20'], 'Properties': ['id', 'val_foo', 'val2_foo'], 'X': array([ 20.,...\n 1460., 1500., 1540., 1580., 1620., 1660., 1700., 1740., 1780.,\n 1820., 1860., 1900., 1940., 1980.]), ...}\n def set_values_to_regular_grid_(self, values: list, l0, l1,\n coords_base: dict):\n \n coords = self.add_cartesian_coords(coords_base)\n \n arrays = self.create_struc_xarrays(values, l0, l1,\n self.grid.regular_grid.resolution)\n \n> self.s_regular_grid = subsurface.StructuredData.from_dict(data_dict=arrays, coords=coords)\nE NameError: name 'subsurface' is not defined\n../gempy/core/xsolution.py:297: NameError\n\n \n \n SKIPPED (Test for gempy_lite) [ 47%]\n\n \n \n SKIPPED (Test for gempy_lite) [ 48%]\n\n \n \n FAILED [ 48%]\n\n test/test_core/test_xsol.py:157 (test_xsol_full)model_horizontal_two_layers = interpolator 2023-03-07 07:25\n def test_xsol_full(model_horizontal_two_layers):\n model_horizontal_two_layers.update_to_interpolator()\n vals = gp.compute_model(model_horizontal_two_layers, set_solutions=False)\n sol = XSolution(\n model_horizontal_two_layers._grid,\n stack=model_horizontal_two_layers._stack,\n surfaces=model_horizontal_two_layers._surfaces)\n> sol.set_values(vals)\ntest_core/test_xsol.py:165: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n../gempy/core/xsolution.py:131: in set_values\n self.set_values_to_regular_grid_(values, l[0], l[1], coords_base.copy())\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nself = <gempy.core.xsolution.XSolution object at 0x7f6828d65de0>\nvalues = [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 ,\n 0.5002 ]]), array([[1.5002, 0.5002]]), ...]\nl0 = 0, l1 = 125000\ncoords_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,\n 7.9, 8.1, 8.3, 8.5, 8.7, 8.9, 9.1, 9.3, 9.5, 9.7, 9.9]), ...}\n def set_values_to_regular_grid_(self, values: list, l0, l1,\n coords_base: dict):\n \n coords = self.add_cartesian_coords(coords_base)\n \n arrays = self.create_struc_xarrays(values, l0, l1,\n self.grid.regular_grid.resolution)\n \n> self.s_regular_grid = subsurface.StructuredData.from_dict(data_dict=arrays, coords=coords)\nE NameError: name 'subsurface' is not defined\n../gempy/core/xsolution.py:297: NameError\n\n \n \n PASSED [ 49%]\n\n \n \n FAILED [ 50%]\n\n test/test_core/test_xsol.py:174 (test_set_meshes)model_horizontal_two_layers = interpolator 2023-03-07 07:25\n def test_set_meshes(model_horizontal_two_layers):\n> import subsurface\nE ModuleNotFoundError: No module named 'subsurface'\ntest_core/test_xsol.py:176: ModuleNotFoundError\n\n \n \n PASSED [ 50%]\n\n \n \n \n \n \n PASSED [ 51%]\n\n \n \n PASSED [ 51%]\n\n \n \n PASSED [ 52%]\n\n \n \n PASSED [ 53%]\n\n \n \n PASSED [ 53%]\n\n \n \n PASSED [ 54%]\n\n \n \n PASSED [ 55%]\n\n \n \n PASSED [ 55%]\n\n \n \n PASSED [ 56%]\n\n \n \n PASSED [ 57%]\n\n \n \n \n \n \n PASSED [ 57%]\n\n \n \n PASSED [ 58%]\n\n \n \n PASSED [ 58%]Active grids: ['regular']\n[ 800. 1000.]\nActive grids: ['regular' 'topography']\nActive grids: ['regular' 'topography' 'sections']\n start stop resolution dist\nsection1 [0, 0] [1000, 1000] [100, 80] 1414.213562\nsection2 [800, 0] [800, 1000] [150, 100] 1000.000000\nsection3 [50, 200] [100, 500] [200, 150] 304.138127\n\n \n \n PASSED [ 59%]Active grids: ['regular']\nActive grids: ['regular' 'sections']\nActive grids: ['regular' 'sections']\n start stop resolution dist\nsection1 [0, 0] [1000, 1000] [100, 80] 1414.213562\nsection2 [800, 0] [800, 1000] [150, 100] 1000.000000\nsection3 [50, 200] [100, 500] [200, 150] 304.138127\n\n \n \n PASSED [ 60%]\n\n \n \n \n \n \n FAILED [ 60%]Active grids: ['regular']\n\n 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\n (\"GITHUBACTION\" in os.environ and os.environ[\"GITHUBACTION\"] == \"true\"),\n reason=\"Skipping this test on Travis CI because gdal.\")\n def test_real_grid_ales():\n resolution = [30, 20, 50]\n extent = np.array([729550.0, 751500.0, 1913500.0, 1923650.0, -50, 800.0])\n path_interf = data_path + \"/2018_interf.csv\"\n path_orient = data_path + \"/2018_orient_clust_n_init5_0.csv\"\n path_dem = data_path + \"/_cropped_DEM_coarse.tif\"\n \n geo_model = gp.create_model('Alesmodel')\n gp.init_data(geo_model, extent=extent, resolution=resolution,\n path_i=path_interf,\n path_o=path_orient)\n \n> geo_model.set_topography(source='gdal', filepath=path_dem)\ntest_core/test_grids/test_topography.py:47: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n../gempy/plot/decorators.py:108: in pst\n topography = func(*args, **kwargs)\n../gempy/core/model.py:428: in set_topography\n self._grid.create_topography(source, **kwargs)\n../gempy/core/data.py:183: in create_topography\n self.topography.load_from_gdal(filepath)\n../gempy/core/grid_modules/topography.py:122: in load_from_gdal\n dem = LoadDEMGDAL(filepath, extent=self.extent)\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nself = <gempy.core.grid_modules.create_topography.LoadDEMGDAL object at 0x7f68264392d0>\npath_dem = '/home/miguel/PycharmProjects/gempy/test/test_core/test_grids/../../input_data/_cropped_DEM_coarse.tif'\ngrid = None\nextent = array([ 7.29550e+05, 7.51500e+05, 1.91350e+06, 1.92365e+06,\n -5.00000e+01, 8.00000e+02])\ndelete_temp = True\n def __init__(self, path_dem, grid=None, extent=None, delete_temp=True):\n \"\"\"\n Args:\n path_dem: path where dem is stored. file format: GDAL raster formats\n if grid: cropped to geomodel extent\n \"\"\"\n if GDAL_IMPORT == False:\n> raise ImportError('Gdal package is not installed. No support for raster formats.')\nE ImportError: Gdal package is not installed. No support for raster formats.\n../gempy/core/grid_modules/create_topography.py:35: ImportError\n\n \n \n [1600. 2000.]\n[[ 0. 0. 1965.35297791]\n [ 0. 40.81632653 1951.7566901 ]\n [ 0. 81.63265306 1938.28261911]\n ...\n [2000. 1918.36734694 1994.00395337]\n [2000. 1959.18367347 1999.46369539]\n [2000. 2000. 1991.12328024]] [[[ 0. 0. 1965.35297791]\n [ 0. 40.81632653 1951.7566901 ]\n [ 0. 81.63265306 1938.28261911]\n ...\n [ 0. 1918.36734694 1981.16072145]\n [ 0. 1959.18367347 1985.82487834]\n [ 0. 2000. 1985.588668 ]]\n [[ 40.81632653 0. 1977.20208871]\n [ 40.81632653 40.81632653 1973.08605219]\n [ 40.81632653 81.63265306 1933.43176595]\n ...\n [ 40.81632653 1918.36734694 1957.60528886]\n [ 40.81632653 1959.18367347 1966.79428539]\n [ 40.81632653 2000. 1976.11353164]]\n [[ 81.63265306 0. 1967.71153881]\n [ 81.63265306 40.81632653 1960.09034566]\n [ 81.63265306 81.63265306 1936.30847248]\n ...\n [ 81.63265306 1918.36734694 1942.74336416]\n [ 81.63265306 1959.18367347 1946.83944313]\n [ 81.63265306 2000. 1963.58112269]]\n ...\n [[1918.36734694 0. 1938.41053067]\n [1918.36734694 40.81632653 1933.36534759]\n [1918.36734694 81.63265306 1933.98495031]\n ...\n [1918.36734694 1918.36734694 1964.2780338 ]\n [1918.36734694 1959.18367347 1966.82838829]\n [1918.36734694 2000. 1958.19672717]]\n [[1959.18367347 0. 1954.76901087]\n [1959.18367347 40.81632653 1936.43380299]\n [1959.18367347 81.63265306 1929.18684525]\n ...\n [1959.18367347 1918.36734694 1993.02967815]\n [1959.18367347 1959.18367347 2000. ]\n [1959.18367347 2000. 1986.34117143]]\n [[2000. 0. 1959.80102413]\n [2000. 40.81632653 1940.01437642]\n [2000. 81.63265306 1927.47976069]\n ...\n [2000. 1918.36734694 1994.00395337]\n [2000. 1959.18367347 1999.46369539]\n [2000. 2000. 1991.12328024]]]\nFAILED [ 61%][1600. 2000.]\nActive grids: ['regular' 'topography']\n\n 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\nartificial_grid = <gempy.core.grid_modules.topography.Topography object at 0x7f68209ec640>\n def test_plot_2d_topography(one_fault_model_no_interp, artificial_grid):\n geo_model = one_fault_model_no_interp\n #geo_model._grid.topography = artificial_grid\n geo_model.set_topography()\n> p2d = gp.plot_2d(geo_model, section_names=['topography'], show_topography=True,\n kwargs_topography={'hillshade': False})\ntest_core/test_grids/test_topography.py:102: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n../gempy/plot/plot_api.py:216: in plot_2d\n p.plot_section_traces(temp_ax)\n../gempy/plot/visualization_2d.py:695: in plot_section_traces\n self.plot_data(ax, section_name='topography', **kwargs)\n../gempy/plot/visualization_2d.py:458: in plot_data\n points_df.plot.scatter(x=x, y=y, ax=ax, c=points_df['surface'].map(self._color_lot),\n../venv/lib/python3.10/site-packages/pandas/plotting/_core.py:1636: in scatter\n return self(kind=\"scatter\", x=x, y=y, s=s, c=c, **kwargs)\n../venv/lib/python3.10/site-packages/pandas/plotting/_core.py:917: in __call__\n return plot_backend.plot(data, x=x, y=y, kind=kind, **kwargs)\n../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/__init__.py:71: in plot\n plot_obj.generate()\n../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py:291: in generate\n self._adorn_subplots()\n../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py:497: in _adorn_subplots\n handle_shared_axes(\n../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/tools.py:432: in handle_shared_axes\n if is_first_col(ax):\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nx = <Axes: ylabel='elevation [m]'>\n> is_first_col = lambda x: x.get_subplotspec().is_first_col()\nE AttributeError: 'NoneType' object has no attribute 'is_first_col'\n../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/tools.py:393: AttributeError\n\n \n \n FAILED [ 62%]\n\n test/test_core/test_grids/test_topography.py:114 (test_plot_3d_structure_topo2)unconformity_model_topo = unconformity_model 2023-03-07 07:24\nartificial_grid = <gempy.core.grid_modules.topography.Topography object at 0x7f68209ec640>\n def test_plot_3d_structure_topo2(unconformity_model_topo, artificial_grid):\n \n geo_model = unconformity_model_topo\n with pytest.raises(AssertionError):\n geo_model._grid.regular_grid.set_topography_mask(artificial_grid)\n \n # geo_model._grid.regular_grid.set_topography_mask(geo_model._grid.topography)\n \n> p2d = gp.plot_2d(geo_model, section_names=['topography'], show_topography=True,\n show_lith=False,\n kwargs_topography={'hillshade': True})\ntest_core/test_grids/test_topography.py:123: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n../gempy/plot/plot_api.py:216: in plot_2d\n p.plot_section_traces(temp_ax)\n../gempy/plot/visualization_2d.py:695: in plot_section_traces\n self.plot_data(ax, section_name='topography', **kwargs)\n../gempy/plot/visualization_2d.py:458: in plot_data\n points_df.plot.scatter(x=x, y=y, ax=ax, c=points_df['surface'].map(self._color_lot),\n../venv/lib/python3.10/site-packages/pandas/plotting/_core.py:1636: in scatter\n return self(kind=\"scatter\", x=x, y=y, s=s, c=c, **kwargs)\n../venv/lib/python3.10/site-packages/pandas/plotting/_core.py:917: in __call__\n return plot_backend.plot(data, x=x, y=y, kind=kind, **kwargs)\n../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/__init__.py:71: in plot\n plot_obj.generate()\n../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py:291: in generate\n self._adorn_subplots()\n../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py:497: in _adorn_subplots\n handle_shared_axes(\n../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/tools.py:432: in handle_shared_axes\n if is_first_col(ax):\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nx = <Axes: ylabel='elevation [m]'>\n> is_first_col = lambda x: x.get_subplotspec().is_first_col()\nE AttributeError: 'NoneType' object has no attribute 'is_first_col'\n../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/tools.py:393: AttributeError\n\n \n \n \n \n \n \n Active grids: ['regular']\nSetting kriging parameters to their default values.\nPASSED [ 62%]\nLithology ids \n [5. 2. 2. ... 1. 1. 1.] \n\n \n \n PASSED [ 63%]\nLithology ids \n [5. 5. 5. ... 1. 1. 1.] \n\n \n \n PASSED [ 64%]\nLithology ids \n [5. 5. 5. ... 3. 3. 1.] \n\n \n \n PASSED [ 64%]Active grids: ['regular']\nSetting kriging parameters to their default values.\nCompiling theano function...\nLevel of Optimization: fast_compile\nDevice: cpu\nPrecision: float64\nNumber of faults: 0\nCompilation Done!\nKriging values: \n values\nrange 1854.723699\n$C_o$ 81904.761905\ndrift equations [3, 3, 3, 3, 3]\nnugget grad 0.01\nnugget scalar 0.000001\nActive grids: ['regular']\n\n \n \n \n \n \n \n \n PASSED [ 65%]Active grids: ['regular']\nFault colors changed. If you do not like this behavior, set change_color to False.\nSetting kriging parameters to their default values.\nCompiling theano function...\nLevel of Optimization: fast_compile\nDevice: cpu\nPrecision: float64\nNumber of faults: 16\nCompilation Done!\nKriging values: \n values\nrange 54089.18561\n$C_o$ 69658095.238095\ndrift equations [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]\n\n \n \n PASSED [ 66%] Fault_2235 ... Rocklea_Inlier_metagranitic_unit\nFault_2235 0.0 ... 0\nFault_2843 0.0 ... 0\nFault_2248 0.0 ... 0\nFault_16769 0.0 ... 0\nFault_2997 0.0 ... 0\nFault_5993 0.0 ... 0\nFault_3496 0.0 ... 0\nFault_7439 0.0 ... 0\nFault_3498 0.0 ... 0\nFault_12647 0.0 ... 0\nFault_12658 0.0 ... 0\nFault_7436 NaN ... 1\nFault_7099 NaN ... 0\nFault_10762 NaN ... 0\nFault_12660 NaN ... 0\nFault_14378 NaN ... 0\n[16 rows x 15 columns]\n\n \n \n FAILED [ 66%]Active grids: ['regular']\n\n test/test_integrations/test_map2loop.py:69 (test_loop2gempy2)@pytest.mark.skipif((\"TRAVIS\" in os.environ and os.environ[\"TRAVIS\"] == \"true\") or\n (\"GITHUBACTION\" in os.environ and os.environ[\"GITHUBACTION\"] == \"true\"),\n reason=\"Skipping this test on Travis CI. For some reason there is a linalg \"\n \"error.\")\n def test_loop2gempy2():\n topo = fp2\n # topo = None\n \n> loop2gempy(contacts_file2, orientations_file2, extent[:4], series_file2,\n extent[4],\n extent[5],\n dtm_reproj_file=topo,\n faults_contact=faults_contact2,\n faults_orientations=faults_orientations2,\n faults_faults_rel=ff,\n faults_groups_rel=fg,\n model_name='testing_map',\n compute=True,\n vtk=True, vtk_path=None, image_2d=True)\ntest_integrations/test_map2loop.py:78: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n../gempy/addons/map2gempy.py:128: in loop2gempy\n geo_model.set_topography(source=source, **topo_kwarg)\n../gempy/plot/decorators.py:108: in pst\n topography = func(*args, **kwargs)\n../gempy/core/model.py:428: in set_topography\n self._grid.create_topography(source, **kwargs)\n../gempy/core/data.py:183: in create_topography\n self.topography.load_from_gdal(filepath)\n../gempy/core/grid_modules/topography.py:122: in load_from_gdal\n dem = LoadDEMGDAL(filepath, extent=self.extent)\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nself = <gempy.core.grid_modules.create_topography.LoadDEMGDAL object at 0x7f6825c44370>\npath_dem = '/home/miguel/PycharmProjects/gempy/test/test_integrations/../input_data/dtm_rp2.tif'\ngrid = None\nextent = array([ 5.15687310e+05, 5.62666860e+05, 7.47344677e+06, 7.52127357e+06,\n -3.20000000e+03, 1.20000000e+03])\ndelete_temp = True\n def __init__(self, path_dem, grid=None, extent=None, delete_temp=True):\n \"\"\"\n Args:\n path_dem: path where dem is stored. file format: GDAL raster formats\n if grid: cropped to geomodel extent\n \"\"\"\n if GDAL_IMPORT == False:\n> raise ImportError('Gdal package is not installed. No support for raster formats.')\nE ImportError: Gdal package is not installed. No support for raster formats.\n../gempy/core/grid_modules/create_topography.py:35: ImportError\n\n \n \n FAILED [ 67%]Active grids: ['regular']\n\n 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\n (\"GITHUBACTION\" in os.environ and os.environ[\"GITHUBACTION\"] == \"true\"),\n reason=\"Skipping this test on Travis CI. For some reason there is a linalg \"\n \"error.\")\n def test_map2loop_model_import_data():\n geo_model = gp.create_model('test_map2Loop')\n gp.init_data(\n geo_model,\n extent=[bbox[0], bbox[2], bbox[1], bbox[3], model_base, model_top],\n resolution=[50, 50, 50],\n path_o=orientations_file,\n path_i=contacts_file\n )\n \n # Load Topology\n> geo_model.set_topography(source='gdal', filepath=fp)\ntest_integrations/test_map2loop.py:106: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n../gempy/plot/decorators.py:108: in pst\n topography = func(*args, **kwargs)\n../gempy/core/model.py:428: in set_topography\n self._grid.create_topography(source, **kwargs)\n../gempy/core/data.py:183: in create_topography\n self.topography.load_from_gdal(filepath)\n../gempy/core/grid_modules/topography.py:122: in load_from_gdal\n dem = LoadDEMGDAL(filepath, extent=self.extent)\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nself = <gempy.core.grid_modules.create_topography.LoadDEMGDAL object at 0x7f681fe811b0>\npath_dem = '/home/miguel/PycharmProjects/gempy/test/test_integrations/../input_data/dtm_rp.tif'\ngrid = None\nextent = array([5.00e+05, 5.45e+05, 7.49e+06, 7.52e+06, 0.00e+00, 8.00e+02])\ndelete_temp = True\n def __init__(self, path_dem, grid=None, extent=None, delete_temp=True):\n \"\"\"\n Args:\n path_dem: path where dem is stored. file format: GDAL raster formats\n if grid: cropped to geomodel extent\n \"\"\"\n if GDAL_IMPORT == False:\n> raise ImportError('Gdal package is not installed. No support for raster formats.')\nE ImportError: Gdal package is not installed. No support for raster formats.\n../gempy/core/grid_modules/create_topography.py:35: ImportError\n\n \n \n FAILED [ 67%]Active grids: ['regular']\nSetting kriging parameters to their default values.\nCompiling theano function...\nLevel of Optimization: fast_compile\nDevice: cpu\nPrecision: float64\nNumber of faults: 0\nCompilation Done!\nKriging values: \n values\nrange 54230.987452\n$C_o$ 70023809.52381\ndrift equations [3, 3]\n\n 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\n (\"GITHUBACTION\" in os.environ and os.environ[\"GITHUBACTION\"] == \"true\"),\n reason=\"Skipping this test on Travis CI. For some reason there is a linalg \"\n \"error.\")\n def test_map2loop_model_no_faults():\n # Location box\n bbox = (500000, 7490000, 545000, 7520000)\n model_base = -3200 # Original 3200\n model_top = 800\n \n # Input files\n geo_model = gp.create_model('test_map2Loop')\n gp.init_data(\n geo_model,\n extent=[bbox[0], bbox[2], bbox[1], bbox[3], model_base, model_top],\n resolution=[50, 50, 80],\n path_o=orientations_file,\n path_i=contacts_file\n )\n \n gp.set_interpolator(geo_model)\n \n # Load Topology\n> geo_model.set_topography(source='gdal', filepath=fp)\ntest_integrations/test_map2loop.py:141: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n../gempy/plot/decorators.py:108: in pst\n topography = func(*args, **kwargs)\n../gempy/core/model.py:428: in set_topography\n self._grid.create_topography(source, **kwargs)\n../gempy/core/data.py:183: in create_topography\n self.topography.load_from_gdal(filepath)\n../gempy/core/grid_modules/topography.py:122: in load_from_gdal\n dem = LoadDEMGDAL(filepath, extent=self.extent)\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nself = <gempy.core.grid_modules.create_topography.LoadDEMGDAL object at 0x7f682024b130>\npath_dem = '/home/miguel/PycharmProjects/gempy/test/test_integrations/../input_data/dtm_rp.tif'\ngrid = None\nextent = array([ 5.00e+05, 5.45e+05, 7.49e+06, 7.52e+06, -3.20e+03, 8.00e+02])\ndelete_temp = True\n def __init__(self, path_dem, grid=None, extent=None, delete_temp=True):\n \"\"\"\n Args:\n path_dem: path where dem is stored. file format: GDAL raster formats\n if grid: cropped to geomodel extent\n \"\"\"\n if GDAL_IMPORT == False:\n> raise ImportError('Gdal package is not installed. No support for raster formats.')\nE ImportError: Gdal package is not installed. No support for raster formats.\n../gempy/core/grid_modules/create_topography.py:35: ImportError\n\n \n \n \n \n \n \n \n Active grids: ['regular']\nFault colors changed. If you do not like this behavior, set change_color to False.\nSetting kriging parameters to their default values.\nPASSED [ 68%]default_project 2023-03-07 07:25 2023-03-07 07:25\n\n \n \n PASSED [ 69%] X Y Z ... G_x G_y G_z\nsurface_points 99 2000 0 400 ... NaN NaN NaN\n 100 2000 500 400 ... NaN NaN NaN\n 101 2000 1000 400 ... NaN NaN NaN\n 0 0 0 800 ... NaN NaN NaN\n 1 0 500 800 ... NaN NaN NaN\n... ... ... ... ... ... ... ...\n 98 1300 1000 799 ... NaN NaN NaN\norientations 3 2000 500 400 ... 8.191520e-01 1.000050e-12 0.573576\n 2 500 500 800 ... 