Repository: lettucecfd/lettuce Branch: master Commit: 121359d2b7ee Files: 256 Total size: 3.9 MB Directory structure: gitextract_tttiteof/ ├── .codeclimate.yml ├── .github/ │ ├── ISSUE_TEMPLATE/ │ │ ├── BUG_REPORT.yml │ │ └── FEATURE_REQUEST.yml │ ├── pull_request_template.md │ └── workflows/ │ └── CI.yml ├── .gitignore ├── .readthedocs.yaml ├── AUTHORS.rst ├── LICENSE ├── README.rst ├── docs/ │ ├── README.md │ ├── authors.rst │ ├── conf.py │ ├── contributing.rst │ ├── history.rst │ ├── index.rst │ ├── installation.rst │ ├── make.bat │ ├── modules.rst │ ├── readme.rst │ └── usage.rst ├── examples/ │ ├── 00_simplest_TGV.py │ ├── 01a_first_example_TGV.ipynb │ ├── 01b_first_example_obstacle.py │ ├── 02_converging_obstacle_flow.py │ ├── 03_outputs_TGV.py │ ├── __init__.py │ ├── advanced_flows/ │ │ ├── FailingTGVandObstacle.py │ │ ├── LidDrivenCavity.ipynb │ │ ├── MixingLayer.ipynb │ │ ├── PartiallySaturatedObstacle.py │ │ └── PorousMedium.ipynb │ ├── advanced_projects/ │ │ ├── __init__.py │ │ └── efficient_bounce_back_obstacle/ │ │ ├── 00_run_parametrized_project.ipynb │ │ ├── 01_script_cylinder_simulation.py │ │ ├── __init__.py │ │ ├── auxiliary_code/ │ │ │ ├── __init__.py │ │ │ ├── data_processing_and_plotting.py │ │ │ └── helperCode.py │ │ ├── boundary/ │ │ │ ├── __init__.py │ │ │ ├── fullway_bounce_back_boundary.py │ │ │ ├── halfway_bounce_back_boundary.py │ │ │ ├── linear_interpolated_bounce_back_boundary.py │ │ │ └── solid_boundary_data.py │ │ ├── flow/ │ │ │ ├── __init__.py │ │ │ └── obstacle_cylinder.py │ │ ├── profile_reference_data/ │ │ │ ├── Fig09_ux_profile_pos1_DI2018.csv │ │ │ ├── Fig09_ux_profile_pos1_KM2000.csv │ │ │ ├── Fig09_ux_profile_pos1_LS1993.csv │ │ │ ├── Fig09_ux_profile_pos1_WR2008.csv │ │ │ ├── Fig09_ux_profile_pos2_DI2018.csv │ │ │ ├── Fig09_ux_profile_pos2_KM2000.csv │ │ │ ├── Fig09_ux_profile_pos2_LS1993.csv │ │ │ ├── Fig09_ux_profile_pos2_WR2008.csv │ │ │ ├── Fig09_ux_profile_pos3_DI2018.csv │ │ │ ├── Fig09_ux_profile_pos3_KM2000.csv │ │ │ ├── Fig09_ux_profile_pos3_LS1993.csv │ │ │ ├── Fig09_ux_profile_pos3_WR2008.csv │ │ │ ├── Fig10_uy_profile_pos1_DI2018.csv │ │ │ ├── Fig10_uy_profile_pos1_KM2000.csv │ │ │ ├── Fig10_uy_profile_pos1_LS1993.csv │ │ │ ├── Fig10_uy_profile_pos1_WR2008.csv │ │ │ ├── Fig10_uy_profile_pos2_DI2018.csv │ │ │ ├── Fig10_uy_profile_pos2_KM2000.csv │ │ │ ├── Fig10_uy_profile_pos2_LS1993.csv │ │ │ ├── Fig10_uy_profile_pos2_WR2008.csv │ │ │ ├── Fig10_uy_profile_pos3_DI2018.csv │ │ │ ├── Fig10_uy_profile_pos3_KM2000.csv │ │ │ ├── Fig10_uy_profile_pos3_LS1993.csv │ │ │ ├── Fig10_uy_profile_pos3_WR2008.csv │ │ │ ├── Fig11_uxux_profile_pos1_DI2018.csv │ │ │ ├── Fig11_uxux_profile_pos1_KM2000.csv │ │ │ ├── Fig11_uxux_profile_pos1_R2016.csv │ │ │ ├── Fig11_uxux_profile_pos2_BM1994.csv │ │ │ ├── Fig11_uxux_profile_pos2_DI2018.csv │ │ │ ├── Fig11_uxux_profile_pos2_KM2000.csv │ │ │ ├── Fig11_uxux_profile_pos2_LS1993.csv │ │ │ ├── Fig11_uxux_profile_pos2_R2016.csv │ │ │ ├── Fig11_uxux_profile_pos3_DI2018.csv │ │ │ ├── Fig11_uxux_profile_pos3_KM2000.csv │ │ │ ├── Fig11_uxux_profile_pos3_R2016.csv │ │ │ ├── Fig12_uyuy_profile_pos1_DI2018.csv │ │ │ ├── Fig12_uyuy_profile_pos1_R2016.csv │ │ │ ├── Fig12_uyuy_profile_pos2_BM1994.csv │ │ │ ├── Fig12_uyuy_profile_pos2_DI2018.csv │ │ │ ├── Fig12_uyuy_profile_pos2_LS1993.csv │ │ │ ├── Fig12_uyuy_profile_pos2_R2016.csv │ │ │ ├── Fig12_uyuy_profile_pos3_DI2018.csv │ │ │ ├── Fig12_uyuy_profile_pos3_R2016.csv │ │ │ ├── Fig13_uxuy_profile_pos1_BM1994.csv │ │ │ ├── Fig13_uxuy_profile_pos1_DI2018.csv │ │ │ ├── Fig13_uxuy_profile_pos1_R2016.csv │ │ │ ├── Fig13_uxuy_profile_pos2_BM1994.csv │ │ │ ├── Fig13_uxuy_profile_pos2_DI2018.csv │ │ │ ├── Fig13_uxuy_profile_pos2_LS1993.csv │ │ │ ├── Fig13_uxuy_profile_pos2_R2016.csv │ │ │ ├── Fig13_uxuy_profile_pos3_BM1994.csv │ │ │ ├── Fig13_uxuy_profile_pos3_DI2018.csv │ │ │ └── Fig13_uxuy_profile_pos3_R2016.csv │ │ ├── reporter/ │ │ │ ├── __init__.py │ │ │ ├── observables_force_coefficients.py │ │ │ ├── reporter_ProfileReporter.py │ │ │ └── reporter_advanced_vtk_reporter.py │ │ └── simulation/ │ │ ├── __init__.py │ │ └── ebb_simulation.py │ ├── development/ │ │ ├── .gitignore │ │ ├── example_progress_reporter_based_on_simplest_TGV.py │ │ └── manually_generate_cuda_native.py │ └── simple_flows/ │ ├── Couette.ipynb │ ├── DecayingTurbulence.ipynb │ ├── LambOseenVortex.py │ ├── Obstacle.ipynb │ └── Poiseuille.ipynb ├── lettuce/ │ ├── __init__.py │ ├── _context.py │ ├── _flow.py │ ├── _simulation.py │ ├── _stencil.py │ ├── _unit.py │ ├── _version.py │ ├── base.py │ ├── cli.py │ ├── cuda_native/ │ │ ├── __init__.py │ │ ├── _default_code_gen.py │ │ ├── _generator.py │ │ ├── _registry.py │ │ ├── _template.py │ │ ├── _transformer.py │ │ ├── _util.py │ │ └── ext/ │ │ ├── __init__.py │ │ ├── _boundary/ │ │ │ ├── __init__.py │ │ │ ├── bounce_back_boundary.py │ │ │ ├── equilibrium_pu.py │ │ │ └── no_boundary.py │ │ ├── _collision/ │ │ │ ├── __init__.py │ │ │ ├── bgk_collision.py │ │ │ └── no_collision.py │ │ ├── _equilibrium/ │ │ │ ├── __init__.py │ │ │ └── quadratic_equilibrium.py │ │ └── _force/ │ │ ├── __init__.py │ │ └── _force.py │ ├── ext/ │ │ ├── __init__.py │ │ ├── _boundary/ │ │ │ ├── __init__.py │ │ │ ├── anti_bounce_back_outlet.py │ │ │ ├── bounce_back_boundary.py │ │ │ ├── equilibrium_boundary_pu.py │ │ │ ├── equilibrium_outlet_p.py │ │ │ └── partially_saturated_boundary.py │ │ ├── _collision/ │ │ │ ├── __init__.py │ │ │ ├── bgk_collision.py │ │ │ ├── kbc_collision.py │ │ │ ├── mrt_collision.py │ │ │ ├── no_collision.py │ │ │ ├── regularized_collision.py │ │ │ ├── smagorinsky_collision.py │ │ │ └── trt_collision.py │ │ ├── _equilibrium/ │ │ │ ├── __init__.py │ │ │ ├── incompressible_quadratic_equilibrium.py │ │ │ ├── quadratic_equilibrium.py │ │ │ └── quadratic_equilibrium_less_memory.py │ │ ├── _flows/ │ │ │ ├── __init__.py │ │ │ ├── _ext_flow.py │ │ │ ├── _flow_by_name.py │ │ │ ├── couette.py │ │ │ ├── decayingturbulence.py │ │ │ ├── doublyshear.py │ │ │ ├── lamboseenvortex.py │ │ │ ├── liddrivencavity.py │ │ │ ├── obstacle.py │ │ │ ├── poiseuille.py │ │ │ └── taylorgreen.py │ │ ├── _force/ │ │ │ ├── __init__.py │ │ │ ├── _force.py │ │ │ ├── guo.py │ │ │ └── shan_chen.py │ │ ├── _reporter/ │ │ │ ├── __init__.py │ │ │ ├── error_reporter.py │ │ │ ├── failure_reporter.py │ │ │ ├── observable_reporter.py │ │ │ ├── progress_reporter.py │ │ │ ├── vtk_reporter.py │ │ │ └── write_image.py │ │ └── _stencil/ │ │ ├── __init__.py │ │ ├── d1q3.py │ │ ├── d2q9.py │ │ ├── d3q15.py │ │ ├── d3q19.py │ │ └── d3q27.py │ └── util/ │ ├── __init__.py │ ├── datautils.py │ ├── moments.py │ └── utility.py ├── native_cuda_synopsis.md ├── pyproject.toml ├── requirements.txt ├── tests/ │ ├── __init__.py │ ├── boundary/ │ │ ├── test_antibounceback_outlet_bc.py │ │ ├── test_bc_masks.py │ │ ├── test_bounceback_bc.py │ │ ├── test_equilibrium_bc_outlet_p.py │ │ ├── test_equilibrium_bc_pu.py │ │ ├── test_equilibrium_pressure_outlet.py │ │ └── test_partiallysaturated_bc.py │ ├── collision/ │ │ ├── test_collision_conserves_mass.py │ │ ├── test_collision_conserves_momentum.py │ │ ├── test_collision_fixpoint_2x.py │ │ ├── test_collision_fixpoint_2x_MRT.py │ │ ├── test_collision_optimizes_pseudo_entropy.py │ │ ├── test_collision_relaxes_shear_moments.py │ │ └── test_force.py │ ├── conftest.py │ ├── flow/ │ │ ├── test_divergence.py │ │ ├── test_einsum.py │ │ ├── test_flow.py │ │ ├── test_initialize_fneq.py │ │ ├── test_initialize_pressure.py │ │ ├── test_obstacle.py │ │ └── test_pressure_poisson.py │ ├── moments/ │ │ ├── test_conserved_moments_d2q9.py │ │ ├── test_getitem.py │ │ ├── test_inverse_transform.py │ │ ├── test_moment_equilibrium_D3Q27Hermite.py │ │ ├── test_moment_equilibrium_dellar.py │ │ ├── test_moment_equilibrium_lallemand.py │ │ ├── test_moments_density.py │ │ └── test_orthogonality.py │ ├── native/ │ │ ├── __init__.py │ │ ├── test_native_bgk_collision.py │ │ ├── test_native_bounce_back.py │ │ ├── test_native_equilibrium_pu.py │ │ ├── test_native_no_streaming_mask.py │ │ ├── test_native_streaming.py │ │ └── test_native_streaming_strategy.py │ ├── reporter/ │ │ ├── test_HDF5Reporter.py │ │ ├── test_energy_spectrum.py │ │ ├── test_generic_reporters.py │ │ ├── test_high_ma_reporter.py │ │ ├── test_nan_reporter.py │ │ ├── test_vtk_reporter_mask.py │ │ ├── test_vtk_reporter_no_mask.py │ │ ├── test_write_image.py │ │ └── test_write_vtk.py │ ├── stencil/ │ │ ├── test_first_zero.py │ │ ├── test_opposite.py │ │ ├── test_symmetry.py │ │ └── test_weights.py │ ├── test_checkpoint.py │ ├── test_cli.py │ ├── test_equilibrium.py │ ├── unit/ │ │ ├── __init__.py │ │ ├── test_consistency.py │ │ ├── test_conversion_reversible.py │ │ └── test_reynolds_number_consistent.py │ └── util/ │ ├── test_grid_fine_to_coarse.py │ └── test_torch_gradient.py └── versioneer.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .codeclimate.yml ================================================ exclude_patterns: - "versioneer.py" - "lettuce/_version.py" - "config/" - "db/" - "dist/" - "features/" - "**/node_modules/" - "script/" - "**/spec/" - "**/test/" - "**/tests/" - "Tests/" - "**/vendor/" - "**/*_test.go" - "**/*.d.ts" ================================================ FILE: .github/ISSUE_TEMPLATE/BUG_REPORT.yml ================================================ name: Bug Report description: File a bug report. title: "[Bug]: " labels: ["bug"] assignees: [] body: - type: markdown attributes: value: | Thanks for taking the time to fill out this bug report! - type: input id: contact attributes: label: Contact Details description: How can we get in touch with you if we need more info? placeholder: ex. email@example.com validations: required: false - type: textarea id: what-happened attributes: label: What happened? description: Also tell us, what did you expect to happen? placeholder: Tell us what you see! value: "A bug happened!" validations: required: true - type: dropdown id: version attributes: label: Version description: What version of our software are you running? options: - 0.2.0 - 0.2.1 - 0.2.2 - 0.2.3 default: 0 validations: required: true - type: dropdown id: browsers attributes: label: What operating system are you seeing the problem on? multiple: true options: - Linux - Windows - MacOs - type: textarea id: logs attributes: label: Relevant log output description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. render: shell ================================================ FILE: .github/ISSUE_TEMPLATE/FEATURE_REQUEST.yml ================================================ name: "Feature Request" description: Suggest an idea for improving lettuce title: "[Feature]: " labels: ["enhancement"] assignees: [] body: - type: markdown attributes: value: | Thanks for suggesting a new and exciting feature! - type: input id: contact attributes: label: Contact Details description: How can we get in touch with you if we need more info? placeholder: ex. email@example.com validations: required: false - type: textarea id: is-related attributes: label: Is your proposal related to a problem? description: Provide a clear and concise description of what the problem is. placeholder: "I'm always frustrated when..." value: "This is related to a problem!" validations: required: true - type: textarea id: solution attributes: label: "Describe the solution you'd like" description: Provide a clear and concise description of what you want to happen. placeholder: (Describe your proposed solution here.) value: "This is the suggested solution." validations: required: true - type: textarea id: alternatives attributes: label: "Describe alternatives you've considered" description: "Let us know about other solutions you've tried or researched." placeholder: (Write your answer here.) value: "This is an alternative." validations: required: true - type: textarea id: context attributes: label: "Additional context" description: "Is there anything else you can add about the proposal? You might want to link to related issues here, if you haven't already." placeholder: (Write your answer here.) value: "This is additional context." validations: required: true ================================================ FILE: .github/pull_request_template.md ================================================ ## Description ## Checklist - [ ] This pull request is associated to an issue - [ ] This PR contains a description - [ ] Did you add a new method? If so, you need to - [ ] add method description - [ ] maybe mention class in the corresponding `__init__` - [ ] add an example using the method in `examples/advanced_flows/` or `examples/simple_flows/` - [ ] add a test in `tests/` - [ ] Add someone else as reviewer and wait for approval before merging. ================================================ FILE: .github/workflows/CI.yml ================================================ name: CI on: push: branches: - "master" pull_request: branches: - "**" schedule: - cron: "0 7 * * 1" # Allows you to run this workflow manually from the Actions tab workflow_dispatch: jobs: test: runs-on: ${{ matrix.cfg.os }} strategy: fail-fast: false matrix: cfg: - { os: ubuntu-latest, python-version: '3.12', extra: 'cpu' } - { os: ubuntu-latest, python-version: '3.12', extra: 'cu124' } - { os: ubuntu-latest, python-version: '3.12', extra: 'cu126' } - { os: ubuntu-latest, python-version: '3.12', extra: 'cu128' } - { os: ubuntu-latest, python-version: '3.12', extra: 'cu130' } - { os: macos-latest, python-version: '3.12', extra: 'cpu' } steps: - uses: actions/checkout@v4 - name: Install uv uses: astral-sh/setup-uv@v6 - name: Install dependencies with ${{ matrix.cfg.extra }} extra shell: bash -el {0} run: | uv sync --extra ${{ matrix.cfg.extra }} - name: Run unit tests shell: bash -el {0} run: | uv run --extra ${{ matrix.cfg.extra }} pytest tests - name: Run integration tests shell: bash -el {0} run: | source .venv/bin/activate if [[ "${{ matrix.cfg.extra }}" == "cpu" ]]; then lettuce --no-cuda convergence --use-no-cuda_native lettuce --no-cuda benchmark --use-no-cuda_native else lettuce --no-cuda convergence --use-no-cuda_native lettuce --no-cuda benchmark --use-no-cuda_native fi ================================================ FILE: .gitignore ================================================ Makefile .idea/ .gitattributes .editorconfig # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python env/ build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ .installed.cfg *.egg # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ .pytest_cache/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # SageMath parsed files *.sage.py # dotenv .env # virtualenv .venv venv/ ENV/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ # data folder data/ ================================================ FILE: .readthedocs.yaml ================================================ # .readthedocs.yaml # Read the Docs configuration file # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details # Required version: 2 # Set the OS, Python version and other tools you might need build: os: ubuntu-22.04 tools: python: "3.12" # You can also specify other tool versions: # nodejs: "19" # rust: "1.64" # golang: "1.19" # Build documentation in the "docs/" directory with Sphinx sphinx: configuration: docs/conf.py # Optionally build your docs in additional formats such as PDF and ePub # formats: # - pdf # - epub # Optional but recommended, declare the Python requirements required # to build your documentation # See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html python: install: - requirements: requirements.txt ================================================ FILE: AUTHORS.rst ================================================ ======= Credits ======= Development Lead ---------------- * Andreas Kraemer Contributors ------------ * Dominik Wilde * Mario Bedrunka * Philipp Spelten * You? ================================================ FILE: LICENSE ================================================ MIT License Copyright (c) 2019, Andreas Kraemer Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: README.rst ================================================ .. image:: https://raw.githubusercontent.com/lettucecfd/lettuce/master/.source/img/logo_lettuce_typo.png .. image:: https://github.com/lettucecfd/lettuce/actions/workflows/CI.yml/badge.svg :target: https://github.com/lettucecfd/lettuce/actions/workflows/CI.yml :alt: CI Status .. image:: https://readthedocs.org/projects/lettucecfd/badge/?version=latest :target: https://lettucecfd.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3757641.svg :target: https://doi.org/10.5281/zenodo.3757641 GPU-accelerated Lattice Boltzmann Simulations in Python ------------------------------------------------------- Lettuce is a Computational Fluid Dynamics framework based on the lattice Boltzmann method (LBM). - **GPU-Accelerated Computation**: Utilizes PyTorch for high performance and efficient GPU utilization. - **Rapid Prototyping**: Supports both 2D and 3D simulations for quick and reliable analysis. - **Advanced Techniques**: Integrates neural networks and automatic differentiation to enhance LBM. - **Optimized Performance**: Includes custom PyTorch extensions for native CUDA kernels. Resources --------- - `Documentation`_ - Presentation at CFDML2021 - `Paper`_ | `Preprint`_ | `Slides`_ | `Video`_ | `Code`_ .. _Paper: https://www.springerprofessional.de/en/lettuce-pytorch-based-lattice-boltzmann-framework/19862378 .. _Documentation: https://lettuceboltzmann.readthedocs.io .. _Preprint: https://arxiv.org/pdf/2106.12929.pdf .. _Slides: https://drive.google.com/file/d/1jyJFKgmRBTXhPvTfrwFs292S4MC3Fqh8/view .. _Video: https://www.youtube.com/watch?v=7nVCuuZDCYA .. _Code: https://github.com/lettucecfd/lettuce-paper Getting Started --------------- To find some very simple examples of how to use lettuce, please have a look at the examples_. These will guide you through lettuce's main features. Please ensure you have Jupyter installed to run the Jupyter notebooks. .. _examples: https://github.com/lettucecfd/lettuce/tree/master/examples Installation ------------ * Install the uv package manager from https://docs.astral.sh/uv/ * Clone this repository from github and change to it:: git clone https://github.com/lettucecfd/lettuce cd lettuce * Create a new virtual environment and activate it:: uv venv source .venv/bin/activate * The `pyproject.toml` file currently requires at least **CUDA 12.4** (we successfully tested CUDA 12.4, 12.6, 12.8 and 13.0). If your GPU does not support this version, you may need to downgrade it. Please note that we cannot guarantee the maintenance for older CUDA versions. * Run the install command, depending on your needs (run one of the three options below): 1. use lettuce (no development) with GPU support:: uv pip install . 2. use lettuce (no development) with CPU only or specific older CUDA versions (if you do not have access to a GPU or an older GPU) use (cpu, cu124, cu126):: uv pip install ".[cpu]" 3. use and **develop** lettuce (code changes take effect in program execution): use the changeable-installation-flag (`-e`):: uv pip install -e . * Check out the convergence order, running on CPU:: lettuce --no-cuda convergence * For running a CUDA-driven LBM simulation on one GPU omit the `--no-cuda`. If CUDA is not found, make sure that CUDA-capable GPU drivers are installed and compatible with the installed cudatoolkit (check cuda version number). * Check out the performance, running on GPU:: lettuce benchmark * Run the test cases:: pytest tests Citation -------- If you use Lettuce in your research, please cite the following paper:: @inproceedings{bedrunka2021lettuce, title={Lettuce: PyTorch-Based Lattice Boltzmann Framework}, author={Bedrunka, Mario Christopher and Wilde, Dominik and Kliemank, Martin and Reith, Dirk and Foysi, Holger and Kr{\"a}mer, Andreas}, booktitle={High Performance Computing: ISC High Performance Digital 2021 International Workshops, Frankfurt am Main, Germany, June 24--July 2, 2021, Revised Selected Papers}, pages={40}, organization={Springer Nature} } Credits ------- We use the following third-party packages: * pytorch_ * numpy_ * pytest_ * click_ * matplotlib_ * versioneer_ * pyevtk_ * h5py_ * mmh3_ This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template. .. _Cookiecutter: https://github.com/audreyr/cookiecutter .. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage .. _pytorch: https://github.com/pytorch/pytorch .. _numpy: https://github.com/numpy/numpy .. _pytest: https://github.com/pytest-dev/pytest .. _click: https://github.com/pallets/click .. _matplotlib: https://github.com/matplotlib/matplotlib .. _versioneer: https://github.com/python-versioneer/python-versioneer .. _pyevtk: https://github.com/pyscience-projects/pyevtk .. _h5py: https://github.com/h5py/h5py .. _mmh3: https://github.com/hajimes/mmh3 License ----------- * Free software: MIT license, as found in the LICENSE_ file. .. _LICENSE: https://github.com/lettucecfd/lettuce/blob/master/LICENSE ================================================ FILE: docs/README.md ================================================ # Building the docs locally ``` conda install -c conda-forge \ sphinx \ sphinxcontrib-napoleon \ numpydoc \ alabaster cd docs make html ``` ================================================ FILE: docs/authors.rst ================================================ .. include:: ../AUTHORS.rst ================================================ FILE: docs/conf.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- # # lettuce documentation build configuration file, created by # sphinx-quickstart on Fri Jun 9 13:47:02 2017. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. # If extensions (or modules to document with autodoc) are in another # directory, add these directories to sys.path here. If the directory is # relative to the documentation root, use os.path.abspath to make it # absolute, like shown here. # import os import sys sys.path.insert(0, os.path.abspath('..')) import lettuce # -- General configuration --------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. # # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = ['sphinx.ext.autodoc', 'sphinx.ext.viewcode', 'sphinx.ext.napoleon'] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # # source_suffix = ['.rst', '.md'] source_suffix = '.rst' # The master toctree document. master_doc = 'index' # General information about the project. project = u'lettuce' copyright = u"2019, Andreas Kraemer" author = u"Andreas Kraemer" # The version info for the project you're documenting, acts as replacement # for |version| and |release|, also used in various other places throughout # the built documents. # # The short X.Y version. version = lettuce.__version__ # The full version, including alpha/beta/rc tags. release = lettuce.__version__ # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # -- Options for HTML output ------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = 'alabaster' # Theme options are theme-specific and customize the look and feel of a # theme further. For a list of options available for each theme, see the # documentation. # # html_theme_options = {} # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # -- Options for HTMLHelp output --------------------------------------- # Output file base name for HTML help builder. htmlhelp_basename = 'lettucedoc' # -- Options for LaTeX output ------------------------------------------ latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass # [howto, manual, or own class]). latex_documents = [ (master_doc, 'lettuce.tex', u'lettuce Documentation', u'Andreas Kraemer', 'manual'), ] # -- Options for manual page output ------------------------------------ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'lettuce', u'lettuce Documentation', [author], 1) ] # -- Options for Texinfo output ---------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'lettuce', u'lettuce Documentation', author, 'lettuce', 'One line description of project.', 'Miscellaneous'), ] ================================================ FILE: docs/contributing.rst ================================================ .. include:: ../CONTRIBUTING.rst ================================================ FILE: docs/history.rst ================================================ .. include:: ../HISTORY.rst ================================================ FILE: docs/index.rst ================================================ Welcome to lettuce's documentation! ====================================== .. toctree:: :maxdepth: 2 :caption: Contents: readme installation usage modules contributing authors history Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` ================================================ FILE: docs/installation.rst ================================================ .. highlight:: shell ============ Installation ============ From sources ------------ The sources for lettuce can be downloaded from the `Github repo`_. You can either clone the public repository: .. code-block:: console $ git clone git://github.com/lettucecfd/lettuce Or download the `tarball`_: .. code-block:: console $ curl -OL https://github.com/lettucecfd/lettuce/tarball/master Once you have a copy of the source, you can install it with: .. code-block:: console $ python setup.py install .. _Github repo: https://github.com/lettucecfd/lettuce .. _tarball: https://github.com/lettucecfd/lettuce/tarball/master ================================================ FILE: docs/make.bat ================================================ @ECHO OFF pushd %~dp0 REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=python -msphinx ) set SOURCEDIR=. set BUILDDIR=_build set SPHINXPROJ=lettuce if "%1" == "" goto help %SPHINXBUILD% >NUL 2>NUL if errorlevel 9009 ( echo. echo.The Sphinx module was not found. Make sure you have Sphinx installed, echo.then set the SPHINXBUILD environment variable to point to the full echo.path of the 'sphinx-build' executable. Alternatively you may add the echo.Sphinx directory to PATH. echo. echo.If you don't have Sphinx installed, grab it from echo.http://sphinx-doc.org/ exit /b 1 ) %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% goto end :help %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% :end popd ================================================ FILE: docs/modules.rst ================================================ API === Simulation ---------- .. automodule:: lettuce.simulation :noindex: :members: :undoc-members: :show-inheritance: Lattices -------- .. automodule:: lettuce.lattices :noindex: :members: :undoc-members: :show-inheritance: Stencils -------- .. automodule:: lettuce.stencils :noindex: :members: :undoc-members: :show-inheritance: Streaming --------- .. automodule:: lettuce.streaming :noindex: :members: :undoc-members: :show-inheritance: Collision --------- .. automodule:: lettuce.collision :noindex: :members: :undoc-members: :show-inheritance: Observables ----------- .. automodule:: lettuce.observables :noindex: :members: :undoc-members: :show-inheritance: Reporters --------- .. automodule:: lettuce.reporters :noindex: :members: :undoc-members: :show-inheritance: Force ----- .. automodule:: lettuce.force :noindex: :members: :undoc-members: :show-inheritance: Equilibrium ----------- .. automodule:: lettuce.equilibrium :noindex: :members: :undoc-members: :show-inheritance: Boundary -------- .. automodule:: lettuce.boundary :noindex: :members: :undoc-members: :show-inheritance: Flows ----- Couette ^^^^^^^ .. automodule:: lettuce.flows.couette :noindex: :members: :undoc-members: :show-inheritance: Poiseuille ^^^^^^^^^^ .. automodule:: lettuce.flows.poiseuille :noindex: :members: :undoc-members: :show-inheritance: Taylor-Green ^^^^^^^^^^^^ .. automodule:: lettuce.flows.taylorgreen :noindex: :members: :undoc-members: :show-inheritance: Decaying-Turbulence ^^^^^^^^^^^^^^^^^^^ .. automodule:: lettuce.flows.decayingturbulence :noindex: :members: :undoc-members: :show-inheritance: Obstacle ^^^^^^^^ .. automodule:: lettuce.flows.obstacle :noindex: :members: :undoc-members: :show-inheritance: Utility ------- .. automodule:: lettuce.util :noindex: :members: :undoc-members: :show-inheritance: Command-Line Interface ---------------------- .. automodule:: lettuce.cli :noindex: :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/readme.rst ================================================ .. include:: ../README.rst ================================================ FILE: docs/usage.rst ================================================ ===== Usage ===== To use lettuce in a project:: import lettuce ================================================ FILE: examples/00_simplest_TGV.py ================================================ """ This file showcases the simplicity of the lettuce code. The following code will run a two-dimensional Taylor-Green vortex on GPU. """ import torch import lettuce as lt flow = lt.TaylorGreenVortex( lt.Context(dtype=torch.float64), # for running on cpu: device='cpu' resolution=128, reynolds_number=100, mach_number=0.05, stencil=lt.D2Q9 ) simulation = lt.Simulation( flow=flow, collision=lt.BGKCollision(tau=flow.units.relaxation_parameter_lu), reporter=[]) mlups = simulation(num_steps=1000) print("Performance in MLUPS:", mlups) ================================================ FILE: examples/01a_first_example_TGV.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# A first example\n", "This is a first example of how to use lettuce.\n", "A two dimensional Taylor Green vortex is initialized and simulated for 10000 steps. Afterwards the energy and the velocity field is plotted." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-04T07:24:40.895024Z", "start_time": "2024-08-04T07:24:39.958634Z" } }, "source": [ "import lettuce as lt\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import torch" ], "outputs": [], "execution_count": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "* for running on GPU: device = \"cuda\". CUDA drivers are required!\n", "* dtype=torch.float32 for single precision - float64 for double precision\n", "* select collision model (here BGKCollision) - try also KBCCollision or RegularizedCollision" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-04T07:24:43.170453Z", "start_time": "2024-08-04T07:24:43.005785Z" } }, "source": [ "context = lt.Context(device=torch.device('cuda:0') if torch.cuda\n", " .is_available() else torch.device('cpu'),\n", " dtype=torch.float32)\n", "flow = lt.TaylorGreenVortex(resolution=256, reynolds_number=100, \n", " mach_number=0.05, stencil=lt.D2Q9,\n", " context=context)\n", "collision = lt.BGKCollision(tau=flow.units.relaxation_parameter_lu)\n", "energyreporter = lt.ObservableReporter(lt.IncompressibleKineticEnergy(flow), interval=1000, out=None)\n", "simulation = lt.Simulation(flow=flow, collision=collision, reporter=[energyreporter])" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "steps time IncompressibleKineticEnergy\n" ] } ], "execution_count": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Reporters will grab the results in between simulation steps (see reporters.py and simulation.py)\n", "* Output: Column 1: simulation steps, Column 2: time in LU, Column 3: kinetic energy in PU\n", "* Output: separate VTK-file with ux,uy,(uz) and p for every 100. time step in ./output" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run simulation" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-04T07:24:50.269735Z", "start_time": "2024-08-04T07:24:45.023922Z" } }, "source": [ "mlups = simulation(10000)\n", "print(\"Performance in MLUPS:\", mlups)" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Performance in MLUPS: 124.98567838266325\n" ] } ], "execution_count": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Post process\n", "### Energy Reporter\n", "* Grab output of kinetic energy reporter" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-04T07:24:50.400266Z", "start_time": "2024-08-04T07:24:50.270799Z" } }, "source": [ "energy = np.array(simulation.reporter[0].out)\n", "print(energy.shape)\n", "plt.plot(energy[:,1],energy[:,2])\n", "plt.title('Kinetic energy')\n", "plt.xlabel('Time')\n", "plt.ylabel('Energy in physical units')\n", "plt.show()" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(11, 3)\n" ] }, { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Velocity\n", "We calculate the speed in Lettuce units depending on the last 'f'. Then we convert this velocity into physical units. For further investigations the tensor must be converted into a Numpy-Array. The norm of the fractions in x and y direction is plotted afterwards." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-04T07:25:00.256202Z", "start_time": "2024-08-04T07:25:00.174826Z" } }, "source": [ "u = flow.u_pu.cpu().numpy()\n", "u_norm = np.linalg.norm(u,axis=0)\n", "plt.imshow(u_norm)\n", "plt.show()" ], "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 6 }, { "metadata": {}, "cell_type": "code", "outputs": [], "execution_count": null, "source": "" } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: examples/01b_first_example_obstacle.py ================================================ import torch import lettuce as lt import matplotlib.pyplot as plt import numpy as np """ Context definitions. The context defines the default device (cpu or cuda) and datatype (e.g., float32 for single, float64 for double precision). Native CUDA is currently not supported for the anti-bounce-back outlet. """ context = lt.Context(torch.device("cuda:0"), use_native=False) """ Flow definitions. We need 1. the resolution in x and y direction 2. the Reynolds number (i.e., how fast the flow behaves compared to the object's length and fluid's viscosity) 3. the Mach number (i.e., how fast the flow is compared to speed of sound; Ma=0.3 is stable, above is discouraged) 4. the physical domain length in x-direction (this defines how lattice units scale to physical units) to initialize the Obstacle flow object. """ nx = 200 ny = 100 Re = 300 Ma = 0.01 lx = 1 flow = lt.Obstacle(context, [nx, ny], reynolds_number=Re, mach_number=Ma, domain_length_x=lx) """ Per default, lt.Obstacle has no solid. It is stored in flow.mask as a fully False numpy array. To add a solid, we set some mask values to True by getting the domain extends from flow.grid and creating a boolean array from it. For a circle, just use a boolean function. Otherwise, you may as well use the array indices. """ x, y = flow.grid r = .05*y.max() # radius x_c = 0.3*x.max() # center along x y_c = 0.5*y.max() # center along y flow.mask = ((x - x_c) ** 2 + (y - y_c) ** 2) < (r ** 2) """ To show 2D images, you need to rotate the outputs. This is because in lettuce, the first axis is downstream, while for imshow it is vertical. """ plt.imshow(context.convert_to_ndarray(flow.mask.t())) plt.show() """ Collision definition. The collision is usually BGK (low dissipation, but may be unstable) or KBC (higher dissipation, but generally stable). BGK is preferred for converging flows, KBC is preferred for driven flows in smaller domains (where energy conversation plays a smaller role, but gradients may be higher). """ collision = lt.KBCCollision(tau=flow.units.relaxation_parameter_lu) """ Simulation object setup. """ simulation = lt.Simulation(flow=flow, collision=collision, reporter=[]) """ Reporters. - Reporter objects are used to extract information later on or during the simulation. - They can be created as separate objects when required later (see 01_example4convergence.py). """ energyreporter = lt.ObservableReporter(lt.IncompressibleKineticEnergy(flow), interval=50) simulation.reporter.append(energyreporter) """ Run num_steps iterations of the simulation. This can be done repeatedly (see 02_converging_flows.py). """ mlups = simulation(num_steps=4000) print("Performance in MLUPS:", mlups) """ Before or after simulation call, physical values can be extracted from the flow. Alternatively, the reporters can be drawn from the simulation.reporters list (see 01_example4convergence.py) """ u = context.convert_to_ndarray(flow.u_pu) u_norm = np.linalg.norm(u, axis=0).transpose() plt.imshow(u_norm) plt.title('Velocity after simulation') plt.colorbar() plt.tight_layout() plt.show() ================================================ FILE: examples/02_converging_obstacle_flow.py ================================================ import lettuce as lt import matplotlib.pyplot as plt import numpy as np import torch """ For descriptions of the initialization, refer to '01b_first_example_obstacle.py'. Here, we use a lower Reynolds number for faster convergence. """ context = lt.Context(device=torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'), dtype=torch.float32, use_native=False) nx = 300 ny = 100 Re = 10 Ma = 0.1 lx = 1 flow = lt.Obstacle(context, [nx, ny], reynolds_number=Re, mach_number=Ma, domain_length_x=lx) x, y = flow.grid r = .05*y.max() # radius x_c = 0.3*x.max() # center along x y_c = 0.5*y.max() # center along y flow.mask = ((x - x_c) ** 2 + (y - y_c) ** 2) < (r ** 2) collision = lt.BGKCollision(tau=flow.units.relaxation_parameter_lu) simulation = lt.Simulation(flow=flow, collision=collision, reporter=[]) simulation.reporter.append(lt.VTKReporter( interval=1000, filename_base="./data/converging_obstacle" )) """ We now add a reporter which we access later. The output can be written to files specified by out="reporter.txt" """ energy = lt.IncompressibleKineticEnergy(flow) simulation.reporter.append(lt.ObservableReporter(energy, interval=1000, out=None)) """ Now, we do not just run the whole simulation for 30,000 steps, but check the energy convergence every 2000 steps. The populations are kept on the GPU until evaluated by [...].cpu() """ nmax = 30000 ntest = 1000 it = 0 i = 0 mlups = 0 energy_old = 1 energy_new = 1 while it <= nmax: i += 1 it += ntest mlups += simulation(num_steps=ntest) energy_new = flow.incompressible_energy().cpu().mean().item() print(f"avg MLUPS: {mlups / i:.3f}, avg energy: {energy_new:.8f}, " f"rel. diff: {abs(energy_new - energy_old)/energy_old:.8f}") if not energy_new == energy_new: print("CRASHED!") break if abs(energy_new - energy_old)/energy_old < 0.0075: print(f"CONVERGED! To {abs(energy_new - energy_old)/energy_old:.2%} " f"after {it} iterations.") break energy_old = energy_new u = context.convert_to_ndarray(flow.u_pu) u_norm = np.linalg.norm(u, axis=0).transpose() plt.imshow(u_norm) plt.colorbar() plt.title(f'Velocities at it={it}') plt.show() ================================================ FILE: examples/03_outputs_TGV.py ================================================ import lettuce as lt import torch import numpy as np import matplotlib.pyplot as plt print("start") # ---------- Set up simulation ------------- device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") context = lt.Context(device=device, dtype=torch.float32) # single precision # - torch.float64 for double precision resolution = 80 # resolution of the lattice, low resolution leads to # unstable speeds somewhen after 10 (PU) flow = lt.TaylorGreenVortex(context, resolution, 1600, 0.1, lt.D3Q27) # select collision model - try also KBCCollision or RegularizedCollision collision = lt.BGKCollision(tau=flow.units.relaxation_parameter_lu) simulation = lt.Simulation(flow, collision, []) # reporters will grab the results in between simulation steps # (see io.py and simulation.py) # Output: Column 1: time in LU, Column 2: kinetic energy in PU energy = lt.IncompressibleKineticEnergy(flow) kinE_reporter = lt.ObservableReporter(energy, interval=1, out=None) simulation.reporter.append(kinE_reporter) # Output: separate VTK-file with ux,uy,uz and p for every time step in ../data VTKreport = lt.VTKReporter(interval=25, filename_base='./data/tgv') simulation.reporter.append(VTKreport) # ---------- Simulate until time = tend (PU) ------------- tend = 10 # [PU] nend = int(simulation.flow.units.convert_time_to_lu(10)) # [LU] print(f"Simulating {nend} steps! Maybe drink some water in the meantime.") # runs simulation, but also returns overall performance in MLUPS (million # lattice units per second) print("MLUPS: ", simulation(nend)) # ---------- Plot kinetic energy over time (PU) ------------- # grab output of kinetic energy reporter E = np.asarray(kinE_reporter.out) # normalize to size of grid, not always necessary E[:, 1] = E[:, 1] / (2 * np.pi) ** 3 # save kinetic energy values for later use np.save("data/TGV3DoutRes" + str(resolution) + "E", E) fig = plt.figure() ax1 = plt.subplot(1, 2, 1) plt.xlabel('Time in physical units') plt.ylabel('Kinetic energy in physical units') ax1.plot(simulation.flow.units.convert_time_to_pu(range(0, E.shape[0])), E[:, 1]) # ---------- Plot magnitude of speed in slice of 3D volume ------------- # grab u in PU u = flow.u_pu # [direction of u: Y, X, Z] (due to ij indexing) uMagnitude = torch.pow(torch.pow(u[0, :, :, :], 2) + torch.pow(u[1, :, :, :], 2) + torch.pow(u[2, :, :, :], 2), 0.5) # select slice to plot uMagnitude = uMagnitude[:, :, round(0.1 * resolution)] # send selected slice to CPU und numpy, to be able to plot it via matplotlib uMagnitude = uMagnitude.cpu().numpy() ax2 = plt.subplot(1, 2, 2) ax2.matshow(uMagnitude) plt.tight_layout() plt.show() plt.savefig('data/tgv3d-output.pdf') ================================================ FILE: examples/__init__.py ================================================ ================================================ FILE: examples/advanced_flows/FailingTGVandObstacle.py ================================================ """ This file showcases: a) interrupting a TGV simulation using a reporter that detects NaN in f b) interrupting an obstacle simulation using a reporter that detects Ma > 0.3 """ import torch import lettuce as lt import os if not os.path.exists("./data"): os.mkdir("./data") ### SIMULATION 1: TGV with NaN Reporter### # a) unstable TGV, that causes NaN values in f, which are detected by the NaN # ... reporter, who interrupts the simulation. flow = lt.TaylorGreenVortex( lt.Context(dtype=torch.float64), resolution=32, reynolds_number=30000, mach_number=0.3, stencil=lt.D2Q9 ) nan_reporter = lt.NaNReporter(100, outdir="./data/nan_reporter", vtk_out=True) simulation = lt.BreakableSimulation( flow=flow, collision=lt.BGKCollision(tau=flow.units.relaxation_parameter_lu), reporter=[nan_reporter]) simulation(10000) print(f"Failed after {nan_reporter.failed_iteration} iterations") ### SIMULATION 2: obstacle with High Ma Reporter ### # b) unstable obstacle flow, that causes high Ma values (Ma > 0.3), which are # ... detected by the HighMa reporter, who interrupts the simulation. flow = lt.Obstacle( lt.Context(dtype=torch.float64,use_native=False), resolution=[32, 32], reynolds_number=100, mach_number=0.01, stencil=lt.D2Q9(), domain_length_x=32 ) flow.mask = ((2 < flow.grid[0]) & (flow.grid[0] < 10) & (2 < flow.grid[1]) & (flow.grid[1] < 10)) high_ma_reporter = lt.HighMaReporter(100, outdir="./data/highma_reporter", vtk_out=True) simulation = lt.BreakableSimulation( flow=flow, collision=lt.BGKCollision(tau=flow.units.relaxation_parameter_lu), reporter=[high_ma_reporter]) simulation(10000) print(f"Failed after {high_ma_reporter.failed_iteration} iterations") ================================================ FILE: examples/advanced_flows/LidDrivenCavity.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lid-driven cavity flow example\n", "This is an example of using the cavity flow\n", "A two dimensional flow is initialized and simulated. Afterwards, the energy and the velocity field are plotted." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T14:45:11.491029Z", "start_time": "2024-08-14T14:45:10.421365Z" } }, "source": [ "import lettuce as lt\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import os" ], "outputs": [], "execution_count": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "* for running on GPU: device = \"cuda\". CUDA drivers are required!