1.000000e-12 1.000000e-12 1.000000\n 0 0 500 450 ... -5.000000e-01 9.999694e-13 0.866025\n 1 0 500 250 ... -2.588190e-01 9.999842e-13 0.965926\n[106 rows x 8 columns]\n values\nStructure isLith True\n isFault True\n number faults 1\n number surfaces 4\n number series 4\n number surfaces per series [1, 1, 2, 0]\n len surfaces surface_points [3, 15, 39, 45]\n len series surface_points [3, 15, 84, 0]\n len series orientations [1, 1, 2, 0]\nOptions dtype float64\n output geology\n theano_optimizer fast_compile\n device cpu\n verbosity None\nKriging range 2872.281323\n $C_o$ 196428.571429\n drift equations [3, 3, 3, 3]\nRescaling rescaling factor 4600\n centers [1150.0, 500.0, 525.0]\n\n \n \n \n \n \n \n PASSED [ 69%]one_fault_model 2023-03-07 07:24\n\n \n \n PASSED [ 70%] X Y Z ... G_x G_y G_z\nsurface_points 52 700 1000 900.000 ... NaN NaN NaN\n 53 600 1000 600.000 ... NaN NaN NaN\n 54 500 1000 300.000 ... NaN NaN NaN\n 55 800 1000 1200.000 ... NaN NaN NaN\n 56 900 1000 1500.000 ... NaN NaN NaN\n 44 0 1000 1500.000 ... NaN NaN NaN\n 45 300 1000 1400.000 ... NaN NaN NaN\n 46 600 1000 1300.000 ... NaN NaN NaN\n 47 1100 1000 1300.000 ... NaN NaN NaN\n 48 1400 1000 1250.000 ... NaN NaN NaN\n 49 1700 1000 1300.000 ... NaN NaN NaN\n 50 1500 500 1200.000 ... NaN NaN NaN\n 51 1500 1500 1150.000 ... NaN NaN NaN\n 31 900 150 920.000 ... NaN NaN NaN\n 32 900 300 920.000 ... NaN NaN NaN\n 33 900 1500 920.000 ... NaN NaN NaN\n 34 900 1700 820.000 ... NaN NaN NaN\n 35 900 1950 870.000 ... NaN NaN NaN\n 36 0 1000 1300.000 ... NaN NaN NaN\n 37 300 1000 1200.000 ... NaN NaN NaN\n 38 600 1000 1100.000 ... NaN NaN NaN\n 39 1100 1000 1100.000 ... NaN NaN NaN\n 40 1400 1000 1050.000 ... NaN NaN NaN\n 41 1700 1000 1100.000 ... NaN NaN NaN\n 42 1500 500 1000.000 ... NaN NaN NaN\n 43 1500 1500 950.000 ... NaN NaN NaN\n 0 800 50 750.000 ... NaN NaN NaN\n 1 800 150 700.000 ... NaN NaN NaN\n 2 800 300 700.000 ... NaN NaN NaN\n 3 800 500 800.000 ... NaN NaN NaN\n 4 800 1000 1000.000 ... NaN NaN NaN\n 5 800 1500 700.000 ... NaN NaN NaN\n 6 800 1700 600.000 ... NaN NaN NaN\n 7 800 1950 650.000 ... NaN NaN NaN\n 8 0 1000 1100.000 ... NaN NaN NaN\n 9 300 1000 1000.000 ... NaN NaN NaN\n 10 450 1000 950.000 ... NaN NaN NaN\n 11 1100 1000 900.000 ... NaN NaN NaN\n 12 1400 1000 850.000 ... NaN NaN NaN\n 13 1700 1000 900.000 ... NaN NaN NaN\n 14 1500 500 800.000 ... NaN NaN NaN\n 15 1500 1500 750.000 ... NaN NaN NaN\n 16 800 50 450.000 ... NaN NaN NaN\n 17 800 150 400.000 ... NaN NaN NaN\n 18 800 300 400.000 ... NaN NaN NaN\n 19 800 500 500.000 ... NaN NaN NaN\n 20 800 1000 700.000 ... NaN NaN NaN\n 21 800 1500 400.000 ... NaN NaN NaN\n 22 800 1700 300.000 ... NaN NaN NaN\n 23 800 1950 350.000 ... NaN NaN NaN\n 24 0 1000 800.000 ... NaN NaN NaN\n 25 300 1000 700.000 ... NaN NaN NaN\n 26 1100 1000 600.000 ... NaN NaN NaN\n 27 1400 1000 550.000 ... NaN NaN NaN\n 28 1700 1000 600.000 ... NaN NaN NaN\n 29 1500 500 500.000 ... NaN NaN NaN\n 30 1500 1500 450.000 ... NaN NaN NaN\norientations 2 500 1000 864.602 ... -0.948683 9.998257e-13 0.316229\n 1 400 1000 1400.000 ... 0.316229 1.000019e-12 0.948683\n 0 1000 1000 950.000 ... 0.316229 1.000019e-12 0.948683\n[60 rows x 8 columns]\n values\nStructure isLith True\n isFault True\n number faults 1\n number surfaces 5\n number series 3\n number surfaces per series [1, 4, 0]\n len surfaces surface_points [5, 8, 13, 16, 15]\n len series surface_points [5, 52, 0]\n len series orientations [1, 2, 0]\nOptions dtype float64\n output geology\n theano_optimizer fast_compile\n device cpu\n verbosity None\nKriging range 3464.101615\n $C_o$ 285714.285714\n drift equations [3, 3, 3]\nRescaling rescaling factor 3800.0\n centers [850.0, 1000.0, 900.0]\n\n \n \n PASSED [ 71%]\n\n \n \n PASSED [ 71%]\nLithology ids \n [6. 6. 6. ... 2. 2. 2.] \n\n \n \n PASSED [ 72%]\n\n \n \n \n \n \n \n PASSED [ 73%]Active grids: ['regular']\nSetting kriging parameters to their default values.\n\n \n \n PASSED [ 73%]Active grids: ['regular']\nSetting kriging parameters to their default values.\n\n \n \n PASSED [ 74%]Active grids: ['regular']\nSetting kriging parameters to their default values.\n\n \n \n \n \n PASSED [ 75%]Active grids: ['regular']\nFault colors changed. If you do not like this behavior, set change_color to False.\nSetting kriging parameters to their default values.\n\n \n \n PASSED [ 75%]Active grids: ['regular']\nFault colors changed. If you do not like this behavior, set change_color to False.\nSetting kriging parameters to their default values.\n\n \n \n PASSED [ 76%]Active grids: ['regular']\nSetting kriging parameters to their default values.\nFault colors changed. If you do not like this behavior, set change_color to False.\n[array([[ 686.66664124, 20. , -1940. ],\n [ 673.33328247, 20. , -1980. ],\n [ 686.66664124, 60. , -1940. ],\n ...,\n [ 1326.66671753, 1940. , -20. ],\n [ 1313.33328247, 1980. , -60. ],\n [ 1326.66671753, 1980. , -20. ]]), array([[ 20. , 20. , -286.66992188],\n [ 20. , 60. , -291.58874512],\n [ 60. , 20. , -289.60845947],\n ...,\n [1980. , 1924.77844238, -420. ],\n [1980. , 1940. , -409.91333008],\n [1980. , 1980. , -383.2472229 ]]), array([[ 20. , 20. , -421.09481812],\n [ 20. , 60. , -424.28619385],\n [ 60. , 20. , -424.64828491],\n ...,\n [1980. , 1940. , -549.03137207],\n [1980. , 1954.659729 , -540. ],\n [1980. , 1980. , -524.12384033]]), array([[ 24.65882838, 20. , -1060. ],\n [ 60. , 54.68887568, -1060. ],\n [ 60. , 20. , -1069.35462952],\n ...,\n [ 1980. , 1900.90560913, -1140. ],\n [ 1980. , 1940. , -1127.25219727],\n [ 1980. , 1980. , -1113.39775085]]), array([[ 20. , 20. , -1587.05173492],\n [ 60. , 58.98488045, -1580. ],\n [ 60. , 20. , -1602.81368256],\n ...,\n [ 1980. , 1900. , -1555.06591797],\n [ 1980. , 1940. , -1551.07387543],\n [ 1980. , 1980. , -1546.51187897]])] [array([[ 2, 1, 0],\n [ 3, 1, 2],\n [ 5, 0, 4],\n ...,\n [3296, 3199, 3195],\n [3299, 3296, 3297],\n [3298, 3296, 3299]], dtype=int32), array([[ 2, 1, 0],\n [ 3, 1, 2],\n [ 6, 5, 4],\n ...,\n [3912, 3991, 3992],\n [3992, 3913, 3912],\n [3993, 3913, 3992]], dtype=int32), array([[ 2, 1, 0],\n [ 3, 1, 2],\n [ 3, 4, 1],\n ...,\n [3819, 3894, 3820],\n [3821, 3820, 3894],\n [3821, 3894, 3895]], dtype=int32), array([[ 2, 1, 0],\n [ 3, 0, 1],\n [ 4, 3, 1],\n ...,\n [3850, 3913, 3914],\n [3914, 3851, 3850],\n [3915, 3851, 3914]], dtype=int32), array([[ 2, 1, 0],\n [ 0, 1, 3],\n [ 4, 3, 1],\n ...,\n [4200, 4122, 4199],\n [4200, 4123, 4122],\n [4201, 4123, 4200]], dtype=int32)]\n\n \n \n PASSED [ 76%]Active grids: ['regular']\nSetting kriging parameters to their default values.\nFault colors changed. If you do not like this behavior, set change_color to False.\n\n \n \n \n PASSED [ 77%]Active grids: ['regular']\nSetting kriging parameters to their default values.\nCompiling theano function...\nLevel of Optimization: fast_compile\nDevice: cpu\nPrecision: float64\nNumber of faults: 0\nCompilation Done!\nKriging values: \n values\nrange 8.485281\n$C_o$ 1.714286\ndrift equations [3]\n[0]\n[0] drift equations\n range $C_o$ drift equations\nvalues 50 1.714286 [0]\nscalar_field_iter __str__ = [ 0. -0.169 0. ]\nn_surface_op_float_sigmoid __str__ = [[0. 1. 2. 0.]]\nn_surface_0 __str__ = [[0.]]\na __str__ = 0.0\nn_surface_1 __str__ = [[1.]]\nb __str__ = -0.16908401533258222\ndrift[slice_init:slice_init+1][0] __str__ = [[1.]]\nmiddle 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 ]]\nn_surface_0 __str__ = [[2.]]\na __str__ = -0.16908401533258222\nn_surface_1 __str__ = [[0.]]\nb __str__ = 0.0\ndrift[slice_init:slice_init+1][0] __str__ = [[2.]]\nmiddle 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. ]]\n values\nStructure isLith True\n isFault False\n number faults 0\n number surfaces 1\n number series 1\n number surfaces per series [1]\n len surfaces surface_points [7]\n len series surface_points [7]\n len series orientations [2]\nOptions dtype float64\n output geology\n theano_optimizer fast_compile\n device cpu\n verbosity [n_surface_op_float_sigmoid, scalar_field_iter, compare, sigma]\nKriging range 50\n $C_o$ 1.714286\n drift equations [0]\nRescaling rescaling factor 12.0\n centers [0.0, 0.0, 1.5]\n[[-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]]\n[[-3. 0. -3.]\n [-3. 0. -1.]\n [-3. 0. 1.]\n [-3. 0. 3.]\n [-1. 0. -3.]\n [-1. 0. -1.]\n [-1. 0. 1.]\n [-1. 0. 3.]\n [ 1. 0. -3.]\n [ 1. 0. -1.]\n [ 1. 0. 1.]\n [ 1. 0. 3.]\n [ 3. 0. -3.]\n [ 3. 0. -1.]\n [ 3. 0. 1.]\n [ 3. 0. 3.]]\n[ 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]\n\n \n \n \n \n \n \n PASSED [ 78%]\n\n \n \n PASSED [ 78%]\n\n \n \n PASSED [ 79%]\n\n \n \n FAILED [ 80%]Active grids: ['regular' 'topography' 'sections']\nActive grids: ['regular' 'topography' 'sections']\n\n 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\n def test_plot_2d_test_labels(one_fault_model_no_interp):\n geo_model = one_fault_model_no_interp\n section_dict = {'section_SW-NE': ([250, 250], [1750, 1750], [100, 100]),\n 'section_NW-SE': ([250, 1750], [1750, 250], [100, 100])}\n geo_model.set_section_grid(section_dict)\n geo_model.set_topography(fd=1.2, d_z=np.array([600, 2000]), resolution=np.array([60, 60]))\n \n> gp.plot_2d(geo_model,\n section_names=['section_NW-SE', 'section_NW-SE', 'topography'],\n direction=['x'], cell_number=['mid'],\n show_topography=True,\n )\ntest_plotting/test_2d.py:31: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n../gempy/plot/plot_api.py:216: in plot_2d\n p.plot_section_traces(temp_ax)\n../gempy/plot/visualization_2d.py:695: in plot_section_traces\n self.plot_data(ax, section_name='topography', **kwargs)\n../gempy/plot/visualization_2d.py:458: in plot_data\n points_df.plot.scatter(x=x, y=y, ax=ax, c=points_df['surface'].map(self._color_lot),\n../venv/lib/python3.10/site-packages/pandas/plotting/_core.py:1636: in scatter\n return self(kind=\"scatter\", x=x, y=y, s=s, c=c, **kwargs)\n../venv/lib/python3.10/site-packages/pandas/plotting/_core.py:917: in __call__\n return plot_backend.plot(data, x=x, y=y, kind=kind, **kwargs)\n../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/__init__.py:71: in plot\n plot_obj.generate()\n../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py:291: in generate\n self._adorn_subplots()\n../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py:497: in _adorn_subplots\n handle_shared_axes(\n../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/tools.py:403: in handle_shared_axes\n layout[row_num(ax), col_num(ax)] = ax.get_visible()\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nx = <Axes: ylabel='elevation [m]'>\n> row_num = lambda x: x.get_subplotspec().rowspan.start\nE AttributeError: 'NoneType' object has no attribute 'rowspan'\n../venv/lib/python3.10/site-packages/pandas/plotting/_matplotlib/tools.py:386: AttributeError\n\n \n \n Active grids: ['sections']\nActive grids: ['sections']\nSetting kriging parameters to their default values.\nPASSED [ 80%]\n\n \n \n PASSED [ 81%]\n\n \n \n PASSED [ 82%]\n\n \n \n PASSED [ 82%]Active grids: ['topography' 'sections']\n\n \n \n \n \n PASSED [ 83%]\n\n \n \n PASSED [ 83%]\n\n \n \n PASSED [ 84%]\n\n \n \n PASSED [ 85%]\n\n \n \n PASSED [ 85%]\n\n \n \n PASSED [ 86%]\n\n \n \n \n \n \n PASSED [ 87%]\n\n \n \n PASSED [ 87%]\n\n \n \n SKIPPED (github actions does not like background plot) [ 88%]\n\n \n \n PASSED [ 89%]foo\n\n \n \n PASSED [ 89%]foo\n\n \n \n PASSED [ 90%]foo\n\n \n \n SKIPPED (github actions does not like background plot) [ 91%]\n\n \n \n PASSED [ 91%]foo\n\n \n \n PASSED [ 92%]\n\n \n \n PASSED [ 92%]\n\n \n \n PASSED [ 93%]\n\n \n \n PASSED [ 94%]foo\n\n \n \n PASSED [ 94%][1600. 2000.]\nActive grids: ['topography' 'sections']\nfoo\n\n \n \n PASSED [ 95%]foo\n\n \n \n PASSED [ 96%]foo\n\n \n \n PASSED [ 96%]foo\n\n \n \n PASSED [ 97%]foo\n\n \n \n PASSED [ 98%]foo\n\n \n \n PASSED [ 98%]\n\n \n \n PASSED [ 99%]\n\n \n \n PASSED [100%]\n\n \n \n \n \n \n\n" }, { "path": "test/__init__.py", "content": "" }, { "path": "test/conftest.py", "content": "import enum\nimport os\n\ninput_path = os.path.dirname(__file__) + '/input_data'\ninput_path2 = os.path.dirname(__file__) + '/../examples/data/input_data/'\nimport numpy as np\n\nnp.random.seed(1234)\n\n\nclass TestSpeed(enum.Enum):\n MILLISECONDS = 0\n SECONDS = 1\n MINUTES = 2\n HOURS = 3\n \n \nclass Requirements(enum.Enum):\n CORE = enum.auto()\n BASE = enum.auto()\n OPTIONAL = enum.auto()\n DEV = enum.auto()\n\n\nTEST_SPEED = TestSpeed.MINUTES # * Use seconds for compile errors, minutes before pushing and hours before release\nREQUIREMENT_LEVEL = Requirements.CORE # * Use CORE for mandatory tests, OPTIONAL for optional tests and DEV for development tests\n" }, { "path": "test/context.py", "content": "\"\"\"\nThe main function of the code you provided is to add the parent directory of the tests folder to the Python path (sys.path). \nhis is done with sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))). \nhis line is adding the directory one level up from the directory of the context.py file to the front of the sys.path.\n\nsys.path is the list of directories that Python checks when it's trying to import a module. By inserting the parent\ndirectory of the tests directory at the front of sys.path, Python will check this directory first when importing modules.\nThis allows test scripts in the tests directory to import modules from the parent directory (like gempy in your case) as\nif they were in the same directory. This is very helpful when you're running tests because it ensures the tests are\nrunning against the source code in your project, rather than an installed version of your package.\n\"\"\"\n# -*- coding: utf-8 -*-\nimport sys\nimport os\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\n" }, { "path": "test/pytest.ini", "content": "[pytest]\naddopts = -v\nnorecursedirs = test\\test_modules\\_geophysics_TO_UPDATE" }, { "path": "test/test_api/__init__.py", "content": "" }, { "path": "test/test_api/test_backends.py", "content": "import pytest\n\nimport gempy as gp\nimport gempy_viewer as gpv\nfrom gempy.core.data.gempy_engine_config import GemPyEngineConfig\nfrom gempy_engine.config import AvailableBackends\nfrom gempy.core.data.enumerators import ExampleModel\nfrom test.conftest import TEST_SPEED, TestSpeed\n\n\n@pytest.mark.skipif(TEST_SPEED.value < TestSpeed.SECONDS.value, reason=\"Global test speed below this test value.\")\nclass TestBackends:\n @pytest.fixture(scope='class')\n def geo_model(self):\n geo_model: gp.data.GeoModel = gp.generate_example_model(\n example_model=ExampleModel.ONE_FAULT,\n compute_model=False\n )\n\n geo_model.interpolation_options.number_octree_levels = 6\n return geo_model\n \n def test_backends_numpy(self, geo_model):\n gp.compute_model(geo_model, engine_config=GemPyEngineConfig(backend=AvailableBackends.numpy))\n gpv.plot_3d(\n model=geo_model,\n show_data=True,\n show_boundaries=True,\n show_lith=False,\n image=True\n )\n\n \n def test_numpy_pykeops(self, geo_model):\n gp.compute_model(\n gempy_model=geo_model,\n engine_config=GemPyEngineConfig(\n backend=AvailableBackends.numpy,\n use_gpu=False,\n ))\n \n gpv.plot_3d(\n model=geo_model,\n show_data=True,\n show_boundaries=True,\n show_lith=False,\n image=True\n )\n\n @pytest.mark.skip(reason=\"Not finished yet. Fault data is not passed to legacy properly.\")\n @pytest.mark.skipif(TEST_SPEED.value <= 1, reason=\"Global test speed below this test value.\")\n def test_backends_legacy(self, geo_model):\n # TODO: (Sep 2023, Miguel) Pass fault data to legacy properly\n \n gp.compute_model(geo_model, engine_config=GemPyEngineConfig(backend=AvailableBackends.legacy))\n from gempy.optional_dependencies import require_gempy_legacy\n gpl = require_gempy_legacy()\n gpl.plot_3d(\n model=geo_model.legacy_model,\n show_data=True,\n show_boundaries=True,\n show_lith=False,\n image=True\n )" }, { "path": "test/test_api/test_fault_api.py", "content": "import numpy as np\n\nimport gempy as gp\nfrom gempy.core.data.structural_group import FaultsRelationSpecialCase\n\n\ndef test_fault_api():\n frame = _create_structural_frame()\n gp.set_is_fault(\n frame=frame,\n fault_groups=['1', '3'],\n faults_relation_type=FaultsRelationSpecialCase.OFFSET_FORMATIONS,\n )\n\n print(frame)\n\n print(frame.fault_relations)\n np.testing.assert_array_equal(\n frame.fault_relations,\n np.array(\n [\n [False, True, False, True],\n [False, False, False, False],\n [False, False, False, True],\n [False, False, False, False]\n ]\n )\n )\n\n\ndef test_fault_api_setter():\n frame = _create_structural_frame()\n\n # Use the setter to set the fault_relations\n frame.fault_relations = np.array(\n [\n [False, True, False, True],\n [False, False, False, False],\n [False, False, False, True],\n [False, False, False, False]\n ]\n )\n\n # Now we verify if the StructuralGroup fault_relations have been updated correctly\n\n assert frame.structural_groups[0].fault_relations == [frame.structural_groups[1], frame.structural_groups[3]]\n assert frame.structural_groups[1].fault_relations == FaultsRelationSpecialCase.OFFSET_NONE\n assert frame.structural_groups[2].fault_relations == FaultsRelationSpecialCase.OFFSET_ALL\n # ! Last group does not matter really. assert frame.structural_groups[3].fault_relations == FaultsRelationSpecialCase.OFFSET_NONE\n\n\ndef _create_structural_frame():\n frame = gp.data.StructuralFrame(\n structural_groups=[\n gp.data.StructuralGroup(\n name='1',\n elements=[],\n structural_relation=gp.data.StackRelationType.ERODE\n ),\n gp.data.StructuralGroup(\n name='2',\n elements=[],\n structural_relation=gp.data.StackRelationType.ERODE\n ),\n gp.data.StructuralGroup(\n name='3',\n elements=[],\n structural_relation=gp.data.StackRelationType.ERODE\n ),\n gp.data.StructuralGroup(\n name='4',\n elements=[],\n structural_relation=gp.data.StackRelationType.ERODE\n ),\n ],\n color_gen=gp.ColorsGenerator() # * Irrlevant for this test\n )\n return frame\n" }, { "path": "test/test_api/test_gempy_legacy_comp.py", "content": "import gempy as gp\nimport gempy_viewer\nfrom gempy.core.data import Solutions\nfrom gempy.API.gp2_gp3_compatibility.gp3_to_gp2_input import gempy3_to_gempy2\nfrom gempy.optional_dependencies import require_gempy_legacy\nfrom test.test_api.test_initialization_and_compute_api import _create_data\n\n# Skip pytest if legacy is not installed\ntry:\n require_gempy_legacy()\nexcept ImportError:\n import pytest\n pytest.skip(\"Legacy is not installed.\", allow_module_level=True)\n\n\n\ndef test_compare_numpy_with_legacy():\n geo_data = _create_data()\n\n gp.map_stack_to_surfaces(\n gempy_model=geo_data,\n mapping_object={\"Strat_Series\": ('rock2', 'rock1')}\n )\n\n sol: Solutions = gp.compute_model(geo_data)\n\n gempy_viewer.plot_2d(geo_data, direction=['y'], show_data=True, show_boundaries=False)\n\n legacy_model = gempy3_to_gempy2(geo_data)\n\n gl = require_gempy_legacy()\n gl.set_interpolator(legacy_model)\n gl.compute_model(legacy_model)\n gl.plot_2d(legacy_model, direction=['y'])\n" }, { "path": "test/test_api/test_initialization_and_compute_api.py", "content": "from pprint import pprint\n\nimport gempy as gp\nimport gempy_viewer\nfrom gempy.core.data import GeoModel\n\nfrom gempy_engine.core.data.solutions import Solutions\n\n\ndef test_api_create_data():\n geo_data = _create_data()\n pprint(geo_data)\n\n\ndef _create_data():\n data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'\n geo_data: GeoModel = gp.create_geomodel(\n project_name='horizontal',\n extent=[0, 1000, 0, 1000, 0, 1000],\n resolution=[50, 5, 50],\n importer_helper=gp.data.ImporterHelper(\n path_to_surface_points=data_path + \"/data/input_data/jan_models/model1_surface_points.csv\",\n path_to_orientations=data_path + \"/data/input_data/jan_models/model1_orientations.csv\"\n )\n )\n return geo_data\n\n\ndef test_map_stack_to_surfaces():\n geo_data = _create_data()\n\n gp.map_stack_to_surfaces(\n gempy_model=geo_data,\n mapping_object={\"Strat_Series\": ('rock2', 'rock1')}\n )\n\n pprint(geo_data.structural_frame)\n\n\ndef test_api_compute_model():\n geo_data = _create_data()\n\n gp.map_stack_to_surfaces(\n gempy_model=geo_data,\n mapping_object={\"Strat_Series\": ('rock2', 'rock1')}\n )\n\n gempy_viewer.plot_2d(geo_data, direction=['y'])\n\n sol: Solutions = gp.compute_model(geo_data)\n\n gempy_viewer.plot_2d(geo_data, direction=['y'], show_data=True, show_boundaries=True, show_lith=False)\n gempy_viewer.plot_2d(geo_data, direction=['y'], show_data=False, show_scalar=True)\n" }, { "path": "test/test_api/test_model_construction_granular.py", "content": "import pooch\nimport gempy as gp\nimport gempy_engine.core.data.solutions\nimport gempy_viewer\nfrom gempy.core.color_generator import ColorsGenerator\nfrom gempy_engine.core.data import InterpolationOptions\nfrom gempy.core.data import GeoModel\nfrom gempy.API.io_API import read_orientations, read_surface_points\nfrom gempy.core.data.orientations import OrientationsTable\nfrom gempy.core.data.structural_element import StructuralElement\nfrom gempy.core.data.structural_frame import StructuralFrame\nfrom gempy.core.data.structural_group import Stack\nfrom gempy.core.data.surface_points import SurfacePointsTable\nfrom gempy.optional_dependencies import require_gempy_viewer\n\n\"\"\"\n- [x] create data\n- [x] map series\n- [ ] plot data\n- [ ] compute\n- [ ] plot results\n\n\"\"\"\n\ndef test_read_input_points():\n _, _ = _read_data()\n\n\ndef _read_data() -> tuple[SurfacePointsTable, OrientationsTable]:\n data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'\n surface_points_file = pooch.retrieve(\n url=data_path + \"data/input_data/jan_models/model1_surface_points.csv\",\n known_hash=\"6f1a39ed77e87a4057f03629c946b1876b87e24409cadfe0e1cf7ab1488f69e4\"\n )\n orientations_file = pooch.retrieve(\n url=data_path + \"data/input_data/jan_models/model1_orientations.csv\",\n known_hash=\"04c307ae23f70252fe54144a2fb95ca7d96584a2d497ea539ed32dfd23e7cd5d\"\n )\n print(pooch.file_hash(surface_points_file))\n print(pooch.file_hash(orientations_file))\n surface_points: SurfacePointsTable = read_surface_points(\n path=surface_points_file,\n )\n orientations: OrientationsTable = read_orientations(\n path=orientations_file\n )\n return surface_points, orientations\n\n\ndef test_create_grid():\n _generate_grid()\n\n\ndef _generate_grid():\n grid: gp.data.Grid = gp.data.Grid(\n extent=[0, 1000, 0, 1000, 0, 500],\n resolution=[50, 5, 50]\n )\n return grid\n\n\ndef test_create_structural_frame():\n # * Structural elements\n _create_structural_frame()\n\n\ndef _create_structural_frame():\n surface_points, orientations = _read_data()\n surface_points_groups = surface_points.get_surface_points_by_id_groups()\n orientations_groups = orientations.get_orientations_by_id_groups()\n structural_elements = []\n color_gen = ColorsGenerator()\n for i in range(len(surface_points_groups)):\n # TODO: Split surface points and orientations by id\n structural_element: StructuralElement = StructuralElement(\n name=\"layer1\",\n surface_points=surface_points_groups[i],\n orientations=orientations_groups[i],\n color=next(color_gen)\n )\n\n structural_elements.append(structural_element)\n # * Structural groups definitions\n default_formation: Stack = Stack(\n name=\"default_formation\",\n elements=structural_elements,\n structural_relation=gp.data.StackRelationType.ERODE\n )\n # ? Should I move this to the constructor?\n structural_frame: StructuralFrame = StructuralFrame(\n structural_groups=[default_formation],\n color_gen=color_gen\n )\n return structural_frame\n\n\ndef test_create_interpolation_options():\n interpolation_options = _generate_interpolation_options()\n\n\ndef _generate_interpolation_options():\n range_ = 1000.0\n interpolation_options: InterpolationOptions = InterpolationOptions.from_args(\n range=range_,\n c_o=(range_ ** 2) / 14 / 3,\n )\n return interpolation_options\n\n\ndef _create_geomodel() -> GeoModel:\n geo_model: GeoModel = GeoModel.from_args(\n name=\"horizontal\",\n structural_frame=_create_structural_frame(),\n grid=_generate_grid(),\n interpolation_options=_generate_interpolation_options()\n )\n\n return geo_model\n\n\ndef test_structural_frame_surface_points():\n structural_frame: StructuralFrame = _create_structural_frame()\n print(structural_frame.surface_points_copy)\n pass\n\n\ndef _interpolate_numpy() -> GeoModel:\n geo_model: GeoModel = _create_geomodel()\n\n solutions: gempy_engine.core.data.solutions.Solutions = gempy_engine.compute_model(\n interpolation_input=geo_model.interpolation_input_copy,\n options=geo_model.interpolation_options,\n data_descriptor=geo_model.input_data_descriptor\n )\n print(solutions)\n geo_model.solutions = solutions\n # TODO: Use gempy API\n if PLOT := True:\n from gempy_engine.core.data.output.blocks_value_type import ValueType\n from gempy_engine.plugins.plotting.helper_functions import plot_block_and_input_2d\n plot_block_and_input_2d(\n stack_number=0,\n interpolation_input=geo_model.interpolation_input_copy,\n outputs=geo_model.solutions.octrees_output,\n structure=geo_model.structural_frame.input_data_descriptor.stack_structure,\n value_type=ValueType.ids\n )\n \n gempy_viewer.plot_3d(geo_model, show_boundaries=True, image=True)\n \n return geo_model\n\n\ndef test_interpolate_aesara():\n geo_model: GeoModel = _create_geomodel()\n\n\ndef test_plot_input():\n geo_model: GeoModel = _create_geomodel()\n gp_viewer: gempy_viewer = require_gempy_viewer()\n # TODO: Add all the plot data in a plot options class\n\n # TODO: Make options required\n gp_viewer.plot_2d(\n geo_model,\n direction=['y'],\n show_results=False\n )\n\n\ndef test_plot_results():\n solved_geo_model: GeoModel = _interpolate_numpy()\n gp_viewer: gempy_viewer = require_gempy_viewer()\n\n gp_viewer.plot_2d(\n solved_geo_model,\n direction=['y'],\n show_boundaries=False, # TODO: Fix boundaries\n show_results=True\n )\n" }, { "path": "test/test_api/test_modify_input_data.py", "content": "from gempy import generate_example_model\nfrom gempy.core.data.enumerators import ExampleModel\nimport gempy_viewer as gp_viewer\nimport gempy as gp\n\n\ndef test_modify_surface_point_by_name_and_index():\n model = generate_example_model(ExampleModel.ONE_FAULT, compute_model=False)\n print(model.structural_frame)\n gp_viewer.plot_2d(\n model,\n direction=['y'],\n show_boundaries=False, # TODO: Fix boundaries\n )\n \n gp.modify_surface_points(\n geo_model=model,\n elements_names=[\"fault\"],\n Z=800 # This can be an array of the same length as the number of points\n )\n\n gp_viewer.plot_2d(\n model,\n direction=['y'],\n show_boundaries=False, # TODO: Fix boundaries\n )\n\n\n\ndef test_modify_surface_point_by_global_index():\n model = generate_example_model(ExampleModel.ONE_FAULT, compute_model=False)\n print(model.surface_points_copy.df)\n gp_viewer.plot_2d(\n model,\n direction=['y'],\n show_boundaries=False, # TODO: Fix boundaries\n )\n\n gp.modify_surface_points(\n geo_model=model,\n slice=0,\n Z=800\n )\n\n print(model.surface_points_copy.df)\n gp_viewer.plot_2d(\n model,\n direction=['y'],\n show_boundaries=False, # TODO: Fix boundaries\n )\n\n" }, { "path": "test/test_api/test_show_input_data.py", "content": "from gempy import generate_example_model\nfrom gempy.core.data.enumerators import ExampleModel\n\n\ndef test_print_structural_frame():\n model = generate_example_model(ExampleModel.ONE_FAULT, compute_model=False)\n print(model.structural_frame)\n" }, { "path": "test/test_community_bugs/__init__.py", "content": "" }, { "path": "test/test_community_bugs/test_community_bugs.py", "content": "\"\"\"\nThis scrip is to add test for specific issues that the community has found. Use the the following template:\n\ndef test_issue_XXX():\n ...\n\n\"\"\"\n" }, { "path": "test/test_core/__init__.py", "content": "" }, { "path": "test/test_core/test_color_generator.py", "content": "import gempy.core.color_generator as cg\n\n\ndef test_color_generator():\n\n # create an instance of ColorsGenerator\n color_iterator: iter = cg.ColorsGenerator()\n\n # generate color dictionary (with optional seaborn palettes if you want)\n\n # get the generator from next_color method\n\n # now you can get colors one by one\n print(next(color_iterator)) # prints first color\n print(next(color_iterator)) # prints second color\n\n left_colors = color_iterator._gempy_default_colors[2:]\n\n assert left_colors == [\n color for color, _ in zip(color_iterator, left_colors)\n ]\n\n # get a random color\n assert color_iterator.up_next() == next(color_iterator)\n\n # override the random color generator\n cg.np.random.randint = lambda a, b: 0x00ff42\n\n assert color_iterator._random_hexcolor() == \"#00ff42\"\n assert next(color_iterator) == \"#00ff42\"\n\n # trigger up_next method for the cache\n assert color_iterator.up_next() == \"#00ff42\"\n\n cg.np.random.randint = lambda a, b: 0x000000\n\n # ensure that the up_next method still returns the same color\n assert color_iterator.up_next() == \"#00ff42\"\n\n # trigger the cache\n assert next(color_iterator) == \"#00ff42\"\n assert next(color_iterator) == \"#000000\"\n\n # etc...\n" }, { "path": "test/test_core/test_installation.py", "content": "def test_installation():\n import gempy\n import gempy_engine" }, { "path": "test/test_core/test_transfoms.py", "content": "from gempy.core.data import GeoModel\nfrom gempy_engine.core.data.transforms import Transform\nfrom test.test_api.test_initialization_and_compute_api import _create_data\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef test_transform_1():\n geo_data: GeoModel = _create_data()\n print(geo_data.input_transform)\n transformed_xyz = geo_data.input_transform.apply(geo_data.surface_points_copy.xyz)\n print(transformed_xyz)\n return\n\n\n# Assuming you have your Transform class defined above this.\n\ndef plot_points(points, title):\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n ax.scatter(points[:, 0], points[:, 1], points[:, 2], c='r', marker='o')\n ax.set_xlabel('X')\n ax.set_ylabel('Y')\n ax.set_zlabel('Z')\n ax.set_title(title)\n plt.show()\n\n\ndef test_transform_operations_scale_move():\n transform = Transform(\n position=np.array([1, 2, 3]),\n rotation=np.array([0, 0, 0]),\n scale=np.array([2, 2, 2]))\n\n original_points = np.array([\n [0, 0, 0],\n [1, 1, 1],\n [-1, -1, -1]\n ])\n\n transformed_points = transform.apply(original_points)\n inv_transformed_points = transform.apply_inverse(transformed_points)\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n\n # Original points\n ax.scatter(original_points[:, 0], original_points[:, 1], original_points[:, 2], c='r', marker='o', label='Original')\n # Transformed points\n ax.scatter(transformed_points[:, 0], transformed_points[:, 1], transformed_points[:, 2], c='g', marker='^', label='Transformed')\n # Inverse transformed points\n ax.scatter(inv_transformed_points[:, 0], inv_transformed_points[:, 1], inv_transformed_points[:, 2], c='b', marker='x', label='Inv Transformed')\n\n ax.legend()\n plt.show()\n\n assert np.allclose(original_points, inv_transformed_points)\n\n\n\ndef test_transform_operations_rotate():\n\n transform = Transform(\n position=np.array([0, 0, 0]),\n rotation=np.array([45, 45, 45]),\n scale=np.array([1, 1, 1]))\n\n original_points = np.array([\n [0, 0, 0],\n [1, 1, 1],\n [-1, -1, -1]\n ])\n\n transformed_points = transform.apply(original_points)\n inv_transformed_points = transform.apply_inverse(transformed_points)\n\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n # Original points\n ax.scatter(original_points[:, 0], original_points[:, 1], original_points[:, 2], c='r', marker='o', label='Original')\n # Transformed points\n ax.scatter(transformed_points[:, 0], transformed_points[:, 1], transformed_points[:, 2], c='g', marker='^', label='Transformed')\n # Inverse transformed points\n ax.scatter(inv_transformed_points[:, 0], inv_transformed_points[:, 1], inv_transformed_points[:, 2], c='b', marker='x', label='Inv Transformed')\n\n ax.legend()\n plt.show()\n\n assert np.allclose(original_points, inv_transformed_points)\n\n\n" }, { "path": "test/test_model_types/__init__.py", "content": "" }, { "path": "test/test_model_types/test_example_models_I.py", "content": "from typing import Any\n\nfrom numpy import dtype, ndarray\n\nimport gempy as gp\nfrom gempy.core.data import GeoModel\nfrom gempy.core.data.enumerators import ExampleModel\nfrom gempy.optional_dependencies import require_gempy_viewer\nfrom gempy_engine.core.data.interp_output import InterpOutput\nfrom gempy_viewer import GemPyToVista\n\nfrom test.verify_helper import gempy_verify_array\nimport pytest\nfrom test.conftest import TEST_SPEED, TestSpeed\n\n# ! When importing the model is computed\n\n# TODO []: Use the generator and do some approval testing\nPLOT = True\n\npytestmark = pytest.mark.skipif(TEST_SPEED.value < TestSpeed.MINUTES.value, reason=\"Global test speed below this test value.\")\n\n\ndef _verify_scalar_field(model, name):\n outputs_centers_: InterpOutput = model.solutions.octrees_output[-1].outputs[0]\n scalar_field = outputs_centers_.exported_fields.scalar_field\n scalar_field = scalar_field[::int(len(scalar_field) / 50)] # Pick 50 values from the scalar field array\n gempy_verify_array(scalar_field, name)\n\n\ndef test_generate_horizontal_stratigraphic_model():\n model = gp.generate_example_model(ExampleModel.HORIZONTAL_STRAT, compute_model=True)\n print(model.structural_frame)\n\n if PLOT:\n gpv = require_gempy_viewer()\n gpv.plot_3d(model, show_data=True, image=True)\n\n if False:\n _verify_scalar_field(\n model=model,\n name=\"Horizontal Stratigraphic Scalar Field\"\n )\n\n\ndef test_generate_fold_model():\n model = gp.generate_example_model(ExampleModel.ANTICLINE, compute_model=True)\n print(model.structural_frame)\n\n if PLOT or False:\n gpv = require_gempy_viewer()\n gpv.plot_3d(model, image=True)\n\n _verify_scalar_field(\n model=model,\n name=\"Anticline Scalar Field\"\n )\n\n\ndef test_generate_fault_model():\n model = gp.generate_example_model(ExampleModel.ONE_FAULT, compute_model=True)\n print(model.structural_frame)\n\n if PLOT:\n gpv = require_gempy_viewer()\n gpv.plot_3d(model, image=True)\n\n _verify_scalar_field(\n model=model,\n name=\"Fault Scalar Field\"\n )\n\n\ndef test_generate_combination_model():\n\n model = gp.generate_example_model(ExampleModel.COMBINATION, compute_model=True)\n print(model.structural_frame)\n\n if PLOT:\n gpv = require_gempy_viewer()\n gpv.plot_2d(model)\n pd3 = gpv.plot_3d(\n model,\n image=True,\n show_lith=True,\n show_octree=True,\n show=True\n )\n\n _verify_scalar_field(\n model=model,\n name=\"Combination Scalar Field\"\n )\n\n\ndef test_generate_greenstone_model():\n model = gp.generate_example_model(ExampleModel.GREENSTONE, compute_model=True)\n print(model.structural_frame)\n\n if PLOT:\n gpv = require_gempy_viewer()\n gpv.plot_3d(model, image=True)\n" }, { "path": "test/test_model_types/test_example_models_I.test_generate_combination_model.Combination Scalar Field.approved.txt", "content": "[-0.48762027 -0.37851606 -0.20602752 0.18466945 0.24586016 0.34157167\n -0.37574516 -0.23997105 -0.38798331 -0.30116176 -0.22103656 -0.22657835\n -0.40356962 -0.26444733 -0.30116176 -0.26664095 -0.28892361 -0.23997105\n -0.12532504 -0.01622083 -0.019569 0.03607991 -0.20879842 -0.06297978\n -0.07856609 0.03942809 0.10950876 0.14957136 0.07279434 0.10396697\n 0.19967849 0.25867558 0.30208635 0.14957136 0.17739582 0.09172883\n 0.22750295 0.34549713 0.27761007 0.25752102 0.23858653 0.33210443\n 0.36327706 0.40668784 0.49016121 0.25752102 0.26975916 0.39110152\n 0.38221155 0.43786047 0.52133384]\n" }, { "path": "test/test_model_types/test_example_models_I.test_generate_fault_model.Fault Scalar Field.approved.txt", "content": "[-1.13708400e-02 -2.52401253e-01 -1.58105889e-01 -2.53046188e-01\n -1.02730053e-01 3.07857424e-02 1.65039406e-02 1.02339531e-02\n -7.49849706e-03 2.03358730e-03 1.68709401e-02 1.16318104e-02\n -5.46883208e-03 -3.66307173e-01 -2.16332415e-01 -3.08865566e-01\n -2.15389261e-01 -1.11256902e-01 2.72083076e-02 4.20445442e-02\n -7.47304561e-02 3.98513512e-02 1.63139249e-02 4.29008353e-02\n -7.33691160e-03 3.78053797e-02 1.68817500e-03 1.19697379e-02\n 2.92719967e-02 1.01708771e-02 2.46994485e-02 2.91498040e-02\n 2.38183394e-02 1.43569767e-01 2.64840495e-01 3.68607253e-01\n 2.35431983e-01 3.12159958e-01 2.13482001e-05 5.67351744e-02\n -8.42197714e-03 1.74023726e-01 1.53633321e-02 6.80918669e-03\n -1.60048318e-03 1.23676916e-01 2.07197240e-01 2.84933975e-01\n 1.27564214e-02 2.58711537e-01 2.99561004e-02]\n" }, { "path": "test/test_model_types/test_example_models_I.test_generate_fold_model.Anticline Scalar Field.approved.txt", "content": "[-0.52188333 -0.50112162 -0.4782676 -0.4049219 -0.52592991 -0.35477513\n -0.32950309 -0.49867861 -0.39645404 -0.3705816 -0.53297922 -0.4170038\n -0.38884941 -0.50066294 -0.52343835 -0.50147135 -0.38406676 -0.49930607\n -0.34000787 -0.36981774 -0.39715035 -0.50713183 -0.38966825 -0.5074257\n -0.53982527 -0.35470835 -0.49995446 -0.383908 -0.54971153 -0.47881636\n -0.54893268 -0.39154008 -0.41173995 -0.50805229 -0.37732251 -0.35443146\n -0.40539656 -0.38387161 -0.54517857 -0.52338116 -0.40620542 -0.47800398\n -0.49943244 -0.50482445 -0.5102548 -0.35318898 -0.55119547 -0.35075437\n -0.41029029 -0.32958424 -0.35520657]\n" }, { "path": "test/test_model_types/test_example_models_I.test_generate_horizontal_stratigraphic_model.Horizontal Stratigraphic Scalar Field.approved.txt", "content": "[-0.53138973 -0.53401697 -0.53639193 -0.53851076 -0.54037354 -0.54198423\n -0.54335025 -0.54448177 -0.54539063 -0.54608928 -0.54658948 -0.54690111\n -0.54703108 -0.5469825 -0.54675437 -0.54634166 -0.54573594 -0.5449263\n -0.54390055 -0.54264645 -0.54115292 -0.53941111 -0.53741528 -0.53516341\n -0.5326574 -0.53138973 -0.53401697 -0.53639193 -0.53851076 -0.54037354\n -0.54198423 -0.54335025 -0.54448177 -0.54539063 -0.54608928 -0.54658948\n -0.54690111 -0.54703108 -0.5469825 -0.54675437 -0.54634166 -0.54573594\n -0.5449263 -0.54390055 -0.54264645 -0.54115292 -0.53941111 -0.53741528\n -0.53516341 -0.5326574 ]\n" }, { "path": "test/test_model_types/test_subduction.py", "content": "import os\n\nimport gempy as gp\nimport gempy_viewer as gpv\n\n\ndef test_subduction():\n # Get the directory containing the test file\n current_dir = os.path.dirname(os.path.abspath(__file__))\n # Navigate to examples directory relative to test location\n data_path = os.path.join(current_dir, '../..', 'examples')\n\n geo_model: gp.data.GeoModel = gp.create_geomodel(\n project_name='Onlap_relations',\n extent=[-200, 1000, -500, 500, -1000, 0],\n resolution=[50, 50, 50],\n refinement=5,\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=data_path + \"/data/input_data/tut-ch1-4/tut_ch1-4_orientations.csv\",\n path_to_surface_points=data_path + \"/data/input_data/tut-ch1-4/tut_ch1-4_points.csv\",\n )\n )\n\n gp.add_structural_group(\n model=geo_model,\n group_index=0,\n structural_group_name=\"seafloor_series\",\n elements=[geo_model.structural_frame.get_element_by_name(\"seafloor\")],\n structural_relation=gp.data.StackRelationType.ERODE,\n )\n\n gp.add_structural_group(\n model=geo_model,\n group_index=1,\n structural_group_name=\"right_series\",\n elements=[\n geo_model.structural_frame.get_element_by_name(\"rock1\"),\n geo_model.structural_frame.get_element_by_name(\"rock2\"),\n ],\n structural_relation=gp.data.StackRelationType.ONLAP\n )\n\n gp.add_structural_group(\n model=geo_model,\n group_index=2,\n structural_group_name=\"onlap_series\",\n elements=[geo_model.structural_frame.get_element_by_name(\"onlap_surface\")],\n structural_relation=gp.data.StackRelationType.ERODE\n )\n\n gp.add_structural_group(\n model=geo_model,\n group_index=3,\n structural_group_name=\"left_series\",\n elements=[geo_model.structural_frame.get_element_by_name(\"rock3\")],\n structural_relation=gp.data.StackRelationType.BASEMENT\n )\n\n gp.remove_structural_group_by_name(model=geo_model, group_name=\"default_formation\")\n \n \n # %%\n from gempy_engine.core.data.options import MeshExtractionMaskingOptions\n from gempy_engine.core.data.options import EvaluationOptions\n evaluation_options: EvaluationOptions = geo_model.interpolation_options.evaluation_options\n evaluation_options.mesh_extraction_masking_options = MeshExtractionMaskingOptions.INTERSECT\n s = gp.compute_model(geo_model)\n\n gpv.plot_2d(geo_model)\n \n # %% \n gpv.plot_3d(\n model=geo_model,\n show_surfaces=True,\n show_data=True,\n image=True,\n show_topography=True,\n kwargs_plot_structured_grid={'opacity': 0.1}\n )\n\n # %%\n # ! White are True, black are False\n if False:\n p = gpv.plot_2d(\n model=geo_model,\n cell_number=2,\n override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix_squeezed[0],\n show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None}\n )\n gpv.plot_2d(\n model=geo_model,\n cell_number=2,\n override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix_squeezed[1],\n show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None}\n )\n\n gpv.plot_2d(\n model=geo_model,\n cell_number=2,\n override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix_squeezed[2],\n show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None}\n )\n\n gpv.plot_2d(\n model=geo_model,\n cell_number=2,\n override_regular_grid=geo_model.solutions.raw_arrays.mask_matrix_squeezed[3],\n show_data=True, kwargs_lithology={'cmap': 'gray', 'norm': None}\n )\n" }, { "path": "test/test_modules/__init__.py", "content": "" }, { "path": "test/test_modules/test_cg/__init__.py", "content": "" }, { "path": "test/test_modules/test_cg/test_cg_solver.py", "content": "import pytest\n\nimport gempy as gp\nfrom gempy.core.data.enumerators import ExampleModel\nfrom gempy.optional_dependencies import require_gempy_viewer\nimport gempy_engine.core.backend_tensor as BackendTensor\n\nfrom gempy_engine.modules.weights_cache.weights_cache_interface import WeightCache\n\nPLOT = True\n\npytest.mark.skip(\"Run explicitly\")\n\n\ndef test_solve_with_cg():\n model = gp.generate_example_model(ExampleModel.GREENSTONE, compute_model=False)\n print(model.structural_frame)\n\n WeightCache.clear_cache()\n BackendTensor.PYKEOPS = False\n\n sol = gp.compute_model(\n gempy_model=model,\n engine_config=gp.data.GemPyEngineConfig(\n backend=gp.data.AvailableBackends.PYTORCH,\n use_gpu=False,\n dtype='float64'\n )\n )\n\n if PLOT:\n gpv = require_gempy_viewer()\n gpv.plot_3d(model, image=True)\n\n\ndef test_save_weights():\n model = gp.generate_example_model(ExampleModel.GREENSTONE, compute_model=False)\n print(model.structural_frame)\n\n sol = gp.compute_model(\n gempy_model=model,\n engine_config=gp.data.GemPyEngineConfig(\n backend=gp.data.AvailableBackends.PYTORCH,\n use_gpu=False,\n dtype='float32'\n )\n )\n weights1 = sol.octrees_output[0].outputs[0].weights\n weights2 = sol.octrees_output[0].outputs[1].weights\n weights3 = sol.octrees_output[0].outputs[2].weights\n\n WeightCache.clear_cache()\n BackendTensor.PYKEOPS = False\n\n sol = gp.compute_model(\n gempy_model=model,\n engine_config=gp.data.GemPyEngineConfig(\n backend=gp.data.AvailableBackends.PYTORCH,\n use_gpu=False,\n dtype='float32'\n )\n )\n\n if PLOT:\n gpv = require_gempy_viewer()\n gpv.plot_3d(model, image=True)\n\n\ndef test_keops_x_torch():\n model = gp.generate_example_model(ExampleModel.GREENSTONE, compute_model=False)\n print(model.structural_frame)\n\n WeightCache.clear_cache()\n