\n", "* dtype=torch.float32 for single precision - float64 for double precision\n", "* select collision model (here BGKCollision) - try also KBCCollision or RegularizedCollision\n", "\n", "### Code:\n", "* Reporter will grab the results in between simulation steps\n", "* Output: Column 1: simulation steps, Column 2: time in LU, Column 3: kinetic energy in PU\n", "* Output: separate VTK-file with ux,uy,(uz) and p for every 100. time step in ./output" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T14:45:11.922912Z", "start_time": "2024-08-14T14:45:11.491874Z" } }, "source": [ "nmax = 100000\n", "nconsole = 1000\n", "nreport = 100\n", "epsilon = 0.0001 # convergence condition: .1 % relative change\n", "Re = 100\n", "\n", "context = lt.Context(use_native=False)\n", "flow = lt.Cavity2D(context=context, resolution=64, reynolds_number=Re, mach_number=0.05)\n", "simulation = lt.Simulation(flow=flow, collision=lt.KBCCollision(), reporter=[])\n", "\n", "Energy = lt.IncompressibleKineticEnergy(flow)\n", "energy_reporter_internal = lt.ObservableReporter(Energy, interval=nreport, out=None)\n", "simulation.reporter.append(energy_reporter_internal)\n", "simulation.reporter.append(lt.ObservableReporter(Energy, interval=nconsole)) # print energy\n", "if not os.path.isdir(\"data\"):\n", " os.mkdir(\"data\")\n", "simulation.reporter.append(lt.VTKReporter(interval=nreport, filename_base=f\"./data/cavity2d_Re{Re}/out\"))" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "steps time IncompressibleKineticEnergy\n", "steps time IncompressibleKineticEnergy\n" ] } ], "execution_count": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run simulations" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T14:46:07.843211Z", "start_time": "2024-08-14T14:45:11.923485Z" } }, "source": [ "energy_new = 0\n", "mlups = 0\n", "iterations = int(nmax//nconsole)\n", "for _ in range(iterations):\n", " energy_old = energy_new\n", " energy_new = Energy(flow.f).mean()\n", " mlups += simulation(nconsole)\n", " if abs((energy_new - energy_old)/energy_new) < epsilon:\n", " print(\"CONVERGENCE! Less than \", epsilon*100, \" % relative change\")\n", " break\n", " if not energy_new == energy_new:\n", " print(\"CRASH\")\n", " break\n", "print(\"avg MLUPS: \", mlups/iterations)" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 0.0 0.0\n", "1000 0.45105489780439517 0.019674744457006454\n", "2000 0.9021097956087903 0.02419084496796131\n", "3000 1.3531646934131856 0.0268400888890028\n", "4000 1.8042195912175807 0.028614206239581108\n", "5000 2.255274489021976 0.029889840632677078\n", "6000 2.706329386826371 0.030847690999507904\n", "7000 3.157384284630766 0.031584933400154114\n", "8000 3.6084391824351614 0.0321597158908844\n", "9000 4.059494080239556 0.03261049836874008\n", "10000 4.510548978043952 0.03296438604593277\n", "11000 4.961603875848347 0.03324209898710251\n", "12000 5.412658773652742 0.033459488302469254\n", "13000 5.863713671457138 0.033629387617111206\n", "14000 6.314768569261532 0.03376169875264168\n", "15000 6.765823467065927 0.03386494889855385\n", "16000 7.216878364870323 0.03394544497132301\n", "17000 7.667933262674718 0.03400781750679016\n", "18000 8.118988160479113 0.034056372940540314\n", "19000 8.570043058283508 0.034093745052814484\n", "20000 9.021097956087903 0.034122712910175323\n", "21000 9.472152853892299 0.034145306795835495\n", "22000 9.923207751696694 0.034162867814302444\n", "23000 10.37426264950109 0.03417631983757019\n", "24000 10.825317547305485 0.034186627715826035\n", "25000 11.27637244510988 0.034194447100162506\n", "26000 11.727427342914275 0.03420044109225273\n", "27000 12.17848224071867 0.03420506417751312\n", "28000 12.629537138523064 0.03420861065387726\n", "29000 13.08059203632746 0.03421135991811752\n", "30000 13.531646934131855 0.034213364124298096\n", "CONVERGENCE! Less than 0.01 % relative change\n", "avg MLUPS: 0.6618240655510539\n" ] } ], "execution_count": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Post process\n", "### Energy Reporter\n", "* Grab output of kinetic energy reporter" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T14:46:07.920150Z", "start_time": "2024-08-14T14:46:07.844066Z" } }, "source": [ "energy = np.array(simulation.reporter[0].out)\n", "print(energy.shape)\n", "plt.plot(energy[:,1],energy[:,2])\n", "plt.title('Kinetic energy')\n", "plt.xlabel('Time')\n", "plt.ylabel('Energy in physical units')\n", "plt.show()" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(301, 3)\n" ] }, { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Velocity\n", "We calculate the speed in Lettuce units depending on the last 'f'. Then we convert this velocity into physical units. For further investigations the tensor must be converted into a Numpy-Array. The norm of the fractions in x and y direction is plotted afterwards." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T14:46:08.012981Z", "start_time": "2024-08-14T14:46:07.920728Z" } }, "source": [ "u_x, u_y = context.convert_to_ndarray(flow.u_pu)\n", "x, y = [context.convert_to_ndarray(_) for _ in flow.grid]\n", "plt.quiver(x.T, y.T, u_x.T, u_y.T)" ], "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 5 }, { "cell_type": "code", "source": [], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-08-14T14:46:08.015002Z", "start_time": "2024-08-14T14:46:08.013613Z" } }, "outputs": [], "execution_count": 5 } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.10" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: examples/advanced_flows/MixingLayer.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example: Mixing Layer\n", "\n", "In this example, a custom flow is created and demonstrated." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T15:15:58.287361Z", "start_time": "2024-08-14T15:15:57.239308Z" } }, "source": [ "from typing import Union, List, Optional\n", "\n", "import torch\n", "\n", "import lettuce as lt\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "from lettuce import UnitConversion" ], "outputs": [], "execution_count": 1 }, { "cell_type": "markdown", "metadata": {}, "source": "## Setup" }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T15:15:58.877072Z", "start_time": "2024-08-14T15:15:58.291376Z" } }, "source": [ "def randu(nx, ny, nz):\n", " # returns values between -1 and 1\n", " return (np.random.rand(nx, ny, nz)-0.5)*2\n", "\n", "\n", "class MixingLayer(lt.ExtFlow):\n", "\n", " def __init__(self, context: lt.Context, resolution, reynolds_number,\n", " mach_number, stencil: 'Stencil'):\n", " self.stencil = stencil\n", " self.d = stencil.d\n", " super().__init__(context, resolution, reynolds_number, mach_number,\n", " stencil=stencil)\n", " self._mask = torch.zeros(self.resolution, dtype=torch.bool)\n", "\n", " def make_resolution(self, resolution: Union[int, List[int]],\n", " stencil: Optional['Stencil'] = None) -> List[int]:\n", " if isinstance(resolution, int):\n", " assert stencil is not None, \"stencil must be provided if resolution is an integer\"\n", " return [resolution] * stencil.d\n", " else:\n", " assert len(resolution) in [2, 3], ('the resolution of the mixing'\n", " ' layer must be 2- or 3-dimensional!')\n", "\n", " def make_units(self, reynolds_number, mach_number,\n", " resolution: List[int]) -> 'UnitConversion':\n", " return UnitConversion(reynolds_number=reynolds_number, mach_number=mach_number,\n", " characteristic_length_pu=resolution[0])\n", "\n", " def initial_pu(self):\n", " x = self.context.convert_to_ndarray(self.grid[0])\n", " y = self.context.convert_to_ndarray(self.grid[1])\n", " p = np.array([0 * x], dtype=float)\n", " nx, ny, *nz = self.resolution\n", " nz = nz[0] if nz else 1\n", " shearlayerthickness = 0.093\n", " amplitude = 1\n", " centering = (np.exp(\n", " -pow(\n", " y/(2*shearlayerthickness),\n", " 2)\n", " ) * amplitude)\n", " u = []\n", " for dim in range(len(self.grid)):\n", " u.append(randu(nx, ny, nz) * centering)\n", " u[dim] = u[dim][..., 0] if self.d == 2 else u[dim]\n", " u[0] = np.tanh(y/(2*shearlayerthickness))\n", " u = np.array(u, dtype=float)\n", " return p, u\n", "\n", " @property\n", " def mask(self):\n", " return self._mask\n", "\n", " @property\n", " def grid(self):\n", " xyz = tuple(torch.linspace(-1, 1, steps=n) for n in self.resolution)\n", " return torch.meshgrid(*xyz, indexing='ij')\n", "\n", " @property\n", " def boundaries(self):\n", " x, y, *z = self.grid\n", " top = np.zeros(np.shape(y), dtype=bool)\n", " bottom = np.zeros(np.shape(y), dtype=bool)\n", " bottom[:, 0] = True # bottom\n", " top[:, -1] = True # top\n", " downstream = np.array([1.0, 0.0, 0.0]) if len(self.grid) == 3\\\n", " else np.array([1.0, 0.0])\n", " return [\n", " # moving fluid on top and bottom\n", " lt.EquilibriumBoundaryPU(self.context, top, downstream),\n", " lt.EquilibriumBoundaryPU(self.context, bottom, -downstream),\n", " ]\n", "\n", "context = lt.Context()\n", "flow = MixingLayer(context=context, resolution=256, reynolds_number=10000,\n", " mach_number=0.05, stencil=lt.D2Q9())\n", "collision = lt.BGKCollision(tau=flow.units.relaxation_parameter_lu)\n", "simulation = lt.Simulation(flow=flow, collision=collision, reporter=[])\n", "energyspectrum = lt.EnergySpectrum(flow)\n", "reporter = lt.ObservableReporter(energyspectrum, interval=500, out=None)\n", "simulation.reporter.append(reporter)" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "steps time EnergySpectrum\n" ] } ], "execution_count": 2 }, { "cell_type": "markdown", "metadata": {}, "source": "### Initialized flow" }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T15:15:59.031508Z", "start_time": "2024-08-14T15:15:58.934769Z" } }, "source": [ "u = context.convert_to_ndarray(flow.u_pu)\n", "u_norm = np.linalg.norm(u,axis=0)\n", "plt.imshow(u_norm.transpose())\n", "plt.colorbar()\n", "plt.title('Initialized velocity')\n", "plt.show()" ], "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 3 }, { "cell_type": "markdown", "metadata": {}, "source": "## Run simulation" }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T15:16:00.324019Z", "start_time": "2024-08-14T15:15:59.038521Z" } }, "source": [ "mlups = simulation(num_steps=2000)\n", "print(\"Performance in MLUPS:\", mlups)" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Performance in MLUPS: 102.13895947138577\n" ] } ], "execution_count": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Velocity\n", "* Velocity field after the simulation" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T15:16:00.412549Z", "start_time": "2024-08-14T15:16:00.328853Z" } }, "source": [ "u = context.convert_to_ndarray(flow.u_pu)\n", "u_norm = np.linalg.norm(u,axis=0)\n", "plt.imshow(u_norm.transpose())\n", "plt.colorbar()\n", "plt.title('Velocity after simulation')\n", "plt.show()" ], "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Vorticity\n", "* Vorticity field after the simulation" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T15:16:00.503982Z", "start_time": "2024-08-14T15:16:00.416895Z" } }, "source": [ "dx = flow.units.convert_length_to_pu(1.0)\n", "grad_u0 = np.gradient(u[0], dx)\n", "grad_u1 = np.gradient(u[1], dx)\n", "vorticity = (grad_u1[0] - grad_u0[1])\n", "plt.imshow(vorticity.transpose(), cmap='Spectral')\n", "plt.colorbar()\n", "plt.title('Vorticity after simulation')\n", "plt.show()" ], "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 6 }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T15:16:00.510631Z", "start_time": "2024-08-14T15:16:00.509362Z" } }, "source": [], "outputs": [], "execution_count": null } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: examples/advanced_flows/PartiallySaturatedObstacle.py ================================================ import torch import os from matplotlib import pyplot as plt from matplotlib.colors import colorConverter, LinearSegmentedColormap import lettuce as lt import numpy as np """ Setting up variable parameters """ dim = 2 context = lt.Context(use_native=False) Re = 250 Ma = 0.01 saturation = 0.5 """ Setting up the Flow object """ class ObstaclePartially(lt.Obstacle): def __init__(self, resolution, domain_length_x): super().__init__(context, resolution, Re, Ma, domain_length_x) self.saturation = saturation return @property def post_boundaries(self): x = self.grid[0] return [ lt.EquilibriumBoundaryPU( context, torch.abs(x) < 1e-6, self.units.characteristic_velocity_pu * self._unit_vector(), pressure=0 ), lt.EquilibriumOutletP( self._unit_vector().tolist(), self ), lt.PartiallySaturatedBC( self.mask, tau=self.units.relaxation_parameter_lu, saturation=self.saturation) ] def initial_solution(self, x): p = np.zeros_like(x[0], dtype=float)[None, ...] ui = np.zeros_like(x[0], dtype=float) u = np.stack((ui, ui) if self.units.lattice.D == 2 else (ui, ui, ui)) return p, u """ Setting up post-processing """ class Show2D: def __init__(self, mask, outdir: str, **kwargs): if len(mask.shape) == 3: mask = mask[:, :, int(mask.shape[2] / 2)] self.mask = context.convert_to_ndarray(mask).transpose() self.outdir = outdir self.dpi = kwargs['dpi'] if 'dpi' in kwargs else 1200 self.save = kwargs['save'] if 'save' in kwargs else True self.show_mask = kwargs['show_mask'] if 'show_mask' in kwargs else True self.mask_alpha = kwargs['mask_alpha'] if 'mask_alpha' in kwargs \ else 1. self.__call__(mask, "solid_mask", "solid_mask") def __call__(self, data, title: str, name: str, vlim=None): fig, ax = plt.subplots(figsize=(16, 4)) data = data[:, :, int(data.shape[2] / 2)] if len(data.shape) > 2 \ else data data = context.convert_to_ndarray(data).transpose() \ if isinstance(data, torch.Tensor) else data.transpose() vmin, vmax = vlim if vlim is not None else None, None p = ax.imshow(data, origin='lower', aspect='auto', interpolation='none', vmin=vmin, vmax=vmax) color1 = colorConverter.to_rgba('white') color2 = colorConverter.to_rgba('black') cmap2 = LinearSegmentedColormap.from_list('my_cmap2', [color1, color2], 256) cmap2._init() alphas = np.linspace(0, 0.8, cmap2.N + 3) cmap2._lut[:, -1] = alphas if self.show_mask: ax.imshow(self.mask, origin='lower', aspect='auto', interpolation='none', cmap=cmap2, vmin=0, vmax=1, alpha=self.mask_alpha) ax.set_title(title) fig.colorbar(p, ax=ax) plt.show() if self.save: fig.savefig(os.path.join(self.outdir, name), dpi=self.dpi) plt.close() """ initializing up Flow and Show2D """ nz = 1 if dim == 2 else 10 ny = 50 nx = 2 * ny flow = ObstaclePartially( resolution=[nx, ny, nz] if dim == 3 else [nx, ny], domain_length_x=10.1 ) x, y, *z = flow.grid # flow.mask = ((x >= 2) & (x < 5) & (y >= x) & (y <= 3)) # triangle flow.mask = (x >= 2) & (x <= 4) & (y >= 1.5) & (y <= 3.5) # rectangle collision = lt.BGKCollision(tau=flow.units.relaxation_parameter_lu) simulation = lt.Simulation(flow=flow, collision=collision, reporter=[]) imgdir = os.path.join(os.getcwd(), "images") if not os.path.exists(imgdir): os.mkdir(imgdir) if not os.path.exists('data'): os.mkdir('data') simulation.reporter.append(lt.VTKReporter( interval=200, filename_base="data/partiallysaturated/out" )) show2d = Show2D(flow.mask, imgdir, save=False, show_mask=False, mask_alpha=saturation) show2d(flow.u_pu[0], "u_x(t=0)", "u_0", vlim=(-.2, 1.5)) """ run simulation """ steps, mlups_sum, n = 0, 0, 0 step_size = 1000 t_max = 20 step_max = min(flow.units.convert_time_to_lu(t_max), 20000) print(f"Step size: {step_size}, max steps: {step_max:.1f}, max time: {t_max}") while steps < step_max and ~torch.isnan(flow.f).any(): mlups_new = simulation(step_size) steps += step_size n += 1 mlups_sum += mlups_new mlups_avg = mlups_sum / n energy = (torch.sum(flow.incompressible_energy()) / ((1 / nz) ** dim)) t = flow.units.convert_time_to_pu(steps) print(f"Step: {steps}, Time: {t:.1f} s, Energy: {energy:.2f}, " f"MLUPS: {mlups_avg:.1f}") u = flow.u_pu p = flow.p_pu # [Pa] grad_u0 = torch.gradient(u[0]) grad_u1 = torch.gradient(u[1]) vorticity = (grad_u1[0] - grad_u0[1]) show2d(torch.abs(vorticity), f"vorticity(it={steps},t={t:.1f})", f"vort_{steps}", vlim=(-.2, 1)) show2d(p * 10 ** -5, f"pressure(it={steps},t={t:.1f}) [bar]", f"rho_{steps}", vlim=(-.001, .001)) show2d(torch.norm(u, dim=0), f"u(it={steps},t={t:.1f}) [m/s]", f"u_{steps}", vlim=(-.2, 1.5)) ================================================ FILE: examples/advanced_flows/PorousMedium.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "33addcd4", "metadata": {}, "source": [ "# Implementation of a flow through a porous medium\n", "modified by Javier E. Santos and Philipp Spelten\n", "\n", "\n", "Description: This notebook calculates the steady-state single-phase laminar flow and the permeability of a domain with overlapping spheres" ] }, { "cell_type": "code", "id": "cc4dc7ad", "metadata": { "ExecuteTime": { "end_time": "2025-08-06T10:17:35.756583Z", "start_time": "2025-08-06T10:17:34.563226Z" } }, "source": [ "from typing import List, Optional\n", "\n", "from matplotlib import pyplot as plt\n", "import torch\n", "import lettuce as lt\n", "import numpy as np\n", "import os" ], "outputs": [], "execution_count": 1 }, { "cell_type": "markdown", "id": "3988ef17", "metadata": {}, "source": [ "## Define Flow and Boundary Condition" ] }, { "cell_type": "code", "id": "8d15f368", "metadata": { "ExecuteTime": { "end_time": "2025-08-06T10:17:35.760246Z", "start_time": "2025-08-06T10:17:35.757289Z" } }, "source": [ "class PeriodicPressureBC(lt.Boundary):\n", " \"\"\"According to Ehsan Evati: 'High performance simulation of fluid flow in porous media...'\n", " \"\"\"\n", "\n", " def __init__(self, delta_rho: float):\n", " self.delta_rho = delta_rho\n", "\n", " def __call__(self, flow: 'Flow'):\n", " flow.f[[1,5,8], 0,:] = (flow.f[[1,5,8], 0,:] + flow.torch_stencil.w[[1,5,8],\n", " None] * self.delta_rho)\n", " flow.f[[3,6,7],-1,:] = flow.f[[3,6,7],-1,:] - flow.torch_stencil.w[[3,6,7],None] * self.delta_rho\n", " # TODO: call flow.collision() as it will otherwise be jumped in \n", " # simulation\n", " return flow.f\n", "\n", " def make_no_collision_mask(self, shape: List[int], context: 'Context') -> Optional[torch.Tensor]:\n", " return \n", "\n", " def make_no_streaming_mask(self, shape: List[int], context: 'Context') -> Optional[torch.Tensor]:\n", " pass\n", "\n", " def native_available(self) -> bool:\n", " return False\n", "\n", " def native_generator(self, index: int) -> 'NativeBoundary':\n", " pass" ], "outputs": [], "execution_count": 2 }, { "metadata": { "ExecuteTime": { "end_time": "2025-08-06T10:17:35.772949Z", "start_time": "2025-08-06T10:17:35.760759Z" } }, "cell_type": "code", "source": [ "class PorousMedium2D(lt.Obstacle):\n", " def __init__(self, context: 'Context', resolution: list[int],\n", " reynolds_number, mach_number, domain_length_x, \n", " delta_rho_lu, stencil: 'Stencil'):\n", " super().__init__(context=context, resolution=resolution, \n", " reynolds_number=reynolds_number, mach_number=mach_number,\n", " domain_length_x=domain_length_x, stencil=stencil)\n", " self.delta_rho_lu = delta_rho_lu\n", " \n", " @property\n", " def boundaries(self):\n", " return [\n", " # left/right: push using PeriodicPressureBC\n", " PeriodicPressureBC(self.delta_rho_lu),\n", " # periodic in y direction\n", " lt.BounceBackBoundary(self.mask)\n", " ]" ], "id": "a692ad9c7e81d8b3", "outputs": [], "execution_count": 3 }, { "cell_type": "markdown", "id": "23823cf0", "metadata": {}, "source": [ "## Setting up domain and flow parameters" ] }, { "cell_type": "code", "id": "worst-deputy", "metadata": { "ExecuteTime": { "end_time": "2025-08-06T10:17:35.784139Z", "start_time": "2025-08-06T10:17:35.773676Z" } }, "source": [ "# user-defined parameters for\n", "nx = 512 # domain length in x-dir\n", "ny = 512 # domain length in y-dir\n", "\n", "# MASK and ITERATION user-defined parameters for\n", "n_buffer = 10 # number of buffer layers\n", "it_check = 500 # check for convergence of floating avg every n-iterations of it_floating_avg\n", "it_floating_avg = 10 # take avg velocity every n-iterations for the floating avg\n", "avgs_per_check = int(it_check//it_floating_avg)\n", "it_max = 1e6 # break after max its is reached\n", "epsilon = 0.1 # break after the diff between its it's less than e %\n", " # lower epsilon makes no sense, as the avg velocity continues changing\n", "\n", "delta_rho_lu = 0.005 # forced pressure differential left and right" ], "outputs": [], "execution_count": 4 }, { "cell_type": "code", "id": "8b4de7ef", "metadata": { "ExecuteTime": { "end_time": "2025-08-06T10:17:36.085609Z", "start_time": "2025-08-06T10:17:35.786027Z" } }, "source": [ "# Set up context\n", "context = lt.Context(dtype=torch.float64, use_native=False)\n", "stencil = lt.D2Q9()\n", "\n", "# FLOW parameters\n", "Ma = 0.05 # I set this arbitrarily\n", "u_lbm = stencil.cs*Ma\n", "\n", "omega = 1.0\n", "nu = (1/omega - 0.5)/(1/stencil.cs)**2\n", "Re = u_lbm*nx/nu\n", "print(Re)\n", "\n", "# Set up FLOW\n", "flow = PorousMedium2D(\n", " context, resolution=[nx, ny], reynolds_number=Re, mach_number=Ma, \n", " domain_length_x=nx, delta_rho_lu=delta_rho_lu, stencil=stencil\n", ")" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "88.68100134752649\n" ] } ], "execution_count": 5 }, { "cell_type": "code", "source": [ "# make porous medium by inserting random CIRCLES\n", "np.random.seed(234269)\n", "n_circles = 125\n", "x, y = flow.grid\n", "for i in range(n_circles):\n", " x0 = np.random.rand()\n", " y0 = np.random.rand()\n", " r = 0.02 + 0.02 * np.random.rand() # radius between 0.02 and 0.04\n", " x0 = x0*nx\n", " y0 = y0*nx\n", " r = r*nx\n", " circle = ((x-x0)**2 + (y-y0)**2 < r**2)\n", " flow.mask[circle] = 1\n", "\n", "# make a BUFFER region on left and right without obstacles\n", "flow.mask[ :n_buffer, :] = 0\n", "flow.mask[-n_buffer:, :] = 0\n", "\n", "# calculate porosity\n", "phi = 1-torch.sum(flow.mask)/(nx*ny)\n", "\n", "# Show MASK\n", "plt.imshow(context.convert_to_ndarray(flow.mask.T), \n", " origin=\"lower\", cmap='gray_r')\n", "plt.title(f'The porosity of the domain is {phi*100:2.1f} %')\n", "\n", "directory = './data/porous_example'\n", "\n", "if not os.path.exists(directory):\n", " print(f\"Directory '{directory}' does not exist. Creating it now...\")\n", " os.makedirs(directory)\n", " print(f\"Directory '{directory}' created.\")\n", "else:\n", " print(f\"Directory '{directory}' already exists.\")\n", "\n", "# Set up SIMULATION\n", "collision = lt.KBCCollision(tau=flow.units.relaxation_parameter_lu)\n", "simulation = lt.Simulation(flow, collision, [])\n", "simulation.reporter.append(lt.VTKReporter(interval=2, filename_base=directory+\"/out\"))" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2025-08-06T10:17:36.237426Z", "start_time": "2025-08-06T10:17:36.086138Z" } }, "id": "788ba01aaac63b34", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Directory './data/porous_example' does not exist. Creating it now...\n", "Directory './data/porous_example' created.\n" ] }, { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 6 }, { "cell_type": "markdown", "id": "59319068", "metadata": {}, "source": [ "## Run Simulation" ] }, { "cell_type": "code", "id": "d80f0401", "metadata": { "ExecuteTime": { "end_time": "2025-08-06T10:18:09.438991Z", "start_time": "2025-08-06T10:17:36.238034Z" } }, "source": [ "# u_avg is the average velocity over the last couple of time steps\n", "u_avg = [np.inf]\n", "for i in range(1,int(it_max//it_check)):\n", " u_avg_new = 0\n", " for j in range(it_floating_avg):\n", " simulation(avgs_per_check)\n", " # print(flow.u().mean())\n", " u_avg_new += flow.u().mean()\n", " u_avg_new = u_avg_new/it_floating_avg\n", " u_avg.append(u_avg_new)\n", " rel_change = ((u_avg[-1]-u_avg[-2])/u_avg[-1]*100).abs()\n", " print(f'it {i*it_check} u_avg[-1] = {u_avg[-1]} the relative change in mean vel is {rel_change} %')\n", " if rel_change < epsilon or not u_avg[-1] == u_avg[-1]:\n", " break" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "it 500 u_avg[-1] = 0.009691138791684406 the relative change in mean vel is inf %\n", "it 1000 u_avg[-1] = 0.009739418591561515 the relative change in mean vel is 0.4957154210307779 %\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001B[31m---------------------------------------------------------------------------\u001B[39m", "\u001B[31mKeyboardInterrupt\u001B[39m Traceback (most recent call last)", "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[7]\u001B[39m\u001B[32m, line 6\u001B[39m\n\u001B[32m 4\u001B[39m u_avg_new = \u001B[32m0\u001B[39m\n\u001B[32m 5\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m j \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(it_floating_avg):\n\u001B[32m----> \u001B[39m\u001B[32m6\u001B[39m \u001B[43msimulation\u001B[49m\u001B[43m(\u001B[49m\u001B[43mavgs_per_check\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 7\u001B[39m \u001B[38;5;66;03m# print(flow.u().mean())\u001B[39;00m\n\u001B[32m 8\u001B[39m u_avg_new += flow.u().mean()\n", "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/lettuce/_simulation.py:204\u001B[39m, in \u001B[36mSimulation.__call__\u001B[39m\u001B[34m(self, num_steps)\u001B[39m\n\u001B[32m 202\u001B[39m \u001B[38;5;28mself\u001B[39m._collide_and_stream(\u001B[38;5;28mself\u001B[39m)\n\u001B[32m 203\u001B[39m \u001B[38;5;28mself\u001B[39m.flow.i += \u001B[32m1\u001B[39m\n\u001B[32m--> \u001B[39m\u001B[32m204\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_report\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 206\u001B[39m end = timer()\n\u001B[32m 207\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m num_steps * \u001B[38;5;28mself\u001B[39m.flow.rho().numel() / \u001B[32m1e6\u001B[39m / (end - beg)\n", "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/lettuce/_simulation.py:193\u001B[39m, in \u001B[36mSimulation._report\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m 191\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_report\u001B[39m(\u001B[38;5;28mself\u001B[39m):\n\u001B[32m 192\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m reporter \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m.reporter:\n\u001B[32m--> \u001B[39m\u001B[32m193\u001B[39m \u001B[43mreporter\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m)\u001B[49m\n", "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/lettuce/ext/_reporter/vtk_reporter.py:47\u001B[39m, in \u001B[36mVTKReporter.__call__\u001B[39m\u001B[34m(self, simulation)\u001B[39m\n\u001B[32m 44\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m d \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(simulation.flow.stencil.d):\n\u001B[32m 45\u001B[39m \u001B[38;5;28mself\u001B[39m.point_dict[\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mu\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[33m'\u001B[39m\u001B[33mxyz\u001B[39m\u001B[33m'\u001B[39m[d]\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m] = (\n\u001B[32m 46\u001B[39m simulation.flow.context.convert_to_ndarray(u[d, ...]))\n\u001B[32m---> \u001B[39m\u001B[32m47\u001B[39m \u001B[43mwrite_vtk\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mpoint_dict\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msimulation\u001B[49m\u001B[43m.\u001B[49m\u001B[43mflow\u001B[49m\u001B[43m.\u001B[49m\u001B[43mi\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mfilename_base\u001B[49m\u001B[43m)\u001B[49m\n", "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/lettuce/ext/_reporter/vtk_reporter.py:11\u001B[39m, in \u001B[36mwrite_vtk\u001B[39m\u001B[34m(point_dict, id, filename_base)\u001B[39m\n\u001B[32m 10\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mwrite_vtk\u001B[39m(point_dict, \u001B[38;5;28mid\u001B[39m=\u001B[32m0\u001B[39m, filename_base=\u001B[33m\"\u001B[39m\u001B[33m./data/output\u001B[39m\u001B[33m\"\u001B[39m):\n\u001B[32m---> \u001B[39m\u001B[32m11\u001B[39m \u001B[43mvtk\u001B[49m\u001B[43m.\u001B[49m\u001B[43mgridToVTK\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43mf\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[38;5;132;43;01m{\u001B[39;49;00m\u001B[43mfilename_base\u001B[49m\u001B[38;5;132;43;01m}\u001B[39;49;00m\u001B[33;43m_\u001B[39;49m\u001B[38;5;132;43;01m{\u001B[39;49;00m\u001B[38;5;28;43mid\u001B[39;49m\u001B[38;5;132;43;01m:\u001B[39;49;00m\u001B[33;43m08d\u001B[39;49m\u001B[38;5;132;43;01m}\u001B[39;49;00m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m 12\u001B[39m \u001B[43m \u001B[49m\u001B[43mnp\u001B[49m\u001B[43m.\u001B[49m\u001B[43marange\u001B[49m\u001B[43m(\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpoint_dict\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mp\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m.\u001B[49m\u001B[43mshape\u001B[49m\u001B[43m[\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 13\u001B[39m \u001B[43m \u001B[49m\u001B[43mnp\u001B[49m\u001B[43m.\u001B[49m\u001B[43marange\u001B[49m\u001B[43m(\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpoint_dict\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mp\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m.\u001B[49m\u001B[43mshape\u001B[49m\u001B[43m[\u001B[49m\u001B[32;43m1\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 14\u001B[39m \u001B[43m \u001B[49m\u001B[43mnp\u001B[49m\u001B[43m.\u001B[49m\u001B[43marange\u001B[49m\u001B[43m(\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpoint_dict\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mp\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m.\u001B[49m\u001B[43mshape\u001B[49m\u001B[43m[\u001B[49m\u001B[32;43m2\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 15\u001B[39m \u001B[43m \u001B[49m\u001B[43mpointData\u001B[49m\u001B[43m=\u001B[49m\u001B[43mpoint_dict\u001B[49m\u001B[43m)\u001B[49m\n", "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/.venv/lib/python3.12/site-packages/pyevtk/hl.py:339\u001B[39m, in \u001B[36mgridToVTK\u001B[39m\u001B[34m(path, x, y, z, cellData, pointData, fieldData, start)\u001B[39m\n\u001B[32m 337\u001B[39m w.appendData((x, y, z))\n\u001B[32m 338\u001B[39m \u001B[38;5;66;03m# Write data\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m339\u001B[39m \u001B[43m_appendDataToFile\u001B[49m\u001B[43m(\u001B[49m\u001B[43mw\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcellData\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpointData\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfieldData\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 341\u001B[39m \u001B[38;5;66;03m# Close file\u001B[39;00m\n\u001B[32m 342\u001B[39m w.save()\n", "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/.venv/lib/python3.12/site-packages/pyevtk/hl.py:122\u001B[39m, in \u001B[36m_appendDataToFile\u001B[39m\u001B[34m(vtkFile, cellData, pointData, fieldData)\u001B[39m\n\u001B[32m 120\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m key \u001B[38;5;129;01min\u001B[39;00m keys:\n\u001B[32m 121\u001B[39m data = pointData[key]\n\u001B[32m--> \u001B[39m\u001B[32m122\u001B[39m \u001B[43mvtkFile\u001B[49m\u001B[43m.\u001B[49m\u001B[43mappendData\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdata\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 124\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m cellData \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m 125\u001B[39m keys = \u001B[38;5;28mlist\u001B[39m(cellData.keys())\n", "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/.venv/lib/python3.12/site-packages/pyevtk/vtk.py:641\u001B[39m, in \u001B[36mVtkFile.appendData\u001B[39m\u001B[34m(self, data)\u001B[39m\n\u001B[32m 638\u001B[39m writeBlockSize(\u001B[38;5;28mself\u001B[39m.xml.stream, block_size)\n\u001B[32m 639\u001B[39m \u001B[38;5;66;03m# else:\u001B[39;00m\n\u001B[32m 640\u001B[39m \u001B[38;5;66;03m# writeBlockSize64Bit(self.xml.stream, block_size)\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m641\u001B[39m \u001B[43mwriteArrayToFile\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mxml\u001B[49m\u001B[43m.\u001B[49m\u001B[43mstream\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 643\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m 644\u001B[39m \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28;01mFalse\u001B[39;00m\n", "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/.venv/lib/python3.12/site-packages/pyevtk/evtk.py:99\u001B[39m, in \u001B[36mwriteArrayToFile\u001B[39m\u001B[34m(stream, data)\u001B[39m\n\u001B[32m 95\u001B[39m \u001B[38;5;66;03m# NOTE: VTK expects data in FORTRAN order\u001B[39;00m\n\u001B[32m 96\u001B[39m \u001B[38;5;66;03m# This is only needed when a multidimensional array has C-layout\u001B[39;00m\n\u001B[32m 97\u001B[39m dd = np.ravel(data, order=\u001B[33m\"\u001B[39m\u001B[33mF\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m---> \u001B[39m\u001B[32m99\u001B[39m bin_pack = \u001B[43mstruct\u001B[49m\u001B[43m.\u001B[49m\u001B[43mpack\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfmt\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43mdd\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 100\u001B[39m stream.write(bin_pack)\n", "\u001B[31mKeyboardInterrupt\u001B[39m: " ] } ], "execution_count": 7 }, { "cell_type": "code", "id": "natural-portrait", "metadata": {}, "source": [ "u_plot = [context.convert_to_ndarray(_) for _ in u_avg[1:]]\n", "plt.plot(np.log10(np.abs(u_plot)))\n", "plt.xlabel(f'{it_check} iterations')\n", "plt.ylabel(f'mean log10(|velocity|) [lus]')" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "id": "63df80e6", "metadata": {}, "source": [ "u = context.convert_to_ndarray(flow.u_pu)\n", "k = nu*u.mean()/(delta_rho_lu/nx)**2\n", "p = context.convert_to_ndarray(flow.p_pu)\n", "unorm = np.linalg.norm(u, axis=0)\n", "\n", "# Plot without outliers due to bounce-back contacts\n", "fig, axes = plt.subplots(1,4, figsize=(12,3), dpi=300)\n", "fig.tight_layout()\n", "axes[0].set_title(\"Pressure\")\n", "axes[0].imshow(p[0].transpose(), origin=\"lower\", )#vmin=p[0,ny-1,0], vmax=p[0].mean(axis=-1).max())\n", "axes[1].set_title(\"Velocity\")\n", "axes[1].imshow(unorm.transpose(), origin=\"lower\", cmap='inferno',\n", " vmin=np.percentile(unorm.flatten(),1),\n", " vmax=np.percentile(unorm.flatten(),95)\n", " )\n", "axes[2].set_title(\"Mean fluid pressure along x\")\n", "axes[2].plot(p[0].sum(axis=-1)/(np.logical_not(context.convert_to_ndarray(flow.mask)).sum(axis=-1)))\n", "axes[3].set_title(\"Mean fluid velocity along x\")\n", "axes[3].plot(u[0].mean(axis=-1));\n" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "id": "needed-courage", "metadata": {}, "source": [ "print(f'Porosity = {phi*100} % and Permeability = {k} [m^2]')" ], "outputs": [], "execution_count": null } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.10" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: examples/advanced_projects/__init__.py ================================================ ================================================ FILE: examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parametrized_project.ipynb ================================================ { "cells": [ { "metadata": {}, "cell_type": "markdown", "source": [ "## ADVANCED PROJECT: CYLINDER FLOW\n", "This .ipynb contains examples for invocation of the `01_script_cylinder_simulation.py` script, that conducts a simulation of the flow around a circular cylinder.\n", "The code is ported from projects MP1, MP2 and Master’s Thesis of M.Bille. Some of the most prominent features implemented are:\n", "- efficient bounce back boundary conditions (Fullway, Halfway, linear Interpolated) for solid bodies, that allow calculation of force on the solid body by Momentum exchange algorithm (MEA)\n", "- simulation-class that allows:\n", " - boundaries can be called after the streaming step\n", " - boundaries can use post-collision populations for their bounce back algorithm (needed for HWBB and IBB algorithm)\n", "- observables: coefficient of lift, coefficient of drag\n", "- reporter: profile reporter for average velocity and reynolds stress profiles\n", "- data-processing and -plotting utilities for the measured observables\n", "\n", "### Examples\n", "1. 2D Simulation at Re = 200: periodic vortex shedding behind a circular cylinder, calculation of force coefficients (lift, drag) and Strouhal number\n", "2. 3D Simulation at Re = 3900: turbulent vortex shedding behind a circular cylinder, calculation of average velocity and reynolds stress profiles and comparison to literature\n", "\n", "### Usage\n", "The example command line calls and parameters below serve as examples for the use of `01_script_cylinder_simulation.py`. There are lots of parameters available through arguments (see script itself). The \"%run\" command is used here, to simulate a bash command line call of `python 01_script_cylinder_simulation.py `\n", "\n", "The script will conduct the simulation and write data to an individually named folder in the directory specified for the `--outdir` parameter. This contains plots, .txt files etc.\n", "The output can be controlled through parameters (see script content (argument parser) itself). Plots etc. are named accordingly in the output data directory.\n", "\n", "You might want to output 2D or 3D vtk-data for further analysis and visualization. But beware the amount of storage you might need and set your parameters accordingly (see script parameters and code).\n", "\n", "### Note\n", "- Some of the functionalities might not work flawlessly through this .ipynb-implementation (warnings etc.): just use a regular python console or call the script with parameters from a bash script.\n", "- Some functionalities throw a warning, when the flow data con not be processed, because it doesn't meet certain physical characteristics (for example: peak finding in a periodic force coefficient timeseries). This is intended. The errors are captured and forwarded to the output, without crashing the script.\n", "- The command line output of the script (including useful messages) might not appear in the notebook, depending on your configuration of Jupyter. See the .txt-output in the output directory for the full log and output, or execute the script in a python console." ], "id": "62eec9273a6c4271" }, { "metadata": { "collapsed": true, "ExecuteTime": { "end_time": "2025-11-14T15:40:54.515141Z", "start_time": "2025-11-14T15:40:54.512769Z" } }, "cell_type": "markdown", "source": [ "### Example 1:\n", "#### 2D Simulation at Re = 200: periodic vortex shedding behind a circular cylinder, calculation of force coefficients (lift, drag) and Strouhal number\n", "NOTE: these default parameters take approx. 2 min to run on an NVIDIA 2060super\n", "\n", "Defaults: `01_script_cylinder_simulation.py --outdir \"./datafolder\" --char_length_lu 20 --t_target 100 --bbbc_type ibb1 --reynolds_number 200 --domain_height_y_in_d 5 --collision bgk --name 2D_simulation_cylinder_Re200 --mach_number 0.1 --stencil D2Q9`" ], "id": "d918fef7d168e9ab" }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-17T16:04:29.456923Z", "start_time": "2025-12-17T16:03:05.111157Z" } }, "cell_type": "code", "source": [ "%matplotlib inline\n", "%run 01_script_cylinder_simulation.py --outdir \"./datafolder\" --char_length_lu 20 --t_target 100 --bbbc_type ibb1 --reynolds_number 200 --domain_height_y_in_d 5 --collision bgk --name 2D_simulation_cylinder_Re200 --mach_number 0.1 --stencil D2Q9" ], "id": "cdd448d543dd26be", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SCRIPT: Writing arguments to dictionary...\n", "SCRIPT: Input arguments are: \n", "{'name': '2D_simulation_cylinder_Re200', 'default_device': 'cuda', 'float_dtype': 'float64', 't_sim_max': 259200, 'text_output_only': False, 'no_data': False, 'outdir': './datafolder', 'outdir_data': None, 'reynolds_number': 200.0, 'mach_number': 0.1, 'char_velocity_pu': 1, 'char_length_lu': 20, 'char_length_pu': 1, 'domain_length_x_in_d': None, 'domain_height_y_in_d': 5.0, 'domain_width_z_in_d': None, 'perturb_init': False, 'u_init_condition': 0, 'lateral_walls': 'periodic', 'n_steps': 100000, 't_target': 100.0, 'collision': 'bgk', 'stencil': 'D2Q9', 'eqlm': False, 'bbbc_type': 'ibb1', 'periodic_region_start_relative': None, 'periodic_region_start_pu': None, 'periodic_region_start_lu': None, 'calc_u_profiles': False, 'output_u_profiles_timeseries': False, 'profile_reference_path': '../profile_reference_data/', 'vtk_full_basic': False, 'vtk_full_basic_interval': None, 'vtk_3D': False, 'vtk_3D_fps': None, 'vtk_3D_step_interval': None, 'vtk_3D_t_interval': None, 'vtk_3D_step_start': None, 'vtk_3D_step_end': None, 'vtk_3D_t_start': None, 'vtk_3D_t_end': None, 'vtk_slice2D': False, 'vtk_slice2D_fps': None, 'vtk_slice2D_step_interval': None, 'vtk_slice2D_t_interval': None, 'vtk_slice2D_step_start': None, 'vtk_slice2D_step_end': None, 'vtk_slice2D_t_start': None, 'vtk_slice2D_t_end': None, 'count_tensors': False}\n", "\n", "SCRIPT: Creating timestamp, simulation ID and creating output directory...\n", "outdir/simID = ./datafolder/251217_170307-2D_simulation_cylinder_Re200\n", "outdir_DATA/simID = ./datafolder/251217_170307-2D_simulation_cylinder_Re200/251217_170307-2D_simulation_cylinder_Re200\n", "SCRIPT: Writing input parameters to file: ./datafolder/251217_170307-2D_simulation_cylinder_Re200/input_parameters.txt\n", "SCRIPT: Saving simulation script to outdir...\n", "-> Saved simulation script to './datafolder/251217_170307-2D_simulation_cylinder_Re200/251217_170307-2D_simulation_cylinder_Re200_01_script_cylinder_simulation.py'\n", "SCRIPT: Starting stdout-LOGGER (see outdir for log file)\n", "SCRIPT: Processing parameters...\n", "\n", "(INFO) parameters set for simulation of 34641 steps, representing 100.000 seconds [PU]!\n", "\n", "SCRIPT: initializing solver components...\n", "-> initializing context...\n", "-> initializing flow...\n", "OBST_Cylinder: self.resolution= [200, 100]\n", "OBST_Cylinder: x_pos, y_pos, radius: 50.5 50.5 10.0\n", "-> initializing collision operator...\n", "\n", "SCRIPT: initializing simulation object...\n", "CYLINDER: the bc_type was given as: ibb1\n", "IBB: calc force is: True\n", "SIMULATION: creating no_collision_mask entries for: i, boundary = 3 \n", "collision index is: 0\n", "-> initializing reporters...\n", "\n", "SCRIPT: spacial and temporal dimensions:\n", "domain shape (LU): [200, 100]\n", "t_target (PU) with 34641 steps (LU): 100.0 seconds\n", "steps to simulate 1 second PU: 346.41 steps\n", "steps to simulate 100.000 (t_target, PU) seconds: 34641.000 steps\n", "\n", "#################################################\n", "\n", "SCRIPT (2025-12-17 17:03:07): running simulation for 34641 steps...\n", "\n", "#################################################\n", "\n" ] }, { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data", "jetTransient": { "display_id": null } }, { "name": "stdout", "output_type": "stream", "text": [ "***** SIMULATION FINISHED AT 2025-12-17 17:04:26 *****\n", "\n", "### STATS ###\n", "MLUPS: 8.869\n", "simulated PU-Time: 99.99995337498915 seconds\n", "simulated LU-steps: 34641\n", "runtime (WALLTIME) of simulation(num_steps): 78.118 seconds (= 1.3 minutes )\n", "\n", "### HARDWARE UTILIZATION ###\n", "current GPU VRAM (MB) usage: 9.747\n", "max. GPU VRAM (MB) usage: 15.871\n", "CPU % avg. over last 1 min, 5 min, 15 min; 2.66 1.05 0.58\n", "current total RAM usage [MB]: 10744.05 of 64219.7 MB\n", "\n", "SCRIPT: processing, plotting and saving data...\n", "\n", "DRAG STATS:\n", "mean_simple = 1.693653928340924\n", "mean_periodcorrected = 1.6928978755967055\n", "min_simple = 1.5952873956700555\n", "max_simple = 1.7953284200403388\n", "max_mean = 1.7873442402797335\n", "min_mean = 1.617481548579985\n", "frequency_fit = 0.1999999999241945\n", "frequency_fft = 0.0\n", "fft_resolution = 0.01999827742257103\n", "\n", "LIFT STATS:\n", "mean_simple = -0.013755539770956112\n", "mean_periodcorrected = -0.0005787575118849577\n", "min_simple = -0.7790682792632936\n", "max_simple = 0.7791679323475318\n", "max_mean = 0.7675340372781098\n", "min_mean = -0.7712608122909177\n", "frequency_fit = 0.20000000029835424\n", "frequency_fft = 0.21998105164828133\n", "fft_resolution = 0.01999827742257103\n" ] }, { "data": { "text/plain": [ "