BackendTensor.PYKEOPS = False\n sol = gp.compute_model(\n gempy_model=model,\n engine_config=gp.data.GemPyEngineConfig(\n backend=gp.data.AvailableBackends.PYTORCH,\n use_gpu=False,\n dtype='float32'\n )\n )\n\n if PLOT:\n gpv = require_gempy_viewer()\n gpv.plot_3d(model, image=True)\n" }, { "path": "test/test_modules/test_compute_times_for_grids.py", "content": "import numpy as np\n\nimport gempy as gp\nimport time\n\nPLOT = True\nDENSE_RESOLUTION = [20, 20, 20]\n\n\ndef test_compute_time_dense_dense():\n geo_model: gp.data.GeoModel = _setup_model()\n geo_model.interpolation_options.evaluation_options.mesh_extraction = True\n\n geo_model.grid.active_grids = gp.data.Grid.GridTypes.DENSE\n start_time = time.perf_counter()\n gp.compute_model(geo_model)\n print(f\"Computing model on grids: {geo_model.grid.active_grids}\")\n end_time = time.perf_counter()\n computation_time_dense_I = end_time - start_time\n\n start_time = time.perf_counter()\n gp.compute_model(geo_model)\n print(f\"Computing model on grids: {geo_model.grid.active_grids}\")\n end_time = time.perf_counter()\n computation_time_dense_II = end_time - start_time\n\n print(f\"Computation only model dense grid 125*50*50: {computation_time_dense_I:.2f} seconds\")\n print(f\"Computation only model dense grid 125*50*50: {computation_time_dense_II:.2f} seconds\")\n \n # Assert that it is not too different\n assert abs(computation_time_dense_I - computation_time_dense_II) < 0.2\n\n\ndef test_compute_time_topo_dense_grid():\n geo_model: gp.data.GeoModel = _setup_model()\n\n geo_model.grid.active_grids = gp.data.Grid.GridTypes.DENSE\n start_time = time.perf_counter()\n gp.compute_model(geo_model)\n print(f\"Computing model on grids: {geo_model.grid.active_grids}\")\n end_time = time.perf_counter()\n computation_time_dense = end_time - start_time\n\n # Compute a solution for the model\n geo_model.grid.active_grids = gp.data.Grid.GridTypes.TOPOGRAPHY\n start_time = time.perf_counter()\n gp.compute_model(geo_model)\n print(f\"Computing model on grids: {geo_model.grid.active_grids}\")\n end_time = time.perf_counter()\n computation_time_topo = end_time - start_time\n\n # Recompute model as a new grid was added\n geo_model.grid.active_grids = gp.data.Grid.GridTypes.TOPOGRAPHY | gp.data.Grid.GridTypes.DENSE\n start_time = time.perf_counter()\n gp.compute_model(geo_model)\n print(f\"Computing model on grids: {geo_model.grid.active_grids}\")\n end_time = time.perf_counter()\n computation_time_topo_dense = end_time - start_time\n\n print(f\"Computation only model dense grid 125*50*50: {computation_time_dense:.2f} seconds\")\n print(f\"Computation time with topography 125*50: {computation_time_topo:.2f} seconds\")\n print(f\"Computation time with topography and dense grid 125*50*50: {computation_time_topo_dense:.2f} seconds\")\n \n # Assert that dense takes longer than topo and that the sum of both is close\n assert computation_time_topo_dense > computation_time_dense\n \n\ndef test_compute_time_custom_dense_grid():\n geo_model: gp.data.GeoModel = _setup_model()\n\n # numpy array with random coordinates within the extent of the model\n custom_coordinates = np.random.uniform(\n low=geo_model.grid.extent[:3],\n high=geo_model.grid.extent[3:],\n size=(1000, 3)\n )\n\n start_time = time.perf_counter()\n gp.compute_model_at(geo_model, custom_coordinates)\n end_time = time.perf_counter()\n computation_time_at = end_time - start_time\n\n print(f\"Computation compute_at with 1000 custom points: {computation_time_at:.2f} seconds\")\n\n\ndef _setup_model():\n # Define the path to data\n data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'\n path_to_data = data_path + \"/data/input_data/jan_models/\"\n # Create a GeoModel instance\n geo_model = gp.create_geomodel(\n project_name='EGU_example',\n extent=[0, 2500, 0, 1000, 0, 1000],\n resolution=DENSE_RESOLUTION,\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=path_to_data + \"model7_orientations.csv\",\n path_to_surface_points=path_to_data + \"model7_surface_points.csv\"\n )\n )\n # Map geological series to surfaces\n gp.map_stack_to_surfaces(\n gempy_model=geo_model,\n mapping_object={\n \"Fault_Series\" : ('fault'),\n \"Strat_Series1\": ('rock3'),\n \"Strat_Series2\": ('rock2', 'rock1'),\n }\n )\n # Define youngest structural group as fault\n gp.set_is_fault(geo_model, [\"Fault_Series\"])\n # Setting a randomly generated topography\n gp.set_topography_from_random(\n grid=geo_model.grid,\n fractal_dimension=2,\n d_z=np.array([700, 950]),\n topography_resolution=np.array([125, 50])\n )\n return geo_model\n" }, { "path": "test/test_modules/test_faults/__init__.py", "content": "" }, { "path": "test/test_modules/test_faults/test_fault_relations.py", "content": "import numpy as np\nimport gempy as gp\nfrom gempy.core.data.enumerators import ExampleModel\nfrom gempy.optional_dependencies import require_gempy_viewer\n\nPLOT = True\n\n\ndef test_fault_relations_implementation():\n # TODO! (Miguel Dec25) These fault description are not serializing!\n model = gp.generate_example_model(ExampleModel.FAULT_RELATION, compute_model=True)\n\n correct_relations = np.array([\n [0, 1, 1, 1],\n [0, 0, 0, 1],\n [0, 0, 0, 0],\n [0, 0, 0, 0]\n ], dtype=bool)\n\n # Assert\n assert np.array_equal(model.structural_frame.fault_relations, correct_relations) == True\n\n if PLOT:\n gpv = require_gempy_viewer()\n gtv: gpv.GemPyToVista = gpv.plot_3d(\n model=model,\n show_data=True,\n image=True,\n show=True\n )\n" }, { "path": "test/test_modules/test_faults/test_finite_faults.py", "content": "import numpy as np\nimport pytest\n\nimport gempy as gp\nimport gempy_viewer as gpv\nfrom gempy.core.data.enumerators import ExampleModel\nfrom test.verify_helper import verify_model_serialization\nfrom gempy_viewer.optional_dependencies import require_pyvista\nfrom test.conftest import TEST_SPEED, TestSpeed\n\ncenter = np.array([500, 500, 500])\nradius = np.array([500, 100, 200]) / 2\nk = np.array([1, 1, 1]) * 1\n\nPLOT = False\n\n\n@pytest.mark.skipif(TEST_SPEED.value < TestSpeed.SECONDS.value, reason=\"Global test speed below this test value.\")\ndef test_finite_fault_scalar_field_on_fault():\n geo_model: gp.data.GeoModel = gp.generate_example_model(\n example_model=ExampleModel.ONE_FAULT,\n compute_model=False\n )\n\n regular_grid = geo_model.grid.regular_grid\n\n # TODO: Extract grid from the model\n scaled_center = geo_model.input_transform.apply(center.reshape(1, -1))[0]\n scaled_radius = geo_model.input_transform.scale_points(radius.reshape(1, -1))[0]\n scalar_funtion: callable = gp.implicit_functions.ellipsoid_3d_factory( # * This paints the 3d regular grid\n center=scaled_center,\n radius=scaled_radius,\n max_slope=k # * This controls the speed of the transition\n )\n transform = gp.data.Transform(\n position=np.array([0, 0, 0]),\n rotation=np.array([0, 60, 0]),\n scale=np.ones(3)\n )\n faults_data = gp.data.FaultsData(\n fault_values_everywhere=np.zeros(0),\n fault_values_on_sp=np.zeros(0),\n thickness=None,\n fault_values_ref=np.zeros(0),\n fault_values_rest=np.zeros(0),\n finite_fault_data=gp.data.FiniteFaultData(\n implicit_function=scalar_funtion,\n implicit_function_transform=transform,\n pivot=scaled_center,\n )\n )\n\n geo_model.structural_frame.structural_groups[0].faults_input_data = faults_data\n\n verify_model_serialization(\n model=geo_model,\n verify_moment=\"after\",\n file_name=f\"verify/{geo_model.meta.name}\"\n )\n \n gp.compute_model(geo_model)\n\n # TODO: Try to do this afterwards\n\n if plot_pyvista := True:\n plot3d = gpv.plot_3d(\n geo_model,\n show_lith=False,\n show=False\n )\n scalar_block_for_viz = _apply(\n regular_grid=regular_grid,\n scalar_funtion=gp.implicit_functions.ellipsoid_3d_factory( # * This paints the 3d regular grid\n center=center,\n radius=radius,\n max_slope=k # * This controls the speed of the transition\n ),\n transform=transform\n )\n _plot_scalar_field(regular_grid, scalar_block_for_viz, plot3d.p, background_field=False)\n \n \ndef test_finite_fault_scalar_field():\n geo_model: gp.data.GeoModel = gp.generate_example_model(\n example_model=ExampleModel.ONE_FAULT,\n compute_model=False\n )\n\n regular_grid = geo_model.grid.regular_grid\n\n # TODO: Extract grid from the model\n scalar_funtion: callable = gp.implicit_functions.ellipsoid_3d_factory( # * This paints the 3d regular grid\n center=center,\n radius=radius,\n max_slope=k # * This controls the speed of the transition\n )\n\n transform = gp.data.Transform(\n position=np.array([0, 0, 0]),\n rotation=np.array([0, 0, 30]),\n scale=np.ones(3)\n )\n\n transformed_points = transform.apply_inverse_with_pivot(\n points=regular_grid.values,\n pivot=center\n )\n scalar_block = scalar_funtion(transformed_points)\n\n # TODO: Try to do this afterwards\n # scalar_fault = scalar_funtion(regular_grid.values)\n\n if plot_pyvista := True:\n _plot_scalar_field(regular_grid, scalar_block)\n\n\n@pytest.mark.skipif(TEST_SPEED.value < TestSpeed.SECONDS.value, reason=\"Global test speed below this test value.\")\ndef test_finite_fault_scalar_field_on_fault_ZERO():\n geo_model: gp.data.GeoModel = gp.generate_example_model(\n example_model=ExampleModel.ONE_FAULT,\n compute_model=True\n )\n\n regular_grid = geo_model.grid.regular_grid\n\n # TODO: Extract grid from the model\n scalar_funtion: callable = gp.implicit_functions.ellipsoid_3d_factory( # * This paints the 3d regular grid\n center=center,\n radius=radius,\n max_slope=k # * This controls the speed of the transition\n )\n\n transform = gp.data.Transform(\n position=np.array([0, 0, 0]),\n rotation=np.array([0, 0, 30]),\n scale=np.ones(3)\n )\n\n transformed_points = transform.apply_inverse_with_pivot(\n points=regular_grid.values,\n pivot=center\n )\n \n scalar_block = scalar_funtion(transformed_points)\n\n # TODO: Try to do this afterwards\n # scalar_fault = scalar_funtion(regular_grid.values)\n\n if plot_pyvista := True:\n plot3d = gpv.plot_3d(\n geo_model,\n show=False\n )\n _plot_scalar_field(regular_grid, scalar_block, plot3d.p)\n\n\ndef _apply(regular_grid, scalar_funtion, transform):\n transformed_points = transform.apply_inverse_with_pivot(\n points=regular_grid.values, # ! This depends on the octree\n pivot=center\n )\n scalar_block = scalar_funtion(transformed_points)\n return scalar_block\n\n\ndef _plot_scalar_field(regular_grid, scalar_block, plotter=None, background_field=True):\n if not PLOT:\n return\n \n pv = require_pyvista()\n p = plotter or pv.Plotter()\n regular_grid_values = regular_grid.values_vtk_format\n grid_3d = regular_grid_values.reshape(*(regular_grid.resolution + 1), 3).T\n regular_grid_mesh = pv.StructuredGrid(*grid_3d)\n regular_grid_mesh[\"lith\"] = scalar_block\n if True:\n area_of_effect = regular_grid_mesh.threshold([.000000001, 1.1])\n p.add_mesh(area_of_effect, show_edges=True, opacity=.4)\n\n area_of_effect_2 = regular_grid_mesh.threshold([.2, 1.1])\n p.add_mesh(area_of_effect_2, show_edges=True, opacity=.8)\n if background_field:\n p.add_mesh(regular_grid_mesh, show_edges=False, opacity=.2)\n p.show_bounds(bounds=regular_grid.extent)\n # * Add the fault\n if False:\n dual_mesh = pv.PolyData(fault_mesh.vertices, np.insert(fault_mesh.edges, 0, 3, axis=1).ravel())\n dual_mesh[\"bar\"] = scalar_fault\n p.add_mesh(dual_mesh, opacity=1, silhouette=True, show_edges=True)\n p.show()\n" }, { "path": "test/test_modules/test_faults/test_finite_faults.test_finite_fault_scalar_field_on_fault.verify/fault.approved.txt", "content": "{\n \"meta\": {\n \"name\": \"fault\",\n \"creation_date\": \"\",\n \"last_modification_date\": null,\n \"owner\": null\n },\n \"structural_frame\": {\n \"structural_groups\": [\n {\n \"name\": \"Fault_Series\",\n \"elements\": [\n {\n \"name\": \"fault\",\n \"is_active\": true,\n \"_color\": \"#527682\",\n \"surface_points\": {\n \"name_id_map\": {\n \"fault\": 65970106,\n \"rock1\": 167239155,\n \"rock2\": 217776925\n },\n \"_model_transform\": null\n },\n \"orientations\": {\n \"name_id_map\": {\n \"fault\": 65970106,\n \"rock1\": 167239155,\n \"rock2\": 217776925\n },\n \"_model_transform\": null\n },\n \"scalar_field_at_interface\": null,\n \"_id\": 65970106\n }\n ],\n \"structural_relation\": 3,\n \"fault_relations\": 1,\n \"faults_input_data\": {\n \"fault_values_everywhere\": [],\n \"fault_values_on_sp\": [],\n \"fault_values_ref\": [],\n \"fault_values_rest\": [],\n \"thickness\": null,\n \"finite_fault_data\": {\n \"implicit_function_transform\": {\n \"position\": [\n 0,\n 0,\n 0\n ],\n \"rotation\": [\n 0,\n 60,\n 0\n ],\n \"scale\": [\n 1.0,\n 1.0,\n 1.0\n ],\n \"_is_default_transform\": false,\n \"_cached_pivot\": null\n },\n \"pivot\": [\n 0.0,\n 0.0,\n 0.0\n ]\n }\n },\n \"solution\": null\n },\n {\n \"name\": \"Strat_Series\",\n \"elements\": [\n {\n \"name\": \"rock2\",\n \"is_active\": true,\n \"_color\": \"#ffbe00\",\n \"surface_points\": {\n \"name_id_map\": {\n \"fault\": 65970106,\n \"rock1\": 167239155,\n \"rock2\": 217776925\n },\n \"_model_transform\": null\n },\n \"orientations\": {\n \"name_id_map\": {\n \"fault\": 65970106,\n \"rock1\": 167239155,\n \"rock2\": 217776925\n },\n \"_model_transform\": null\n },\n \"scalar_field_at_interface\": null,\n \"_id\": 217776925\n },\n {\n \"name\": \"rock1\",\n \"is_active\": true,\n \"_color\": \"#9f0052\",\n \"surface_points\": {\n \"name_id_map\": {\n \"fault\": 65970106,\n \"rock1\": 167239155,\n \"rock2\": 217776925\n },\n \"_model_transform\": null\n },\n \"orientations\": {\n \"name_id_map\": {\n \"fault\": 65970106,\n \"rock1\": 167239155,\n \"rock2\": 217776925\n },\n \"_model_transform\": null\n },\n \"scalar_field_at_interface\": null,\n \"_id\": 167239155\n }\n ],\n \"structural_relation\": 1,\n \"fault_relations\": 3,\n \"faults_input_data\": null,\n \"solution\": null\n }\n ],\n \"color_generator\": {\n \"_index\": 4\n },\n \"basement_color\": \"#728f02\",\n \"is_dirty\": true,\n \"binary_meta_data\": {\n \"sp_binary_length\": 792,\n \"ori_binary_length\": 300\n }\n },\n \"grid\": {\n \"_octree_grid\": {\n \"resolution\": [\n 64,\n 64,\n 64\n ],\n \"extent\": [\n 0.0,\n 1000.0,\n 0.0,\n 1000.0,\n 0.0,\n 1000.0\n ],\n \"_transform\": null,\n \"_base_resolution\": [\n 2,\n 2,\n 2\n ]\n },\n \"_dense_grid\": null,\n \"_custom_grid\": null,\n \"_topography\": null,\n \"_sections\": null,\n \"_centered_grid\": null,\n \"_transform\": null,\n \"_octree_levels\": -1,\n \"binary_meta_data\": {\n \"custom_grid_binary_length\": 0,\n \"topography_binary_length\": 0\n },\n \"active_grids\": 1025\n },\n \"geophysics_input\": null,\n \"input_transform\": {\n \"position\": [\n -500.0,\n -500.0,\n -500.0\n ],\n \"rotation\": [\n 0.0,\n 0.0,\n 0.0\n ],\n \"scale\": [\n 0.0005,\n 0.0005,\n 0.0005\n ],\n \"_is_default_transform\": false,\n \"_cached_pivot\": null\n },\n \"_interpolation_options\": {\n \"kernel_options\": {\n \"range\": 1.7,\n \"c_o\": 10.0,\n \"uni_degree\": 1,\n \"i_res\": 4.0,\n \"gi_res\": 2.0,\n \"number_dimensions\": 3,\n \"kernel_function\": \"cubic\",\n \"kernel_solver\": 1,\n \"compute_condition_number\": false,\n \"optimizing_condition_number\": false,\n \"condition_number\": null\n },\n \"evaluation_options\": {\n \"_number_octree_levels\": 6,\n \"_number_octree_levels_surface\": 4,\n \"octree_curvature_threshold\": -1.0,\n \"octree_error_threshold\": 1.0,\n \"octree_min_level\": 2,\n \"mesh_extraction\": true,\n \"mesh_extraction_masking_options\": 3,\n \"mesh_extraction_fancy\": true,\n \"evaluation_chunk_size\": 500000,\n \"compute_scalar\": true,\n \"compute_scalar_gradient\": false,\n \"verbose\": false\n },\n \"debug\": false,\n \"cache_mode\": 3,\n \"cache_model_name\": \"fault\",\n \"block_solutions_type\": 1,\n \"sigmoid_slope\": 5000000\n }\n}\n" }, { "path": "test/test_modules/test_gempy_subsurface.py", "content": "import pytest\n\nimport gempy as gp\nimport gempy_viewer as gpv\n\nfrom gempy import optional_dependencies\nfrom gempy.core.data.enumerators import ExampleModel\n\nimport numpy as np\n\nfrom ..conftest import REQUIREMENT_LEVEL, Requirements\n\npytestmark = pytest.mark.skipif(\n condition=REQUIREMENT_LEVEL.value < Requirements.DEV.value and False,\n reason=\"This test needs higher requirements.\"\n)\n\npd = pytest.importorskip(\"pandas\", reason=\"Pandas is not installed\")\n\n\ndef test_gempy_to_subsurface():\n model = gp.generate_example_model(ExampleModel.ANTICLINE, compute_model=True)\n if False:\n gpv.plot_3d(model)\n\n vertex: list[np.ndarray] = model.solutions.raw_arrays.vertices\n simplex_list: list[np.ndarray] = model.solutions.raw_arrays.edges\n\n idx_max = 0\n for simplex_array in simplex_list:\n simplex_array += idx_max\n idx_max = simplex_array.max() + 1\n\n vertex_id_array = [np.full(v.shape[0], i + 1) for i, v in enumerate(vertex)]\n cell_id_array = [np.full(v.shape[0], i + 1) for i, v in enumerate(simplex_list)]\n\n concatenated_id_array = np.concatenate(vertex_id_array)\n concatenated_cell_id_array = np.concatenate(cell_id_array)\n\n subsurface = optional_dependencies.require_subsurface()\n ss = subsurface\n meshes: ss.UnstructuredData = ss.UnstructuredData.from_array(\n vertex=np.concatenate(vertex),\n cells=np.concatenate(simplex_list),\n vertex_attr=pd.DataFrame({'id': concatenated_id_array}),\n cells_attr=pd.DataFrame({'id': concatenated_cell_id_array})\n )\n\n trisurf = subsurface.core.structs.unstructured_elements.triangular_surface.TriSurf(meshes)\n pyvista_mesh = ss.visualization.to_pyvista_mesh(trisurf)\n ss.visualization.pv_plot([pyvista_mesh], image_2d=True)\n\n\ndef test_gempy_to_subsurface_II():\n model: gp.data.GeoModel = gp.generate_example_model(ExampleModel.ANTICLINE, compute_model=True)\n subsurface = optional_dependencies.require_subsurface()\n ss = subsurface\n \n meshes: ss.UnstructuredData = model.solutions.raw_arrays.meshes_to_subsurface()\n trisurf = subsurface.core.structs.unstructured_elements.triangular_surface.TriSurf(meshes)\n pyvista_mesh = ss.visualization.to_pyvista_mesh(trisurf)\n ss.visualization.pv_plot([pyvista_mesh], image_2d=True)\n\n\ndef test_gempy_to_subsurface_III():\n model = gp.generate_example_model(ExampleModel.ANTICLINE, compute_model=True)\n subsurface = optional_dependencies.require_subsurface()\n ss = subsurface\n meshes: ss.UnstructuredData = model.solutions.meshes_to_unstruct()\n\n trisurf = subsurface.core.structs.unstructured_elements.triangular_surface.TriSurf(meshes)\n pyvista_mesh = ss.visualization.to_pyvista_mesh(trisurf)\n ss.visualization.pv_plot([pyvista_mesh], image_2d=True)\n" }, { "path": "test/test_modules/test_geophysics/__init__.py", "content": "import pytest\n\npytestmark = pytest.mark.skip(reason=\"Geophysics module is not yet updated\")" }, { "path": "test/test_modules/test_geophysics/test_gravity.py", "content": "# Importing GemPy\nimport gempy as gp\n\n# Importing auxiliary libraries\nimport numpy as np\n\nfrom test.verify_helper import verify_model_serialization\n\n\ndef test_gravity():\n color_generator = gp.data.ColorsGenerator()\n element1 = gp.data.StructuralElement(\n name='surface1',\n color=next(color_generator),\n surface_points=gp.data.SurfacePointsTable.from_arrays(\n x=np.array([3, 9]),\n y=np.array([0, 0]),\n z=np.array([3.05, 3.05]),\n names='surface1'\n ),\n orientations=gp.data.OrientationsTable.from_arrays(\n x=np.array([6]),\n y=np.array([0]),\n z=np.array([4]),\n G_x=np.array([0]),\n G_y=np.array([0]),\n G_z=np.array([1]),\n names='surface1'\n )\n )\n\n element2 = gp.data.StructuralElement(\n name='surface2',\n color=next(color_generator),\n surface_points=gp.data.SurfacePointsTable.from_arrays(\n x=np.array([3, 9]),\n y=np.array([0, 0]),\n z=np.array([1.02, 1.02]),\n names='surface2'\n ),\n orientations=gp.data.OrientationsTable.initialize_empty()\n )\n\n frame = gp.data.StructuralFrame(\n structural_groups=[gp.data.StructuralGroup(\n name='default',\n elements=[element1, element2],\n structural_relation=gp.data.StackRelationType.ERODE\n )\n ],\n color_gen=color_generator\n )\n\n geo_model: gp.data.GeoModel = gp.create_geomodel(\n project_name=\"2-layers\",\n extent=[0, 12, -2, 2, 0, 4],\n resolution=[500, 1, 500],\n refinement=1,\n structural_frame=frame,\n )\n\n gp.compute_model(geo_model, validate_serialization=True)\n\n import gempy_viewer as gpv\n gpv.plot_2d(geo_model, cell_number=0)\n\n gp.set_centered_grid(\n grid=geo_model.grid,\n centers=np.array([[6, 0, 4]], dtype=\"float\"),\n resolution=np.array([10, 10, 100], dtype=\"float\"),\n radius=np.array([16000, 16000, 16000], dtype=\"float\") # ? This radius makes 0 sense but it is the original one in gempy v2\n )\n\n gravity_gradient = gp.calculate_gravity_gradient(geo_model.grid.centered_grid)\n geo_model.geophysics_input = gp.data.GeophysicsInput(\n tz=gravity_gradient,\n densities=np.array([2.6, 2.4, 3.2]),\n )\n\n verify_model_serialization(\n model=geo_model,\n verify_moment=\"after\",\n file_name=f\"verify/{geo_model.meta.name}\"\n )\n\n gp.compute_model(geo_model)\n\n print(geo_model.solutions.gravity)\n np.testing.assert_almost_equal(geo_model.solutions.gravity, np.array([-1624.1714]), decimal=4)\n" }, { "path": "test/test_modules/test_geophysics/test_gravity.test_gravity.verify/2-layers.approved.txt", "content": "{\n \"meta\": {\n \"name\": \"2-layers\",\n \"creation_date\": \"\",\n \"last_modification_date\": null,\n \"owner\": null\n },\n \"structural_frame\": {\n \"structural_groups\": [\n {\n \"name\": \"default\",\n \"elements\": [\n {\n \"name\": \"surface1\",\n \"is_active\": true,\n \"_color\": \"#015482\",\n \"surface_points\": {\n \"name_id_map\": {\n \"surface1\": 57292991\n },\n \"_model_transform\": null\n },\n \"orientations\": {\n \"name_id_map\": {\n \"surface1\": 57292991\n },\n \"_model_transform\": null\n },\n \"scalar_field_at_interface\": 0.09000000000000002,\n \"_id\": -1\n },\n {\n \"name\": \"surface2\",\n \"is_active\": true,\n \"_color\": \"#9f0052\",\n \"surface_points\": {\n \"name_id_map\": {\n \"surface2\": 21816406\n },\n \"_model_transform\": null\n },\n \"orientations\": {\n \"name_id_map\": null,\n \"_model_transform\": null\n },\n \"scalar_field_at_interface\": -0.2483333333333333,\n \"_id\": -1\n }\n ],\n \"structural_relation\": 1,\n \"fault_relations\": null,\n \"faults_input_data\": null,\n \"solution\": null\n }\n ],\n \"color_generator\": {\n \"_index\": 3\n },\n \"basement_color\": \"#ffbe00\",\n \"is_dirty\": true,\n \"binary_meta_data\": {\n \"sp_binary_length\": 144,\n \"ori_binary_length\": 60\n }\n },\n \"grid\": {\n \"_octree_grid\": null,\n \"_dense_grid\": {\n \"resolution\": [\n 500,\n 1,\n 500\n ],\n \"extent\": [\n 0.0,\n 12.0,\n -2.0,\n 2.0,\n 0.0,\n 4.0\n ],\n \"_transform\": null,\n \"_base_resolution\": [\n 2,\n 2,\n 2\n ]\n },\n \"_custom_grid\": null,\n \"_topography\": null,\n \"_sections\": null,\n \"_centered_grid\": {\n \"centers\": [\n [\n 6.0,\n 0.0,\n 4.0\n ]\n ],\n \"resolution\": [\n 10.0,\n 10.0,\n 100.0\n ],\n \"radius\": [\n 16000.0,\n 16000.0,\n 16000.0\n ]\n },\n \"_transform\": null,\n \"_octree_levels\": -1,\n \"binary_meta_data\": {\n \"custom_grid_binary_length\": 0,\n \"topography_binary_length\": 0\n },\n \"active_grids\": 1058\n },\n \"geophysics_input\": {\n \"gravity_input\": {\n \"tz\": [],\n \"densities\": [\n 2.6,\n 2.4,\n 3.2\n ]\n },\n \"magnetics_input\": null\n },\n \"input_transform\": {\n \"position\": [\n -6.0,\n -0.0,\n -2.51\n ],\n \"rotation\": [\n 0.0,\n 0.0,\n 0.0\n ],\n \"scale\": [\n 0.08333333333333333,\n 0.16778523489932887,\n 0.08333333333333333\n ],\n \"_is_default_transform\": false,\n \"_cached_pivot\": null\n },\n \"_interpolation_options\": {\n \"kernel_options\": {\n \"range\": 1.7,\n \"c_o\": 10.0,\n \"uni_degree\": 1,\n \"i_res\": 4.0,\n \"gi_res\": 2.0,\n \"number_dimensions\": 3,\n \"kernel_function\": \"cubic\",\n \"kernel_solver\": 1,\n \"compute_condition_number\": false,\n \"optimizing_condition_number\": false,\n \"condition_number\": null\n },\n \"evaluation_options\": {\n \"_number_octree_levels\": 1,\n \"_number_octree_levels_surface\": 4,\n \"octree_curvature_threshold\": -1.0,\n \"octree_error_threshold\": 1.0,\n \"octree_min_level\": 2,\n \"mesh_extraction\": true,\n \"mesh_extraction_masking_options\": 3,\n \"mesh_extraction_fancy\": true,\n \"evaluation_chunk_size\": 500000,\n \"compute_scalar\": true,\n \"compute_scalar_gradient\": false,\n \"verbose\": false\n },\n \"debug\": false,\n \"cache_mode\": 3,\n \"cache_model_name\": \"2-layers\",\n \"block_solutions_type\": 2,\n \"sigmoid_slope\": 5000000\n }\n}\n" }, { "path": "test/test_modules/test_grids/__init__.py", "content": "" }, { "path": "test/test_modules/test_grids/test_custom_grid.py", "content": "import numpy as np\nimport pytest\n\nfrom test.verify_helper import verify_model_serialization\nfrom test.conftest import TEST_SPEED, TestSpeed\nimport gempy as gp\nfrom gempy.core.data.enumerators import ExampleModel\n\npytestmark = pytest.mark.skipif(TEST_SPEED.value < TestSpeed.SECONDS.value, reason=\"Global test speed below this test value.\")\n\nxyz_coord = np.array(\n [[0, 0, 0],\n [1000, 0, 0],\n [0, 1000, 0],\n [1000, 1000, 0],\n [0, 0, 1000],\n [1000, 0, 1000],\n [0, 1000, 1000],\n [1000, 1000, 1000]],\n dtype=float\n)\n\n\ndef test_custom_grid():\n geo_model: gp.data.GeoModel = gp.generate_example_model(\n example_model=ExampleModel.ANTICLINE,\n compute_model=False\n )\n\n geo_model.interpolation_options.number_octree_levels = 2\n\n gp.set_custom_grid(\n grid=geo_model.grid,\n xyz_coord=xyz_coord\n )\n\n verify_model_serialization(\n model=geo_model,\n verify_moment=\"after\",\n file_name=f\"verify/{geo_model.meta.name}\"\n )\n \n sol: gp.data.Solutions = gp.compute_model(geo_model, validate_serialization=True)\n np.testing.assert_array_equal(\n sol.raw_arrays.custom,\n np.array([3., 3., 3., 3., 1., 1., 1., 1.])\n )\n\n\ndef test_compute_at():\n geo_model: gp.data.GeoModel = gp.generate_example_model(\n example_model=ExampleModel.ANTICLINE,\n compute_model=False\n )\n\n geo_model.interpolation_options.number_octree_levels = 2\n sol: np.ndarray = gp.compute_model_at(\n gempy_model=geo_model,\n at=xyz_coord\n )\n \n np.testing.assert_array_equal(\n sol,\n np.array([3., 3., 3., 3., 1., 1., 1., 1.])\n )\n" }, { "path": "test/test_modules/test_grids/test_custom_grid.test_custom_grid.verify/fold.approved.txt", "content": "{\n \"meta\": {\n \"name\": \"fold\",\n \"creation_date\": \"\",\n \"last_modification_date\": null,\n \"owner\": null\n },\n \"structural_frame\": {\n \"structural_groups\": [\n {\n \"name\": \"Strat_Series\",\n \"elements\": [\n {\n \"name\": \"rock2\",\n \"is_active\": true,\n \"_color\": \"#9f0052\",\n \"surface_points\": {\n \"name_id_map\": {\n \"rock1\": 67239155,\n \"rock2\": 117776925\n },\n \"_model_transform\": null\n },\n \"orientations\": {\n \"name_id_map\": {\n \"rock1\": 67239155,\n \"rock2\": 117776925\n },\n \"_model_transform\": null\n },\n \"scalar_field_at_interface\": null,\n \"_id\": 117776925\n },\n {\n \"name\": \"rock1\",\n \"is_active\": true,\n \"_color\": \"#015482\",\n \"surface_points\": {\n \"name_id_map\": {\n \"rock1\": 67239155,\n \"rock2\": 117776925\n },\n \"_model_transform\": null\n },\n \"orientations\": {\n \"name_id_map\": {\n \"rock1\": 67239155,\n \"rock2\": 117776925\n },\n \"_model_transform\": null\n },\n \"scalar_field_at_interface\": null,\n \"_id\": 67239155\n }\n ],\n \"structural_relation\": 1,\n \"fault_relations\": null,\n \"faults_input_data\": null,\n \"solution\": null\n }\n ],\n \"color_generator\": {\n \"_index\": 3\n },\n \"basement_color\": \"#ffbe00\",\n \"is_dirty\": true,\n \"binary_meta_data\": {\n \"sp_binary_length\": 1296,\n \"ori_binary_length\": 120\n }\n },\n \"grid\": {\n \"_octree_grid\": {\n \"resolution\": [\n 32,\n 32,\n 32\n ],\n \"extent\": [\n 0.0,\n 1000.0,\n 0.0,\n 1000.0,\n 0.0,\n 1000.0\n ],\n \"_transform\": null,\n \"_base_resolution\": [\n 2,\n 2,\n 2\n ]\n },\n \"_dense_grid\": null,\n \"_custom_grid\": {},\n \"_topography\": null,\n \"_sections\": null,\n \"_centered_grid\": null,\n \"_transform\": null,\n \"_octree_levels\": -1,\n \"binary_meta_data\": {\n \"custom_grid_binary_length\": 192,\n \"topography_binary_length\": 0\n },\n \"active_grids\": 1029\n },\n \"geophysics_input\": null,\n \"input_transform\": {\n \"position\": [\n -500.0,\n -500.0,\n -510.0\n ],\n \"rotation\": [\n 0.0,\n 0.0,\n 0.0\n ],\n \"scale\": [\n 0.0005,\n 0.0005,\n 0.0005\n ],\n \"_is_default_transform\": false,\n \"_cached_pivot\": null\n },\n \"_interpolation_options\": {\n \"kernel_options\": {\n \"range\": 1.7,\n \"c_o\": 10.0,\n \"uni_degree\": 1,\n \"i_res\": 4.0,\n \"gi_res\": 2.0,\n \"number_dimensions\": 3,\n \"kernel_function\": \"cubic\",\n \"kernel_solver\": 1,\n \"compute_condition_number\": false,\n \"optimizing_condition_number\": false,\n \"condition_number\": null\n },\n \"evaluation_options\": {\n \"_number_octree_levels\": 2,\n \"_number_octree_levels_surface\": 4,\n \"octree_curvature_threshold\": -1.0,\n \"octree_error_threshold\": 1.0,\n \"octree_min_level\": 2,\n \"mesh_extraction\": true,\n \"mesh_extraction_masking_options\": 3,\n \"mesh_extraction_fancy\": true,\n \"evaluation_chunk_size\": 500000,\n \"compute_scalar\": true,\n \"compute_scalar_gradient\": false,\n \"verbose\": false\n },\n \"debug\": false,\n \"cache_mode\": 3,\n \"cache_model_name\": \"fold\",\n \"block_solutions_type\": 1,\n \"sigmoid_slope\": 5000000\n }\n}\n" }, { "path": "test/test_modules/test_grids/test_diamond_square.py", "content": "import gempy.core.data.grid_modules.diamond_square\nimport pytest # to add fixtures and to test error raises\nimport numpy as np # as another testing environment\n\n\ndef test_class_nocrash():\n \"\"\"Simply check if class can be instantiated\"\"\"\n gempy.core.data.grid_modules.diamond_square.DiaomondSquare(size=(5, 5))\n\n\ndef test_grid_generation():\n \"\"\"Test grid generation and extension for non-suitable grid sizes\"\"\"\n ds = gempy.core.data.grid_modules.diamond_square.DiaomondSquare(size=(5, 5))\n assert ds.grid.shape == (5, 5)\n ds = gempy.core.data.grid_modules.diamond_square.DiaomondSquare(size=(8, 10))\n assert ds.grid.shape == (9, 17)\n\n\ndef test_diamond_selection():\n \"\"\"Test selection of diamond positions\"\"\"\n ds = gempy.core.data.grid_modules.diamond_square.DiaomondSquare(size=(5, 5))\n z = ds.get_selection_diamond(1)\n assert np.all(z == np.array([[2, 0, 0, 0, 2],\n [0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0],\n [2, 0, 0, 0, 2]]))\n z = ds.get_selection_diamond(0)\n assert np.all(z == np.array([[2, 0, 2, 0, 2],\n [0, 1, 0, 1, 0],\n [2, 0, 2, 0, 2],\n [0, 1, 0, 1, 0],\n [2, 0, 2, 0, 2]]))\n\n\ndef test_square_selection():\n \"\"\"Test selection of diamond positions\"\"\"\n ds = gempy.core.data.grid_modules.diamond_square.DiaomondSquare(size=(5, 5))\n z = ds.get_selection_square(0)\n assert np.all(z == np.array([[0, 0, 2, 0, 2, 0, 0],\n [0, 2, 1, 2, 1, 2, 0],\n [2, 1, 2, 1, 2, 1, 2],\n [0, 2, 1, 2, 1, 2, 0],\n [2, 1, 2, 1, 2, 1, 2],\n [0, 2, 1, 2, 1, 2, 0],\n [0, 0, 2, 0, 2, 0, 0]]))\n z = ds.get_selection_square(1)\n assert np.all(z == np.array([[0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 1, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 1, 0, 2, 0, 1, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 1, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0]]))\n\n\ndef test_random_initialization():\n \"\"\"Test random initialization of corner points\"\"\"\n ds = gempy.core.data.grid_modules.diamond_square.DiaomondSquare(size=(5, 6), seed=52062)\n ds.random_initialization()\n m_pow_max = min(ds.n, ds.m)\n step_size = int(2 ** m_pow_max)\n np.testing.assert_array_almost_equal(ds.grid[::step_size, ::step_size],\n np.array([[0.35127005, 0.55476571, 0.93745213],\n [0.66668382, 0.85215985, 0.53222795]]))\n\n\ndef test_random_initialization_level():\n \"\"\"Test random initialization on lower level\"\"\"\n ds = gempy.core.data.grid_modules.diamond_square.DiaomondSquare(size=(33, 33), seed=52062)\n level = 3\n ds.random_initialization(level=level)\n step_size = int(2 ** level)\n np.testing.assert_array_almost_equal(ds.grid[::step_size, ::step_size],\n np.array([[0.35127005, 0.55476571, 0.93745213, 0.66668382, 0.85215985],\n [0.53222795, 0.55800027, 0.20974513, 0.74837501, 0.64394326],\n [0.0359961, 0.22723278, 0.56347804, 0.13438884, 0.32613594],\n [0.20868763, 0.03116471, 0.1498014, 0.20755495, 0.86021482],\n [0.64707457, 0.44744272, 0.36504945, 0.52473407, 0.27948164]]))\n\ndef test_reset_grid():\n ds = gempy.core.data.grid_modules.diamond_square.DiaomondSquare(size=(5, 6), seed=52062)\n ds.random_initialization()\n ds.reset_grid()\n np.testing.assert_array_almost_equal(ds.grid,\n np.array([[0., 0., 0., 0., 0., 0., 0., 0., 0.],\n [0., 0., 0., 0., 0., 0., 0., 0., 0.],\n [0., 0., 0., 0., 0., 0., 0., 0., 0.],\n [0., 0., 0., 0., 0., 0., 0., 0., 0.],\n [0., 0., 0., 0., 0., 0., 0., 0., 0.]]))\n\n\ndef test_random_func():\n \"\"\"Test random function implementation\"\"\"\n ds = gempy.core.data.grid_modules.diamond_square.DiaomondSquare(size=(33, 33), seed=52062)\n np.testing.assert_array_almost_equal(ds.random_func(2, 2),\n np.array([-0.14872995, 0.05476571]))\n # testing for correct default implementation\n ds.r_type = 'default'\n np.testing.assert_array_almost_equal(ds.random_func(2, 2),\n np.array([0.43745213, 0.16668382]))\n # testing long-range correlation\n ds.r_type = 'long_range'\n np.testing.assert_array_almost_equal(ds.random_func(2, 2),\n np.array([0.04401998, 0.00402849]))\n # testing level-scale correlation\n ds.r_type = 'level_scale'\n np.testing.assert_array_almost_equal(ds.random_func(2, 2),\n np.array([0.18600009, 0.06991504]))\n # testing deterministic implementation (no random value)\n ds.r_type = 'deterministic'\n assert ds.random_func(2, 2) == 0.0\n\n\ndef test_random_func_raises_error():\n \"\"\"Test if random function raises NonImplementedError correctly\"\"\"\n ds = gempy.core.data.grid_modules.diamond_square.DiaomondSquare(size=(33, 33), seed=52062)\n ds.r_type = 'fail'\n\n with pytest.raises(NotImplementedError):\n ds.random_func(2, 2)\n\n\ndef test_interpolate():\n \"\"\"Test interpolation step itself\"\"\"\n ds = gempy.core.data.grid_modules.diamond_square.DiaomondSquare(size=(9, 9), seed=52062)\n ds.interpolate()\n np.testing.assert_array_almost_equal(ds.grid,\n np.array([[0., 0.2951411, 0.21781267, 0.29361906, 0.01037812,\n -0.0376406, -0.59889259, 0.01136296, 0.],\n [-0.13102895, -0.07079394, 0.40240191, -0.24139454, -0.64535709,\n -0.25358984, -0.20811689, -0.38977623, -0.02280871],\n [-0.32311967, -0.08246826, 0.03236034, -0.72313104, -0.6863271,\n -0.09742037, 0.16154592, -0.41643384, -0.23968483],\n [-0.53344647, -0.09313507, -0.66247738, -0.42849468, -0.06519284,\n -0.50628043, -0.31159035, 0.53516982, 0.07387422],\n [0.23421434, 0.32817758, -0.45156142, -0.24627659, -0.2974599,\n 0.16071127, 0.36261452, 0.62070397, 0.60516641],\n [-0.20172896, -0.05668301, -0.26331217, -0.22196496, 0.42029741,\n 0.35078669, 0.77129922, 0.38999358, 0.95701668],\n [-0.41296032, -0.04377428, -0.23235603, 0.60954786, 0.72643437,\n 0.37788456, 0.62211967, 0.16198846, 0.61709021],\n [0.00192109, -0.28399285, 0.28596529, 0.54081866, 1.00235637,\n 0.25454729, 0.1248549, 0.85789169, 0.23511424],\n [0., -0.54507578, -0.33592062, 0.62216544, 0.77575097,\n 0.5338132, 0.22007596, 0.02926128, 0.]]))\n" }, { "path": "test/test_modules/test_grids/test_grids_I.py", "content": "import pytest\n\nimport gempy as gp\nimport gempy_viewer as gpv\nfrom gempy.core.data.enumerators import ExampleModel\nfrom gempy_engine.core.data.output.blocks_value_type import ValueType\nfrom gempy_engine.plugins.plotting.helper_functions import plot_block_and_input_2d\nfrom test.conftest import TEST_SPEED, TestSpeed\n\n\npytestmark = pytest.mark.skipif(TEST_SPEED.value < TestSpeed.MINUTES.value, reason=\"Global test speed below this test value.\")\n\n\ndef test_octree():\n geo_model: gp.data.GeoModel = gp.generate_example_model(\n example_model=ExampleModel.ANTICLINE,\n compute_model=False\n ) \n \n geo_model.interpolation_options.number_octree_levels = 4\n gp.compute_model(geo_model)\n\n plot_block_and_input_2d(\n stack_number=0,\n interpolation_input=geo_model.interpolation_input_copy,\n outputs=geo_model.solutions.octrees_output,\n structure=geo_model.structural_frame.input_data_descriptor.stack_structure,\n value_type=ValueType.ids\n )\n \n gpv.plot_3d(geo_model, show_data=True, show_boundaries=True, show_lith=False, image=True)\n \n geo_model.interpolation_options.number_octree_levels = 2\n gp.compute_model(geo_model)\n\n plot_block_and_input_2d(\n stack_number=0,\n interpolation_input=geo_model.interpolation_input_copy,\n outputs=geo_model.solutions.octrees_output,\n structure=geo_model.structural_frame.input_data_descriptor.stack_structure,\n value_type=ValueType.ids\n )\n\n gpv.plot_3d(geo_model, show_data=True, show_boundaries=True, show_lith=False, image=True)\n" }, { "path": "test/test_modules/test_grids/test_grids_sections.py", "content": "import numpy as np\nimport pytest\n\nimport gempy as gp\nimport gempy_viewer as gpv\nfrom gempy.core.data.enumerators import ExampleModel\nfrom test.verify_helper import verify_model_serialization\n\nfrom test.conftest import TEST_SPEED, TestSpeed\n\n\n\ndef test_section_grids():\n geo_model: gp.data.GeoModel = gp.generate_example_model(\n example_model=ExampleModel.ANTICLINE,\n compute_model=False\n )\n\n geo_model.grid.set_octree_grid_by_levels(\n octree_levels=2,\n evaluation_options=geo_model.interpolation_options.evaluation_options\n )\n\n gp.set_section_grid(\n grid=geo_model.grid,\n section_dict={\n 'section_SW-NE': ((250., 250.), (1750., 1750.), (100, 100)),\n 'section_NW-SE': ((250., 1750.), (1750., 250.), (100, 100))\n }\n )\n\n gp.set_topography_from_random(\n grid=geo_model.grid,\n fractal_dimension=1.2,\n d_z=np.array([200, 1000]),\n topography_resolution=np.array([60, 60])\n )\n\n verify_model_serialization(\n model=geo_model,\n verify_moment=\"after\",\n file_name=f\"verify/{geo_model.meta.name}\"\n )\n \n gp.compute_model(geo_model, validate_serialization=True)\n gpv.plot_2d(\n model=geo_model,\n section_names=['section_SW-NE', 'section_NW-SE', 'topography'],\n direction=['x'], cell_number=['mid'],\n show_lith=[False, True, False, True],\n show_boundaries=[False, False, False, True],\n show_topography=True,\n show_section_traces=True # TODO: Test this one\n )\n\n gpv.plot_2d(geo_model, show_boundaries=False, section_names=['topography'])\n gpv.plot_3d(\n geo_model,\n show_data=True,\n show_boundaries=True,\n show_lith=False,\n image=True\n )\n\n\n\n@pytest.mark.skipif(TEST_SPEED.value < TestSpeed.MINUTES.value, reason=\"Global test speed below this test value.\")\ndef test_topography_II():\n geo_model: gp.data.GeoModel = gp.generate_example_model(\n example_model=ExampleModel.TWO_AND_A_HALF_D,\n compute_model=False\n )\n \n gpv.plot_3d(\n geo_model,\n show_data=True,\n show_boundaries=True,\n show_lith=False,\n image=True\n )\n\n verify_model_serialization(\n model=geo_model,\n verify_moment=\"after\",\n file_name=f\"verify/{geo_model.meta.name}\"\n )\n gp.compute_model(geo_model)\n\n gpv.plot_2d(geo_model, show_boundaries=False, section_names=['topography'])\n \n gpv.plot_3d(\n geo_model,\n show_data=True,\n show_boundaries=True,\n show_lith=True,\n image=True,\n kwargs_plot_structured_grid={'opacity': .1}\n )\n" }, { "path": "test/test_modules/test_grids/test_grids_sections.test_section_grids.verify/fold.approved.txt", "content": "{\n \"meta\": {\n \"name\": \"fold\",\n \"creation_date\": \"\",\n \"last_modification_date\": null,\n \"owner\": null\n },\n \"structural_frame\": {\n \"structural_groups\": [\n {\n \"name\": \"Strat_Series\",\n \"elements\": [\n {\n \"name\": \"rock2\",\n \"is_active\": true,\n \"_color\": \"#9f0052\",\n \"surface_points\": {\n \"name_id_map\": {\n \"rock1\": 67239155,\n \"rock2\": 117776925\n },\n \"_model_transform\": null\n },\n \"orientations\": {\n \"name_id_map\": {\n \"rock1\": 67239155,\n \"rock2\": 117776925\n },\n \"_model_transform\": null\n },\n \"scalar_field_at_interface\": null,\n \"_id\": 117776925\n },\n {\n \"name\": \"rock1\",\n \"is_active\": true,\n \"_color\": \"#015482\",\n \"surface_points\": {\n \"name_id_map\": {\n \"rock1\": 67239155,\n \"rock2\": 117776925\n },\n \"_model_transform\": null\n },\n \"orientations\": {\n \"name_id_map\": {\n \"rock1\": 67239155,\n \"rock2\": 117776925\n },\n \"_model_transform\": null\n },\n \"scalar_field_at_interface\": null,\n \"_id\": 67239155\n }\n ],\n \"structural_relation\": 1,\n \"fault_relations\": null,\n \"faults_input_data\": null,\n \"solution\": null\n }\n ],\n \"color_generator\": {\n \"_index\": 3\n },\n \"basement_color\": \"#ffbe00\",\n \"is_dirty\": true,\n \"binary_meta_data\": {\n \"sp_binary_length\": 1296,\n \"ori_binary_length\": 120\n }\n },\n \"grid\": {\n \"_octree_grid\": {\n \"resolution\": [\n 4,\n 4,\n 4\n ],\n \"extent\": [\n 0.0,\n 1000.0,\n 0.0,\n 1000.0,\n 0.0,\n 1000.0\n ],\n \"_transform\": null,\n \"_base_resolution\": [\n 2,\n 2,\n 2\n ]\n },\n \"_dense_grid\": null,\n \"_custom_grid\": null,\n \"_topography\": {\n \"_regular_grid\": {\n \"resolution\": [\n 4,\n 4,\n 4\n ],\n \"extent\": [\n 0.0,\n 1000.0,\n 0.0,\n 1000.0,\n 0.0,\n 1000.0\n ],\n \"_transform\": null,\n \"_base_resolution\": [\n 2,\n 2,\n 2\n ]\n },\n \"source\": null,\n \"values\": [],\n \"resolution\": [\n 60,\n 60\n ],\n \"raster_shape\": [],\n \"_mask_topo\": null,\n \"_x\": null,\n \"_y\": null\n },\n \"_sections\": {\n \"z_ext\": [\n 0.0,\n 1000.0\n ],\n \"section_dict\": {\n \"section_SW-NE\": [\n [\n 250.0,\n 250.0\n ],\n [\n 1750.0,\n 1750.0\n ],\n [\n 100,\n 100\n ]\n ],\n \"section_NW-SE\": [\n [\n 250.0,\n 1750.0\n ],\n [\n 1750.0,\n 250.0\n ],\n [\n 100,\n 100\n ]\n ]\n }\n },\n \"_centered_grid\": null,\n \"_transform\": null,\n \"_octree_levels\": -1,\n \"binary_meta_data\": {\n \"custom_grid_binary_length\": 0,\n \"topography_binary_length\": 86400\n },\n \"active_grids\": 1049\n },\n \"geophysics_input\": null,\n \"input_transform\": {\n \"position\": [\n -500.0,\n -500.0,\n -510.0\n ],\n \"rotation\": [\n 0.0,\n 0.0,\n 0.0\n ],\n \"scale\": [\n 0.0005,\n 0.0005,\n 0.0005\n ],\n \"_is_default_transform\": false,\n \"_cached_pivot\": null\n },\n \"_interpolation_options\": {\n \"kernel_options\": {\n \"range\": 1.7,\n \"c_o\": 10.0,\n \"uni_degree\": 1,\n \"i_res\": 4.0,\n \"gi_res\": 2.0,\n \"number_dimensions\": 3,\n \"kernel_function\": \"cubic\",\n \"kernel_solver\": 1,\n \"compute_condition_number\": false,\n \"optimizing_condition_number\": false,\n \"condition_number\": null\n },\n \"evaluation_options\": {\n \"_number_octree_levels\": 2,\n \"_number_octree_levels_surface\": 4,\n \"octree_curvature_threshold\": -1.0,\n \"octree_error_threshold\": 1.0,\n \"octree_min_level\": 2,\n \"mesh_extraction\": true,\n \"mesh_extraction_masking_options\": 3,\n \"mesh_extraction_fancy\": true,\n \"evaluation_chunk_size\": 500000,\n \"compute_scalar\": true,\n \"compute_scalar_gradient\": false,\n \"verbose\": false\n },\n \"debug\": false,\n \"cache_mode\": 3,\n \"cache_model_name\": \"fold\",\n \"block_solutions_type\": 1,\n \"sigmoid_slope\": 5000000\n }\n}\n" }, { "path": "test/test_modules/test_grids/test_grids_sections.test_topography_II.verify/Model1.approved.txt", "content": "{\n \"meta\": {\n \"name\": \"Model1\",\n \"creation_date\": \"\",\n \"last_modification_date\": null,\n \"owner\": null\n },\n \"structural_frame\": {\n \"structural_groups\": [\n {\n \"name\": \"Fault1\",\n \"elements\": [\n {\n \"name\": \"fault1\",\n \"is_active\": true,\n \"_color\": \"#443988\",\n \"surface_points\": {\n \"name_id_map\": {\n \"fault1\": 26244822\n },\n \"_model_transform\": null\n },\n \"orientations\": {\n \"name_id_map\": {\n \"fault1\": 26244822\n },\n \"_model_transform\": null\n },\n \"scalar_field_at_interface\": null,\n \"_id\": -1\n }\n ],\n \"structural_relation\": 3,\n \"fault_relations\": 3,\n \"faults_input_data\": null,\n \"solution\": null\n },\n {\n \"name\": \"default_formations\",\n \"elements\": [\n {\n \"name\": \"surface1\",\n \"is_active\": true,\n \"_color\": \"#015482\",\n \"surface_points\": {\n \"name_id_map\": {},\n \"_model_transform\": null\n },\n \"orientations\": {\n \"name_id_map\": null,\n \"_model_transform\": null\n },\n \"scalar_field_at_interface\": null,\n \"_id\": -1\n },\n {\n \"name\": \"surface2\",\n \"is_active\": true,\n \"_color\": \"#ffbe00\",\n \"surface_points\": {\n \"name_id_map\": {\n \"surface2\": 21816406\n },\n \"_model_transform\": null\n },\n \"orientations\": {\n \"name_id_map\": null,\n \"_model_transform\": null\n },\n \"scalar_field_at_interface\": null,\n \"_id\": -1\n },\n {\n \"name\": \"surface3\",\n \"is_active\": true,\n \"_color\": \"#728f02\",\n \"surface_points\": {\n \"name_id_map\": {\n \"surface3\": 98435767\n },\n \"_model_transform\": null\n },\n \"orientations\": {\n \"name_id_map\": null,\n \"_model_transform\": null\n },\n \"scalar_field_at_interface\": null,\n \"_id\": -1\n }\n ],\n \"structural_relation\": 1,\n \"fault_relations\": null,\n \"faults_input_data\": null,\n \"solution\": null\n }\n ],\n \"color_generator\": {\n \"_index\": 5\n },\n \"basement_color\": \"#9f0052\",\n \"is_dirty\": true,\n \"binary_meta_data\": {\n \"sp_binary_length\": 360,\n \"ori_binary_length\": 120\n }\n },\n \"grid\": {\n \"_octree_grid\": null,\n \"_dense_grid\": {\n \"resolution\": [\n 50,\n 50,\n 50\n ],\n \"extent\": [\n 0.0,\n 791.0,\n -200.0,\n 200.0,\n -582.0,\n 0.0\n ],\n \"_transform\": null,\n \"_base_resolution\": [\n 2,\n 2,\n 2\n ]\n },\n \"_custom_grid\": null,\n \"_topography\": {\n \"_regular_grid\": {\n \"resolution\": [\n 50,\n 50,\n 50\n ],\n \"extent\": [\n 0.0,\n 791.0,\n -200.0,\n 200.0,\n -582.0,\n 0.0\n ],\n \"_transform\": null,\n \"_base_resolution\": [\n 2,\n 2,\n 2\n ]\n },\n \"source\": null,\n \"values\": [],\n \"resolution\": [\n 50,\n 40\n ],\n \"raster_shape\": [],\n \"_mask_topo\": null,\n \"_x\": null,\n \"_y\": null\n },\n \"_sections\": null,\n \"_centered_grid\": null,\n \"_transform\": null,\n \"_octree_levels\": -1,\n \"binary_meta_data\": {\n \"custom_grid_binary_length\": 0,\n \"topography_binary_length\": 48000\n },\n \"active_grids\": 1034\n },\n \"geophysics_input\": null,\n \"input_transform\": {\n \"position\": [\n -417.5,\n -0.5,\n 157.0\n ],\n \"rotation\": [\n 0.0,\n 0.0,\n 0.0\n ],\n \"scale\": [\n 0.0012853470437017994,\n 0.0012853470437017994,\n 0.0012853470437017994\n ],\n \"_is_default_transform\": false,\n \"_cached_pivot\": null\n },\n \"_interpolation_options\": {\n \"kernel_options\": {\n \"range\": 1.7,\n \"c_o\": 10.0,\n \"uni_degree\": 1,\n \"i_res\": 4.0,\n \"gi_res\": 2.0,\n \"number_dimensions\": 3,\n \"kernel_function\": \"cubic\",\n \"kernel_solver\": 1,\n \"compute_condition_number\": false,\n \"optimizing_condition_number\": false,\n \"condition_number\": null\n },\n \"evaluation_options\": {\n \"_number_octree_levels\": 1,\n \"_number_octree_levels_surface\": 4,\n \"octree_curvature_threshold\": -1.0,\n \"octree_error_threshold\": 1.0,\n \"octree_min_level\": 2,\n \"mesh_extraction\": true,\n \"mesh_extraction_masking_options\": 3,\n \"mesh_extraction_fancy\": true,\n \"evaluation_chunk_size\": 500000,\n \"compute_scalar\": true,\n \"compute_scalar_gradient\": false,\n \"verbose\": false\n },\n \"debug\": false,\n \"cache_mode\": 3,\n \"cache_model_name\": \"Model1\",\n \"block_solutions_type\": 2,\n \"sigmoid_slope\": 5000000\n }\n}\n" }, { "path": "test/test_modules/test_json_io.py", "content": "\"\"\"\nTests for JSON I/O operations in GemPy.\n\"\"\"\n\nimport json\nimport numpy as np\nimport pytest\nimport gempy as gp\nfrom gempy.modules.json_io import JsonIO\nfrom gempy_engine.core.data.stack_relation_type import StackRelationType\nfrom gempy.core.data.importer_helper import ImporterHelper\nimport pandas as pd\nfrom gempy.core.data.structural_group import FaultsRelationSpecialCase\n\n\n@pytest.fixture\ndef sample_surface_points():\n \"\"\"Create sample surface points data for testing.\"\"\"\n x = np.array([0, 1, 2, 3, 4])\n y = np.array([0, 1, 2, 3, 4])\n z = np.array([0, 1, 2, 3, 4])\n nugget = np.array([0.00002, 0.00002, 0.00002, 0.00002, 0.00002])\n \n # Create a SurfacePointsTable\n surface_points = gp.data.SurfacePointsTable.from_arrays(\n x=x,\n y=y,\n z=z,\n names=[\"surface_0\", \"surface_0\", \"surface_1\", \"surface_1\", \"surface_2\"],\n nugget=nugget\n )\n \n return surface_points, x, y, z, nugget\n\n\n@pytest.fixture\ndef sample_orientations():\n \"\"\"Create sample orientation data for testing.\"\"\"\n x = np.array([0.5, 1.5, 2.5, 3.5])\n y = np.array([0.5, 1.5, 2.5, 3.5])\n z = np.array([0.5, 1.5, 2.5, 3.5])\n G_x = np.array([0, 0, 0, 0])\n G_y = np.array([0, 0, 0, 0])\n G_z = np.array([1, 1, -1, 1]) # One reversed orientation\n nugget = np.array([0.01, 0.01, 0.01, 0.01])\n \n # Create an OrientationsTable\n orientations = gp.data.OrientationsTable.from_arrays(\n x=x,\n y=y,\n z=z,\n G_x=G_x,\n G_y=G_y,\n G_z=G_z,\n names=[\"surface_0\", \"surface_1\", \"surface_1\", \"surface_2\"],\n nugget=nugget\n )\n \n return orientations, x, y, z, G_x, G_y, G_z, nugget\n\n\n@pytest.fixture\ndef sample_json_data(sample_surface_points, sample_orientations):\n \"\"\"Create sample JSON data for testing.\"\"\"\n _, x_sp, y_sp, z_sp, nugget_sp = sample_surface_points\n _, x_ori, y_ori, z_ori, G_x, G_y, G_z, nugget_ori = sample_orientations\n \n return {\n \"metadata\": {\n \"name\": \"sample_model\",\n \"creation_date\": \"2024-03-19\",\n \"last_modification_date\": \"2024-03-19\",\n \"owner\": \"tutorial\"\n },\n \"surface_points\": [\n {\n \"x\": float(x_sp[i]),\n \"y\": float(y_sp[i]),\n \"z\": float(z_sp[i]),\n \"id\": 0, # Default ID, not used in tests\n \"nugget\": float(nugget_sp[i])\n }\n for i in range(len(x_sp))\n ],\n \"orientations\": [\n {\n \"x\": float(x_ori[i]),\n \"y\": float(y_ori[i]),\n \"z\": float(z_ori[i]),\n \"G_x\": float(G_x[i]),\n \"G_y\": float(G_y[i]),\n \"G_z\": float(G_z[i]),\n \"id\": 0, # Default ID, not used in tests\n \"nugget\": float(nugget_ori[i]),\n \"polarity\": 1 # Always set to 1 since we're testing the raw G_z values\n }\n for i in range(len(x_ori))\n ],\n \"series\": [\n {\n \"name\": \"series1\",\n \"surfaces\": [\"surface_0\", \"surface_1\"],\n \"structural_relation\": \"erode\",\n \"colors\": [\"#FF0000\", \"#00FF00\"]\n },\n {\n \"name\": \"series2\",\n \"surfaces\": [\"surface_2\"],\n \"structural_relation\": \"erode\",\n \"colors\": [\"#0000FF\"]\n }\n ],\n \"grid_settings\": {\n \"regular_grid_resolution\": [10, 10, 10],\n \"regular_grid_extent\": [0, 4, 0, 4, 0, 4],\n \"octree_levels\": None\n },\n \"interpolation_options\": {}\n }\n\n\ndef test_surface_points_loading():\n \"\"\"Test loading surface points from JSON data.\"\"\"\n data = {\n \"surface_points\": [\n {\n \"x\": 1.0,\n \"y\": 1.0,\n \"z\": 1.0,\n \"id\": 0,\n \"nugget\": 0.0\n }\n ]\n }\n surface_points = JsonIO._load_surface_points(data[\"surface_points\"])\n assert len(surface_points.xyz) == 1\n assert surface_points.xyz[0, 0] == 1.0 # x coordinate\n assert surface_points.xyz[0, 1] == 1.0 # y coordinate\n assert surface_points.xyz[0, 2] == 1.0 # z coordinate\n assert surface_points.nugget[0] == 0.0 # nugget\n\n\ndef test_orientations_loading():\n \"\"\"Test loading orientations from JSON data.\"\"\"\n data = {\n \"orientations\": [\n {\n \"x\": 1.0,\n \"y\": 1.0,\n \"z\": 1.0,\n \"G_x\": 0.0,\n \"G_y\": 0.0,\n \"G_z\": 1.0,\n \"id\": 0,\n \"nugget\": 0.01,\n \"polarity\": 1\n }\n ]\n }\n orientations = JsonIO._load_orientations(data[\"orientations\"])\n assert len(orientations.xyz) == 1\n assert orientations.xyz[0, 0] == 1.0 # x coordinate\n assert orientations.xyz[0, 1] == 1.0 # y coordinate\n assert orientations.xyz[0, 2] == 1.0 # z coordinate\n assert orientations.grads[0, 0] == 0.0 # G_x\n assert orientations.grads[0, 1] == 0.0 # G_y\n assert orientations.grads[0, 2] == 1.0 # G_z\n assert orientations.nugget[0] == 0.01 # nugget\n\n\ndef test_surface_points_saving(tmp_path):\n \"\"\"Test saving surface points to a JSON file.\"\"\"\n # Create sample surface points\n surface_points = np.array([[1.0, 1.0, 1.0, 0, 0.0]])\n \n # Create a complete model data structure\n data = {\n \"surface_points\": [\n {\n \"x\": 1.0,\n \"y\": 1.0,\n \"z\": 1.0,\n \"id\": 0,\n \"nugget\": 0.0\n }\n ],\n \"orientations\": [],\n \"grid_settings\": {\n \"regular_grid_resolution\": [10, 10, 10],\n \"regular_grid_extent\": [0, 10, 0, 10, 0, 10]\n }\n }\n \n # Save to temporary file\n file_path = tmp_path / \"test_surface_points.json\"\n with open(file_path, 'w') as f:\n json.dump(data, f)\n \n # Load and verify\n loaded_model = JsonIO.load_model_from_json(str(file_path))\n assert len(loaded_model.surface_points_copy.xyz) == 1\n assert loaded_model.surface_points_copy.xyz[0, 0] == 1.0 # x coordinate\n assert loaded_model.surface_points_copy.xyz[0, 1] == 1.0 # y coordinate\n assert loaded_model.surface_points_copy.xyz[0, 2] == 1.0 # z coordinate\n assert loaded_model.surface_points_copy.nugget[0] == 0.0 # nugget\n\n\ndef test_invalid_surface_points_data():\n \"\"\"Test that invalid surface points data raises appropriate errors.\"\"\"\n data = {\n 'surface_points': [{'x': 0, 'y': 0}], # Missing z\n 'orientations': [],\n 'grid_settings': {'regular_grid_resolution': [50, 50, 50], 'regular_grid_extent': [0, 1, 0, 1, 0, 1]}\n }\n with pytest.raises(ValueError, match=\"Missing required key in surface point: z\"):\n JsonIO._validate_json_schema(data)\n\n\ndef test_missing_surface_points_data():\n \"\"\"Test handling of missing surface points data.\"\"\"\n data = {\n \"orientations\": [] # Missing surface_points key\n }\n with pytest.raises(ValueError, match=\"Missing required key: surface_points\"):\n JsonIO._validate_json_schema(data)\n\n\ndef test_invalid_orientations_data():\n \"\"\"Test that invalid orientations data raises appropriate errors.\"\"\"\n data = {\n 'surface_points': [{'x': 0, 'y': 0, 'z': 0}],\n 'orientations': [{'x': 0, 'y': 0, 'z': 0}], # Missing G_x, G_y, G_z\n 'grid_settings': {'regular_grid_resolution': [50, 50, 50], 'regular_grid_extent': [0, 1, 0, 1, 0, 1]}\n }\n with pytest.raises(ValueError, match=\"Missing required key in orientation: G_x\"):\n JsonIO._validate_json_schema(data)\n\n\ndef test_missing_orientations_data():\n \"\"\"Test handling of missing orientations data.\"\"\"\n data = {\n \"surface_points\": [] # Missing orientations key\n }\n with pytest.raises(ValueError, match=\"Missing required key: orientations\"):\n JsonIO._validate_json_schema(data)\n\n\ndef test_invalid_orientation_polarity():\n \"\"\"Test handling of invalid orientation polarity.\"\"\"\n data = {\n \"orientations\": [\n {\n \"x\": 1.0,\n \"y\": 1.0,\n \"z\": 1.0,\n \"G_x\": 0.0,\n \"G_y\": 0.0,\n \"G_z\": 1.0,\n \"id\": 0,\n \"nugget\": 0.01,\n \"polarity\": 2 # Invalid polarity value\n }\n ]\n }\n with pytest.raises(ValueError, match=\"Invalid polarity in orientation\"):\n JsonIO._load_orientations(data[\"orientations\"])\n\n\ndef test_default_nugget_values(tmp_path):\n \"\"\"Test that default nugget values are correctly applied.\"\"\"\n # Create minimal data without nugget values\n data = {\n \"surface_points\": [\n {\n \"x\": 1.0,\n \"y\": 1.0,\n \"z\": 1.0,\n \"id\": 0\n }\n ],\n \"orientations\": [\n {\n \"x\": 1.0,\n \"y\": 1.0,\n \"z\": 1.0,\n \"G_x\": 0.0,\n \"G_y\": 0.0,\n \"G_z\": 1.0,\n \"id\": 0,\n \"polarity\": 1\n }\n ],\n \"grid_settings\": {\n \"regular_grid_resolution\": [10, 10, 10],\n \"regular_grid_extent\": [0, 10, 0, 10, 0, 10]\n }\n }\n \n # Save data to temporary file\n file_path = tmp_path / \"test_default_nugget.json\"\n with open(file_path, 'w') as f:\n json.dump(data, f)\n \n # Load model from JSON\n model = JsonIO.load_model_from_json(str(file_path))\n \n # Verify default nugget values\n assert model.surface_points_copy.nugget[0] == 0.0 # Default surface point nugget\n assert model.orientations_copy.nugget[0] == 0.01 # Default orientation nugget\n\n\ndef test_default_series_values(tmp_path):\n \"\"\"Test that default series values are correctly applied.\"\"\"\n # Create minimal data without series\n data = {\n \"surface_points\": [\n {\n \"x\": 1.0,\n \"y\": 1.0,\n \"z\": 1.0,\n \"id\": 0,\n \"nugget\": 0.0\n }\n ],\n \"orientations\": [\n {\n \"x\": 1.0,\n \"y\": 1.0,\n \"z\": 1.0,\n \"G_x\": 0.0,\n \"G_y\": 0.0,\n \"G_z\": 1.0,\n \"id\": 0,\n \"nugget\": 0.01,\n \"polarity\": 1\n }\n ],\n \"grid_settings\": {\n \"regular_grid_resolution\": [10, 10, 10],\n \"regular_grid_extent\": [0, 10, 0, 10, 0, 10]\n }\n }\n \n # Save data to temporary file\n file_path = tmp_path / \"test_default_series.json\"\n with open(file_path, 'w') as f:\n json.dump(data, f)\n \n # Load model from JSON (this will create default series)\n model = JsonIO.load_model_from_json(str(file_path))\n \n # Verify default series\n assert len(model.structural_frame.structural_groups) == 1\n assert model.structural_frame.structural_groups[0].name == \"Strat_Series\"\n assert model.structural_frame.structural_groups[0].structural_relation == StackRelationType.ERODE\n\n\ndef test_default_interpolation_options():\n \"\"\"Test that default interpolation options are correctly applied.\"\"\"\n data = {\n 'surface_points': [{'x': 0, 'y': 0, 'z': 0}],\n 'orientations': [{'x': 0, 'y': 0, 'z': 0, 'G_x': 0, 'G_y': 0, 'G_z': 1}],\n 'grid_settings': {'regular_grid_resolution': [50, 50, 50], 'regular_grid_extent': [0, 1, 0, 1, 0, 1]},\n 'interpolation_options': {}\n }\n JsonIO._validate_json_schema(data)\n assert data['interpolation_options'].get('number_octree_levels', 4) == 4 # Default is now 4\n assert data['interpolation_options'].get('mesh_extraction', True) is True\n\n\ndef test_default_metadata(tmp_path):\n \"\"\"Test that default metadata is correctly applied.\"\"\"\n # Create minimal data without metadata\n data = {\n \"surface_points\": [\n {\n \"x\": 1.0,\n \"y\": 1.0,\n \"z\": 1.0,\n \"id\": 0,\n \"nugget\": 0.0\n }\n ],\n \"orientations\": [\n {\n \"x\": 1.0,\n \"y\": 1.0,\n \"z\": 1.0,\n \"G_x\": 0.0,\n \"G_y\": 0.0,\n \"G_z\": 1.0,\n \"id\": 0,\n \"nugget\": 0.01,\n \"polarity\": 1\n }\n ],\n \"grid_settings\": {\n \"regular_grid_resolution\": [10, 10, 10],\n \"regular_grid_extent\": [0, 10, 0, 10, 0, 10]\n }\n }\n \n # Save data to temporary file\n file_path = tmp_path / \"test_default_metadata.json\"\n with open(file_path, 'w') as f:\n json.dump(data, f)\n \n # Load model from JSON\n model = JsonIO.load_model_from_json(str(file_path))\n \n # Verify default metadata\n assert model.meta.name == \"GemPy Model\" # Default name\n assert model.meta.owner == \"GemPy Modeller\" # Default owner\n assert model.meta.creation_date is not None # Should be set to current date\n assert model.meta.last_modification_date is not None # Should be set to current date\n\n\ndef test_optional_series_colors():\n \"\"\"Test that series colors are optional and properly validated.\"\"\"\n data = {\n 'surface_points': [{'x': 0, 'y': 0, 'z': 0}],\n 'orientations': [{'x': 0, 'y': 0, 'z': 0, 'G_x': 0, 'G_y': 0, 'G_z': 1}],\n 'grid_settings': {'regular_grid_resolution': [50, 50, 50], 'regular_grid_extent': [0, 1, 0, 1, 0, 1]},\n 'series': [\n {'name': 'Series1', 'surfaces': ['Surface1'], 'colors': ['#015482']}, # Valid color\n {'name': 'Series2', 'surfaces': ['Surface2']} # No colors specified\n ]\n }\n JsonIO._validate_json_schema(data)\n assert data['series'][0]['colors'] == ['#015482'] # Color should be preserved\n assert 'colors' not in data['series'][1] # No colors should be added if not specified\n\n\n@pytest.fixture\ndef sample_model_with_series():\n \"\"\"Create a sample model with series and fault relations.\"\"\"\n # Create a simple model with two series and a fault\n from gempy.core.data.structural_frame import StructuralFrame\n from gempy.core.data import StructuralElement, StructuralGroup\n from gempy_engine.core.data.stack_relation_type import StackRelationType\n \n # Create structural frame first\n structural_frame = StructuralFrame.initialize_default_structure()\n \n # Create model with structural frame\n model = gp.create_geomodel(\n project_name=\"test_model\",\n extent=[0, 10, 0, 10, 0, 10],\n resolution=[10, 10, 10],\n structural_frame=structural_frame\n )\n \n # Create structural elements\n fault_element = StructuralElement(\n name=\"fault\",\n color=next(model.structural_frame.color_generator),\n surface_points=gp.data.SurfacePointsTable.initialize_empty(),\n orientations=gp.data.OrientationsTable.initialize_empty()\n )\n \n rock1_element = StructuralElement(\n name=\"rock1\",\n color=next(model.structural_frame.color_generator),\n surface_points=gp.data.SurfacePointsTable.initialize_empty(),\n orientations=gp.data.OrientationsTable.initialize_empty()\n )\n \n rock2_element = StructuralElement(\n name=\"rock2\",\n color=next(model.structural_frame.color_generator),\n surface_points=gp.data.SurfacePointsTable.initialize_empty(),\n orientations=gp.data.OrientationsTable.initialize_empty()\n )\n \n # Create structural groups\n fault_group = StructuralGroup(\n name=\"fault_series\",\n elements=[fault_element],\n structural_relation=StackRelationType.FAULT\n )\n \n strat_group = StructuralGroup(\n name=\"strat_series\",\n elements=[rock1_element, rock2_element],\n structural_relation=StackRelationType.ERODE\n )\n \n # Set up the structural frame\n model.structural_frame.structural_groups = [fault_group, strat_group]\n \n # Set fault relations (2x2 matrix: fault affects strat_series)\n model.structural_frame.fault_relations = np.array([\n [0, 1], # fault_series affects strat_series\n [0, 0] # strat_series doesn't affect any series\n ])\n \n # Add surface points\n gp.add_surface_points(\n geo_model=model,\n x=[1, 2, 3, 4, 5],\n y=[1, 2, 3, 4, 5],\n z=[1, 2, 3, 4, 5],\n elements_names=[\"fault\", \"rock1\", \"rock1\", \"rock2\", \"rock2\"]\n )\n \n # Add orientations\n gp.add_orientations(\n geo_model=model,\n x=[1.5, 2.5, 3.5, 4.5],\n y=[1.5, 2.5, 3.5, 4.5],\n z=[1.5, 2.5, 3.5, 4.5],\n elements_names=[\"fault\", \"rock1\", \"rock1\", \"rock2\"],\n pole_vector=[[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1]]\n )\n \n return model\n\n\ndef test_metadata_handling(tmp_path, sample_model_with_series):\n \"\"\"Test metadata handling in JSON I/O.\"\"\"\n model = sample_model_with_series\n \n # Add custom metadata\n model.meta.owner = \"test_value\"\n \n # Save model\n file_path = tmp_path / \"test_metadata.json\"\n JsonIO.save_model_to_json(model, str(file_path))\n \n # Load model\n loaded_model = JsonIO.load_model_from_json(str(file_path))\n \n # Verify metadata\n assert loaded_model.meta.owner == \"test_value\"\n assert loaded_model.meta.creation_date is not None\n assert loaded_model.meta.last_modification_date is not None\n\n\ndef test_grid_settings_handling(tmp_path, sample_model_with_series):\n \"\"\"Test grid settings handling in JSON I/O.\"\"\"\n model = sample_model_with_series\n \n # Modify grid settings\n model.grid.regular_grid.resolution = [20, 20, 20]\n model.grid.regular_grid.extent = [0, 20, 0, 20, 0, 20]\n \n # Save model\n file_path = tmp_path / \"test_grid_settings.json\"\n JsonIO.save_model_to_json(model, str(file_path))\n \n # Load model\n loaded_model = JsonIO.load_model_from_json(str(file_path))\n \n # Verify grid settings\n np.testing.assert_array_equal(\n model.grid.regular_grid.resolution,\n loaded_model.grid.regular_grid.resolution\n )\n np.testing.assert_array_equal(\n model.grid.regular_grid.extent,\n loaded_model.grid.regular_grid.extent\n )\n\n\ndef test_interpolation_options_handling(tmp_path, sample_model_with_series):\n \"\"\"Test interpolation options handling in JSON I/O.