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" 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" 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" 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" 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" 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" 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" 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" 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" 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" }, "metadata": {}, "output_type": "display_data", "jetTransient": { "display_id": null } }, { "name": "stdout", "output_type": "stream", "text": [ "Strouhal number is: 0.20000000029835424\n", "\n", "♬ THE END ♬\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data", "jetTransient": { "display_id": null } } ], "execution_count": 1 }, { "metadata": {}, "cell_type": "markdown", "source": [ "### Example 2:\n", "#### 3D Simulation at Re = 3900: turbulent vortex shedding behind a circular cylinder, calculation of average velocity and reynolds stress profiles and comparison to literature\n", "Note: This simulation may take very long (hours) to finish (reasons: 3D stencil, high Reynolds number,...), depending on your settings\n", "\n", "The following settings take 1:30 minutes to run on an RTX 2060super:\n", "\n", "`01_script_cylinder_simulation.py --outdir \"./datafolder\" --char_length_lu 20 --t_target 1 --bbbc_type ibb1 --reynolds_number 3900 --domain_height_y_in_d 5 --domain_width_z_in_d 3 --collision kbc --name 3D_simulation_cylinder_Re3900 --mach_number 0.1 --stencil D3Q27 --calc_u_profiles --profile_reference_path \"./profile_reference_data/\" --show_u_profiles`\n", "\n", "as a showcase to see, if the code works. For physically meaningful data, you need to increase `--t_target` to >10²-10³ seconds..." ], "id": "fc77a746f992e219" }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-17T16:45:15.690110Z", "start_time": "2025-12-17T16:43:45.533416Z" } }, "cell_type": "code", "source": [ "%matplotlib inline\n", "%run 01_script_cylinder_simulation.py --outdir \"./datafolder\" --char_length_lu 20 --t_target 1 --bbbc_type ibb1 --reynolds_number 3900 --domain_height_y_in_d 5 --domain_width_z_in_d 3 --collision kbc --name 3D_simulation_cylinder_Re3900 --mach_number 0.1 --stencil D3Q27 --calc_u_profiles --profile_reference_path \"./profile_reference_data/\" --show_u_profiles" ], "id": "a25110bac2f3c90a", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SCRIPT: Writing arguments to dictionary...\n", "SCRIPT: Input arguments are: \n", "{'name': '3D_simulation_cylinder_Re3900', 'default_device': 'cuda', 'float_dtype': 'float64', 't_sim_max': 259200, 'text_output_only': False, 'no_data': False, 'outdir': './datafolder', 'outdir_data': None, 'reynolds_number': 3900.0, 'mach_number': 0.1, 'char_velocity_pu': 1, 'char_length_lu': 20, 'char_length_pu': 1, 'domain_length_x_in_d': None, 'domain_height_y_in_d': 5.0, 'domain_width_z_in_d': 3.0, 'perturb_init': False, 'u_init_condition': 0, 'lateral_walls': 'periodic', 'n_steps': 100000, 't_target': 1.0, 'collision': 'kbc', 'stencil': 'D3Q27', 'eqlm': False, 'bbbc_type': 'ibb1', 'periodic_region_start_relative': None, 'periodic_region_start_pu': None, 'periodic_region_start_lu': None, 'calc_u_profiles': True, 'show_u_profiles': True, 'output_u_profiles_timeseries': False, 'profile_reference_path': './profile_reference_data/', 'vtk_full_basic': False, 'vtk_full_basic_interval': None, 'vtk_3D': False, 'vtk_3D_fps': None, 'vtk_3D_step_interval': None, 'vtk_3D_t_interval': None, 'vtk_3D_step_start': None, 'vtk_3D_step_end': None, 'vtk_3D_t_start': None, 'vtk_3D_t_end': None, 'vtk_slice2D': False, 'vtk_slice2D_fps': None, 'vtk_slice2D_step_interval': None, 'vtk_slice2D_t_interval': None, 'vtk_slice2D_step_start': None, 'vtk_slice2D_step_end': None, 'vtk_slice2D_t_start': None, 'vtk_slice2D_t_end': None, 'count_tensors': False}\n", "\n", "SCRIPT: Creating timestamp, simulation ID and creating output directory...\n", "outdir/simID = ./datafolder/251217_174345-3D_simulation_cylinder_Re3900\n", "outdir_DATA/simID = ./datafolder/251217_174345-3D_simulation_cylinder_Re3900/251217_174345-3D_simulation_cylinder_Re3900\n", "SCRIPT: Writing input parameters to file: ./datafolder/251217_174345-3D_simulation_cylinder_Re3900/input_parameters.txt\n", "SCRIPT: Saving simulation script to outdir...\n", "-> Saved simulation script to './datafolder/251217_174345-3D_simulation_cylinder_Re3900/251217_174345-3D_simulation_cylinder_Re3900_01_script_cylinder_simulation.py'\n", "SCRIPT: Starting stdout-LOGGER (see outdir for log file)\n", "SCRIPT: Processing parameters...\n", "\n", "(INFO) parameters set for simulation of 346 steps, representing 1.000 seconds [PU]!\n", "\n", "SCRIPT: initializing solver components...\n", "-> initializing context...\n", "-> initializing flow...\n", "OBST_Cylinder: self.resolution= [200, 100, 60]\n", "OBST_Cylinder: x_pos, y_pos, radius: 50.5 50.5 10.0\n", "-> initializing collision operator...\n", "\n", "SCRIPT: initializing simulation object...\n", "CYLINDER: the bc_type was given as: ibb1\n", "IBB: calc force is: True\n", "SIMULATION: creating no_collision_mask entries for: i, boundary = 3 \n", "collision index is: 0\n", "-> initializing reporters...\n", "ProfileReporters at x-positions: p1: 71.2 p2: 80.8 p3: 90.4\n", "ProfileReporter: requested position is off grid! Interpolating u(71.2) = 0.7999999999999972 * u(71) + 0.20000000000000284 * u(72)\n", "ProfileReporter: requested position is off grid! Interpolating u(80.8) = 0.20000000000000284 * u(80) + 0.7999999999999972 * u(81)\n", "ProfileReporter: requested position is off grid! Interpolating u(90.4) = 0.5999999999999943 * u(90) + 0.4000000000000057 * u(91)\n", "\n", "SCRIPT: spacial and temporal dimensions:\n", "domain shape (LU): [200, 100, 60]\n", "t_target (PU) with 346 steps (LU): 0.999 seconds\n", "steps to simulate 1 second PU: 346.41 steps\n", "steps to simulate 1.000 (t_target, PU) seconds: 346.410 steps\n", "\n", "#################################################\n", "\n", "SCRIPT (2025-12-17 17:43:46): running simulation for 346 steps...\n", "\n", "#################################################\n", "\n" ] }, { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data", "jetTransient": { "display_id": null } }, { "name": "stdout", "output_type": "stream", "text": [ "***** SIMULATION FINISHED AT 2025-12-17 17:45:12 *****\n", "\n", "### STATS ###\n", "MLUPS: 4.857\n", "simulated PU-Time: 0.9988159656980528 seconds\n", "simulated LU-steps: 346\n", "runtime (WALLTIME) of simulation(num_steps): 85.493 seconds (= 1.42 minutes )\n", "\n", "### HARDWARE UTILIZATION ###\n", "current GPU VRAM (MB) usage: 577.019\n", "max. GPU VRAM (MB) usage: 1845.631\n", "CPU % avg. over last 1 min, 5 min, 15 min; 4.18 5.31 5.19\n", "current total RAM usage [MB]: 11127.89 of 64219.7 MB\n", "\n", "SCRIPT: processing, plotting and saving data...\n", "\n", "(WARNING!) peak finding for drag didn't work... This might just be because there is no converged periodic region!\n", "Analyze Periodic Timeseries (verbose):-> see Python Stack Trace below:\n", "\n", "--- Python Stack Trace ---\n", "Traceback (most recent call last):\n", " File \"/home/mbille/lettuce_25/lettuce/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py\", line 129, in analyze_periodic_timeseries\n", " if peaks_min[0][0] < peaks_max[0][0]:\n", " ~~~~~~~~~~~~^^^\n", "IndexError: index 0 is out of bounds for axis 0 with size 0\n", "\n", "--------------------------\n", "\n", "DRAG STATS:\n", "mean_simple = 0.1732521376257104\n", "mean_periodcorrected = None\n", "min_simple = -0.724629230527121\n", "max_simple = 1.1230686001217727\n", "max_mean = None\n", "min_mean = None\n", "frequency_fit = 0.19893275083624243\n", "frequency_fft = 1.9794866372215736\n", "fft_resolution = 1.9794866372215736\n", "\n", "LIFT STATS:\n", "mean_simple = 0.00042755743250013793\n", "mean_periodcorrected = -0.002531614794975356\n", "min_simple = -0.02564869868377675\n", "max_simple = 0.025483797803982536\n", "max_mean = 0.01726717693382942\n", "min_mean = -0.02071004755938259\n", "frequency_fit = 0.202221662229376\n", "frequency_fft = 1.9794866372215736\n", "fft_resolution = 1.9794866372215736\n" ] }, { "data": { "text/plain": [ "
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" 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" 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" 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" 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" 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" 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" 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" 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" }, "metadata": {}, "output_type": "display_data", "jetTransient": { "display_id": null } }, { "name": "stdout", "output_type": "stream", "text": [ "Strouhal number is: 0.202221662229376\n" ] }, { "data": { "text/plain": [ "
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LMk9Ag0Ys33WSMV0iZeiTy9hepQkrhYc2M2HCPzz00EOVtklISACUNo2no1KpaNeuXYV1zZo14++//6ZDhw7Odf/++y/XXXcdLVu2BKsZU9JuHh8Qw8CB/TFZrLzx2Y+88c0i3CLbUnJiJ/PmzSMmJoaPV+5kVX4QAN6qElL3bOLaDtEM79meKD8XXDUqZ15HjRpFSUmJM9A8lbSTFPWFBItC1CN2u500QwkH0gro07wBSUlJxMTE4DHwcfTNulXa3lqUS37aEbpdfSu2U3oo//HfcuPhys+jUqlwNGd++eWXuf3222ncuDEqlYr4+PhaeS1RUVEVAs28vDxnNbVv37twbdCIyS+/Tcd3J9GsWbNaeU5xabHb7Rwr9QCKMR7ewjert/P5559Xu/3IkSP59ddfK5XSHTt2rMbn6d27N59++mmF0sZNmzZhs9kqtHUcPHgw7du3p0+fPkRERNDTr5BlC6YBEBMTQ4cOHfht0gw0bQbh4h+Owe6GR+u+bCqFTX9lo1HDD/f1oEtjf+Lj4zFr3FG76/hm7kcVLrJAKe2cMmUK69atq/QYSPW2uLRIsCjEJSAxMZGsrKwK60wWO8kGCztOZHGyCDLNrhzPN1NYqgRxn90QRPrxgxiNRoIs+ViKcmnoYUdfkoU18xgj+nTijZefJePkSRo1asSJE0W88sorDBo0CIDffvuNl156CYCnn36abt26ER4eTlhYGCdPnqRbNyX4HDp0KFFRUVXm21HiU57jxH3s2LEqA8vqTuy+vr707NmT+Ph4stMOAaAKaMy///4rweJl6lBGIcm5xdgtpZSc2IndbHaW4JXnKKUzmUzVltIBZGVlnfF3burUqYwbN46vv/6aadOUgNButzt7Wp+qb9++9OvXj9buhexd8gJx+w7x6Ix3WL4lgcgOvchVeYOHH9ENPJ3H8u56Iz7dRvH6ARVtjKU0D/aiRYgXPhQx9QWl89a4cePO+P0qr6ZSyar+T8qTQFOcLQkWhahjJ06coE3XXlg9gylJ2o3drHQi8bvmXry7jCi3ZSkAdpsVc04yg2+chDnzOACmuF9JXfI+yeW2XvB2Gg08tIwbN45Ro0bRpUsXevXqRXh4uHMbR7A4ZsyYCqV8GRnlRtA+RWBgIG5ubpSUlNR4ops2bZrzJFwVxxh65X3++eccPXqUq/rfQICrhdirY7n99l7VHkPUb6sT0gEoSdyFm1ZNsVkpwTu1aQPAL7/8wtChQ6sNJm+66SamTJlS4/NVFUyWvxBasWIFubm5rFmzhvj4eBITE0lLSwOgoKAAi8XC6tWrASUYXPzJaxQXF9M4oAj78ePcctf9/LXiN/7991+ysrLQuAdgt9swlKrZcCSbDUeync8V9OC3JM8ez9dz3icmJoZjeWawQ5iXltV/LGfatGnOphqnSkhIYNy4cVWWSqampjJ69GhKSkqqfR+k+lucLQkWhbgIHFf6JoudJIOZE3kWjucryyM5JQTc9TEAr/QLIMrTisVi4c8jhfxwoBTDiQTMmceI8FJjz01m9/pVYDVXOH5qihIment7079/fwYOHIi7u7vz8Y4dO3LLLbfUymuJjIzk559/rvbEDTVXoTlOdFU1+A8ICODEiRNY8tOZOyKiyqBBXD4WrNsLaCk+so3Y2Fg2btxY7baO70t1weT+/furLU07k2DSzc2NmJgYIiMjnb8Vx5A9AD///DOtW7d2bl9SUkKLFi3YsWMHN954I3fddRcA99xzD1988YVzu5w/5+ASEIFLg8a4BjbCpUEjXAIboXbRYS3MYe7cuYwdO5avDruQomoAgLe2PcG3vMqf+QEcWL6bDs0aMnn0NWjUZe0hoeZSSVdX1yqr7B2/v6ysLAkWxRmTYFGIM3S6qp3yAZLdbie72MbxPDMepbncO1a50vfsMISA6x88ZU81dpsVd3M+K//cybw3n2Py5Mk0aRrN4VllgwbnVvGcLi4umM1mhg0bxpNPPkm3bt0qDHJdG6qqanZ0UqnuxF0bFi9eTPPmzfH09Lwgxxd1z7/oOAnHCyk+spXO48fUGCyeTmRkZI3BT3XBpCN4eu2118jKyqqwTfnvflRUlNIZ5j/u7u488cQTjBs3DldXV+f6YcOG4e/vT3JyMj/88AN2s4mRfTsTFKR0isnK2s+GZV9wIkUpsVyzZg1ubm4c0LbGs1k30HlgsKhxa9SO48DxAti43UhG/LPs3LkDrVZLoldr/Ps/SGx0OP26tCU6LIBQbx2erir279/PuHHjKC0tJTQ0VC64RK2QYFGIM3D0+Anade5BqcoFtbsPlryTWAuUKiWXoCh8uo1C7e6N2t0bjd5Hue2iBI5Zy96lpKQElUqFtjANqzGf0ozjmDOPUZpxlKE9O7Jg7rtgtXD9w0pwmJ6ezqhRo2jXrh27du0C4PHHH6dz586EhIQ409KlS7n99tsZM2YMPXv2POfXd2pAmJqaik6nw2QyVVt6odfraxwH72yf89T106dPJy8/H427D0P79+Gaa6455+cSl6bPnrqdRo0aYSkspHPnzhf0uaoLJgMDA9Hr9TWWOrq6upKamlqpCruq9rkRERHceuutJCQk8MMPyjBUTz75ZIWgrXyJ5eTJk2nVqhVNduxAx0Zuu20CPQfeiItfGO17XU+J1ovY2Fi2/bDVWQUeetcAvDpGcQQ4cgA4kKcc2FqKu7ms1iE1NZV/D2WhUkGojxthvmW1DUKcDQkWxRWp1GIjo6CEtDwjR1MyOJGWTYDdAAXppKensy+jhO32KExoMat14Kon8N65zv1jCnewYvbz3HDDDXQYcDNfJ/lWeg671YI5JxlsZcPQ5B2KI+/QWNRqNbGxsTSNaUrbhr4ssFoYNGgQt99+O9OnT8fHxweVSsWXX37pPKncdtttlUoJzmdImTNpe3ghxohznJxrqkJzcXFBHdKCReqelKamUPrrrxIsXob27dtHYWEh/v7+VTZnuBgiIyNJSEiostSxfPu/mjrW1NQ+t6pAs/yF0vjx4yv8rvPz87lrWD8++eQTHnr2XudYpX9EPsX48eNZtGgRKzf9hGtgI7zDo7HofFB7BqDx9AONK/m5Ruex/ve//5HcehzqgEZl+bGXEnrXBzyx9Djdj6l5pn8TPD09UavV7D2Zj6tGTQMvHT7uLjJklXCSYFFcVux2OzlFpaQbTKQbStCZckk7spfQ0FBUwc2Z/ts+UvOM5BZbKu2bvXI2hTuWA6CLaEvIbTNPObYNVakRc2EugaG+9O/fn969e9Mw3I+cr+diMxro3a0DbZpF4aa28v0XH5OTksy111zDkr3KMWbPno2Hhwft27cnNjYWUKYiAxg7dixdunQ5p9d9up7H1T3+2muvMWXKlGrbHl6IXpM1nZwdMjIyGDpmPCpXd9yCm9CnW+NazYOoW1abnYXbU1j/+yoABg4ciEajAU5f4nwh1FSFfeDAgTNuflLemQSabm5ulUrnfXx86NWrF5988kmF9Y6BysePH8+cOXN48MEH+XDePG677TZSU1MZd8ddpBtMtG/ZiiX/7aPT6SjOOEGQjx8WnTcmK5SqXHENiuKoESwHM+g/637i4uLw9vZGP/IVtIFKYKlR2fF106BXmQnx9aBt42BeGFY2K86RzELcXTQ08NI5p2oUly8JFsUlq6ioiOXLl3Po0CEOHjzIkRPJlGrc0VhNaK1KTz+TvgH54d2xunphdfXE6uIJmrKv9VW6JL6f/iDjx4/nmpvvY19q2awhdosZa1EuVmMetmIDQd5u9Bo0iODgYPKKLaxc+D8aeLkzY9rTmAtyuXf8bVx37TWkpaVx+1tvcf10ZaTClJQUbm4fyGefLWLs1LudJQEvPj4RjUZDfHw8S5Yof9/du3ev1TZEjh7Fp+t5XNPjer2eXr16XdTG7qdrX1ZcXIytMAtrYS4aTz+adDr3KnZx6dmZnMcTP+3EbmoNKjWDBw8+oxLn8236cC5O912tSXWBZk3tJOH0F3nlt1epVISFhfHXqpWAUsW95Bulw9xLL72Er68ver0egLlff8fJvGJ++X0VDSKaMHHW6zz9STpWq5Xc3FxciwtQFRegcffCaleRXWwjGw1JxhK27d/M6rcmAsp4l/NzGpNuUv5r1WYjrlYjsS2aEOqnJzrYh4f6NnWWTGYWmPBy0+Lmojmn91HUPQkWxXmzWq0kJiaSmppKeno6hYWFFBYWUlRUVGlpNBqdgz2/8OJLNIpugaHYwtYNa5j/1WcMGDCAzv0GM3t5PFlFZjbtPIDG0x+t7yDUwR6AowRwLQC6hq0JaX9XpTypS4sozklF3VDFVVddRdOmTfFTG0n/6SUaBfnwv+eepEl4GGFhPXjuuec4mnSUF6e/SJ8+fQA4efIkK1asICwsjIEDr1f+sO02Xn/99UrBXnh4OH379uWzzz6rsN5RUnKmqjoxlC9NqamjSXUlg1A/B/d1d3cnKiqKgrRD6KO7sis5j06N/Oo6W6KWrDuoBDvGY8rvasCAAQQGBp62xPlS/K7W5HzaScK5DT9VXnJyMl5eXhQUFABwy8hhJCQk8N3sWSz65TM6dmzKyEOHyMjIYP369WzYsAGT6TimIisHE1M5npZDTrEFjYcfdouZ43uV/129Xk9SyGB0QY2xq9TYXPSUuOjZdMIAJww09svl26nKlKChoaEcaX4LJrcAdGo7ni7gobVjKcpDh5kWYX4MjFTh5eVFREQEqUV2ggP9iQjyQ++qkarwS4QEi1c4u92O0WisMrgrKCigsLCQoUOH4u/vD8Dy5cv5/vvv6dWrF3fdfQ95xWYOHkui3/WDUbt5oNb9l9w8KD6+E3PGUQBcQ5rhd829qH3Kthk+PxFIBKCteT9Lf/0Vi8VCgVswq09qAQ3ujWMr5NdmMtJ/4GBue+5uADZt38OnKz/B313DI/eOp3GIH706t2P0yBtZu3YtA775httfWQ8onUZmPjyWpk2bcsPQAc5jnhrkAYSFhXH33XfX8rtdtTMtHaypo8nFLhm8GGJiYvg39SD66K5sPZLBTe0bSM/oy8Sek/kAmJITCAwMdJYWnk8pXn1yJk0xzmT4qby8vEoXmGfbOc3FxYXw8HDGjBnDmDFjKm1bVFTE0aNHOXbsGKWlykgOu3fvxr75d27sPpJ+A4eyYs1GLFo9L772NhoPP257dQaTpsVjNpvZsWMH4VE3o3UDk02FyQTZJhWgnFMO7DzOJ5MnAcrc3tuCBuEaqHwHNFhx16rJz87AXlpM/x6xzBreHL1ez4IFC5i5cAst2nRg8IBr8dRp8XDVsvavVfh4uDGgX0+ujomo4VMQZ0OCxcuEzWZDpVI5r8IOHz7M7t27adSokbMkLDExkdtuu42MjAwMBgOFhYUVSvoA1B6+6MJj0Lh7o3bzRO3uzd9F4VjUrmQXltCw4BDffvstNpuN0sDmvLG5EIDQO96plKfoE3/R1deIp6cnB3Ot7AxsXWkbAFtpCR7e3nz00UfKDCJNWvLp8m+IbODDg3feSrC3G8Hebnhrrbi7qHF3d0erVb66I0aM4MVnilGr1Xh4eDiPuXz5crRabYUhLYKDg097JX+hnUvpoGNMtao6mUD9K205UzExMaz+5S8AFq/bQeuCbUycOLGOcyVqQ8J/zUHUhpNMnjy5jnNTN84nMD6TKvva6pzm4eFB27ZtK0yVOHr06ArbNB8/CoBHbx1IQUEBXl5eXH/wIDt27OC7775j82/TcPX2wy84AquLHquLnuSMXFTu3oT4exN11VVkZ2fj7++PymZFbbdiU2mwoqHQAhqfYABO5JbQtGlTCgoKUKlUhN7zEXvsDdmz4kC53ARBNnz/2Qb6F6/Bw8MDd3d3vkvxx61BJGGBvthLi/D3cCM7LQmdysr1vbpxZ49I3N3dyc7OJu5AIg2CgoiKCEPvqkHvqnWOcXmlkmDxPJjNZgwGQ7WPl5aWOqtky6dhw4Y5rxgXLVrEmjVruO666xgyZAgAR44cYezYsVit1mqPbSo1U1BSSlFxKcVmKyaLnZ9+XcTOvfvJyS+gIPkAX3z4Fg8//DDpxSo+/G0zdlc9BwJ7om7ojcbdE193L/zdvMlZ+SFF+/7Bw8MDt6axeA56osJz/X2iBFDaCAZ6ejqrYo8a/huiwVxMWAM/vN1c8HZzYe+ObeSkJzPyhp48d/eNAMTt2c8zb39BwyB/Hp30gLKtuwvbt2ygqMBKp06DK8wscuD7V87oM9BqtXh5eVVa72ijU9uqa2RfUxujM73SvxxLB89HTEwMpWmfAuAS0JBsw77T7CHqg/xiM8m5xQAc2PwX7hr7afYQpzqTksm6uIhUqVR4e3sD0LhxYxo3bsyIESPO+jgWi4W1G7bw7Y+/Et64Kd2u7o3RbCMsOIh+U5Xe3na7ncJdf6LxCkCtc0flqkft4obK1R21qzvFhdl8/JPSdnPkyJGU+PfHio5DWcWAGgylQDDYIXNbKm/dN4Tk5GTUajVBt81CF94SKPu/t1tKwWJCay7Ef4vS+ah3796sN/jh27AZ4SEN2LDmb3y93CkuyMdSXMiUifezau7/OHr0KDk5OaTnF+Pl5Ul4A39Uditubm6YzWbCwsJo27YtYWFhBAUF0bZtW6Kjo53vRX5+Pmq1Gj+/sqY4+fn5uLq6Vph84UJS2csXK4kaGQwGfHx8yM/Px9vbm82bN9O9e3cAVC461O4+aPQ+mHOSsZcqf4YugY1wi2wLGhdUag0qjZbJjz1ORmY2BcZi7IfXM//jN5k6dSrRVw3i49UJuLjqOHI8EZXWFZVWh8rFFbVWR86qORQf3QaAPqY3DYY9VW1eu9oPkLT2J0aOHElpgxbM3V99+7mh4SW8P/FG1Go13/z2N8/+sgMvnZrB1/bFV++Cn96F776cy/GDe3nx4bt5YJxyFZmRmcmyZctpEtWY3r17O49nt9svu3YmiYmJxMTEYDQaT79xNS7EMDSXsy1bttCtWzfCbnyaR++7g7t7NcXfw/X0O4pL2uaj2dz86SZCfXRsnHpdXWdH1DM2m43i4mJn4UtBQQFFRUWVmlGVv33NNdeQb3XB1cuf5PRsvvxuAcGRTdB5+VFosnL9NX15/fbezlqe4Ftn4hrcFJWLDpW64rnTYsgk5WOlnfzw4cPZ7NXrv8CyMr2LGvXCJ9m7VxkOI+iml3FvogyFZreUYjMZlVRqxFZSRMaC5wB48MEHCe8xDJVnAGqLiRkvPo+/rzefzf0UV60aF7WaJ8bfwOZNm4iMjKRp285ERTenWdMomkc3pWXzZoSHhaDTatCqVWjUSq3jqTHM2ZBg8Sw43uiJ//uIn3Zk4hMcjtGiRq33Ru3i5twu7fupmBJ3o1ar8ek4BO9r76/2mIO9kvApPEHfvn35+2ghP5xwq3bbfm4nuKlrYwAWbzvGSmNjsNtwd9Uq1bOuWgpys7GaS5h8XUvuG9wVgFWbdvLqT+sJ8fdmQN+r8dW74qt3IS3xCG5qG13btiAs6OL2MKyPzmYGl6pIQHh2SktL8fHxoaSkhISEhAqzZ4j666v1x3jpt3145B2lYNkbfPbZZ85hYYSoKxaLpUKg6QhE8wuLKCyxsHTFKjJy8hg0eAitwnyc+61KyMQ9MBzfgCAOn0jCrnbBWGqh2GwnNCSImyOVwDY9PZ1PD7mRaqs6SNPYzLQ58CXp6en4+/sT59kN9yZVD1SvAoLXvMLmTZsACBwxFY8WV1f72hKmD8TdVXNewaJUQ5+DIqsG9yadKKXiG2i3mrEW5fHh7I+4e/BVqFQqps/5gQ9XrCWoQQBDBg5Aq1HjqlGzYvnvGAsMXH/PMEb0ewAA7ZbdJKVvIKRBIFd174abiwZ3VzXxWzZjMhYw6vobaNlEabDbp28xxuJiAvx8UatrHuOqf/f29O/evvIDLYJq6R25Mlwpje/rWvmgPCYmhu3btzN//nxndZYE3fXboLahfPr+m+zYsoHipCTnNHhC1CWtVouPjw8+Pj5VPj529PAq1w8eXP5euxqf4y6UMUaLSi0UlFgoLLFQUGKmoMRMqdXOgNYjAJg1axZH1x+nib6A8MbRFJksFJeUsmnrVlBriY2N5ff168nJyeHRRx9lea4JlbkYNFrsqOGU0lAXzfnX9EnJ4llwROWb9x5lV5qRIG93IoL8CPDQ4e/pijE/B7vdjr+/v3N+3uLiYgwGA+7u7mcdyQtxpamuul/t4YtrcFNKjsah1+tJSEiQgLEeSkxMJD09nX79+lFUVMRzzz3HsGHDnB3W5EJAiKrZbDaysrKw2+0EBQU5m3oVFBRgNBrR6/XO9vvFxSVk5+Zitavw8fN3zsZzRVZDr127ljfeeIO4uDhSU1NZuHDhaRvS/vPPPzz22GPs3buXiIgInn/+ee68884zfs7zeaOFEKfnmDPX0TN87dq1TP/fa3jd9SkqtYanWuYz8a6xxMXF1erg5uLCO5N2v3IhIMSFcz4xTL2do6eoqIj27dsze/bsM9r+2LFjDBkyhH79+rFjxw6mTJnCvffey8qVKy9wToUQZysmJoaOHTvyyCOPcPzIQXz4L8Dwb1TzjuKSlZWVhUnjwYS3f+LLZRuIi4urkObNm4fRaKyxXbAQom7U2zaLgwYNYtCgQWe8/Zw5c4iKiuKtt94C/hvw999/eeeddxgwYMBp9hZC1AW1Wo23tzfXd4nh57hkEvMrz+kt6g9dwxj+SHdn68mjjAtJ4+67764wHIgQ4tJUb0sWz9bGjRu57rqKwzQMGDCAjRs3VruPyWTCYDBUSEKIi69ZkDJziwSL9ZtrUBQAKXs38cQTT5CWllbHORJCnIkrJlhMS0sjODi4wrrg4GAMBgPFxcVV7jNz5kxn7ygfHx8iImTqICEuttWrV/PSo/cBcDzXVMe5EefDNagJACWpR5gwYUKl/2QhxKXpigkWz8XUqVPJz893pqSkpLrOkhBXnODgYDIP7wIgtdAK6nrbeuaK5/JfyWKwzsKnn37qnHNeCHFpu2L+dUNCQkhPT6+wLj09HW9v72qny9HpdDUOsiyEuDDKT6totVoZMaAvW0xFqHUeuPiH1WHOxLnKK7Gi9fTHbrfRMlRGkxCiPrligsUePXqwbNmyCutWrVpFjx496ihHQohTBQYGotfrq5xD26tjCbZSI662EgIDZcah+uZ4ntLe1JKbir+3R6U51Kubd10IUffqbbBYWFjI4cOHnfePHTvGjh078Pf3JzIykqlTp5KSksI333wDwAMPPMCHH37IU089xd13381ff/3Fjz/+yO+//15XL0EIcYrIyEgSEhJqHD5FBm6unzLNyrzepRnH+GbxN87/5vL0er1cCAhxCaq3g3L/888/9OvXr9L6O+64g6+++oo777yT48eP888//1TY59FHH2Xfvn00bNiQadOmyaDcQtQDhw8f5q233sJisTB37ty6zo44B2arjfCYzuTl5WLOPE7Xrl35+OOPK2wjFwJCXDhX5AwudUGCRSHqRkJCAq3atkcf0YrZX37PnX1j6jpL4ixlZWXRoEEDAE6ePIndbicsTNqfCnGxXJEzuAghrhwtWrRAq/emwc3/4+UVRygxW+s6S+IsOdokNmrUiNDQUAkUhahHJFgUQlzy1Go1QV46rCWF2FFxNLOorrMkzsLu5HzeWJuGR6u+xMRIqbAQ9Y0Ei0KIeuHqq6/GnHkCgEMZBXWcG3E2Nh/LZleBHn3zq8jOzmbGjBmkpKTUdbaEEGdIgkUhRL3QrFkzzFmJABxIk2CxPtmTkg+AKf0wu3fv5oUXXiA3N7eOcyWEOFP1dugcIcSVJTo6GvPKRQBSDV3P7P4vWCxNP8L1119PYGAgjRo1quNcCSHOlASLQoh6IT8/H3OuUnV5LEuCxfqiyGTh6H+f18i+nXnkvrvo1q1bHedKCHE2JFgUQtQLXbt2xZLzOqAEizabHbVaVce5EqezL9WA3Q4h3m5899qcus6OEOIcSJtFIUS90K1bN/T2ErKXv8/0axsgA8TWD472im3CfcjJyaGwsLCOcySEOFsSLAoh6gWNRkPzZtEU7voDXX4SGilVrBdS80sACNdbefHFF/Hy8uL555+v41wJIc6GVEMLIeqNZs2asW3bNg4dOlTXWRFn6NnBMfQLKqFnr9642M0AhISE1HGuhBBnQ4JFIUS9UVRUhNY3hF92pNFhXzr9WwXXdZbEGSgtMuCKheYtWrBu3TrUaqnUEqI+kWBRCFFv2O123Bq1Jz3yWr7bfEKCxXqiT58+GI1GSkpKcHd3r+vsCCHOklzeCSHqjQkTJnDPmBsAGT6nPth0NJtbP93E+6sPoVKpJFAUop6SYFEIUW/ccMMNTJ10DwBJOUZKLbY6zpGoyf5UAxuPZrP3ZD4pKSlMmDCBWbNm1XW2hBBnSYJFIUS94mY34aZRYbNDYo6xrrMjanA8W/l8kvfv4Oabb+azzz7js88+q+NcCSHOlrRZFELUG4mJiQwePJj8zhPQhUTz5+ZdGMLdnI8HBgYSGRlZhzkUiYmJZGVlAbD7WI6ybl8ce9avp1+/fvTo0aMusyeEOAcSLAoh6oXExERiYmIwGo0ENj2JLiSap195E8OWhc5t9Ho9CQkJEjDWkfKfEUDYvR/jEhDB4e0bAfj777/ZvHkz999/v3xGQtQjUg0thKgXsrKyMBqN3H777bibDQCMuWcScXFxxMXFMW/ePIxGo7NUS1x8js9o3rx5bNm6DfcGEQC4W5VZW2bOnCmfkRD1kJQsCiHqlSlTpvBSRHPS8kuIDvYkyMvt9DuJiyomJobgJjFYbGm4qFXkphwDoH379nWcMyHEuZBgUQhR7zRp4EmTBp51nQ1Rg5zCUgI9dbhpbBy221CpVAwePLiusyWEOAdSDS2EqFfMZjPXX389UVFR5Ofn13V2RDXaNvRh2/PX8VI3V0CZ21sIUT9JsCiEqFcyMzNZs2YNGa5hvPjrdhmc+xKXkXYSgJ49e7JmzZo6zo0Q4lxINfSVJicHqmpcrlKd2/FO3c9ur7g89XZ1qtrvTJ+7fB7Kr6sqqdVlS7UaNJqy5am3HffP9b0RF4TFYqG0tBS/Tjew6EARfWJziQr0qOtsiWqkpKQAEB4ejqenNB245NjtYLWWJZut8m2bTUl2e+Wl4xiOZVW3y29zOlX9r9fGfmdzbqlue60WmjQ5u+NcJiRYvNJ89hk8/XRd56J+KR9AarVlycWlbOnqWrZ0pFPvu7qCTlcxubmBu3tZOvW+uzvo9eDhUXGpvXJ/utnZ2XTs2JHj2Um4Rbbl312HaWTPICEhoa6zJv6TkJDAq+tyKLXaYfdRALRa7ZX9GdntUFICRiMUFSlLRyourjmZTGWptLRsaTYrS0eq6r4jWSzKsnxgaLWefRB1JYuIgMTEus5FnVDZ7fJNOVMGgwEfHx/y8/Px9vau6+ycm6QkOHas4rra/grUxpXh6fY90ytax9JxFey4Ii6farqqtlgqPm6xlKXyf8SOVN0ft+PP3XHbkUpKyk4IJSVn/l7pdODpqSQPD/DyAh+fisnbW1lffhkYWJY8POpVqempY/h5dRyKf/8HMB7cSObC/wEyzmJdK/8ZRUz5EbVOT8rcB7DkJDu3qZefkcWi1MpkZio1M3l5YDBAQYGSDAbIz6+YDAYlKCwsVFJRkfKfciY0mooXjKdeZDouPMtfmLq4VL2u/EWtVlux5qS6VFUNiyOVr6Ep/199Jsua/m+qK5E8E6ermaqtmjOdDrp1O7djXQLOJ4a5cosnrlQREUoSlx5HyUP5EgVHqYOjJKKoqCw5TkCFhcoJKz8fcnPh+HHltuMkVlRNmz43NwgIAH9/8PMrWwYFQViYkkJDy27rdBf17ThVZGQkCQkJZGVlkZ2dzbAJTwIQFXsVK56PA2QGl7rm+IwOJ6dz95IMVICv1oKj4cvMmTO57bbb6v4zstshOxtSUuDkSSWlpiopO1v5HeXmKgFiTo5yuypabdnFWPkLtYgIZb3jgs5xUefpWbGGwJFOrVlwcbm474cQpyHBohCXCpWq7KRRm6xWJWDMz1dOhI7SEcfScWLMzYV9++Cff5ST5qklnUFBykmwYUMliHQEmo7UrBm0aKGUOlwgkZGRREZGcvLkSYL/e5syjTZat2uPTiu9bS8FkZGRpFo8gAxCvF3ZlJGGSqXiiSeeYOzYsURc6IvV7GzYuRMyMpTbOTll3/vkZCWlpCgl++UFBVX8XjdqVHYR1aBBxVJ5Pz8lQNTp6lXpvBDnSoJFIS53Go1SyuHtfealyna7UtXmKHVxnGSTk5WmDJs2lZW8FBSU7eflBZ07Q5cu0KmTcvL18ytL7u61cnJ96KGHOLF/JxEDi0DnwfEsIy1CvM77uKJ2HEhXvhPRDfTc8L//kZaWxqxZs2rn4Gaz8t089SJnyxbYuhWOHi3b1sWlLPgLDFQudLp3V5bh4UoKC4PgYKXqVghRJQkWhRCVqVRlAV7r1jVvW1qqBI3lT9jffw9VBQfe3tC+PXToUJZatTrrardmzZoRHByMOSsJXXhLDmcUSrB4CUlIVYLFdpEBPDnh2XM7iN0OaWkQHw/btyvL+Hg4caLytu7u0LEjDB9edqESFlbv2uUKcamSDi5n4bLo4CLExZKdXVbN7Wj3lZSknPi3b4dDh5TtXFyUgLF9e4iNVZatWimlPTWc6LOysgiL6YzNZCT50G5CgoMvzusSpzXyo/XEJ+bx/q0diLClo9FoaNmyJa7Vld4ZjXDwIOzerVQhO1JmpvK4n58SDHboADExZe1r/fzA11cJDK/gEQKEOBPSwUUIcekJCFBSdQoKYNcu2LFDCQx27IAffyxrK+njAy1bKql167ISIy+lBDEwMJDGfq4cOnSChH37JFi8hPjpXfFxdyH36B7efu9V1qxZw0cffcSD990H+/crJdA7dii3DxyoWFoYFaVcMDz0kLLs2BEiI6WEUIg6JMGiEKJueHnB1VcrycFigcOHlSCifPr5Z6WTjkqllDp27QpduuD3X0lVYWFhHb0IUZXP7+xCSXExXt7eWCwWvFxdaTd3rjLGa0GB8jk2b66UEt5yi3JB0KKFclEgtTZCXHKkGvosSDW0EHXEalWCxs2bYcsWjBs3Mm7XLv7UuqLufhO9Iluw0C8Dbbeu0KMHNG0qJVEXm8GgfD6bNsGWLSRt2sTIrCxSgOSQEOjeHXW3bso4dZ06SVAoxEV2PjGMBItnQYJFIS4Ndrsdd3d3TKZSop9ciFmt5e8/XiVq+wZlgwYNlF6v3bsr1dedOyvt20TtsFhg716lM5MjQNy7F+x2rH5+aLp0Ud73Ll2wduyIRsZ2FaLOSZtFIcQVRaVSMXfuXHx9ffnoiDcJaYUc+vYXokJdlcBl40YlvfZa2dA+TZsqQWP37tCrl9IeTjpFnJnUVFi3DtavVwLE7duVtqVqtVJ1fNVV8Pjj0L07D28rYvfJfJ4f0ooBrUOQ0S+FqP/kn1IIUS/dfvvtAKz+YTsJaYUczizk+tbRMHiwkkCZXu3QIdi2TQlytm6FRYuUAZk9PZUgp1cvpd1kly7KuiudzaZ0OtmwAf79VwkSjxxRHmvSRKlGvukm5f3q0EEZnqachKX/kJRTjE4D77zzDosXL+buu+9m/PjxdfBihBC1oV4Hi7Nnz+aNN94gLS2N9u3b88EHH9C1a9cqt/3qq6+46667KqzT6XSUnM18vEKIS8qOHTvITdwPeHM4vYpOLmq10nGiRQsYO1ZZZzIpweO6dUp6802YNk3Ztn17pc1j9+7KMD4tW17eU6/Z7Uqp4c6dynuyYYNSMpuXp7T5bN9eCbx79YKePZVB1mtQXGrleLYyveSQqzugs5vIzc1lsCN4F0LUS/U2WFywYAGPPfYYc+bMoVu3brz77rsMGDCAAwcOEBQUVOU+3t7eHDhwwHlfJQ3ghai3jEYjH3zwAT/9m0DQjc9xKOMMe0TrdGW9sJ95RilJS0hQAqWNG2H1avjoI2VbV1elmtUxBmSHDsqyPrZZtlqVEkPHOJeO4Yqy/pu52ddXCZIfe0wJmLt2PevXeTC9ALsdfHQqTuSkofPzY968eXTo0KHWX44Q4uKpt8Hi22+/zYQJE5ylhXPmzOH333/niy++4JlnnqlyH5VKRUhIyMXMphDiAtm0aRNffPEFnmFNATiSWYjNZketPsuLQEe7u9atYcIEZV1+fsUxIHfuVGalccwn3LSpEjh27qwEVZda715H73FH1Xt8vPIaiouVxxs3VgLgiROVZfv2yrrznNd7X6oBAB+MAHTv3p2xjhJdIUS9VS+DxdLSUuLi4pg6dapznVqt5rrrrmPjxo3V7ldYWEijRo2w2Wx07NiRV199ldY1TGVmMpkwlZts3mAw1M4LEEKct9jYWMLDw+nQsQ0HtEqQk15QQqiP+/kf3MdHqXrt1atsndmsBGCOkrn4eJgxo2z8x5YtlQCyYUNlRpGwMKXaNiREmZfYx6f2hvMpKVFKBDMylGrkkyfLlvv3Q1wcFBYqz9eihRLM3nRTWcnoBeoZvjslX7mRkwhAp06dLsjzCCEurnoZLGZlZWG1Wgk+ZcaG4OBg9u/fX+U+LVq04IsvvqBdu3bk5+fz5ptvctVVV7F3714aNmxY5T4zZ87k5ZdfrvX8CyHOn7+/P8nJyQAczyqioZ87J1OSiT+SVe0+gYGBREZGntsTurhA27ZKcnTWOLUEb9cupc3fyZNlM9GU3z8wUEmensp8xo7k5lY5kDSblZJAo7FsmZOjBImnDkKuUkFQkBKgNmkCzz9/wUo8ExMTycqq+j3efFBZn30o3rlu06ZNdO/evVbzIIS4uOrlOIsnT54kPDycDRs20KNHD+f6p556ijVr1rB58+bTHsNsNhMTE8Ott97KjBkzqtymqpLFiIgIGWdRiEtMZmYmiYmJ9O7dG6PRWO12er2ehISEcw8Yz5TdrlRlp6QopX+ZmUqQ51gWFioBoCOV+59x0morBpTu7sqcyA0aKAGnYxkWpsyjfRGGAUpMTCQmJqba99jv2vtwj2hN9pLXMeWcVNb5+ZGTk3PB8yaEqNkVN85iYGAgGo2G9PT0CuvT09PPuE2ii4sLHTp04PDhw9Vuo9Pp0Ol055VXIcSF9fTTTzNr1izuuOMOjEYj8+bNIyYmptJ2CQkJjBs3jqysrAsfLKpUSocRX1+lLeRlIisr64zeYwAfHx+ioqLkwlqIy0C9DBZdXV3p1KkTq1evZsSIEQDYbDZWr17NpEmTzugYVquV3bt3y5AOQtRjf/75J9999x0qFx2baUHo3R/StHlLOnbsWNdZu6zFxMSc9j3u3Lkzf/7550XKkRDiQqqXwSLAY489xh133EHnzp3p2rUr7777LkVFRc7e0ePHjyc8PJyZM2cCMH36dLp37050dDR5eXm88cYbnDhxgnvvvbcuX4YQ4jyoVCpSUlIAKPJpjKu7N8fyzEgLuYsvOdeI2VrWqkkCdiEuH/U2WLz55pvJzMzkhRdeIC0tjdjYWFasWOHs9JKYmIi63DAQubm5TJgwgbS0NPz8/OjUqRMbNmygVatWdfUShBDnqWPHjixatIjRo0dTkrIffXRXDmeb6zpbV6QJ38RxKN2AW6P2lJzYKWMrCnEZqbfBIsCkSZOqrXb+559/Ktx/5513eOeddy5CroQQF4ufnx/Dhw+nT58+bEs9iD66KwdzSus6W1ecErOVg+kFWG1gzlFKevfv30+fPn246aabzrh5kBDi0nR+I7AKIcQlYPDgwZhOKrMzHcqRksWLbV+qAavNjhtmrAVZhIWF8e+//7J27Vo0Gk1dZ08IcZ7qdcmiEEJs2bJFGeYq9SAAaYVW/tm4DW9dxWvhhISEusjeZenU93LZIWU+aHJOAHDjjTfy+OOPs27dOvr06XOxsyeEqGUSLAoh6rVZs2bxyy+/4Ovrizk7CZeACAaNe5CSo9sqbavX6wkMDKyDXF4eAgMD0ev1zuFxHAIGT8azbX/SErag0+m45557iIqKIioqqo5yKoSoTRIsCiHqtV69epGfn49arSYuZT9enp68/e57dAt3q7Ttec3gIoiMjCQhIaHSDC5TVmaSmG/h9acnMqjdTHmPhbjM1MsZXOrK+Yx+LoS4sJYvX87gwUNo2DCcxMREVLU1D7OokbHUQpsXV2Kzw+ZnryXY241PPvkEX19fBgwYgK+vb11nUQjB+cUw0sFFCHFZ6Nu3L25uOpKTk9mxY0ddZ+eKYbXZ6euXT8ne1Xz50bvYbDaeffZZbrnlFg4cOFDX2RNC1AIJFoUQlwWdTuecken9D2djsljrOEdXBi83Fwq2LiR96TvodDqMRiPjxo2je/fuMjC3EJcJCRaFEPXefffdh7e3N+3btyd06BTWNhjO77tS6zpbV4wFCxawcuVK7rjjDjw8PHjvvffYuHEjLi4udZ01IUQtkGBRCFHvqVQqioqKMJlMTLj7Tqyo2ZGUV9fZuuxZrDZ+jksmJa+E/v374+/vX9dZEkJcANIbWghR7z355JM8+uijREdHs3xvBl9vTpZg8TwkJiZW6vFcnqNX+dbDqTzx00683LTseOF6NCo4duwYUVFR0sFIiMuIBItCiHovOjraeTs2wheAvSn5/LLoN0aNuKGOclU/JSYmEhMTg9ForHYbvV5PQkICb369FGhEiLoQjVrFiRMnaNq0KZGRkRw+fFiqoYW4TEiwKIS4rDT0c8fXxUqeWcOT73zFiBsGo9Fozri07EqXlZWF0Whk3rx5xMTEVHo8ISGBcePGkZKSwubjOWgjG9HCX5nS7/Dhw7i7uxMSEiKBohCXEQkWhRCXhaVLl7J+/XrGjh3LqC6N+XxDEg2vGoHNZiMlJeWMS8skYFTExMTU2Jv5jz9WoQpqAcDdQ3sBcM0115CZmUlSUtJFyaMQ4uKQYFEIcVmYM2cOv//+Ow0bNmTUjbfz+YYk0jSBFFvPvLQsKytLgsUz9O3Sv9Bc2wkXrLSL8HOu9/DwoGXLlnWYMyFEbZNgUQhxWejXrx+hoaH06dOHmFAvbuwQTodIXzQqFWazGTh9aZk4cxl2b/yBzo390WrUyGRgQly+JFgUQlwWHn/88Qr337k5FoDdu3dzxx131EGOLk/5+fkA6CLaANCzRQgA9957LydPnuSFF16gR48edZY/IUTtk3EWhRCXtZ07dzqnnTt48GAd56b+++STTwDwPvA7H97SniFtQzGbzfzyyy+sWLECi8VSxzkUQtQ2KVkUQlxWkpOTWbhwIZMmTSLXaIbonnQZcQ9bF33OCy+8wMiRI3F1da3rbF7yEhISKq07dOgQP/74IwBPPvIgQ2MbOh/btGkTS5cu5aqrrrpoeRRCXBwSLAohLhsmk4nWrVtjMBjo1KkT+6whvPJ7AtFdR8Kizzl06BDTpk3j9ddfr+usXrICAwPR6/WMGzeu2m00Go1zHm6Hli1bSscWIS5TEiwKIS4bOp2OkSNHcvToUex2O0PbhfG/ZQkczrej8WqAtSCTWbNmERQURL9+/Zz7VVWKdqWKjIwkISGh0piUf/zxB1OnTsXNJ4BuT89j6dFS7m9oR6OWmVqEuNyp7NKF7YwZDAZ8fHzIz8/H29u7rrMjhKiCxWJBqy27Dr75k41sPpZD4fp5ZP/7Q7X7yTiL1SsoKKBVq1YkJydz6/Oz2WBuRKtQb5ZN7sXhw4f5+OOPGTFiBL169arrrAohqnE+MYyULAohLivlA0WAYbFhbD6WQ4fh9/LKm1N44IEH2LFjB9HR0Xz11Ve4u7sDMoNLTU6cOIGLiwtNmjShJKQdJOVzY4dwABYuXMjbb7/Nnj17WLlyZZ3kry5n55GZgcSVQIJFIcQFZzKZMJvNeHp6XrTnNJvN5OfnM7hNKC8u3suhrBICorqxbNkyOnTowOHDh5kzZw7ffPMNKpVUpdakTZs27Nmzh817j3DXL4moVUoQDtC1a1fGjx/PNddcUyd5S0xMpEWLFpSUlFS7jZubGz///DOhoaGVHgsMDASoNuBLTU0FqHLf1NRUbrrpJoqLi8/6uU933OoeA+X3pNPpqn1OCVBFbZNgUYhL3OlKLs7nxFHTsVNTU7FarXh6emI2m2nQoIHzsbVr12KxWLjxxhtp2rQpoJxsf/vtNzw9Pbnpppuc2w4cOJCVK1fy5JNPMmvWrBpfa2355ptveOihhxg+fDjfffcdvZoF8veBTOZvTuSFG1rx448/cs011zBv3jyuuuoqHnzwwYuSr/pMr9ez2+AGwNXRgQR7K7f79OlDnz59Lkoeli9fzuzZsxk+fDgTJkwAlO+dI1BcsWKF83v6448/MmfOHAoLCykpKWHo0KFVHtPNzQ2bzUZpaek55+vdd9+tVAWfmprKqFGjanzuC0WaVIjaJsGiELXkdIEXnH0pwpmUXJzOqSUbFosFg8FASkoKDz74YNmxNVqw2cBuA0Ct98XFPxyVqxtqFzdU/yW1ixsqVx1F+9aQlpbGbbfdRmK+mV/iklm8YBE+LuAW0Ya20ZEEeevw8PAAqDAwdkJCAvPmzePee+8lKirqnF9bdSIiIigqKmLXrl0A3Hl1FGsOZlJstgLQu3dvXnvtNZ588kkmT55Mnz59aNWqVa3n41J1plWnX375JUajkQceeAC1Ws2i7SkAjIgNvyj5tNvtFUp99+3bx++//05RUZEzWHTQaDS0a9fO+T3/7bffnAOIA86pHnv27ElxcTFLlizBYDBU6PU9aNAgXnnlFef96667jtzc3Ar7p6amcuLECcLCwtizZw/Tpk0jMDCQDh06VMhrfHw8JpOpwr4Ojuklz/YxgGXLljFt2jRmzJhRqUd6+f13794t1eOi1kiwKMRZqO4km5qayujRo2usCjsfVZVcgHLieGHGqzz06NN4+gdhsmuIatGaIrONtGwD3y74GUP8786SDX2Lq/HqPNwZ/PnfNQe9ty8WNNjskP7Dczx372iioqJ44I1vCRj4cLV5Kk0/xltvvcVbb72FR+t+BA59HP9r7wPg4UVHgaMAeLedwOeTXqFVq7JhVebOncs777zD3r17WbRoUe29Uf+56qqr2LFjB23btgWgd7NA/nmiH5EBeuc2jz/+OH/99RfLly/no48+4qmnnroiTq6JiYnExMRgNBqr3Uav17N27VqmTJmCwWDA39+fVj0HcTSrCDcXNQPahDBz5kzS09P53//+57wgqE2OoOeLL76gffv2ANx0000YjUZGjx5dafstW7ZUuOB65JFHiImJ4eabbwbKpnr85ptvsFgs9OvXr8Ig7Y899hj9+vVzTgdpNBrx9PR0BouO/V9//XWeeeYZJkyYUKFE1cvLC4DDhw8TEhJSIW+LFy9m7Nixzvvz5s1z3nYcNy4ujvXr11docxsTE0N+fj6BgYG0aNECV1dXZ8/9qKioGqeuPN3/kZQ+irMhwaIQZ+hMTrKurq78+uuvFU5aNZUU7NuXwPh7JqDWefL57Hdo21p57EiOmb92n2DVmvXkFJbwd3Eku3ZZMZSY2XvoOKa/P+adl54kKioKrw5DWEpnyPnvoJvynMf37jaa4qPxTLhtFBMmTGDl4SI+iTdUyHNpufEQRoy+meuvvxatVovVmI85O4nmTaNo4OuNu6sGvauG4oJ8flv4M1ZDhvP1HMwuZV1iMbklNlKyCzmckoFnYDgmqx1DiZX2TYKdpS6GEjN9+l3D3r17ue+++87lozgtnU7nDDAAVCpVhUDRse6ll17ihhtuoHfv3mcUQF0OJ9esrCyMRmOVpVZQ9n212WzMnDmTRYsWMWbMGA5nGhkeG4abVoMhO4OXXnqJ0tJSevbsWWXwdr5eeOEF4uPjmTx5Mv/884/zQm3IkCEUFxcTHx/vzK9K58G/+1PZb07kYHoBB9IKOJlfjI/Gr8Ix7/lqK4czAwD4+OOtmEwmQu+Zjd1sIrFRW4YOvc657ffx6Yx46Ru++fIz7JZSlh4sUo5f6ktM/1sqlIhnFdsx6XyxW0rJLixFX2LGbC37YTlKGEEpLZ09e3al17tixQqef/55hg8fXmHb4cOHU1BQwP79+2nRooXzseXLl9O8eXO6dOlS5ftXUlJy2s84Kyur3n+fxcUhwaIQZ6iqk6zJYqeg1MbeA4d5ZtpLqF3dSbT5YSKIpLRMmmhynA3oPVr15fMjepI2xKHSeaL3DcBQ7Evkoz8BMP29WcyZ+Tz9+/dn8z9HWJ6eBS2vxxvYkQfkZSgZcfEhPdvAunXrSEtLw1ZSgMpiwlpSgNpSgqdOg6eLmpjoxiz+6XssRTkEBQWRkpLC4E4dad9Shd5Vw/q1f/PytOd4Y+Yr3DL6RtxdNXi4DkajVhEfH0/xoU0UH9rEb3FxFUow4uPj+fqhj4GyUpGOwC3lHu/UaQTbtm2jeet27E8roGNk2Un7rZUHWLXPnUnT5zKgS4Rz/Z49e2jZsmWl3sznq7i4mDvvvJNJkybRq1cvjmQWEn8il5s6R9C1a1e6du1KfHz8GQVQl9PJ1fHZVUej0fDQQw/x4IMPolKpaBHixXu3dHA+vmTJEhYtWsSoUaMuSP4++eQT9Ho9b7zxRo0XaiG3v0XklAW8vQvYtbvCY6Gemgr3T+aXcCK74jFcAxsBcCzPzB9//MHXX39NYWEh+yNuwOQZiu/VtwLwxQ4D7NgNNMS14y24ux/j33//BWBxqifhE5RpEId8thtQ8tHo6aXYTEYmDfZ1Pt9zv+6iyUNzycvKxGYq4q2NuUQe302GVzt63jeDmIASFi9eDMCJ7CKi2vcg5fgRAkLCsNnKAtDvvvsOd3d3Z7Bot9u55557KlSFx8TE0KZNG7RaLWq1zO4rzp0Ei+KKYbPZMZqtFJks/yUrhSYLB4+eIESVR7vWMYSGhvLX/nSWxx9j247d2DSuNGkeQ5HJSmaegbD7PmXZms00a9aMrl278vqK/Xz8zxHAm9DxbwPw+vpcQKm6OvnlI4zsp/yZa7wCOWp0BS+lF2mp0ezMm91iJiU9mx07dtC/f39ahnoxqHUDdm7ZSMLOOIYOuJYAbz3HDyawJ34zpelHePPNN537F+6sOGRJixYteHj+fL568DMAvv76a6ZPn86ff/7J4GuvBWDZF/GYM47y9Ydv8OfC+RX2L9/Wa+7cuXTs2JGQkBAaNmzImVKpVHi5udClsb9zndVm5+8DmZzML+HZhbv5fksiLw1rjTFxD4MGDWLIkCHMmzcPFxeXM36e03n11Vf58ccfWbt2Lau37GboR5vRqFX0aBpAQ7+KpY0tW7asMYC6Up3adhBgwIABDBgwoNae4+TJk6xevZrbb78dAH9/f77++msAZzA/7eMfyNAEMaGjNzabjc2bN/PlUT3ZgMWQSbCbDX9NCW6mXNTGbLL3nnQe/7777uObJX85263eesstJCYlgsYFtVbH8++/x9Gju5g/X/kteMaa0fqGoHbRodLqUGldleSiw2g28ejr/3Me21UNXm5aTBYbpRZbpddmNBr58ssvCQ0NJSHZhWJdALpwpYRzfVIJ65MSATUq/w6MGhXCazOVY/980Ez+1Y/geTV0nrkWABeVDw0nzcNFbadHp7Ln+OjPffyWGYDNXILfNfdiM5fwa0Ih07+fzeqVy5g4tBuvvaq0x0wzlKD1C+OJF15h/jdfEujjhVajZvPmzezdu5c2bdrQtWvXWvhUxeVCgkVxSbLZbBQXF6PX6ykx2ygwmflt+Sqyj+9n9OhRNGnShK3Hc/hp1QY++2oeDcIiuGXcHRT8Fwiu27SVw19P5eu5H3Prrbcy/bd9fLn+GNWNQJ/yyb188f4sbr/9drYcy+WnXdmgDgM7nDiQ6dzOxS+Mn758jUB3Fdu2bcMU1AmNCkqNBdhLi7GVFmM3F2MzGZXbllIWLFgAQPGRrVgLsrCWFGI3FWIrKcJeasRaXIDdUsoHH3zAgQMH6NWrF0888QQf3z6c79SHGPfht8xb922lPDdv3hw/Pz82b97MwIED6dKlC56ennh6ehIUFFRh21atWuHv7090dLRz3aFDhwDYvn0727dvr/azmDNnjvN206ZNnXMDg3ICzsnJYf78+XTv3h2ADRs2APDaa69x00030aRJkwqBxqx+3qxP8+GrbZnsTsln1Mcb6NrAhkWrJy8vD6vVWqvB4rPPPsvOnTt58sknaRURSPcmAWw4ks3sv48wc6TSpnHJkiWAUrrZqVOnmg532UtKSgJwDjH07aYT/LU/g9dGtsNWlIOXl5ezfV5tsdlsjBihlEYHBwdz/fXXV3j8YHYpwbfO5JvjnoCRFl6lfPryFPbv34/GuwE2kxG7qYiUGp7j+PHjtAorG4j42tgm7HMtYe1aJQjrGOqGNuIq3n77befvKCUlhSeffBKAyZMn4+PjTmFhHgUFBRTccgt79uxhz5499LTvYfAwpbPJzbfcwuGjx5jy+JO8/+HHqDQatvtM4uWXXwbApUEUGg9fVC461Do93gEhuHv74+LhhdbVnSmTyy78XDUq/PQuGEosWP8rVTTbVWg8fLEBPXs059NPP6WoqIiFGf54tO5b4TXP210AmuYEDG5OfuGf5OXl4ePjw/zdBsLv+5SjQPdZSumoq1YN5hKK8u0M2/2LM1j8JS6JZ9/9Ei83F0beMJhAX088dFpWLfuNNX/+wQMj+/H0448CYDRZuPfee2kYFsy05593fk8cPdYDAgKc45qK+kWCRVGrbDY7eUXFWEuK8PLyws3NjUPpBew8lMTm+B2oXd1p3b4DRSYrxlILK1b/g2bXYl5+6UW6devG26sO8vOmQySezMBF74Vap3f+SYKapPdmEBXVmCZNmrBoewo/HlHh3et2TMDXG0+UZcQrErtWGRIjNzcXNTZnoGi32XDXqnBRWdFipSAnEx8fX7755htWrlxJis0Hb7U/OZmp2EqL0dos2C0lvPjs00y6/16G9evOhg0beP/99/n6m2/ZPnUYjz32GJ9//rnz6VUqFQ0aNKB1RChEBLBz507MWSfwthdiMpkoKirCbrfj5uaGxaIM2XHVVVexYsUK/v33X+66664K76tGo6FTp0707NmTnj17cvXVVxMUFMR3333H5s2bGTduXIUG9ICzTRfAK6+8UqnELDY2lkWLFjFhwgRnoOdw4sQJpk+fDsCIESOwWCykpaVVqoJNTU3l5MmTFYJBR+D5008/8dNPP1X5PdHr9ayP28V3uwv5KS6ZLZlqoiZ9yRM3t8fNza3Kfc6Vu7u7MxgEeLR/czYc2chP25IY3akhnRr5sW3bNkAZYLp8r+0r0VdffQUobejGPfAoM5ftp9hsZeXeVL5+/h6OHDnCggULKn1nzofRaCQmJoYDBw7QqFEj5/ojmYW8seIAK/Zm4xbZFhc13N2zCUHGA+zfvx9/f3/atWvN2rVrq70QBGWg9ldeeaXCb2LChAkkJCQ4g8WEhARiYmIqdFqx2cpKCbt06VKpicK6deuYMmUK06ZNY9q0aRUee3fWTEBpx2y32+nRowdZWVlkZWWRe/yYc7uiGvLd3zuVR7rFYLfbue/BSezYm8DQEaNY/sdqHpj0CD8u2O58XrcmnXAJiPiv85oOlYs7atf/li5uzPnlfeZ88C7u7u7c8uZCrCWFaHV67CqlerrUYgOVKy6+IQR5ZnDXXXcp1fFeHTGFd8IEfLohuVzuoqDX/az+91uu6b2V6Oho3vzzKBvCb8Jus7LirQ146rR4umnJSksh5fgRRkeamP2GUrq59mAGj8+cja+nnpEjhuHn5YGbi4ZCQx6lxkL6tGpI00ZKr/vsgmJ27EnARaMitm1r9K5atBo1qampZGZmEhwcTHBwcA3vpDhfEixe4ux2O3l5eaSlpVVIBoOBwsJCZ2ratCkvvfQSGRkZWK1WXn31VQCmTJlCQFAwmdl5ZOXmkV9oJCMrh6zcPJKST2I4sRetVkt+fj7JJa6k5BrJKyjE1c0Dv4BAQsIbMmL0zfzw488YN/2Am5sbJSUlDH76Y7YlGTiamILKxQ2bWotdowOtKwAnZg0jqnEjPD09McbejCUsFvBRXtTxvWUvUNWQpL/XYsh/lMLCQjo98BYni0DrE4QdygJFuw27uYQGoWG8/fbb/PTTTxgC2xDiHo4WC3oXNS5Y0GJl9IgboLSYbvdv4babRjJu3Dh+WrSUDU/1ptiQx7LfFvPII5V7+f55Yn+Nn0XbwGcxpSTw7FPzWLp0KREREYSFhuDt7c1DDz3kDBaXLVvGdddd5ywdU9rwKaVVf/zxBx07dnSWnG7cuJH+/fs7n+PFF1/k7rvvrhTYPfvss4wYMcJ5Pzk5meTkZI4dU046x44dq3AihNPPd+woZYyMjCQ2NrbCY+XHbRw9enSloT0c3nvvPcLDw2ndurVznaPhf4sWLTh27FiF8ev8/Pxo164da9aswWbM542bOnJbt0heWrKXI5lFtG8c6Nw2JycHf/+yKuzaEuFWSjtfM7vyXHhgXhy/TerJrbfeyu+//87kyZNr/fnqk6SkJJYuXQrAnXfdzZM/76TYbKVHkwCuiXTh5ePHyczMrDDmZm3w9PTk66+/dn7mBSVmXl22nx+3JWG12VFhp2DXn8x/dizX94zBbm9J/uzZjB07Fh8fH37/Xenxf2qb0/KjFFQ3lqabmxsqlarCEDpVqe7xmgbddjy348LrbFX1nEvmfwHABy89XmF9ydE4So7GAUpwbLFYuP766zEVmUhLS8Pk7UV+fj6enp5M6t6ALycOJC4ujqeemcrf6zbw/kef0H/wDRSUWEjcbmH6tGeV1xd1FNcQ5UJf7apH5eqO2tUdlasetc6dP5f9xqpFSu3JI99uAkCl1mAosWAosUA+oPLBPaojOVnL6dChA56enhiaXEd+aFcygddXHa30Olt/O5dJt4/Cy8uLf3O9mbNeGWKMX5WmBRoVqOwWTEWFdDF+Ts/WjdBqtRyzBfJT/Ek02Iht2xpvTz0ebq5kZ6aTfjKZG1p4M26E0nwiw6Rl8ivvU2Is5MH7JuCqAW9PDwrzc8lKT6Vfpxj8PJT/wlIbaHXu+Hl54uaiQf3fnOgGg4GioiI8PDycU+jZ7fay73JBAWazGS8vL1xdXc/4s7fZbBQVFVFcXExAQAAajeb0O11AEiyehzVr1rB9+3YKCgooLCystHR1dcXLywsPDw/0ej2+vn40adacG0ePYc3af7EU5bJjxw5MJhODx0/CYLKSkZ3L5m3b2bQ1DoOxhMLiUiwmI4YtC53P69v3LlwbNEKlDUClCUGldWFjtjd/zfqbxKOHOPHJ/Xh5eVFQUMCxqOHsyagqCPLCZm5E0hdlfzgNRr+IvkcXR0hHCXAcePfPQ+DfnhN7ptEgMIDMzEza5pVwOM8G3lXPMKBycXMGMj6+7XFXe2I3l2AvLcFmLsFTp2X8bTdzMvE4nT/6iA/feYM9e/YwOcjCndd0wl5qZP2av3hx6lP/Ve2WDQGRfgw2bdoEVC618vb2ZvFrk5z3HZ1Lig05hPl7gb8X7du347rrrnNWNXl6euLh4eGsXvP09Ky0DAgIIDs723ncF154odrvRU5ODrt3lzW0Lx9cnRrApaenV3gsJiaGxo0bk5OTQ05OjnMolxkzZjBjxoxqn7Oqkg0HV1dXUlNTKwWTjmPXtC9Uf5J0dXXF3d0dnU5XYQiS8nkaPnw4K1euZPHixSxdupTc3FzWrFkDwBdffEFsbCwdIv1Y+NDVHMooJMjLDbvdzttvv8306dP5999/ncPf1IbU1FRiY2PJMRTR7fmfSC4wcf+325jaTRnWx8fH5zRHuHxUdTHxxhtvYLFYAEjWR7N1Zy4erhpmjW5HhL+e3bt3s3XrVudA7LXJ0dv5+PHjWGx21iRkYrXZiVDnsnnuc5izEnF7ZiSglNw/9NBDzn0dgVpVnXYOHDhw2iGR4NxmcHHsX12np5qe+0LO4JKamsrQoUMZP358heC5pKQEg8FQ4bN/+sknuGXMCfr17kbTYKXa2NfShtdee63Cf6QjabVaEhMTOXz4MIcP7+Fw984cPnwYjUbDO7d15X//tQu/ZdwdbNi6nWdfnE6PXn0pNFkwHyvlls+VnuD6DBtujTP/awfqikrz31Kr3F65dDHLvlcuwF9ZtB271YxKU9Y8RelsrkXj4YumRMXjjyvnMu8eY/DrPR6AndlAthEwAjpwa8q0/z3FUw8pNTfvr9jN8UClhPzFFWWlvQ6/LHmTv+cr+fVs15+AQWUXk3arGZXVgt1iwlJagmrbD/iZlIKcd+f/zv3v/ITabiUqIpyD+/fSrnUMHdq1IS8rg77Rfrz82P14enri5hdMqVc45pIiSooKMBryKDLkYSzIw242YS0pxEUNjRs35rvvvnN2aEpOTiYnJ4cmTZo4Z8ay2+3O369DbTXpkWDxHMz9YzsnTmawdftOEg4ddQ5UbDXmk7++rKNAg9Ev4uL936DGWjdUJh2qfRpenr4KuyGNxI/vxcPDg6KiIrYG9OdYrqMEJgRaDEEFeAGWgmwMWxbSuHFjoqKiyGzXiwK3im3SLEBijhE8AlCr1dx///1otVpOeOoho9ywDVYzKpsVNTZsJcrV0LBhw5QeoZaGJJdYMJcUo3PR4OvtiV7nQkRoMPnZGQxdsQJLqQk3Nzd0YS0YkWfkZNJxbKXF+Hm5o9OocNOA3WxC3/eXGnu1uru7c/XVbYA2AMS2bkFBQQGxsbHOP+9mATq6xTR27mM2mykqKqpQouoIzAsLC/H09CQ4OLhCY/z58+ej1+srVG327t2bVatWnfXn7hhvraqTbGpqKjqdDpPJVGMJxbk8VtNUZVD9iaN8yUZNs1dUd+zqTlhnclwAX19fPD09GTVqFKNGjcJsNrNu3TpmzpzJn3/+yezZszl69CjffPONMo5ciHKistlsLPp3N0aVOz/88EOtBouhoaFcf/317Ny5k5evC+PxlensTM7n7yClBDshIQGz2UxiYmKFgOh0pbT1SWBgIHq9vsbvokd4c+bvKQTguSExRPgrHYE8PDzo27dvreWloKCA119/nTFjxtCjR48KvZ11kW3BauVEyj7nupSUmlolVi0yMvKMerBfiF7uZ/rctS0xMfG0n7FerycwMLDKDl1Nmzbl6aefrnbfbt26VVpnMplQq1VK1bNOy1svPU1OTg6tW7cmNFQZdzIzQsfy5csr/Y8r/+UGCgoKKCoqoiC3gMIOrSgoKKCkpITnhsfy3PBYbDY7Dz38CCtWrebRJ55mxKjRlJhtmHOa4mrMxGq1klGiImPnDxhNZuxqLSVmKyaLDbtKAxoXbIVZzlK6hr5utNAbsau1ZOXkkZqRRXhkI2wqLbmGAppEhLLWUaKnqRh0qTQuoHFB5eqOix4ycvNIOboHgOMZBvTRSpvPVMArNpJjwLGEQkBP+pF1HD2qlKbqm19Fg+5lQ0+5/pcc40dkL3+fwl1/cOjQIQ4Z1Nz1/FJcNGArNZGZlkKg33aaNIrAUmKkX6QLT9w2EACXwEYE9r2d7t26ERwcglqjptRYWO1nejoqu91eU1MPUY7BYMDHx4eIKT+i1ukrPW7JSSZl7gNMnTqVmJgY3j/kRWZp1VG9a6mB4K0fER0drRRVtxnF3sRMjh8+iLeHG82aNMbHwx0fTz3r/1pJ6baf+eKLL+jWrRt/7kvnSHIahxL2EtEwjPZtWuGqVaPTatBp1bQJLysdMZSYUaE0XnbVqGUO3PNwJuMsXoh5YM9nMOgznamjNo/rGGYm7pQhdxzi4uLo3LmzM7iOiIhg165d+Pr6ArBybxoTv4vHT1vKX88Owcut9jq7ABQWFqLVanFzc2P94Sx2JOUxtIkLrVq1uiLGWYTKn19mZiZ33303J0+eJLZ7L1yGPE9GoZnezQJI/+klJtx77wUZS3HatGm88sortG7dmiNFLkx8fCq3XRWN47Tk+L9aunQpL7744mlnLanuO3clulC//SuV1Wpl1Z+rsWtcCGgQjKGomNyCIvILjeQXFBHioSbE31upoQprSlxiPhnZuei9fSk2mSkxW9m2fSeHjp3gxrYNGNC1FUVFRaw/ks2cf5NQu+ho1CQai12N2QYFxSYsdhWdLft4e/KtHD16lDzPRjz6S/UXrs0N8az6WKn1cotsR/Ctr1Z43GYykvTuGPLz851V5mdKgsWz4AgWr3vpR06eTCEk0J/unTvgodPi7qLhyN54OnobGTp0KEFBQcQn5mI0FqO2mWng54OPpxvuLhrcXTRoNdWPeVXVMBXi0iB/wKfnaKNZ3Ynb8firr77KJ598wtVXX+2sQgLIMlp55q8ccowW+rcK5pNxnZztgy6E3Nxc/Pz8SExM5OTJk4wbN44jR45w7bXX8vrrrzt/i5frZ2swGOjduzc7d+4kOjqaH5f9xRNLjmC3w3Xs4LknH8XX15djx445A/rasn79eu655x76j5/MkpwQ1FpXvrm7C/PfeRFPT09mzZqFSqVytkmsyeUUzIsri9lsJi8vr0Jb4G+//ZYlS5YwatRobrlFmYUoK7+QATfegl3twrsfzMam1mI0WVj+x5/88+8GhnaL4akHlCr4NIOJ0VNmoFapeODBB/HQ6ykuKuChAe3PKVjEXo99+OGH9kaNGtl1Op29a9eu9s2bN9e4/Y8//mhv0aKFXafT2du0aWP//fffz+r58vPz7YA9Pz//fLItxGUtLi7ODtjj4uKqfHzp0qV2oMbkHdXe3uzZ3+2Nnl5qf/33Pfa1a9dekLwuWLDA7uXlZf/jjz/sRSaz/amfdtp/X7PZrtVq7YB9/vz5F+R5LxUmk8l+7bXX2gF7UFCQ/ciRI3a73W7PM5baE7OL7BaLxT5x4kT7P//8c8HysHJ3sr3p1KX2Rk8vtY9+b5X9ngn32wG7SqWyb9261W63l32n5s2bZ4+Li6synThx4oLlUYjLwfnEMPW2zeKCBQt47LHHmDNnDt26dePdd99lwIABHDhwoNIYc6CM/Xbrrbcyc+ZMhg4dyvz58xkxYgTx8fG0adOmDl6BEJe36tr5OYbWObVKMTk5mffff5+mTZvy6aef8kInbz7Yks/svw/z4tePkLQvztmetbasXLmSgoICPv/8c/7IC+KnuGS2NfDg8ede4vWXn2fixIn07du32iYC9ZnNZuPOO+9k9erVSm/kn3+jSZMmWK1WPF3V+LgrTW0+/PDDWn9ui8WCVqslLb+ER3/cjcUGxgMbKD2wn88X/Yparearr76ic+fOFfY73awzQogLo95WQ3fr1o0uXbo4/8hsNhsRERE8/PDDPPPMM5W2v/nmmykqKnIOCwHQvXt3YmNjKww6XBNHNfQ5FeEKcYU4k7adoLRDGzJkiPP+G2+8wVNPPUVwcDDp6enExcXx3g4Law5mQtJ2FjzSv8qG9eejpKSETz/9lIceeohso4XhH64nzVBCk0APcn95kR2b1jJmzBjnwOr1xZk0l3j//fd566230Gq1zPx6KZ/stfJov8b89vYTtGvbltdff/2C5O3QoUNcf/31vPHGG6wxN+W3nSdpHuDCqmcGg82KWq3m22+/5bbbbnPuc7qmDUKI0zufGKZeliyWlpYSFxfH1KlTnevUajXXXXcdGzdurHKfjRs38thjj1VYN2DAABYtWlTt85hMpgoTwBsMhvPLuBBXgMjISBISEk7bAebU0rpHHnmEhIQEBg4cyM03K210nh0cw7pDmdgiOqALa1HreXVzc+ORRx4BINhby3f3dGHs51s4mlVE2NBn0e7dy48//sjkyZO56qqrav35L4QzCdZdXV2d41++9vHXfLHfjtlqZ/3uI6xYvpw1//zDxIkTL0j7v0mTJnH8+HHe+e53UlqMRq2CRpmbwKZMw/fZZ59VCBSFEHXvvINFm83GV199xd13310b+TkjWVlZWK3WSiO2BwcHs39/1QMrp6WlVbl9Wlpatc8zc+ZM5xRNQogzdy5Dhuh0Or744osK40G2CPHi/j5NifDT0ybswpfmf/buTHJW/EODG1/kZIGF6Ptnc+iTiUycOJG5c+eiVlfdMe1S6vySlZWF0WisNEC1gyNY//bbbzGrdXyeFEBRaTE9mgTw1T1d6WR/iz59+lyw17NgwQJWrFjBAW0TvtyWSbdAM58/+RIAN910E+3btz/rAeaFEBfWeQeLarWaTz755KIGixfL1KlTK5RGGgwGIiIi6jBHQlw5HCWTTw9UBsy22+0cOHCAFi1qv4QRlDH/5s+fT2JiInMeL+TbFH+ScyF45PPs+PpR52C4VbkUe+Kern1feJMWvBNXQnKugQg/Nz4a2xEXjbpSDUxt8/X15ZZbblHyoFnNpDvGY7fb0Wq1p50isrbbrAohzkytVEN37tyZDz/8kEmTJp1+41oQGBiIRqOpMPsFKLNhhISEVLlPSEjIWW0PSklHTaPkCyFqn2M+3iFDhrBx40Y6d+5Mbm4u11xzDfsOHOLI4UM0DKv9DideXl7ExcWxcOFCJowbzdC8Yh6cF4ePMYN5QEBAAEuWLKk0d7WjpC4rK+uSChZrovUN4dm/skkttGItNuB5/Gd83Ptd1Dzs3r2bJ+4ahakgn5EjR/Lmm286B76vyqVUeivElaZWgsXk5GSWL1/Om2++yVVXXUXbtm1p27btacfFOleurq506tSJ1atXO+fLtdlsrF69utqAtUePHqxevZopU6Y4161atYoePXpckDwKIWpWXdXigQMHAKXH7GOPPcaaNWvw8/PDHtaWoO6P8M5vcbx1/4X5bwkMDGTChAkAhPu68/1dHbj99rcByM7OZvVffzPt+ecuyHNfLI888ghujTuQWmilgV5N0i+vMfKpSRd8bNeCggJuu+02/GOvp1/7KJ566G7y8/OJjY3l8ccfJzc3VwJCIS5RtRIs/vTTT/zzzz+4ubmh0WhISEjgzz//vGDBIsBjjz3GHXfcQefOnenatSvvvvsuRUVF3HWXMufj+PHjCQ8PZ+bMmQBMnjyZPn368NZbbzFkyBB++OEHtm3bxqeffnrB8iiEqOxMpptzlN6tW7eOhQsXMnLkSMbfN5H3N2bxZ5oL+cVmfNxrd2aXqjz44IMsXKjMy64Lb8kniYEM2HeMrq2iLvhzXwg//fQT69evB2Diw4/w2E198XpyE+7u7hf8uefPn8/yv9cT3uIe/tliI9eifH47duzg6quvBi7N6nwhRC0Fi6NGjSI0NJRff/0VPz8/jEZjrc7nWpWbb76ZzMxMXnjhBdLS0oiNjWXFihXOTiyJiYkVGqNfddVVzJ8/n+eff55nn32WZs2asWjRIhljUYiL7HS9pUEJKD/77DNmzJjBU089xZAhQ5h8Q1eWH1nLoYxCFmxN5L7eTavdv7ZMnTqVtWvXcuLECcKHTsbq25BJC4/wQ4MGNGngecGfvzZ99NsmnnlgknOaxTGxQQR5uZ1+x1oSHByMV+dhqLQuNFAVUOJi4eNffqFx48ZA/azOF+JKUSvjLLZv356dO3cSGxvLjh07mD17NidOnGDWrFm1kcdLhoyzKMTFU1hYSLNmzUhLS+PNN9/k8ccfZ8HWRJ7+ZTeh3jrWPtUPF63mgudjy5YtdOvWjZVrNvJmnImD6YUEeemYP6E70UGel9wYgI78OHpD2+12Fuwt5Md9hbjkHsdl/Sck7N190fO7btM2xi44ilrnwdzxnenbzB8Xl7LS4UvtfRTiclPn4yw6qowcY3dNnDix3oxJJoS4tJQfUPq+++5j+vTpvPTSS8TGxhLh5YPGUkyqAT5bsYUHh174NsdarfI3GejpyltDghn2zp9kEMbNn2zgh/t6kJqaClTfBvNit8OrUM2vUuM/YCJe7QcAkJmwify9u+ukZ/Gqo0bUOg8aemu5tmXQBZ3vWwhRu2olWHzkkUfIyclh1KhRPPDAA1x99dU1VjEJIURVqhtQurCwkOuuuw4Av97j8e4xhm83J/PghWsWXUlCQgLLli0jceFSQm79H9lEMfLDtRz7YgpAtW0wL2Q7vOpmavnxxx/Jzc1jSYqOLbnuYLeRvfIj7u7TnDvejbvoAezKP1fzy55CQEszy3EucF8aIUQtq5VgcezYsQA8/fTTfPXVV+zdu5eff/65Ng4thLiCVDWg9JYtW3jwwQfRaDS8+uqrPDvjdXyvuomTZj0H0wtoHux1QfNUVYectPlTCbr5FQiJxi12KKWZs1m4cGGlWWkuZDu8083U4nPVLfj2GofdbiNryRtYjm5m6BuP1UkV75R35lPcdiSWgiz2/P0lTLvvoudBCHHuan26vzvvvLO2DymEuMKUH1C6Y8eOLF++nCVLlvDjjz9iLczhrlgfBnZrTbOgC9/JpKoOOStWrCCmfUt+SSjku5Uf0iK6KU2aNKlyxpQLpaaZWlYdMfJxXD4AOas+wXRoA7/88gv9+/e/aPlzKC0tJTAkjBMmI4ati3n3qScu+DA9QojaVSvB4r59+1i8eDG+vr60bt2atm3b4ufnVxuHFkII3nvvPVatWkVcXBwAQ5p50LGx/0V7/lOnL3QEsmGBm/nOauHAgQP88ccqPIIaERmgv2j5grLAOicnhxkzZvDss88yNMSVz7f+ScbGXynasYxvv/2W4cOHX9R8Obi6urLu8xn8s34T18xehsk0qtJ0fiBT+glxKauVYHHYsGE8/PDDFBUV8fnnn7N7927y8/M5cuRIbRxeCHGFa9y4MS+++CLPPPMMACdOnGDevHnEx8fz5+q/0GqqnrP5QnP05o3t0AFDk2sZ/P46PrujMx0beuHq6urcrqZAqDbaD1qtVvr06cOePXsoKiri/vvv58jse7AZ83nuueecTYXqUpOIMNxdNDWOrylT+glxaaqVYDEkJITJkydXWGe1Wmvj0EIIASgD8c+dO5cjR45gNpv5aO6X6LuNoc/rf/LXU9eiuwjD6JzKEQTu3L2HoL3HKTTBHV9swXv3j3QMceWaa64Bqu/8ArXTAUaj0fDie5/z+ptvc+utt/L4449jMyrV0CNHjjzn456v3/7dzo59h1jy2Zs8+MADZzS+poyxKMSl57yCxccee4zY2Fj69OnD559/zj333ON8TKO5+H/cQojLl4uLC7Nnz2bgwIFER0cz46VpzMtvTorBzNKdqYzq1PCi58nRVtBuMbPt7XsYOP1H1h8vIKPFjfy8+iNat1bmo58xYwaDBw+utP/pOsBU19vZsa/D0cxCZq7Pp7DjHcQnGVizZo1z8O269PS8dRh9m5KnbcKWLVu4++67JRgUoh46r2Cxb9++7Nq1i0OHDvHLL7/w+uuv07lz5ws+N7QQ4vJWXbVt+cDpyccfQ//3Yd5YeYD5WxLrJFh0aNKkCUePHiX+g4fo+eA7/Jtcilf/SWzKTgYgKiqKli1botefeXvG0/V2dli1cQcLV+SSXVRK61AvPp75JKCUZn7++efn/qLO045j6Rh9m2K32yjav46xr/9QZ3kRQpyf8woWhw0bxrBhw0hOTiYkJASLxcKePXvYtWsXq1evlmBRCHFWzmTeaL1ej16v56mnniK4UTM06nDiTuRyKL2AZhd4GJ1TOfJ79OhRAPbv28v+hwfgd919eHe6gQ2mCLy7jaagoICIiAhefPFFJk2aVGEqUqg6OE5ISMBoNPLuu+/Sq1evCo8VFxczevRosko1zD3qicWlhAhvLY0Tl7Fs/14CAgLo0aNHnQaL87alAWA8sIEwL61z/mchRP1TK20WBw8ezLp16/Dx8aFz5840bdpU5lwWQpy1M503euXKlbzxxhtERkbS/O43OVSsZ8HWJJ4f2uoi5rZifjdu3MjDDz+M3W5nYFABYTEe/JJQiCU/nTVr1pCTk8OqVat4+OGHAbDZbOzcuROouU3jM888w/79+0lJSSE6OpqgoCAAnpzxFu/ssGFx8aA04xgbPnief/9rp5idnc29995bZx1GMgpKWLLjJACGrQu5d/xtlQJkIUT9UStzQ3fs2LHCUAg2m40uXbo4h7m4XMjc0EJcGoxGI6NGjcLNzY2Vu5IJuukl/PQubHq2bjq6OHz66afcf//9ADz88MP0HXEbo67twdatW5U5j3teS5fW0YAy5Fjr1q0BmD59OkOGDAFg+/btmM1mAB588EEAbrzxRhYuXMhLL73Eiy++SFKOkaHvrSHfZCPEzcprA8IoLcjhrrvuwtPTk3nz5qHRaOqsw8hrv+9hzroTlJ7cT+q3T7Bnzx7naxVC1I06nxu6YcOGrFu3zllVolarKS0trY1DCyFEJXq9nuXLl7NlyxZSHnkEo9pCrhFW7UtnaLuwOsvXfffdh1qt5r777uODDz5wzhutVqsZMuZ2rnlrDX2b5/HUwJYkJCSg0WiwWq00adLEOXbj/fffz7Zt23j33Xedx+3atSvLli2juLgYgFAfN1o1cOWfbXt4/f6r6dOjMwB//vknubm5dOnS5eK+8HJKzFa+WHMQ1Dryty6ic+fOEigKUc/VSrD44YcfMnjwYHr06EHXrl3ZvXu39HgTQtS6U3sHa7Va5nz0Eb8dKCTfZKOBuubOIBfDvffei0aj4Z577nFOe7p3715WHzNisdr4Y186fyakc22UP1Omvshbr7xQYf+mTZtSXFyMv3/ZoOP9+vXjnnvuISBAqVLWatQ82t2XBU88x8aWzzuDxYs5g0x1knOLsZUUYjHnYzywgTs/eL+usySEOE+1EixGRkayfft2Fi5cyO7du2nevDmvvvpqbRxaCCGAM+sd/EktjFlYG+666y40Gg133nkndrud8ePHA+ASEIFv7/Hom/dg1VEjNnMrfHuNw9XT17nvDz8ovYbLN+1Ra7R8EZdNYnYSH97WAZVKRdKJ49hNRUybNo2ePXvSs2fPi/oaq+NaksMLXdTcPekJXLQaYmJiKrwWGUtRiPrnrIPFnJycCle8Di4uLowZM4YxY8bUSsaEEKK8quZCTkxMZOTIkdjtdu6//34++eSTascsvNjGjx+PRqPhjjvuYMqUKdx2223OxxIyS/lml4ED2eBz1S3M2qlh9DB7pTmTVS46dOExTF2RxOFCZbaYritNtA3SOYPm2NjYS6aa99SA3gpce+21FbapjUHIhRAX11kHi4GBgYSHh9O+ffsKqXnz5jI5vBDignPMhQxK57obb7yRX3/9lU8+nYtbk07M3prHp7F2NOq6/z8aO3YsnTt3pkWLFhXWdwRuu97OnKUbmbEwnm5Xt3H+fxpKzPSZ9TfeOjURkxeg0mg5XAi20hKyV7zPna+vdR7H3d2d77//Hj8/v4v5sqq1cncyxaUWvL29MRgMvPPOO/Tu3dv5+OkGIRdCXJrOeiyD3bt389prr9GqVSu2bt3KxIkTadWqFZ6ennTr1u1C5FEIIar15JPKINSoNQQOfZzVx4pZeyizbjNVTvlAMScnhyFDhrB//35UKhXdwt1I/fJhOrumEB8fr8x1vSGeXKOZE7kmVBotFkMGrfQFtEldRsmBf7n22muJi4sjLi6O/fv3Ex0dXYevrszhjEL+ty6X8Ac+p6DETGBgIBMnTqRjx47OdCm0qRRCnL2zLlls3bo1rVu3dk5Mb7fbWbFiBQ8//HCl6gYhhLjQunfvTteuXdmyZQtFe//Gu/NwFmxJol+LoLrOWiWPPfYYy5YtIzk5me3btzsH9b5rfLlxFjVaXPzC0XgFYMlJgaJs/nFxcfaE9vLyok2bNri6utbRq6jal+uPAWA6eQB7aTHDht2Ki4tLHedKCFEbzruDi0qlYtCgQcybN49PP/20NvIkhBBn5ZlnnmHkyJEU7vwD787D+TMhncwCEw28dHWdtQreeOMN0tLSmDVrFmq1msjISObNm0dKSgrFxcUYjUbn0mAwsHTpCbItFiwWC927d+eNN964ZDqylJdbVMov8crUhoatixk4cCCvvPJKHedKCFFbaqU3NChX97feemttHU4