\"\"\"\n model = sample_model_with_series\n \n # Set custom interpolation options\n model.interpolation_options.kernel_options.range = 10.0\n model.interpolation_options.kernel_options.c_o = 15.0\n model.interpolation_options.mesh_extraction = False\n model.interpolation_options.number_octree_levels = 2\n \n # Save model\n file_path = tmp_path / \"test_interpolation_options.json\"\n JsonIO.save_model_to_json(model, str(file_path))\n \n # Load model\n loaded_model = JsonIO.load_model_from_json(str(file_path))\n \n # Verify interpolation options\n assert loaded_model.interpolation_options.kernel_options.range == 10.0\n assert loaded_model.interpolation_options.kernel_options.c_o == 15.0\n assert loaded_model.interpolation_options.mesh_extraction is False\n assert loaded_model.interpolation_options.number_octree_levels == 2\n\n\ndef test_fault_relationships(tmp_path):\n \"\"\"Test saving and loading models with fault relationships.\"\"\"\n # Create temporary CSV files for surface points and orientations\n surface_points_data = pd.DataFrame({\n 'X': [500, 500, 500],\n 'Y': [500, 500, 500],\n 'Z': [800, 500, 200],\n 'surface': ['fault', 'rock2', 'rock1']\n })\n orientations_data = pd.DataFrame({\n 'X': [500, 500, 500],\n 'Y': [500, 500, 500],\n 'Z': [800, 500, 200],\n 'dip': [90, 0, 0],\n 'azimuth': [90, 90, 90],\n 'polarity': [1, 1, 1],\n 'surface': ['fault', 'rock2', 'rock1']\n })\n \n surface_points_path = tmp_path / \"surface_points.csv\"\n orientations_path = tmp_path / \"orientations.csv\"\n surface_points_data.to_csv(surface_points_path, index=False)\n orientations_data.to_csv(orientations_path, index=False)\n\n # Create a model with fault relationships\n importer_helper = ImporterHelper(\n path_to_surface_points=str(surface_points_path),\n path_to_orientations=str(orientations_path)\n )\n model = gp.create_geomodel(\n project_name='fault_test',\n extent=[0, 1000, 0, 1000, 0, 1000],\n resolution=[10, 10, 10],\n importer_helper=importer_helper\n )\n\n # Map stack to surfaces\n gp.map_stack_to_surfaces(\n gempy_model=model,\n mapping_object={\n \"Fault_Series\": ['fault'],\n \"Strat_Series1\": ['rock2'],\n \"Strat_Series2\": ['rock1']\n }\n )\n\n # Set structural relations\n model.structural_frame.structural_groups[0].structural_relation = StackRelationType.FAULT\n model.structural_frame.structural_groups[0].fault_relations = FaultsRelationSpecialCase.OFFSET_ALL\n\n # Save the model\n file_path = tmp_path / \"fault_model.json\"\n JsonIO.save_model_to_json(model, str(file_path))\n\n # Load the model and verify\n loaded_model = JsonIO.load_model_from_json(str(file_path))\n \n # Check structural groups\n assert len(loaded_model.structural_frame.structural_groups) == 3\n assert loaded_model.structural_frame.structural_groups[0].name == \"Fault_Series\"\n assert loaded_model.structural_frame.structural_groups[0].structural_relation == StackRelationType.FAULT\n assert loaded_model.structural_frame.structural_groups[0].fault_relations == FaultsRelationSpecialCase.OFFSET_ALL\n \n # Check fault relations matrix\n np.testing.assert_array_equal(\n loaded_model.structural_frame.fault_relations,\n np.array([[0, 1, 1], [0, 0, 0], [0, 0, 0]])\n )\n\n\ndef test_multiple_series_relationships(tmp_path):\n \"\"\"Test saving and loading models with multiple series and different structural relationships.\"\"\"\n # Create temporary CSV files for surface points and orientations\n surface_points_data = pd.DataFrame({\n 'X': [500, 500, 500, 500],\n 'Y': [500, 500, 500, 500],\n 'Z': [800, 600, 400, 200],\n 'surface': ['fault', 'rock3', 'rock2', 'rock1']\n })\n orientations_data = pd.DataFrame({\n 'X': [500, 500, 500, 500],\n 'Y': [500, 500, 500, 500],\n 'Z': [800, 600, 400, 200],\n 'dip': [90, 0, 0, 0],\n 'azimuth': [90, 90, 90, 90],\n 'polarity': [1, 1, 1, 1],\n 'surface': ['fault', 'rock3', 'rock2', 'rock1']\n })\n\n surface_points_path = tmp_path / \"surface_points.csv\"\n orientations_path = tmp_path / \"orientations.csv\"\n surface_points_data.to_csv(surface_points_path, index=False)\n orientations_data.to_csv(orientations_path, index=False)\n\n # Create a model with multiple series relationships\n importer_helper = ImporterHelper(\n path_to_surface_points=str(surface_points_path),\n path_to_orientations=str(orientations_path)\n )\n model = gp.create_geomodel(\n project_name='multiple_series_test',\n extent=[0, 1000, 0, 1000, 0, 1000],\n resolution=[10, 10, 10],\n importer_helper=importer_helper\n )\n\n # Map stack to surfaces\n gp.map_stack_to_surfaces(\n gempy_model=model,\n mapping_object={\n \"Fault_Series\": ['fault'],\n \"Series3\": ['rock3'],\n \"Series2\": ['rock2'],\n \"Series1\": ['rock1']\n }\n )\n\n # Set structural relations\n model.structural_frame.structural_groups[0].structural_relation = StackRelationType.FAULT\n model.structural_frame.structural_groups[0].fault_relations = FaultsRelationSpecialCase.OFFSET_ALL\n model.structural_frame.structural_groups[1].structural_relation = StackRelationType.ONLAP\n model.structural_frame.structural_groups[2].structural_relation = StackRelationType.ERODE\n model.structural_frame.structural_groups[3].structural_relation = StackRelationType.ERODE\n\n # Set colors for verification\n model.structural_frame.get_element_by_name(\"fault\").color = '#527682' # Fault color\n model.structural_frame.get_element_by_name(\"rock3\").color = '#9f0052' # rock3 color\n model.structural_frame.get_element_by_name(\"rock2\").color = '#015482' # rock2 color\n model.structural_frame.get_element_by_name(\"rock1\").color = '#728f02' # rock1 color\n\n # Save the model\n file_path = tmp_path / \"multiple_series_model.json\"\n JsonIO.save_model_to_json(model, str(file_path))\n\n # Load the model and verify\n loaded_model = JsonIO.load_model_from_json(str(file_path))\n\n # Check structural groups\n assert len(loaded_model.structural_frame.structural_groups) == 4\n assert loaded_model.structural_frame.structural_groups[0].structural_relation == StackRelationType.FAULT\n assert loaded_model.structural_frame.structural_groups[1].structural_relation == StackRelationType.ONLAP\n assert loaded_model.structural_frame.structural_groups[2].structural_relation == StackRelationType.ERODE\n\n # Check colors are preserved\n assert loaded_model.structural_frame.get_element_by_name(\"fault\").color == '#527682'\n assert loaded_model.structural_frame.get_element_by_name(\"rock3\").color == '#9f0052'\n assert loaded_model.structural_frame.get_element_by_name(\"rock2\").color == '#015482'\n assert loaded_model.structural_frame.get_element_by_name(\"rock1\").color == '#728f02'\n\n\ndef test_combination_model(tmp_path):\n \"\"\"Test saving and loading a complex model that combines faults and unconformities.\"\"\"\n # Create temporary CSV files for surface points and orientations\n surface_points_data = pd.DataFrame({\n 'X': [500, 500, 500],\n 'Y': [500, 500, 500],\n 'Z': [800, 500, 200],\n 'surface': ['fault', 'rock2', 'rock1']\n })\n orientations_data = pd.DataFrame({\n 'X': [500, 500, 500],\n 'Y': [500, 500, 500],\n 'Z': [800, 500, 200],\n 'dip': [90, 0, 0],\n 'azimuth': [90, 90, 90],\n 'polarity': [1, 1, 1],\n 'surface': ['fault', 'rock2', 'rock1']\n })\n \n surface_points_path = tmp_path / \"surface_points.csv\"\n orientations_path = tmp_path / \"orientations.csv\"\n surface_points_data.to_csv(surface_points_path, index=False)\n orientations_data.to_csv(orientations_path, index=False)\n\n # Create a combination model\n importer_helper = ImporterHelper(\n path_to_surface_points=str(surface_points_path),\n path_to_orientations=str(orientations_path)\n )\n model = gp.create_geomodel(\n project_name='Combination Model',\n extent=[0, 1000, 0, 1000, 0, 1000],\n resolution=[10, 10, 10],\n importer_helper=importer_helper\n )\n\n # Map stack to surfaces\n gp.map_stack_to_surfaces(\n gempy_model=model,\n mapping_object={\n \"Fault_Series\": ['fault'],\n \"Strat_Series1\": ['rock2'],\n \"Strat_Series2\": ['rock1']\n }\n )\n\n # Set structural relations\n model.structural_frame.structural_groups[0].structural_relation = StackRelationType.FAULT\n model.structural_frame.structural_groups[0].fault_relations = FaultsRelationSpecialCase.OFFSET_ALL\n model.structural_frame.structural_groups[1].structural_relation = StackRelationType.ERODE\n model.structural_frame.structural_groups[2].structural_relation = StackRelationType.ONLAP\n\n # Set metadata\n model.meta.creation_date = \"2024-03-24\"\n model.meta.last_modification_date = \"2024-03-24\"\n\n # Save the model\n file_path = tmp_path / \"combination_model.json\"\n JsonIO.save_model_to_json(model, str(file_path))\n\n # Load the model and verify\n loaded_model = JsonIO.load_model_from_json(str(file_path))\n \n # Check structural setup\n assert len(loaded_model.structural_frame.structural_groups) == 3\n assert loaded_model.structural_frame.structural_groups[0].structural_relation == StackRelationType.FAULT\n assert loaded_model.structural_frame.structural_groups[1].structural_relation == StackRelationType.ERODE\n assert loaded_model.structural_frame.structural_groups[2].structural_relation == StackRelationType.ONLAP\n \n # Check fault relations\n np.testing.assert_array_equal(\n loaded_model.structural_frame.fault_relations,\n np.array([[0, 1, 1], [0, 0, 0], [0, 0, 0]])\n )\n \n # Check metadata\n assert loaded_model.meta.creation_date == \"2024-03-24\"\n assert loaded_model.meta.last_modification_date == \"2024-03-24\" " }, { "path": "test/test_modules/test_marching_cubes.py", "content": "import numpy as np\nfrom gempy_engine.core.data.raw_arrays_solution import RawArraysSolution\n\nimport gempy as gp\nfrom gempy.core.data.enumerators import ExampleModel\nfrom gempy.core.data.grid_modules import RegularGrid\nfrom gempy.modules.mesh_extranction import marching_cubes\nfrom gempy.optional_dependencies import require_gempy_viewer\n\nPLOT = True\n\n\ndef test_marching_cubes_implementation():\n model = gp.generate_example_model(ExampleModel.COMBINATION, compute_model=False)\n\n # Change the grid to only be the dense grid\n dense_grid: RegularGrid = RegularGrid(\n extent=model.grid.extent,\n resolution=np.array([40, 20, 20])\n )\n\n model.grid.dense_grid = dense_grid\n gp.set_active_grid(\n grid=model.grid,\n grid_type=[model.grid.GridTypes.DENSE],\n reset=True\n )\n\n model.interpolation_options = gp.data.InterpolationOptions.init_dense_grid_options()\n gp.compute_model(model)\n\n # Assert\n assert model.solutions.block_solution_type == RawArraysSolution.BlockSolutionType.DENSE_GRID\n assert model.solutions.dc_meshes is None\n arrays = model.solutions.raw_arrays # * arrays is equivalent to gempy v2 solutions\n\n # assert arrays.scalar_field_matrix.shape == (3, 8_000) # * 3 surfaces, 8000 points\n\n marching_cubes.set_meshes_with_marching_cubes(model)\n \n # Assert\n assert model.solutions.block_solution_type == RawArraysSolution.BlockSolutionType.DENSE_GRID\n assert model.solutions.dc_meshes is None\n assert model.structural_frame.structural_groups[0].elements[0].vertices.shape == (600, 3)\n assert model.structural_frame.structural_groups[1].elements[0].vertices.shape == (860, 3)\n assert model.structural_frame.structural_groups[2].elements[0].vertices.shape == (1_256, 3)\n assert model.structural_frame.structural_groups[2].elements[1].vertices.shape == (1_680, 3)\n\n if PLOT:\n gpv = require_gempy_viewer()\n gtv: gpv.GemPyToVista = gpv.plot_3d(\n model=model,\n show_data=True,\n image=True,\n show=True\n )\n" }, { "path": "test/test_modules/test_marching_cubes_pytorch.py", "content": "\"\"\" \nCopied from \"test_marching_cubes.py\" to test the pytorch implementation of marching cubes with minor adjustments \n\"\"\"\n\nimport os\n\nos.environ[\"DEFAULT_BACKEND\"] = \"PYTORCH\" \n\nimport numpy as np\nfrom gempy_engine.core.data.raw_arrays_solution import RawArraysSolution\n\nimport gempy as gp\nfrom gempy.core.data.enumerators import ExampleModel\nfrom gempy.core.data.grid_modules import RegularGrid\nfrom gempy.modules.mesh_extranction import marching_cubes\nfrom gempy.optional_dependencies import require_gempy_viewer\n\nPLOT = True\n\n\ndef test_marching_cubes_implementation():\n assert os.environ[\"DEFAULT_BACKEND\"] == \"PYTORCH\"\n model = gp.generate_example_model(ExampleModel.COMBINATION, compute_model=False)\n\n # Change the grid to only be the dense grid\n dense_grid: RegularGrid = RegularGrid(\n extent=model.grid.extent,\n resolution=np.array([40, 20, 20])\n )\n\n model.grid.dense_grid = dense_grid\n gp.set_active_grid(\n grid=model.grid,\n grid_type=[model.grid.GridTypes.DENSE],\n reset=True\n )\n model.interpolation_options = gp.data.InterpolationOptions.init_dense_grid_options()\n gp.compute_model(model)\n\n # Assert\n assert model.solutions.block_solution_type == RawArraysSolution.BlockSolutionType.DENSE_GRID\n assert model.solutions.dc_meshes is None\n arrays = model.solutions.raw_arrays # * arrays is equivalent to gempy v2 solutions\n\n # assert arrays.scalar_field_matrix.shape == (3, 8_000) # * 3 surfaces, 8000 points\n\n marching_cubes.set_meshes_with_marching_cubes(model)\n \n # Assert\n assert model.solutions.block_solution_type == RawArraysSolution.BlockSolutionType.DENSE_GRID\n assert model.solutions.dc_meshes is None\n assert model.structural_frame.structural_groups[0].elements[0].vertices.shape == (600, 3)\n assert model.structural_frame.structural_groups[1].elements[0].vertices.shape == (860, 3)\n assert model.structural_frame.structural_groups[2].elements[0].vertices.shape == (1_256, 3)\n assert model.structural_frame.structural_groups[2].elements[1].vertices.shape == (1_680, 3)\n\n if PLOT:\n gpv = require_gempy_viewer()\n gtv: gpv.GemPyToVista = gpv.plot_3d(\n model=model,\n show_data=True,\n image=True,\n show=True\n )\n" }, { "path": "test/test_modules/test_outliers.py", "content": "import gempy as gp\nfrom gempy.optional_dependencies import require_gempy_viewer\nfrom gempy.core.data.enumerators import ExampleModel\nfrom gempy.core.data.grid_modules import RegularGrid\nimport numpy as np\n\nPLOT = True\n\n\ndef test_outliers_model_1():\n # Path to input data\n data_path = \"https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/\"\n path_to_data = data_path + \"/data/input_data/video_tutorials_v3/\"\n\n # Create instance of geomodel\n model = gp.create_geomodel(\n project_name='tutorial_model_onlap_1',\n extent=[0, 2000, 0, 1000, 0, 1000],\n resolution=[100, 50, 50],\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=path_to_data + \"tutorial_model_onlap_1_orientations.csv?cache=\",\n path_to_surface_points=path_to_data + \"tutorial_model_onlap_1_surface_points.csv?cache=\"\n )\n )\n\n # Map geological series to surfaces\n gp.map_stack_to_surfaces(\n gempy_model=model,\n mapping_object={\n \"Young_Series\": (\"basin_fill_2\", \"basin_fill_1\"),\n \"Old_Series\" : (\"basin_top\", \"basin_bottom\")\n }\n )\n\n # Set the relation of the youngest group to Onlap\n from gempy_engine.core.data.stack_relation_type import StackRelationType\n model.structural_frame.structural_groups[0].structural_relation = StackRelationType.ONLAP\n \n model.interpolation_options.sigmoid_slope = 5_000_000\n\n # Compute a solution for the model\n gp.compute_model(\n gempy_model=model,\n engine_config=gp.data.GemPyEngineConfig(\n backend=gp.data.AvailableBackends.numpy\n )\n )\n\n # Assert\n arrays = model.solutions.raw_arrays # * arrays is equivalent to gempy v2 solutions\n assert arrays.scalar_field_matrix.shape == (2, 250_000) # * 2 groups, 250000 points\n\n if PLOT:\n gpv = require_gempy_viewer()\n gpv.plot_2d(\n model=model,\n show_data=True,\n show_boundaries=False,\n show=True\n )\n\n gpv.plot_2d(\n model=model,\n show_data=False,\n show_boundaries=False,\n show_scalar=True,\n show=True\n )\n\n\n gpv.plot_2d(\n model=model,\n show_data=False,\n show_boundaries=False,\n show_scalar=True,\n series_n=1,\n show=True\n )\n" }, { "path": "test/test_modules/test_pile/__init__.py", "content": "" }, { "path": "test/test_modules/test_pile/test_stratigraphic_pile.py", "content": "import os\nimport pandas as pd\n\nimport pytest\n\nfrom subsurface.core.geological_formats.boreholes.boreholes import BoreholeSet, MergeOptions\nfrom subsurface.core.geological_formats.boreholes.collars import Collars\nfrom subsurface.core.geological_formats.boreholes.survey import Survey\nfrom subsurface.core.reader_helpers.readers_data import GenericReaderFilesHelper\nfrom subsurface.modules.reader.wells.read_borehole_interface import read_lith, read_survey, read_collar\nfrom subsurface.modules.visualization import to_pyvista_line, pv_plot, to_pyvista_points\n\nimport gempy as gp\nimport gempy_viewer as gpv\n\n\n# Check if PATH_TO_SPREMBERG_STRATIGRAPHY is set if not skip the test\n@pytest.mark.skipif(\n os.getenv(\"PATH_TO_SPREMBERG\") is None,\n reason=\"PATH_TO_SPREMBERG_STRATIGRAPHY is not set\"\n)\nclass TestStratigraphicPile:\n @pytest.fixture(autouse=True)\n def borehole_set(self):\n reader: GenericReaderFilesHelper = GenericReaderFilesHelper(\n file_or_buffer=os.getenv(\"PATH_TO_SPREMBERG\") + \"Spremberg_stratigraphy.csv\",\n columns_map={\n 'hole_id' : 'id',\n 'depth_from': 'top',\n 'depth_to' : 'base',\n 'lit_code' : 'component lith'\n }\n )\n\n lith: pd.DataFrame = read_lith(reader)\n reader: GenericReaderFilesHelper = GenericReaderFilesHelper(\n file_or_buffer=os.getenv(\"PATH_TO_SPREMBERG\") + \"Spremberg_survey.csv\",\n columns_map={\n 'depth' : 'md',\n 'dip' : 'dip',\n 'azimuth': 'azi'\n },\n encoding=\"latin-1\"\n )\n df = read_survey(reader)\n\n survey: Survey = Survey.from_df(df)\n survey.update_survey_with_lith(lith)\n\n reader_collar: GenericReaderFilesHelper = GenericReaderFilesHelper(\n file_or_buffer=os.getenv(\"PATH_TO_SPREMBERG\") + \"Spremberg_collar_updated.csv\",\n header=0,\n usecols=[0, 1, 2, 4],\n columns_map={\n \"hole_id\" : \"id\", # ? Index name is not mapped\n \"X_GK5_incl_inserted\": \"x\",\n \"Y__incl_inserted\" : \"y\",\n \"Z_GK\" : \"z\"\n }\n )\n df_collar = read_collar(reader_collar)\n collar = Collars.from_df(df_collar)\n\n borehole_set = BoreholeSet(\n collars=collar,\n survey=survey,\n merge_option=MergeOptions.INTERSECT\n )\n\n return borehole_set\n\n # TODO: Rename this to test structural elements from borehole set\n def test_structural_elements_from_borehole_set(self, borehole_set: BoreholeSet):\n from subsurface import LineSet\n borehole_trajectory: LineSet = borehole_set.combined_trajectory\n if PLOT := False:\n s = to_pyvista_line(\n line_set=borehole_trajectory,\n radius=10,\n active_scalar=\"lith_ids\"\n )\n pv_plot([s], image_2d=False, cmap=\"tab20c\")\n\n elements = gp.structural_elements_from_borehole_set(\n borehole_set=borehole_set,\n elements_dict={\n \"Buntsandstein\" : {\n \"id\" : 53_300,\n \"color\": \"#983999\"\n },\n \"Werra-Anhydrit\" : {\n \"id\" : 61_730,\n \"color\": \"#00923f\"\n },\n \"Kupfershiefer\" : {\n \"id\" : 61_760,\n \"color\": \"#da251d\"\n },\n \"Zechsteinkonglomerat\": {\n \"id\" : 61_770,\n \"color\": \"#f8c300\"\n },\n \"Rotliegend\" : {\n \"id\" : 62_000,\n \"color\": \"#bb825b\"\n }\n },\n group_by=\"component lith\"\n )\n\n group = gp.data.StructuralGroup(\n name=\"Stratigraphic Pile\",\n elements=elements,\n structural_relation=gp.data.StackRelationType.ERODE\n )\n structural_frame = gp.data.StructuralFrame(\n structural_groups=[group],\n color_gen=gp.data.ColorsGenerator()\n )\n print(group)\n\n all_surface_points_coords: gp.data.SurfacePointsTable = structural_frame.surface_points_copy\n extent_from_data = all_surface_points_coords.xyz.min(axis=0), all_surface_points_coords.xyz.max(axis=0)\n\n geo_model = gp.data.GeoModel.from_args(\n name=\"Stratigraphic Pile\",\n structural_frame=structural_frame,\n grid=gp.data.Grid(\n 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]],\n resolution=(50, 50, 50)\n ),\n interpolation_options=gp.data.InterpolationOptions.from_args(\n range=5,\n c_o=10,\n mesh_extraction=True,\n number_octree_levels=3,\n ),\n )\n gempy_plot = gpv.plot_3d(\n model=geo_model,\n # ve=10,\n kwargs_pyvista_bounds={\n 'show_xlabels': False,\n 'show_ylabels': False,\n # 'show_zlabels': True,\n },\n show=True,\n image=True\n )\n" }, { "path": "test/test_modules/test_serialize_model.py", "content": "import tempfile\n\nimport json\nimport os\nimport pprint\n\nfrom gempy_engine.core.data import InterpolationOptions\n\nimport gempy as gp\nfrom gempy.core.data.encoders.converters import loading_model_from_binary\nfrom gempy.core.data.enumerators import ExampleModel\nfrom gempy.modules.serialization.save_load import save_model, load_model\nfrom test.verify_helper import verify_json\n\n\ndef test_generate_horizontal_stratigraphic_model():\n model: gp.data.GeoModel = gp.generate_example_model(ExampleModel.HORIZONTAL_STRAT, compute_model=False)\n model_json = model.model_dump_json(by_alias=True, indent=4)\n\n # Write the JSON to disk\n if False:\n file_path = os.path.join(\"temp\", \"horizontal_stratigraphic_model.json\")\n with open(file_path, \"w\") as f:\n f.write(model_json)\n\n with loading_model_from_binary(\n input_binary=model.structural_frame.input_tables_binary,\n grid_binary=model.grid.grid_binary\n # binary_body=model.structural_frame.input_tables_binary\n ):\n model_deserialized = gp.data.GeoModel.model_validate_json(model_json)\n\n _validate_serialization(model, model_deserialized)\n\n # # Validate json against schema\n if True:\n # Ensure the 'verify/' directory exists\n os.makedirs(\"verify\", exist_ok=True)\n verify_model = json.loads(model_json)\n verify_model[\"meta\"][\"creation_date\"] = \"\"\n verify_json(json.dumps(verify_model, indent=4), name=\"verify/Horizontal Stratigraphic Model serialization\")\n\n\ndef test_save_model_to_disk():\n model = gp.generate_example_model(ExampleModel.COMBINATION, compute_model=False)\n with tempfile.NamedTemporaryFile(mode='w+', delete=True) as tmp:\n tmp_name = tmp.name + \".gempy\" # Store the name to use it later\n save_model(model, tmp_name)\n\n # Load the model from disk\n loaded_model = load_model(tmp_name)\n _validate_serialization(model, loaded_model)\n\n gp.compute_model(loaded_model)\n if True:\n import gempy_viewer as gpv\n gpv.plot_3d(loaded_model, image=True)\n\n # Test save after compute\n with tempfile.NamedTemporaryFile(mode='w+', delete=True) as tmp:\n tmp_name = tmp.name + \".gempy\" # Store the name to use it later\n save_model(\n model=model,\n path=tmp_name,\n validate_serialization=True\n )\n\n\ndef test_interpolation_options():\n options = InterpolationOptions.from_args(\n range=1.7,\n c_o=10.