IIc7YsGHDaNiwIcnJJ/C35ZODD7/GJ3N/n6Z1nbUKGjRowIoVKyqsmzBhAtnZ2dXuEx0dzWuvvcbIkSMv2Xbh87ckUmK2EeWr5UTSbv636CtCQ0PrOltCiFpy1sGip6cnbdu2pX379rRr14727dvTsmVLtm7dSkFBwYXIoxBCOFU1jzJA586dSU5O5sif3+J3/SQWbEvivt5NLtkAy6FNmzbk5+fj6elZKbVv35477rjjkq7OLbXY+GbjcQCa2ZP5p05zI4S4EM46WPz555/ZsWMHO3bs4L333uPIkSPY7cqQDzNmzLgQeRRCCGf7vprmUQYo2LuGoAEPcTSziO1JeXSMvDR6Clfnn3/+qessnJdlu1NJN5iwFeUy940HgOoD+urWCyEubWccLL7wwgsMHz6cgQMHMnDgQOd6o9HIsWPHCAgIICQk5IJkUgghIiMjSUhIqDCDy6mee+45VqxYgT1pOy0798Fosl7EHF6Z9p7MB6CJLZkkmwWgxoBer9cTGBh4UfImhKgdZxwsJicnM2jQIFxdXbnhhhsYPnw411xzDXq9/pIZEFacgd9+g/nzK68vX1XnuF1+WdPtU5NaffplVUmjqTmp1aDVVlyn1VafXFwq33esc9w+NanPejQpcRFFRkbWOD7f/PnziY6O5uiC//F0n1B6NutzEXN3ZXpuSCtu7hLBgZ0e5KxrS4sWLZg6dWq128sMLpcwux3M5rJUWlp222JRUvnbVd0/9TGrVblttZ5ZstnKlo7kuG+3K6m621Xdt9vLXlv511l+/amPVScgAD78sPbf93pAZbfX9M5UZLPZWL9+Pb/99huLFy8mNTWV/v37M3z4cIYOHVrlzC6XE4PBgI+PD/n5+Xh7e9d1ds7NDz/A3LlVP1bVj+d0t2tKjh/tqT9sq7Xs9ql/BjX9iVgsNf+Qa4NGA66up086XdlSpwM3t7KlI7m7K6n87VPv6/VlS0fS1lq/syvSe++9x5QpUwgODubQoUN4eXnVdZauGHa7naKiIjw9Pes6K/WPI1ArKgKjEYqLlaUjlZQo6xypqvunJpNJCfgc6dT7pyaz+cK+xjMpFHAUDDiWp6byBQ+n3i5/HyquL3+/ptvlnbouKKjqwpZ64nximLMKFk+VkJDgDBzj4uLo2rUrw4YN49ZbbyU8PPxcD3vJuiyCxfrObq8cQDqWNV3pOtaVX56aHH+WNf2Zlv+zddw2mZRU/k/61D90i+XMX6OrK3h4KMnTs/rkeNzDA7y8wMencvL2VoLYK8ju3bu59tpriY6OZtkfq0lIN9KjaUBdZ+uyk2EowWKz88k7r9GjRw/69++PRqOp62xdPHa78vs2GCA/X0mn3i4qgsLC06eiIiVZz6LZRFUXoeWT40K2/IWti0vZfUeqbl1V6dQam1PXaTQVHy9fA1Q+iBN1os6CxfIyMzNZsmQJS5YsoVevXjzxxBO1cdhLigSL4pxZLGWB46klAo6SBKOx7Lbj5FH+ZFNQULZ0rHcsS0urf26dTgkafXzA17dsWT75+VVcOpKPj1LaWU/+5BMTE1m3bh3jxo3DzT+UiAfmYrPDl8OC8XBVSxVoLXppyV6+3XiczNWfUxS3mOPHjxMREVHX2TpzdrvyW8vLq5hycysuT035+crSYKi5JM7dveqLOsfFnuO248LQkfT6sqWj5uHUpNPVm9+kuHRcEsHilUCCRXHJKi2tWKpRvpTj1PVVnQBzc6s/8bm4QIMGEByspKAgCAysXMoZGAhhYRAaqmx/kdt/JiYmEhMTg9FodK4LvesDXIOiyF7xAYU7V6LX60lISJCA8TzlG830eG01xlIr6T88R8mJnQwYMKDSGJIXjd2ufIdPnlRSamrZxZXjgio/H9LTlZSRoSSTqerjubpWvnAqfwHl61t2AeYowS9/29tb+d0IcQk5nximVhpHvffee0yePLk2DiWEOBeurkqwdq69TO12pZTTUZriCCrz8yEnBzIzy060hw/D5s1lJ+GCgsrVZ1qtElj6+VWsFvf3V4LNoKCy4DMwUGk47uurVFmdo6ysLIxGI/PmzXPOQfzm0h1sMEKnkQ8y4cnbGTduHFlZWRIsnofExEQ+XXccY6kVW04SJSd2otfrcXNzIz4+/vxLb2025QInO1tJju+dI2VlVb4gyshQSuzL0+kqlup5eyvft44dy757ju9d+UDQz0+pxpWSOyGcaiVY3L17N/fffz8fffQRGo2Gffv2MWPGDL7//vvaOLwQ4kJTqcqqvc62vbHdrpTQZGWVleo4lo6AMz9fWbdnj3Jiz8xUgoJT8+Drq5zAHYGvIzVoUBZkOlJAQJVV5DExMXTs2BGA0nc/wh46nONGLQGRzc79/RHAf6W3rdvgN/5DtF4B5Gz8CVCGUFu8eDGLFy+uWHrraNfnuOBwlOg5vgNZWWXLrCwlOMzNrXzxoVIpn7fj4sLXF5o2LbsIadBA+d6GhSkpJOSKa6srxIVUK8HiZ599xjvvvMPAgQPx8fHh+PHjPPPMM7VxaCHEpU6lUkpiGjZU0pmwWpUAwlFSlJOjJEdpUna2sv7gQdiwQQku8vIqH0erLWtr6aj2mzYNWrYk3mSiaUYyKX7ppLqHsva/uYspLlaCGCk5OjsmE1kHDqCK7ITWKwCMeRQlrGV0u3ZM7dABcnNJSEpi3PbtZPXuTWRJiRL4VdWe1tNTCfAaNFCCv6ZNoVs3JSB0JH9/ZRkUpGwnowQIUWdq5de3detW1q1bR25uLkePHuWvv/6iUaNGtXFoIcTlSKMpCxbOVGmpEkBmZChBZk5OxY4Ihw7Bvn2QkgJHjjDx8GE2Wa1MSbGwcMijrNmToRynZ0+l2v7U6sdT256d2gHIy6tyBwRX10s/6LRYKnaaMhqV5gPVdd4oX8Vb/v0tLsYOeN3xLgC525YQZrXw8ZEjBBYUKJ+lh4fynL16QYsWFd8/R4lwgwbKeyeEqDdqJVh89NFHeeGFF7j++uvZunUrI0aM4MMPP+Tqq6+ujcMLIYQSmDmqGasSHw+LFsEXX0DHjgx55RUCNm6kz7g7WL5HRUpABNqAhvDoA0oA4wiEcnOVwMhggKSkih2BiotrzpNaXXGMzFOHMXH0XD01nUvnH7u94lBNpw7ZVL6nvaN3fXFxzT3lQSmxO7VtqY+PUuV7Sk/59HQDLqkh2MwlFO1cyesffMCw+fO55ppreOWVV5TPoFMnePRRpW2gEOKycNbBYk5OTqXBt//991/n7S5durB06VLGjBnD+vXrzz+HQghxDp5//nnn7c2zf2DZd59wIjsZBg0680DGZCoLKh09a8uX0p06cLLRWDmAKypSSkHLB3hnOgjFqaWWjjHxyg8E7+tbMTh1c6s89Er5ElFHcpT6ncXQSObFi0meNhZdaHOee2IyBcXFbNy4EV9f3zN7PUKIeumsg8XAwEDCw8Np3769M8XGxtKsWTNU//3hhIeHs3r16lrPrBBCnE5CQkKldVt+ncuujf+c/cF0urKes1e40tJSnn32WeymImL8YNq0aQD4+fldlpMwCCHKnFFdyGuvvUbef43Ld+/ezWuvvUarVq3YunUrEydOJCYmBk9PT7p16+bcx83N7YJkGJTSzbFjx+Lt7Y2vry/33HMPhYWFNe7Tt29fVCpVhfTAAw9csDwKIS6uwMBA9Ho948aNo1OnThXSX3/95dzO28enDnNZf01+djr79u0D4JVXXkGr1aLVarn33nsZNGhQHedOCHEhnVHJ4quvvsqYMWPw9fWldevWtG7dmrFjxwLKXKArVqzg4Ycf5tprr72gmXUYO3YsqamprFq1CrPZzF133cV9993H/NPM2ThhwgSmT5/uvK+XRtZCXDYiIyNJSEggKyvLuW79+vU8+eSTNG3alAyzDlX7YTz2026WPNO0DnNa/+w9dJxl9o6E3P4WGT+9yMmTJ4mLi3PWJjlUVaorhKj/zihYrGmSF5VKxaBBg5g3bx6ffvpprWWsOgkJCaxYsYKtW7fSuXNnAD744AMGDx7Mm2++SVh1jd9RgsOQkJD/s3ffUVFcbwPHv7vUpUsTBBTs2LD3WBJ7773GlkRjr4ktUWNMTGKJscSaqK+JNXZ/xhZbLBjEglgRFRAR6W3Lff/Y7IaVYhfU+zlnDjszd2buDOzy7K2vPI+SJOWNwoULmwwI7enpyYgRI0hPT6drt57s1AQQHKvlfnwqBR1VeZjTN8uhO2oUFlb4Fi1GglLHoEGDckxrY2OD6/MODi9JUr700ubjqlmzJocOHXpZp8uRoTG1IVAEaNSoEUqlklOnTuV67Lp163B1daVcuXJMmjTJZFqw7KSnp5OQkGCySJL05vD09CQ0NJRr164xY8zHqKOugtKML9fuz+usvTHSNVrWnLwNwIS2VTl48CBWVlYolUp+//13AgMDTRY5naIkvX2eqmTxs88+M/aAtrOzo3z58gQEBFChQgUCAgIoXbo0Z86cITEx8ZVmFiAqKgp3d3eTbebm5jg7OxMVFZXjcT169KBIkSIUKlSI4OBgJkyYQGhoKFu2bMnxmNmzZ/PFF1+8tLxLkvT6lSihn7mlQIEC1PWAU8CeqwlotTrMzF7v/NVvog0nrvEgMR0PB2taVvDEwsyL27dvc+TIETp37pzX2ZMk6TV4qmBx0qRJxtebNm0iKCiIoKAg5s+fz40bNxBCoFAomDFjxnNnZOLEicyZMyfXNC/SHmbw4MHG1+XLl8fT05MPPviAGzduUKxY9u2XJk2axOjRo43rCQkJ+Pj4PHceJEnKOzqdjhmD2tFo4WlQFWDBxv2M6tY0r7OVL4WHhxMTE4NOp2Pa+n/AqRCV7RO4cD4I0Hcm6tKlS95mUpKk1+aZh85p1qwZzZo1M66npKRw69YtXFxcXqg94JgxY+jXr1+uaYoWLYqHhwfR0dEm2zUaDbGxsc90fUPP7evXr+cYLFpZWWEl5xeVpDfehg0b+Pzzz+nVqxfFzT24iR0/H7wsg8VshIeH4+/vT0pKCtZFAijYbRa6jFSWjuvH4vRkANP5nyVJeuu98AwuNjY2lC1b9oUz4ubmhttTTP1Vq1Yt4uLiCAwMpEqVKgAcPHgQnU5nMnTPkwQFBQH6Nk2SJL39bt68ybp165j09QJmnBUkOxVj/rLVvFe1AqAvLZPBD8TExJCSksLatWs5lOTJn7dSqeKcwaKN/0fnzp2pXr06R48eJSYmRj4vSXpHvHEzs/v7+9OsWTMGDRrEkiVLUKvVDBs2jG7duhl7Qt+7d48PPviAX375herVq3Pjxg3Wr19PixYtcHFxITg4mFGjRlGvXj0qVKiQx3ckSdKrVrlyZSwtLblx4wYDO7fEuelQMqKuM2reYYQ6HZClZY/z9/enW8VK7LsURXkvRzatWUp6ejpqtTqvsyZJ0mv2xgWLoO/VPGzYMD744AOUSiUdO3ZkwYIFxv1qtZrQ0FBjb2dLS0v+/PNP5s2bR3JyMj4+PnTs2NFkOjBJkt5eSUlJZGRksHbtWvz9/blx4wZduixCqVSyY+dO4uLi6NWrlywty+Thw4eYKRW0KK+vfRk1ahS1a9fm5s2b/P3333mcO0mSXqc3Mlh0dnbOdQBuX19fk7EhfXx8OHLkyOvImiRJ+Zi/vz+VK1emcuXKtGvXDh8fHypVqpTrSArvIqW1PV179KJs6ZJs2LDBOJ1fjRo1sLCwyOPcSZL0uslxIyRJemecPHmSpk2bsmzZMtas/42q3UezJig+r7OV7xRo+CE2XedyR1eAgnJebEl6572RJYuSJEnP4+bNm/zvf//j0aNHNGzbnal/XMJMqaBayyd3rntX3E9SY1u2IQozc7q0aY6ZmRnNmzenfPnyjB8/Pq+zJ0lSHpDBoiRJ74wWLVpgY2NDv379KFbQnqq+BTgb9oiZ6w/mddbyjaWHr6EwK0DGnYu0blmbLVu2sHfvXg4cOECbNm24fft2XmdRkqTXTAaLkiS9MwoUKGAyeUD7cq6cDXvEdeEOCtkq52FSOkGJdmAGj05soN76icZ9arWa9957D5DzP0vSu0YGi5IkvTMenwWqmJXATJsODu6o/CrnUa7yj682nQQzCzKirrF50awcJzqQY1JK0rtFBouSJL31XF1dsbGxoVevXln2FXh/IA7V2uFYtfU7XVqWmKZm26VHYGZJRcv7tGgxMq+zJElSPiGDRUmS3nqFCxcmJCSEmJgYAH755Rfmz59PkSJFmD95ACP2PcTKrwpqlTMajQZz87f7o9Ew93Nm24PuoTWzRP3wDp92rA/AkiVLsLCwoEuXLtjb2+dFViVJygfe7k9ESZKkfxUuXNhYdVqiRAkiIyMZPnw4derUZNedsySnpPDJJ0MpXciJ+fPn53FuX53Mcz8/zsLND6W1LZ3W3yQ4OJhp06YRHR2Nm5sbbdq0yYPcSpKUH8hgUZKkd469vT2//fabcX1iPTf27dnNJ5t+4YCZGXXq1KF48eLG/W9TG73Mcz/7+/sDEBcXR8uWLUl7cIuJEyfy9ddfExUVxejRo9m1axfNmzfP41xLkpSXZLAoSdI7LTw8nPJlyxhL2rRaLV27djVJ8zbOG22YzUat1TF11rekpaVRsWJFOnXqxNdff41KpWLChAlMmDAhr7MqSVIek2NFSJL0zkpOTmb+/PmkpKTw66+/smbjDlw+GIDC3Ipvv/2WwMBA1q5dS0pKSpY2fm+LbefusD6xNE71+zJu3DgUCkVeZ0mSpHxGBouSJL2z4uLi+PHHHwFITExidZgddlXbY1v+A9avX0+lSpWMVbVvI51OsPRoGAozc2pXrUTnzp2N+4KCgvIuY5Ik5SsyWJQk6Z3l5eXFoEGDAKhcuRJ9a/sC4FS9A/8EnWfPnj15mLtXb3/Ifa5HJ2FvZc7/zfwECwsLTp06BcCQIUO4f/9+HudQkqT8QAaLkiS90z788EMALCws6FzVGycbC8ycPFCVqMGsWbMQQuRxDl8NIQQ/Hb4BQJ/aRXCwtgCgaNGiAHTt2pWCBQvmWf4kSco/ZLAoSZL0L5WFGb1qFAHAqUZHTpw4QWBgYB7n6tXYG3iN83fiUOg0uMZe5Ny5c5w7d87YNnPUqFF5nENJkvIL2RtakiQJOHv2LMOHD+eT0ROxNDODQqWx8vJn5cqVeZ21l8owm838vRexKVmLhOD9fPjtIpM0NjY2uLm55VEOJUnKb2TJoiRJErBjxw6OHz/OnC8+p15hKwAca3Q0tuF7WxQuXJhT/1zAuUxtAD7wVnL27FkWLlxIz5492bFjx1s3TJAkSS9GlixKkvROM5S07dy5E4Dg4GAuf94Xz15zyXh4FwAzM7O3at7ociWLcnKSN0evx9AmoCUA48aN49ChQxQqVIhWrVrlcQ4lScpPZLAoSdI77fF5ow3SNYLIeq3p9NcatFotGRkZeZTDly82NhZnZ2faBBQybhs3bhwWFhZ8+umneZgzSZLyI4V4W7v6vQIJCQk4OjoSHx+Pg4NDXmdHkqRXKDk5GVtbW7799lvq1KlD7dq18zpLL8WVsAga1qpC+/bt+e6771CpVHmdJUmSXoMXiWFkyaIkSVImSUlJjB8/ns2bN3PlyhXqdviQ8NjkvM7WMwkPD892xpl0jY6ev91EfDCKQ6e25kHOJEl6E8lgUZIkKROVSsVff/1FdHQ0s5dv5PeHXqjMlXhoojHXpWdbEufq6ppvOoSEh4fj7+9vnOs6M7uAprg0+xRzeze+mzkNlUrFihUr0Ol0dO/eHTs7uzzIsSRJ+Z0MFiVJkjIxMzNjxYoVAFSrVo1/vj/EtZg02o6fR/yxddkeY2Njk296EMfExJCSksLatWtNpiq8dv0GY/6MBSDh7B94DJqKRqNh6tSpREREYGdnR/fu3fMq25Ik5WNy6BxJkqTH1KhRgxo1aqBUKmlX0hoAp+rtUFjaMGzYMAIDA43L2rVrSUlJybbaNy/5+/tTuXJlKleuTJkyZZi+9HfMnb1RaNJICv4fABqNhtGjR1O7dm06dOiQxzmWJCm/kiWLkiRJuajhZU1GTDiWroUZvnATPwxqhkKhyOtsPZMvv/ySR+6VsQaaFLVhWUYqANbW1owZM4YxY8bkbQYlScrXZMmiJElSNtRqNTNmzKBb164knPwdgCNRZqSqtXmcs2eTmprK0o17sC5SAaVC0LHC2zNepCRJr4csWZQkScqGubk5v/32Gzdu3ADFLcp1n0BUkpr1p8LpV6sw5uZvxsfn77//jvCtCUDrAC9cbfSjpW3bto2EhATq16//xpWUvsty6ulukJ86W0lvjzfj006SJOk1UygUfPHFF1y+fJmpU6fSobQdu2+p2f/H70zq8B3Hjx+nZMmSeZ3NJ1qyZAmPTp2mw3sVGNqwHkn3rgEwa9YsZsyYwdGjR6lbt24e51J6XHZBYWRkJJ06dSItLS3H4/JTZyvp7SGDRUmSpBx07NgRPz8/pk6dSkNfFWM61KFN67nExMSwZs0aZs2alddZzFFISAihoaH8/fffmJub07VWcZLuXSMkJATQ35uZmZkMFF+x3EoCIyMjAfD09Myy/UlBobW1NZs2bTI5NiQkhF69enH06FGTnvCZyZJH6XnIYFGSXoInVQ2lp6djZWWV7b6c/mE8zbFPs1/+c3g5roZewUypoF69euzevZsVK1bQrl07rl69mtdZM2GY67pXr14ozC1BaY5Go6FJkybGNDY2NsydOxdvb+88zGn+8TwBncGT3tudO3cmNTX1ufJlaWnJli1bjNc2BIMzZsxgypQpeHp6Urly5Sx57dWrV47nlCWP0vOQwaIkPYPnrRrKS/Kfw4txdnbG0tLyv3/AZubYVWxOQkoc1atXB/TP2NU1f3QcMcx1/eDBAwZ9v5FoJ386FFXSr2FZdDodSqVSfoHIJLdBzF+WefPm8d5775lsMwR+QJYxMQ37MjIysgSEAH5+ftleJy4uDoAZM2bQokWLLPsN542JiZG/f+mZyGBRkoAzZwO5d/cOTZo0wcbGhtjkDG7cvsuVKyF4eHhSunRpIiIiaNSoEampqWgToo3HKm0cUVg6YGbpgJWVJYsXL8Hd3Q0hBDdv3uTkgd2sW7uW0aNHU7VuQ27evsvFixdxcXHGzc2dGTNmADBlymQqly6Ku5srFhYWAOzevZspU6bk+OH/pP1Hjx5l5MiROVZLyaDhyYoUKUKRIkW4du0aEyZMwLZSS1b8k4A6NgIvYvn6q1lYW1sTExOTbelUXjzjwoUL4+7phdL/EeZJ6dRrUIHKlX34+OOPycjIYM6cOa81P/lZToOYQ+4BHTz9+9PV1dUY8CUlJRETE4O7u7sxnb+/P7du3SI8PJz+/fubnKNDhw40adKEZcuWGbctXboUgNDQUON54+PjiYiIAPTBpGH73bt30Wg0eHh4GI9PTU0lLCwMKysrkxLTiIgIMjIyKFiwoHGmosjISP7++2/s7Oxo3LixMe2qVau4d+8eXbp0MbbdvX//Pjt37sTT09PkecTHx2NhYYFKpZKdqd5QMlh8g9y/f5+goCDjEhkZiZ2dHXZ2dtjb2/P9999jb28P6IOEy5cvU6NGDSpWrAhAdHQ0W7fq54M1MzMzHvv4Ymtri1arxcrKCqVSibW1NWod6ITATGjRarVYW1tzJSqJlAwN6RodaWot6Rod6Rotqeka7oVdx0sXja+vL9evX2dvSAxXb4YT++gRlv9+aAz5aAj29vac+/sY907uIDw8nL59++JVqzWXr98m+MJFzgSew9rGFqEwA6UZiY9i0F3aR6FChTA3Nyfepy7JShtS0zKwsLLG1t6BkqX9sVLZEns/gjq6C9SsWZOAgABmHorkZGgkCUnJKC0sMbe0RqPVodYKdBmphP/Qmb///pty5coxbN15Ttx89O+Tj4Tt+uodl36LALg9pxWdOnWidu3aHEj15WKCpfH3NPUMwIN/1xwI37ARgO+//x6XKwK78h+Aq49+dwx4f7wSgFUx8GWXHhQu6Eznzp2xqduXtUkBeH+6jtUPFWzZGo6FQmCuEKQmJVA0+hhmGYn6/Ggc+L+QNCLu3MbRTkW1SgGkJsYzbfkfWPtVpveHgxD/jq2H0gx0+uFfsmv3lJkMJvUdXSZPnsy1a9cYNGgQLgULsSnkT+KdCxFh5kHXrl3RanMeTievSnY3n7tLTFI6hRytaVvRi+TkZFavXk1aWhp9+vShfv36rzU/eeVJTUQMVbeGQcxz4u/vT6VKlTh27Bh37ui/WBpK+KysrDhz5gyFCxemefPmxmN69OgB6D97DTZt2kT//v2pU6eOcZtGo2HYsGFERUXx3nvvoVAoUJhbITTp3L59m7CwMK5FPCT0fhKWHsW5HJ2GVeHynI9WY3fuNjohiL/0F+PHjwfgWqI56/6+TUZGBjNmzODBgweMGzcOC0sL7AKa8eeBg0yfNpU6derwxZINhD9KxcLcgq+++oo7d+4wfMRwypQpo7+3e//QuWMHatasiX2xytx4kATADzvOcuvGNdI8AqieaodCp0VzJ5iBAwdSqlQpigdU52GyGgszBaOGf8rfJ08we9ZMOrVvi4WZkrth1xg5fDglSpRg+crVaHQ6kpNT2Ld3Lw9iYvjggw8oXrwYGRlqYu5HsnvXTmxtbenQuQsarb5H/6FDh7gTHk6d2jUpV8YfhUKBTqdDrVbj7OxsfL4pKSkIIbC2tsbMzAzQD42VkZHxpD+fLGxtbZ/5mMdlZGSgVqtzPG9aWhparRZLS0tjwYFWqzWpvcp8L6+DQgghXtvV3nAJCQk4OjoSHx+Pvb099+/f5++//8bJyQlbW1t0Oh1BQUHY29vTsWNHLC0tUSgUnDp1irDwuxQv5U+RosVI1+hIzdAgMlLxLVhAX1IlFJy9l0Lk/WjuRUTy8FEcoCD87j1u3Qrj+vmTxIWe1mdEocShWntQKlEolPp//kol48aNR6PVcWDrOm4f/o3w8HDmzJnDo+LNCLlylWs3bnA/+gEKpRko9MdmRN8k/sQG4z169puPwsoWhbklCnNLlP/+BLBOCCdj77fcunWLkSNHcrhAcx6lqLM+KCA98hpRv4wyrnsNWY65k0e2abWP7nF32RAAJk6cSKB7M67eT8o2rSY+mntLPjSue/T+HqtC2fdIFWlJhM/vBkC/fv1IrTWYv2/GZptWp07nzvcdjetu7T/H2q8y8N/bw9LSioyMdJQKJffmd0Gj0dC7d29Ezb4cuREHKEzSG9xd0BOhScfS0hKnxp9gXeo9Yyr9Efo1pVJJxJIBqJMe0aVLF3w7f85vZ+9km1+Au4v6ok16CECB9wfiUK1djmnvLf+IGWOH0qRJEzZcTGTblQSSo++gfhSJJi4KTfx9NHH30cTfRx17D3QaQFZh5+THg9eY+7+rqGPCiVgxlH79+vLpp59mSWcomQoMDMw1EHleOQVCx46f4JeHvsSkKZjWugz96/ih0Wg4ePAghw4d4quvvnonSnieporZysqK9PR0pk2bxsiRI3FycgLgwIEDzJ49mwMHDgAYf4fOzs48evSIf/75h0uXLtGrVy8GDx7MsmXLaNGiBdt37CQxNYNrYXdp3rYDSWkaylaqinNBL5KSEnl4bh9hYWHUrVuXfzTeWLj7obSywczaDrsCrpjbOJCq1qFOSeTugv+mXyzYbRbWRQKyvQehVRM+tz1mZmZotVqazdpKSIJFjvd8+9u2xi+Mrq3HYlumQY5pC538nnth12nWrBkFmgxlw5mn/Ez6YBAOVdvmmPbessFoHkXg7+9P3++3sPjwjRzTRqz8FPWDWwA41uqKU73e2aYTGjX3N0wi/d4VihQpwpyNf/HT4RuEh4WRnpqMrcoaH69CuLkU4H7kPU4umUjGff11rQqXx658YxBahE4HWg1CpwWhQ+i0JF88gCIhinbt2jFjwXIOXolm1YrlXLt6lUKeBWnauBE1qlXj5o3r7F6zgKv/nESr1VK4VDmSzAuQmppMQlwc6WmpCK0WIbSg06KJj4b0JC5cuICPbzEepWr55OMh7N2zmzlfz+bDfv0wM1Nw7K8jtGzWFITOeL8qlSrbAp/x48fTqFGjLM8ncwzj4OCQ4/POjixZfA6fbbnA0VNnibz/wBhUKcwsUJhbkHz5CAmnt9CzZ08snQriMeAnMLNAobCGwFvALeN5Ev/ZQ9yfi9HpdIyf8iW/ZWT+Z2L4llEC/EpglmIOoacpVaoUFSpW5rRvzyz5+vl4OAApiZY8CA+nXbt2lChRgrFn7qLVqcCtHHZupscoLPVTmXl5eZGSkoK5owdK6+y/OcUlJnP/lj7/dnZ2+DjboE2LJuZ+JEKdgdBmIDQZCI0adexdAAoWLEiZMmVQ26STln6PhIQEHBwc8fAoSMGCHpiZKQn+O5SS779Pjx49qFGjBlY3Fag0SYTdvIGVpTlFfLwxU4C5Aq5Gh1O2SRM6dOiAm5sbZ2OURD16xMOYaOxtVLi7ulKjWhVsbaz5ZeVynCpUIDY2Fj8/P7q3LcfVm7f56/BBvv/uW/2HgVaL0KnhsZKhB1uz9nJdvnw5AwcOJDAwkNhme/j111+pV68elSoVYO3QRvz666/07t2bdevWUa1aNWxtbbGzs2NbwDL69u3LypUr6dnT9Pd27tw5qlSpAuj/GbkNP09wcDCurq6UruCP7uJu5v/4E26eXpStUBGdwhyd0pzbdyNwd7ShQOGCXL58GcWD68Sf2ozSwgqFhRUKcyvKV6rK5Ws3UVqqcFJZsn//fgoVKoS9c1UydElYuBbBwrVIlvuMWDmMqcMHYG9vz6SF6xn7y1/ER9ygmLsjXZvWxd5KP5b/u1zq2LtWEZb+dZNE18LYlKzF2rVr+eijj6hRo8Zry0NugZBN6fdwazsBXWoCtT30QaG5uTlNmjQx6ejytnu8ivnWrVscPXoUFxcXWrZsaVLN/MUXX9CuXTtjTczdu3eNgaLSypYbsWqiLkRSss1QkhXWzD+TyN37tri2GU9o6Cn8/Py4du0axYf+jHD0AsCu01fYAQ//XbSqR9y99iPjxo2jW7dutJj7P6wLlzfmNx1Iz9AHAwoz03/P2tREtMmPEFo1QqvR/9SoQafR/wQqVqxIYGAgPioNnh4F2bljJwBt27VDqVQQFxfHoYOHIFMZUXrkNRRmluTk75PHadOiGUuXLuXXk2E0LVuQbdu2IQQ0btIUM0sr1BododeucUeTbjxOl5aMJv4+KM1RmOkXlOYozC30hRw6LWXKlOHrr7/m8jP/ZrOnMLfQB3roOwfFpai5/TAF7N2xtAc1cDNBcDMhFlChtPrvf52Fszd25d7P8dzpdy6RGhPOjh076DI+nq/3XIFCdXEoVJckYHM4bA4PBuDB7VhS7t8HIMU5A7f2A1ACTtmc9+Ge+SQF76ds2bLsDLzJ0N8vQ9FueA/txsI7sHDGfmNa+8otSQzcAYCVlz9u3b5CCB0aIO7fBeCjA2lMsLzJoHpFAbhwN55OS06gTX/+drkyWHwO289HoHQsjo1j8Sz70u+FGF9rMtJRmGftJWdppsBCCQlaNZaWlqSlpZGekkiAnxP3wm/z4H4UCB0qlRX2trY42NuRXtCW2uPGMXv2bFAoGb8pmAfR90mIj6Oguyse7u4olQqETkeM2yMqNFvKwIED9aVVTtcJDw/nwf0ovAoVoqhfEcyUChCCS2diqdLxN9q0aYO1tTVnw2JJTkoCnRobKwuc7O1wsFVhoYT9e9OIf28pbdq0wcHBARsbG2JiYoiKiuLatWsIIUhKSkKpVFKqVGtKlZr3dN9eelcFRhtXy5UDKJtD4jYma7nNZttq3lRgqsm2EgXL0aJmWaaNGPjkfD3m0qVLxteNGjUyfnM7d+4cgLGkpnTp0pQoUcKY9lmqCnx8fPDx8TGuVyzhg/rhHb6ZPztLoAmwbt06evXqxfzxA2jUqBHBwcHGpU/dAjT+YiIAAwYMYMWKFZQsWZIfFvSkQwV3Fqxcx/rt/8PNtzQFvIsjbF2I05hT2tuV4sWL069fP2zrD+B0UgFwqEpoGuz+4z7qh3dIjwglI/IqRc1jWTB3DvXq1XuGJ/nmunXrFpMnT6ZKlSr0q92ShQev41SvDxE3TtOpUyfOnTuHm5vbk0/0EuTU1i4iIpKRe6LQAAlnt5McXwrwfS15ym8ePNA3BzFUMV+6dIn58+dTv359pkyZYpK2Qq2G/LTlEDHrD5GSrsYq7DgtW7Zk165dePSey7g/Y4AY8NAPcH7oRgJggbVPeY5s/8Z4noLVHmHt6IXQZIA6FU1qEnZWZrg52VPA04b5mzfj7+9PamoqCWf/IPnSIaZMHEulcv64Otpib21B2LUQmjTUv6dWrFhBqVKlAIiKiqJnz4Gkp6eTncDAQADallTRokVNUrvpSyINbQXPnTvH78O/4tixY1SqVMl4nE6nIyUlhcTERBITE0lKSvrvZ+ufjL3me9fypXctXz6poG9K5OfnZ6wmzciojOaLjjwNrU6gnHIDMzMzrK2taarRMfz9EqSk6oMZaytrlEr9l1K1Wo1yygWU/36+qrU6NLr/gl2dEGi0ArVWoNHpKDDpLEkJcaSlpeFY0Iuqvs4kpaRy6sxZHsTEUrpsOQp6FiItQ0PJD//gr//txMrKiioftOXvW7FodIJrN27yMPYRHp6FcHZ1Q6sTNOnzG4EHd6JSqSjsYkOnKt4kp6RyNyKStPQMCji7YOfgSGJSMv6fDCDsdBGsrKwoWa8Nu+4oQakkNS2d9Aw1VtYqLCyt0OgEk7/9mvljIklISMDWxhaVhRlaIVCrNQiF6SR7X8/+iqFN9MHiht83MnJ7GNnVD2gEJs9IIEjX6ECryyb103kjg8VZs2axa9cugoKC9N8e/u0BlhshBNOmTePnn38mLi6OOnXqsHjxYpN/6k9rVOMSWFlaYm1hhoOtDdaWZliZm2FlrsRrTH0KOy0gOTmZhMQkIuNTsTRTYmmmwNJcgYVSgbe3N+bm5qSlvU909Oe4u7tjZWX1b7BR54nXB/iuS/bVEXrlTdaGNiwOZA1sAaht2quuqq8z4Jxt0q6ds34QuLq64urqSjl9hPdGUCgU2NjYPPNxr7N9yLNSKBR4eHjg4eFhLDkyBLEA9evXp3Tp0gQEBGBtYUbZwq4Utkgi6Z/dJP2zO1N5t1737vrqL7OwIFycncHWBTMnD1LN7bFw8cHCxQfKN+LCvK7GZxkalUj4tcvs3fY7Xbp0ea2lbK/L4cOHWb9+Pfv27ePytUH8euImcS7eFG7cn/C9P9OjRw/27t37Wv9WHm9rp3SLQ3PsOFZmkHhuFzCOM2fOcOzYMdq3b4+vr+9ry1te6tq1K7///rvJtqpVq9KlSxeqVavGseMnWLzpfxRoOABV8erEO3vxvwxACVoSubt9At266ZuxaOLu4+LpQ/StEDRxUYz/ZACu9tZcCjrL4vkLqVevHu+99x6+vr54+PhRqkRRihYpzIYNG+jVa8i/HWD+C85SU1MJCQkh9drfAHjp+uCidkHEQAKQHheNpVKQjv6LXnZyG2cxLi6Of/75J8sxhjE2VSpVls9AOzs7k043uQkIyPr/x9LSEkvLnEsoc2Nprg+KVJb2WfapLJ/9vVTA8b/zuDtYAwWo798m27QVSg4zvq5c9N/7b5z9GJW1yvyXtppv9v8n/9PH+GrcE1IOvHDB+DpkRjMAYmNjSUhIwM7eAXtHR3Q6UCBoGRYGgLtnIY6XKYoQgvj4eJKSkrC1tcPJyREAB9V/zRBKedhzbEJDEuITKPvDEzKTgzcyWMzIyKBz587UqlWLFStWPNUx33zzDQsWLGDNmjX4+fkxZcoUmjZtyuXLl7G2tn6m6w+oW/SJJWZOTk44OTlR2CfnNNbW1u9sNd7b6Na/VfSGD+THt9+6dcskgHs87ePHPenYZzl32bJl6d3btJ2Pl5e+qmz48OEUKlSImzdvcuPGDW7evEl4eDharZaUqye4dvWE8RjvoqWYu2ojZ8NiWbVhK9M/n2Cstpu+/RInb8aQFuXOkfkb2bk4AA/HZ3tv5Xd9+vTh1KlTDBkyBPcC9vQNcGDO70f4vFtjJv61jj///JMffviBsWPH5lkeK/o4sWNYXfafvsCYNH0HqOXLl7Ns2TJCQkJMetW+LeLj49mwYQPVqlUzbss8HWNISAjnzp0jMDCQyMhIpkyZgkPrSaiKVsOhuv4YoVVjnhqLo5kae0UGHUaOws5G3yM4evOXfLNqJRRzw8mpBF5uySiVqUTEhZJ6/TSN+5r2hk6Me8T5uEfG9qRTpkzJUpKZWU7jIubWAS27ZiCZx9jMSX4a5knKmbOzs0knHYMiRf5rOuRlqQ8IvQvkXvhhZW6GdwEbEsw0z52fN7qDy+rVqxk5cuQTSxaFEBQqVIgxY8YYP8Tj4+MpWLAgq1evNn57fJIXaRwqvfkM7Qsfr/bL7+MsWltbExoamuUfi6EKe+3atVmquE+dOkXNmvrqtv79+xMaGsqZM2ewtrYmNjaW4OBgqlSpwrhx4yhQoAC9+/RjxPZbBN5+ZHKe90u7M6CWD5qo0Ley921gYCBVq1YlMDCQc+fOsW7dOpYvX06xYsWAnP9mMnvetp+Gcxs6Xly5coWlS5fy2Wef4ebmZrL/1q1bzJ49m2+++Yb338+5XdabKDk5GQ8PD5KSsu8UZ+7kiap4dWyKV+fBttno0vTpPD/oi3VAKxKunEB9+xyJ107/N2LAS5RbwPekAb+f529Dzh0t5UR2cHmCW7duERUVZdI7yNHRkRo1anDy5Mkcg8X09HSTtiEJCQmvPK9S/vU039pz+seQFzO4GKqkvv7662zHADSUSmbH0A4JYNiwYVSuXJmUlBSuXr1qUmKzatUqYmJiKFGiBJs/7sS9uFT2Xoxiz4VIAsMfcfBKNAevRJMYtJdmy5axbt26HK/5JjK0Uw0JCaFSpUqUKR9AfHy8sZT36NGjwPPPqJHbP/7MJcfp6en06NGD81ducPfuXTZu3GiStmPHjnTokFsL3zdHRkYGQUFBxgHRbW1tqVy5Mn/99RefffYZbdu2pfPHE0lw8KVwzRbEC5Xx2GYfjqFrjaLUrFkT7yJFuXThPNWrdTaODZidF31/vu7grHDhwjIYlF66dyJYjIqKAvQ9czMrWLCgcV92Zs+ezRdffPFK8ya9OQwzY7wp39oNwe3IkSNzTRcTE5NrFbaBjY2NscrZ8A+0Q4cOhISE4OPjYzzH9V0b8U5NZfSAoey8nsaGU2Fo7l2ibtvOAKSptZgpFViYKbNc403x4MEDFi9ezKVLl7CxsaH3h4Nweq83Kr/KRKz6FLSmQ0plN4MH5D6jxtPOLBIZGcn69eu5FJWM99A1+FUpRHYVRm/6UDnh4eFcuHCB/v37k5SUxK5du0hJSeHXX381/r1WfL8NX/ydjqLxGByBeAFCq8HLMpWL+/6PlsO6Uq5caZKSkrhyKZiroVcAsp0lRZKk/+SbYHHixIlPnFUgJCSE0qVLv6YcwaRJkxg9+r9eugkJCSY9VaV3z5v0rf1Jwa1h3trcgklra+ss7ZvCw8Pp1KkTgLH9m6HKOrMff/yRQYMGsWPsZ9h+sgYPd30v4V9P3mbZkWt8WNWNj5pVeiODmJSUFKZPnw78W3pobsWnex8Qm6pj7PK93Nm7jJ07dzJjxgxGjx7Ne++998zBSG4zi8B/s4Ns3LiRNWvW4N75CxRmFsTEJfHPP/8YA6j9+/dToUIFkxLhN012gXN21elpcTFcva/AxtKMJmUKEuBmxqed3ic8Tj/239D+27McI9vwSdKT5ZtPjzFjxtCvX79c0xQtWvS5zm2Y5uj+/fsmVQn37983lpRkx8rKKtfqBUnK754U3F65ciXbYDK3KuyQkBBj+8zHAxmNRsMvv/zCokWL0Gq1LFmyhF9//ZVx48YxZswYhBD8EXSXB8ka5hyJZNv5KJYMrI+f64vPivA6FSlShHHjxlG+fHkqV66MSqXiC+sIPv2/f9h+PR2X2BSSk5ON06/lxFBCm11JrmFbTjOLGI5ds2YNVl7+qIpWQWg1/DqxB6vi9WO8KZVKJk6ciLm5OWPGjHmhe84Ljx49YtGiRTRr1sxYinj06FHWrFlDeoYam9J1KVLxPWpY3WP16tUo0+L4sccHVPd1poCtvmfuB+fPcfToUWP73McD7/xUGyBJ+VW+CRbd3Nxe2fhkfn5+eHh4cODAAWNwmJCQwKlTp/j4449fyTUl6U2QUzD5NFXY1tbWvPfee1mOf/DgAYsWLWLy5Mls3bqVS5cuMX36dBYuXMhHH33EpFZtGDp/J4+8ahEaZ0nTeX/xacPiDKlfzDiExpvg8ZqQVhU8+b/T4Zy48ZDCbUfz5fjhODs7M3fu3GyPz1xCm1ubRkNQ+LjMc/16NNaPG9qkhD0fH9wN6Men+7//+z+2bt1K3759n/7G8gmNRkPdunW5fPmy8RksWrSIv0+dxta/Hj6NPkStciYFKFs8AVavBqBpWdOZogoXLmwMEJ80pZ8kSdnLN8HiswgPDyc2NtY4vEdQUBAAxYsXx87ODtAPjDx79mzat2+PQqFg5MiRzJw5kxIlShiHzilUqBDt2rXLuxuRpHwqtypsQ6njpk2bsm1nZwiAZs6cabLv4cOHzJo1i6+++oqff/6ZohWKszI4haPXYvhu/1W2nrvDD90qE+Dj9Mru61VSKBR82bYszecf5e87yQxsWBXS7gFkO4hyTExMjiW08F81c3ajPQghWLVqFQC2vgFQsBQWZgqmda5pMoxGjRo1+P77740DHL9JzM3NGTduHF9++aWxCv2fe0l4DVqMeQEv1ICjyoIBdf2wuXcmbzMrSW+5NzJYnDp1KmvWrDGuG0aiP3ToEA0aNAAgNDSU+Ph4Y5rx48eTnJzM4MGDiYuLo27duuzdu/eZx1iUpHfFk6qws+sdmlMApFar2bx5M4sXLyYpKYmBAwfSunVr5s2bx4Uq3kzdep4bD5LYsX0bFT7p+8a0YxRCsG3bNhYuXMiOHTso7m7PgLpFWXLkBtN3XOLbhvoBcnv16sWYMWP45JNPsj1PdiVe2VVNg77EbeTIkSxatEh/bOexPAC6VPXJdry1NylQ1Gq1pKamGr/0t2vXjn379rHwp6UU+GAwDlX1AysXsLFgUL2i9K5ZBHtrC9atOw3kPB5pTs9SkqSn80YGi6tXr2b1v1UOOXm8N6BCoeDLL7/kyy+/fIU5kyTJ4PEAqEaNGrRs2ZIGDRpgZmbGjh072L9/P3/88QfVovfyf4fOca6MO3yirzKNik/L94N6q9VqRo8eTVhYGIsWLWL8+PF8+n5x/gi6R0Kqmtvx+l7RN2/eZOjQocTExNCqVSvg+QKYpKQkunXrxq5du1AoFCjt3UiwcMYSxb8zNemHlhk6dCjDhw+nfPnyTzhj/qHVaunfvz/Xrl1j3759BAUF0atXL+7cuYO5jSM2pesC0L16YT5rURp76/+Gd3JycgJyH/xadmSRpOf3RgaLkiS9mezt9dNwffXVV2zatImbN29iZWXFRwP64OKgokWLFty5c4dEc0c6Lj5Bv9p+jG1SEvN8OsyOpaUl33zzDcHBwXz00UcA2FqZs7hXFbwLqEiJvY9KpSI1VT/Y87Rp05g2bZrJObLrcZ6ZobQsJiaG4cOHExoaipWVFUOGDGHBggUsbulGhoMPhZz04wkuXbqU5cuXs2fPHm7dumUyZmZ+dvLkSbZv305SUhJr164l5Oo17ty5g7e3NwMGDGDOL/P48ccfGdAiawBsKOV+FYOfS5L0hs/g8rrJGVwkKfdZSQztGQHjzCKZ7dq1y1iylhOVSsWU9UdZ+rd+DNRaRV1Y0L0SbvZv5sgE4eHhREdHM2PGDLZv3465uTnDhw8nICCAvn37snPnTlq2bJnluKd5VtkN6H3t2jU+++wzGjVqxJAhQ176/bwKjw+NY1moFK4tRhF/YgPJlw8b0z1pvMrs/uYkSdKTM7hIkvTaPO1MNtmVlhk6a8yYYTqXLvzXoSM1NZWUwG08+OMw7q1Gc/LmQ1otPMpPPStTpUjWuVLzu5BES2J17mzZsoWePXvy22+/8f3331O8uL7aOKeZQQzbFy1aZBzH8v79+2g0GhxcPTkXlUbHKlnblZYoUYKNGzdmOzB3fqNWq4mKiuLBgwekpKTgW7QYLScuYffNdHQCKvWcyA9N53L82DFGjhyZa+98Wc0sSa+ODBYlSXomz9tTOjM/P78sJUBKpZLvvvuOuLg4/P39sY7+iQ+0gYR5NuZ6dBJdl/7NV+3L06Va/hwY/8SJEyxdupTmzZsbpxA9cSOGIb8GYmmupHLhAqxfv55GjRoxceJErl+/DsCIESOYOnUqLi4uJudbsGABoB/0O3PHGJ1O8OGaMxwOjUfYpjI6h8ec3zsJ6XQ6+vfvz5EjR/jmm29QWKpIqdqXnTf0Pcc7VPJiWuuyONpYoPz3XmQ1syTlDRksSpL0zJ7UUzouLi7bXqm5zUddsWJFtm3bRoMGDShdujRBQUF4e3uTkqFlwuZgdgZHMnFLML6utlT3y38ljIcOHeKXX37h3r17xmCxVlEXGpZy41DoA0b+FsS2obUZOHAg7du3Z/jw4axfv55jx47RpEmTHM8bFhaGEMIY/C04eI3DoQ+wMlfSrNx/pZJjx47FxsaGsWPHvhHNZGJjYzl79ixRUVGE3Y/Do+c3WLr7obIw4/suATQvn7XEVY6TKEl5QwaLkiS9NE9TRQ3/9V59nKEDjOH1unXr6NWrFwu6VeT2jWv4Fi1GBW/Hl5nll6ZHjx7cu3fP5N4VCgVzOlWg2byjhEQm8MP+a0xsXhoXFxfWrVtH165dmTRpEpcvXwagY8eOfPbZZ4B+RIc7d+7Qtm1bY6B46Eo08w9cA+Cr9uUpU0gfFN68eZN58+ah1Wpp3Lhxtu368hsbGxsWLVpEbGIas88rsXTX4WSt5JeBNang7ZTX2ZMkKRMZLEqS9NI8aT5qQzV1Tu30DFJTU6lbty6XLl1Co9Fw/vx5ds5fQMuWLbDqkXV+3/zAz8+Pn376Kct2d3trZncoz5BfA1n61w3qFnelbgl927o2bdrQsmVLli9fzqRJkwgMDKRixYrGsRGrVKliPM/liARG/haEENC7ZhE6VvE2ufbvv//OkSNH8nWgGB4eztWrV7Gzs2PUqFEEBQXx/fffU97Onf13ohndvLAMFCUpH5LBoiRJL9WTqqhzY5jWLSwsjGrVqnHp0iUGDhzIkCFDsFFZ06B+fRQKBTqdYN2p23Su6oO1hdnLzP4r0bSsB12qevP72bv0XXWasU1K8XGDYgCYmZkxZMgQ+vTpox87MZtBtI9f17d9TErXUKmwE5NbmbbbUygUdOjQgQ4dOryW+3ke4eHhlChRgoyMDP0GpTnoNMb2mApLFUPWCRpUC5VtDyUpn5HBoiRJr112A1JHRkbSsWNHwHSuZK1WayyxmzZtGl26dOHH04/4/exdzoQ9Yn63ivmqM0dSUhKffvopbdu2NZlOdHqbsqSqdew4H4F3AVWW41SqrNsMElLVJGdoqOHnzLI+VbEy1wfIGRkZ6HS6N2ImqgcPHhgDRaf3eqHyrcjEGrbUrVXdWOKchr5DT3ZDMkmSlHfkOIvPQI6zKEkv5vHx9LJjaWnJli1b8PT0RKPRMGfOHLZs2WLcf+bMGVIdCtNv1Rm0AkY1KsmIRiVeR/afytdff82kSZNwdXUlLCwMW1tbk/2Bt2OpXLiAMcC9eC+eom622Fiafnd/kJhuMrbkodBoahdzMQaKhmutWLGCpUuX8v7777/Cu3px586do0q1Grg0H45dOX1eF3avROuAQoSHh1OqVCnjVJHZyW5MSUmSnt6LxDAyWHwGMliUpBcXHh6e67A7jw9SLYRgzpw5TJo0CYCaNWuSmJhIolt5FDX0JZALuleiTUCh13MDT5CRkUHXrl0ZOXIk9evXzzVtbHIGjb8/glKpoGZRFyr5OOFqb8Uf/9zjUGg0Oz6tS9lC2Xfo0Wg0+Pv7c/36ddasWUOfPn1exe28sIcPH+Li4sKEydNZc8MS6yIBKBF83THAZBgkwyDkOQ2PI4fGkaQXIwflliTpjfGkNo2Pd35RKBRMnDgRIQSfffYZf//9NxYWFqju3KFv78Fsv5rC2I3n8S6gonLhAq86+09kaWnJ1q1bnyrtvUepWJoriYxPY8f5CHacjzDZf+L6wxyDRXNzcwIDA1m9ejW9e/d+4Xy/CsHBwTRo0ICuvfrxR6Iv1kWKYSbUrBpQh3ol3UzSGn7vcngcScp/8ueEq5IkSY9p2rQpAA4ODqjVapydnfm4diEa+RckQ6Nj8C9nuZ+QczVmXnnw4AHBwcHZ7ivv7cihsQ1YN7AGY5uU5IPS7pQsaMeg9/z4c3R9BtUrmuu5HRwcGD58eL5qs5nZihUriEtMZtvDglgWLIY2+RHfNPHIEihKkpS/yZJFSZLeKKtWrWLcuHHcvHmTBvXrsWvfn9x9ZM/NmGSC78bTuEz+6exx/vx5mjdvjkqlIjg4OEv7RQBrCzPqFHelTvGnm6ouPj6es2fP8sEHH7zs7L50M2bMYNvBv9G5+IA6lfu/T8Ov/8a8zpYkSc9IlixKkvRG8fX15eTJk1SqVIno6GiGDOjHlPc92fpJbRqXKZjX2TPh5+eHubk5lpaWREREPPmApzB27FgaNWrE9OnTX8r5XhWdTkefPn0Iv3gazb5vGV1VhTr6JiEhIZw7dy7LIns8S1L+JUsWJUnKV3IKGjJvd3d3588//+T999/n3LlzzJwwnN27dxv3Z54eLy85ODiwb98+/Pz8XsrwNjqdDpVKhVKpzLe9n69fv87iJUuwcHDjjz/+wMLCgq8/G4mrA1hbW+c6u4+NjQ2urk9XwipJ0usje0M/A9kbWpJenacZVufx4VOuXLlCxYoV6d69O4sXLyY9PZ1bCYLp2y+xtHcVCjrknyrpl+nmzZsULZp7e8a8EB4eTvHixVFVaYdjzc482DqLtNvnTdJYW1uzadOmbGfxkT2eJenVkUPnvCZP+6C1Wi1qtfo15kyS3g4RERE8evQox/0ZGRlYWlqabHvw4AFubm789ddfrF69mjIDvyM8QUMFb0e+7RSAudl/rW0sLS2znSHldRBCsGnTJm7fvs3YsWOf6VjDZ0p+H3z77NmzNBw4GZdmnwLQv6IDrUv+107TMDxSYGCg7PEsSa+ZHDonnxBCEBUVRVxcXF5nRZLeWDY2Ntlu12g0xMTEkN332wcPHuDv78+cOXNQ2VqjMbdBJ+BS6HUcVBbGdEqlEj8/vywB5+tw4sQJunTpAkDjxo0JCAh4quO0Wi39+vXj/v37bN++Pd8GjFqtltE//Ipz06EAfNKgGOOblc7jXEmS9DLIYPElMgSK7u7u2NjY5Is2U5L0tkhJSSE9PR0vLy+srKyy7E9NTSUyMhJ3d3es7Z2IjNcPo+NeQIWtlQU6nY6IiAgiIyMpXLjwa39/1qlTh9GjR5OUlPTUgSJAaGgoW7duJT09nb///psGDRq8uky+gDX7TnO7UEMUCiWVHVIY17RUXmdJkqSXRAaLL4lWqzUGii4uLnmdHUl662i1WkDfaSS7IWjCw8MBiImJobynJxmYE5ucwf1kQQk7S6zNlLi5uREREYFGo8HCwiLLOV617777Dp1OZ1xPTU3l3Llz1KlTJ8djypQpw+7du3n48GG+DRSPX7vPzMPRKMwsSL5ylEmTO8svy5L0FpFD57wkhjaKOVWhSZL0ahm+pHl7e6NUKtHEP8DKTIFGpyP8YQpCCGP1syHwzAuZ20wOHz6cunXrcvDgQeO206dPU69ePQYMGGDcVq9ePdq3b/9a8/ksZqw/hE5pjjo8iJgd32GmlIGiJL1NZLD4kslv05KUt2xtbbl79y4xMQ9Qx0WiVCgwUyrQ5ZPhdAy0Wq2xlDEt7b+ZZ+Li4jh69Cjnzp3Lq6w9taSkJNq2bcuIms6USg+lT9F00GnyOluSJL1kshpakqS3TqFChXj06BFpyUk4WcdRpJC+jWJ+GqPAzMyMFStWMHjwYIoUKWLcHhAQwKZNm3B0zH5O6PwiJimdr7/4gu3bt3Px4kVCQ0MJDg7m61kznmqsTEmS3hwyWJRo0KABFStWZN68eXmdFUl6KSwsLLC2tiYpKYm4hw+IsbPBzc0NIQS6fDZaWI0aNUzWCxYsSMeOHfMoN08WHh7OlfD7jNsbyb0LqYCC4cOHExwcTGRkJCqVSg68LUlvGRksSs9k9erVjBw5MsvwQL6+vowcOZKRI0fmSb6kd0fmKtvstht+enh4EB4eTkZGBrdv30at0ZCKJTFJ6fhotOTPAWjyt/DwcMpUrolj+2lYuHhj5VMepbWtyfve2tqanTt3ZjvoNsiBtyXpTSSDRUmS3gjm5uYolUpu3bqVa7qc9kdE3sfCyYMMjWDJkRuMb1nhVWTzrXbrXjQObT7DwsUbTXw093+bzIK5X1OrVi3gv0G3PT095aDbkvQWkR1cJBPp6emMHTsWLy8vbG1tqVGjBocPHwbg8OHD9O/fn/j4eBQKBQqFgunTp9OgQQNu377NqFGjjNsBpk+fTsWKFU3OP2/ePHx9fU22rVy5krJly2JlZYWnpyfDhg0z7ouLi2PgwIG4ubnh4ODA+++/z/nzptOHSe8GKysrypYti7+/f7aLn58fAH5+fln2OTo6gk6DJikGgD+CIthxPiIvb+eNk5SuYebRWCzd/dAmP+L+hs+ZNm44Q4cOpXLlylSuXBl/f/+8zqYkSa+ADBYlE8OGDePkyZNs2LCB4OBgOnfuTLNmzbh27Rq1a9dm3rx5ODg4EBkZSWRkJGPHjmXLli14e3vz5ZdfGrc/rcWLFzN06FAGDx7MhQsX2L59O8WLFzfu79y5M9HR0ezZs8c4RdgHH3xAbGzsq7h9KZ+zsrLC1tY228Uws4m1tbXJdhsbG+zt7VEoFIiMNESGfu7piZuDufEgKS9v542RptYycM0ZrsWq0aYmcH/DZBrXqsikSZPyOmuSJL0Gshr6VUtJgStXXu81S5eGnMZ7TEqChATTbRkZkJRE+JkzrFq1ivDTpynk4QEKBWN79mTvH3+wauFCvpo0CUdAAXgAKBSQnAyAmUKBPeChVOq3P3igv3eNBh4+1F9HodBv0+ng3zaPM2fMYMywYYzo31+fxtOTav7+kJTEsZMnOX36NNFhYVj9GwjMnTmTbVu3smnDBgYPHKg/Z+bFcJ18NESKlPdiY2ON0wTq0pLQRN8gWeXLJ2vPsW1oHVSWZnmcw/wt8PYjTt+KRZeeQvTvUynn48zWrVsxN5f/QqR/CaH/bNdo/lvUatBq/1s3vM7uZ06LTpf7a50u6+vHFyH+y5/h9ePbMt9HThwcYMSIV/8s8yH5Tn/VrlyBKlVe7zUDAyGn9kIpKfpALrOMDEhN5cLff6PVailZt67J7nS1GhcbG4iK0gd5Oh1EZKrCE0L/ho+Lg39n0QD06xkZkLkNWUyMPu3160THxhIRGckHxYrB1atZsnp+/36SkpJw8fIy2Z6ans6NU6egevXcn8PjgWROQWVuiyH4fdL23Naze/34TxngvjIKhQIfHx/i4+OJiorC0tKSmP3LMGv+OaH3Ydr2i3zT6emn33sXJVw7w8Ptc0hPiCUj6jr+7/fIdspF6SXSaiE9HdLS9Et6uumSkZH1dUaG6ZLdtscXtfq/n4YAL7vFsC/zz8df5zWFAszM9J+rhiW3z9vs/i8YzpP5p0GhQjJYlF6R0qX1wdvrvmZO3N31S2Z2duDmRpK7O2ZmZgT+8w9mZmaPJbEDDw84f17/Znw8GLW0BB8fqFpVvy4ESi8vhEqlT/vvtzX1gQP6tAEBqBIT9WmLFoXy5f/7RvfvzyR7ezw9PDi8Z89/3wL/3e/k6AjOzqbHZF6y2/asi+Hbak77snv9IgwfZjl9wD2+L7v1x/c9Kb1h/S1nb2+PUqkkKioKR0dHzDSpJP65CM+On9EmwOvJJ3hHaXWCPbt30bFjRzIyMqhWrRpn7kKfPn3yOmuvhxD6gCs1NeclLS3ndUOgl3nJvD019b+A8PGfLxJ8WVjoP2ctLcHK6r/XFhb6dcN+C4v/XltZ6f8XGLYZFnPz3F9n/mlm9t9rw7rh9ePbMv/MbVEqs//5+Db5hfuVksHiq2Zjk3MpXz5TqVIltFot0dHRvPfee9mmsbS0zHaqtCzbFQrc3N2JiopCKBQo/p3iLCg4WL/fwgJ7Z2d8fX058NdfNGzSJMs5K9esSdQXX2Du4JClU0y+ZAgWcwokDa+f52fm14YA9vHtmdefxdN8G8/uAzu7NI9/kD+eLh8wMzNj5MiR9OzZEysbO6wtZBV0do5fj2HGzssUv3+OjIwMOnXqxNixY6lZsyYxMTHZzjDz2gbdNgRxhuAqORkSE/XNbAw/swvAMu9PSso+oEtJ0Z8vJeW/ZjNPy9wcVCqwttYvKlXWdWtrfXWmSqUP0Aw/ra31Pw2vDUvmbYbXhsUQ5GV+bWGRb95r0ttDBouSUcmSJenZsyd9+vThu+++o1KlSjx48IADBw5QoUIFWrZsia+vL0lJSRw4cICAgABsbGywsbHB19eXv/76i27dumFlZYWrqysNGjTgwYMHfPPNN3Tq1Im9e/eyZ88eHBwcjNecPn06H330Ee7u7jRv3pzExESOHz/Op59+SqNGjahVqxbt2rXjm2++oWTJkkRERLBr1y7at29PVUMpZn5h+IA2y+PgI3NwmltAmVtbn8eDVbVa/4/08fTPGphmDjafZcmuxPTx7f+WxKSlpmZ7acP4iw8fPuS7776jRo0aVMnUROTa/UTsrM3xdFQ933N/i4TFJPPJunPEp6qpWqM9v/ziR7du3YiMjMTGxib3QbdVKlzT0/VNSzIHYykp/5W4GV5nt+3xJTlZH9gZfqak6KtNn5aFxX+Bl729vvTMzg5sbfWLnZ1pYGZrq/+Sb9hvY2Ma9BleP75YW+uDRUl6C8m/bMnEqlWrmDlzJmPGjOHevXu4urpSs2ZNWrVqBUDt2rX56KOP6Nq1Kw8fPmTatGlMnz6dL7/8kiFDhlCsWDHS09MRQuDv789PP/3EV199xYwZM+jYsSNjx45l2bJlxuv17duXtLQ0fvjhB8aOHYurqyudOnUC9G3Ndu/ezeeff07//v158OABHh4e1KtXj4IFC+bJ83kjZG4f+aplV22fXQD6eLCaXaCq1eqD0pwaqT+BOfrhHW6FheWYRgGYazSogPABA6js7c1vMTFYF63EFO+mFNEl83vGWWwszUyr8jJX2T1e/ZZdVdzjQXF2JbdParOaW8l0ds/Q8DO7dmePdzrQaHJsw5ag1tHbtg7x1s6USIqg5bezGXUxmBNjx9LLwYHfnZ3BwQFPw+/L0Dbu35oF19RUCteu/eS/HWtr00BMpTJdt7UFV1fT4M7OTp/GENhlDvDs7PTBoL29fl2l0v/eXsf7QJLecgohnrVo4N2VkJCAo6Mj8fHxJqVjoC+1uHXrFn5+fsYhPCRJeklyCpgeC57SMzLQaDTZ93zU6dDqdIRHRmL366/4xsczNjCQ727coLVfae62/5JYCxtaRF7gx1NrUKof6yiQOeh6lqrJ/CRz27HMbdn+rb7UWlnTPaAHp91LoUmMIWrNKLTJj7KcxsbCgpBPPqGwu3vOpW2Zq1Izp7Gx0V9PBnGS9FrlFsM8yRtZsjhr1ix27dpFUFAQlpaWWaaey06/fv1Ys2aNybamTZuyd+/eV5RLSZJemqcsLbX6d8lOeno66cnJaJOTeThkCLEKBdWuXcN2wAC8mzVkUOeyjN4Rxm7P8sxb+AejG5fM+UKGqvnHS/EMgWRuQ3o83mnKENRm184st7ahhm3ZNf7ProOCmdkT27J9szuE03/dRKdOJ3H3XDatXYm3tzf79+8nPj6eLl26GGdpienTh8JvSHtsSZJezBsZLGZkZNC5c2dq1arFihUrnvq4Zs2asWrVKuO6HPpBkt4N6enpXLp0CZ1OR0xMDB999BG3b9827l+8eDFr1qzh+60nmH3wLgsOXKO4ux1tAgplf0Kl8r+OBW+J7ecjWPrXTQAe7pnP9xOH0a5dO4D81z5YkqTX6o2sB/jiiy8YNWoU5cuXf6bjrKys8PDwMC4FChR4RTmUJCk/0Wg06HQ6vLy88PT0ZPPmzQQGBtK2bVsAihUrRkpKCkUV0XSp4AzAuI3nCboTl4e5fr2qFXagcmEnOvrbkhLyF97e3iQahreSJOmd9kYGi8/r8OHDuLu7U6pUKT7++GMeGmYWyUF6ejoJCQkmiyRJby4rKyssLS0pW7YslStXZt26dXz00UcsXboUgP79+7N5ah/qFnUkXaNj5bFbTzjj2yEwMJA6VSowppI53cvZAzBixAiqV6/OtWvX8jh3kiTltXcmWGzWrBm//PILBw4cYM6cORw5coTmzZtnO2agwezZs3F0dDQuPj4+rzHHkiS9ara2tixevNhYyyCE4OaN61xZNZGh9X35rsvbPbNLaoaWtYeCadmyJbdu3eLbObNR/tuuMSEhgfj4eOzt7fM4l5Ik5bV8EyxOnDgRhUKR63LlBeZY7tatG23atKF8+fK0a9eOnTt3cubMGQ4fPpzjMZMmTSI+Pt643Llz57mvL0lS/hUZGQlAx44dcXBwIPDv4/wxawj/BJ7l3LlzhIeH87YNHCGEYMT6M0zed4fUovUJCAjgl19+Me5ft24dO3fuxMPDIw9zKUlSfpBvOriMGTOGfv365ZqmaNGiL+16RYsWxdXVlevXr/PBBx9km8bKykp2gpGkt1x4eLhxbM+FCxcatx8/fpwaNWqAQolbk4/4ZGA/pneqllfZfOkW/hnK/648RGg1OKRHs2vXLpNhv6Kjo3FxcckyU8trm6VFkqR8I98Ei25ubri5ub226929e5eHDx/i6en52q4pSVL+ExMTY5zdxczMjKZNm9KgQQMmTpyITqejce/hXPVsxOqz0dQsHUWzcm9+Sdufl6P4/s/roFCQemwNB9csJCIiglq1ajFv3rwnz9JiY4Orq+trzLEkSXkp3wSLzyI8PJzY2FjCw8PRarUEBQUBULx4cezs7AAoXbo0s2fPpn379iQlJfHFF1/QsWNHPDw8uHHjBuPHj6d48eI0bdo0D+9EkqTXKT09nYyMDC5duoTi37Z5mUvKtmzZQps2bQB9QDRs2DD2/zKPAh8k41C1LaN/D8LXtTalPZ5tQNv85Pr9RIasPglKS5KC9jCrX1MyMjIYN24cd+7cYd68efz555+51qq4urpSuHDh15hrSZLy0hsZLE6dOtVkgO1KlSoBcOjQIRo0aABAaGgo8fHxgL60IDg4mDVr1hAXF0ehQoVo0qQJM2bMkNXMkvQOMDc3R6lUcu/evWzHWQSwtramYsWKxvWhQ4dy+/Ztvv32Wx4dXEH5uk25nWbNoF/Osn1oXQrYWr7mu3hx8SkZtJm7G62ZHWl3LvFw/1I+2qcxSXP06FEaNWpESEiIDAglSQLkdH/P5FVP9xceHk5MTEyO++W3+fyvX79+xMXFsW3bthzT+Pr6MnLkSEaOHAno58DeunWrcQDkpxUaGkr9+vW5du1avuuxmpGRQcmSJdm0aVO+GdA5PT2d5ORk7ty5g0ajMSlZ7NWrFzt37qRly5YAPHr0iJ9//plhw4bRoUMH9u3bh51LQcqMWM39ZC11iruwpn91zM3yTR/Bp9J7ykKOqouiSYjmfU0gwwb2wdzctMzA8DwCAwOpLGdokaS3xotM94eQnlp8fLwARHx8fJZ9qamp4vLlyyI1NfW5zn379m1hY2MjgBwXGxsbcfv27Re9jSz69u0r2rZt+9LPm19FR0eLjh07CicnJ2Fvby/q168vrly58lTHLlu2TFSoUEHY2toKR0dHUbFiRfHVV18Z9z/Ns4yOjhbJycnGdUBs3br1me+jffv2YubMmcb11NRU0bdvX1GuXDlhZmaWYz5+/PFHUbp0aWFtbS1Kliwp1qxZY7I/IyNDfPHFF6Jo0aLCyspKVKhQQezZs8ckzU8//STKly8v7O3thb29vahZs6bYvXu3SZqFCxeK999//5nv61XK7n0aGBgoABEYGCiEEEKn04kyZcoIQCxdulScPHnS+B70KltNlP58tygyYaf4YvulvLqN5zJ37lz950jpusKyYDFx9uxZUb9+fbF69WqTdI8/D0mS3g65xTBP8kZWQ7+NYmJiSElJYe3atfj7+2fZb5yPNSYm35cuZmRkYGmZf6voJkyYwNmzZ43Dgjze2zMnK1euZOTIkSxYsID69euTnp5OcHAwFy9efKbrv4yOXOHh4ezcudOk965Wq0WlUjF8+HA2b96c7XGLFy9m0qRJ/Pzzz1SrVo3Tp08zaNAgChQoQOvWrQGYPHkya9eu5eeff6Z06dLs27eP9u3bc+LECWOTD29vb77++mtKlCiBEII1a9bQtm1b/vnnH8qWLQtAz549GTNmDJcuXTJuexMoFAoGDx7MypUr8fLyMv4tFy1alJuXzvB+lT+579uE6n7OeZzTp7djx07Gjh0LQL/3K/DTT8c4e/YsR44c4fTp07Rv3/7ZSxokSXp3vPzY9e31KksWn/Rt/lV+239Sadjhw4dFtWrVhKWlpfDw8BATJkwQarXauL9+/fpi6NChYsSIEcLFxUU0aNBACCHEhQsXRLNmzYStra1wd3cXvXr1Eg8ePDA57tNPPxXjxo0TBQoUEAULFhTTpk0zufajR4/E4MGDhbu7u7CyshJly5YVO3bsMO7ftGmTKFOmjLC0tBRFihQRc+fOfeL9DhgwQHTv3v0pn85/2rZtK/r165drGsOz/Pbbb4WHh4dwdnYWn3zyicjIyDCmKVKkiPjhhx+M64D4+eefRbt27YRKpRLFixcXf/zxR67X+fbbb0XVqlWfmI/H1apVS4wdO9Zk2+jRo0WdOnWM656enuLHH380SdOhQwfRs2fPXPNUoEABsXz5cpNtDRs2FJMnT871uNcpt5LFtWvXisDAQBEYGChOnTolzp49KwIDA8XatWsFIHbv3i26du0qYmNjxcOk9Dy8i2dz4nqMaLvwqGjduaeYOHGiOHv2rADEsWPHxIIFC8SMGTNM0suSRUl6O8mSRemVuXfvHi1atKBfv3788ssvXLlyhUGDBmFtbc306dON6dasWcPHH3/M8ePHAYiLi+P9999n4MCB/PDDD6SmpjJhwgS6dOnCwYMHTY4bPXo0p06d4uTJk/Tr1486derQuHFjdDodzZs3JzExkbVr11KsWDEuX76MmZkZoJ+irEuXLkyfPp2uXbty4sQJPvnkE1xcXHIds7Nt27Z07NiRPn360KxZs6d+Fh4eHhw5coTbt29TpEiRHNMdOnQIT09PDh06xPXr1+natSsVK1Zk0KBBOR7zxRdf8M033/Dtt9+ycOFCevbsye3bt3F2zr706ujRo8/VFjA9PT1Lm1qVSsXp06dRq9VYWFjkmObYsWPZnlOr1bJx40aSk5OpVauWyb7q1atz9OjRZ87n6+Tq6vrEoWKsra3R6XSMHz+eW7f0UwCGoZ9zWmHrTBEfb9wdnq+t8qsUEhnP4F/PkpimoXePCXzZthz//PMPoP+dfvrpp3mcQ0mS3givIHh9a72LJYufffaZKFWqlNDpdMZtixYtEnZ2dkKr1Qoh9CWElSpVMjluxowZokmTJibb7ty5IwARGhpqPK5u3bomaapVqyYmTJgghBBi3759QqlUGtM/rkePHqJx48Ym28aNGyfKlCmT471eunRJ2NnZidmzZwsvLy/x+++/G/cZSlwyl35mFhERIWrWrCkAUbJkSdG3b1/x22+/GZ+DEPpnWaRIEaHRaIzbOnfuLLp27Wpcz65kMXPpW1JSkgCytBPMLCAgQHz55Zc57s/pdzpp0iTh4eEhzp49K3Q6nThz5owoWLCgAERERIQQQoju3buLMmXKiKtXrwqtViv+97//CZVKJSwtLU3OFRwcLGxtbYWZmZlwdHQUu3btynK9+fPnC19f3xzz+brl9D69ffu2sVQx87Jjxw5hYWGRYztiS8+SwufTtaLF9wdEmlqTw1VfP51OJ0ZPniFKjtsoikzYKTotPi5SM/T5y8vPGkmS8o4sWZRemZCQEGrVqmXsOQpQp04dkpKSuHv3rrH9ZJUqVUyOO3/+PIcOHTKOe5nZjRs3KFmyJAAVKlQw2efp6Ul0dDQAQUFBeHt7G9Nml7e2bduabKtTpw7z5s1Dq9UaSyAzmz59Os2bN2fixIk0adKExo0b8/DhQz766CMuXLhA6dKlcxxs2NPTk5MnT3Lx4kX++usvTpw4Qd++fVm+fDl79+5FqdT3jC1btqzJtT09Pblw4UK25zTI/BxsbW1xcHAwPofspKamPlev+ylTphAVFUXNmjURQlCwYEH69u3LN998Y8z//PnzGTRoEKVLl0ahUFCsWDH69+/PypUrTc5VqlQpgoKCiI+PZ9OmTfTt25cjR45QpkwZYxqVSkVKSsoz5/N1K1y4cLZtgU+cOGEscd24caNxfvjY2Fg6dOhAamoiQmnOpfupTN56kW86VTB5r+SV46fPsSHCGQs3FS4WaoYFWHL5wnkA9u7dC8CqVauyPVbO0CJJ0uNksCi9FLa2tibrSUlJtG7dmjlz5mRJm3nWHAsLC5N9CoUCnU4H6AONly04OJi+ffsCULlyZbZv307Tpk2JiYlh79699O/f/4nnKFeuHOXKleOTTz7ho48+4r333uPIkSM0bNgQyP2ecvKsx7i6uvLo0aMn5vVxKpWKlStXsnTpUu7fv4+npyfLli3D3t7e2PHGzc2Nbdu2kZaWxsOHDylUqBATJ07MMt2mpaUlxYsXB/RfFs6cOcP8+fNZunSpMU1sbOxrnZnpZTNUqw8aNIgWLVqY/J727dvHunXrWPHHHDy7zWBj4F08HKwY07R0XmUXgHSNlrmnErFwK4Im8SHnfx1Lg5kPsqT78ccf+fHHH7M9h5yhRZKkzGSwmM/k9K0+r77t+/v7s3nzZoQQxhKT48ePY29vj7e3d47HVa5cmc2bN+Pr65tlHLenVaFCBe7evcvVq1ezLV309/c3tpE0OH78OCVLlsy2VBHAy8uLo0ePMmnSJEBfErl161ZatWqFs7Mzw4YNe6Y8GkrRkpOTn+m4F1WpUiUuX7783MdbWFgYf38bNmygVatWxpJFA2tra7y8vFCr1WzevJkuXbrkek6dTkd6errJtosXLxp7UL+JDH/zAwYMyBLQ16pVCysrKxYtWsTASg4sO5fAwkM3CP7nLEtGdHwlX3ZyExUVhUajYd3FJIIiktGlpzCykgXvD9hrku7Ro0dMmDCBwMDAHEdfkGO6SpKUmQwW84mnaWT/Kr/tx8fHG6dNNHBxceGTTz5h3rx5fPrppwwbNozQ0FCmTZvG6NGjswQXmQ0dOpSff/6Z7t27M378eJydnbl+/TobNmxg+fLlOQZzmdWvX5969erRsWNHvv/+e4oXL86VK1dQKBQ0a9aMMWPGUK1aNWbMmEHXrl05efIkP/74Iz/99FOO5xw3bhwtWrRg6NChfPzxx6jVao4cOYKlpSUPHjxgx44ddO3aNdtjP/74YwoVKsT777+Pt7c3kZGRzJw5Ezc3tywdO161pk2bMnDgwCzV7ZcvXyYjI4PY2FgSExONv1PDzCRXr17l9OnT1KhRg0ePHvH9999z8eJFkxmRTp06xb1796hYsSL37t1j+vTpxs4dBpMmTaJ58+YULlyYxMRE1q9fz+HDh9m3b59JPo8ePcqMGTNe3YN4zXQ6HXFxcVk6HhVRh+P98Dp3XapyJK4Avk360qmCG507d6Zo0aKvNPDSaDQsWrSIqVOn4unpyZY9B9hyxozgDbN4v/dKKleubPJlD2DZsmVUqVIFf39/OfC2JElPJIPFfKJw4cKEhITk2Qwuhw8fzlICNGDAAJYvX87u3bsZN24cAQEBODs7M2DAACZPnpzr+QoVKsTx48eZMGECTZo0IT09nSJFitCsWbNcg8zHbd68mbFjx9K9e3eSk5MpXrw4X3/9NaAvvfz999+ZOnUqM2bMwNPTky+//DLXntDNmjXjwIEDTJs2jTp16qBUKmnUqBGnT59my5Yt9OvXDx8fH2rXrp3l2EaNGrFy5UoWL17Mw4cPcXV1pVatWhw4cAAXF5envqeXoXnz5pibm/Pnn3+azG/eokULk2nsDL9T8e9ETVqtlu+++47Q0FAsLCxo2LAhJ06cwNfX13hMWloakydP5ubNm9jZ2dGiRQt+/fVXnJycjGmio6Pp06cPkZGRODo6UqFCBfbt20fjxo2NaU6ePEl8fDydOnV6RU/h9YqKiqJPnz4kJiby119/YWFhQWRkJIDxS55j3R441emBonBVflo8iZ9++glzc3MOHTpE3bp1X3qejh07xtChQ7kUFok2IYFSpUpBajwLmrlRc6q+jWJSUhJt27Y1vhclSZKelZzu7xm86un+JOlZLFq0iO3bt2cpzcsvunbtSkBAAJ999lleZ8XoWd+n586do0qVKgQGBuLs7EzFihXJyMjg8OHDVK9enXXr1tGrVy9mzJhBixYtEEKw73oSGddOsG7NSq5fvw7oq7Pbtm3LqFGjeO+99164E8z9+/cZNmwYmzZtwq5ic5wbDeE9i5uM7PAeSqXSZMq+JUuW8PPPP+Ph4cGNGzewsbExuS9ZsihJ74YXme5PlixK0htqyJAhxMXFkZiYmC/nhi5fvjyjRo3K66y8FCEhIfj7+zNz5kw8PDwwNzfn3LlzxjEX/fz8jEGXfmCA+kz7fCKLFi1i1JRZaOKi2LZtG9u2baNUqVL06NGDJk2aYGlpmWuNQWxsLFevXiUyMtK4REREsGnTJpIyBK5tJ2JbWl9iuTfoNutmjzY5PjIyku+//x5vb2+aNm2KjY3NK3tGkiS9vWTJ4jOQJYuS9GZ71vdpeHg4/v7+Txz+Z+fOnbRs2dJkmxCCYYt3s/O2jqR/dvPor18R6Vk7QtnY2BASEkLv3r0JDAxk48aNNG/eHND3WM5u4GzrIgG4tByNub0LZgroUd6edqVsjSWWu3fvZsqUKaxdu5aePXtmOV6WLErSu0eWLEqSJL0CubUlvnPnDsuXL2fnzp0mw0EZ6ATcT0xHobDCvnIrfGq3oUsJMyL+3sHG339HqVQyd+5cevXqxdGjR3n48CHJycmEhoZSsGBBQF/d7ObmRsGCBXF1dcXZrSAPveoQZqUfxshdBcsH1KGCt5PJtQ2jJ9y6dSvbuc/lWIqSJD0LGSxKkiTlIrsBuxMTE2nSpAkPHz7M8TgzpYL23qns/Gk65frNJCYNll3QUSegJ8c+n4m1OoHg4GAAk1EQsqu6f/BAP06ilU95PHr01ufh3C7un1yPU/9gwMkkvaFZwpQpU5gyZUq2+ZNjKUqS9LRksChJkvSM7O3tmTNnDgsXLuT8+fO5pk0Pv8CnxRNJ9KrOwoPXOX79IS0XnuD/BtcgLi4OgGlfzqRVi2Yo/61GTtcI9gReZ9HWwyhtnJjXuZxxPMQNFxOxSo5i5pzFAMTExGQJZg3BpZ2dHTt37sy2TascS1GSpKclg0VJkqTnMGDAAMqXL0+NGjVyrNY1dIAxV8Kw90vQOqAQU/+4xJGrD7C3/m+Q7yj7UvTaFk1xdzsK2FhyJiyWNLUd9pVbIYQOTz8PY9vCypX1bQ5n5pI3w5BJM2bMoH79+i/nhiVJemfJYFGSJOk5eXh4PHEwfcA4RmURF1tW96/G8esP8XWx5cy/+x+kK0lT67h4L8F4jItKSdiJXaTeOI1tp8XPlb969eo913GSJEmZyWBRkiTpORk6wKxevZoZM2ZQu3ZtvvnmG+PUgIbxDjN3gFEoFNQtYdpWsK1XKnMHNOXa/UTuJ6ZTpXABUiOvUXX6IgAszHIel9FQqimEYPHixbRr1474+PiXfauSJL3DZLAo5WvTp09n27ZtWaYifFOvk1dWr17NyJEjjW3kXkTv3r3x9/fPV4NtG9SsWZNx48bRsWPH13bNwoUL07hxY2bOnEmJEiWoXr16lkG3n1RNbaYAP1db/FxtjfvOReUcILq6umJtbU1aWlqWUs0VK1YAsgOLJEkvz9PPuya9tfr160e7du1Mtm3atAlra2u+++67vMnUW2Lt2rWULl0aa2trfH19n3qe5AYNGqBQKIxTG2bWsmVLFAoF06dPf+p8dO3alatXrz51+pycP3+e3bt3M3z4cADUajUTJkygfPny2NraUqhQIfr06UNERITJcbGxsfTs2RMHBwecnJwYMGAASUlJJmmCg4N57733sLa2xsfHh2+++SbL9Tdu3Gh8nuXLl2f37t0m+ydPnszEiRPR6XQvfK/PolatWhw7doyffvrJJFDMPOd7lSpVsiyGnsqZp1J8GoULF2bTpk2A/m8sMDCQnTt3UrZsWSZOnEhgYCAhISGyA4skSS+FLFmUsli+fDlDhw5lyZIl9O/fP9s0GRkZWFpavuacvVnCwsLo06cP48ePZ8iQITx48OCZAjYfHx9Wr17NxIkTjdvu3bvHgQMHsh3XLzcqlQqVSvVMx2Rn4cKFdO7cGTs7OwBSUlI4d+4cU6ZMISAggEePHjFixAjatGnD2bNnjcf17NmTyMhI9u/fj1qtpn///gwePJj169cD+sFimzRpQqNGjViyZAkXLlzgww8/xMnJicGDBwNw4sQJunfvzuzZs2nVqhXr16+nXbt2nDt3jnLlygH6ObMHDhzInj17sgyS/apVr17d+FoIwdixY6lTp06uc74bqqnj4uKyjIeYuTQyu5JJQymxv7+/sfNLkyZNMDc3f+HpBCVJkkwI6anFx8cLQMTHx2fZl5qaKi5fvixSU1Oz7EtKShJJSUlCp9MZt6Wnp4ukpCSRlpaWbVqtVmvclpGRIZKSkrKcO7u0z6Nv376ibdu2Qggh5syZI6ytrcWWLVtM0tSvX18MHTpUjBgxQri4uIgGDRoIIYT47rvvRLly5YSNjY3w9vYWH3/8sUhMTBRC6J+XtbW12L17t8m5tmzZIuzs7ERycrIQQog7d+6Ibt26iQIFCggbGxtRpUoV8ffffwshhJg2bZoICAgQv/zyiyhSpIhwcHAQXbt2FQkJCcbzabVa8dVXXwlfX19hbW0tKlSoIDZu3Gjcf+jQIQGIP//8U1SpUkWoVCpRq1YtceXKFWMaw3UMrl+/Lvz8/MTQoUOFTqcTsbGxonfv3sLJyUmoVCrRrFkzcfXq1Vyfa1hYmFAqlSI0NPRpfg1ZnvfHH38sXFxcxLFjx4zbZ82aJVq3bi0CAgLEtGnTjNuflL9Vq1YJR0fHLPeb23N9nEajEY6OjmLnzp255v306dMCELdv3xZCCHH58mUBiDNnzhjT7NmzRygUCnHv3j0hhBA//fSTKFCggEhPTzemmTBhgihVqpRxvUuXLqJly5Ym16pRo4YYMmSIybb+/fuLXr16ZZu33N6nL9OWLVsEICwsLIz3mJ3bt28LGxsbATzXYmZmJpYsWfJK70WSpLdDbjHMk8hq6NfAzs4OOzs7k9KFb7/9Fjs7O4YNG2aS1t3dHTs7O8LDw43bFi1ahJ2dHQMGDDBJ6+vri52d3UubjWHChAnMmDGDnTt30r59+yz716xZg6WlJcePH2fJkiUAKJVKFixYwKVLl1izZg0HDx5k/PjxADg4OBhLgDJbt24d7dq1w8bGhqSkJOrXr8+9e/fYvn0758+fZ/z48SbViDdu3GDbtm3s3LmTnTt3cuTIEZPq2dmzZ/PLL7+wZMkSLl26xKhRo+jVqxdHjhwxue7nn3/Od999x9mzZzE3N+fDDz/M9jkEBwdTt25devTowY8//ohCoaBfv36cPXuW7du3c/LkSYQQtGjRArVanePz9PLyomrVqgwbNoy0tLQnPP2sLC0t6dmzJ6tWrTJuW716dbb5fp78Pem5Pi44OJj4+HiqVq2aa77j4+NRKBTGqtWTJ0/i5ORkclyjRo1QKpWcOnXKmKZevXompdVNmzYlNDSUR48eGdM0atTI5FpNmzbl5MmTJtuqV6/O0aNHc83jq9a6dWuGDx/OpEmTKFSoUI7pDB1kAgMDs10Mv5vs9n366adotVomTZpEYmLia7w7SZLeOS89dH2LPW/JIv+WAkRHRxu3zZw5UwBi4MCBJmkNpQy3bt0ybvvhhx8EIHr06GGS1tXVVQDi4sWLL3Rfffv2FZaWlgIQBw4cyDZN/fr1RaVKlZ54ro0bNwoXFxfj+tatW01KEQ2ljXv27BFCCLF06VJhb28vHj58mO35pk2bJmxsbExKvMaNGydq1KghhBAiLS1N2NjYiBMnTpgcN2DAANG9e3chhGnJosGuXbsEYPx9GUrajh8/LgoUKCDmzp1rTHv16lUBiOPHjxu3xcTECJVKJX7//fccn0W/fv1EtWrVxJAhQ0SDBg1M/m5atWolhg4dmuOx9evXFyNGjBBBQUHC3t5eJCUliSNHjgh3d3ehVqtNShafJn/ZlSzm9lyzs3XrVmFmZmZSQv641NRUUblyZZO/1VmzZomSJUtmSevm5iZ++uknIYQQjRs3FoMHDzbZf+nSJQGIy5cvCyGEsLCwEOvXrzdJs2jRIuHu7m6y7Y8//hBKpTLbEvfXVbJokDkPERERYu7cuUKj0byUc6vVatGzZ0+T37skSVJOXqRkUbZZfA0MDfltbGyM28aNG8fIkSMxNzf9FURHRwOYtC8bOnQogwYNwszMzCRtWFhYlrTPq0KFCsTExDBt2jSqV69ubJOWWZUqVbJs+/PPP5k9ezZXrlwhISEBjUZDWloaKSkp2NjY0KJFCywsLNi+fTvdunVj8+bNODg4GEuIgoKCqFSpEs7OzjnmzdfX12QGCk9PT+Nzun79OikpKTRu3NjkmIyMDOPAxJnvMfM5QP+8DZ0AwsPDady4MbNmzWLkyJHGtCEhIZibm1OjRg3jNhcXF0qVKpVjqe7ly5dZvXo1ly5dwt/fn379+tGgQQP27t2Lu7s7Fy9efOLYfAABAQGUKFGCTZs2cejQIXr37p3lb+Z58ge5P9fspKamYmVllWN7OLVaTZcuXYxDuOQVlUqFTqcjPT39pbw3XoRSqa+8EUIwaNAgdu3aRWhoKMuWLXum86jValatWmUsCVYqlZibm7N27dpXkW1JkiQTshr6NbC1tcXW1tbkn6ylpSW2trZYWVllm9bwTwbAwsICW1tbrK2tn5j2eXl5eXH48GHu3btHs2bNsq3WsrW1NVkPCwujVatWVKhQgc2bNxMYGMiiRfpx4TIyMoz32alTJ2NV9Pr16+natasx4Hmaf+aGMesMFAqFsZraEIjv2rWLoKAg43L58mVjb9HszmP4XWSu7nZzc6N69er83//9HwkJCbyI4OBgrKysKFOmDAqFgpUrV1K0aFHq1KnDzz//TGJiIm3atHmqc3344YcsWrSITZs25Vh1/jxye67ZcXV1JSUlxfi7zcwQKN6+fZv9+/fj4OBg3Ofh4ZElCNVoNMTGxuLh4WFMc//+fZM0hvUnpTHsN4iNjcXW1jbPA8XHderUCTc3N2NPcoALFy4wc+bMLFMGajQaY/U76OeiHj9+PHv27Mnydy1JkvSqyWBRMipSpAhHjhwhKioqx4Axs8DAQHQ6Hd999x01a9akZMmSWYZMAX1P2L1793Lp0iUOHjxIz549jfsqVKhAUFAQsbGxz5XnMmXKYGVlRXh4OMWLFzdZfHx8nulcKpWKnTt3Ym1tTdOmTY337+/vj0ajMbavA3j48CGhoaGUKVMm23N5eXmRnp5uPMbMzIz169dTrFgxBg8ezOeff/7UwUyPHj24cOEC5cqVy/Z6z5O/51GxYkVAX2qamSFQvHbtGn/++ScuLi4m+2vVqkVcXByBgYHGbQcPHkSn0xlLQ2vVqsVff/1l0sZy//79lCpVigIFChjTHDhwwOTc+/fvp1atWibbLl68mKVUOa8Z2r3evn3b2HMb9EMBTZkyha+++sq4LTIyEgsLC9zc3BBCAODs7MysWbOYP38+bdu2fe35lyTp3SaDRcmEj48Phw8fJjo6mqZNm+Zawla8eHHUajULFy7k5s2b/Prrr8aOL5nVq1cPDw8PevbsiZ+fn0l1affu3fHw8KBdu3YcP36cmzdvsnnz5iydFnJib2/P2LFjGTVqFGvWrOHGjRucO3eOhQsXsmbNmme+f1tbW3bt2oW5uTnNmzcnKSmJEiVK0LZtWwYNGsSxY8c4f/48vXr1wsvLK8d/3HXr1qV27dp07dqVbdu2cePGDfbu3UtERAS2trasX7+elJSUp8pTgQIFiIyMzBIoGTxP/p6Hm5sblStX5tixY8ZtarWaTp06cfbsWdatW4dWqyUqKoqoqChjCaS/vz/NmjVj0KBBnD59muPHjzNs2DC6detm7PzRo0cPLC0tGTBgAJcuXeK3335j/vz5jB492nitESNGsHfvXr777juuXLnC9OnTOXv2bJZOYkePHqVJkyYv7b5fpse/IFSpUoV27drRoUMH4zZHR0cAtFotycnJxu1Dhw5l+PDhWWojJEmSXjUZLEpZeHt7c/jwYWJiYnINGAMCAvj++++ZM2cO5cqVY926dcyePTtLOoVCQffu3Tl//rxJqSLoq6n/97//4e7uTosWLShfvjxff/11lvaZuZkxYwZTpkxh9uzZxsBk165d+Pn5PduN/8vOzo49e/YghKBly5YkJyezatUqqlSpQqtWrahVqxZCCHbv3p2lKjfzPe/du5dOnToxevRoypQpw6RJkxgwYABXr14lKiqKnj17PvXg0U5OTlmaAWT2rPl7XgMHDmTdunXGdUMv9rt371KxYkU8PT2Ny4kTJ4zp1q1bR+nSpfnggw9o0aIFdevWNWm35+joyP/+9z9u3bpFlSpVGDNmDFOnTjWOsQhQu3Zt1q9fz7JlywgICGDTpk1s27bNpKTu3r17nDhxIsfxQfObtm3bsnXrVrp27WrcplKpePDgARkZGdm2HZYkSXrdFMJQzyE9UUJCAo6OjsTHx5u0yQJIS0vj1q1b+Pn5ZWlbKElvi9TUVEqVKsVvv/2Wpfo3P5gwYQKPHj3KsQOJfJ9KkvSuyi2GeRLZG1qSpKemUqn45ZdfcpyRJK+5u7ubVF1LkiRJL04Gi5IkPZMGDRrkdRZyNGbMmLzOgiRJ0ltHtlmUJEmSJEmSciSDRUmSJEmSJClHMliUJEmSJEmScvTGBYthYWEMGDAAPz8/VCoVxYoVY9q0adnOKpFZWloaQ4cOxcXFBTs7Ozp27JhlNghJkiRJkiTJ1BsXLF65cgWdTsfSpUu5dOkSP/zwA0uWLOGzzz7L9bhRo0axY8cONm7cyJEjR4iIiDAZCFeSJEmSJEnK6q0YZ/Hbb79l8eLF3Lx5M9v98fHxuLm5sX79ejp16gTog05/f39OnjxJzZo1n+o6cpxFSXqzyfepJEnvqhcZZ/GNK1nMTnx8PM7OzjnuDwwMRK1W06hRI+O20qVLU7hw4VynlUtPTychIcFkkSRJkiRJepe88cHi9evXWbhwIUOGDMkxTVRUFJaWljg5OZlsL1iwIFFRUTkeN3v2bBwdHY2Lj4/Py8r2G+/w4cMoFAri4uJe6DxhYWEoFAqCgoKe+pjp06dTsWLFF7pufrB69WqTv8mnva8pU6aYTIOXn0ycOJFPP/00r7MhSZIkvUT5JlicOHEiCoUi1+XKlSsmx9y7d49mzZrRuXNnBg0a9NLzNGnSJOLj443LnTt3Xvo18tqSJUuwt7dHo9EYtyUlJWFhYZFl8GVDgHjjxg1q165NZGQkjo6OL3R9Hx8fIiMjTeb3fVXWrl1L6dKlsba2xtfXlxkzZjzVcQ0aNDD+DVpbW1OyZElmz57Ny27BMXbsWA4cOJBrmqioKObPn8/nn39u3DZ79myqVauGvb097u7utGvXjtDQUJPjnqaDV3h4OC1btsTGxgZ3d3fGjRtn8ncB+jmeAwICsLGxwdPTkw8//JCHDx+a3MOaNWtybBIiSZIkvXnyTbA4ZswYQkJCcl2KFi1qTB8REUHDhg2pXbt2jvPAGnh4eJCRkZGlFOz+/ft4eHjkeJyVlRUODg4my9umYcOGJCUlcfbsWeO2o0eP4uHhwalTp0hLSzNuP3ToEIULF6ZYsWJYWlri4eGBQqF4oeubmZnh4eGBufmrnUwoLCyMPn360K5dO0JCQvj999/x8/N76uMHDRpEZGQkoaGhTJo0ialTp7JkyZKXmkc7OztcXFxyTbN8+XJq165NkSJFjNuOHDnC0KFD+fvvv9m/fz9qtZomTZqQnJxsTPOkDl5arZaWLVuSkZHBiRMnWLNmDatXr2bq1KnGNMePH6dPnz4MGDCAS5cusXHjRk6fPm3yRc3V1ZWmTZuyePHil/FIJEmSpPxAvIHu3r0rSpQoIbp16yY0Gs0T08fFxQkLCwuxadMm47YrV64IQJw8efKprxsfHy8AER8fn2VfamqquHz5skhNTc2yLykp6ZkXtVptPF6tVoukpCSRkpLyxPM+D09PTzF79mzj+vjx48XQoUOFv7+/OHTokHF7vXr1RN++fYUQQhw6dEgA4tGjR0IIIcLCwkSrVq2Ek5OTsLGxEWXKlBG7du0SQggRGxsrevToIVxdXYW1tbUoXry4WLlypRBCiFu3bglA/PPPPybn/fPPP0WVKlWESqUStWrVEleuXDHmY9q0aSIgIMC4fv36deHn5yeGDh0qdDpdtvcYFhYmlEqlCA0NfebnU79+fTFixAiTbZUrVxbt27c3rsfGxorevXsLJycnoVKpRLNmzcTVq1dNjlm1apXw8fERKpVKtGvXTsydO1c4OjrmeF/ZKVu2rPjxxx9zTRMdHS0AceTIESHEf3//GzduNKYJCQkx+fvfvXu3UCqVIioqyphm8eLFwsHBQaSnpwshhPj2229F0aJFTa61YMEC4eXlZbJtzZo1wtvbO9c85pXc3qeSJElvs9ximCfJNyWLT+vevXs0aNCAwoULM3fuXB48eEBUVJRJ28N79+5RunRpTp8+DYCjoyMDBgxg9OjRHDp0iMDAQPr370+tWrWeuif0i7Czs3vmZevWrcbjt27dip2dHc2bNzc5r6+vb5bjnkfDhg05dOiQcf3QoUM0aNCA+vXrG7enpqZy6tQpGjZsmO05hg4dSnp6On/99RcXLlxgzpw5xvxMmTKFy5cvs2fPHkJCQli8eDGurq655unzzz/nu+++4+zZs5ibm/Phhx9mmy44OJi6devSo0cPfvzxxxxLOr28vKhatSrDhg0zKS19VkIIjh49ypUrV7C0tDRu79evH2fPnmX79u2cPHkSIQQtWrRArVYDcOrUKQYMGMCwYcMICgqiYcOGzJw585muHRsby+XLl6latWqu6eLj4wGMnb6epoPXyZMnKV++PAULFjSmadq0KQkJCVy6dAmAWrVqcefOHXbv3o0Qgvv377Np0yZatGhhcv3q1atz9+5dwsLCnun+JEmSpPzp1db9vQL79+/n+vXrXL9+HW9vb5N94t82ZGq1mtDQUFJSUoz7fvjhB5RKJR07diQ9PZ2mTZvy008/vda851cNGzZk5MiRaDQaUlNT+eeff6hfvz5qtdpY1Xry5EnS09NzDBbDw8Pp2LEj5cuXBzBpMhAeHk6lSpWMQY6vr+8T8zRr1izq168P6NuztmzZkrS0NJPhTk6cOEGrVq34/PPPGTNmTK7nGzRoEEIIihYtSvPmzfnjjz+MzQpat25NkSJF+PHHH3M8/qeffmL58uVkZGSgVquxtrZm+PDhAFy7do3t27dz/PhxateuDejb9vn4+LBt2zY6d+7M/PnzadasGePHjwegZMmSnDhxgr179z7xWRiEh4cjhKBQoUI5ptHpdIwcOZI6deoY24E+TQevqKgok0DRsN+wD6BOnTqsW7eOrl27kpaWhkajoXXr1ixatMjkOEP+bt++/VS/a0mSJCl/e+NKFvv164cQItvFwNfXFyGESQcNa2trFi1aRGxsLMnJyWzZsiXX9oovU1JS0jMv7du3Nx7fvn17kpKS2LNnj8l5w8LCshz3PBo0aEBycjJnzpzh6NGjlCxZEjc3N+rXr29st3j48GGKFi1K4cKFsz3H8OHDmTlzJnXq1GHatGkEBwcb93388cds2LCBihUrMn78eE6cOPHEPFWoUMH42tPTE4Do6GjjtvDwcBo3bszUqVOfGChevnyZ1atXs3r1ahYvXkzhwoVp0KCB8XwXL17kvffey/UcPXv2JCgoiOPHj9O8eXM+//xzY2AYEhKCubk5NWrUMKZ3cXGhVKlShISEGNNk3g/6krpnkZqaCpDr+IBDhw7l4sWLbNiw4ZnO/TQuX77MiBEjmDp1KoGBgezdu5ewsDA++ugjk3QqlQrA5MuaJEmS9OZ644LFN5Gtre0zL5k7fJibm2Nra2v8J5zbeZ9H8eLF8fb25tChQxw6dMhYoleoUCF8fHw4ceIEhw4d4v3338/xHAMHDuTmzZv07t2bCxcuULVqVRYuXAhA8+bNuX37NqNGjSIiIoIPPviAsWPH5ponCwsL42tD1bJOpzNuc3Nzo3r16vzf//3fE8e/DA4OxsrKijJlyqBQKFi5ciVFixalTp06/PzzzyQmJtKmTZtcz+Ho6Ejx4sWpVq0av//+Oz/++CN//vlnrse8bIaq+0ePHmW7f9iwYezcuZNDhw6ZlLo/TQcvDw+PLL2jDeuGNLNnz6ZOnTqMGzeOChUqGEvnV65cSWRkpPG42NhYQP87kiRJkt58MliUAH1V9OHDhzl8+LBJiWy9evXYs2cPp0+fzrEK2sDHx4ePPvqILVu2MGbMGH7++WfjPjc3N/r27cvatWuZN2/eE3uwP4lKpWLnzp1YW1vTtGlTEhMTc0zr5eVFeno6p06dAvQ9sNevX0+xYsUYPHgwn3/+eZZAPDd2dnaMGDGCsWPHIoTA398fjUZjPD/Aw4cPCQ0NpUyZMgD4+/ub7Af4+++/n+WWKVasGA4ODly+fNlkuxCCYcOGsXXrVg4ePJill3eVKlWwsLAwGZYnNDSU8PBwY+lmrVq1uHDhgknp7f79+3FwcDDeQ0pKCkql6UeGmZmZMQ8GFy9exMLCgrJlyz7T/UmSJEn5kwwWJUAfLB47doygoCBjySJA/fr1Wbp0KRkZGbkGiyNHjmTfvn3cunWLc+fOcejQIfz9/QGYOnUqf/zxB9evX+fSpUvs3LnTuO9F2NrasmvXLszNzWnevHmO1fB169aldu3adO3alW3btnHjxg327t1LREQEtra2rF+//pmrTIcMGcLVq1fZvHkzJUqUoG3btgwaNIhjx45x/vx5evXqhZeXF23btgX01fR79+5l7ty5XLt2jR9//PGZ2isCKJVKGjVqxLFjx0y2Dx06lLVr17J+/Xrs7e2NHb4M1dZP08GrSZMmlClTht69e3P+/Hn27dvH5MmTGTp0KFZWVoC+beeWLVuMU2seP36c4cOHU716dZN2lEePHuW99957pgBckiRJysdeQm/sd8bzDp3zJjAMYVO6dGmT7WFhYQIQpUqVMtn++NA5w4YNE8WKFRNWVlbCzc1N9O7dW8TExAghhJgxY4bw9/cXKpVKODs7i7Zt24qbN2+aXPfxoXMM5xVCiH/++UcA4tatW0KIrEPMJCYmitq1a4t69erlOHxQQkKCGDNmjPDz8xOWlpaiTJkyYt68eeLevXvC29tbtGvXTmi12myPzW7oHCGEGDJkiChbtqzQarXGoXMcHR2FSqUSTZs2zTJ0zooVK4S3t7dQqVSidevWzzV0zu7du4WXl5dJXoFsl1WrVhnTpKamik8++UQUKFBA2NjYiPbt24vIyEiTc4eFhYnmzZsLlUolXF1dxZgxY0yGcBJCP1ROmTJlhEqlEp6enqJnz57i7t27JmlKlSol/u///i/X+8grb/r7VJIk6Xm9yNA5CiFe8jQUb7HcJuFOS0vj1q1b+Pn55doBQZJehBCCGjVqMGrUKLp3757X2cliz549jBkzhuDg4Fc+0PrzkO9TSZLeVbnFME8iq6El6Q2iUChYtmxZlmn48ovk5GRWrVqVLwNFSZIk6fnIT3RJesNUrFiRihUr5nU2stWpU6e8zoIkSZL0ksmSRUmSJEmSJClHMliUJEmSJEmSciSDRUmSJEmSJClHMliUJEmSJEmSciSDRUmSJEmSJClHMliUJEmSJEmSciSDRUmSJEmSJClHMliUnolCoWDbtm1Pnb5BgwaMHDnSuO7r68u8efNeer5ehbCwMBQKBUFBQc98bL169Vi/fv3Lz9RL0K1bN7777ru8zoYkSZL0hpDBokS/fv1QKBQoFAosLCwoWLAgjRs3ZuXKleh0OpO0kZGRNG/ePNdztWvXLsf9Z86cYfDgwS8r68+Vh1dt+/bt3L9/n27dugEQGxvLp59+SqlSpVCpVBQuXJjhw4cTHx9vclx4eDgtW7bExsYGd3d3xo0bZzJTS2RkJD169KBkyZIolUqTIDyzefPmGa/l4+PDqFGjSEtLM+6fPHkys2bNynJ9SZIkScqODBYlAJo1a0ZkZCRhYWHs2bOHhg0bMmLECFq1amUSsHh4eGBlZfXc13Fzc8PGxuZlZDnfWrBgAf3790ep1L+9IiIiiIiIYO7cuVy8eJHVRC02UAAAE85JREFUq1ezd+9eBgwYYDxGq9XSsmVLMjIyOHHiBGvWrGH16tVMnTrVmCY9PR03NzcmT55MQEBAttdev349EydOZNq0aYSEhLBixQp+++03PvvsM2OacuXKUaxYMdauXfuKnoAkSZL0NpHB4muQkqHJcUlTa19q2udlZWWFh4cHXl5eVK5cmc8++4w//viDPXv2sHr1amO6Z62Gftzj1dDh4eG0bdsWOzs7HBwc6NKlC/fv38/1HHfu3KFLly44OTnh7OxM27ZtCQsLA2D69OmsWbOGP/74w1haevjw4WzPs3fvXurWrYuTkxMuLi60atWKGzduPPe9ATx48ICDBw/SunVr47Zy5cqxefNmWrduTbFixXj//feZNWsWO3bsMAbi//vf/7h8+TJr166lYsWKNG/enBkzZrBo0SIyMjIA/bObP38+ffr0wdHRMdvrnzhxgjp16tCjRw98fX1p0qQJ3bt35/Tp0ybpWrduzYYNG17oXiVJkqR3g5wb+jUoM3VfjvsalnJjVf/qxvUqM/4k9bGg0KCGnzO/DallXK875xCxyRkmacK+bvmCuf3P+++/T0BAAFu2bGHgwIEv7bwGOp3OGCgeOXIEjUbD0KFD6dq1a44BnlqtpmnTptSqVYujR49ibm7OzJkzadasGcHBwYwdO5aQkBASEhJYtWoVAM7OztmeKzk5mdGjR1OhQgWSkpKYOnUq7du3JygoyFgq+KyOHTuGjY0N/v7+uaaLj4/HwcEBc3P9W/DkyZOUL1+eggULGtM0bdqUjz/+mEuXLlGpUqWnun7t2rVZu3Ytp0+fpnr16ty8eZPdu3fTu3dvk3TVq1dn1qxZpKenv1BJsSRJkvT2k8GilKvSpUsTHBz8Ss594MABLly4wK1bt/Dx8QHgl19+oWzZspw5c4Zq1aplOea3335Dp9OxfPlyFAoFAKtWrcLJyYnDhw/TpEkTVCoV6enpeHh45Hr9jh07mqyvXLkSNzc3Ll++TLly5Z7rnm7fvk3BggVzDTZjYmKYMWOGSdvNqKgok0ARMK5HRUU99fV79OhBTEwMdevWRQiBRqPho48+MqmGBihUqBAZGRlERUVRpEiRpz6/JEmS9O6RweJrcPnLpjnuU/4b8BgETmn01GmPTWj4Yhl7CkIIY1D2soWEhODj42MMFAHKlCmDk5MTISEh2QaL58+f5/r169jb25tsT0tLe+Yq5GvXrjF16lROnTpFTEyMsTNPeHj4cweLqampWFtb57g/ISGBli1bUqZMGaZPn/5c18jN4cOH+eqrr/jpp5+oUaMG169fZ8SIEcyYMYMpU6YY06lUKgBSUlJeeh4kSZKkt4sMFl8DG8unf8yvKu3zCgkJwc/P75Vf52klJSVRpUoV1q1bl2Wfm5vbM52rdevWFClShJ9//plChQqh0+koV66csY3g83B1deXRo0fZ7ktMTKRZs2bY29uzdetWLCwsjPs8PDyytCs0tN18UglpZlOmTKF3797GZgPly5cnOTmZwYMH8/nnnxtLPGNjY4Fnf2aSJEnSu0d2cJFydPDgQS5cuJCluvZl8ff3586dO9y5c8e47fLly8TFxVGmTJlsj6lcuTLXrl3D3d2d4sWLmyyGTh+WlpZotdm3+zR4+PAhoaGhTJ48mQ8++AB/f/8cg7xnUalSJaKiorKcKyEhgSZNmmBpacn27duzlD7WqlWLCxcuEB0dbdy2f/9+HBwccnwW2UlJSclSBW5mZgboS4kNLl68iLe3N66urk99bkmSJOndJINFCdAPyxIVFcW9e/c4d+4cX331FW3btqVVq1b06dPnlVyzUaNGlC9fnp49e3Lu3DlOnz5Nnz59qF+/PlWrVs32mJ49e+Lq6krbtm05evQot27d4vDhwwwfPpy7d+8C+l7DwcHBhIaGEhMTg1qtznKeAgUK4OLiwrJly7h+/ToHDx5k9OjRT8xz6dKl2bp1a477K1WqhKurK8ePHzduMwSKycnJrFixgoSEBKKiooiKijIGtU2aNKFMmTL07t2b8+fPs2/fPiZPnszQoUNNOqAEBQURFBREUlISDx48ICgoiMuXLxv3t27dmsWLF7NhwwZu3brF/v37mTJlCq1btzYGjQBHjx6lSZMmT7xfSZIkSZLV0BKgH0bG09MTc3NzChQoQEBAAAsWLKBv377P3TP4SRQKBX/88Qeffvop9erVQ6lU0qxZMxYuXJjjMTY2Nvz1119MmDCBDh06kJiYiJeXFx988AEODg4ADBo0iMOHD1O1alWSkpI4dOgQDRo0MDmPUqlkw4YNDB8+nHLlylGqVCkWLFiQJd3jQkNDcx3M2szMjP79+7Nu3TpatWoFwLlz5zh16hQAxYsXN0l/69YtfH19MTMzY+fOnXz88cfUqlULW1tb+vbty5dffmmSPnOv6MDAQNavX0+RIkWMQwdNnjwZhULB5MmTuXfvHm5ubrRu3ZpZs2YZj0tLS2Pbtm3s3bs313uVJEmSJACFyFw3JeUqISEBR0dH47AnmaWlpXHr1i38/Pxy7eAgvf2ioqIoW7Ys586dy5c9jRcvXszWrVv53//+l9dZee3k+1SSpHdVbjHMk8hqaEl6yTw8PFixYgXh4eF5nZVsWVhY5Fp6K0mSJEmZyWpoSXoF8nJu6id5FQOsS5IkSW8vWbIoSZIkSZIk5UgGi5IkSZIkSVKO/r+9+4+Jsn7gAP5+wO7k/IEeOwTljh/nilmTNVKUnN0pS1rDaMbWaHIEXcHQzVkNbDFWrZmTLTYyItsOnTprKUFzLZQ8/KPQophJceuuGHHEvBCwznXY3dMfzvvKVw64k7sH7t6vjc17eJ7n3nx23t73PM89H5bFWcbvCxHNXfz/SUTkP5bFWXJ7Ng5On0Y0d92enefOe04SEdHU+AWXWRIdHY1ly5Z5Z+BQKBRBm1OZiPzn8XjgcDigUCiwYAHf+oiIZorvmLPo9hy+d07ZRkRzR1RUFDQaDT/IERH5gWVxFgmCgMTERMTHx086xRwRSUsmkwVtRiIionDFshgE0dHRvCaKiIiIwsK8+4jd19eH0tJSpKamIiYmBlqtFjU1Nd4L133R6XQQBGHCT1lZWYhSExEREc1P8+7IYm9vLzweDxobG7F69WpcuXIFRqMRTqcTtbW1U25rNBrx5ptveh8rFIpgxyUiIiKa1+ZdWczNzUVubq73cVpaGiwWCxoaGqYtiwqFwvslFCIiIiKa3rwri5MZGxuDUqmcdr3jx4/j2LFjSEhIQF5eHqqrq6c8uuhyueByuSY8DwBcv3793kMTERERhcjt7hLI5ATzvixarVbU19dPe1SxsLAQycnJWLlyJS5fvozKykpYLBacPn3a5zb79+/HG2+8cddytVp9z7mJiIiIQm14eBixsbF+bSOIc2T+q6qqKhw4cGDKdX7++Wekp6d7H9vtdjz22GPQ6XT46KOP/Hq+r776Clu3boXVaoVWq510nf8/sjg6Oork5GT09/f7PdAUuOvXr0OtVuP333/H0qVLpY4TETjm0uC4hx7HXBoc99AbGxuDRqPByMgIli1b5te2c+bI4ssvv4zi4uIp10lLS/P+e3BwEHq9HtnZ2fjwww/9fr6srCwAmLIsyuVyyOXyu5bHxsbyxS2BpUuXctxDjGMuDY576HHMpcFxD71A7jU7Z8qiSqWCSqWa0bp2ux16vR6ZmZkwmUwB/eHd3d0AgMTERL+3JSIiIooU8+4+i3a7HTqdDhqNBrW1tXA4HBgaGsLQ0NCEddLT03Hp0iUAgM1mw1tvvYWuri709fWhtbUVRUVF2Lx5M9auXSvVn0JEREQ0582ZI4szdfbsWVitVlitViQlJU343e3LL2/evAmLxYIbN24AuDXF17lz51BXVwen0wm1Wo0dO3bg9ddf9+u55XI5ampqJj01TcHDcQ89jrk0OO6hxzGXBsc99O5lzOfMF1yIiIiIaO6Zd6ehiYiIiCh0WBaJiIiIyCeWRSIiIiLyiWWRiIiIiHxiWQzQ9u3bodFosHDhQiQmJmLnzp0YHByUOlZY6+vrQ2lpKVJTUxETEwOtVouamhqMj49LHS2svf3228jOzoZCofD7rv80c4cOHUJKSgoWLlyIrKws762/KDguXLiAvLw8rFy5EoIg4LPPPpM6Utjbv38/1q1bhyVLliA+Ph75+fmwWCxSxwp7DQ0NWLt2rfcG6Bs3bsQXX3zh1z5YFgOk1+vxySefwGKx4NSpU7DZbHjmmWekjhXWent74fF40NjYiJ6eHrz77rv44IMP8Nprr0kdLayNj4+joKAA5eXlUkcJWx9//DH27t2LmpoafP/998jIyMC2bdtw9epVqaOFLafTiYyMDBw6dEjqKBGjo6MDFRUV6OzsxNmzZ3Hz5k08/vjjcDqdUkcLa0lJSXjnnXfQ1dWF7777Dlu2bMFTTz2Fnp6eGe+Dt86ZJa2trcjPz4fL5cJ9990ndZyIcfDgQTQ0NODXX3+VOkrYa2pqwp49ezA6Oip1lLCTlZWFdevW4b333gMAeDweqNVq7N69G1VVVRKnC3+CIKC5uRn5+flSR4koDocD8fHx6OjowObNm6WOE1GUSiUOHjyI0tLSGa3PI4uz4Nq1azh+/Diys7NZFENsbGwMSqVS6hhEARsfH0dXVxdycnK8y6KiopCTk4NvvvlGwmREwTU2NgYAfA8PIbfbjZMnT8LpdGLjxo0z3o5l8R5UVlZi0aJFiIuLQ39/P1paWqSOFFGsVivq6+vx0ksvSR2FKGB//vkn3G43VqxYMWH5ihUrJkxjShROPB4P9uzZg0cffRQPPfSQ1HHC3o8//ojFixdDLpejrKwMzc3NWLNmzYy3Z1m8Q1VVFQRBmPKnt7fXu/6rr76KH374AW1tbYiOjkZRURF4Vt9//o47cGv+79zcXBQUFMBoNEqUfP4KZMyJiGZLRUUFrly5gpMnT0odJSI88MAD6O7uxsWLF1FeXg6DwYCffvppxtvzmsU7OBwODA8PT7lOWloaZDLZXcsHBgagVqvx9ddf+3Vol/wf98HBQeh0OmzYsAFNTU2IiuJnHn8F8lrnNYvBMT4+DoVCgU8//XTCNXMGgwGjo6M8YxECvGYxtHbt2oWWlhZcuHABqampUseJSDk5OdBqtWhsbJzR+guCnGdeUalUUKlUAW3r8XgAAC6XazYjRQR/xt1ut0Ov1yMzMxMmk4lFMUD38lqn2SWTyZCZmYn29nZvWfF4PGhvb8euXbukDUc0i0RRxO7du9Hc3Ayz2cyiKCGPx+NXX2FZDMDFixfx7bffYtOmTVi+fDlsNhuqq6uh1Wp5VDGI7HY7dDodkpOTUVtbC4fD4f1dQkKChMnCW39/P65du4b+/n643W50d3cDAFavXo3FixdLGy5M7N27FwaDAY888gjWr1+Puro6OJ1OPP/881JHC1t///03rFar9/Fvv/2G7u5uKJVKaDQaCZOFr4qKCpw4cQItLS1YsmSJ95rc2NhYxMTESJwufO3btw9PPPEENBoN/vrrL5w4cQJmsxlffvnlzHcikt8uX74s6vV6UalUinK5XExJSRHLysrEgYEBqaOFNZPJJAKY9IeCx2AwTDrm58+flzpaWKmvrxc1Go0ok8nE9evXi52dnVJHCmvnz5+f9HVtMBikjha2fL1/m0wmqaOFtZKSEjE5OVmUyWSiSqUSt27dKra1tfm1D16zSEREREQ+8YIvIiIiIvKJZZGIiIiIfGJZJCIiIiKfWBaJiIiIyCeWRSIiIiLyiWWRiIiIiHxiWSQiIiIin1gWiYiIiMgnlkUiIiIi8ollkYhoDjhy5Ag2bdoEANDpdBAEAYIgQC6XY9WqVcjLy8Pp06clTklEkYhlkYhoDmhpacH27du9j41GI/744w/YbDacOnUKa9aswbPPPosXX3xRwpREFIlYFomIgujo0aOIi4uDy+WasDw/Px87d+4EAPzzzz9oa2ubUBYVCgUSEhKQlJSEDRs24MCBA2hsbMThw4dx7ty5kP4NRBTZWBaJiIKooKAAbrcbra2t3mVXr17FmTNnUFJSAgBob2/HqlWrkJ6ePuW+DAYDli9fztPRRBRSLItEREEUExODwsJCmEwm77Jjx45Bo9FAp9MBuPsUtC9RUVG4//770dfXF6S0RER3Y1kkIgoyo9GItrY22O12AEBTUxOKi4shCAJEUcTnn38+o7IIAKIoQhCEYMYlIpqAZZGIKMgefvhhZGRk4OjRo+jq6kJPTw+Ki4sBAJcuXcK///6L7Ozsaffjdrvxyy+/IDU1NciJiYj+Z4HUAYiIIsELL7yAuro62O125OTkQK1WA7h1CvrJJ59EdHT0tPs4cuQIRkZGsGPHjmDHJSLy4pFFIqIQKCwsxMDAAA4fPuz9YgsAtLa2TnoK+saNGxgaGsLAwAA6OztRWVmJsrIylJeXQ6/XhzI6EUU4QRRFUeoQRESRoKioCGfOnMHg4CDkcjlsNhsefPBBDA8PY9GiRd71dDodOjo6AAAymQxxcXHIzMxESUkJnn76aaniE1GE4mloIqIQsdvteO655yCXywHcOgW9ZcuWCUURAMxmswTpiIgmx7JIRBRkIyMjMJvNMJvNeP/9973Lk5KSsG/fPgmTERFNj6ehiYiCLCUlBSMjI6iursYrr7widRwiIr+wLBIRERGRT/w2NBERERH5xLJIRERERD6xLBIRERGRTyyLREREROQTyyIRERER+cSySEREREQ+sSwSERERkU8si0RERETk038bEp4jOF7xGwAAAABJRU5ErkJggg==" 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" 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" 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" 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evXpG5UyaNEkBlISEBJPn0K1bN+Wtt95Sl9u0aaM88cQTRjFNmzZVJk2aZLKM7du3K7a2tsq8efMUHx8fZffu3eq2devWKfb29ib3Xb58ueLk5KQoiqI0bNhQWblypWIwGJQaNWoov/zyi/K///1PqVKlihrfqlUr5dVXXzUq4+WXX1a6du2qLgPK+vXrFUW5/dx9/fXX6vaTJ08qgBIeHm6yXk5OTso333xjcnubNm2UsWPH5lvft29fpV69esrZs2cVvV6v/PHHH4qNjY1iaWmpxrRs2VJp06aNcu3aNSUnJ0f59ttvFa1Wq9SuXVuN+fDDD5UOHTqofzdVqlRR/ve//+U73ptvvqm0bdu2wDoW9toqSf4hLYtCCCEeGqmpqcV+9OzZU92/Z8+epKamsnnzZqNyL126VOC+9+Ppp58mNDSU0NBQDh48SKdOnejSpQuXL18G4NixY5w/fx4HBwe1BdPV1ZXMzEwuXLgAQExMDK+++iq1atXCyckJR0dHUlNTiYyMLHI9wsPDad68udG6li1bGi3r9XpmzZpFUFAQrq6u2Nvbs3Xr1nzHadCggdGyj48PsbGxJo89efJkRo4cyYQJE1i6dCndu3dn48aNAISFhfHEE08U6Rz+7//+j+XLl7Nr1y7S0tLo2rVrgefZunVro3WtW7cmPDy80LLvPCcfHx+AQs8pIyMjX+tfUSxYsIBatWpRt25dLC0tGTVqFEOHDkWrvZ1iffvttyiKgp+fH1ZWVnz66af07dtXjTl8+DALFixQB/8UxsbGRm2NfVDMH+jRhBBCiELY2dmVaH9zc3PMzfN/tJW03LvLqlmzprr89ddf4+TkxJIlS5g9ezapqak0btyY7777Lt++Hh4eAAwePJibN2+yYMECqlSpgpWVFS1btlQvU+clEcodA0ays7OLXdd58+axYMEC5s+fT1BQEHZ2dowbN049Tp67+8dpNBoMBoPJco8fP86bb74JQJcuXVi6dCkvv/wyn3/+OStWrGDu3LlFql///v15++23mTFjBgMHDizwd3e/7jynvASssHNyd3cnISGh2Mfx8PBgw4YNZGZmcvPmTXx9fZk8eTLVq1dXY2rUqKEmxMnJyfj4+NC7d281Zs+ePcTGxhoN7tHr9bz11lvMnz/fqC9qfHy8+nf0oEjLohBCCFECGo0GrVarDqpp1KgR586dw9PTk5o1axo98gZJ7N27lzFjxtC1a1cCAwOxsrIiLi5OLTMvGYiKilLX3TnYBSAgICBf38N//vnHaHnv3r306NGDAQMGEBwcTPXq1Tl79myJz9nPz4/du3eryy+++CJffvklw4cPx9nZmZdffrlI5bi6uvLcc8+xa9cu/u///q/AmICAAPbu3Wu0bu/evdSrV+/+T6AAISEhnDp16r73t7a2xs/Pj5ycHH766Sd69OiRL8bOzg4fHx8SEhLYunWrGjNw4ECOHz+utliHhobi6+vLxIkT2bp1q1EZJ06cICQk5L7reT+kZVEIIYQoBp1OR3R0NAAJCQl8/vnnpKam0r17dyC3tWzevHn06NGDmTNnUqlSJS5fvszPP//M22+/TaVKlahVqxbffvstTZo0ITk5mYkTJ2JjY6Mew8bGhhYtWvDRRx9RrVo1YmNjmTp1qlE9Xn/9dT755BMmTpzIK6+8wuHDh1mxYoVRTK1atVi3bh379u3DxcWF//73v8TExJQ40Xr77bd544038Pb2pnfv3iQlJbF//35sbW05ffo0e/fuLfKl6BUrVvDFF1+oA4TuNnHiRHr16kVISAjt27dn48aN/Pzzz/z5558lOoe7derUiZUrV+Zbn5ekp6amcuPGDUJDQ7G0tFSfwwMHDnDt2jUaNmzItWvXmDFjBgaDgbffflstY+vWrSiKQp06dTh//jwTJ06kbt26DB06FAA3N7d8529hYYG3tzd16tRR16Wnp3P48GE+/PDDUj33e5GWRSGEEKIYtmzZgo+PDz4+PjRv3pxDhw7x448/0rZtWwBsbW3ZvXs3/v7+vPDCCwQEBDBs2DAyMzNxdHQEcucTTEhIoFGjRgwcOJAxY8bkm3Zm2bJl5OTk0LhxY8aNG8fs2bONtvv7+/PTTz+xYcMGgoODWbx4cb4kYurUqTRq1IhOnTrRtm1bvL29ef7550v8HLz22musXbuWjRs30rhxY5577jmysrI4ffo0AwcO5Pnnn+fcuXNFKsvGxsZkogjw/PPPs2DBAj7++GMCAwP58ssvWb58ufp8l5b+/ftz8uRJzpw5Y7Q+JCSEkJAQDh8+zOrVqwkJCTHqW5mZmcnUqVOpV68ePXv2xM/Pj7///htnZ2c1JikpiZEjR1K3bl0GDRrEE088wdatWwucHqcwv/zyC/7+/jz55JMlOtfi0ihKEWfQFKUiOTkZJycnkpKS1DcNIYR4nGRmZhIREUG1atXua0CBEGVl4sSJJCcn8+WXX5Z3VQrUokULxowZU+BclFD4a6sk+Ye0LAohhBBCkDs3Y5UqVQodCFNe4uLieOGFF+jbt+8DP7b0WRRCCCGEAJydnXnnnXfKuxoFcnd3N+oH+SBJy6IQQgghhDBJkkUhhBBCCGGSJItCCCGEEMIkSRaFEEIIIYRJkiwKIYQQQgiTJFkUQgghhBAmSbIohBBCCCFMkmRRCCHEIycyMpIjR46YfERGRpZ3FcU9DBky5J63HqxatSrz589XlzUaDRs2bCj2sc6cOYO3tzcpKSnF3resZWVlUbVqVf7999/yropJkiwKIYR4pERGRhIQEEDjxo1NPgICAsokYSxKglOR3Lhxg5deegkXFxccHR1p27Ztvnsnm7JkyRKCg4Oxt7fH2dmZkJAQ5syZU6zjHzp0iOHDh99P1Y1MmTKF0aNH4+DgAOTeFm/IkCEEBQVhbm5u8ne6cOFCAgICsLGxoU6dOnzzzTdG27Ozs5k5cyY1atTA2tqa4OBgtmzZYhSzaNEiGjRogKOjI46OjrRs2ZLNmzer2y0tLZkwYQKTJk0q8XmWFbmDixBCiEdKXFwc6enprFq1ioCAgHzbw8PDGTBgAHFxcfj7+5dDDYsuKysLS0vL8q6GSZMmTeLff//lt99+w9vbmyNHjhRpv2XLljFu3Dg+/fRT2rRpg06n4/jx45w4caJYx/fw8LifahuJjIzkt99+47PPPlPX6fV6bGxsGDNmDD/99FOB+y1atIgpU6awZMkSmjZtysGDB3n11VdxcXGhe/fuAEydOpVVq1axZMkS6taty9atW+nZsyf79u0jJCQEgEqVKvHRRx9Rq1YtFEVh5cqV9OjRg6NHjxIYGAhA//79eeuttzh58qS67qGiiAcqKSlJAZSkpKTyrooQQpSLjIwM5dSpU0pGRsZ97X/48GEFUA4fPnxf20ti8ODBSo8ePUxu37lzp9K0aVPF0tJS8fb2ViZNmqRkZ2er29u0aaOMHDlSGTt2rOLm5qa0bdtWURRFCQsLUzp37qzY2dkpnp6eyoABA5QbN24Y7Td69Ghl4sSJiouLi+Ll5aW89957RsdOSEhQhg8frnh6eipWVlZKYGCgsnHjRnX7unXrlHr16imWlpZKlSpVlI8//vie5zts2DClb9++RXx2buvRo4cyZMiQQmPynst58+Yp3t7eiqurq/LGG28oWVlZakyVKlWU//3vf+oyoCxZskR5/vnnFRsbG6VmzZrKL7/8Uuhx5s2bpzRp0uSe9bhby5YtlQkTJhitGz9+vNK6dWt12cfHR/n888+NYl544QWlf//+hdbJxcVF+frrr43WPf3008rUqVML3e9eCnttlST/kMvQQgghRCm4du0aXbt2pWnTphw7doxFixaxdOlSZs+ebRS3cuVKLC0t2bt3L4sXLyYxMZFnnnmGkJAQ/v33X7Zs2UJMTAy9evXKt5+dnR0HDhxg7ty5zJw5k23btgFgMBjo0qULe/fuZdWqVZw6dYqPPvoIMzMzAA4fPkyvXr3o06cPYWFhzJgxg2nTprFixYpCz6lHjx6sW7cu36XVe/H29uaff/7h8uXLhcbt2LGDCxcusGPHDlauXMmKFSvuWaf333+fXr16cfz4cbp27Ur//v2Jj483Gb9nzx6aNGlSrPoD6HQ6rK2tjdbZ2Nhw8OBBsrOzC435+++/CyxTr9ezZs0a0tLSaNmypdG2Zs2asWfPnmLX84EoUQr7CPj888+VKlWqKFZWVkqzZs2UAwcOmIxdvny5Ahg9rKysjGIMBoMybdo0xdvbW7G2tlbatWunnD17tsj1kZZFIcTjrqK2LL7zzjtKnTp1FIPBoK5buHChYm9vr+j1ekVRclsIQ0JCjPabNWuW0rFjR6N1V65cUQDlzJkz6n5PPPGEUUzTpk2VSZMmKYqiKFu3blW0Wq0af7d+/fopHTp0MFo3ceJEpV69eibP9eTJk4q9vb0yZ84cxc/PT/nhhx/Ubf/++68CGLV+3un69etKixYtFECpXbu2MnjwYGXt2rXq86Aouc9llSpVlJycHHXdyy+/rPTu3VtdLqhl8c7Wt9TUVAVQNm/ebPI8goODlZkzZ5rcbup3OmXKFMXb21v5999/FYPBoBw6dEjx8vJSAOX69euKoihK3759lXr16ilnz55V9Hq98scffyg2NjaKpaWlUVnHjx9X7OzsFDMzM8XJyUn5/fff8x1vwYIFStWqVU3WsyikZfE+rF27lvHjx/Pee+9x5MgRgoOD6dSpE7GxsSb3cXR0JCoqSn3c/a1o7ty5fPrppyxevJgDBw5gZ2dHp06dyMzMLOvTEUII8RALDw+nZcuWaDQadV3r1q1JTU3l6tWr6rrGjRsb7Xfs2DF27NiBvb29+qhbty4AFy5cUOMaNGhgtJ+Pj4/6eRYaGkqlSpWoXbu2ybq1bt3aaF3r1q05d+4cer2+wH1mzJhBly5dmDx5Mr/++iuvv/46ixcvBiAsLIy6devi7u5e4L4+Pj7s37+fsLAwxo4dS05ODoMHD6Zz584YDAY1LjAwUG39vPucTLnzebCzs8PR0bHQfTIyMvK1/hXFtGnT6NKlCy1atMDCwoIePXowePBgALTa3PRpwYIF1KpVi7p162JpacmoUaMYOnSouj1PnTp1CA0N5cCBA4wYMYLBgwdz6tQpoxgbGxvS09OLXc8HoUIni//973959dVXGTp0KPXq1WPx4sXY2tqybNkyk/toNBq8vb3Vh5eXl7pNURTmz5/P1KlT6dGjBw0aNOCbb77h+vXr9zWUXwghxOPHzs7OaDk1NZXu3bsTGhpq9Dh37hxPPfWUGmdhYWG0n0ajURMvGxubUq/n8ePH1UEajRo14tdff2XChAnMnj2br7/+mqFDh96zjPr16/PGG2+watUqtm3bxrZt29i1a5e6vbBzMqW4+7i7u5OQkHDPut7NxsaGZcuWkZ6ezqVLl4iMjKRq1ao4ODioA288PDzYsGEDaWlpXL58mdOnT2Nvb0/16tWNyrK0tKRmzZo0btyYOXPmEBwczIIFC4xi4uPjS2VAT1mosKOhs7KyOHz4MFOmTFHXabVa2rdvz/79+03ul5qaSpUqVTAYDDRq1IgPP/xQHZkUERFBdHQ07du3V+OdnJxo3rw5+/fvp0+fPvnK0+l06HQ6dTk5Obk0Tk8IIR574eHhxVpf1gICAvjpp59QFEVtXdy7dy8ODg5UqlTJ5H6NGjXip59+omrVqpib39/HcoMGDbh69Spnz54tsHUxICCAvXv3Gq3bu3cvtWvXNmrZu5Ofnx979uxRP0dbt27N+vXrefbZZ3F1dWXUqFHFqmO9evUASEtLK9Z+JRUSEpKvFa84LCws1N/fmjVrePbZZ/O1HFpbW+Pn50d2djY//fRTvv6mdzMYDEa5AcCJEyfU5PxhU2GTxbi4OPR6vVHLIICXlxenT58ucJ86deqwbNkyGjRoQFJSEh9//DGtWrXi5MmTVKpUiejoaLWMu8vM23a3OXPm8P7775fCGQkhhIDcliJbW1sGDBhgMsbW1tbkJdKSSkpKIjQ01Gidm5sbb7zxBvPnz2f06NGMGjWKM2fO8N577zF+/Ph8ycWdRo4cyZIlS+jbty9vv/02rq6unD9/njVr1vD111+bTObu1KZNG5566ilefPFF/vvf/1KzZk1Onz6NRqOhc+fOvPXWWzRt2pRZs2bRu3dv9u/fz+eff84XX3xhssyJEyfStWtXRo4cyYgRI8jOzmbXrl1YWlpy48YNNm7cSO/evQvcd8SIEfj6+vLMM89QqVIloqKimD17Nh4eHvkGdpS1Tp068corr6DX642ey1OnTpGVlUV8fDwpKSnq77Rhw4YAnD17loMHD9K8eXMSEhL473//y4kTJ1i5cqVaxoEDB7h27RoNGzbk2rVrzJgxA4PBwNtvv63GTJkyhS5duuDv709KSgqrV69m586dbN261aiee/bsYdasWWX3RJRAhU0W70fLli2N/ohbtWpFQEAAX3755X3/AqdMmcL48ePV5eTkZCpXrlziugohxOPK39+f8PBw4uLiTMa4u7uX2RyLO3fuzNcCNGzYML7++ms2bdrExIkTCQ4OxtXVlWHDhjF16tRCy/P19WXv3r1MmjSJjh07otPpqFKlCp07dy40ybzbTz/9xIQJE+jbty9paWnUrFmTjz76CMhtvfzhhx+YPn06s2bNwsfHh5kzZzJkyBCT5XXu3Jnt27fz3nvv0bp1a/Xq3MGDB/n5558ZMmQIlStXplWrVvn2bd++PcuWLWPRokXcvHkTd3d3WrZsyfbt23FzcyvyOZWGLl26YG5uzp9//kmnTp3U9V27djUal5D3O1UUBcgdufzJJ59w5swZLCwsePrpp9m3bx9Vq1ZV98nMzGTq1KlcvHgRe3t7unbtyrfffouzs7MaExsby6BBg4iKisLJyYkGDRqwdetWOnTooMbs37+fpKQkXnrppTJ6FkpGo+Q9KxVMVlYWtra2rFu3zmhm9sGDB5OYmMgvv/xSpHJefvllzM3N+f7777l48SI1atTg6NGj6jcPyP1G17Bhw3z9DwqSnJyMk5MTSUlJODo6Fve0hBDikZeZmUlERATVqlW7r4EHQhTXwoUL+fXXX/O15j0sevfuTXBwMO+8806JyinstVWS/KPCDnCxtLSkcePGbN++XV1nMBjYvn17kZvA9Xo9YWFh+Pj4AFCtWjW8vb2NykxOTubAgQMPvFldCCGEEEXz2muv8dRTTz2094YOCgrizTffLO+qmFShL0OPHz+ewYMH06RJE5o1a8b8+fNJS0tTR3ANGjQIPz8/9V6VM2fOpEWLFtSsWZPExETmzZvH5cuXeeWVV4DcEVfjxo1j9uzZ1KpVi2rVqjFt2jR8fX0fq3uFCiGEEI8Sc3Nz3n333fKuRoEsLS3v2VWhvFXoZLF3797cuHGD6dOnEx0dTcOGDdmyZYs6QCUyMtKoP0hCQgKvvvoq0dHRuLi40LhxY/bt26eO4AJ4++23SUtLY/jw4SQmJvLEE0+wZcsWuZQihBBCiAqpwvZZfFhJn0UhxONO+iwKUTakz6IQQgghhHjgJFkUQgghhBAmSbIohBBCCCFMkmRRCCGEEEKYJMmiEEII8YANGTKk1KdcW7FihdGdQ8rTpUuX0Gg0+W6LWBRPPfUUq1evLv1KlYI+ffrwySeflHc1HjhJFoUQQogiGjJkCBqNBo1Gg4WFBdWqVePtt98mMzOzWOUsWLCAFStWlGrdevfuzdmzZ0tURlkkscXx66+/EhMTQ58+fQCIj49n9OjR1KlTBxsbG/z9/RkzZgxJSUlG+0VGRtKtWzdsbW3x9PRk4sSJ5OTkqNujoqLo168ftWvXRqvVMm7cuAKPn5iYyMiRI/Hx8cHKyoratWuzadMmdfvUqVP54IMP8h2/oqvQ8ywKIYQQpa1z584sX76c7OxsDh8+zODBg9FoNPznP/8pchlOTk6lXi8bGxtsbGxKvdwH6dNPP2Xo0KHqHMjXr1/n+vXrfPzxx9SrV4/Lly/z+uuvc/36ddatWwfk3m2tW7dueHt7s2/fPqKiohg0aBAWFhZ8+OGHAOh0Ojw8PJg6dSr/+9//Cjx2VlYWHTp0wNPTk3Xr1uHn58fly5eNWmvr169PjRo1WLVqFSNHjizbJ+NhoogHKikpSQGUpKSk8q6KEEKUi4yMDOXUqVNKRkZGeVel2AYPHqz06NHDaN0LL7yghISEqMtxcXFKnz59FF9fX8XGxkapX7++snr16kLL2bx5s9K6dWvFyclJcXV1Vbp166acP39e3R4REaEAyk8//aS0bdtWsbGxURo0aKDs27dPjVm+fLni5ORUaP0jIyOVl19+WXFyclJcXFyU5557TomIiFAURVHee+89BTB67Nixo8Byilrfo0ePFlqfO8XGxioajUY5ceJEoXE//PCDYmlpqWRnZyuKoiibNm1StFqtEh0drcYsWrRIcXR0VHQ6Xb7927Rpo4wdOzbf+kWLFinVq1dXsrKyCj3++++/rzzxxBNFOKMHr7DXVknyD7kMLYQQ4qGRlpZm8nH3pd7CYjMyMooUW1InTpxg3759WFpaqusyMzNp3Lgxv//+OydOnGD48OEMHDiQgwcPFnre48eP599//2X79u1otVp69uyJwWAwinv33XeZMGECoaGh1K5dm759+xpdbi1MdnY2nTp1wsHBgT179rB3717s7e3p3LkzWVlZTJgwgV69etG5c2eioqKIioqiVatWJapvcfz999/Y2toSEBBQaFzepNLm5rkXR/fv309QUJB6dzaATp06kZyczMmTJ4t8/F9//ZWWLVsycuRIvLy8qF+/Ph9++CF6vd4orlmzZhw8eBCdTleMs3u0yWVoIYQQDw17e3uT27p27crvv/+uLnt6epKenl5gbJs2bdi5c6e6XLVqVeLi4vLFKfdxE7PffvsNe3t7cnJy0Ol0aLVaPv/8c3W7n58fEyZMUJdHjx7N1q1b+eGHH2jWrFmBZb744otGy8uWLcPDw4NTp05Rv359df2ECRPo1q0bAO+//z6BgYGcP3+eunXr3rPea9euxWAw8PXXX6PRaABYvnw5zs7O7Ny5k44dO2JjY4NOp8Pb27vQsopa3+K4fPkyXl5eRrfhvVtcXByzZs1i+PDh6rro6GijRBFQl6Ojo4t8/IsXL/LXX3/Rv39/Nm3axPnz53njjTfIzs7mvffeU+N8fX3JysoiOjqaKlWqFLn8R5m0LAohhBDF8PTTTxMaGsqBAwcYPHgwQ4cONUqe9Ho9s2bNIigoCFdXV+zt7dm6dSuRkZEmyzx37hx9+/alevXqODo6UrVqVYB8+zRo0ED92cfHB4DY2Ngi1fvYsWOcP38eBwcH7O3tsbe3x9XVlczMTC5cuFDU0y9WfYsjIyOj0Ns/Jicn061bN+rVq8eMGTPu+zimGAwGPD09+eqrr2jcuDG9e/fm3XffZfHixUZxef1CTX1RqYikZVEIIcRDIzU11eQ2MzMzo+XCkqS7W6cuXbpUonrdyc7Ojpo1awK5LWrBwcEsXbqUYcOGATBv3jwWLFjA/PnzCQoKws7OjnHjxpGVlWWyzO7du1OlShWWLFmCr68vBoOB+vXr59vHwsJC/TmvdbCol35TU1Np3Lgx3333Xb5tHh4eRSqjuPUtDnd3dxISEgrclpKSQufOnXFwcGD9+vVGz4O3t3e+S/wxMTHqtqLy8fHBwsLC6O8sICCA6OhosrKy1K4G8fHxQPGfs0eZJItCCCEeGnZ2duUeWxxarZZ33nmH8ePH069fP2xsbNi7dy89evRgwIABQG4yd/bsWerVq1dgGTdv3uTMmTMsWbKEJ598Esjtv1faGjVqxNq1a/H09MTR0bHAGEtLy3x99B5UfUNCQoiOjiYhIQEXFxd1fXJyMp06dcLKyopff/01X+tjy5Yt+eCDD4iNjcXT0xOAbdu24ejoaPI5L0jr1q1ZvXo1BoNB/bJx9uxZfHx8jPqknjhxgkqVKuHu7l6S032kyGVoIYQQogRefvllzMzMWLhwIQC1atVi27Zt7Nu3j/DwcF577TW1pasgLi4uuLm58dVXX3H+/Hn++usvxo8fX+r17N+/P+7u7vTo0YM9e/YQERHBzp07GTNmDFevXgVy+3YeP36cM2fOEBcXR3Z2dqnVt27duqxfv97k9pCQENzd3dm7d6+6Ljk5mY4dO5KWlsbSpUtJTk4mOjqa6OhoNant2LEj9erVY+DAgRw7doytW7cydepURo4ciZWVlVpWaGgooaGhpKamcuPGDUJDQzl16pS6fcSIEcTHxzN27FjOnj3L77//zocffphvipw9e/bQsWPHe55vRSLJohBCCFEC5ubmjBo1irlz55KWlsbUqVNp1KgRnTp1om3btnh7exc60bVWq2XNmjUcPnyY+vXr8+abbzJv3rxSr6etrS27d+/G39+fF154gYCAAIYNG0ZmZqba0vjqq69Sp04dmjRpgoeHh1HiVtL6njlzptDJrM3MzBg6dKjRZfIjR45w4MABwsLCqFmzJj4+PurjypUr6n6//fYbZmZmtGzZkgEDBjBo0CBmzpxpVH5ISAghISEcPnyY1atXExISQteuXdXtlStXZuvWrRw6dIgGDRowZswYxo4dy+TJk9WYzMxMNmzYwKuvvnrP861INMr9DAUT9y05ORknJyd16L8QQjxuMjMziYiIoFq1aoUOaKjI+vbti5mZGatWrSrvqjxUoqOjCQwM5MiRIw/lSONFixaxfv16/vjjj/KuSoEKe22VJP+QlkUhhBDiAcnJyeHUqVPs37+fwMDA8q7OQ8fb25ulS5eWaFR1WbKwsOCzzz4r72o8cDLARQghhHhATpw4QatWrXj66ad5/fXXy7s6D6XyvDf1vbzyyivlXYVyIcmiEEII8YA0bNjwsZqfT1QMchlaCCGEEEKYJMmiEEIIIYQwSZJFIYQQQghhkiSLQgghhBDCJEkWhRBCCCGESZIsCiGEEEIIkyRZFEIIIYQQJkmyKIQQQpQBjUbDhg0bihzftm1bxo0bpy5XrVqV+fPnl3q9ysKlS5fQaDSEhoYWe9+nnnqK1atXl36lSkGfPn345JNPyrsa5U6SRSGEEKKIhgwZgkajQaPRYGFhgZeXFx06dGDZsmUYDAaj2KioKLp06VJoWYXdreTQoUMMHz68tKp+X3Uoa7/++isxMTH06dMHgPj4eEaPHk2dOnWwsbHB39+fMWPGkJSUZLRfZGQk3bp1w9bWFk9PTyZOnEhOTo66PSoqin79+lG7dm20Wq1REn6n+fPnq8eqXLkyb775JpmZmer2qVOn8sEHH+Q7/uNGkkUhhBCiGDp37kxUVBSXLl1i8+bNPP3004wdO5Znn33WKGHx9vbGysrqvo/j4eGBra1taVT5ofXpp58ydOhQtNrcdOT69etcv36djz/+mBMnTrBixQq2bNnCsGHD1H30ej3dunUjKyuLffv2sXLlSlasWMH06dPVGJ1Oh4eHB1OnTiU4OLjAY69evZrJkyfz3nvvER4eztKlS1m7di3vvPOOGlO/fn1q1KjBqlWryugZeDRIsiiEEOKhkZ6VY/KRma0v9dj7YWVlhbe3N35+fjRq1Ih33nmHX375hc2bN7NixQo1rriXoe9292XoyMhIevTogb29PY6OjvTq1YuYmJhCy7hy5Qq9evXC2dkZV1dXevTowaVLlwCYMWMGK1eu5JdfflFbS3fu3FlgOVu2bOGJJ57A2dkZNzc3nn32WS5cuHDf5wZw48YN/vrrL7p3766uq1+/Pj/99BPdu3enRo0aPPPMM3zwwQds3LhRTcT/+OMPTp06xapVq2jYsCFdunRh1qxZLFy4kKysLCD3uVuwYAGDBg3CycmpwOPv27eP1q1b069fP6pWrUrHjh3p27cvBw8eNIrr3r07a9asKdG5Purk3tBCCCEeGvWmbzW57ek6Hiwf2kxdbjzrTzLuSgrzNK/mytrXWqrLT/xnB/FpWfniLn3UrQS1ve2ZZ54hODiYn3/+mVdeeaVUyryTwWBQE8Vdu3aRk5PDyJEj6d27t8kELzs7m06dOtGyZUv27NmDubk5s2fPpnPnzhw/fpwJEyYQHh5OcnIyy5cvB8DV1bXAstLS0hg/fjwNGjQgNTWV6dOn07NnT0JDQ9VWweL6+++/sbW1JSAgoNC4pKQkHB0dMTfPTVn2799PUFAQXl5eakynTp0YMWIEJ0+eJCQkpEjHb9WqFatWreLgwYM0a9aMixcvsmnTJgYOHGgU16xZMz744AN0Ol2JWoofZZIsCiGEEKWgbt26HD9+vEzK3r59O2FhYURERFC5cmUAvvnmGwIDAzl06BBNmzbNt8/atWsxGAx8/fXXaDQaAJYvX46zszM7d+6kY8eO2NjYoNPp8Pb2LvT4L774otHysmXL8PDw4NSpU9SvX/++zuny5ct4eXkVmmzGxcUxa9Yso76b0dHRRokioC5HR0cX+fj9+vUjLi6OJ554AkVRyMnJ4fXXXze6DA3g6+tLVlYW0dHRVKlSpcjlVySSLAohhHhonJrZyeQ27a2EJ8/hae2LHPv3pKdLVrEiUBRFTcpKW3h4OJUrV1YTRYB69erh7OxMeHh4gcnisWPHOH/+PA4ODkbrMzMzi30J+dy5c0yfPp0DBw4QFxenDuaJjIy872QxIyMDa2trk9uTk5Pp1q0b9erVY8aMGfd1jMLs3LmTDz/8kC+++ILmzZtz/vx5xo4dy6xZs5g2bZoaZ2NjA0B6enqp1+FRIcmiEEKIh4atZdE/lsoq9n6Fh4dTrVq1Mj9OUaWmptK4cWO+++67fNs8PDyKVVb37t2pUqUKS5YswdfXF4PBQP369dU+gvfD3d2dhISEArelpKTQuXNnHBwcWL9+PRYWFuo2b2/vfP0K8/pu3quF9E7Tpk1j4MCBareBoKAg0tLSGD58OO+++67a4hkfHw8U/zmrSGSAixBCCFFCf/31F2FhYfku15aWgIAArly5wpUrV9R1p06dIjExkXr16hW4T6NGjTh37hyenp7UrFnT6JE36MPS0hK9vuB+n3lu3rzJmTNnmDp1Ku3atSMgIMBkklccISEhREdH5ysrOTmZjh07Ymlpya+//pqv9bFly5aEhYURGxurrtu2bRuOjo4mn4uCpKen57sEbmZmBuS2Euc5ceIElSpVwt3dvchlVzSSLAohhBDFoNPpiI6O5tq1axw5coQPP/yQHj168OyzzzJo0KAyOWb79u0JCgqif//+HDlyhIMHDzJo0CDatGlDkyZNCtynf//+uLu706NHD/bs2UNERAQ7d+5kzJgxXL16FcgdNXz8+HHOnDlDXFwc2dnZ+cpxcXHBzc2Nr776ivPnz/PXX38xfvz4e9a5bt26rF+/3uT2kJAQ3N3d2bt3r7ouL1FMS0tj6dKlJCcnEx0dTXR0tJrUduzYkXr16jFw4ECOHTvG1q1bmTp1KiNHjjQagBIaGkpoaCipqancuHGD0NBQTp06pW7v3r07ixYtYs2aNURERLBt2zamTZtG9+7d1aQRYM+ePXTs2PGe51uRyWVoIYQQohi2bNmCj48P5ubmuLi4EBwczKeffsrgwYPve2TwvWg0Gn755RdGjx7NU089hVarpXPnznz22Wcm97G1tWX37t1MmjSJF154gZSUFPz8/GjXrh2Ojo4AvPrqq+zcuZMmTZqQmprKjh07aNu2rVE5Wq2WNWvWMGbMGOrXr0+dOnX49NNP88Xd7cyZM4VOZm1mZsbQoUP57rvvePbZZwE4cuQIBw4cAKBmzZpG8REREVStWhUzMzN+++03RowYQcuWLbGzs2Pw4MHMnDnTKP7OUdGHDx9m9erVVKlSRZ06aOrUqWg0GqZOncq1a9fw8PCge/fufPDBB+p+mZmZbNiwgS1bthR6rhWdRrmzrVWUueTkZJycnNSpAIQQ4nGTmZlJREQE1apVK3SAg6j4oqOjCQwM5MiRIw/lSONFixaxfv16/vjjj/KuSpEU9toqSf4hl6GFEEIIUS68vb1ZunQpkZGR5V2VAllYWBTaevu4kMvQQgghhCg35Xlv6nspiwnWH0XSsiiEEEIIIUySZFEIIUS5kC7zQpSusnpNVfhkceHChVStWhVra2uaN2+ebyLPOy1ZsoQnn3wSFxcXXFxcaN++fb74IUOGqDdcz3t07ty5rE9DCCEqjLwJlh/nO2IIURbyJkm/c+qf0lCh+yyuXbuW8ePHs3jxYpo3b878+fPp1KkTZ86cwdPTM1/8zp076du3L61atcLa2pr//Oc/dOzYkZMnT+Ln56fGde7cWb3pOvDY3lhcCCHuh5mZGc7Ozuqkyra2tmV2mzwhHhcGg4EbN25ga2uLuXnppncVeuqc5s2b07RpUz7//HMg94msXLkyo0ePZvLkyffcX6/X4+Liwueff65OtDpkyBASExPZsGHDfdVJps4RQojcy2XR0dEkJiaWd1WEqDC0Wi3VqlXD0tIy37aS5B8VtmUxKyuLw4cPM2XKFHWdVqulffv27N+/v0hlpKenk52djaurq9H6nTt34unpiYuLC8888wyzZ8/Gzc2twDJ0Oh06nU5dTk5Ovo+zEUKIikWj0eDj44Onp2eBdw0RQhSfpaVlmUwMX2GTxbi4OPR6PV5eXkbrvby8OH36dJHKmDRpEr6+vrRv315d17lzZ1544QWqVavGhQsXeOedd+jSpQv79+8vsI/AnDlzeP/990t2MkIIUUGZmZmVev8qIUTpqrDJYkl99NFHrFmzhp07dxrNgt6nTx/156CgIBo0aECNGjXYuXMn7dq1y1fOlClTjO6hmZycTOXKlcu28kIIIYQQpaTCjoZ2d3fHzMyMmJgYo/UxMTF4e3sXuu/HH3/MRx99xB9//EGDBg0Kja1evTru7u6cP3++wO1WVlY4OjoaPYQQQgghHhUVNlm0tLSkcePGbN++XV1nMBjYvn07LVu2NLnf3LlzmTVrFlu2bKFJkyb3PM7Vq1e5efMmPj4+pVJvIYQQQoiHSYVNFgHGjx/PkiVLWLlyJeHh4YwYMYK0tDSGDh0KwKBBg4wGwPznP/9h2rRpLFu2jKpVqxIdHU10dDSpqakApKamMnHiRP755x8uXbrE9u3b6dGjBzVr1qRTp07lco5CCCGEEGWpQvdZ7N27Nzdu3GD69OlER0fTsGFDtmzZog56iYyMNBo1tGjRIrKysnjppZeMynnvvfeYMWMGZmZmHD9+nJUrV5KYmIivry8dO3Zk1qxZMteiEEIIISqkCj3P4sNI5lkUQgghxINWkvyjQl+GFkIIIYQQJSPJohBCCCGEMEmSRSGEEEIIYZIki0IIIYQQwiRJFoUQQgghhEmSLAohhBBCCJMkWRRCCCGEECZJsiiEEEIIIUySZFEIIYQQQpgkyaIQQgghhDBJkkUhhBBCCGGSJItCCCGEEMIkSRaFEEIIIYRJkiwKIYQQQgiTJFkUQgghhBAmSbIohBBCCCFMkmRRCCGEEEKYJMmiEEIIIYQwSZJFIYQQQghhkiSLQgghhBDCJEkWhRBCCCGESZIsCiGEEEIIkyRZFEIIIYQQJkmyKIQQQgghTJJkUQghhBBCmCTJohBCCCGEMEmSRSGEEEIIYZIki0IIIYQQwiRJFoUQQgghhEmSLAohhBBCCJMkWRRCCCGEECZJsiiEEEIIIUySZFEIIYQQQpgkyaIQQgghhDBJkkUhhBBCCGGSJItCiApLp9NhMBjKuxpCCPFIk2RRCFFhGAwGlixZQp06dbC3t8fa2pqoqCh1++HDh9m2bRuKopRjLYUQ4tEiyaIQokI4cuQIrVq1Yvjw4Zw9e5a0tDQA4uPjAVAUhXHjxtGxY0fatWvHP//8U57VFUKIR4Z5eVdACCFKIikpialTp/LFF19gMBhwcHDg/fffp0ePHtg7OrP7Uhp19QYUfQ5NmjTh4MGD7Nixg5YtW/Lcc88xe/ZsgoKCyvs0ysSFCxf44YcfyMzMBMDZ2Zk333xT3f7ll19y/fp1HB0d8fDwUB/16tXD1ta2vKothHjIaBS5HvNAJScn4+TkRFJSEo6OjuVdHSEeebGxsdSpU4fE5BRaD5/FrNde5umGNQH4dv8lpv1ykkouNrzZvjYvNPLjypUrzJw5k+XLl6v9Gbt378706dNp0qRJeZ5KqTl37hwffPABq1atQq/Xq+urVq1KRESEutykSRMOHz6cb38XFxdGjRrFu+++i5WV1QOpsxCibJUk/5Bk8QGTZFGIktPr9ZiZmanLn638gXXX7LmcrFDT055NY57E0lzLD/9eYe6W08SlZgEwqGUVpj9bD3MzLWfOnGH69On8+OOPKIrCd999R79+/crrlErN1KlTmTNnjpoId+jQgdq1awPg5ubG+++/r8bOnTuXy5cvk5iYyI0bN7hx4wZXr14lLi6O4OBgjh49ikajKZfzEEKUrpLkH3IZWgjxSLlw4QI9e/Zk7ty5tG3XgS93XeSLcw7ocgw42Vgwtl0tLM1zu2P3alKZ7g18+Wr3ReZvP8s3+y9zLSGDT/uGUKdOHdauXcusWbP4+uuvefnll9VjJCYm4ujoiFb76HXrrlmzJgaDgW7dujF9+nSaNWtmMvbtt9/Ot06v17NhwwYcHBzURDE1NZW3336bMWPGULdu3TKruxDi4SQtiw+YtCwKcf+2bdtG7969SUhIoHab5/HuNobLN9MBeLKWO/NeCsbbybrAfTeHRTFubSi6HAP1/RxZNrgpno75Y48cOcKLL77IsGHDmDp1apmeT2kICwsjLi6Op59+GoDs7GzCwsJo1KhRgfEGg0JGth47q9ttBd/+cxlFUbAy1+LtZIOPkzV+zjZqzP/+9z/Gjx+PRqOhd+/eLFy4EFdX17I/OSFEqSlJ/vHofW0upoULF1K1alWsra1p3rw5Bw8eLDT+xx9/pG7dulhbWxMUFMSmTZuMtiuKwvTp0/Hx8cHGxob27dtz7ty5sjwFIR5bBoOBQ4cO8f7779O8eXM6depEQkICDTv1QtfiFS7fTMfTwYpP+4bwzf81M5koAnQJ8uH74S1wtbPkQmwaMcm6AuOOHTvGpUuXmD59Olu2bCmrUysxnU7HtGnTaNSoEf379ycxMREACwsLGjVqhMGgcCU+nb9Ox7B41wXe+uEYPT7/m/oztvLO+jCjsmZuPMn0X04y6acwBi87SMf/7SZoxlaeX7iXJbsv8uSTT9KjRw8URWHNmjU0bty4wL6OQoiKqUIni2vXrmX8+PG89957HDlyhODgYDp16kRsbGyB8fv27aNv374MGzaMo0eP8vzzz/P8889z4sQJNWbu3Ll8+umnLF68mAMHDmBnZ0enTp3U0YZCiNIzb948mjVrxsz//JfQq8koCgwbNox961fQqoYbw56oxva32vBcsG+R+tY18ndh/Rut+GpQY4IqOQG5XwDvvMAydOhQhg8fjqIo9OvXz2hAyMNAURS2bNlCw4YNmT17Njk5OTRr1oxM3e3kNzNbT5MP/uTJuTv4vxX/8tHm0/x05CrHriaRnqXn4o00ozI71/eha5A3bWp7UNfbAScbCwwKhF5J5NjVRJo0acKGDRs4dOgQ1doP4GpyDq1atWLJkiUP+vSFEOWgQl+Gbt68OU2bNuXzzz8HclspKleuzOjRo5k8eXK++N69e5OWlsZvv/2mrmvRogUNGzZk8eLFKIqCr68vb731FhMmTAByp+3w8vJixYoV9OnT5551ymsGXr0nHFt7hwJjAn0dqemZuy0+LYs9524YbT96NJT09NxLbz42BrxtDGg0GjL0Gi7pbI0uP504cZK0tFQAvGwU/GwVNBoNmXo4m2pFo0YhauyZM2dJSUnJjbUFf3vQaDRk6eFEopaGwcFw668lIiKCpORkADxsoLqjBo1Gg96gcDgOgoKC0JD74R155QrJSUkAuNloqOWsRaPRYFAUDsYo1K1bBzNt7mCFqOgoEhNzY12ttQS4m6tJwD/Xs6lWvQbmFrmXxmJjb5B0q1xnay1Bnpa3Y6/p8PGrjKWlRe7zGJ9AUlIiAA5WZoR4W6mxB65l4ubpjaWlJQBJSclqufZWZjT1s1FjD17LwM7JDSur3NjU1FSSbz0PNhZmtPS3U2MPXU3Hws5JjU3PyCD11vNraablqeqOauy/V9MwWNiqsZmZOnWeQHMzDe1quaixR66mkoGFWt+srGwyMnL/HjQaDZ3quqmxR6+mkJStVZ+HnBw9Ol3eFxsNnQLcMTfL/c547FoKN9L1mJvnPr8Gg4GsrGy13I4BHlhZ5P6ewq4lczVJpw4yMRgUDAa9Wm77AA/srCzIysri4PlorqfosbPLnYolOSWVQ0eOkpquIy1Th2/mJTKTE0hOTuZ0qhXVn+yBt58/mdl6LkbFcy46Ca1V7r4bhgbSsE5VAPQGBTNtyQdf/Hkqhq/2XOS97vUI9M1NIHU6HU8++SSHDh0iJCSEvXv3YmNjU+JjFdndb8u3lv/55x8mv/MOu3btwszeDc96LWjX6xWSLN2wtTRjzbDb/RM7f76XizfSqO5uRy1PO2p52FPb046a7nZUcbXBwqzwtoLrSZn8feEmlVxsaFXVBYBzsal0+OIAADnJsWReCmVoh6ZMGdQdVxuLgs8j71zu/PnOcyxqzF3l3kzPJiEzh5quub+Xcxcv8s7KjcQk5L7G+r/0It4eHgAcPHKUvfv/xin5duKf7FAFvVnuSO/ePXpQydcXgKMnwtixZxfOSRfV2BT7yuSY5x7nhW7dqFbFH9Bw4vRp/tj+Jy7JF9BoNGi1WlLs/MixsAWNhq4dOlCreo1b9bvAH3/9hWf6ZTSAVqslydqTLHM7QEPbJ5+gTs3cUfyR166x7a8d+GZdRavJfW9NsHAlQ5v7/tKiaVPq1KoFQHRMDNt37aKSIQZzTe5rNV7rRKrGFo0GgoOCqFsrd4DTzYR4du7eg78mHktt7mfBTexJwhYNULd2berUzi03JSWFXXv3Us0iBWuz3HLj9NbE661BA9WqVsutgyb3vW3fP/9QwyYTW7PccuOyLYnJtkSjAT+/StSqWQPQkJWdxcGDh6hpn42Dxa1ys8y4nmmBRgMeHp5Ur14NAL3ewNHQo9RyAmer3E+UOJ2GyDQtoMHZxZmqVark/kkAJ0+cpJaLFncbLRqtlpuZCueTFDRosHewp1KlSurv9MKFC9RyNsPLPvczJj7DQPjNbDQaLdY21nh7eamx169HUcPFnMrOuZ8bCRl6jsVkotFosLCwxM3tdpeMmzfjqeFmSTVXm9zfcaaeQ1dSAA3m5mY4ONz+3E9LS6eGmzW1PHN/r6lZBv6+mICG3L+lvM8DgJycHGq42xHo56R+hm8Pj8GUau52NKjkDOR+edx6Mhpdeiq9W9eV0dB3ysrKwtbWlnXr1vH888+r6wcPHkxiYiK//PJLvn38/f0ZP34848aNU9e99957bNiwgWPHjnHx4kVq1KjB0aNHadiwoRrTpk0bGjZsyIIFC/KVqdPp0N3xjT85OZnKlStTedwP6gfg3aZ2C+CVJ6sDcCQygRe+2GfyPBP3rCJp3xoALDyq4vt/n5uMTfrnRxJ3rQTA3Nkbv9e+Nhmb/O+vJGz/CgCtnTOVR60yGZt6/A9ubv4UAI2lDf5v/mgyNi18N3G/zs1d0Gip8vavJmPTzx3gxs+z1GX/t35GY25ZYGzGpWPErn1XXa405nvMbApOxnXXTxP97QR12W/EcswdPQqMzYqNIGr5aHXZ95VFWLhVLjA2OyGK61+9qi57D56PlXfNAmP1qQlcXThQXfbq9x+sKwcWGGvQpXNlfi912fPl97Gp3rjAWMWgJ3JeD3XZ4/l3sK3TqsBYgMsf9wR9bkLo1m089vWfMRl7ZUEfDJm5XzxcO47EIaSLydirXwxBnxIHgMszr+DY9HmTsde+Gk5OwnUAnJ8cgFOrgr90+abG8dXW+dSPu5S7orCkoijbbrUodun/Cac9qqJRDPQJ28b4fd/jkZbIFUWhkV5PHDBEo2HZrQ/t+1ZYHYvgPNC4xcvYVG2IhZs/ZvYuRtstc7IIm98LK30OANcd3PFIS8DCoC+gtPsT5lWDj9oO5VClemTd8VrUKAYCYy4yfs8qnrn4b6kd7247qjdheePuhHtW44a9K8HXzzDv27eYDXwHeD9ir0/356dgV6e1qdMt5uuzL4bM3ES5WK/Pp4fh2KynydhrS14nJ/4qAE5PDsDZxOsTIGrFWLJiLgDg2PwlXNoOMRkb/d0kdFdPAuDQ6FlcO7xuMjbmh+lkRhwBwL5BB9y6jDUZe2P9h6Sfzf3MtA14Co/n8g/iyhP32yekndwBgE2NZni+NN1k7M2tC0kN3QyAdZVgvPp8YDI2/q+lpBxaD4Clbx18Bn5iMtboM9y9Cr7DFpqMzfsM9/f3Z/eRU7SZt9Nk7OCWVXi/R30AYlMyafbBdqyVTM7MfUlGQ98pLi4OvV6P1x3fDgC8vLw4ffp0gftER0cXGB8dHa1uz1tnKuZuc+bMMZqqIk/L6m5Y2NgVuI+v8+0WDEdrc56o6W60/dSpU2RkZuQe288N+2eeQVEUsq2cSMq4ToOgBmpseHi42kLl6m6P3RNPoCgKekt7klOuEBJyu2Xx9JnTaitZJScLajdvDoDezIrkhAiaNmkCUbkf6meiY0hMzy3XU5+Kv78/AAYzS1Kjw2lerbr6wXouJpq41DRAwUUXj5efX24skBl1iubVqmOm1QIK52NjiU3OfcNzSI/F5dZzrQDZ0adpWr0mlrdasy7euMH1pCRAwSb5OlXd3NRYYs7QuGYdbCxyP9Ai4uK4mpAAgEXidfycnfMaSdHGnCPE2Qq7Wy11l+Nvculm7l0/tMmxeNjb3y439jwNXO1xtM7tG3clIYELcXEogJKWgNOt1idFUSD2AoEezrjcmtz4WmISZ251gVB0qdjeOp6iKBhiL1DX2xN3u9y/iaiUZE5F5f5NGXJ0mN8alasA2bERBPlVwvvWN9SY1BSOX8/9vdydgGTduESgfzX8nHJbzOLS0jh67ertgDvis+Mi8U+7hr+LMwAJ6Rn8e/XK7dA7ko7s+Cv4pVyhmmtuwpKcqePAlcjbsbc+4AByEq7hnXSZmrd+P7qcHMKjrmGJASsMdAkJxltbD0crK1JcnTEnAm8ba6w0Ch7aHKqZZVFZm4W1qwLD75ra5s7kLe/nu//P+/nO9bd+1mg0LNWb8VFSKhvT7fm+QSfWB3dggH0qw52SWXPuFB0/+4wVikKnoUPp07QpxVZQgnlXHTMVDZHZ5lzKNuNytjkR2WZcyLYgOkfLzipxaDRQU6Ohxt4E4j1yRyNrUQiwyqGJTTaNbLJpYpON5ZIl3GrQx7eg58HUc3UvigIaDUHkJmUZhjQOpGbyV5KWgzobTmeaccK7JpqJE8ExBzQaorM13MzREGirFPjcF1gPE7+7HAU+uWLGouvGraHnnVwI0GrVaYI8dHE459jktlA5OmF5q5U8JSMDg72B90eNUvfdbGlJvD43YXJ3dMTKPLdlNDUzA112Ju+NHKnG/mFpy42c3Fg3BwesLXJj03SZpGYn8+5rr6ndGXbaOBCdFQsouDk4YGtpdStWx82UZMYMGqTGHrR3JCozCgXwcHDE/tb7SppOR0xiIoN69kSLAUVROO7sSFT6NVDAw9ERp1vvK+k6Hdfi43m+UwfM9VkoisI5N0eiUnJfux6Ojrjeeg/LyNJx6UYcz7RuiWVOBoqiEOluT0zSZUDBw9EJj1vvK5nZ2ZyPiaFpwwZY6aqgADFudtxIuKjW18f5Vkt8dg6nr18noHYNrPycUIAEV1vi487finWg0q33imy9nrArV2lWyRtLx9znIdXJmqTYs7m/C3t7qnrkfubpDQYOX7pMPVdHzDXVURSFTFsLMqLDAXC1s6eWlxegoCjwz8UL+Nuao/X1RVEUcqw05Fw/hYKCq60dAT4+6u9034ULuGtzcHN1zX3/NlcwXD0BKDjb2NLgjlbIvRfOY5edhqWdHYqioDEzkH01DBRwdHSkcePbXw727t2LNiMRKysrFEVBm6Mj81IoAPb2drRo0fJ2HfbtIyfxdu6gZGeScekoADbWNjzxxBPqtn8O/ENOwu1bl1pbmOXLDe5U3cNe/dnSTMsTNd3R5qRzxuQehauwLYvXr1/Hz8+Pffv20bLl7V/O22+/za5duzhw4EC+fSwtLVm5ciV9+/ZV133xxRe8//77xMTEsG/fPlq3bs3169fxueOPrlevXmg0GtauXZuvTFMtizIaWpSVvJd03v+aO1rEDAYDBoMhX0ze/2ZmZuplaL1ej06nMxlraWmpTtick5NDamqq0XYAc3Nz7O3tH4m5+g5GxPPBpnCOXUkEwMpcyztdA4jd+yO//fYbmzZtws6u4C94BoOC9o5L4pvDojh6JZG4VB3xaVnEp2VxMzWLhPQscgwKB99ph7Nt7peFaRtO8O0/l03W6+chATSqm3ulYefpGOLSsqntZU9NT3tsLR+O7/uxKZl8/9dR/q9jIxxscv8m5mwK58vdF2nk78zAllXo3sBX7fJQHDdSdIz5/ij7L94EcufKfD7ED0dDKvXr1kKn01W4SdXF4yHvvTjv/zsfgFHXl9TUVPR6vbrN2dm52MeTeRYL4O7ujpmZGTExxtf0Y2Ji8Pb2LnAfb2/vQuPz/o+JiTFKFmNiYowuS9/JyspK7oAgHijNHa1md9NqtUWeO9DMzKzIt3wzNze/rzevh0mzaq5seKMVu87eYMH2cxyNTMTfzZZBEyYwZswY9l9K4p+LV6jqZoujjQUXYlM5d+txJT6do9M7qP0At4XH8PORa0U6bo4h983fwcqcqu52VHGzJSP2Mr99v4zEy+FMOxXI77/mdptpW9ersKLKza9rVzHxjTeInTiRDz7IvTyXnqXHXKvhSGQiRyIT+fnINT7v1wingvo2FiL0SiL7L97E1kJLZ9ebzOzR7dYWFxYuXEhISIjJaYKEeJjlvRffeYMBU+zt7e8ZU5YqbLJoaWlJ48aN2b59u9pn0WAwsH37dkbdcSniTi1btmT79u1GfRa3bdumtkxWq1YNb29vtm/friaHycnJHDhwgBEjRpTl6QghHgCNRkPbOp60qe3Bv5cTaFIld2CRlZUVu87eYPneSyb3vXwzTR2Y1raOJ252lrjaWeFmZ4mbvSWudrkPCzMtDta3E6ZJneswsVMdXGwtSEhIYNy4cXz77bdA7u34/jtvbpmec2mws7MjOzubDz/8kCZNmtCzZ09mPV+f0e1qsvbgFb7YeYE95+J44Yu9LB3clKruBbfQFuSJao48YXmZ31Ys4H9XztKrZU1atGgB5I6MF0I8AEoFtmbNGsXKykpZsWKFcurUKWX48OGKs7OzEh0drSiKogwcOFCZPHmyGr93717F3Nxc+fjjj5Xw8HDlvffeUywsLJSwsDA15qOPPlKcnZ2VX375RTl+/LjSo0cPpVq1akpGRkaR6pSUlKQASlJSUumerBCiTP1xMlp55YutiufLMxTvgR8rbl3fVBybv6jUa99bGfPOTGXvvn2KXq+/r7J3796t9OzZU7G0tFQARavVKlOnTlWysrJK+SzKzptvvqkAioODgxIeHm60LexqotLiwz+VKpN+U4Lf36ocjUwotKzv/rmsXIlPUzZs2KBUrVpVIbe7rtKkSRPl4MGDZXgWQlRcJck/KnSyqCiK8tlnnyn+/v6KpaWl0qxZM+Wff/5Rt7Vp00YZPHiwUfwPP/yg1K5dW7G0tFQCAwOV33//3Wi7wWBQpk2bpnh5eSlWVlZKu3btlDNnzhS5PpIsCvFoO3nypDJnzhyldevWilarVRMZQBk1apQal56ernz77bfK7t27lR07digbN25UVq9erXz55ZfKxx9/rOzdu1eNXbZsmVpGcHCw0bZHRVZWltKmTRsFUOrUqaN89913ytmzZ9XtMUkZynOf7VFazdmuxCZnFliGLluvTN8QplSZ9JtSe8wKRWOemzxXqlRJ+f777xWDwfCgTkeICqck+UeFHeDysJLb/QlRccTFxbFlyxY2btzIli1beOedd5g0aRIAJ06cICgoyOS+7733HjNmzABy52v96KOP6NevX6H7POxiYmJo3Lgx167l9tfs168f3333HZA7YGr+Z19QqVYgHVuF4OLigi5Hz5LdF/FytGbHmVh2n40jVZc79U/CjuVkHP2VCRMm8M4775R7ny0hHnUywEUIIcqBu7s7AwYMYMCAAWRnZ6vTVEHuCPGnn36ayMhILC0tsbe3x8HBQf2/fv36aqyTkxNz5swpj1MoVV5eXuzYsYP58+cTGhpqNBPF+fPnmfDmGHW5cuXKuDTqQlLd54zK8HSw4hnnOEJdUvn0xAlq1679wOovhCiYtCw+YNKyKIR4HJ08eZJ3331Xvfc25E5u7NSyFxoLa1pWdeK9V18kyM/pjukWH/4pl4R4VEjLohBCiIdaYGAgGzZsAHIvu588eZKYmBhu3rzJzZs3eeqpegRXdi7XOgohCibJohBCiAfKycmJVq1M34ZSCPFwKf50+kIIIYQQ4rFRJsmiwWBg2bJlZVG0EEIIIYR4gMokWdRqtXz55ZdlUbQQQgghhHiAyuwydJMmTfj888/LqnghhBBCCPEAlNkAl6tXr7J582Y+/vhjWrVqRVBQEEFBQTz77LNldUghhBBCCFHKyixZ/OWXXwBITU3l5MmThIWFsW3bNkkWhRBCCCEeIWWWLGZnZ7Nz506sra2pV68ezZs3L6tDCSGEEEKIMlJmyeILL7yAj48PP//8My4uLqSnp9OgQQM2b95cVocUQgghhBClrMySxcjISDZu3MjBgwcJDQ1l4cKFXL58uawOJ4QQQgghykCZJYvW1tYAWFpakpWVxciRI2XGfiGEEEKIR0yZJYtjxowhPj6eF198kddff53WrVsTFxdXVocTQgghhBBlQKMoilLWB1mxYgUnT55k4MCBNGjQoKwP91BLTk7GycmJpKQkHB0dy7s6QgghhHgMlCT/KLOWxVOnTvHLL7/g7OxMYGAgPXr0wMXFpawOJ4QQQgghykCZ3cHlueeew9bWlrS0NJYuXUq7du2oUaNGWR1OCCGEEEKUgTJrWfT29mbs2LFG6/R6fVkdTgghhBBClIFSb1kcP34833zzDW3atGHp0qVG28zMzEr7cEIIIYQQogwVO1nMyMjg2rVr+dafPHkSgLZt2xIZGcm5c+eYN28etWvXpl+/fsyZM4fffvut5DUWQgghhBAPTLFGQ69bt45x48bh7u6OwWBgyZIl6m38GjVqxJEjR9TYq1ev4u3tTU5ODidOnOD48eOEhYXxv//9r/TP4hEio6GFEEII8aCVJP8oVsvi7NmzOXz4MKGhoSxfvpxhw4axevVqAO7OObt27UpaWhrW1tY0adKEnj170rdv32JVTgghhBBClK9iDXDJzs7Gy8sLgMaNG7N792569uzJ+fPn0Wg0xgWbm+Pk5KQuOzk5MWLECA4fPlwK1RZCCCGEEA9CsVoWPT09OX78uLrs6urKtm3bCA8PN1oPUKlSJfbs2XP7QFotWVlZJayuEEIIIYR4kIrVZ/Hq1auYm5vj7e2db9vevXtp3bq1uhwZGUnXrl1p2bIlzZo1IywsjAsXLvD777+XTs0fUdJnUQghhBAPWknyjzK93V92djbr168nLCwMLy8vhgwZgr29fVkd7pEgyaIQQgghHrRyTRbj4+NxdXUtSRGPFUkWhRBCCPGgleu9od3d3fHz8yM4ONjoUbt27XyDXoQQQgghxKOlxMliWFgYoaGhHDt2jEOHDvHVV18RHx+PtbU19evX58CBA6VRTyGEEEIIUQ5KnCwGBgYSGBhI//79gdz5Frds2cLo0aNp165diSsohBBCCCHKT6nfG1qj0dClSxdWrVpFdHR0aRcvhBBCCCEeoFJPFvO0aNGCHTt2lFXxQgghhBDiASjxZWh7e3uCgoIIDg6mQYMGBAcHU7duXQ4dOkRKSkpp1FEIIYQQQpSTEieL69atIzQ0lNDQUBYsWMCFCxdQFAWNRsOsWbNKo45CCCGEEKKclPqk3Onp6URERODm5lbgnV4edzLPohBCCCEetJLkH/fdZ3H69OkcPnw433pbW1sCAwMlURRCCCGEqADuO1m8evUqXbp0oVKlSowYMYLNmzeTlZVVmnUTQgghhBDl7L6TxWXLlhEdHc3333+Pg4MD48aNw93dnRdffJFvvvmG+Pj40qynEEIIIYQoB0Xus2hmZoZery80Jjw8nI0bN/LLL79w+PBhmjVrxnPPPUffvn3x8/MrlQo/6qTPohBCCCEetJLkH0VOFrVaLQaDocgF37hxQ00cn3zySSZMmFCsilVUkiwKIYQQ4kErSf5R5KlzNBpNsQr28PDg4MGDfPXVV3h5eRVrXyGEEEII8XAoszu4AHTt2pVu3boxY8YM0tLSyvJQ+cTHx9O/f38cHR1xdnZm2LBhpKamFho/evRo6tSpg42NDf7+/owZM4akpCSjOI1Gk++xZs2asj4dIYQQQohycd/J4sKFCzlx4kShMc899xwHDhzAy8uLVq1asXjx4mJdyi6J/v37c/LkSbZt28Zvv/3G7t27GT58uMn469evc/36dT7++GNOnDjBihUr2LJlC8OGDcsXu3z5cqKiotTH888/X4ZnIoQQQghRfu57gMu0adNYv349Hh4ejBo1ip49e6LVFpx7RkZG8tdffzFhwgTc3d2ZN28e3bt3L50zKEB4eDj16tXj0KFDNGnSBIAtW7bQtWtXrl69iq+vb5HK+fHHHxkwYABpaWmYm+desddoNKxfv/6+E0TpsyiEEEKIB61cJuWeNWsWJ06cYPXq1aSlpTF79ux8MZ07d6ZKlSr069eP48eP89lnn/Hdd9+xYcMGxo0bd7+Hvqf9+/fj7OysJooA7du3R6vVcuDAgSKXk/eE5iWKeUaOHIm7uzvNmjVj2bJlFJZv63Q6kpOTjR5CCCGEEI+KEt8b2sfHh0GDBhW47aOPPiIoKAgzMzOj9UuXLqVu3bolPbRJ0dHReHp6Gq0zNzfH1dWV6OjoIpURFxfHrFmz8l26njlzJs888wy2trb88ccfvPHGG6SmpjJmzJgCy5kzZw7vv//+/Z2IEEIIIUQ5K5UBLqYm4G7YsGG+RDHPpk2bin2cyZMnFzjA5M7H6dOni13u3ZKTk+nWrRv16tVjxowZRtumTZtG69atCQkJYdKkSbz99tvMmzfPZFlTpkwhKSlJfVy5cqXE9RNCCCGEeFBK3LII4O7ujp+fH8HBwUaP2rVrm5xyp3r16sU+zltvvcWQIUMKjalevTre3t7ExsYarc/JySE+Pv6e96xOSUmhc+fOODg4sH79eiwsLAqNb968ObNmzUKn02FlZZVvu5WVVYHrhRBCCCEeBaWSLIaFhREaGsqxY8c4dOgQX331FfHx8VhbW1O/fv1i9RMsjIeHBx4eHveMa9myJYmJiRw+fJjGjRsD8Ndff2EwGGjevLnJ/ZKTk+nUqRNWVlb8+uuvWFtb3/NYoaGhuLi4SEIohBBCiAqpVJLFwMBAAgMD6d+/PwCKorBlyxZGjx5Nu3btSuMQxRIQEEDnzp159dVXWbx4MdnZ2YwaNYo+ffqoI6GvXbtGu3bt+Oabb2jWrBnJycl07NiR9PR0Vq1aZTQYxcPDAzMzMzZu3EhMTAwtWrTA2tqabdu28eGHH8rdaYQQQghRYZVKsng3jUZDly5dWLVqFV999VVZHOKevvvuO0aNGkW7du3QarW8+OKLfPrpp+r27Oxszpw5Q3p6OgBHjhxRW0Br1qxpVFZERARVq1bFwsKChQsX8uabb6IoCjVr1uS///0vr7766oM7MSGEEEKIB+i+51ksqmrVqhEREVHs/SoqmWdRCCGEEA/aA7k3dGHs7e0JCgoiODiYBg0aEBwcTN26dTl06BApKSmlcQghhBBCCFEOSiVZXLduHaGhoYSGhrJgwQIuXLiAoihoNBpmzZpVGocQQgghhBDloEwuQ6enpxMREYGbm9s9p6p53MhlaCGEEEI8aOVyu7/C2NraEhgYyNq1a8uieCGEEEII8YCUSbKYJywsjNdee01tkTx16hR9+/Yty0MKIYQQQohSdN/J4sKFCzlx4kShMV9//TV169alc+fOvPTSSwwaNIgXX3zxfg8phBBCCCEesPse4BIdHU2fPn3w8PBg1KhR9OzZE63WOPc8dOgQe/bsISEhgYsXL/LXX39RpUqVEldaCCGEEEI8GPfdsjhr1ixOnDjB6tWrSUtLY/bs2fli3nzzTV5//XX+/fdf1qxZw/PPP8/evXtLVGEhhBBCCPHglMpo6Pj4eFxdXe9ZxrVr1+jVq9djnTDKaGghhBBCPGjlPim3u7s7fn5+BAcHGz1q166NRqNR4/z8/Ni+fXtpHFIIIYQQQjwApZIshoWFERoayrFjxzh06BBfffUV8fHxWFtbU79+ffWeywDW1talcUghhBBCCPEAlEqyGBgYSGBgIP379wdAURS2bNnC6NGjadeuXWkcQgghhBBClIMymWdRo9HQpUsXVq1aRXR0dFkcQgghhBBCPABlOil3ixYt2LFjR1keQgghhBBClKFSuQxtb29PUFAQwcHBNGjQgODgYOrWrcuhQ4dISUkpjUMIIYQQQohyUCrJ4rp16wgNDSU0NJQFCxZw4cIFFEVBo9Ewa9as0jiEEEIIIYQoB0WeZ1Gr1WIwGIpUaHp6OhEREbi5ueHt7V2iClY0Ms+iEEIIIR60BzLPYlETRQBbW1sCAwOLVREhhBBCCPHwKZXL0EII8aiIjIwkLi7O5HZ3d3f8/f0fYI2EEOLhJsmiEOKxERkZSUBAAOnp6SZjrK2tWbduHT4+PgVul2RSCPG4kWRRCPHYiIuLIz09nVWrVhEQEJBv+549exg3bhzPPvusyTJsbW0JDw+XhFEI8diQZFEI8dgJCAigUaNG+daHh4cDMGvWLLp27Vrg9gEDBhAXFyfJohDisSHJohCiwjHVLzEvGYyKiip0/2rVqhWYTAohxONIkkUhRIVSlH6JL730EmfOnJHWQSGEKAJJFoUQFUph/RLzLiNnZmbe16XkvBbJvBbKu8ngFyFERSTJohCiQjLVL/F+RUZG8tJLLwEwYMCAAmNk8IsQoiKSZFEI8VgqqHUwIiLCZHxcXByZmZkABbZa5o2k3rNnT4EjraXVUQjxqJJkUQjx2HB3d8fa2prMzEy1dVBjboXWxh59agIouXeqiouL48iRI0b73plc3t1qGRkZyeTJkwFpdRRCVDySLAohHkn3M+K5cuXKzJkzhzfffJPaHQeQHfwiBq0FANbmGqraKxze+iOT569E9/YUlKyMfGVYW1vj7u5utO5erY4y5Y4Q4lEmyaIQ4pFT3BHPBoOBTxavYNU3Kzl+YDcAVy9dxCMkN1FUDHoyc8w4nQh2zV/GrvnLDAhy4IUAe7W8vIRv3bp1hSZ8pd1XUgghypski0KIR05RRzzn9R/cfTmdLy65kerZDHPzfeTk5DDypfbEpe4h/OgBjh85RLatB1Z+AVhXqkeleo1589k2uDnlJotX4tPJ1isAJm8DKIQQFZUki0KIR1ZBrXh390t0bPEyLm0Gg9Ycra0LOYbc/oOjXhumthDq9XpOnz7N3Llz+eabT4j7DVqur8WWLVuoWrUaw789THxyGnb12mJQlGLXU6bcEUI8yiRZFEI8tO6nX6K/vz/r1q3j2ed6UH/wLFI8GwDQvbYdg17qhNnbnfMlZ2ZmZgQGBrJy5Up69erFa6+9hrW1NZUqVeJKQjo3U3XEpulx7z6BURuvMSxaRyNvKzQajVF9TJ2DTLkjhHiUSbIohHgoleROLGZWdni+OJ0UzwZoUJjxXH0Gt6papON269aNEydOEBcXh6WlJVXcLNk6uiVjv9jAzhgLorHjgz0JZEaGkbBrJVnXT6v7yuAXIURFJMmiEOKhVJI7sXwRlo1N9cZo9Fks6BPCc42rFuvYzs7OODs73y7vs/l8O3UqnZ57gbrPj+b3c2ngH4TPwI/54Bk3uHGBAQMG8NFHHxEXF2fUGlrYlDtCCPEokGRRCPFQK06CpSgKGo2GvvUdOHzmJP99MbDYiWJBbGxssLW1ZVDvF+nXry2TkjKYv+0c52JT6NehOVeu+GJra8u4tyaAPqfAMgpqdRRCiEeBJItCiHJjqk8iFN4PsKC4I8fC+O+8//Dxxx9jSEjg+pLXqDX8YKnUc/z48fTt2xdvb28AfJxsaKQPx88Qh17fHH9/f/49doIB352mdWUbXgywx8FKq9avKFPuCCHEw0qSRSFEuShKn0QoeBDL3SOeras3wb3LWGLjs+jWrRuQO2ikNFvy7pwyJzk5mbFjxxITE8NnC+bTp08fnBp14WaGgV/PprHjso7X2lTn/56oVuD+BSksOZbR0kKI8iTJohCiXBTWJxFg06ZNTJs2jcTExHzb1BHPzz7LsFlfsi3ZG43WDP8Og1nx+jPY2NiUaYJla2vLe++9x9SpUzl//jyzZ88GZlPjie44tB5Ags6Oj/84y8r9l+lZywq0ZibLykuGTY2UzjuejJYWQpQXSRaFEOXKVJ/Ee12G9vHxwaFpT/5M9UOjBcvroeydPxIPN9eyqqrK3NycESNGMGDAADZu3MiPP/7I5s2bufD3Rvj7N2wDnsLjmaHcwJ2vjujw/b/POXay4PM5evQoALNmzaJr1675tstoaSFEeZNkUQhRpu5nrsQ7RUREcOTIEaN1iqLw5opduD4zDADLiL/Z/em4B5Io3snBwYF+/frRr18/UlJS+P333/nxxx/ZtGkTlxftxaFhZ5xa9SEr+jz/N+iTQssKCQmRkdJCiIeSRlHu43YE4r4lJyfj5OREUlISjo6O5V0dIUqFqYQwKiqKl156SZ1nsCDW1tYFzpX4+++/8+yzz+bfQWuGW+fR2Ae1B8Arej/bP5+Mvb19/thykpqaqiaOm7f9RWaOgcoezvz0009Ep+pZcSyZfvUdqOJsobYcHj58uMBk8ciRIzRu3NjkdiGEKIqS5B/SsihEGSpstG9ei1pBAx8K2wag0+mwsrJ6KMotSkJoaWnJzz//bLTvveZKzIu9s09jXFwcEydNJtrOGcWgp5nmPD8sn6XeSeVhYW9vT+/evenduzdpaWmsWbMGJycnGjduzOjvj3Louo5D1zKpY5XAq0/VLLQsuVXgo6ew1z2gDrwy9QUrMTERZ2fnAl9rUPrvC3l1kr8jYUqFTRbj4+MZPXo0GzduRKvV8uKLL7JgwYJCWx/atm3Lrl27jNa99tprLF68WF2OjIxkxIgR7NixA3t7ewYPHsycOXMwN6+wT6W4T0Ud7VtRFJYQZmVl4ePjc18tYwEBAfhUrcWaH9fx8cypXL9+Hacr15j86TLe7De+NE+hTNjZ2TFs2DB1+c32tTh/4SLhqdacyXJlwp/x+AxZwMazadxM/YdWjYOws7MD5FaBZe1eSV1ZfXmytrZGo9GQkZFxH7UuG/J3JApTYTOc/v37ExUVxbZt28jOzmbo0KEMHz6c1atXF7rfq6++ysyZM9VlW1tb9We9Xk+3bt3w9vZm3759REVFMWjQICwsLPjwww/L7FzEo6kodyCB/LeAK2wb3B4lXNCAiPIstyQJYUGtZuHh4WBmwYbTqfywbhfx4ee5cf06AQEBbNiwgdq1axf7OA+D6h72LP2/lnyxdhP/prlwPt0avGqwPDSZJTuuE9exLW3btqVr165UqVJFbhVYCgpKCouS1JVEQV+eoGivpREjRrBo0SKj7WX1+r1zu/wdCVMqZLIYHh7Oli1bOHToEE2aNAHgs88+o2vXrnz88cf4+vqa3NfW1ladePduf/zxB6dOneLPP//Ey8uLhg0bMmvWLCZNmsSMGTOwtLQsch2/++cSNnYO6uWzvKtoGqBRFRcCfZ0AuJGiY+vJ6Ltibu8T5OdEfb/c2MT0rNux3FHgrf/qejsSVCk3NiUzmz9OxhiXe0f51T3saFDJGYDMbP0dddDcWSwaDVR2sSW4cm5stt7An6di1PM0vjqowcfJWo01GBS2n469YytG+7nbW6mxALvO3sBwq4utcawGF1sLtb4A+y/cRG9Q8tVBr8/BoMsg0McejUaDRqMhLCqVbH3unT+0Go26g1ajwdbSjEBfR2xsbNBqtZy4lkS6LguDQcHMTItWa4ZGk1sfK3Mz6vne7gcSmZSNpXctzL1qYO5ZXX1uNRqwTcxW4wICAnDxr0N6Vu6dP6wTs7HwqAYo2PjUxN6vFrW9HNT4fcdOY+Huj61PDRz8ahmdo2NyjlG5fjXrkabLXeeUkoO5iy8oCs6VauFWpQ6VXWzRanN3PhR2GnMnL5z8auBRta5RuTfS9KDRgmIgICCAGnWDyMjWAxCfocfM3lX9OTY5Ezd7K8xulauxtEFjbklipp64VJ1aPzNbZ2xcPMlIjFM/yMxd/bD0qoGFiy/mLr5UGv4V3xxPATQ4+lbnzXmfMPr14Q9V/8T74evry+w3XwFg1/5/eXbkDAK6DiEq8l90Oh1bt25l67btOLcZhKV3TbKiz5OYmEi9evWwtra+/wMrSu7DYCj457wu7EX9uaD/7z7e/SisW8Hdb1hAYmYOGo0GJxsLAK4kZvL5viukZumJS05jz6Ejue8dt6qTdnIHqce2AGBm74bncxMIqF0bC0tLLMzM0Gi13Lx5k/MXL/Ckvx2vP5X7OkvNhmXnNKSlpxN28iQoCg3qB2Jvb48GCHAG/8yLjBgzhmyDhu8uWGIXnYGiKFhaWmJmpiUxwQv3ZyeQeS0cL3d3Kvn4oDcozP7zMgkJLrh1HcsVnxa4dbFg801XDp7KpranLXXv+Fzam+FL6DkFxWDA0tICC3NzzlrVw6XdcC5bVqFSpUpq7OK9V7ma5IZz26EA7El240yEggbwcrTipWBP8oYubDidytZrx9Hr9VhaWmJpaYEGDQaDHlttDj0beKrlrj8eS3KmXv1VaNT/NThYm/FcfQ/c3NwwMzPjl9BrxCSmYWlhYfSZpwFsLM15qfHt+m47FUNcqs6ovFv/sDTX0qOhnxq773wcsSm6O/4kNOp+Wo2GrkG3E/UjkQnEJuvuqOvtWI0G2tT2VN+zwqOSc8u9ow55+6GBplVdsTDLnXQ/Ii6NGyk6o+eAW/EAgb6OWJnnTp11PTGDm6lZRn/Cmjtiq3vYqbFxqToS07PUsu6sM4CPs7Uam5yZTXJGtvE53ZEnuNhaYmmupSQq5ACXZcuW8dZbb5GQkKCuy8nJwdramh9//JGePXsWuF/btm05efIkiqLg7e1N9+7dmTZtmtq6OH36dH799VdCQ0PVfSIiIqhevTpHjhwhJCQkX5k6nQ6d7vYHZHJyMpUrV6byuB/QWtnmiweY2i2AV57MTS4OX07gxUX7TJ7rWx1qM7pd7htZeFQyXRbsMRk7om0NJnXOTQIuxaXR9uOdJmOHtKrKjOcCAYhNzqTZh9tNxvY+u5v/7P8GgBQLa4L6fW4ytnvEQT77+2sAcrRm1Oy/yGRs+6vH+Hr37S4AtXt/SpaZRYGxraNP893Oz9Tl4J5zSbKyKzBWd+000asmqMt+b6zA3KHgyZuzYiOIWj6aiOrVqWphwTPtJnHRwbPA2OyE61z/anjuixXwGjwfS++C+6PpU+O5unAQAIerV+fDdm/yr2u1AmMVXToZn/UFct8szF+YjkW1glvvFIOeyHk9csutXZsvWw1jq1dggbEANp/3QavPQQMkdxyNNqCNydgrC/pgyEzlcP36/BjSi+99TbcgVvpmFBZp8aTo9Vxs3hfHps+bjL225DUGmWcz0s+PJXW7sdm/hdF2b10KE67+zQtxJ3OTeSgwYVDdT//FoiY+RU2mCkrC7n7c2h6p0xFw/jzpiqIm5AA2tVri+cK7AGTFXSYt7C8M4Ttpq0uho4UFHc3NSdXraZKaymFbWxppNLePeff/d9axgrji6Mm2Wi34o1YLDlYOpOmOZfj8+wtxwDX3KkQMW2hy36T9P+K6eyUXgQkuvvw4/CuTscn//krC9tztZvauVBr5jcnYlGNbsdryGVGAxtIW/zd/MBmbdmoXcRvn5S5ozagy8ReTsenn/iHl59nktYHWmLCeHBPvhRmXQoldO1VdrjR2DWbWBX+5ynsv3KvR0FpRCHljJfEObgXG5r0X5vF99UssXP0KjM17L7xkZUUVjYau/eZxyqPg9zePtAQOrRihLr/4wvsc9qlTYKyDLp2wZa+or/GB3Sazp3JQgbFmBj0Xvh6iLr/acRzbqjYuMBbgzPJXsDLkgEbD2DbD+aVGS5Oxx4LTcer7MgBTfj7O9wevmIzdP+UZfJxsAHh/40mW771kMnb7W22o4ZH7u5q39TQLd1wwGfvb6CfUhqKFO84zb+sZk7E/vt6SplVdZYDL3aKjo/H0NP5ANzc3x9XVlejoaJP79evXjypVquDr68vx48eZNGkSZ86c4eeff1bL9fLyMtonb9lUuXPmzOH999/Pt75zoBcWtrdewHmfM7d+qOp2O8lxtrWgc6C3uk39TLq1vZrH7Vh7K3Pa1fU02n7nd4Hq7rdjbSzNeKq2R74YNfaOci3MtLSq4QaKgnL5sro+b6/qgdUh5A0AzBQtzTJTbm3XqFFqfWtVgnrDb314QUhWqtH2O3+u6u8JQ4ao6wOzM8lWdHeUfTvW38cF8vp0KQq19XpSSDN+LoD07BzOJBn/rnLir2PQpd81SCL355zkG7mLzzwDTk74ac2JTb9Jgk6Hi5U1rjbWKGjIMRiISLmpHkcBctIS0CbFqGWpX0sBfXrS7UO1aIGLkx12uhSSsrKwNjcnU5/7jd3N2oa49ERu3FoGcNelob1zf7idIBkMt9cFB2Pj6oxVTiYZej1mGg16RQE02FmYk5adw+nMTPVexm7ZOmyzMo2bbSkg8apeHTMXZ8wMenKUO37LGo16jH9SU9CnJAPgkr+EfOxdXGgUGMjTthqiMqJJu3mVKjmpvOhgRpvMKGyt9ODrW3CCdi9FjTOViN75d1HQtjtao42WC3toterP/hoN4ampxOl0t7cBF6zcWJF4ljC7Kli6V8Hy6aEobQZx8NJR/jrxF2PP/YOXTW5/uvCePcHLK3ffO8p2d3DA383NaN3dxy/wYepc7lx/93Nzr+fMlKL8fu74vW+8qeWL61rC041bSrZ7VOHmrZ+1qTex37USJSsDQ3YmSlYGimK41UqlIfvmVfwqVYKrVwno/zI3fp2Ls60dienpzOrdi2qeXuw9c4bFf24nO/6qegxDZhpxmxaorUfG56YhO/4qVs7OkJiIos/iVctIVu//hyvx8bz2zDME+PpyJT6eT7ZsJTv+mtH5JexYhtqEhvFznJNwHSwsIDv3isRLFlH8efoM52/coEdwQxpUqkTYtev8cuwY2YnG728p//6KxsJKLUtzR9k5Sbeu7EyYAPPm8YxjOvuuXuRMTAwtq9cguHJlFAViUlL46cxeo3LTz+zFzM7ljve12+Xq0xNzg6ZMARcXnkp2RhNziapuuYmogib3fVIBJxst5HX9UhSapLrjkpOqvo+qDwVsLBWYNk2tQ2CaB4actLyPE6OHGQpMnqzG1kjz5GZOulEMd/yvHTdWXfLL9CYgOyPf55IaW+d2FxgPeyuqu9vdqqOi1jV3H0VtrQRwtLbAx8n6Vn0Vtd63Th3zO2JtLMxwtrW49VZ3KwO44/P/znLNtBqszLVqzJ35gkKB7+LF9ki1LE6ePJn//Oc/hcaEh4fz888/s3LlSs6cMc60PT09ef/99xkxYoSJvY399ddftGvXjvPnz1OjRg2GDx/O5cuX2bp1qxqTnp6OnZ0dmzZtokuXLvnKMNWyKFPnlJ+8P3lFUe75yIvLuwwNkJGRQWZmJlZWVmqrs16v5+bNm0b7Hj9+nM6dO7N582bq169vtC0sLIzu3bsDqFOixMfHExcXx6VLl+jUqRMAhw4dwsbGxqi+v//+O1OmTOGDDz6gS5cuRuWGh4czaNAgo3JjYmI4d+4cUVFR9OrVS92WmpqKXq9X9/3zzz/56KOPmDRpEk8//bRRuefOnWPcuHFG5UZHR7N//35u3LjBa6+9pm67fv06aWm5iXpERARTpkwBYNSoUfj55bZEWFhY4OHhQVJSEmPGjJFpYUw4cuQITVo+yZxVWzkUp+Xfy7evlsSuHIsuNgLDnV8Q7lKRBi1k5RgY/0Movx3PHVii1UCzaq50qOeNc1okqxbPx9nZGWdnZ5ycnHBycsLBwYG4uDimTJnC33//TatWrdQvhXlTEq1atarAqYu+++47BgwYwKpVq+jfv79RXfL2BfLtV9i2ou5bUJ3uVe791vfO7fI6rNgem5bFt956iyF3tDQVpHr16nh7exMbG2u0Picnh/j4eJP9EQvSvHlzADVZ9Pb25uDBg0YxMTG5/fNMlWtlZWVyNJ0oH7f7zNzf9y0bGxs1gctjZmaWrzU7b7Skp6enUR8igJs3b3I3V1dXXF1dSU1NVddptVoCA40vIx8/fhyAKlWq5Ov6kJfQ3snLywsvL698E1s/9dRTRst5reNBQUFqsprn7n0h92++Z8+eRtvCw8ONOs/b29tjZWWFTqfj888L7p5Q2vdwrmiUrHQ61LBl0suNiIhL4+cjVzkbk8KgdSt54oknABgwbx1uTvZ8/tZA9Ck32LJlC3FxuX1BT5w4gZeX1yP/PmRprs1tTVEMJO5dQxsfhTVzVt+adaDrPWcdSExMfOimWBLiUfFIJYseHh54eHjcM65ly5YkJiZy+PBh9dvUX3/9hcFgUBPAosjrm5g3mq1ly5Z88MEHxMbGqonBtm3bcHR0pF69esU8G/G4MDnS18T2wrZBbmtd3v93J3HlUW5UVJSaEJqa3sXa2pp169YVOMebzO92b3c+50+7w9PuWsLDLwGgsbLjQII1WXE5VH5jGR4kkeoeSDWX3OR/5syZ9O7dm06dOtGjRw+6deuGq+uDvdNNSSiKoiZ5M5+rT9caNvzv2E1GDBsHlOwe42Xtfl/7e/fuLTS2NF+/ptYJcadH6jJ0cXTp0oWYmBgWL16sTp3TpEkTdeqca9eu0a5dO7755huaNWvGhQsXWL16NV27dsXNzY3jx4/z5ptvUqlSJXXuRb1eT8OGDfH19WXu3LlER0czcOBAXnnllSJPnSN3cHl8PG7zLEpCWPqK8jdkbefI/J93s/NSBvsv3m6xtjbXEHf0D6wiD3L9xH51vZmZGU888QQ9evTgueeeo0aNGmV6DvdLURQW7brA/lOXaZB2lIkTJxQYd69LqKYuz+btN2vWLKZNm5Yv2bzfqWiioqJ48cUXjbof3U3mWRTloST5R4VNFuPj4xk1apTRpNyffvqpOuXGpUuXqFatGjt27KBt27ZcuXJFvWSTlpZG5cqV6dmzJ1OnTjV6Ui9fvsyIESPYuXMndnZ2DB48mI8++qjIk3JLsvh4eRzu4JJHEsKyUdi9tQcMGMBvv/1Gt27dALgSn876o9f46chVLt/MTTAHBNnTo7Ytv/76Kxt++YUTYWFG5QQGBtKjRw9eeOEF9UpMeTMYFD7cFM7Xf+e2lsWum8miaW8QFJR/5GtBz8Od8pLFu5O+sp5n8V5fnkDu4CIeLEkWHyGSLAohSkNhLWqKovDZ95t4/9utzO7dguYNcqfM2n05g1/DE3BJOMP1A79zaN9u9HeMtG/SpAkjR46kT58+JZvPsQRy9AYm/RTGT0dyRyHH//U1zZ1S2bdvX+EtrMW9x/hd+xaU2EnyJSoSSRYfIZIsCiFKQ2HJYmRkJHXq1MnXaubZayY2t+boVHKyaFvTmeqaG5zY/hMbf/0FnU6HjY0N165dw8WlKJMela6sHAMjVx9h26kYFIOem5sX0Kd5NV5//XWaNm16z7shFTbS11SfRpDETjweHpvR0EIIIYyZGrBQ0G0Cb6br2XU5g61nE7mBJbsupbMLO7wbDmdyn0lYnfmD9PQ0o0RxwoQJ9OzZk9atW5fpeehy9Iz87gh/hsei5GRx45eP6N+2AV9++aU62DAgIOC+p3Ypyb5CPO4kWRRCiEeQu7s7tra2JkegQ+7l1SeffNKo1azDE9Dz8GFaPduX/5v1JfuuZROdnMmpGzp+mHJ7EuOMLD2nwkL55JNPWLBgAZcuXVLnySwL4VEp7DgdgyE7ixs/z2Jgh6YsXry4wOmgTJYhI32FKBOSLAohxCPI39+f8PDwQge/rFu3rsDLqxqNhqzoc7zayIn5Q4L5KzwWB+vbt4+LS9Xx1NwdNKtsT48R71LZItUoUczOzsbCouDbzd2v0O0buP7DQtBn061JTYYPH662KOYlfHn9BO/k7u6OtbU1mZmZJhNnmctTiJKRZFEIIR5R/v7+hfa1MzX4Ik9eEuYFkA1HjkQCsONSOulZenZeSALHlsS52LBibwS9mlbmwplwOnfuzIIFC3jppZdKVP/MbD03UnRs27CGYcOGqet/unyMn376KV/8Sy+9lG8Qi7+/P+vWrePZZ5812S9R+iQKUTKSLAohxGMmr4WusEvYDv4BvDZnGX+eT+ZqQgYzNp5i/vZzuMSFEX0ziZdffpmBAwfy2Wef4eTkVOw6ZGTpee3bfzl8PoqzX96+3++3336b7yYHeS2lmZmZxMXF5Uv88pJi6ZcoRNmQZFEIIR4zeXczKWjCabidnDVULvBC57rsuJTOr2fTiE7NJtW+Lv0GDua7pYv49ttv2blzJ0uXLqVDhw5FPv6V+HRe+/Ywp6KSMWTpMbNzoU+3Z1i1ahX16tUrNOGTfolCPHiSLAohRAVlKonKuzVctWrVCkzMCmx51Gixrd0ScycvVh38GWtrazw9PUmp3YXnx35Aa///8tGHH9yzZW/PuRuM/v4oienZuNlZ0tb5Mj5jh/DMM7nJYkGkX6IQ5UuSRSGEqGCKMlIawNnZucD19255fIEBAwbw4aJveHd3KjTuTnhGCh2nfkNjr1V88OYr1KxRHVsrSyzNc0cz779wk693hPPX+UQUNNRytWBiK2fcbVvcKtN066D0SxSifEmyKIQQFUxhI6Xh9mXmew2AMdXymMfPxY7xHXxZufciN3HAvkEHzgAvrY4AIkj541Ne6dCQ//znP5y4eJXt55MADSnHtvLntsX8qc/OV2ZBI55B+iUKUZ4kWRRCiAroXiOlS8O1i2d4IiCAll3dCY/LYlNYNAejsjBY2gOQmalTY/2sdCTsWok2/jLP1K/E+F07sLGxUbdv2rSJadOmqa2aQoiHhySLQgghiuWeo6m15lhZWfHpgvlUq1qFI0eOkHDlHMn//MjatWvp1atXvl3yLkNHRERw5MgRk9uFEA+eJItCCPGYutcAGFNM9WmMioripZdeIjMzE11GDq8NfzXfvoMHD6ZFixb5Wj3z+k9OmzaNadOm5dsPZBCLEOVFkkUhhHjMlHQATJ67+zQeOXKEzMxMZs2axbRp04wGoxR1rkRTA1jy6i2DWIR48CRZFEKIx0xRB8AkJiYWeEn4Xi2P1apVA+5vMIoMYBHi4SPJohBCPIYKGwBTWi2PQoiKQZJFIYQQRkpr6h1T5C4sQjxaJFkUQgiRT2lPvSN3YRHi0SXJohBCiPtyd2vgndPf3E3uwiLEo0uSRSGEEMVyrz6N06ZNw9ramqioKKMBMnlT7sggFiEeLZIsCiGEKBZTfRrvnGcxMzOTZ599Nt++cqlZiEePJItCCCGKzVSfxjNnzpgcGANyqVmIR5Eki0IIIUrNg7gntRDiwdKWdwWEEEIIIcTDS5JFIYQQQghhkiSLQgghhBDCJEkWhRBCCCGESZIsCiGEEEIIkyRZFEIIIYQQJkmyKIQQQgghTJJkUQghhBBCmCTJohBCCCGEMEmSRSGEEEIIYZIki0IIIYQQwiRJFoUQQgghhEmSLAohhBBCCJMkWRRCCCGEECZJsiiEEEIIIUySZFEIIYQQQpgkyaIQQgghhDBJkkUhhBBCCGGSJItCCCGEEMIkSRaFEEIIIYRJFTZZjI+Pp3///jg6OuLs7Mz/t3ff8VHV6R7HP2dmkklvkBAigVCUKuXSBBFBWEFs8bKwXLkUaaKii4IK7qp311V0cdW1YgOxsCoKC64NBAF3VUoUAekBDKQC6W0mM3PuH4NDIhkNAhkI3/frlRecc57zm+ecTM488/udMnHiREpLS/3GHzhwAMMwav1ZvHixL6625W+//XZ9bJKIiIhIvbMFOoEzZfTo0WRnZ7Ny5Uqqqqq46aabmDJlCosWLao1Pjk5mezs7BrzXnrpJebOnctVV11VY/6CBQsYOnSobzomJua05y8iIiJyNmiQxeKOHTv45JNP2LhxIz169ADgmWeeYdiwYTz++OMkJSWdsI7VaiUxMbHGvKVLlzJy5EgiIiJqzI+JiTkh1h+Hw4HD4fBNFxcXn+zmiIiIiARMgxyG/uqrr4iJifEVigCDBw/GYrGwfv36OrWRlpbG5s2bmThx4gnLbrvtNho3bkyvXr2YP38+pmn6bWfOnDlER0f7fpKTk09+g0REREQCpEEWizk5OSQkJNSYZ7PZiIuLIycnp05tvPrqq7Rv356+ffvWmP/nP/+Zd999l5UrVzJ8+HBuvfVWnnnmGb/tzJ49m6KiIt/PwYMHT36DRERERALknBqGnjVrFo899tjPxuzYseOUX6eiooJFixZx//33n7Cs+rxu3bpRVlbG3LlzueOOO2pty263Y7fbTzknERERkUA4p4rFGTNmMH78+J+NadWqFYmJieTl5dWY73K5yM/Pr9O5hu+99x7l5eWMHTv2F2N79+7NQw89hMPhUFEoIiIiDc45VSzGx8cTHx//i3F9+vShsLCQtLQ0unfvDsDq1avxeDz07t37F9d/9dVXue666+r0Wps3byY2NlaFooiIiDRI51SxWFft27dn6NChTJ48mXnz5lFVVcW0adMYNWqU70rozMxMBg0axOuvv06vXr186+7du5d169bx0UcfndDuBx98QG5uLpdccgkhISGsXLmSRx55hJkzZ9bbtomIiIjUpwZZLAK89dZbTJs2jUGDBmGxWBg+fDhPP/20b3lVVRW7du2ivLy8xnrz58+nWbNmXHnllSe0GRQUxHPPPcedd96JaZq0adOGJ554gsmTJ5/x7REREREJBMP8ufu+yGlXXFxMdHQ0RUVFREVFBTodEREROQ+cSv3RIG+dIyIiIiKnh4pFEREREfFLxaKIiIiI+KViUURERET8UrEoIiIiIn6pWBQRERERv1QsioiIiIhfKhZFRERExC8ViyIiIiLil4pFEREREfFLxaKIiIiI+KViUURERET8UrEoIiIiIn6pWBQRERERv1QsioiIiIhfKhZFRERExC8ViyIiIiLil4pFEREREfFLxaKIiIiI+KViUURERET8UrEoIiIiIn7ZAp2AiMjZoLLKTfrhUvbklrInr4RIu41r2oRw4MABDhw4wOu7wOn20Cgmiit7X0zbxEjaJUbRJMqOYRiBTl9E5IxRsSgi563Ccicfbs1m2eYsNh3Ix2MeX1aRvolb3vs/33TynYuxBIdyIB/SPt7pm29zO2gb4WDZH0Zgs+mQKiINj4ahReS8VOX20PeRFfxh6TY27PcWitGhQfRMiaV08yeUbl3pi7VYLJT8+w1K1s7Huv1jru7clDYJEVgMcFntrN+URlhYGKNHj6agoIC/rdjFxgP5eKpXnyIi5yjDNE0dzepRcXEx0dHRFBUVERUVFeh0RM4rhwrK2fXNVzz66KN88cUXRAy+jeD4FMq2r+GLRU9zcaskDMPg1Vdf5cILL6RTp07ExcX5be/rjWmMnXY3B/alU3UkA4CQpLY0GfM3AC6ICeWh1I5c0a5JvWyfiIg/p1J/qFisZyoWRepfUUUV19z3EhlByeS8dQ/OrF3eBdYgEhrF8t///d/MmTOHmJiYX9V+Xl4eU6dOZfny5VhimxHVezhhF/bGYg8H4Hc9kvnjNe2JDAk6TVskInJyTqX+0DC0iDRIxcXFuN1uPvgui0F/W8tBewqGxUpoSjcaNWrE+PHj2bdnF7m5ubzwwgu/ulAESEhIYMmSJRQWFjIu9TeUrHyWQ8+OIbV9JIYB72w6yNCnvuDL9COnbwNFROqJehbrmXoWRc6swsJCJk2axPLVX9J9yl/JJhaApuEW4vZ9yuP33Ez79u3PaA4ul4slS5YwcuRIvt53lJmLv+NQQQXNY+ysvvsKbFZ9TxeR+nUq9Ycu3RORBiE/P59JkyaxbNkywrsOI/GmZ8nGTrDVwm0D2zB1QCvstqvqJRebzcbIkSMBuKRVI9ofWMKO/fDtri9Y1e2vDBkypF7yEBE5HfT1VkTOaUeOHCE1NZX4+HiWLl2Kx+MBtxNLkJ1eLaL5ZPpl/H7whdht1oDl2Lt7VwpXvkDpgS0MHTqUe++9l8+257L/SFnAchIRqSsNQ9czDUOLnF6NGzemoLQSW0wi7iMHGDVqFC+88AIbM8sZ2DbhrLlh9pYtW7jssssoLi7G3qwjif/zMHERoSy4qRddkmMCnZ6INHAahhaR80ZeXh4Wi4XGjRsDMGTSLNaVNSU0JJgvZv2GJo1iALiiXWQAszxR586dyc7Opn///ny7Ix1H7n7yLRfyuxe/ZN6YHgxomxDoFEVEaqVhaBE5J2RlZXHVVVeRmJjImDFjOFzi4JY30/gP7bGGx9C8aQKlnrP7+29YWBibNm3irlsnkfv2fVTs/4ZKl8mkhZtY8s2hQKcnIlIrFYsiclZLT09n4MCBNGvWjE8++QTTNNmQa3Llk2v5eFsONovBHYMu5KM7LqN1fESg062TuXPn8vHypfTzbCO1axIuj8ld737HvLXp6MwgETnbnN1fw0XkvLVt2zYmTpzIhg0bfPOCQkLpcsvTHA5uSkF5FR2aRjF3RGc6JkUHMNNfZ8iQIQwZMgSPxyQhKoSX1u3j0Y930qpxOFd2TAx0eiIiPupZFJGz0vXXX+8rFENCQpg+fTqlRYX07vFfBFkN7vrNRSybduk5WShWZ7EYjLjQRtGa+ZRuW82Gpa8GOiURkRp0NXQ909XQIrVbtWoVYWFh9OnTB4ClS5cyduxYxtzxB+65cxopjb1DzEdLHeSVOGjftOH8/Xg8Htq3b8/u3bsB+OSTT3QvRhE5rfS4PxE5Zy1dupSUlBQGDx7MuHHjfPO79PsNY+at4SP3xcz9dLdvfqMIe4MqFAEsFgsbN24kMtJ7Bfd1qTdw62tf8un3OQHOTERE5yyKSD1zuVy88cYbvPHGG2zcuJHS0lLfspKSEg7ll/Lcmv28u+kgbo+JYUBIkBW3x8RqOTvumXgmREVF8eWXX9K1a1fsHQbx0c4CVu39ljcn9aZnSlyg0xOR85iKRRGpV0lJSRw+fLjGvAsuuIA/z5lLXuNuDHriCxwuDwCD2iVw99C2tEtsWD2J/nTq1ImFCxfyv2PGEpLSDS7szcTXNrJ4al/aJp5d940UkfOHhqFF5LRLT0/nvvvuo3v37oSHh7N//37fsosvvhjwFohjx45lw4YNHDp0CGfzXsxbm47D5aF7i1gWT+3Dq+N7njeF4o9Gjx7NHbdP48jyv+LI2klxpYtx8zeQWVgR6NRE5DylC1zqmS5wkYZo9erVPPfcc2zevJnMzEwcDkeN5XfeeSdPPPEEADk5OTgtIXx5oJgwu43ruiQBUO50MeX1NMb3TWFQ+7PnMX2BMmnSJG6ZPpN7V+SyN6+UNgkRvDe1DzFhwYFOTUTOQbrA5Scefvhh+vbtS1hYGDExMXVaxzRNHnjgAZo2bUpoaCiDBw9mz549NWLy8/MZPXo0UVFRxMTEMHHixBrnW4k0ZHl5ebz33nvcc889XHXVVSxfvty3bPHixSxZsoR9+/b5CsXg4GA6dOjA7bffzs0330zG0XJe+WIft/9zH5c/+R9mLdnKi2vTfW2EBdt4c1JvBndoct4XigCvvPIK3Tu14/UJvUiMCmFvXik3v5Gmm3aLSL1rkOcsOp1ORowYQZ8+fXj11brds+yvf/0rTz/9NAsXLqRly5bcf//9DBkyhO3btxMSEgJ4h4eys7NZuXIlVVVV3HTTTUyZMoVFixadyc0ROW0qKytJT08nIyODrKws8vLyyMvL4+jRo+Tn5zNy5EjGjh0LwDvvvMPNN9+Mw+HA6XTi8XhqtGW327nuuusASE1NZcmSJbRv357LLruMESNG0LlzZwCeWLmbaR9msydvb431OzeLZkjHRDweE0sDvnDlVCXFhNJ09/tkhvdifK+OKqRFpN416GHo1157jenTp1NYWPizcaZpkpSUxIwZM5g5cyYARUVFNGnShNdee41Ro0axY8cOOnTowMaNG+nRowfgvRfasGHDOHToEElJSXXK6cdu4G/2HiIy8ng38MZNmygrLQMgIsj7A+B0Q7HbRr9+l/pi09K+oaSkBIDwIIg8FlvlgQKnhf79L/PFbt78nW/7w20QeWwEy+WBI5UwYMDlvtitW7dx9OhRAMJsEHUs1m3C4Qro168fNpsVgO3bt5OX571IIcQKMXZvrMeEvAro06cPdru3gR07dpKbm3tCrGlCbgX07t2b0FBvQb5nz14yMzMBsFsh1n583+WUQ48ePYiICAcgPX0fBw8eBCDYAnEhx2Nzy6FLt67ERHtv2Lx//wF++OEHAGwWaFwtNq8CLu7chdjYGAAyMg6yb9++WmMPV0C7jh2Jb9wYgMysLPbs9vZAWw2IDz0ee6QS2lzUjsTEJgBkZWX77qNnGNCkWuzRSkhpcyEXHHsf5eUdZuvWrZSXl+N0VBJSVUx5eTkVFRUcrfBw6eVX0KtXT8D7fnj5lZepqqrCUemgPGcfpaWllJWVUWkJ5X/GjGfmzBkAvPTSyzz51JO+13UdzQS8hwBbXDP6DR7G/z30F5wuDys+W8ULL74MVhuGxUbp1pUYpoewsDAadx1M237D+K9efahwuihzuqlwuilzuvB4TJbceqnvyuXpb3/LPzdnYbUY9EqJY0jHJlzZMZGkmGo7QPzatWsX7du3x7RYadk8mb1792KxNMhBofPSli1bKC4upqSkBLfbXWNZbGwsl156/Nj/6aefUlVVVWs7UVFR9O/f3ze9atUqKipqP881LCyMK664wje9evVqysvLa4212+385je/8U2vW7eO4uLiWmODgoJq3B/0P//5DwUFBbXGWiwWhg0b5ptev379CRe+VTds2DDf+37jxo2+z5TqTNN7NBsyZAg2mw3TNPn222/Jysr2vqbhPfYCuD3gAQYOHEhISAimCdu2bSXjoPcZ7UEWbzx4P1tdHu9nYHhEOKYJO3bsICMjA/B+rv0Y63B74y+55BIiIyMxgT2797D/wAHA+zlsPfbnW+mCSjf06NGdmJhYTEz2799Perr38yciyJsHQIULyl3QuUsXGjdqhGnCwUMH2X3s8yc6GIKtx2MbN2tJ8xYtOF7hmZSWltCt9QW/ahhaxSKwb98+WrduzbfffkvXrl198y+//HK6du3K3//+d+bPn8+MGTNqvPFdLhchISEsXryYG264oda2HQ5HjfO3iouLSU5OJnn6u1jsYbWuk7/6FUo2/hOA4KS2NB3zN7+5F37xJkVfvg1AUHwKSROe9Rtb9PViCtcuBMAWk8gFN7/iN7Z403IKVr0EgCU8huRpb/qNLd2ygqMfPw2AERxK8zsX+40t27GOI8v/6p0wLLS4Z7nf2PI96zm85CHfdPMZSzBstZ+vVXHgO/Le+YNvutkd/8AaWvvVo46sneS8MdM3fcEtC7BFxdca68zbT/aC233TSZNeIKhRcq2xVQXZZL002TedOO4p7Iltao11lxZw6LkxvukmNz5GSHLHWmM9jnIOPjXSN50w4k+Etupea6zpcZMx93rfdHzqfYS17VtrLEDu4zdgd1cRYhiEXjMTs8PlfmOXzZ9MF48TgNn9buIf7Qb4jV35/n1cWJgFhkFaQhuOhkbSM3cPsQ7vFyJq6x37cd5P/61LjGH8/Po/XX4yMb82R3/TdY2pZm5ODvdkez/wLg4JYUm3vqxNaMvEH7762fX88vd6P90fdY35Nfv+p/F1+b+/1/i5nE9yvWJLMP+KacMRWyguLLgNC05ge3klZW4P4fkZxOz9D4VVVRR4TA5cOoGoYDudY2JxGRYcwJr8QkyLFceh7RR98QamaWICieP/js0aRLuICMxjL/l9ifdUJkfmDvI/ecaXR+LYJ7DZw2kTHo55LL89Zd6/H2duOkeWPXY8dszj2MJjaREaiok39oeKCjCg6shB8hY/6IttcuNjBMUkkhRix8TABHIcDsCgqiiH3DfvPh476mGC4lOIDwryxhqQX1WFiYG7LJ/sV2/zxSaM/DP2pLZEW22YgGlAqduDifcYlvn88Xuoxg9/gJAWXQi1WHzb5vCYYIDpdnPwyd/6YhunzibswkuwHoszMfBgYhjeKuqHudeDx1tkN752JuEdBpz4+z0m46nfYR47BsUNvZ3ILv5vfH/oubG4S/MBiB00mage1/uNzXxpCq6CLABi+o8lus9Iv7FZC26nKs97wV9Un5HE9h/rNzbnzZk4MncCENnjOuIGTfEbm/vOH6k8sBmAiC5DaDT09hNiQnGw87Hhv6pYbJDD0CcrJ8d749smTZrUmN+kSRPfspycHBISEmost9lsxMXF+WJqM2fOHP70pz+dMD861IY1JMg3nZ+ff/xcJFeVb6jJMD14KkqIa3T8PmsF+QV4TO+QoOlyVos1cVcU06hRo+OxBQW+4cPqscAJsYUFhbiP/dHVaBcDd0UxcbFxGBXeb5+FDifuH3NwVvJjq8axdmPsdt8fd5HDicsXW0H1Q7W7opjoYDu2Y98Yi50Oqjz+YkuIDgsnyGI9Fuuk6sd8neU1Yj2VpUQGBRF8rCe0xOHEeSzW4/hJrKOMEFcEITbvn0Op04nD7T/W7qog1Ob93ZVVOal0ubw5OMpqxJqOcoKrKggLCgIDyp1VVLiqjrVTWjPWWU5QVTnhwd5iuKKqivJjPQims5JgqxWLYWAxDKwuh7fdYG8OTrebcqc31oJJv5QUYkJDaRwWxr64aI54nARZa++J2vmXvxBieN93c8qbsLqqkmDDJAiTYMzj/zc8tLzrTjC8v5tLnVFEuQ4TZngIx0Oo4SHc8BCKm3DDQ/z40WDx7sPuvu+jxwrR6t9Pf/z/T7+z/lxMbetUn1db3M8t/zWvUVvOv7QdPxfzC8Xi3cCO1atZsHMn3xvBDO41GUIiCU5JYYwr42fXPW05/nT+qewzf7+nk42pa44/mS6w2Nltj2VPcAx7g2No68jnf4p2AVDosXJfkv8vTWWVazhybJQCi5UWbfpQDlT/FAiKbAGAq7IUd7Ucghq3wLDa2F0tNtjuPba7S4/WOCYExSZhCYlgf/V5wd6REk9ZYY1Ya2Q81shGHKo2zxbs/bLsqah5rLGGx2KNbET1fjlrkHe0xvOTY64lJBJrWDT51efZvCMCpstZMzY4FIs9nJJq8wyr9zMBj7tGrGELwhJkp/plcEa1Q1SNWMOCYbFS/SQYo1rEyZyUYfj5v7/Yurbt/U7zy9Gm6fFuT7XPbNPtwmq1Yj12jHa5XHjcHn4c8fG163HjcVYSbA8mOMh77HdUVlLlqvKGmubxWLcLq9tBeHj48dcywP6TU4lOxjnTszhr1iwee+yxn43ZsWMH7dq1803XtWfxyy+/5NJLLyUrK4umTZv65o8cORLDMHjnnXd45JFHWLhwIbt27aqxbkJCAn/605+45ZZbam3bX8+iroYWkZM1YcIEFixYQPSl/0NMv9EYBjx/439x1cVNf3nl81i508U7Gw+yemceO7KLOVLqrLG8RVAJxhfz2LJlCwUFBTS+fhZ2w8VN48ZhtRjYLAZP/O1xDDx48g9iHPwWu91OSGgowR2vJLlZEhPGj8dmMbBaDN5/bzFhIXbiI4K4KM5GeHg4ERER/FAZQmxsLM2Tj41OGGB6TCwWC9GhQXRIOv6ZkPZDPm5PtQ7RY/MNA8Ltthq3lNqWWUSV23NsuVEjNiTIykVNjo+y7M4tweny+JZ72zYwDAiyWmiTEOGLPXCkDOeP7VZrE7z7JKVxuC82q7DCd3/UmrFgMQyS446PpB0uceBwuX35/nSdptHHT1EpLHf68qVavj/GNgoP9rVR6nBRVcu2/bhupN3mOz+6ssqNy2PWmq+Bgd1m8cVWuT14TLNGe9V/L1aL4cuhekl1tp1ffCpXQ58zPYszZsxg/PjxPxvTqlWrX9V2YmIiALm5uTWKxdzcXN+wdGJiInl5eTXWc7lc5Ofn+9avjd1ux263+10uIlJX8+fPx2q18sorr2ANjyOy21X8/u3NxIYHc0mrRr/cwHnCNE0OlzhIiPKecGy1GDz+6S7KnMfPCbwgJoTsnd9S8MN2NmbtpGL38SH9o8sfo2XLljyUOtc374/XzK/z6w/vfucpb0P3FnV/ak+nC6LrHFu9cPwl1YvBX3Iy5yDHR9b9M/FkbhUVYbdBHZsOCbLWuV1/IzO1OdsKxNPlnCkW4+PjiY+v/byyU9WyZUsSExNZtWqVrzgsLi5m/fr1vh7DPn36UFhYSFpaGt27e88XW716NR6Ph969e5+RvEREfurll1/GarWya/cOLuo4nk+/z2Xywk28O7VPg3tm9slwujx8te8on23P5bMduYQEWVk943IMw8BuszKhb3P27vyeqcN/Q5uECMLtNuLiRlBYUIDVaqVFixb07duX3/3ud1x99dXYbOfMx6PIGdcg/xoyMjLIz88nIyMDt9vN5s2bAWjTpg0REd4u9nbt2jFnzhxuuOEGDMNg+vTp/OUvf+HCCy/03TonKSmJ1NRUANq3b8/QoUOZPHky8+bNo6qqimnTpjFq1Kg6XwktInI6zJs3D/AOpY19dQMbDuQzbv4Gltzal2axtV8411AdzC/nyZW7WbE9l1KHyzc/LNjK8pVreXfhy3z++edkH7tAaHyfrwlP9n7Bf/TRR3E6ndx66626wlzkZzTIYvGBBx5g4cKFvulu3boB8PnnnzNgwADAezuKoqIiX8w999xDWVkZU6ZMobCwkH79+vHJJ5/47rEI8NZbbzFt2jQGDRqExWJh+PDhPP300/WzUSIiPxESZGXOsBYMeOg7Dh+pItjoE+iU6tU7GzN4YNn3vnPl4iPtpAQVs+3TRez98mNSH6p5OxjDMPj3v//tGw2aMsX/1aUictw5c4FLQ6HH/YnI6fToo4/yh788hukoJzG+EVu2bKHxsfuANnQb9ucz8sWv6Nu6ETOuvIhuybHMmHEXTz31lC8mKiqKnj17Mnr0aEaPHk1wsB6XKOcnPe5PROQ8NWvWLKbfPAHT5SQ7O5uUlBRe+WSj7+rYhmT/kTI+3JLtm+7VMo5r7Dtp9cOHdG8Rh8VicOutt9KpUyfuu+8+Dh48SFFREZ999hk33XSTCkWRX0k9i/VMPYsiciY8//zzTJs2jag+vyPmsv+lSxM7C6b0Jy783C+Qyp0unvt8Ly+v24/FAqtmDOBoxh6uueYaDh48iGEYpKen07Jly0CnKnLWUs+iiMh57tZbb+Xjjz/GlbcPj7OC73IdXP33dWzLLPrllc9Spmny0dZsBv9tLc99no7T7aFnSiy///10unbt6nvcZ7du3QgN1eMjRc4U9SzWM/UsisiZtHLlSq4ZPZnGN/yBoNgk7DYLj9xwMcO7Nwt0aidlb14J/7d8O//eewSAZrGhDE0o5a+//19KSrzPCQkLC+Oll15i9OjRgUxV5JxwKvWHisV6pmJRRM60559/nuat27IkO4rVO70PExjbpwV/vLoDwbazf0CpuLKKPo+soszpJthmYerlrelmz+OKyy/zxaSmpvKPf/yjxh0rRMQ/FYvnEBWLIlJfPB6Tv6/aw99X7cFiwOKpfeneIjbQadXJ45/uYnt2Ef93bSeaN/LeO7JNmzaUlpaydOlS+vQ5v24TJHKqdM6iiIicwGIx8Gz5gLz3/ky78u/P2kJxZ04xo176im8zCgDvgxW+eGEWb0y+lG//vdIXt3nzZrKyslQoitQz9SzWM/Usikh96tKlC1u2bAHg5ptvZt68eWzPKuaVf+/jj1d3COjV0kdKHTy7ei9vfP0Dbo/JhdGQ+4/ZbN261RfTuXNnvvvuu4DlKNJQnEr90SCf4CIiIl5paWlcfPHF7Ny5kxdffBGX282RruPZfLCQNbsO88A1Hbi+axKGYdRbTocKynl53T7e3njQ9/QV1/6NfP7x87hLDgNgt9sZOXIkTzzxRL3lJSK1U89iPVPPoojUN5fLRZcuXdi+fTsAv715JiXtrmVnjveq4ssviufhGzrVy3Oln/psN8+u3ovL4/3o6dQ0glVPTafywGYAmjRpwowZM5gxY4ae1yxyGumcRRER8ctms7F161Y6duwIwHsvPs7uZycx88qLCLZaWLv7MAMfX8OU1zeR9kPBaX/96n0S5XkHcXlM+raKY9Gk3nxwR38Gtm9K165dWbVqFTk5Odx9990qFEXOIhqGFhE5D1gsFrZs2ULPnj355ptviIoIY9oVFzK0U1P+uHQrX+/PZ8X2XH5b7X6MlVVugq0WLJaTG6I2TZNDBRVsyyziHxsP0qNpMLv+9TLvv/8+uYePEJzQijaTR9G3jfdClY8++ui0bquInF4qFkVEzhMWi4W0tDT+9a9/kZSUBECbhAgu92xm+cK/8V+pk+iS0NsX//zne3n/m0xSuyXRvUUsF8SE0Sw2lHB7zY8O0zRZtjmLbZlFfJ9VzPbsYooqqnzLV63PJuulFwBvD2NkVT7Nmzc/8xssIqeFzlmsZzpnUUTONikpKfzwww8AGIZB//79ueuuu5i3P4bt2SUnxMeGBdGiUThLb+3ruzCm76OryCqs9MVYDZPy7HQch7ZTkrYcS3k+vXr1Yvbs2QwbNqx+NkxEfHRT7nOIikUROdvk5OQwbdo0li1bhsvl8s23BNlp2e86hkyaxf7DZWQWVvh6DIOrSuhXsIL8/Hw2bdpEWesrCI9pzBP330nHpChaNw4npXkz2rZty/Tp07n++ut1HqJIAKlYPIeoWBSRs1VlZSWzZ8/m9ddfJz8/H/Dewqay8niPYUhkLJ7QGCzBoTgyd9RY32KxUFVVpaJQ5Cyk+yyKiMgpCwkJ4cknn+TJJ5+ktLSUN954g/3799eI6dXtYrKysqisLMYZH4/NZqNz585cffXVjB49WoWiSAOknsV6pp5FERERqW+6z6KIiIiInBEqFkVERETELxWLIiIiIuKXikURERER8UvFooiIiIj4pWJRRERERPxSsSgiIiIifqlYFBERERG/VCyKiIiIiF8qFkVERETELxWLIiIiIuKXikURERER8UvFooiIiIj4pWJRRERERPxSsSgiIiIifqlYFBERERG/VCyKiIiIiF+2QCdwvjFNE4Di4uIAZyIiIiLnix/rjh/rkJOhYrGeHT16FIDk5OQAZyIiIiLnm6NHjxIdHX1S66hYrGdxcXEAZGRknPQvS05OcXExycnJHDx4kKioqECn02BpP9cf7ev6o31df7Sv60dRURHNmzf31SEnQ8ViPbNYvKeJRkdH64+inkRFRWlf1wPt5/qjfV1/tK/rj/Z1/fixDjmpdc5AHiIiIiLSQKhYFBERERG/VCzWM7vdzoMPPojdbg90Kg2e9nX90H6uP9rX9Uf7uv5oX9ePU9nPhvlrrqEWERERkfOCehZFRERExC8ViyIiIiLil4pFEREREfFLxaKIiIiI+KViMYCuu+46mjdvTkhICE2bNmXMmDFkZWUFOq0G58CBA0ycOJGWLVsSGhpK69atefDBB3E6nYFOrUF6+OGH6du3L2FhYcTExAQ6nQblueeeIyUlhZCQEHr37s2GDRsCnVKDs27dOq699lqSkpIwDIN//vOfgU6pQZozZw49e/YkMjKShIQEUlNT2bVrV6DTapBeeOEFOnfu7LvpeZ8+ffj4449Pqg0ViwE0cOBA3n33XXbt2sX7779Peno6v/3tbwOdVoOzc+dOPB4PL774It9//z1PPvkk8+bN47777gt0ag2S0+lkxIgR3HLLLYFOpUF55513uOuuu3jwwQf55ptv6NKlC0OGDCEvLy/QqTUoZWVldOnSheeeey7QqTRoa9eu5bbbbuPrr79m5cqVVFVVceWVV1JWVhbo1BqcZs2a8eijj5KWlsamTZu44ooruP766/n+++/r3IZunXMWWb58OampqTgcDoKCggKdToM2d+5cXnjhBfbt2xfoVBqs1157jenTp1NYWBjoVBqE3r1707NnT5599lkAPB4PycnJ3H777cyaNSvA2TVMhmGwdOlSUlNTA51Kg3f48GESEhJYu3Yt/fv3D3Q6DV5cXBxz585l4sSJdYpXz+JZIj8/n7feeou+ffuqUKwHRUVFv+ph6iKB4HQ6SUtLY/Dgwb55FouFwYMH89VXXwUwM5HTo6ioCEDH5TPM7Xbz9ttvU1ZWRp8+feq8norFALv33nsJDw+nUaNGZGRksGzZskCn1ODt3buXZ555hptvvjnQqYjUyZEjR3C73TRp0qTG/CZNmpCTkxOgrEROD4/Hw/Tp07n00kvp1KlToNNpkLZu3UpERAR2u52pU6eydOlSOnToUOf1VSyeZrNmzcIwjJ/92blzpy/+7rvv5ttvv2XFihVYrVbGjh2Lzgyom5Pd1wCZmZkMHTqUESNGMHny5ABlfu75NftaRKQubrvtNrZt28bbb78d6FQarLZt27J582bWr1/PLbfcwrhx49i+fXud19c5i6fZ4cOHOXr06M/GtGrViuDg4BPmHzp0iOTkZL788suT6h4+X53svs7KymLAgAFccsklvPbaa1gs+q5UV7/mfa1zFk8fp9NJWFgY7733Xo3z58aNG0dhYaFGJM4QnbN45k2bNo1ly5axbt06WrZsGeh0zhuDBw+mdevWvPjii3WKt53hfM478fHxxMfH/6p1PR4PAA6H43Sm1GCdzL7OzMxk4MCBdO/enQULFqhQPEmn8r6WUxccHEz37t1ZtWqVr3DxeDysWrWKadOmBTY5kV/BNE1uv/12li5dypo1a1Qo1jOPx3NStYaKxQBZv349GzdupF+/fsTGxpKens79999P69at1at4mmVmZjJgwABatGjB448/zuHDh33LEhMTA5hZw5SRkUF+fj4ZGRm43W42b94MQJs2bYiIiAhscuewu+66i3HjxtGjRw969erFU089RVlZGTfddFOgU2tQSktL2bt3r296//79bN68mbi4OJo3bx7AzBqW2267jUWLFrFs2TIiIyN9595GR0cTGhoa4OwaltmzZ3PVVVfRvHlzSkpKWLRoEWvWrOHTTz+teyOmBMSWLVvMgQMHmnFxcabdbjdTUlLMqVOnmocOHQp0ag3OggULTKDWHzn9xo0bV+u+/vzzzwOd2jnvmWeeMZs3b24GBwebvXr1Mr/++utAp9TgfP7557W+f8eNGxfo1BoUf8fkBQsWBDq1BmfChAlmixYtzODgYDM+Pt4cNGiQuWLFipNqQ+csioiIiIhfOnFLRERERPxSsSgiIiIifqlYFBERERG/VCyKiIiIiF8qFkVERETELxWLIiIiIuKXikURERER8UvFooiIiIj4pWJRRERERPxSsSgico5YuHAh/fr1A2DAgAEYhoFhGNjtdi644AKuvfZalixZEuAsRaShUbEoInKOWLZsGdddd51vevLkyWRnZ5Oens77779Phw4dGDVqFFOmTAlgliLS0KhYFBEJsNdff51GjRrhcDhqzE9NTWXMmDEAVFZWsmLFihrFYlhYGImJiTRr1oxLLrmExx57jBdffJGXX36Zzz77rF63QUQaLhWLIiIBNmLECNxuN8uXL/fNy8vL48MPP2TChAkArFq1igsuuIB27dr9bFvjxo0jNjZWw9EictqoWBQRCbDQ0FBuvPFGFixY4Jv35ptv0rx5cwYMGACcOATtj8Vi4aKLLuLAgQNnKFsROd+oWBQROQtMnjyZFStWkJmZCcBrr73G+PHjMQwD0zT54IMP6lQsApimiWEYZzJdETmPqFgUETkLdOvWjS5duvD666+TlpbG999/z/jx4wHYsGEDLpeLvn37/mI7brebPXv20LJlyzOcsYicL2yBTkBERLwmTZrEU089RWZmJoMHDyY5ORnwDkFfffXVWK3WX2xj4cKFFBQUMHz48DOdroicJ9SzKCJylrjxxhs5dOgQL7/8su/CFoDly5fXOgRdXl5OTk4Ohw4d4uuvv+bee+9l6tSp3HLLLQwcOLA+UxeRBswwTdMMdBIiIuI1duxYPvzwQ7KysrDb7aSnp9OxY0eOHj1KeHi4L27AgAGsXbsWgODgYBo1akT37t2ZMGECN9xwQ6DSF5EGSMPQIiJnkczMTEaPHo3dbge8Q9BXXHFFjUIRYM2aNQHITkTORyoWRUTOAgUFBaxZs4Y1a9bw/PPP++Y3a9aM2bNnBzAzETnfaRhaROQskJKSQkFBAffffz8zZ84MdDoiIj4qFkVERETEL10NLSIiIiJ+qVgUEREREb9ULIqIiIiIXyoWRURERMQvFYsiIiIi4peKRRERERHxS8WiiIiIiPilYlFERERE/Pp/OPi7aehSaDQAAAAASUVORK5CYII=" }, "metadata": {}, "output_type": "display_data", "jetTransient": { "display_id": null } }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "♬ THE END ♬\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data", "jetTransient": { "display_id": null } } ], "execution_count": 3 } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: examples/advanced_projects/efficient_bounce_back_obstacle/01_script_cylinder_simulation.py ================================================ """ This script contains the code to run the efficient bounce back project with the circular cylinder obstacle. It is supposed to be invoked with parameters (arguments)! Examples are given in the respective .ipynb file. Content: - ARGUMENT PARSING: read all command line arguments or apply defaults - PARAMETER PROCESSING: all relevant parameters are processed, missing stuff ist calculated - INITIALIZATION AND ASSEMBLING OF SOLVER COMPONENTS: stencil, context etc. - SIMULATION CALL: running the simulation - DATA POST-PROCESSING AND PLOTTING: does what it's name suggests... Work-In-Progress NOTE: In some places there are TODOs and placeholders for future functionality! This script still works, they are just future improvements! """ #################### # IMPORT import numpy as np import torch import sys import os import psutil import shutil import resource import matplotlib.pyplot as plt import time import datetime from pyevtk.hl import imageToVTK from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter # LETTUCE RELATED import lettuce as lt from examples.advanced_projects.efficient_bounce_back_obstacle.flow.obstacle_cylinder import ObstacleCylinder from examples.advanced_projects.efficient_bounce_back_obstacle.simulation.ebb_simulation import EbbSimulation from examples.advanced_projects.efficient_bounce_back_obstacle.reporter.reporter_ProfileReporter import ProfileReporter from examples.advanced_projects.efficient_bounce_back_obstacle.reporter.reporter_advanced_vtk_reporter import VTKReporterAdvanced, VTKsliceReporter from examples.advanced_projects.efficient_bounce_back_obstacle.reporter.observables_force_coefficients import DragCoefficient, LiftCoefficient # AUX. CODE from examples.advanced_projects.efficient_bounce_back_obstacle.auxiliary_code.helperCode import Logger from examples.advanced_projects.efficient_bounce_back_obstacle.auxiliary_code.data_processing_and_plotting import plot_force_coefficient, analyze_periodic_timeseries, draw_circular_mask, ProfilePlotter #################### # ARGUMENT PARSING: this script is supposed to be called with arguments, # detailing all simulation- and system-parameters parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter) # context and I/O parser.add_argument("--name", default="cylinder_lowRe", help="name of the simulation, appears in output directory name") parser.add_argument("--default_device", default="cuda", type=str, help="run on cuda or cpu") parser.add_argument("--float_dtype", default="float64", choices=["float32", "float64", "single", "double", "half"], help="data type for floating point calculations in torch") parser.add_argument("--t_sim_max", default=(72*60*60), type=float, help="max. wall time [s] to run the simulation(); default is 72 h. simulation will stops at 0.99*t_max_sim; IMPORTANT: this whole script may take longer to execute, depending on I/O etc.") parser.add_argument("--text_output_only", action='store_true', help="if you don't want pngs etc. to open, please use this flag; data is still saved to files") parser.add_argument("--no_data", action='store_true', help="set, if you want no directories created and no date saved. Only direct output") parser.add_argument("--outdir", default=os.getcwd(), type=str, help="directory to save output files to; default is CWD") parser.add_argument("--outdir_data", default=None, type=str, help="directory to save large/many files to; if not set, everything os saved to outdir") # flow physics and geometry parser.add_argument("--reynolds_number", default=200, type=float, help="Reynolds number") parser.add_argument("--mach_number", default=0.1, type=float, help="Mach number (should stay < 0.3, and < 0.1 for highest accuracy. low Ma can lead to instability because of round of errors ") parser.add_argument("--char_velocity_pu", default=1, type=float, help="characteristic velocity of the flow in physical units (PU)") #TODO (optional): add char_density_PU, char_pressure, viscosity_pu as parameters/arguments parser.add_argument("--char_length_lu", default=1, type=int, help="characteristic length of the flow in lattice units. Number of gridpoints per diameter for a circular cylinder") parser.add_argument("--char_length_pu", default=1, type=float, help="characteristic length of the flow in physical units. Diameter of the cylinder in PU") parser.add_argument("--domain_length_x_in_d", default=None, type=float, help="domain length in x-direction (direction of flow) in number of cylinder-diameters") parser.add_argument("--domain_height_y_in_d", default=None, type=float,help="domain height in y-direction (orthogonal to flow and cylinder axis) in number of cylinder-diameters") parser.add_argument("--domain_width_z_in_d", default=None, type=float,help="domain width in z-direction (orthogonal to flow, parallel to cylinder axis) in number of cylinder-diameters; IMPORTANT: if not set, 2D-Simulation is performed") parser.add_argument("--perturb_init", action='store_true', help="perturb initial velocity profile to trigger vortex shedding") parser.add_argument("--u_init_condition", default=0, type=int, help="initial velocity field: # 0: uniform u=0, # 1: uniform u=1, # 2: parabolic, amplitude u_char_lu (similar to poiseuille-flow)") parser.add_argument("--lateral_walls", default='periodic', help="OPTIONS: 'periodic' or 'bounceback'; add lateral walls, converting the flow to a cylinder in a channel. The velocity profile will be adjusted to be parabolic!") # LBM solver settings parser.add_argument("--n_steps", default=100000, type=int, help="number of steps to simulate, overwritten by t_target, if t_target is >0") parser.add_argument("--t_target", default=0, type=float, help="time in PU to simulate, overwrites n_steps if t_target > 0") parser.add_argument("--collision", default="bgk", type=str, choices=["kbc", "bgk", "reg", 'reg', "bgk_reg", 'kbc', 'bgk', 'bgk_reg'], help="collision operator (bgk, kbc, reg)") parser.add_argument("--stencil", default="D3Q27", choices=['D2Q9', 'D3Q15', 'D3Q19', 'D3Q27'], help="stencil (D2Q9, D3Q27, D3Q19, D3Q15), IMPORTANT: should match number of dimensions inferred from domain_width! Otherwise default D2Q9 or D3Q27 will be chosen for 2D and 3D respectively") parser.add_argument("--eqlm", action="store_true", help="use Equilibrium LessMemory to save ~20% on GPU VRAM, sacrificing ~2% performance") #TODO: implement EQLM-usage -> how to use EQLM in lettuce 2025? (no documentation/example (as of 16.3.26)) parser.add_argument("--bbbc_type", default='fwbb', help="bounce back algorithm (fwbb, hwbb, ibb1) for the solid obstacle") # reporter and observable settings parser.add_argument("--periodic_region_start_relative", default=None, type=float, help="RELATIVE (0.0-1.0) assumed start of the periodic region for measurement of temporal and spacial averaging of observables (drag, lift , velocity profiles...)") parser.add_argument("--periodic_region_start_pu", default=None, type=float, help="ABSOLUTE PU-time; assumed start of the periodic region for measurement of temporal and spacial averaging of observables (drag, lift , velocity profiles...)") parser.add_argument("--periodic_region_start_lu", default=None, type=int, help="ABSOLUTE LU-steps; assumed start of the periodic region for measurement of temporal and spacial averaging of observables (drag, lift , velocity profiles...)") parser.add_argument("--calc_u_profiles", action='store_true', help="calculate average velocity and reynolds stress profiles similar to [Di Ilio et al. 2018] and output plots and time-averages data for plots") parser.add_argument("--show_u_profiles", action='store_true', help="plot average velocity and reynolds stress profiles") parser.add_argument("--output_u_profiles_timeseries", default=False, help="output average velocity and reynolds stress profiles over time (full timeseries)") parser.add_argument("--profile_reference_path", default="../profile_reference_data/", type=str, help="path to reference profiles from [Di Ilio et al. 2018]") parser.add_argument("--vtk_full_basic", action='store_true', help="output vtk files of full domain each interval steps") parser.add_argument("--vtk_full_basic_interval", type=int, help="step interval for output of basic full vtk files") parser.add_argument("--vtk_3D", action='store_true', help="output vtk files of full domain each interval steps between start and end (if start and end are defined!)") parser.add_argument("--vtk_3D_fps", type=float) parser.add_argument("--vtk_3D_step_interval", type=float) parser.add_argument("--vtk_3D_t_interval", type=float) parser.add_argument("--vtk_3D_step_start", type=int) parser.add_argument("--vtk_3D_step_end", type=int) parser.add_argument("--vtk_3D_t_start", type=float) parser.add_argument("--vtk_3D_t_end", type=float) parser.add_argument("--vtk_slice2D", action='store_true', help="toggle vtk-output of 2D slice of WHOLE DOMAIN (!) to outdir_data, if set True (1)") parser.add_argument("--vtk_slice2D_fps", type=float) parser.add_argument("--vtk_slice2D_step_interval", type=float) parser.add_argument("--vtk_slice2D_t_interval", type=float) parser.add_argument("--vtk_slice2D_step_start", type=int) parser.add_argument("--vtk_slice2D_step_end", type=int) parser.add_argument("--vtk_slice2D_t_start", type=float) parser.add_argument("--vtk_slice2D_t_end", type=float) ## FUTURE IMPROVEMENTS (which need arguments/parameters) #TODO (optional): add NAN/failure reporter (on/off, interval,...) #TODO (optional): add watchdog/progress reporter (on/off, interval,...) #TODO (optional): add highMa reporter (on/off, interval,...) #TODO (optional): implement/add 2D-mp4-animation-reporter... (fps_video, number of frames OR fps_pu) # Checkpointing # TODO (optional): add checkpointing-utilities (read, write): ''' Checkpointing is NOT implemented in lettuce 2025 at the moment. It was present in lettuce 0.2.3 Relevant parameters and functionalities would/could include: - checkpoint IN path (where to READ a checkpoint from) - checkpoint OUT path (where to WRITE a checkpoint to) - checkpoint i_start, or t_start (which timestep (in LU or PU) does the checkpoint correspond to. - should stuff continue on, or start from 0? (observables, time (step number), statistics (mean, max, min of observables etc.) ''' # for debugging purposes: parser.add_argument("--count_tensors", action="store_true", help="(for debugging: count all tensors, their sizes and memory consumption on the GPU, to find memory leaks") ########################################################### # put arguments in dictionary print("SCRIPT: Writing arguments to dictionary...") args = vars(parser.parse_args()) # print all arguments print(f"SCRIPT: Input arguments are: \n{args}\n") ########################################################### # CREATE timestamp, sim-ID, outdir and outdir_data print("SCRIPT: Creating timestamp, simulation ID and creating output directory...") name = args["name"] outdir = args["outdir"] outdir_data = args["outdir_data"] default_device = args["default_device"] float_dtype = args["float_dtype"] t_sim_max = args["t_sim_max"] text_output_only = args["text_output_only"] no_data_flag = args["no_data"] timestamp = datetime.datetime.now().strftime("%y%m%d_%H%M%S") sim_id = str(timestamp) + "-" + name os.makedirs(outdir+"/"+sim_id) # create output dir print(f"outdir/simID = {outdir}/{sim_id}") # save data to regular outdir, if data-dir is not specified if outdir_data is None: outdir_data = outdir # adding individual sim-ID to outdir path to get individual DIR per simulation outdir = outdir+"/"+sim_id outdir_data = outdir_data+"/"+sim_id if not os.path.exists(outdir_data): # create output dir for large/many files, if specified os.makedirs(outdir_data) print(f"outdir_DATA/simID = {outdir}/{sim_id}") # save input arguments/parameters to file in outdir: print(f"SCRIPT: Writing input parameters to file: {outdir}/input_parameters.txt") output_file = open(outdir+"/input_parameters.txt", "a") for key in args: output_file.write('{:30s} {:30s}\n'.format(str(key), str(args[key]))) output_file.close() ### SAVE SCRIPT: save this script to outdir print(f"SCRIPT: Saving simulation script to outdir...") temp_script_name = sim_id + "_" + os.path.basename(__file__) shutil.copy(__file__, outdir+"/"+temp_script_name) print(f"-> Saved simulation script to '{str(outdir+'/'+temp_script_name)}'") # START LOGGER -> get all terminal output into file print(f"SCRIPT: Starting stdout-LOGGER (see outdir for log file)") old_stdout = sys.stdout sys.stdout = Logger(outdir) ########################################################### # PROCESS AND SET PARAMETERS print(f"SCRIPT: Processing parameters...") # calculate domain and obstacle geometry and infer dimensions (2D, 3D) if args["domain_height_y_in_d"] is None or args["domain_height_y_in_d"] <= 1: domain_height_y_in_d = 3 else: domain_height_y_in_d = args["domain_height_y_in_d"] if args["domain_length_x_in_d"] is None or args["domain_length_x_in_d"] <=1 : # D/X = domain length in X- / flow-direction domain_length_x_in_d = 2 * domain_height_y_in_d else: domain_length_x_in_d = args["domain_length_x_in_d"] if args["domain_width_z_in_d"] is None: # will be 2D dims = 2 domain_width_z_in_d = None else: # will be 3D dims = 3 if args["domain_width_z_in_d"] <= 1/args["char_length_lu"] : # if less than 1 lattice node... ->set to 1 lattice node domain_width_z_in_d = 1/args["char_length_lu"] print("(!) domain_width_z_in_d is less than 1 lattice node: " "setting domain_width_z_in_d to 1 lattice node") else: domain_width_z_in_d = args["domain_width_z_in_d"] # CORRECT GPD for symmetry: ''' if D/Y (height of the domain in number of cylinder diameters) is even, the resulting GPD (gridpoints per diameter) can't be odd for a symmetrical cylinder and symmetrical domain! - if D/Y is even, GPD will be corrected to be even for symmetrical cylinder => use odd D/Y value to use an odd GPD value! ''' gpd_correction = False if domain_height_y_in_d % 2 == 0 and args["char_length_lu"] % 2 != 0: gpd_correction = True # gpd will be corrected gpd_setup = args["char_length_lu"] # store old gpd for output char_length_lu = int(gpd_setup / 2) * 2 # make gpd even print("(!) domain_height_y_ind_d (DpY) is even, " "gridpoints per diameter (GPD, char_length_lu) will be set to" + str(char_length_lu) + ". Use odd domain_height_Y_in_D (DpY) " "to enable use of odd GPD (char_length_lu)!") else: char_length_lu = args["char_length_lu"] char_length_pu = args["char_length_pu"] char_velocity_pu = args["char_velocity_pu"] reynolds_number = args["reynolds_number"] mach_number = args["mach_number"] perturb_init = args["perturb_init"] u_init_condition = args["u_init_condition"] # calculate lu-domain-resolution, # ...total number of gridpoints and check correct stencil if dims == 2: resolution = [int(domain_length_x_in_d * char_length_lu), int(domain_height_y_in_d * char_length_lu)] number_of_gridpoints = char_length_lu ** 2 * domain_length_x_in_d * domain_height_y_in_d if args["stencil"] == "D2Q9": stencil = lt.D2Q9() else: print("(!) WARNING: wrong stencil choice for 2D simulation, D2Q9 is used") stencil= lt.D2Q9() else: resolution = [int(domain_length_x_in_d * char_length_lu), int(domain_height_y_in_d * char_length_lu), int(domain_width_z_in_d * char_length_lu)] number_of_gridpoints = (char_length_lu ** 3 * domain_length_x_in_d * domain_height_y_in_d * domain_width_z_in_d) if args["stencil"] == "D3Q15": stencil = lt.D3Q15() elif args["stencil"] == "D3Q19": stencil = lt.D3Q19() elif args["stencil"] == "D3Q27": stencil = lt.D3Q27() else: print("(!) WARNING: wrong stencil choice for 3D simulation, D3Q27 is used") stencil = lt.D3Q27() # read dtype if float_dtype == "float32" or float_dtype == "single": float_dtype = torch.float32 elif float_dtype == "double" or float_dtype == "float64": float_dtype = torch.float64 elif float_dtype == "half" or float_dtype == "float16": float_dtype = torch.float16 # OVERWRITE n_steps, if t_target is given n_steps = args["n_steps"] t_target = args["t_target"] if args["t_target"] > 0: n_steps = int(t_target * (char_length_lu / char_length_pu) * (char_velocity_pu / (mach_number * 1 / np.sqrt(3)))) else: t_target = n_steps / (char_length_lu/char_length_pu * char_velocity_pu/(mach_number*1/np.sqrt(3))) # calculate relative starting point for observables that are calculated over a # ...temporally converged or periodic region: ''' - the temporal beginning of the periodic region depends on the time the flow needs to converge into - for example - a von Karman vortex street. => This time is also dependent on the domain size! EXAMPLE: For an Re=100 in a reasonable domain size, the flow needs t_PU = 75-100 seconds to reach it's periodic state IMPORTANT: the start of the periodic region of the time signal depends on: - (!) Domain dimensions: larger domain -> longer settling period - resolution and numerical parameters: dampening, physical accuracy etc. - (!) Reynolds number ''' #TODO (optional): change periodic_start parameter to abs. step-number, instead of relative start: # - ease of use for reporters and processing # - no round-off error in processing LU-parameter, if set if args["periodic_region_start_lu"] is not None and args["periodic_region_start_lu"] >= 0: periodic_start = args["periodic_region_start_lu"] / n_steps elif args["periodic_region_start_pu"] is not None and args["periodic_region_start_pu"] >= 0: periodic_start = args["periodic_region_start_pu"] / t_target elif (args["periodic_region_start_relative"] is not None and 0 <= args["periodic_region_start_relative"] < 1.0): periodic_start = args["periodic_region_start_relative"] else: if args["reynolds_number"] > 1000: periodic_start = 0.4 else: periodic_start = 0.9 # check EQLM parameter if args["eqlm"]: # TODO: use EQLM ( QuadraticEquilibriumLessMemory() ) how is this used in new lettuce? print("SCRIPT: EQLM-parameter was set, but usage of EQLM is not implemented yet!" "...using default equilibrium") # print temporal parameters: steps, T_PU print(f"\n(INFO) parameters set for simulation of {n_steps} steps, " f"representing {t_target:.3f} seconds [PU]!\n") ########################################### # INITIALIZE SOLVER COMPONENTS print("SCRIPT: initializing solver components...") # CONTEXT print("-> initializing context...") context = lt.Context(device=default_device, dtype=float_dtype,use_native=False) # FLOW print("-> initializing flow...") flow = ObstacleCylinder(context=context, resolution=resolution, stencil=stencil, reynolds_number=reynolds_number, mach_number=mach_number, char_length_pu=char_length_pu, char_length_lu=char_length_lu, char_velocity_pu=char_velocity_pu, bc_type=str(args["bbbc_type"]), calc_force_coefficients=True, lateral_walls=args["lateral_walls"]) # COLLISION OPERATOR print("-> initializing collision operator...") collision_operator = None if args["collision"].casefold() == "reg" or args["collision"].casefold() == "bgk_reg": collision_operator = lt.RegularizedCollision(tau=flow.units.relaxation_parameter_lu) elif args["collision"].casefold() == "kbc": # if dims == 2: # collision_operator = lt.KBCCollision2D(tau=flow.units.relaxation_parameter_lu) # else: # collision_operator = lt.KBCCollision3D(tau=flow.units.relaxation_parameter_lu) collision_operator = lt.KBCCollision(tau=flow.units.relaxation_parameter_lu) else: # default to bgk collision_operator = lt.BGKCollision(tau=flow.units.relaxation_parameter_lu) # SIMULATION print("\nSCRIPT: initializing simulation object...") simulation = EbbSimulation(flow, collision_operator,reporter=[]) # REPORTERS: initialize and append to simulation.reporter print("-> initializing reporters...") # DRAG and LIFT (Force Coefficients) and respective reporters: cylinder_cross_sectional_area = flow.char_length_pu if dims==2 else ( flow.char_length_pu*domain_width_z_in_d) DragObservable = DragCoefficient(flow, simulation.post_streaming_boundaries[-1], solid_mask=simulation.post_streaming_boundaries[-1].mask, area_pu=cylinder_cross_sectional_area) DragReporter = lt.ObservableReporter(DragObservable, interval=1, out=None) simulation.reporter.append(DragReporter) LiftObservable = LiftCoefficient(flow, simulation.post_streaming_boundaries[-1], solid_mask=simulation.post_streaming_boundaries[-1].mask, area_pu=cylinder_cross_sectional_area) LiftReporter = lt.ObservableReporter(LiftObservable, interval=1, out=None) simulation.reporter.append(LiftReporter) # VELOCITY- and REYNOLDS-STRESS profiles over space and time: if args["calc_u_profiles"]: # define positions position_1 = flow.x_pos_lu - 0.5 + 1.06 * flow.radius_lu * 2 position_2 = flow.x_pos_lu - 0.5 + 1.54 * flow.radius_lu * 2 position_3 = flow.x_pos_lu - 0.5 + 2.02 * flow.radius_lu * 2 print("ProfileReporters at x-positions:" + " p1: " + str(position_1) + " p2: " + str( position_2) + " p3: " + str(position_3)) # create and append profileReporters ProfileReporter1 = ProfileReporter(flow=flow, interval=1, position_lu=position_1, i_start= int(n_steps * periodic_start)) simulation.reporter.append(ProfileReporter1) ProfileReporter2 = ProfileReporter(flow=flow, interval=1, position_lu=position_2, i_start= int(n_steps * periodic_start)) simulation.reporter.append(ProfileReporter2) ProfileReporter3 = ProfileReporter(flow=flow, interval=1, position_lu=position_3, i_start= int(n_steps * periodic_start)) simulation.reporter.append(ProfileReporter3) # NAN REPORTER (if nan detected -> stop simulation) # TODO (optional): add NaN reporter (see PR for that topic) # - NEEDS breakable simulation (!) # HighMa Reporter (if Ma>0.3 detected -> report and/or stop simulation) # TODO (optional): HighMa Reporter # Watchdog/Progress-Reporter # TODO (optional): Progress-Reporter # VTK Reporter: for visualization etc. # BASIC lettuce vtk output (see also: advanced vtk reporter below) if args["vtk_full_basic"]: vtk_reporter = lt.VTKReporter(interval=args["vtk_full_basic_interval"], filename_base=outdir_data+"/vtk/out") # export obstacle mask for visualization mask_dict = dict() if dims ==2: mask_dict["mask"] = flow.obstacle_mask[...,None].astype(int) else: mask_dict["mask"] = flow.obstacle_mask.astype(int) imageToVTK( path=outdir_data+"/vtk/obstacle_point", pointData=mask_dict, ) imageToVTK( path=outdir_data+"/vtk/obstacle_cell", cellData=mask_dict, ) simulation.reporter.append(vtk_reporter) # ADVANCED vtk output # 3D if args["vtk_3D"] and dims == 3: if args["vtk_3D_t_start"] is not None: #print("(vtk) overwriting vtk_step_start with {}, because vtk_t_start = {}") vtk_3d_i_start = int(round(flow.units.convert_time_to_lu(args["vtk_3D_t_start"]))) elif args["vtk_3D_step_start"] is not None: vtk_3d_i_start = int(args["vtk_3D_step_start"]) else: vtk_3d_i_start = 0 if args["vtk_3D_t_end"] is not None: #print("(vtk) overwriting vtk_step_end with {}, because vtk_t_end = {}") vtk_3d_i_end = int(flow.units.convert_time_to_lu(args["vtk_3D_t_end"])) elif args["vtk_3D_step_end"] is not None: vtk_3d_i_end = args["vtk_3D_step_end"] else: vtk_3d_i_end = n_steps if args["vtk_3D_t_interval"] is not None and args["vtk_3D_t_interval"] > 0: vtk_3d_interval = int(flow.units.convert_time_to_lu(args["vtk_3D_t_interval"])) elif args["vtk_3D_step_interval"] is not None and args["vtk_3D_step_interval"] > 0: vtk_3d_interval = args["vtk_3D_step_interval"] elif args["vtk_3D_fps"] is not None and args["vtk_3D_fps"] > 0: vtk_3d_interval = int(flow.units.convert_time_to_lu(1 / args["vtk_3D_fps"])) else: vtk_3d_interval = 1 if vtk_3d_interval < 1: vtk_3d_interval = 1 vtk_3d_reporter = VTKReporterAdvanced(flow, interval=int(vtk_3d_interval), filename_base=outdir_data + "/vtk/out", imin=vtk_3d_i_start, imax=vtk_3d_i_end) simulation.reporter.append(vtk_3d_reporter) vtk_3d_reporter.output_mask(flow.solid_mask, outdir_data + "/vtk", "solid_mask", point=True) # slice2D (2D slice at Z/2) if args["vtk_slice2D"]: if args["vtk_slice2D_t_start"] is not None and args["vtk_slice2D_t_start"] > 0: #print("(vtk) overwriting vtk_step_start with {}, because vtk_t_start = {}") vtk_slice2d_i_start = int(round(flow.units.convert_time_to_lu(args["vtk_slice2D_t_start"]))) elif args["vtk_slice2D_step_start"] is not None and args["vtk_slice2D_step_start"] > 0: vtk_slice2d_i_start = int(args["vtk_slice2D_step_start"]) else: vtk_slice2d_i_start = 0 if args["vtk_slice2D_t_end"] is not None and args["vtk_slice2D_t_end"] > 0: #print("(vtk) overwriting vtk_step_end with {}, because vtk_t_end = {}") vtk_slice2d_i_end = int(flow.units.convert_time_to_lu(args["vtk_slice2D_t_end"])) elif args["vtk_slice2D_step_end"] is not None and args["vtk_slice2D_step_end"] > 0: vtk_slice2d_i_end = int(args["vtk_slice2D_step_end"]) else: vtk_slice2d_i_end = n_steps if args["vtk_slice2D_t_interval"] is not None and args["vtk_slice2D_t_interval"] > 0: vtk_slice2d_interval = int(flow.units.convert_time_to_lu(args["vtk_slice2D_t_interval"])) elif args["vtk_slice2D_step_interval"] is not None and args["vtk_slice2D_step_interval"] > 0: vtk_slice2d_interval = int(args["vtk_slice2D_step_interval"]) elif args["vtk_slice2D_fps"] is not None and args["vtk_slice2D_fps"] > 0: vtk_slice2d_interval = int(flow.units.convert_time_to_lu(1 / args["vtk_slice2D_fps"])) else: vtk_slice2d_interval = 1 if vtk_slice2d_interval < 1: vtk_slice2d_interval = 1 if args["vtk_slice2D"]: vtk_domainSlice_reporter = VTKsliceReporter(flow, interval=int(vtk_slice2d_interval), filename_base=outdir_data + "/vtk/slice_domain/slice_domain", sliceXY=([0,resolution[0]-1],[0,resolution[1]-1]), sliceZ=int(resolution[2]/2), imin=vtk_slice2d_i_start, imax=vtk_slice2d_i_end) simulation.reporter.append(vtk_domainSlice_reporter) # DRAW CYLINDER-MASK in 2D (xy-plane) try: draw_circular_mask(flow, flow.char_length_lu, filebase=outdir_data, output_data=True) except: print("(!) Drawing of circular cylinder mask did not work...") ################################################## # PRINT relevant PARAMETERS prior to simulation: print(f"\nSCRIPT: spacial and temporal dimensions:") print("domain shape (LU):", flow.resolution) print("t_target (PU) with", n_steps, "steps (LU):", round(n_steps * (flow.char_length_pu / flow.char_length_lu) * (mach_number * 1 / np.sqrt(3) / flow.char_velocity_pu), 3), "seconds") print("steps to simulate 1 second PU:", round((flow.char_length_lu / flow.char_length_pu) * (flow.char_velocity_pu / (mach_number * 1 / np.sqrt(3))), 3), "steps") print(f"steps to simulate {t_target:.3f} (t_target, PU) seconds: {t_target * round((flow.char_length_lu / flow.char_length_pu) * (flow.char_velocity_pu / (mach_number * 1 / np.sqrt(3))), 3):.3f} steps") ################################################## # RUN SIMULATION: print(f"\n#################################################") print(f"\nSCRIPT ({datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}): " f"running simulation for {n_steps} steps...