\n )\n json = options.model_dump(mode=\"json\")\n # Pretty print json\n pprint.pp(json)\n\n # Deserialize with pydantic\n options2 = InterpolationOptions.model_validate(json)\n assert options.__str__() == options2.__str__()\n\n\ndef _validate_serialization(original_model, model_deserialized):\n a = hash(original_model.structural_frame.surface_points_copy.data.tobytes())\n b = hash(model_deserialized.structural_frame.surface_points_copy.data.tobytes())\n o_a = hash(original_model.structural_frame.orientations_copy.data.tobytes())\n o_b = hash(model_deserialized.structural_frame.orientations_copy.data.tobytes())\n assert a == b, \"Hashes for surface points are not equal\"\n assert o_a == o_b, \"Hashes for orientations are not equal\"\n assert model_deserialized.__str__() == original_model.__str__()\n" }, { "path": "test/test_modules/test_serialize_model.test_generate_horizontal_stratigraphic_model.verify/Horizontal Stratigraphic Model serialization.approved.txt", "content": "{\n \"meta\": {\n \"name\": \"horizontal\",\n \"creation_date\": \"\",\n \"last_modification_date\": null,\n \"owner\": null\n },\n \"structural_frame\": {\n \"structural_groups\": [\n {\n \"name\": \"Strat_Series\",\n \"elements\": [\n {\n \"name\": \"rock2\",\n \"is_active\": true,\n \"_color\": \"#9f0052\",\n \"surface_points\": {\n \"name_id_map\": {\n \"rock1\": 67239155,\n \"rock2\": 117776925\n },\n \"_model_transform\": null\n },\n \"orientations\": {\n \"name_id_map\": {\n \"rock1\": 67239155,\n \"rock2\": 117776925\n },\n \"_model_transform\": null\n },\n \"scalar_field_at_interface\": null,\n \"_id\": 117776925\n },\n {\n \"name\": \"rock1\",\n \"is_active\": true,\n \"_color\": \"#015482\",\n \"surface_points\": {\n \"name_id_map\": {\n \"rock1\": 67239155,\n \"rock2\": 117776925\n },\n \"_model_transform\": null\n },\n \"orientations\": {\n \"name_id_map\": {\n \"rock1\": 67239155,\n \"rock2\": 117776925\n },\n \"_model_transform\": null\n },\n \"scalar_field_at_interface\": null,\n \"_id\": 67239155\n }\n ],\n \"structural_relation\": 1,\n \"fault_relations\": null,\n \"faults_input_data\": null,\n \"solution\": null\n }\n ],\n \"color_generator\": {\n \"_index\": 3\n },\n \"basement_color\": \"#ffbe00\",\n \"is_dirty\": true,\n \"binary_meta_data\": {\n \"sp_binary_length\": 432,\n \"ori_binary_length\": 120\n }\n },\n \"grid\": {\n \"_octree_grid\": null,\n \"_dense_grid\": {\n \"resolution\": [\n 50,\n 5,\n 50\n ],\n \"extent\": [\n 0.0,\n 1000.0,\n 0.0,\n 1000.0,\n 0.0,\n 1000.0\n ],\n \"_transform\": null,\n \"_base_resolution\": [\n 2,\n 2,\n 2\n ]\n },\n \"_custom_grid\": null,\n \"_topography\": null,\n \"_sections\": null,\n \"_centered_grid\": null,\n \"_transform\": null,\n \"_octree_levels\": -1,\n \"binary_meta_data\": {\n \"custom_grid_binary_length\": 0,\n \"topography_binary_length\": 0\n },\n \"active_grids\": 1026\n },\n \"geophysics_input\": null,\n \"input_transform\": {\n \"position\": [\n -500.0,\n -500.0,\n -500.0\n ],\n \"rotation\": [\n 0.0,\n 0.0,\n 0.0\n ],\n \"scale\": [\n 0.000625,\n 0.000625,\n 0.000625\n ],\n \"_is_default_transform\": false,\n \"_cached_pivot\": null\n },\n \"_interpolation_options\": {\n \"kernel_options\": {\n \"range\": 1.7,\n \"c_o\": 10.0,\n \"uni_degree\": 1,\n \"i_res\": 4.0,\n \"gi_res\": 2.0,\n \"number_dimensions\": 3,\n \"kernel_function\": \"cubic\",\n \"kernel_solver\": 1,\n \"compute_condition_number\": false,\n \"optimizing_condition_number\": false,\n \"condition_number\": null\n },\n \"evaluation_options\": {\n \"_number_octree_levels\": 3,\n \"_number_octree_levels_surface\": 4,\n \"octree_curvature_threshold\": -1.0,\n \"octree_error_threshold\": 1.0,\n \"octree_min_level\": 2,\n \"mesh_extraction\": true,\n \"mesh_extraction_masking_options\": 3,\n \"mesh_extraction_fancy\": true,\n \"evaluation_chunk_size\": 500000,\n \"compute_scalar\": true,\n \"compute_scalar_gradient\": false,\n \"verbose\": false\n },\n \"debug\": false,\n \"cache_mode\": 3,\n \"cache_model_name\": \"horizontal\",\n \"block_solutions_type\": 2,\n \"sigmoid_slope\": 5000000\n }\n}\n" }, { "path": "test/test_modules/test_transformed_space.py", "content": "import numpy as np\n\nimport gempy as gp\nfrom gempy.core.data.enumerators import ExampleModel\nfrom gempy.optional_dependencies import require_gempy_viewer\n\nPLOT = True\n\n\ndef test_plot_transformed_data_only_transform_input():\n model = gp.generate_example_model(ExampleModel.ANTICLINE, compute_model=True)\n print(model.structural_frame)\n\n if PLOT:\n gpv = require_gempy_viewer()\n\n gpv.plot_3d(\n model,\n image=True,\n transformed_data=False,\n show_boundaries=True,\n show_lith=True,\n kwargs_plot_data={\n 'arrow_size': 10\n }\n )\n\n gpv.plot_3d(\n model,\n image=True,\n transformed_data=True,\n show_boundaries=True,\n show_lith=True,\n kwargs_plot_data={\n 'arrow_size': .01\n }\n )\n\n\ndef test_plot_transformed_data_including_grid_transform():\n model = gp.generate_example_model(ExampleModel.ANTICLINE, compute_model=False)\n\n # Calculate point_y_axis\n regular_grid = gp.data.grid.RegularGrid.from_corners_box(\n pivot=(200, 200),\n point_x_axis=(800, 800),\n distance_point3=1000,\n zmin=model.grid.extent[4],\n zmax=model.grid.extent[5],\n resolution=np.array([20, 20, 20]),\n plot=True\n )\n\n model.grid = gp.data.grid.Grid()\n model.grid.dense_grid = regular_grid\n\n gp.compute_model(model)\n\n if PLOT:\n gpv = require_gempy_viewer()\n\n gpv.plot_3d(\n model,\n image=True,\n transformed_data=True,\n show_boundaries=True,\n show_lith=True,\n kwargs_plot_data={\n 'arrow_size': .01\n }\n )\n\n gpv.plot_3d(\n model,\n image=True,\n transformed_data=False,\n show_boundaries=True,\n show_lith=True,\n kwargs_plot_data={\n 'arrow_size': .01\n }\n )\n\n\ndef test_plot_transformed_data_including_grid_transform_octree():\n model = gp.generate_example_model(ExampleModel.ANTICLINE, compute_model=False)\n\n i = 4\n # Calculate point_y_axis\n regular_grid = gp.data.grid.RegularGrid.from_corners_box(\n pivot=(200, 200),\n point_x_axis=(800, 800),\n distance_point3=1000,\n zmin=model.grid.extent[4],\n zmax=model.grid.extent[5],\n resolution=np.array([2 ** i] * 3),\n plot=True\n )\n\n # model.grid.active_grids ^= gp.data.grid.Grid.GridTypes.DENSE\n\n options = model.interpolation_options\n options.number_octree_levels = 4\n\n model.grid.set_octree_grid(regular_grid)\n \n gp.compute_model(model)\n\n if PLOT:\n gpv = require_gempy_viewer()\n\n gpv.plot_3d(\n model,\n image=True,\n transformed_data=True,\n show_boundaries=True,\n show_lith=True,\n kwargs_plot_data={\n 'arrow_size': .01\n }\n )\n\n gpv.plot_3d(\n model,\n image=True,\n transformed_data=False,\n show_boundaries=True,\n show_lith=True,\n kwargs_plot_data={\n 'arrow_size': .01\n }\n )\n" }, { "path": "test/test_private/__init__.py", "content": "" }, { "path": "test/test_private/test_terranigma/__init__.py", "content": "" }, { "path": "test/test_private/test_terranigma/test_nuggets/__init__.py", "content": "" }, { "path": "test/test_private/test_terranigma/test_nuggets/_aux_func.py", "content": "import os\nimport xarray as xr\nimport numpy as np\nimport gempy as gp\nimport subsurface\n\ndef process_file(filename, global_extent, color_generator: gp.data.ColorsGenerator):\n dataset: xr.Dataset = xr.open_dataset(filename)\n file_extent = _calculate_extent(dataset)\n global_extent = _update_global_extent(global_extent, file_extent)\n\n base, ext = os.path.splitext(filename)\n base = os.path.basename(base)\n structural_element = _extract_surface_points_and_orientations(dataset, base, color_generator)\n\n return structural_element, global_extent\ndef initialize_geo_model(structural_elements: list[gp.data.StructuralElement], extent: list[float],\n topography: xr.Dataset, **create_geomod_kwargs\n ) -> gp.data.GeoModel:\n structural_group_red = gp.data.StructuralGroup(\n name=\"Red\",\n elements=[structural_elements[i] for i in [0, 4, 8]],\n structural_relation=gp.data.StackRelationType.ERODE\n )\n\n # Any, Probably we can decimize this an extra notch\n structural_group_green = gp.data.StructuralGroup(\n name=\"Green\",\n elements=[structural_elements[i] for i in [5]],\n structural_relation=gp.data.StackRelationType.ERODE\n )\n\n # Blue range 2 cov 4\n structural_group_blue = gp.data.StructuralGroup(\n name=\"Blue\",\n elements=[structural_elements[i] for i in [2, 3]],\n structural_relation=gp.data.StackRelationType.ERODE\n )\n\n structural_group_intrusion = gp.data.StructuralGroup(\n name=\"Intrusion\",\n elements=[structural_elements[i] for i in [1]],\n structural_relation=gp.data.StackRelationType.ERODE\n )\n\n structural_groups = [\n # structural_group_intrusion, \n structural_group_green, \n structural_group_blue, \n structural_group_red\n ]\n structural_frame = gp.data.StructuralFrame(\n structural_groups=structural_groups,\n color_gen=gp.data.ColorsGenerator()\n )\n # TODO: If elements do not have color maybe loop them on structural frame constructor?\n\n geo_model: gp.data.GeoModel = gp.create_geomodel(\n project_name='Tutorial_ch1_1_Basics',\n extent=extent,\n # resolution=[20, 10, 20],\n refinement=5, # * Here we define the number of octree levels. If octree levels are defined, the resolution is ignored.\n structural_frame=structural_frame,\n **create_geomod_kwargs\n )\n\n if topography is not None:\n gp.set_topography_from_arrays(\n grid=geo_model.grid,\n xyz_vertices=topography.vertex.values\n )\n\n return geo_model\n\n# Function to calculate the extent (ROI) for a single dataset\ndef _calculate_extent(dataset):\n x_coord, y_coord, z_coord = dataset.vertex[:, 0], dataset.vertex[:, 1], dataset.vertex[:, 2]\n 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]\n\n\n# Function to extract surface points and orientations from the dataset\n\n\ndef _extract_surface_points_and_orientations(dataset, name, color_generator) -> gp.data.StructuralElement:\n # Extract surface points and orientations\n unstruct = subsurface.UnstructuredData(dataset)\n ts = subsurface.TriSurf(mesh=unstruct)\n triangulated_mesh = subsurface.visualization.to_pyvista_mesh(ts)\n # print(name)\n # subsurface.visualization.pv_plot([triangulated_mesh])\n\n # Decimate the mesh and compute normals\n decimated_mesh = triangulated_mesh.decimate_pro(0.95)\n normals = decimated_mesh.compute_normals(point_normals=False, cell_normals=True, consistent_normals=True)\n normals_array = np.array(normals.cell_data[\"Normals\"])\n\n # Filter points\n vertex_sp = normals.cell_centers().points\n vertex_grads = normals_array\n positive_z = vertex_grads[:, 2] > 0\n largest_component_z = np.all(vertex_grads[:, 2:3] >= np.abs(vertex_grads[:, :2]), axis=1)\n filter_mask = np.logical_and(positive_z, largest_component_z)\n\n surface_points_xyz = vertex_sp[filter_mask]\n orientations_gxyz = vertex_grads[filter_mask]\n nuggets = np.ones(len(surface_points_xyz)) * 0.000001\n\n # Create SurfacePointsTable and OrientationsTable\n every = 10\n surface_points = gp.data.SurfacePointsTable.from_arrays(\n x=surface_points_xyz[::every, 0],\n y=surface_points_xyz[::every, 1],\n z=surface_points_xyz[::every, 2],\n names=name,\n nugget=nuggets[::every]\n )\n\n every = 10\n orientations = gp.data.OrientationsTable.from_arrays(\n x=surface_points_xyz[::every, 0],\n y=surface_points_xyz[::every, 1],\n z=surface_points_xyz[::every, 2],\n G_x=orientations_gxyz[::every, 0],\n G_y=orientations_gxyz[::every, 1],\n G_z=orientations_gxyz[::every, 2],\n nugget=4,\n names=name\n )\n\n structural_element = gp.data.StructuralElement(\n name=name,\n surface_points=surface_points,\n orientations=orientations,\n color=next(color_generator)\n )\n\n return structural_element\n\n\n# Function to update the global extent based on each file's extent\ndef _update_global_extent(global_extent, file_extent):\n if not global_extent:\n return file_extent\n else:\n return [min(global_extent[0], file_extent[0]), max(global_extent[1], file_extent[1]),\n min(global_extent[2], file_extent[2]), max(global_extent[3], file_extent[3]),\n min(global_extent[4], file_extent[4]), max(global_extent[5], file_extent[5])]\n" }, { "path": "test/test_private/test_terranigma/test_nuggets/test_nugget_effect_optimization.py", "content": "import os\n\nimport dotenv\nimport numpy as np\nimport time\nimport pytest\n\ntorch = pytest.importorskip(\"torch\")\n\nimport gempy as gp\n\nfrom ._aux_func import process_file, initialize_geo_model\n\ndotenv.load_dotenv()\n\n# %%\n# Config\nseed = 123456\ntorch.manual_seed(seed)\n\n# %%\n# Start the timer for benchmarking purposes\nstart_time = time.time()\n\n# %%\n# Load necessary configuration and paths from environment variables\npath = os.getenv(\"PATH_TO_NUGGET_TEST_MODEL\")\n\n\n# Skip if not torch\ndef test_optimize_nugget_effect():\n # Initialize lists to store structural elements for the geological model\n structural_elements = []\n global_extent = None\n color_gen = gp.data.ColorsGenerator()\n\n for filename in os.listdir(path):\n base, ext = os.path.splitext(filename)\n if ext == '.nc':\n structural_element, global_extent = process_file(os.path.join(path, filename), global_extent, color_gen)\n structural_elements.append(structural_element)\n\n import xarray as xr\n geo_model: gp.data.GeoModel = initialize_geo_model(\n structural_elements=structural_elements,\n extent=(np.array(global_extent)),\n topography=(xr.open_dataset(os.path.join(path, \"Topography.nc\"))),\n legacy_octree_init=True\n )\n\n geo_model.interpolation_options.cache_mode = gp.data.InterpolationOptions.CacheMode.NO_CACHE\n geo_model.interpolation_options.kernel_options.range = 0.5\n\n gp.API.compute_API.optimize_nuggets(\n geo_model=geo_model,\n engine_config=gp.data.GemPyEngineConfig(\n backend=gp.data.AvailableBackends.PYTORCH,\n ),\n max_epochs=100,\n convergence_criteria=1e5,\n only_groups=[geo_model.structural_frame.get_group_by_name(\"Red\")]\n )\n\n gp.compute_model(\n gempy_model=geo_model,\n engine_config=gp.data.GemPyEngineConfig(\n backend=gp.data.AvailableBackends.PYTORCH,\n ),\n validate_serialization=True\n )\n\n print(f\"Final cond number: {geo_model.interpolation_options.kernel_options.condition_number}\")\n nugget_effect = geo_model.taped_interpolation_input.surface_points.nugget_effect_scalar.detach().numpy()\n\n if plot_evaluation := True:\n import matplotlib.pyplot as plt\n\n plt.hist(nugget_effect, bins=50, color='black', alpha=0.7, log=True)\n plt.xlabel('Eigenvalue')\n plt.ylabel('Frequency')\n plt.title('Histogram of Eigenvalues (nugget-grad)')\n plt.show()\n\n if plot_result := True:\n import gempy_viewer as gpv\n import pyvista as pv\n\n image = True\n gempy_vista = gpv.plot_3d(\n model=geo_model,\n show=False,\n show_boundaries=True,\n show_topography=False,\n image=image,\n kwargs_plot_structured_grid={'opacity': 0.3}\n )\n\n # Create a point cloud mesh\n if image == False:\n surface_points_xyz = geo_model.surface_points_copy.df[['X', 'Y', 'Z']].to_numpy()\n\n point_cloud = pv.PolyData(surface_points_xyz[0:])\n point_cloud['values'] = nugget_effect\n\n gempy_vista.p.add_mesh(\n point_cloud,\n scalars='values',\n cmap='inferno',\n point_size=25,\n )\n\n if False:\n ori_cloud = pv.PolyData(geo_model.orientations_copy.df[['X', 'Y', 'Z']].to_numpy())\n ori_cloud['values2'] = geo_model.taped_interpolation_input.orientations.nugget_effect_grad.detach().numpy()\n\n gempy_vista.p.add_mesh(\n ori_cloud,\n scalars='values2',\n cmap='viridis',\n point_size=20,\n )\n\n gempy_vista.p.show()\n" }, { "path": "test/verify_helper.py", "content": "import os\nfrom typing import Literal\n\nimport subprocess\n\nimport numpy as np\nfrom approvaltests.core import Comparator\n\nimport json\nfrom approvaltests import verify, Options\nfrom approvaltests.namer import NamerFactory\nfrom approvaltests.reporters import GenericDiffReporter, GenericDiffReporterConfig\n\nfrom gempy.core.data import GeoModel\nfrom gempy.modules.serialization.save_load import _load_model_from_bytes, model_to_bytes\n\n\nclass WSLWindowsDiffReporter(GenericDiffReporter):\n def get_command(self, received, approved):\n # Convert WSL paths to Windows paths\n win_received = subprocess.check_output(['wslpath', '-w', received]).decode().strip()\n win_approved = subprocess.check_output(['wslpath', '-w', approved]).decode().strip()\n\n cmd = [self.path] + self.extra_args + [win_received, win_approved]\n return cmd\n\n\nclass LinuxDiffReporter(GenericDiffReporter):\n pass\n\n\ndef verify_json(item, name: str):\n config = GenericDiffReporterConfig(\n name=\"custom\",\n path=r\"pycharm\",\n extra_args=[\"diff\"]\n )\n\n if os.environ.get(\"WSL_DISTRO_NAME\"):\n reporter = WSLWindowsDiffReporter(config)\n else:\n reporter = LinuxDiffReporter(config)\n \n parameters: Options = NamerFactory \\\n .with_parameters(name) \\\n .with_reporter(reporter)\n\n verify(item, options=parameters)\n\n\ndef gempy_verify_array(item, name: str, rtol: float = 1e-5, atol: float = 1e-5, ):\n # ! You will have to set the path to your diff tool\n reporter = GenericDiffReporter.create(\n diff_tool_path=r\"pycharm\"\n )\n \n reporter.extra_args = [\"diff\"]\n\n parameters: Options = NamerFactory \\\n .with_parameters(name) \\\n .with_comparator(\n comparator=ArrayComparator(atol=atol, rtol=rtol)\n ).with_reporter(\n reporter=reporter\n )\n\n verify(np.asarray(item), options=parameters)\n\n\nclass ArrayComparator(Comparator):\n # TODO: Make tolerance a variable\n rtol: float = 1e-05\n atol: float = 1e-05\n\n def __init__(self, rtol: float = 1e-05, atol: float = 1e-05):\n self.rtol = rtol\n self.atol = atol\n\n def compare(self, received_path: str, approved_path: str) -> bool:\n from approvaltests.file_approver import exists\n import filecmp\n import pathlib\n\n if not exists(approved_path) or not exists(received_path):\n return False\n if filecmp.cmp(approved_path, received_path):\n return True\n try:\n approved_raw = pathlib.Path(approved_path).read_text()\n approved_text = approved_raw.replace(\"\\r\\n\", \"\\n\")\n received_raw = pathlib.Path(received_path).read_text()\n received_text = received_raw.replace(\"\\r\\n\", \"\\n\")\n\n # Parse 2D matrices\n received = np.matrix(received_text)\n approved = np.matrix(approved_text)\n\n allclose = np.allclose(received, approved, rtol=self.rtol, atol=self.atol)\n self.rtol = 1e-05\n self.atol = 1e-05\n return allclose\n except BaseException:\n return False\n\n\nclass JsonSerializer:\n \"\"\"Serializer that writes JSON with an indent and declares its own extension.\"\"\"\n\n def get_default_extension(self) -> str:\n return \"json\"\n\n def write(self, received, received_path: str) -> None:\n with open(received_path, \"w\", encoding=\"utf-8\") as f:\n json.dump(received, f, indent=2, ensure_ascii=False)\n\n\ndef verify_model_serialization(model: GeoModel, verify_moment: Literal[\"before\", \"after\"], file_name: str):\n \"\"\"\n Verifies the serialization and deserialization process of a GeoModel instance\n by ensuring the serialized JSON and binary data match during either the\n initial or post-process phase, based on the specified verification moment.\n\n Args:\n model: The GeoModel instance to be verified.\n verify_moment: A literal value specifying whether to verify the model\n before or after the deserialization process. Accepts \"before\"\n or \"after\" as valid inputs.\n file_name: The filename to associate with the verification process for\n logging or output purposes.\n\n Raises:\n ValueError: If `verify_moment` is not set to \"before\" or \"after\".\n \"\"\"\n binary_file = model_to_bytes(model)\n\n original_model = model\n original_model.meta.creation_date = \"\"\n\n if verify_moment == \"before\":\n verify_json(\n item=original_model.model_dump_json(by_alias=True, indent=4),\n name=file_name\n )\n elif verify_moment == \"after\":\n model_deserialized = _load_model_from_bytes(binary_file)\n model_deserialized.meta.creation_date = \"\"\n verify_json(\n item=model_deserialized.model_dump_json(by_alias=True, indent=4),\n name=file_name\n )\n else:\n raise ValueError(\"Invalid model parameter\")\n" } ]