\n") print(f"#################################################\n") t_start = time.time() mlups = simulation(num_steps=n_steps) t_end = time.time() runtime = t_end - t_start print(f"***** SIMULATION FINISHED AT " f"{datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')} *****\n") ################################################## # OUTPUT STATS: print(f"### STATS ###") print(f"MLUPS: {mlups:.3f}") print(f"simulated PU-Time: {flow.units.convert_time_to_pu(n_steps)} seconds") print("simulated LU-steps: ", n_steps) print(f"runtime (WALLTIME) of simulation(num_steps): " f"{runtime:.3f} seconds (= ", round(runtime / 60, 2), "minutes )") print("") # GPU VRAM print("### HARDWARE UTILIZATION ###") print(f"current GPU VRAM (MB) usage: " f"{torch.cuda.memory_allocated(device=context.device)/1024/1024:.3f}") print(f"max. GPU VRAM (MB) usage: " f"{torch.cuda.max_memory_allocated(device=context.device)/1024/1024:.3f}") # CPU [cpuLoad1, cpuLoad5, cpuLoad15] = [x / psutil.cpu_count() * 100 for x in psutil.getloadavg()] print("CPU % avg. over last 1 min, 5 min, 15 min; ", round(cpuLoad1, 2), round(cpuLoad5, 2), round(cpuLoad15, 2)) # RAM ram = psutil.virtual_memory() print("current total RAM usage [MB]: " + str(round(ram.used / (1024 * 1024), 2)) + " of " + str( round(ram.total / (1024 * 1024), 2)) + " MB") ### export stats if not no_data_flag: output_file = open(outdir + "/stats.txt", "a") output_file.write("DATA for " + timestamp) output_file.write("\n\n### SIM-STATS ###") output_file.write( f"\nruntime: {runtime:.3f} seconds (= {round(runtime / 60, 2)} " f"min = {round(runtime / 3600, 2)} h)") output_file.write("\nMLUPS = " + str(mlups)) output_file.write("\n") output_file.write("\nVRAM_current [MB] = " + str( torch.cuda.memory_allocated(context.device) / 1024 / 1024)) output_file.write("\nVRAM_peak [MB] = " + str( torch.cuda.max_memory_allocated(context.device) / 1024 / 1024)) output_file.write("\n") output_file.write("\nCPU load % avg. over last 1, 5, 15 min: " + str( round(cpuLoad1, 2)) + " %, " + str(round(cpuLoad5, 2)) + " %, " + str( round(cpuLoad15, 2)) + " %") output_file.write("\nCurrent total RAM usage [MB]: " + str( round(ram.used / (1024 * 1024), 2)) + " of " + str( round(ram.total / (1024 * 1024), 2)) + " MB") output_file.write("\nmaximum total RAM usage ('MaxRSS') [MB]: " + str( round(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024, 2)) + " MB") output_file.close() ################################################## # PLOTTING 2D IMAGE of VELOCITY fig, axes = plt.subplots(1, 2, figsize=(10, 3)) fig.subplots_adjust(right=0.85) u = flow.u_pu.cpu().numpy() if dims == 2: # 2D im1 = axes[0].imshow(context.convert_to_ndarray(flow.solid_mask.T), origin="lower") im2 = axes[1].imshow(u[0, ...].T, origin="lower") else: # 3D im1 = axes[0].imshow(context.convert_to_ndarray(flow.solid_mask[:, :, int(flow.solid_mask.shape[2] / 2)].T), origin="lower") im2 = axes[1].imshow(u[0, ...][:, :, int(flow.solid_mask.shape[2] / 2)].T, origin="lower") cbar_ax = fig.add_axes((0.88, 0.15, 0.04, 0.7)) fig.colorbar(im2, cax=cbar_ax) plt.show() #TODO (optional): plot image of pressure ################################################## # PROCESS DATA: calculate and SAVE OBSERVABLES AND PLOTS: print("\nSCRIPT: processing, plotting and saving data...\n") # DRAG drag_timeseries = np.array(np.array(DragReporter.out)) plot_force_coefficient(drag_timeseries, ylabel="Coefficient of Drag $C_{D}$", ylim=(0.5, 1.6), secax_functions_tuple=(flow.units.convert_time_to_lu, flow.units.convert_time_to_pu), filenamebase=outdir_data+"/drag", periodic_start=periodic_start, adjust_ylim=True) drag_stats = analyze_periodic_timeseries(drag_timeseries, periodic_start_rel=0.5, name="drag", verbose=True, pu_per_step=flow.units.convert_time_to_pu(1), outdir=outdir_data) print(f"DRAG STATS:") #\n{drag_stats}") for key, value in drag_stats.items(): print(f"{key:<20} = {str(value)}") print("") ''' reminder: STATS ARE: {"mean_simple": mean_simple, "mean_periodcorrected": mean_periodcorrected, "min_simple": min_simple, "max_simple": max_simple, "max_mean": max_mean, "min_mean": min_mean, "frequency_fit": frequency_fit, "frequency_fft": freq_peak, "fft_resolution": freq_res} ''' # LIFT lift_timeseries = np.array(np.array(LiftReporter.out)) plot_force_coefficient(lift_timeseries, ylabel="Coefficient of Lift$C_{L}$", ylim=(-1.1, 1.1), secax_functions_tuple=(flow.units.convert_time_to_lu, flow.units.convert_time_to_pu), filenamebase=outdir_data+"/lift", periodic_start=periodic_start) lift_stats = analyze_periodic_timeseries(lift_timeseries, periodic_start_rel=0.5, name="lift", verbose=True, pu_per_step=flow.units.convert_time_to_pu(1), outdir=outdir_data) print(f"LIFT STATS:") for key, value in lift_stats.items(): print(f"{key:<20} = {str(value)}") # plot DRAG and LIFT together: try: fig, ax = plt.subplots(layout="constrained") drag_ax = ax.plot(drag_timeseries[:, 1], drag_timeseries[:, 2], color="tab:blue", label="Drag") ax.set_xlabel("physical time / s") ax.set_ylabel("Coefficient of Drag $C_{D}$") ylim_adjusted = (drag_timeseries[int(drag_timeseries.shape[0] * periodic_start - 1):, 2].min() * 0.5, drag_timeseries[int(drag_timeseries.shape[0] * periodic_start - 1):, 2].max() * 1.2) ax.set_ylim(ylim_adjusted) secax = ax.secondary_xaxis('top', functions=(flow.units.convert_time_to_lu, flow.units.convert_time_to_pu)) secax.set_xlabel("timesteps (simulation time / LU)") ax2 = ax.twinx() lift_ax = ax2.plot(lift_timeseries[:, 1], lift_timeseries[:, 2], color="tab:orange", label="Lift") ax2.set_ylabel("Coefficient of Lift $C_{L}$") ax2.set_ylim((-1.1, 1.1)) fig.legend(loc="upper left", bbox_to_anchor=(0, 1), bbox_transform=ax.transAxes) if not no_data_flag: try: plt.savefig(outdir_data + "/dragAndLift_coefficient.png") except: print("(!) saving dragAndLift_coefficient.png didn't work!") plt.show() except: print("(!) plotting drag and lift together didn't work!") # STROUHAL number # f = Strouhal for St=f*D/U and D=U=1 in PU print("Strouhal number is: ", lift_stats["frequency_fit"] * flow.char_length_pu/flow.char_velocity_pu) # AVERAGE VELOCITY and REYNOLDS STRESS PROFILES if args["calc_u_profiles"] and args["profile_reference_path"] is not None: profile_plotter = ProfilePlotter(flow, output_path=outdir_data, reference_data_path=args["profile_reference_path"], i_timeseries=ProfileReporter1.i_out, u_timeseries1=ProfileReporter1.out, u_timeseries2=ProfileReporter2.out, u_timeseries3=ProfileReporter3.out) profile_plotter.process_data() profile_plotter.plot_velocity_profiles(show_reference=True, save=True, show=args["show_u_profiles"] if args["show_u_profiles"] is not None else False) profile_plotter.plot_reynolds_stress_profiles(show_reference=True, save=True, show=args["show_u_profiles"] if args["show_u_profiles"] is not None else False) # EXPORT OBSERVABLES: if not no_data_flag: ### CUDA-VRAM-summary: output_file = open(outdir_data + "/" + timestamp + "_GPU_memory_summary.txt", "a") output_file.write("DATA for " + timestamp + "\n\n") output_file.write(torch.cuda.memory_summary(context.device)) output_file.close() if args["count_tensors"]: try: ### list present torch tensors: output_file = open(outdir_data + "/" + timestamp + "_GPU_list_of_tensors.txt", "a") total_bytes = 0 import gc for obj in gc.get_objects(): try: if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)): output_file.write("\n" + str(obj.size()) + ", " + str(obj.nelement() * obj.element_size())) total_bytes = total_bytes + obj.nelement() * obj.element_size() except: pass output_file.close() ### count occurrence of tensors in list of tensors: from collections import Counter my_file = open(outdir_data + "/" + timestamp + "_GPU_list_of_tensors.txt", "r") data = my_file.read() my_file.close() data_into_list = data.split("\n") c = Counter(data_into_list) output_file = open(outdir_data + "/" + timestamp + "_GPU_counted_tensors.txt", "a") for k, v in c.items(): output_file.write("type,size,bytes: {}, number: {}\n".format(k, v)) output_file.write("\ntotal bytes for tensors:" + str(total_bytes)) output_file.close() except: print("(!) counting tensors didn't work!") ############################################ # OUTPUT parameters, stats and observables if not no_data_flag: output_file = open(outdir_data + "/" + timestamp + "_parms_stats_obs.txt", "a") output_file.write("DATA for " + timestamp) output_file.write("\n\n### SIM-Parameters ###") output_file.write("\n{:30s} {:30s}".format("Re", str(reynolds_number))) output_file.write("\n{:30s} {:30s}".format("Ma", str(mach_number))) output_file.write("\n{:30s} {:30s}".format("n_steps", str(n_steps))) output_file.write("\n{:30s} {:30s}".format("t_target [s]", str(t_target))) output_file.write("\n{:30s} {:30s}".format("gridpoints_per_diameter (GPD)", str(flow.char_length_lu))) if gpd_correction: output_file.write("\n(!) gpd was corrected from: " + str(gpd_setup) + " to " + str(flow.char_length_lu) + " because D/Y is even") output_file.write("\nDpX (D/X) = ".ljust(31) + str(domain_length_x_in_d)) output_file.write("\nDpY (D/Y) = ".ljust(31) + str(domain_height_y_in_d)) if flow.stencil.d == 3: output_file.write("\nDpZ (D/Z) = ".ljust(31) + str(domain_width_z_in_d)) output_file.write("\nshape_LU: ".ljust(31) + str(flow.resolution)) output_file.write(("\ntotal_number_of_gridpoints: ".ljust(31) + str(flow.rho(flow.f).numel()))) output_file.write("\nbc_type = ".ljust(31) + str()) output_file.write("\nlateral_walls = ".ljust(31) + str(flow.lateral_walls)) output_file.write("\nstencil = ".ljust(31) + str(flow.stencil)) output_file.write("\ncollision = ".ljust(31) + str(collision_operator)) output_file.write("\n") # output_file.write("\nMa = " + str(mach_number)) output_file.write("\nrelaxation parameter tau [LU]".ljust(31) + str(flow.units.relaxation_parameter_lu)) output_file.write("\ngrid_reynolds_number (Re_g) = ".ljust(31) + str(flow.units.characteristic_velocity_lu/ ((1 / np.sqrt(3.0))**2 * (flow.units.relaxation_parameter_lu - 0.5)))) output_file.write("\n") output_file.write("\ncylinder diameter PU = ".ljust(31) + str(flow.units.characteristic_length_pu)) output_file.write("\ncharacteristic velocity PU = ".ljust(31) + str(flow.units.characteristic_velocity_pu)) output_file.write("\nperturb_init = ".ljust(31) + str(perturb_init)) output_file.write("\n") output_file.write("\n\n### SIM-STATS ###") output_file.write("\nruntime = ".ljust(31) + str(runtime) + " seconds (=" + str(runtime / 60) + " minutes)") output_file.write("\nMLUPS = ".ljust(31) + str(mlups)) output_file.write("\n") output_file.write("\nVRAM_current [MB] = ".ljust(31) + str(torch.cuda.memory_allocated(context.device) / 1024 / 1024)) output_file.write("\nVRAM_peak [MB] = ".ljust(31) + str(torch.cuda.max_memory_allocated(context.device) / 1024 / 1024)) output_file.write("\n") output_file.write("\nCPU load % avg. over last 1, 5, 15 min: ".ljust(31) + str(round(cpuLoad1, 2)) + " %, " + str(round(cpuLoad5, 2)) + " %, " + str(round(cpuLoad15, 2)) + " %") output_file.write("\ntotal current RAM usage [MB]: ".ljust(31) + str(round(ram.used / (1024 * 1024), 2)) + " of " + str(round(ram.total / (1024 * 1024), 2)) + " MB") output_file.write("\n\n### OBSERVABLES ###") output_file.write("\nCoefficient of drag between " + str(round(drag_timeseries[int(drag_timeseries.shape[0] * periodic_start - 1), 1], 2)) + " s and " + str(round(drag_timeseries[int(drag_timeseries.shape[0] - 1), 1], 2)) + " s:") output_file.write("\nCd_mean, simple = ".ljust(31) + str(drag_stats["mean_simple"])) output_file.write("\nCd_mean, peak_finder = ".ljust(31) + str(drag_stats["mean_periodcorrected"])) output_file.write( "\nCd_min = ".ljust(31) + str(drag_stats["min_mean"] if drag_stats["min_mean"] is not None else drag_stats["min_simple"])) output_file.write( "\nCd_max = ".ljust(31) + str(drag_stats["max_mean"] if drag_stats["max_mean"] is not None else drag_stats["max_simple"])) output_file.write("\n") output_file.write("\nCoefficient of lift:") output_file.write("\nCl_min = ".ljust(31) + str(lift_stats["min_mean"] if lift_stats["min_mean"] is not None else lift_stats["min_simple"])) output_file.write("\nCl_max = ".ljust(31) + str(lift_stats["max_mean"] if lift_stats["max_mean"] is not None else lift_stats["max_simple"])) output_file.write("\n") output_file.write("\nStrouhal number:") output_file.write("\nFFT: St +- df = " + str(lift_stats["frequency_fft"]) + " +- " + str(lift_stats["fft_resolution"]) + " Hz") output_file.write( "\nSINE-FIT: St = " + str(lift_stats["frequency_fit"])) output_file.write("\n") output_file.close() # export flow physics to file: output_file = open(outdir+"/flow_physics_parameters.txt", "a") output_file.write('\n{:30s}'.format("FLOW PHYSICS and units:")) output_file.write('\n') output_file.write('\n{:30s} {:30s}'.format("Ma", str(reynolds_number))) output_file.write('\n{:30s} {:30s}'.format("Re", str(mach_number))) output_file.write('\n') output_file.write('\n{:30s} {:30s}'.format("Relaxation Parameter LU", str(flow.units.relaxation_parameter_lu))) output_file.write('\n{:30s} {:30s}'.format("l_char_LU", str(flow.units.characteristic_length_lu))) output_file.write('\n{:30s} {:30s}'.format("u_char_LU", str(flow.units.characteristic_velocity_lu))) output_file.write('\n{:30s} {:30s}'.format("viscosity_LU", str(flow.units.viscosity_lu))) output_file.write('\n{:30s} {:30s}'.format("p_char_LU", str(flow.units.characteristic_pressure_lu))) output_file.write('\n{:30s} {:30s}'.format("rho_char_LU", str(flow.units.characteristic_density_lu))) output_file.write('\n') output_file.write('\n{:30s} {:30s}'.format("l_char_PU", str(flow.units.characteristic_length_pu))) output_file.write('\n{:30s} {:30s}'.format("u_char_PU", str(flow.units.characteristic_velocity_pu))) output_file.write('\n{:30s} {:30s}'.format("viscosity_PU", str(flow.units.viscosity_pu))) output_file.write('\n{:30s} {:30s}'.format("p_char_PU", str(flow.units.characteristic_pressure_pu))) output_file.write('\n{:30s} {:30s}'.format("rho_char_PU", str(flow.units.characteristic_density_pu))) output_file.write('\n') output_file.write('\n{:30s} {:30s}'.format("grid reynolds number Re_g", str(flow.units.characteristic_velocity_lu/ ((1 / np.sqrt(3.0))**2 * (flow.units.relaxation_parameter_lu - 0.5))))) output_file.write('\n{:30s} {:30s}'.format("flow through time PU [s]", str(domain_length_x_in_d *flow.units.characteristic_length_pu/ flow.units.characteristic_velocity_pu))) output_file.write('\n{:30s} {:30s}'.format("flow through time LU", str(domain_length_x_in_d *flow.units.characteristic_length_lu/ flow.units.characteristic_velocity_lu))) output_file.write('\n') output_file.close() ## END OF SCRIPT print(f"\n♬ THE END ♬") # reset stdout (from LOGGER, see above (beginning)) sys.stdout = old_stdout ================================================ FILE: examples/advanced_projects/efficient_bounce_back_obstacle/__init__.py ================================================ from examples.advanced_projects.efficient_bounce_back_obstacle.boundary.solid_boundary_data import SolidBoundaryData from examples.advanced_projects.efficient_bounce_back_obstacle.boundary.fullway_bounce_back_boundary import FullwayBounceBackBoundary from examples.advanced_projects.efficient_bounce_back_obstacle.boundary.halfway_bounce_back_boundary import HalfwayBounceBackBoundary from examples.advanced_projects.efficient_bounce_back_obstacle.boundary.linear_interpolated_bounce_back_boundary import LinearInterpolatedBounceBackBoundary __all__ = [ 'FullwayBounceBackBoundary', 'HalfwayBounceBackBoundary', 'LinearInterpolatedBounceBackBoundary', 'SolidBoundaryData' ] ================================================ FILE: examples/advanced_projects/efficient_bounce_back_obstacle/auxiliary_code/__init__.py ================================================ ================================================ FILE: examples/advanced_projects/efficient_bounce_back_obstacle/auxiliary_code/data_processing_and_plotting.py ================================================ """ THIS FILE CONTAINS THE UTILITIES TO PROCESS AND PLOT THE REPORTED DATA FROM THE CYLINDER OBSTACLE SIMULATION SCRIPT - plotting of force coefficients - analysis of periodic (force coefficient) signals - drawing of circular cylinder mask in 2D - plotting- and analysis utility for average velocity and reynolds stress profiles """ import os import numpy as np import matplotlib.pyplot as plt from typing import Any, Optional import traceback from scipy.signal import find_peaks from scipy.optimize import curve_fit from collections import Counter from lettuce import Flow def plot_force_coefficient(data_array: np.ndarray, ylabel: str, ylim: tuple[float, float], secax_functions_tuple: tuple[Any,Any], filenamebase: str, save_timeseries: bool = False, periodic_start: float = 0, adjust_ylim: bool = False): """ - plot force coefficient timeseries - (opt.) save png to outdir - contains exception-capture functionality if data is not plot-abel, save-able etc. """ # PLOT try: fig, ax = plt.subplots(constrained_layout=True) ax.plot(data_array[:, 1], data_array[:, 2]) ax.set_xlabel("physical time / s") ax.set_ylabel(str(ylabel)) ax.set_ylim(ylim) # change y-limits secax = ax.secondary_xaxis('top', functions=secax_functions_tuple) secax.set_xlabel("timestep (simulation time / LU)") except Exception as e: print(f"(WARNING!) plotting of {ylabel} didn't work...") print("\n--- Python Stack Trace ---") full_trace = traceback.format_exc() print(full_trace) print("--------------------------\n") # SAVE PNG try: plt.savefig(filenamebase + ".png") except Exception as e: print(f"(WARNING!) saving of {ylabel} PLOT to {filenamebase}.png didn't work...") print("\n--- Python Stack Trace ---") full_trace = traceback.format_exc() print(full_trace) print("--------------------------\n") # PLOT with ylim automatically ADJUSTED to fit data (additionally) if adjust_ylim: try: fig, ax = plt.subplots(constrained_layout=True) ax.plot(data_array[:, 1], data_array[:, 2]) ax.set_xlabel("physical time / s") ax.set_ylabel(str(ylabel)) ylim_2 = ( data_array[int(data_array.shape[0] * periodic_start - 1):,2].min() * 0.5, data_array[int(data_array.shape[0] * periodic_start - 1):,2].max() * 1.2) ax.set_ylim(ylim_2) # change y-limits secax = ax.secondary_xaxis('top', functions=secax_functions_tuple) secax.set_xlabel("timestep (simulation time / LU)") except Exception as e: print(f"(WARNING!) plotting of {ylabel} didn't work...") print("\n--- Python Stack Trace ---") full_trace = traceback.format_exc() print(full_trace) print("--------------------------\n") # SAVE PNG try: plt.savefig(filenamebase + "_ylimadjusted.png") except Exception as e: print(f"(WARNING!) saving of {ylabel} PLOT " f"to {filenamebase}_ylimadjusted.png didn't work...") print("\n--- Python Stack Trace ---") full_trace = traceback.format_exc() print(full_trace) print("--------------------------\n") # SAVE .txt timeseries #TODO (optional): make this a seperate function... if save_timeseries: try: np.savetxt(filenamebase + ".txt", data_array, header=f"stepLU | timePU | {ylabel}") except Exception as e: print(f"(WARNING!) saving of {ylabel} TIMESERIES to {filenamebase}.txt didn't work...") print("\n--- Python Stack Trace ---") full_trace = traceback.format_exc() print(full_trace) print("--------------------------\n") def analyze_periodic_timeseries(data_array: np.ndarray, periodic_start_rel: float, prominence: float = None, name: str = "periodic timeseries", outdir: Optional[str] = None, pu_per_step: Optional[float] = None, verbose: bool = False): """ ANALYZE PERIODIC SIGNAL (verbose = plot peak-finding), - outputs: min, max, mean (simple + corrected for integer number of periods) - RETURN: dictionary mit: Name, min, max, mean_simple, mean_n_periodic, mean-min, mean-max (v.a. für Cl, nach neuen Skripten) - print error if peak-finding didn't work - do FFT, print FFT spectrum - detect frequency of signal by sine-functino fitting (can be more accurate than FFT for strictly sinusoidal signals!) """ values_periodic = data_array[int(data_array.shape[0] * periodic_start_rel - 1):, 2] steps_LU_periodic = data_array[int(data_array.shape[0] * periodic_start_rel - 1):, 0] mean_periodcorrected = None max_mean = None # mean maximum value from all high peaks min_mean = None # mean minimum value from all low peaks if prominence is None: prominence = ((values_periodic.max() - values_periodic.min()) * 0.5) # The prominence value is up for debate; # The value given here only reliably catches all peaks, # if the signal is simple and periodically converged try: peaks_max = find_peaks(values_periodic, prominence=prominence) peaks_min = find_peaks(-values_periodic, prominence=prominence) if peaks_min[0].shape[0] - peaks_max[0].shape[0] > 0: peak_number = peaks_max[0].shape[0] else: peak_number = peaks_min[0].shape[0] if peaks_min[0][0] < peaks_max[0][0]: first_peak = peaks_min[0][0] last_peak = peaks_max[0][peak_number - 1] else: first_peak = peaks_max[0][0] last_peak = peaks_min[0][peak_number - 1] if verbose: peak_max_y = values_periodic[peaks_max[0]] peak_max_x = steps_LU_periodic[peaks_max[0]] peak_min_y = values_periodic[peaks_min[0]] peak_min_x = steps_LU_periodic[peaks_min[0]] plt.subplots(constrained_layout=True) plt.plot(steps_LU_periodic, values_periodic) plt.scatter(peak_max_x[:peak_number], peak_max_y[:peak_number]) plt.scatter(peak_min_x[:peak_number], peak_min_y[:peak_number]) plt.scatter(steps_LU_periodic[first_peak], values_periodic[first_peak]) plt.scatter(steps_LU_periodic[last_peak], values_periodic[last_peak]) max_mean = values_periodic[peaks_max[0]].mean() plt.axhline(y=max_mean, color="r", ls="--", lw=0.5) min_mean = values_periodic[peaks_min[0]].mean() plt.axhline(y=min_mean, color="r", ls="--", lw=0.5) if outdir is not None: plt.savefig(outdir + f"/{name}_peakfinder.png") mean_periodcorrected = values_periodic[first_peak:last_peak].mean() except Exception as e: print(f"(WARNING!) peak finding for {name} didn't work... " f"This might just be because there is no converged periodic region," f"so don't worry if you expected this!") if verbose: print("Analyze Periodic Timeseries (verbose):-> see Python Stack Trace below:") print("\n--- Python Stack Trace ---") full_trace = traceback.format_exc() print(full_trace) print("--------------------------\n") # simple mean, max, min calculation mean_simple = values_periodic.mean() min_simple = values_periodic.min() max_simple = values_periodic.max() # sine-fit # (for better statistics and frequency analysis of purely sinusoidal signals) def sine_func(xx, a, b, c, d): return a * np.sin(2 * np.pi * b * xx + c) + d frequency_fit = None try: coefficients, values = curve_fit(sine_func, steps_LU_periodic, values_periodic, p0=(0.7, 0.2, 0.5, 0)) fig, ax = plt.subplots(constrained_layout=True) if verbose: plt.plot(steps_LU_periodic, values_periodic, steps_LU_periodic, sine_func(steps_LU_periodic, *coefficients)) plt.legend(["timeseries", "sine-fit"]) ax.set_xlabel("physical time / s") ax.set_ylabel("timeseries") # ax.set_ylim([-1, 1]) if outdir is not None: plt.savefig(outdir + f"/{name}_sine-fit.png") frequency_fit = coefficients[1] except Exception as e: print(f"(WARNING!) sine-fitting for {name} didn't work...") if verbose: print("\n--- Python Stack Trace ---") full_trace = traceback.format_exc() print(full_trace) print("--------------------------\n") # FFT # run FFT on signal and get dominant frequency freq_peak = None freq_res = None if pu_per_step is not None: try: X = np.fft.fft(values_periodic) # fft result (amplitudes) N = len(X) # number of freqs n = np.arange(N) # freq index T = N * pu_per_step # total time measured (T_PU) freq = n / T # frequencies (x-axis of spectrum) if verbose: plt.figure() # plot spectrum |X|(f) plt.stem(freq, np.abs(X), 'b', markerfmt=" ", basefmt="-b") plt.xlabel("Freq (Hz)") plt.ylabel("FFT Amplitude |X(freq)|") plt.xlim(0, 1) # print("max. Amplitude np.abx(X).max():", np.abs(X).max()) # uncomment for debugging # ylim, where highes peak is on left half of full spectrum: plt.ylim(0, np.abs(X[:int(X.shape[0] * 0.5)]).max()) if outdir is not None: plt.savefig(outdir + f"/{name}_fft.png") freq_res = freq[1] - freq[0] # frequency-resolution # get |X| Amplitude for left half of full spectrum X_abs = np.abs(X[:int(X.shape[0] * 0.4)]) freq_peak = freq[np.argmax(X_abs)] # find frequency with the highest amplitude except Exception as e: print(f"(WARNING!) fft for {name} didn't work...") if verbose: print("\n--- Python Stack Trace ---") full_trace = traceback.format_exc() print(full_trace) print("--------------------------\n") return {"mean_simple": mean_simple, "mean_periodcorrected": mean_periodcorrected, "min_simple": min_simple, "max_simple": max_simple, "max_mean": max_mean, "min_mean": min_mean, "frequency_fit": frequency_fit, "frequency_fft": freq_peak, "fft_resolution": freq_res} def draw_circular_mask(flow: 'Flow', gridpoints_per_diameter: int, output_data: bool = False, filebase: str =".", print_data: bool =False): """ calculate and draw a 2D representation of: - the circular cylinder - the basic solid mask """ grid_x = gridpoints_per_diameter + 2 if print_data: print("GPD = " + str(gridpoints_per_diameter)) # define radius and position for a symmetrical circular Cylinder-Obstacle radius_lu = 0.5 * gridpoints_per_diameter y_pos_lu = 0.5 * grid_x + 0.5 x_pos_lu = y_pos_lu # get x,y,z meshgrid of the domain (LU) # tupel of list indizes (1-n (non-zero-based!)) xyz = tuple(np.linspace(1, n, n) for n in (grid_x, grid_x)) # meshgrid of x- and y- indizes -> * unpacks the tuple to be two values and now a tuple x_lu, y_lu = np.meshgrid(*xyz, indexing='ij') # define cylinder (LU) (circle) obstacle_mask_for_visualization = np.sqrt( (x_lu - x_pos_lu) ** 2 + (y_lu - y_pos_lu) ** 2) < radius_lu nx, ny = obstacle_mask_for_visualization.shape # number of x- and y-nodes (Skalar) # for all the solid nodes, neighboring fluid nodes rand_mask = np.zeros((nx, ny), dtype=bool) # same, but including q-dimension rand_mask_f = np.zeros((flow.stencil.q, nx, ny), dtype=bool) rand_xq = [] # list of all x-values (incl. q-multiplicity) rand_yq = [] # list of all y-values (incl. q-multiplicity) # np.array: list of # (a) x-coordinates and # (b) y-coordinates of the obstacle_mask_for_visualization a, b = np.where(obstacle_mask_for_visualization) # ...to iterate over all boundary/object/wall nodes for p in range(0,len(a)): # for all True-nodes in obstacle_mask_for_visualization for i in range(0,flow.stencil.q): # for all stencil directions c_i (lattice.stencil.e) try: # try in case the neighboring cell does not exist # (an f pointing out of the simulation domain) if not obstacle_mask_for_visualization[ a[p] + flow.stencil.e[i][0], b[p] + flow.stencil.e[i][1]]: # if neighbor in +(e_x, e_y; e is c_i) is False, # we are on the object-surface (self True with neighbor False) rand_mask[a[p], b[p]] = 1 rand_mask_f[flow.stencil.opposite[i], a[p], b[p]] = 1 rand_xq.append(a[p]) rand_yq.append(b[p]) except IndexError: pass # just ignore this iteration since there is no neighbor there rand_x, rand_y = np.where(rand_mask) # list of all surface coordinates x_pos = sum(rand_x) / len(rand_x) # x-coordinate of circle center y_pos = sum(rand_y) / len(rand_y) # y-coordinate of circle center # calculate all radii and r_max and r_min r_max = 0 r_min = gridpoints_per_diameter # list of all radii (without q-dimension) in LU radii = np.zeros_like(rand_x, dtype=float) for p in range(0, len(rand_x)): # for all nodes # calculate distance to circle center radii[p] = np.sqrt((rand_x[p] - x_pos) ** 2 + (rand_y[p] - y_pos) ** 2) if radii[p] > r_max: r_max = radii[p] if radii[p] < r_min: r_min = radii[p] # calculate all radii (with q-multiplicity) radii_q = np.zeros_like(rand_xq, dtype=float) for p in range(0, len(rand_xq)): radii_q[p] = np.sqrt((rand_xq[p] - x_pos) ** 2 + (rand_yq[p] - y_pos) ** 2) ### all relative radii in relation to gpd/2 radii_relative = radii / (radius_lu - 0.5) # (subtract 0.5 because "true" boundary location is 0.5LU # further out than node-coordinates) radii_q_relative = radii_q / (radius_lu - 0.5) # calc. mean rel_radius r_rel_mean = sum(radii_relative) / len(radii_relative) rq_rel_mean = sum(radii_q_relative) / len(radii_q_relative) ## AREA calculation area_theory = np.pi * (gridpoints_per_diameter / 2) ** 2 # area = pi*r² in LU² # area in LU = number of nodes, because every node has a cell of 1LU x 1LU around it area = len(a) if output_data: output_file = open(filebase + "/obstacle_mask_info.txt", "a") output_file.write("GPD = " + str(gridpoints_per_diameter) + "\n") output_file.write( "\nr_rel_mean: " + str(sum(radii_relative) / len(radii_relative))) output_file.write("\nrq_rel_mean: " + str( sum(radii_q_relative) / len(radii_q_relative))) output_file.write("\nr_rel_min: " + str(r_max / (radius_lu - 0.5))) output_file.write("\nr_rel_max: " + str(r_min / (radius_lu - 0.5))) output_file.write("\n\narea_rel: " + str(area / area_theory)) output_file.write("\n\nradii: " + str(Counter(radii))) output_file.write("\nradii_q: " + str(Counter(radii_q)) + "\n\n") output_file.close() if print_data: print("area_rel: " + str(area / area_theory)) ### PLOT Mask plt.figure() plt.imshow(obstacle_mask_for_visualization) # plt.xticks(np.arange(gridpoints_per_diameter + 2), minor=True) # plt.yticks(np.arange(gridpoints_per_diameter + 2), minor=True) ax = plt.gca() xmin, xmax = ax.get_xlim() ymax, ymin = ax.get_ylim() if gridpoints_per_diameter >= 10: plt.xticks(np.arange(0, xmax, int(xmax / 10))) plt.yticks(np.arange(0, ymax, int(ymax / 10))) else: plt.xticks(np.arange(0, xmax, 1)) plt.yticks(np.arange(0, ymax, 1)) plt.title("GPD = " + str(gridpoints_per_diameter)) ax.set_xticks(np.arange(-.5, xmax, 1), minor=True) ax.set_yticks(np.arange(-.5, ymax, 1), minor=True) # grid thickness, circle, node marker x, y = np.meshgrid(np.linspace(0, int(xmax), int(xmax + 1)), np.linspace(0, int(ymax), int(ymax + 1))) if gridpoints_per_diameter < 30: ax.grid(which="minor", color="k", axis='both', linestyle='-', linewidth=2) circle = plt.Circle((xmax / 2 - 0.25, ymax / 2 - 0.25), gridpoints_per_diameter / 2, color='r', fill=False, linewidth=1) ax.add_patch(circle) plt.plot(x, y, marker='.', linestyle='', color="b", markersize=1) elif gridpoints_per_diameter < 70: ax.grid(which="minor", color="k", axis='both', linestyle='-', linewidth=1) circle = plt.Circle((xmax / 2 - 0.25, ymax / 2 - 0.25), gridpoints_per_diameter / 2, color='r', fill=False, linewidth=0.5) ax.add_patch(circle) elif gridpoints_per_diameter < 100: ax.grid(which="minor", color="k", axis='both', linestyle='-', linewidth=0.5) elif gridpoints_per_diameter < 150: ax.grid(which="minor", color="k", axis='both', linestyle='-', linewidth=0.25) if output_data: plt.savefig(filebase + "/obstacle_mask_GPD" + str( gridpoints_per_diameter) + ".png") if print_data: plt.show() else: plt.close() class ProfilePlotter: """ utility to handle profile reporter data - load reference values - process profile reporter data - plot and save data with or without references """ def __init__(self, flow: 'Flow', output_path: str, reference_data_path: str, i_timeseries: np.ndarray, u_timeseries1: np.ndarray, u_timeseries2: np.ndarray, u_timeseries3: np.ndarray): self.flow = flow self.output_path = output_path self.i_timeseries = i_timeseries self.u_timeseries1 = u_timeseries1 self.u_timeseries2 = u_timeseries2 self.u_timeseries3 = u_timeseries3 os.makedirs(self.output_path + "/ProfileReporter_Data/") self.import_profile_reference_data(reference_data_path) def import_profile_reference_data(self, data_path: str): # import reference data: # (data is: first column Y/D, second column u_d/u_char) # ux self.p1_LS1993_ux = np.genfromtxt( data_path + 'Fig09_ux_profile_pos1_LS1993.csv', delimiter=';') self.p2_LS1993_ux = np.genfromtxt( data_path + 'Fig09_ux_profile_pos2_LS1993.csv', delimiter=';') self.p3_LS1993_ux = np.genfromtxt( data_path + 'Fig09_ux_profile_pos3_LS1993.csv', delimiter=';') self.p1_KM2000_ux = np.genfromtxt( data_path + 'Fig09_ux_profile_pos1_KM2000.csv', delimiter=';') self.p2_KM2000_ux = np.genfromtxt( data_path + 'Fig09_ux_profile_pos2_KM2000.csv', delimiter=';') self.p3_KM2000_ux = np.genfromtxt( data_path + 'Fig09_ux_profile_pos3_KM2000.csv', delimiter=';') self.p1_WR2008_ux = np.genfromtxt( data_path + 'Fig09_ux_profile_pos1_WR2008.csv', delimiter=';') self.p2_WR2008_ux = np.genfromtxt( data_path + 'Fig09_ux_profile_pos2_WR2008.csv', delimiter=';') self.p3_WR2008_ux = np.genfromtxt( data_path + 'Fig09_ux_profile_pos3_WR2008.csv', delimiter=';') self.p1_DI2018_ux = np.genfromtxt( data_path + 'Fig09_ux_profile_pos1_DI2018.csv', delimiter=';') self.p2_DI2018_ux = np.genfromtxt( data_path + 'Fig09_ux_profile_pos2_DI2018.csv', delimiter=';') self.p3_DI2018_ux = np.genfromtxt( data_path + 'Fig09_ux_profile_pos3_DI2018.csv', delimiter=';') # uy self.p1_LS1993_uy = np.genfromtxt( data_path + 'Fig10_uy_profile_pos1_LS1993.csv', delimiter=';') self.p2_LS1993_uy = np.genfromtxt( data_path + 'Fig10_uy_profile_pos2_LS1993.csv', delimiter=';') self.p3_LS1993_uy = np.genfromtxt( data_path + 'Fig10_uy_profile_pos3_LS1993.csv', delimiter=';') self.p1_KM2000_uy = np.genfromtxt( data_path + 'Fig10_uy_profile_pos1_KM2000.csv', delimiter=';') self.p2_KM2000_uy = np.genfromtxt( data_path + 'Fig10_uy_profile_pos2_KM2000.csv', delimiter=';') self.p3_KM2000_uy = np.genfromtxt( data_path + 'Fig10_uy_profile_pos3_KM2000.csv', delimiter=';') self.p1_WR2008_uy = np.genfromtxt( data_path + 'Fig10_uy_profile_pos1_WR2008.csv', delimiter=';') self.p2_WR2008_uy = np.genfromtxt( data_path + 'Fig10_uy_profile_pos2_WR2008.csv', delimiter=';') self.p3_WR2008_uy = np.genfromtxt( data_path + 'Fig10_uy_profile_pos3_WR2008.csv', delimiter=';') self.p1_DI2018_uy = np.genfromtxt( data_path + 'Fig10_uy_profile_pos1_DI2018.csv', delimiter=';') self.p2_DI2018_uy = np.genfromtxt( data_path + 'Fig10_uy_profile_pos2_DI2018.csv', delimiter=';') self.p3_DI2018_uy = np.genfromtxt( data_path + 'Fig10_uy_profile_pos3_DI2018.csv', delimiter=';') # uxux self.p1_DI2018_uxux = np.genfromtxt( data_path + 'Fig11_uxux_profile_pos1_DI2018.csv', delimiter=';') self.p1_KM2000_uxux = np.genfromtxt( data_path + 'Fig11_uxux_profile_pos1_KM2000.csv', delimiter=';') self.p1_R2016_uxux = np.genfromtxt( data_path + 'Fig11_uxux_profile_pos1_R2016.csv', delimiter=';') self.p2_BM1994_uxux = np.genfromtxt( data_path + 'Fig11_uxux_profile_pos2_BM1994.csv', delimiter=';') self.p2_DI2018_uxux = np.genfromtxt( data_path + 'Fig11_uxux_profile_pos2_DI2018.csv', delimiter=';') self.p2_KM2000_uxux = np.genfromtxt( data_path + 'Fig11_uxux_profile_pos2_KM2000.csv', delimiter=';') self.p2_LS1993_uxux = np.genfromtxt( data_path + 'Fig11_uxux_profile_pos2_LS1993.csv', delimiter=';') self.p2_R2016_uxux = np.genfromtxt( data_path + 'Fig11_uxux_profile_pos2_R2016.csv', delimiter=';') self.p3_DI2018_uxux = np.genfromtxt( data_path + 'Fig11_uxux_profile_pos3_DI2018.csv', delimiter=';') self.p3_KM2000_uxux = np.genfromtxt( data_path + 'Fig11_uxux_profile_pos3_KM2000.csv', delimiter=';') self.p3_R2016_uxux = np.genfromtxt( data_path + 'Fig11_uxux_profile_pos3_R2016.csv', delimiter=';') # uyuy self.p1_DI2018_uyuy = np.genfromtxt( data_path + 'Fig12_uyuy_profile_pos1_DI2018.csv', delimiter=';') self.p1_R2016_uyuy = np.genfromtxt( data_path + 'Fig12_uyuy_profile_pos1_R2016.csv', delimiter=';') self.p2_BM1994_uyuy = np.genfromtxt( data_path + 'Fig12_uyuy_profile_pos2_BM1994.csv', delimiter=';') self.p2_DI2018_uyuy = np.genfromtxt( data_path + 'Fig12_uyuy_profile_pos2_DI2018.csv', delimiter=';') self.p2_LS1993_uyuy = np.genfromtxt( data_path + 'Fig12_uyuy_profile_pos2_LS1993.csv', delimiter=';') self.p2_R2016_uyuy = np.genfromtxt( data_path + 'Fig12_uyuy_profile_pos2_R2016.csv', delimiter=';') self.p3_DI2018_uyuy = np.genfromtxt( data_path + 'Fig12_uyuy_profile_pos3_DI2018.csv', delimiter=';') self.p3_R2016_uyuy = np.genfromtxt( data_path + 'Fig12_uyuy_profile_pos3_R2016.csv', delimiter=';') # uxuy self.p1_BM1994_uxuy = np.genfromtxt( data_path + 'Fig13_uxuy_profile_pos1_BM1994.csv', delimiter=';') self.p1_DI2018_uxuy = np.genfromtxt( data_path + 'Fig13_uxuy_profile_pos1_DI2018.csv', delimiter=';') self.p1_R2016_uxuy = np.genfromtxt( data_path + 'Fig13_uxuy_profile_pos1_R2016.csv', delimiter=';') self.p2_BM1994_uxuy = np.genfromtxt( data_path + 'Fig13_uxuy_profile_pos2_BM1994.csv', delimiter=';') self.p2_DI2018_uxuy = np.genfromtxt( data_path + 'Fig13_uxuy_profile_pos2_DI2018.csv', delimiter=';') self.p2_LS1993_uxuy = np.genfromtxt( data_path + 'Fig13_uxuy_profile_pos2_LS1993.csv', delimiter=';') self.p2_R2016_uxuy = np.genfromtxt( data_path + 'Fig13_uxuy_profile_pos2_R2016.csv', delimiter=';') self.p3_BM1994_uxuy = np.genfromtxt( data_path + 'Fig13_uxuy_profile_pos3_BM1994.csv', delimiter=';') self.p3_DI2018_uxuy = np.genfromtxt( data_path + 'Fig13_uxuy_profile_pos3_DI2018.csv', delimiter=';') self.p3_R2016_uxuy = np.genfromtxt( data_path + 'Fig13_uxuy_profile_pos3_R2016.csv', delimiter=';') def process_data(self, save: bool = False): """CALCULATE temporal velocity averages""" avg_u1_temporal = np.mean(np.array(self.u_timeseries1), axis=0) avg_u2_temporal = np.mean(np.array(self.u_timeseries2), axis=0) avg_u3_temporal = np.mean(np.array(self.u_timeseries3), axis=0) self.avg_u1_x = avg_u1_temporal[0] self.avg_u1_y = avg_u1_temporal[1] self.avg_u2_x = avg_u2_temporal[0] self.avg_u2_y = avg_u2_temporal[1] self.avg_u3_x = avg_u3_temporal[0] self.avg_u3_y = avg_u3_temporal[1] if save: np.save( self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_" + "pos1" + "_t-avg.npy", avg_u1_temporal) np.save( self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_" + "pos2" + "_t-avg.npy", avg_u2_temporal) np.save( self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_" + "pos3" + "_t-avg.npy", avg_u3_temporal) # Y_inD for y-axis and plotting self.y_in_D = ((np.arange(self.avg_u1_x.shape[0]) + 1 - self.flow.y_pos_lu) / self.flow.char_length_lu) if save: np.save( self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_YinD.npy", self.y_in_D) # CALCULATE turbulent reynolds stresses # diff between timeseries and time_average -> u' u1_diff = self.u_timeseries1 - avg_u1_temporal u2_diff = self.u_timeseries2 - avg_u2_temporal u3_diff = self.u_timeseries3 - avg_u3_temporal # square of diff -> u'^2 u1_diff_sq = u1_diff ** 2 u2_diff_sq = u2_diff ** 2 u3_diff_sq = u3_diff ** 2 # ux'*uy' u1_diff_xy = u1_diff[:, 0, :] * u1_diff[:, 1, :] u2_diff_xy = u2_diff[:, 0, :] * u2_diff[:, 1, :] u3_diff_xy = u3_diff[:, 0, :] * u3_diff[:, 1, :] # time_average of u'² and ux'uy' self.u1_diff_sq_mean = np.mean(u1_diff_sq, axis=0) # time average self.u2_diff_sq_mean = np.mean(u2_diff_sq, axis=0) # time average self.u3_diff_sq_mean = np.mean(u3_diff_sq, axis=0) # time average self.u1_diff_xy_mean = np.mean(u1_diff_xy, axis=0) # time average self.u2_diff_xy_mean = np.mean(u2_diff_xy, axis=0) # time average self.u3_diff_xy_mean = np.mean(u3_diff_xy, axis=0) # time average if save: # save reynolds stresses np.save( self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_1_ReStress_x.npy", np.array([self.y_in_D, self.u1_diff_sq_mean[0]])) np.save( self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_2_ReStress_x.npy", np.array([self.y_in_D, self.u2_diff_sq_mean[0]])) np.save( self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_3_ReStress_x.npy", np.array([self.y_in_D, self.u3_diff_sq_mean[0]])) np.save( self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_1_ReStress_y.npy", np.array([self.y_in_D, self.u1_diff_sq_mean[1]])) np.save( self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_2_ReStress_y.npy", np.array([self.y_in_D, self.u2_diff_sq_mean[1]])) np.save( self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_3_ReStress_y.npy", np.array([self.y_in_D, self.u3_diff_sq_mean[1]])) np.save( self.output_path + "/ProfileReporter_Data" +"/ProfileReporter_1_ReShearStress.npy", np.array([self.y_in_D, self.u1_diff_xy_mean])) np.save( self.output_path + "/ProfileReporter_Data" +"/ProfileReporter_2_ReShearStress.npy", np.array([self.y_in_D, self.u2_diff_xy_mean])) np.save( self.output_path + "/ProfileReporter_Data" +"/ProfileReporter_3_ReShearStress.npy", np.array([self.y_in_D, self.u3_diff_xy_mean])) def save_timeseries_to_files(self, basepath: str): """save the FULL timeseries to files""" # timeseries (i) np.save(basepath + "/ProfileReporter_Data" + "/ProfileReporter_" + "_timeseries_steps.npy", np.array(self.i_timeseries)) #timeseries (u) np.save(basepath + "/ProfileReporter_Data" + "/ProfileReporter_" + "pos1" + "_timeseries_data.npy", np.array(self.u_timeseries1)) np.save(basepath + "/ProfileReporter_Data" + "/ProfileReporter_" + "pos2" + "_timeseries_data.npy", np.array(self.u_timeseries2)) np.save(basepath + "/ProfileReporter_Data" + "/ProfileReporter_" + "pos3" + "_timeseries_data.npy", np.array(self.u_timeseries3)) def plot_velocity_profiles(self, show_reference: bool = False, save: bool = False, show: bool = False): """plot tht average velocity profiles""" cm = 1 / 2.54 if not show_reference: # PLOT ux fig, (ax_ux, ax_uy) = plt.subplots(1, 2, constrained_layout=True, figsize=(30 * cm, 10 * cm)) ax_ux.plot(self.y_in_D, self.avg_u1_x, self.y_in_D, self.avg_u2_x, self.y_in_D, self.avg_u3_x) ax_ux.set_xlabel("y/D") ax_ux.set_ylabel(r"$\bar{u}_{x}$/$u_{char}$") ax_ux.legend(["x/D = 1.06", "x/D = 1.54", "x/D = 2.02"]) # PLOT uy ax_uy.plot(self.y_in_D, self.avg_u1_y, self.y_in_D, self.avg_u2_y, self.y_in_D, self.avg_u3_y) ax_uy.set_xlabel("y/D") ax_uy.set_ylabel(r"$\bar{u}_{y}$/$u_{char}$") ax_uy.legend(["x/D = 1.06", "x/D = 1.54", "x/D = 2.02"]) if save: plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_velocity_noReference.png") if show: plt.show() else: plt.close() else: # PLOT ux against references fig, ax = plt.subplots(constrained_layout=True) my_data = ax.plot(self.y_in_D, self.avg_u1_x, self.y_in_D, self.avg_u2_x - 1, self.y_in_D, self.avg_u3_x - 2) plt.setp(my_data, ls="-", lw=1, marker="", color="red", label="lettuce") ref_LS = ax.plot(self.p1_LS1993_ux[:, 0], self.p1_LS1993_ux[:, 1], self.p2_LS1993_ux[:, 0], self.p2_LS1993_ux[:, 1], self.p3_LS1993_ux[:, 0], self.p3_LS1993_ux[:, 1]) plt.setp(ref_LS, ls="", lw=1, marker="s", fillstyle='none', color="k", label="Lorenco & Shih (1993)") ref_KM = ax.plot(self.p1_KM2000_ux[:, 0], self.p1_KM2000_ux[:, 1], self.p2_KM2000_ux[:, 0], self.p2_KM2000_ux[:, 1], self.p3_KM2000_ux[:, 0], self.p3_KM2000_ux[:, 1]) plt.setp(ref_KM, ls="dotted", lw=1.5, marker="", color="k", label="Kravchenko & Moin (2000)") ref_WR = ax.plot(self.p1_WR2008_ux[:, 0], self.p1_WR2008_ux[:, 1], self.p2_WR2008_ux[:, 0], self.p2_WR2008_ux[:, 1], self.p3_WR2008_ux[:, 0], self.p3_WR2008_ux[:, 1]) plt.setp(ref_WR, ls="dashdot", lw=1.5, marker="", color="k", label="Wissink & Rodi (2008)") ref_DI = ax.plot(self.p1_DI2018_ux[:, 0], self.p1_DI2018_ux[:, 1], self.p2_DI2018_ux[:, 0], self.p2_DI2018_ux[:, 1], self.p3_DI2018_ux[:, 0], self.p3_DI2018_ux[:, 1]) plt.setp(ref_DI, ls="--", lw=1.5, marker="", color="tab:blue", label="Di Ilio et al. (2018)") ax.set_xlabel("y/D") ax.set_ylabel(r"$\bar{u}_{x}$/$u_{char}$") ax.set_ylim((-2.5, +2)) ax.set_xlim((-3, 3)) ax.legend(handles=[my_data[0], ref_LS[0], ref_KM[0], ref_WR[0], ref_DI[0]], loc='best') if save: plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_ux_withReference.png") if show: plt.show() else: plt.close() # PLOT uy against references fig, ax = plt.subplots(constrained_layout=True) my_data = ax.plot(self.y_in_D, self.avg_u1_y, self.y_in_D, self.avg_u2_y - 1, self.y_in_D, self.avg_u3_y - 2) plt.setp(my_data, ls="-", lw=1, marker="", color="red", label="lettuce") ref_LS = ax.plot(self.p1_LS1993_uy[:, 0], self.p1_LS1993_uy[:, 1], self.p2_LS1993_uy[:, 0], self.p2_LS1993_uy[:, 1], self.p3_LS1993_uy[:, 0], self.p3_LS1993_uy[:, 1]) plt.setp(ref_LS, ls="", lw=1, marker="s", fillstyle='none', color="k", label="Lorenco & Shih (1993)") ref_KM = ax.plot(self.p1_KM2000_uy[:, 0], self.p1_KM2000_uy[:, 1], self.p2_KM2000_uy[:, 0], self.p2_KM2000_uy[:, 1], self.p3_KM2000_uy[:, 0], self.p3_KM2000_uy[:, 1]) plt.setp(ref_KM, ls="dotted", lw=1.5, marker="", color="k", label="Kravchenko & Moin (2000)") ref_WR = ax.plot(self.p1_WR2008_uy[:, 0], self.p1_WR2008_uy[:, 1], self.p2_WR2008_uy[:, 0], self.p2_WR2008_uy[:, 1], self.p3_WR2008_uy[:, 0], self.p3_WR2008_uy[:, 1]) plt.setp(ref_WR, ls="dashdot", lw=1.5, marker="", color="k", label="Wissink & Rodi (2008)") ref_DI = ax.plot(self.p1_DI2018_uy[:, 0], self.p1_DI2018_uy[:, 1], self.p2_DI2018_uy[:, 0], self.p2_DI2018_uy[:, 1], self.p3_DI2018_uy[:, 0], self.p3_DI2018_uy[:, 1]) plt.setp(ref_DI, ls="--", lw=1.5, marker="", color="tab:blue", label="Di Ilio et al. (2018)") ax.set_xlabel("y/D") ax.set_ylabel(r"$\bar{u}_{y}$/$u_{char}$") ax.set_ylim((-2.5, +1.5)) ax.set_xlim((-3, 3)) ax.legend(handles=[my_data[0], ref_LS[0], ref_KM[0], ref_WR[0], ref_DI[0]], loc='best') if save: plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_uy_withReference.png") if show: plt.show() else: plt.close() def plot_reynolds_stress_profiles(self, show_reference: bool = False, save: bool = False, show: bool = False): """plot average reynolds stress profiles""" cm = 1 / 2.54 if not show_reference: fig, (ax_xx, ax_yy, ax_xy) = plt.subplots(1, 3, figsize=(40 * cm, 10 * cm), constrained_layout=True) ax_xx.plot(self.y_in_D, self.u1_diff_sq_mean[0], self.y_in_D, self.u2_diff_sq_mean[0], self.y_in_D, self.u3_diff_sq_mean[0]) ax_xx.set_xlabel("y/D") ax_xx.set_ylabel(r"$\overline{u_{x}'u_{x}'}$/$u_{char}^2$") ax_xx.legend(["x/D = 1.06", "x/D = 1.54", "x/D = 2.02"]) ax_yy.plot(self.y_in_D, self.u1_diff_sq_mean[1], self.y_in_D, self.u2_diff_sq_mean[1], self.y_in_D, self.u3_diff_sq_mean[1]) ax_yy.set_xlabel("y/D") ax_yy.set_ylabel(r"$\overline{u_{y}'u_{y}'}$/$u_{char}^2$") ax_yy.legend(["x/D = 1.06", "x/D = 1.54", "x/D = 2.02"]) ax_xy.plot(self.y_in_D, self.u1_diff_xy_mean, self.y_in_D, self.u2_diff_xy_mean, self.y_in_D, self.u3_diff_xy_mean) ax_xy.set_xlabel("y/D") ax_xy.set_ylabel(r"$\overline{u_{x}'u_{y}'}$/$u_{char}^2$") ax_xy.legend(["x/D = 1.06", "x/D = 1.54", "x/D = 2.02"]) if save: plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_reynoldsStresses_noReference.png") if show: plt.show() else: plt.close() else: # plot reynolds stresses against reference # uxux - streamwise fig, ax = plt.subplots(constrained_layout=True) my_data = ax.plot(self.y_in_D, self.u1_diff_sq_mean[0], self.y_in_D, self.u2_diff_sq_mean[0] - 0.5, self.y_in_D, self.u3_diff_sq_mean[0] - 1) plt.setp(my_data, ls="-", lw=1, marker="", color="red", label="lettuce") ref_LS = ax.plot(self.p2_LS1993_uxux[:, 0], self.p2_LS1993_uxux[:, 1]) plt.setp(ref_LS, ls="", lw=1, marker="s", fillstyle='none', color="k", label="Lorenco & Shih (1993)") ref_R = ax.plot(self.p1_R2016_uxux[:, 0], self.p1_R2016_uxux[:, 1], self.p3_R2016_uxux[:, 0], self.p3_R2016_uxux[:, 1], self.p3_R2016_uxux[:, 0], self.p3_R2016_uxux[:, 1]) plt.setp(ref_R, ls="--", lw=1.5, marker="", color="k", label="Rajani et al. (2016)") ref_KM = ax.plot(self.p1_KM2000_uxux[:, 0], self.p1_KM2000_uxux[:, 1], self.p2_KM2000_uxux[:, 0], self.p2_KM2000_uxux[:, 1], self.p3_KM2000_uxux[:, 0], self.p3_KM2000_uxux[:, 1]) plt.setp(ref_KM, ls="dotted", lw=1.5, marker="", color="k", label="Kravchenko & Moin (2000)") ref_BM = ax.plot(self.p2_BM1994_uxux[:, 0], self.p2_BM1994_uxux[:, 1]) plt.setp(ref_BM, ls="dashdot", lw=1.5, marker="", color="k", label="Beaudan & Moin (1994)") ref_DI = ax.plot(self.p1_DI2018_uxux[:, 0], self.p1_DI2018_uxux[:, 1], self.p2_DI2018_uxux[:, 0], self.p2_DI2018_uxux[:, 1], self.p3_DI2018_uxux[:, 0], self.p3_DI2018_uxux[:, 1]) plt.setp(ref_DI, ls="--", lw=1.5, marker="", color="tab:blue", label="Di Ilio et al. (2018)") ax.set_xlabel("y/D") ax.set_ylabel(r"$\overline{u_{x}'u_{x}'}$/$u_{char}^2$") ax.set_ylim((-1.2, 0.8)) ax.set_xlim((-3, 3)) ax.legend( handles=[my_data[0], ref_LS[0], ref_R[0], ref_KM[0], ref_BM[0], ref_DI[0]], loc='best') if save: plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_uxux_withReference.png") if show: plt.show() else: plt.close() # uyuy - cross-stream fig, ax = plt.subplots(constrained_layout=True) my_data = ax.plot(self.y_in_D, self.u1_diff_sq_mean[1], self.y_in_D, self.u2_diff_sq_mean[1] - 0.5, self.y_in_D, self.u3_diff_sq_mean[1] - 1) plt.setp(my_data, ls="-", lw=1, marker="", color="red", label="lettuce") ref_BM = ax.plot(self.p2_BM1994_uyuy[:, 0], self.p2_BM1994_uyuy[:, 1]) plt.setp(ref_BM, ls="dashdot", lw=1.5, marker="", color="k", label="Beaudan & Moin (1994)") ref_LS = ax.plot(self.p2_LS1993_uyuy[:, 0], self.p2_LS1993_uyuy[:, 1]) plt.setp(ref_LS, ls="", lw=1, marker="s", fillstyle='none', color="k", label="Lorenco & Shih (1993)") ref_R = ax.plot(self.p1_R2016_uyuy[:, 0], self.p1_R2016_uyuy[:, 1], self.p3_R2016_uyuy[:, 0], self.p3_R2016_uyuy[:, 1], self.p3_R2016_uyuy[:, 0], self.p3_R2016_uyuy[:, 1]) plt.setp(ref_R, ls="--", lw=1.5, marker="", color="k", label="Rajani et al. (2016)") ref_DI = ax.plot(self.p1_DI2018_uyuy[:, 0], self.p1_DI2018_uyuy[:, 1], self.p2_DI2018_uyuy[:, 0], self.p2_DI2018_uyuy[:, 1], self.p3_DI2018_uyuy[:, 0], self.p3_DI2018_uyuy[:, 1]) plt.setp(ref_DI, ls="--", lw=1.5, marker="", color="tab:blue", label="Di Ilio et al. (2018)") ax.set_xlabel("y/D") ax.set_ylabel(r"$\overline{u_{y}'u_{y}'}$/$u_{char}^2$") ax.set_ylim((-1.2, 0.8)) ax.set_xlim((-3, 3)) ax.legend(handles=[my_data[0], ref_BM[0], ref_LS[0], ref_R[0], ref_DI[0]], loc='best') if save: plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_uyuy_withReference.png") if show: plt.show() else: plt.close() # uxuy - Reynolds shear stress fig, ax = plt.subplots(constrained_layout=True) my_data = ax.plot(self.y_in_D, self.u1_diff_xy_mean, self.y_in_D, self.u2_diff_xy_mean - 0.5, self.y_in_D, self.u3_diff_xy_mean - 1) plt.setp(my_data, ls="-", lw=1, marker="", color="red", label="lettuce") ref_BM = ax.plot(self.p2_BM1994_uxuy[:, 0], self.p2_BM1994_uxuy[:, 1]) plt.setp(ref_BM, ls="dashdot", lw=1.5, marker="", color="k", label="Beaudan & Moin (1994)") ref_LS = ax.plot(self.p2_LS1993_uxuy[:, 0], self.p2_LS1993_uxuy[:, 1]) plt.setp(ref_LS, ls="", lw=1, marker="s", fillstyle='none', color="k", label="Lorenco & Shih (1993)") ref_R = ax.plot(self.p1_R2016_uxuy[:, 0], self.p1_R2016_uxuy[:, 1], self.p3_R2016_uxuy[:, 0], self.p3_R2016_uxuy[:, 1], self.p3_R2016_uxuy[:, 0], self.p3_R2016_uxuy[:, 1]) plt.setp(ref_R, ls="--", lw=1.5, marker="", color="k", label="Rajani et al. (2016)") ref_DI = ax.plot(self.p1_DI2018_uxuy[:, 0], self.p1_DI2018_uxuy[:, 1], self.p2_DI2018_uxuy[:, 0], self.p2_DI2018_uxuy[:, 1], self.p3_DI2018_uxuy[:, 0], self.p3_DI2018_uxuy[:, 1]) plt.setp(ref_DI, ls="--", lw=1.5, marker="", color="tab:blue", label="Di Ilio et al. (2018)") ax.set_xlabel("y/D") ax.set_ylabel(r"$\overline{u_{x}'u_{y}'}$/$u_{char}^2$") ax.set_ylim((-1.2, 0.8)) ax.set_xlim((-3, 3)) ax.legend(handles=[my_data[0], ref_BM[0], ref_LS[0], ref_R[0], ref_DI[0]], loc='best') if save: plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_uxuy_withReference.png") if show: plt.show() else: plt.close() ================================================ FILE: examples/advanced_projects/efficient_bounce_back_obstacle/auxiliary_code/helperCode.py ================================================ import os import sys class Logger(object): """logging functionality proposed by P.Spelten to log stdout to file""" def __init__(self, outdir): self.terminal = sys.stdout self.filename = os.path.join(outdir, "log") def write(self, message): self.terminal.write(message) f = open(self.filename, "a") f.write(message) f.close() def flush(self): # this flush method is needed for python 3 compatibility. # This handles the flush command by doing nothing. # you might want to specify some extra behavior here. pass ================================================ FILE: examples/advanced_projects/efficient_bounce_back_obstacle/boundary/__init__.py ================================================ ================================================ FILE: examples/advanced_projects/efficient_bounce_back_obstacle/boundary/fullway_bounce_back_boundary.py ================================================ import torch import numpy as np from typing import List, Optional from lettuce import Boundary, Flow, Context __all__ = ["FullwayBounceBackBoundary"] class FullwayBounceBackBoundary(Boundary): """ FULLWAY BOUNCE BACK BOUNDARY CONDITION (FWBB): - inverts populations on solid boundary nodes, instead of colliding them - (!) mind observable-artifacts on solid regions. They do not affect the flow in the fluid region, but can mess with visualization and non-local statistics (mean velocity etc.) - able to calculate fluid force on boundary by momentum exchange """ def __init__(self, context: 'Context', flow: 'Flow', mask: np.ndarray | torch.Tensor, global_solid_mask: Optional[np.ndarray | torch.Tensor]=None, periodicity: tuple[bool,bool,bool|None] = None, calc_force: bool = False): self.context = context self.flow = flow self.mask = mask if periodicity is None: periodicity = (False, False, False if self.flow.stencil.d == 3 else None) # global_solid_mask (optional) to filter out all "fake" fluid neighbors, # ...which are outside the FWBB but not in the fluid region if global_solid_mask is None: global_solid_mask = np.zeros_like(self.mask, dtype=bool) # exclude self.mask from global_solid_mask for "other" solid mask other_solid_bc_mask = np.where(~self.mask, global_solid_mask, False) if calc_force: # summed force vector on all boundary nodes, in D dimensions (x,y,(z)) self.force_sum = torch.zeros_like(self.context.convert_to_tensor( self.flow.torch_stencil.e[0])) self.calc_force = True else: self.calc_force = False ### create f_index_fwbb, needed for force-calculation # ...(marks all fs which streamed into the boundary in prior streaming step) # ... in other words: marks all fs that need to be bounced self.f_index_fwbb = [] if self.flow.stencil.d == 2: nx, ny = mask.shape # domain size in x and y ix_sp, iy_sp = np.where(mask) # np.arrays: list of (ix_sp) x-indizes and (iy_sp) y-indizes in the boundary.mask # ...to enable iteration over all boundary/wall/object-nodes for sp_index in range(0, len(ix_sp)): # for all TRUE-nodes in boundary.mask for q_index in range(0, self.flow.stencil.q): # for all stencil-directions c_i (lattice.stencil.e in lettuce) # check for boundary-nodes neighboring the domain-border. # ...they have to take the periodicity into account... border = np.zeros(self.flow.stencil.d, dtype=int) if (ix_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 0] == -1 and periodicity[0]): # searching border on left border[0] = -1 elif (ix_sp[sp_index] == nx - 1 and self.flow.stencil.e[q_index][ 0] == 1 and periodicity[0]): # searching border on right border[0] = 1 if (iy_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 1] == -1 and periodicity[1]): # searching border on left border[1] = -1 elif (iy_sp[sp_index] == ny - 1 and self.flow.stencil.e[q_index][ 1] == 1 and periodicity[1]): # searching border on right border[1] = 1 try: # try in case the neighboring cell does not exist # (= an f pointing out of the simulation domain) if (not mask[ix_sp[sp_index] + self.flow.stencil.e[q_index][ 0] - border[0]*nx, iy_sp[sp_index] + self.flow.stencil.e[q_index][ 1] - border[1]*ny] and not other_solid_bc_mask[ix_sp[sp_index] + self.flow.stencil.e[q_index][ 0] - border[0]*nx, iy_sp[sp_index] + self.flow.stencil.e[q_index][ 1] - border[1]*ny]): # if the neighbor of sp_index is False in the boundary.mask, # ...sp_index is ix_sp solid node, neighboring ix_sp fluid node: # the direction pointing from the fluid neighbor to solid # ...sp_index is marked on the solid sp_index # list of [q, nx, ny], marks all fs on the boundary-border, which point into the boundary/solid self.f_index_fwbb.append([self.flow.stencil.opposite[q_index], ix_sp[sp_index], iy_sp[sp_index]]) except IndexError: pass # just ignore this iteration since there is no neighbor there if self.flow.stencil.d == 3: # like 2D, but in 3D...guess what... nx, ny, nz = mask.shape ix_sp, iy_sp, iz_sp = np.where(mask) for sp_index in range(0, len(ix_sp)): for q_index in range(0, self.flow.stencil.q): border = np.zeros(self.flow.stencil.d, dtype=int) if (ix_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 0] == -1 and periodicity[0]): # searching border on left border[0] = -1 elif (ix_sp[sp_index] == nx - 1 and self.flow.stencil.e[q_index][ 0] == 1 and periodicity[0]): # searching border on right border[0] = 1 if (iy_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 1] == -1 and periodicity[1]): # searching border on left border[1] = -1 elif (iy_sp[sp_index] == ny - 1 and self.flow.stencil.e[q_index][ 1] == 1 and periodicity[1]): # searching border on right border[1] = 1 if (iz_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 2] == -1 and periodicity[2]): # searching border on left border[2] = -1 elif (iz_sp[sp_index] == nz - 1 and self.flow.stencil.e[q_index][ 2] == 1 and periodicity[2]): # searching border on right border[2] = 1 try: # try in case the neighboring cell does not exist # (and f pointing out of simulation domain) if (not mask[ix_sp[sp_index] + self.flow.stencil.e[q_index][ 0] - border[0] * nx, iy_sp[sp_index] + self.flow.stencil.e[q_index][ 1] - border[1] * ny, iz_sp[sp_index] + self.flow.stencil.e[q_index][ 2] - border[2] * nz] and not other_solid_bc_mask[ix_sp[sp_index] + self.flow.stencil.e[q_index][ 0] - border[0] * nx, iy_sp[sp_index] + self.flow.stencil.e[q_index][ 1] - border[1] * ny, iz_sp[sp_index] + self.flow.stencil.e[q_index][ 2] - border[2] * nz]): self.f_index_fwbb.append([self.flow.stencil.opposite[q_index], ix_sp[sp_index], iy_sp[sp_index], iz_sp[sp_index]]) except IndexError: pass # just ignore this iteration since there is no neighbor there self.f_index_fwbb = torch.tensor(np.array(self.f_index_fwbb), device=flow.context.device, dtype=torch.int64) # the batch-index has to be integer self.opposite_tensor = torch.tensor(self.flow.stencil.opposite, device=flow.context.device, dtype=torch.int64) # batch-index has to be ix_sp tensor def __call__(self, flow: 'Flow'): # FULLWAY-BBBC: inverts populations on all boundary nodes # calc force on boundary:# if self.calc_force: self.calc_force_on_boundary(flow.f) # bounce (invert populations on boundary nodes) # -> basically an efficient version of: # f = torch.where(self.mask, f[self.flow.stencil.opposite], f) if self.flow.torch_stencil.d == 2: flow.f[self.opposite_tensor[self.f_index_fwbb[:, 0]], self.f_index_fwbb[:, 1], self.f_index_fwbb[:, 2]] = flow.f[self.f_index_fwbb[:, 0], self.f_index_fwbb[:, 1], self.f_index_fwbb[:, 2]] if self.flow.torch_stencil.d == 3: flow.f[self.opposite_tensor[self.f_index_fwbb[:, 0]], self.f_index_fwbb[:, 1], self.f_index_fwbb[:, 2], self.f_index_fwbb[:, 3]] = flow.f[self.f_index_fwbb[:, 0], self.f_index_fwbb[:, 1], self.f_index_fwbb[:, 2], self.f_index_fwbb[:, 3]] def calc_force_on_boundary(self, f: torch.Tensor): """calculate fluid force on all relevant boundary nodes by momentum exchange""" if self.flow.torch_stencil.d == 2: self.force_sum = 2 * torch.einsum('i..., id -> d', f[self.f_index_fwbb[:, 0], self.f_index_fwbb[:, 1], self.f_index_fwbb[:, 2]], self.flow.torch_stencil.e[self.f_index_fwbb[:, 0]]) if self.flow.torch_stencil.d == 3: self.force_sum = 2 * torch.einsum('i..., id -> d', f[self.f_index_fwbb[:, 0], self.f_index_fwbb[:, 1], self.f_index_fwbb[:, 2], self.f_index_fwbb[:, 3]], self.flow.torch_stencil.e[self.f_index_fwbb[:, 0]]) def make_no_collision_mask(self, f_shape: List[int], context: 'Context') -> Optional[torch.Tensor]: return self.context.convert_to_tensor(self.mask, dtype=bool) def make_no_streaming_mask(self, shape: List[int], context: 'Context') -> Optional[torch.Tensor]: """FWBB needs streaming to invert the populations on the solid itself!""" pass def native_available(self) -> bool: return False def native_generator(self, index: int): # not implemented yet pass ================================================ FILE: examples/advanced_projects/efficient_bounce_back_obstacle/boundary/halfway_bounce_back_boundary.py ================================================ import torch import numpy as np from typing import List, Optional from lettuce import Boundary, Flow, Context from solid_boundary_data import SolidBoundaryData __all__ = ["HalfwayBounceBackBoundary"] class HalfwayBounceBackBoundary(Boundary): """ HALFWAY BOUNCE BACK BOUNDARY CONDITION (HWBB): - inverts populations on fluid nodex, neighboring boundary - improved temporal accuracy in comparison to FWBB - able to calculate fluid force on boundary by momentum exchange """ def __init__(self, context: 'Context', flow: 'Flow', solid_boundary_data: SolidBoundaryData, global_solid_mask: Optional[np.ndarray | torch.Tensor]=None, periodicity: tuple[bool,bool,bool|None] = None, calc_force: bool = False): self.context = context self.flow = flow self.mask = solid_boundary_data.solid_mask # global_solid_mask to filter out all "fake" fluid neighbors, # which are outside this HWBB but not in the fluid region if global_solid_mask is None: global_solid_mask = self.mask if periodicity is None: periodicity = (False, False, False if self.flow.stencil.d == 3 else None) if calc_force: # summed force vector on all boundary nodes, in D dimensions (x,y,(z)) self.force_sum = torch.zeros_like(self.context.convert_to_tensor( self.flow.stencil.e[0])) self.calc_force = True else: self.calc_force = False self.f_index = [] self.f_collided = None # COMPATIBILITY to unified Solid Boundary Data object! # -> combine f_index_lt and f_index_gt (needed for IBB1) to self.f_index # if solid_boundary_data contains batch_indices, use them if ((hasattr(solid_boundary_data, "f_index_gt") or hasattr(solid_boundary_data, "f_index_lt")) and len(solid_boundary_data.f_index_lt.shape) == len(solid_boundary_data.f_index_gt.shape)): print("HWBB: SBD has got attributs f_index_gt and f_index_lt!") self.f_index = np.concatenate((solid_boundary_data.f_index_lt, solid_boundary_data.f_index_gt), axis=0) elif (hasattr(solid_boundary_data, "f_index_gt") and solid_boundary_data.f_index_lt.shape[0] == 0): print("HWBB: SBD has got attributf_index_gt (NO f_index_lt)!") self.f_index = solid_boundary_data.f_index_gt elif (hasattr(solid_boundary_data, "f_index_lt") and solid_boundary_data.f_index_gt.shape[0] == 0): print("HWBB: SBD has got attributf_index_lt (NO f_index_gt)!") self.f_index = solid_boundary_data.f_index_lt else: #else do extra neighbor_search below print("(INFO) HWBB didn't find solid_boundary_data, " "doing legacy neighbour_search on mask...") # searching boundary-fluid-interface and append indices to f_index if self.flow.stencil.d == 2: nx, ny = self.mask.shape # domain size in x and y # x- and y-index of boundaryTRUE nodes for iteration over boundary area a, b = np.where(self.mask) for p in range(0, len(a)): # for all TRUE-nodes in boundary.mask for i in range(0, self.flow.stencil.q): # for all stencil-directions c_i (lattice.stencil.e in lettuce) # check for boundary-nodes neighboring the domain-border. # ...they have to take the periodicity into account... border = np.zeros(self.flow.context.d, dtype=int) if (a[p] == 0 and self.flow.stencil.e[i][ 0] == -1 and periodicity[0]): # searching border on left [x] border[0] = -1 elif (a[p] == nx - 1 and self.flow.stencil.e[i][ 0] == 1 and periodicity[0]): # searching border on right [x] border[0] = 1 if (b[p] == 0 and self.flow.stencil.e[i][ 1] == -1 and periodicity[1]): # searching border on left [y] border[1] = -1 elif (b[p] == ny - 1 and self.flow.stencil.e[i][ 1] == 1 and periodicity[1]): # searching border on right [y] border[1] = 1 try: # try in case the neighboring cell does not exist # (= an f pointing out of the simulation domain) if (not self.mask[a[p] + self.flow.stencil.e[i][ 0] - border[0] * nx, b[p] + self.flow.stencil.e[i][ 1] - border[1] * ny] and not global_solid_mask[ a[p] + self.flow.stencil.e[i][ 0] - border[0] * nx, b[p] + self.flow.stencil.e[i][ 1] - border[1] * ny]): # if the neighbor of p is False in the boundary.mask, # p is a solid node, neighboring a fluid node: # ...the direction pointing from the fluid neighbor # to solid p is marked on the neighbor self.f_index.append([self.flow.stencil.opposite[i], a[p] + self.flow.stencil.e[i][ 0] - border[0] * nx, b[p] + self.flow.stencil.e[i][ 1] - border[1] * ny]) except IndexError: pass # just ignore this iteration since there is no neighbor there if self.flow.stencil.d == 3: # like 2D, but in 3D...guess what... nx, ny, nz = self.mask.shape a, b, c = np.where(self.mask) for p in range(0, len(a)): for i in range(0, self.flow.stencil.q): border = np.zeros(self.flow.context.d, dtype=int) # x - direction if (a[p] == 0 and self.flow.stencil.e[i][ 0] == -1 and periodicity[0]): # searching border on left border[0] = -1 elif (a[p] == nx - 1 and self.flow.stencil.e[i][ 0] == 1 and periodicity[0]): # searching border on right border[0] = 1 # y - direction if (b[p] == 0 and self.flow.stencil.e[i][ 1] == -1 and periodicity[1]): # searching border on left border[1] = -1 elif (b[p] == ny - 1 and self.flow.stencil.e[i][ 1] == 1 and periodicity[1]): # searching border on right border[1] = 1 # z - direction if (c[p] == 0 and self.flow.stencil.e[i][ 2] == -1 and periodicity[2]): # searching border on left border[2] = -1 elif (c[p] == nz - 1 and self.flow.stencil.e[i][ 2] == 1 and periodicity[2]): # searching border on right border[2] = 1 try: # try in case the neighboring cell does not exist # (an f pointing out of simulation domain) if (not self.mask[a[p] + self.flow.stencil.e[i][ 0] - border[0] * nx, b[p] + self.flow.stencil.e[i][ 1] - border[1] * ny, c[p] + self.flow.stencil.e[i][ 2] - border[2] * nz] and not global_solid_mask[ a[p] + self.flow.stencil.e[i][ 0] - border[0] * nx, b[p] + self.flow.stencil.e[i][ 1] - border[1] * ny, c[p] + self.flow.stencil.e[i][ 2] - border[2] * nz]): self.f_index.append([self.flow.stencil.opposite[i], a[p] + self.flow.stencil.e[i][ 0] - border[0] * nx, b[p] + self.flow.stencil.e[i][ 1] - border[1] * ny, c[p] + self.flow.stencil.e[i][ 2] - border[2] * nz]) except IndexError: pass # just ignore this iteration since there is no neighbor there # convert relevant tensors: self.f_index = torch.tensor(np.array(self.f_index), device=self.flow.context.device, dtype=torch.int64) # the batch-index has to be integer self.opposite_tensor = torch.tensor(self.flow.stencil.opposite, device=self.flow.context.device, dtype=torch.int64) # batch-index has to be a tensor def __call__(self, flow: 'Flow'): # HWBB: bounce populations on neighboring fluid nodes # calc force on boundary: if self.calc_force: self.calc_force_on_boundary() # bounce (invert populations on fluid nodes neighboring solid nodes) # efficient version of: # f = torch.where(self.f_mask[self.flow.stencil.opposite], # f_collided[self.flow.stencil.opposite], f) if self.flow.torch_stencil.d == 2: flow.f[self.opposite_tensor[self.f_index[:, 0]], self.f_index[:, 1], self.f_index[:, 2]] = self.f_collided[:, 0] if self.flow.torch_stencil.d == 3: flow.f[self.opposite_tensor[self.f_index[:, 0]], self.f_index[:, 1], self.f_index[:, 2], self.f_index[:, 3]] = self.f_collided[:, 0] def make_no_collision_mask(self, f_shape: List[int], context: 'Context' ) -> Optional[torch.Tensor]: # INFO: for the halfway bounce back boundary, # a no_collision_mask ist not necessary, because the no_stream_mask # prevents interaction between nodes inside and outside the boundary region. # INFO: pay attention to the initialization of observable/moment-fields # (u, rho,...) on the boundary nodes, in the initial solution of your flow, # especially if visualization or post-processing uses the field-values # in the whole domain (including the boundary region)! return self.context.convert_to_tensor(self.mask, dtype=bool) def make_no_streaming_mask(self, f_shape: List[int], context: 'Context' ) -> Optional[torch.Tensor]: # no_stream_mask has to be dimensions: (q,x,y,z) (z optional), # ...but CAN be (x,y,z) (z optional). # ...in the latter case, torch.where() broadcasts the mask to (q,x,y,z), # ...so ALL q populations of a lattice-node are marked equally return self.context.convert_to_tensor(self.mask, dtype= bool) def calc_force_on_boundary(self): """calculate the fluid force on boundary by momentum exchange""" # calculate force on boundary by momentum exchange method (MEA, MEM) # according to Kruger et al., 2017, pp.215-217: # momentum (f_i*c_i - f_i_opposite*c_i_opposite = 2*f_i*c_i for a resting boundary) # is summed for all populations pointing at the surface of the boundary self.force_sum = 2 * torch.einsum('i..., id -> d', self.f_collided[:, 0], self.flow.torch_stencil.e[self.f_index[:, 0]]) def store_f_collided(self, f_collided: torch.Tensor): """store populations between collision and streaming, because they are needed for calculation of bounce and force!""" if self.flow.torch_stencil.d == 2: self.f_collided[:, 0] = torch.clone( f_collided[self.f_index[:, 0], # q self.f_index[:, 1], # x self.f_index[:, 2]]) # y self.f_collided[:, 1] = torch.clone( f_collided[self.opposite_tensor[self.f_index[:, 0]], # q self.f_index[:, 1], # x self.f_index[:, 2]]) # y if self.flow.torch_stencil.d == 3: self.f_collided[:, 0] = torch.clone( f_collided[self.f_index[:, 0], # q self.f_index[:, 1], # x self.f_index[:, 2], # y self.f_index[:, 3]]) # z self.f_collided[:, 1] = torch.clone( f_collided[self.opposite_tensor[self.f_index[:, 0]], # q self.f_index[:, 1], # x self.f_index[:, 2], # y self.f_index[:, 3]]) # z def initialize_f_collided(self): """initialize the tensors to store post-collision populations""" f_collided = torch.zeros_like(self.f_index[:, 0], dtype=self.flow.context.dtype) f_collided_opposite = torch.zeros_like(self.f_index[:, 0], dtype=self.flow.context.dtype) self.f_collided = torch.stack((f_collided, f_collided_opposite), dim=1) def native_available(self) -> bool: return True def native_generator(self, index: int): # not implemented yet pass ================================================ FILE: examples/advanced_projects/efficient_bounce_back_obstacle/boundary/linear_interpolated_bounce_back_boundary.py ================================================ import torch from typing import List, Optional from lettuce import Boundary, Flow, Context from solid_boundary_data import SolidBoundaryData __all__ = ["LinearInterpolatedBounceBackBoundary"] class LinearInterpolatedBounceBackBoundary(Boundary): """Interpolated Bounce Back Boundary Condition (IBB or IBB1) first introduced by Bouzidi et al. (2001), as described in Kruger et al. (2017) - linear interpolation of populations to retain the true boundary location between fluid- and solid-node - improved accuracy in comparison to FWBB and HWBB - able to calculate fluid force on boundary by momentum exchange - (!) RELIES on the SolidBoundaryData object to be provided, which supplies the f_index and interpolation constants. """ def __init__(self, context: 'Context', flow: 'Flow', solid_boundary_data: SolidBoundaryData, calc_force: bool =False): self.context = context self.flow = flow self.mask = solid_boundary_data.solid_mask if calc_force: # summed force vector on all boundary nodes, in D dimensions (x,y,(z)) self.force_sum = torch.zeros_like(self.context.convert_to_tensor( self.flow.stencil.e[0])) self.calc_force = True else: self.calc_force = False # convert relevant data to tensors: self.f_index_lt = torch.tensor(solid_boundary_data.f_index_lt, device=self.context.device, dtype=torch.int64) # the batch-index has to be integer self.f_index_gt = torch.tensor(solid_boundary_data.f_index_gt, device=self.context.device, dtype=torch.int64) # the batch-index has to be integer self.d_lt = self.context.convert_to_tensor(solid_boundary_data.d_lt) self.d_gt = self.context.convert_to_tensor(solid_boundary_data.d_gt) self.opposite_tensor = torch.tensor(self.flow.stencil.opposite, device=self.context.device, dtype=torch.int64) # batch-index has to be a tensor # TODO (optional): replace self.opposite_tensor with flow.torch_stencil.opposite # in old lettuce (pre-2025) there was no torch_stencil! self.f_collided_lt = None self.f_collided_gt = None # will be populated in initialize_f_collided() method def __call__(self, flow: 'Flow'): """ IBB1: interpolate bounced populations from two fluid nodes""" ## reminder: f_collided_lt = [f_collided_lt, f_collided_lt.opposite] # (!) this is for compact storage-layout if self.flow.stencil.d == 2: # BOUNCE # if d <= 0.5 if len(self.f_index_lt) != 0: flow.f[self.opposite_tensor[self.f_index_lt[:, 0]], self.f_index_lt[:, 1], self.f_index_lt[:, 2]] = (2 * self.d_lt * self.f_collided_lt[:, 0] + (1 - 2 * self.d_lt) * flow.f[ self.f_index_lt[:, 0], self.f_index_lt[:, 1], self.f_index_lt[:, 2]]) # if d > 0.5 if len(self.f_index_gt) != 0: flow.f[self.opposite_tensor[self.f_index_gt[:, 0]], self.f_index_gt[:, 1], self.f_index_gt[:, 2]] = ((1 / (2 * self.d_gt)) * self.f_collided_gt[:, 0] + (1 - 1 / (2 * self.d_gt)) * self.f_collided_gt[:, 1]) if self.flow.stencil.d == 3: # BOUNCE # if d <= 0.5 if len(self.f_index_lt) != 0: flow.f[self.opposite_tensor[self.f_index_lt[:, 0]], self.f_index_lt[:, 1], self.f_index_lt[:, 2], self.f_index_lt[:, 3]] = (2 * self.d_lt * self.f_collided_lt[:, 0] + (1 - 2 * self.d_lt) * flow.f[ self.f_index_lt[:, 0], self.f_index_lt[:, 1], self.f_index_lt[:, 2], self.f_index_lt[:, 3]]) # if d > 0.5 if len(self.f_index_gt) != 0: flow.f[self.opposite_tensor[self.f_index_gt[:, 0]], self.f_index_gt[:, 1], self.f_index_gt[:, 2], self.f_index_gt[:, 3]] = ((1 / (2 * self.d_gt)) * self.f_collided_gt[:, 0] + (1 - 1 / (2 * self.d_gt)) * self.f_collided_gt[:, 1]) # CALC. FORCE on boundary (MEM, MEA) if self.calc_force: self.calc_force_on_boundary(flow.f) def make_no_streaming_mask(self, f_shape: List[int], context: Context): # no_stream_mask has to be dimensions: (q,x,y,z) (z optional), # but CAN be (x,y,z) (z optional). # in the latter case, torch.where broadcasts the mask to (q,x,y,z), # so ALL q populations of a lattice-node are marked equally return self.context.convert_to_tensor(self.mask, dtype=bool) def make_no_collision_mask(self, f_shape: List[int], context: Context): # INFO: pay attention to the initialization of observable/moment-fields (u, rho,...) # on the boundary nodes, in the initial solution of your flow, # especially if visualization or post-processing uses the field-values # in the whole domain (including the boundary region)! return self.context.convert_to_tensor(self.mask, dtype=bool) def calc_force_on_boundary(self, f_bounced: torch.Tensor): """calculate the fluid force on the boundary by Momentum Exchange""" ### basically: force = e * (f_collided + f_bounced[opp.]) if self.flow.stencil.d == 2: self.force_sum = torch.einsum('i..., id -> d', self.f_collided_lt[:, 0] + f_bounced[ self.opposite_tensor[self.f_index_lt[:, 0]], self.f_index_lt[:, 1], self.f_index_lt[:, 2]], self.flow.torch_stencil.e[self.f_index_lt[:, 0]]) \ + torch.einsum('i..., id -> d', self.f_collided_gt[:, 0] + f_bounced[ self.opposite_tensor[self.f_index_gt[:, 0]], self.f_index_gt[:, 1], self.f_index_gt[:, 2]], self.flow.torch_stencil.e[self.f_index_gt[:, 0]]) if self.flow.stencil.d == 3: self.force_sum = torch.einsum('i..., id -> d', self.f_collided_lt[:, 0] + f_bounced[ self.opposite_tensor[self.f_index_lt[:, 0]], self.f_index_lt[:, 1], self.f_index_lt[:, 2], self.f_index_lt[:, 3]], self.flow.torch_stencil.e[self.f_index_lt[:, 0]]) \ + torch.einsum('i..., id -> d', self.f_collided_gt[:, 0] + f_bounced[ self.opposite_tensor[self.f_index_gt[:, 0]], self.f_index_gt[:, 1], self.f_index_gt[:, 2], self.f_index_gt[:, 3]], self.flow.torch_stencil.e[self.f_index_gt[:, 0]]) def store_f_collided(self, f_collided: torch.Tensor): """store populations between collision and streaming, because they are needed for calculation of bounce and force!""" if self.flow.stencil.d == 2: if len(self.f_collided_lt) != 0: self.f_collided_lt[:, 0] = torch.clone(f_collided[self.f_index_lt[:, 0], # q self.f_index_lt[:, 1], # x self.f_index_lt[:, 2]]) # y self.f_collided_lt[:, 1] = ( torch.clone(f_collided[self.opposite_tensor[self.f_index_lt[:,0]], # q self.f_index_lt[:, 1], # x self.f_index_lt[:, 2]])) # y if len(self.f_collided_gt) != 0: self.f_collided_gt[:, 0] = torch.clone(f_collided[self.f_index_gt[:, 0], # q self.f_index_gt[:, 1], # x self.f_index_gt[:, 2]]) # y self.f_collided_gt[:, 1] = ( torch.clone(f_collided[self.opposite_tensor[self.f_index_gt[:,0]], # q self.f_index_gt[:, 1], # x self.f_index_gt[:, 2]])) # y if self.flow.stencil.d == 3: if len(self.f_collided_lt) != 0: self.f_collided_lt[:, 0] = torch.clone(f_collided[self.f_index_lt[:, 0], # q self.f_index_lt[:, 1], # x self.f_index_lt[:, 2], # y self.f_index_lt[:, 3]]) # z self.f_collided_lt[:, 1] = ( torch.clone(f_collided[self.opposite_tensor[self.f_index_lt[:,0]], # q self.f_index_lt[:, 1], # x self.f_index_lt[:, 2], # y self.f_index_lt[:, 3]])) # z if len(self.f_collided_gt) != 0: self.f_collided_gt[:, 0] = torch.clone(f_collided[self.f_index_gt[:, 0], # q self.f_index_gt[:, 1], # x self.f_index_gt[:, 2], # y self.f_index_gt[:, 3]]) # z self.f_collided_gt[:, 1] = ( torch.clone(f_collided[self.opposite_tensor[self.f_index_gt[:, 0]], # q self.f_index_gt[:, 1], # x self.f_index_gt[:, 2], # y self.f_index_gt[:, 3]])) # z def initialize_f_collided(self): """initialize the tensors to store post-collision populations""" # float-tensor with number of (x_b nodes with d<=0.5) values f_collided_lt = torch.zeros_like(self.d_lt) # float-tensor with number of (x_b nodes with d>0.5) values f_collided_gt = torch.zeros_like(self.d_gt) f_collided_lt_opposite = torch.zeros_like(self.d_lt) f_collided_gt_opposite = torch.zeros_like(self.d_gt) self.f_collided_lt = torch.stack((f_collided_lt, f_collided_lt_opposite), dim=1) self.f_collided_gt = torch.stack((f_collided_gt, f_collided_gt_opposite), dim=1) def native_available(self) -> bool: return False def native_generator(self, index: int): # not implemented yet return None ================================================ FILE: examples/advanced_projects/efficient_bounce_back_obstacle/boundary/solid_boundary_data.py ================================================ import numpy as np __all__ = ['SolidBoundaryData'] # index lists for Bounce Back Boundaries: populations for FWBB, HWBB; IBB. # - d and the differentiation of lt (less than 0.5) # and gt (greater than 0.5) is only for IBB class SolidBoundaryData(dict): f_index_lt: np.ndarray f_index_gt: np.ndarray d_lt: np.ndarray d_gt: np.ndarray points_inside: np.ndarray solid_mask: np.ndarray not_intersected: np.ndarray = np.ndarray([]) ================================================ FILE: examples/advanced_projects/efficient_bounce_back_obstacle/flow/__init__.py ================================================ ================================================ FILE: examples/advanced_projects/efficient_bounce_back_obstacle/flow/obstacle_cylinder.py ================================================ from typing import Union, List, Optional import numpy as np import torch from lettuce.ext._flows import ExtFlow from lettuce import UnitConversion, Context, Stencil, Equilibrium from lettuce.util import append_axes from lettuce.ext import (EquilibriumBoundaryPU, EquilibriumOutletP) from examples.advanced_projects.efficient_bounce_back_obstacle import SolidBoundaryData, FullwayBounceBackBoundary, HalfwayBounceBackBoundary, LinearInterpolatedBounceBackBoundary __all__ = ['ObstacleCylinder'] class ObstacleCylinder(ExtFlow): """ FLOW AROUND A CIRCULAR CYLINDER in 2D or 3D Flow:< - inflow (EquilibriumBoundaryPU) at x=0, outflow (EquilibriumOutletP) at x=xmax - further boundaries depend on parameters: - lateral (y direction): periodic, no-slip wall, slip wall - lateral (z direction): periodic (only if 3D) - obstacle: cylinder obstacle centered at (y_lu/2+x_offset, y_LU/2+y_offset), with radius=char_length, uniform symmetry in z-direction - boundary condition for obstacle can be chosen: hwbb, fwbb, ibb1 - FWBB: fullway bounce back - HWBB: halfway bounce back - IBB1: linear interpolated bounce back - initial perturbation (trigger Von Kármán vortex street for Re>46) can be initialized in y and z direction - initial velocity can be 0, u_char or a parabolic profile (parabolic if lateral_walls are True) - inlet/inflow velocity can be uniform u_char or parabolic """ def __init__(self, context: Context, resolution: Union[int, List[int]], reynolds_number: float, mach_number: float, char_length_pu: float, char_length_lu: float, char_velocity_pu: float=1, lateral_walls: str = 'periodic', bc_type: str = 'fwbb', perturb_init: bool = True, u_init: int = 0, x_offset: float=0, y_offset: float=0, calc_force_coefficients: bool=False, stencil: Optional[Stencil] = None, equilibrium: Optional[Equilibrium] = None): self.char_length_pu = char_length_pu self.char_length_lu = char_length_lu self.char_velocity_pu = char_velocity_pu # domain "shape" in LU. # If only one INT, a square/cube-shaped domain is assumed self.resolution = self.make_resolution(resolution, stencil) # flow and boundary settings self.perturb_init = perturb_init # toggle: introduce asymmetry in initial solution to trigger # v'Karman Vortex Street self.u_init = u_init # toggle: initial solution velocity profile type self.lateral_walls = lateral_walls # toggle: lateral walls to be bounce back (bounceback), # slip wall (slip) or periodic (periodic) self.bc_type = bc_type # toggle: bounce back algorithm: # - halfway (hwbb), # - fullway (fwbb), # - linearly interpolated (ibb1) self.calc_force_coefficients = calc_force_coefficients self.periodicity = (False, False, True if len(self.resolution) == 3 else None) # initialize masks (init with zeros) self.solid_mask = np.zeros(shape=self.resolution, dtype=bool) # marks all solid nodes (obstacle, walls, ...) self.wall_mask = np.zeros_like(self.solid_mask) # marks lateral (top+bottom) walls self._obstacle_mask = np.zeros_like(self.solid_mask) # marks all obstacle nodes (for fluid-solid-force_calculation) # initialize super class with unit conversion, equilibrium, context etc. ExtFlow.__init__(self, context, resolution, reynolds_number, mach_number, stencil, equilibrium) self.in_mask = np.zeros(self.grid[0].shape, dtype=bool) # marks all inlet nodes # cylinder geometry in LU self.x_offset = x_offset self.y_offset = y_offset self.radius_lu = char_length_lu / 2 self.y_pos_lu = self.resolution[1] / 2 + 0.5 + self.y_offset # y_position of cylinder-center in 1-based indexing self.x_pos_lu = self.y_pos_lu + self.x_offset # keep symmetry of cylinder in x and y direction # MESHGRID of x, y, (z) index (LU) xyz = tuple(np.linspace(1, n, n) for n in self.resolution) # Tupel of index-lists (1-n (one-based!)) if self.stencil.d == 2: # meshgrid of x-, y-index x_lu, y_lu = np.meshgrid(*xyz, indexing='ij') elif self.stencil.d == 3: # meshgrid of x-, y- and z-index x_lu, y_lu, z_lu = np.meshgrid(*xyz, indexing='ij') else: x_lu, y_lu, z_lu = 1,1,1 print("CYLINDER OBSTACLE -> WARNING: something went wrong in LU-gird-index generation," " lattice.D must be 2 or 3!") # Options for obstacle definition: # 1) basic mask (no interpolation) -> see right below # 2) IBB-index and mask -> see method make_ibb_index_lists below # TODO: 3) OCC-enabled -> read externally supplied SolidBoundaryData # Opt1: basic mask (no interpolation) condition = np.sqrt((x_lu - self.x_pos_lu) ** 2 + (y_lu - self.y_pos_lu) ** 2) < self.radius_lu self.obstacle_mask[np.where(condition)] = 1 self.solid_mask[np.where(condition)] = 1 print("CYLINDER OBSTACLE: x_pos, y_pos, radius:", self.x_pos_lu, self.y_pos_lu, self.radius_lu) # MASKS for solid boundaries (lateral walls, obstacle, solid (= obstacle and walls), inlet) # (INFO): indexing doesn't need z-Index for 3D, everything is broadcasted along z! if self.lateral_walls == 'bounceback' or self.lateral_walls == 'slip': # if top and bottom are link-based BC self.wall_mask[:, [0, -1]] = True # don't mark wall nodes as inlet self.solid_mask[np.where(self.wall_mask)] = 1 # mark solid walls self.in_mask[0, 1:-1] = True # inlet on the left, except for top and bottom wall (y=0, y=y_max) else: # if lateral_wals == 'periodic', no walls self.in_mask[0, :] = True # inlet on the left (x=0) # generate parabolic velocity profile for inlet BC if lateral_walls # (top and bottom) are bounce back walls (== channel-flow) self.u_inlet = self.units.characteristic_velocity_pu * self._unit_vector() # u = [ux,uy,uz] = [1,0,0] in PU // uniform characteristic velocity in x-direction if self.lateral_walls == 'bounceback': """ parabolic velocity profile, zeroing on the edges ## How to parabola: ## 1.parabola in factorized form (GER: "Nullstellenform"): y = (x-x1)*(x-x2) ## 2.parabola with a maximum and zero at x1=0 und x2=x0: y=-x*(x-x0) ## 3.scale parabola, to make y_s(x_s)=1 the maximum: y=-x*(x-x0)*(1/(x0/2)²) ## (4. optional) scale amplitude with 1.5 to have a mean velocity of 1, also making the integral of a homogeneous velocity profile with u=1 and the parabolic profile being equal """ (nx, ny, nz) = self.resolution # number of gridpoints in y direction parabola_y = np.zeros((1, ny)) y_coordinates = np.linspace(0, ny, ny) # linspace() creates n points between 0 and ny, including 0 and ny: # top and bottom velocity values will be zero to agree with wall-boundary-condition parabola_y[:, 1:-1] = - 1.5 * np.array(self.u_inlet).max() * y_coordinates[1:-1] * ( y_coordinates[1:-1] - ny) * 1 / (ny / 2) ** 2 # parabolic velocity profile # scale with 1.5 to achieve a mean velocity of u_char! if self.stencil.d == 2: # in 2D u1 needs Dimension 1 x ny (!) velocity_y = np.zeros_like(parabola_y) # y-velocities = 0 # stack/pack u-field self.u_inlet = np.stack([parabola_y, velocity_y], axis=0) elif self.stencil.d == 3: ones_z = np.ones(nz) parabola_yz = parabola_y[:, :, np.newaxis] * ones_z parabola_yz_zeros = np.zeros_like(parabola_yz) # create u_xyz inlet yz-plane and stack/pack u-field self.u_inlet = np.stack([parabola_yz, parabola_yz_zeros, parabola_yz_zeros], axis=0) def make_units(self, reynolds_number: float, mach_number: float, resolution: List[int] ) -> 'UnitConversion': return UnitConversion( reynolds_number=reynolds_number, mach_number=mach_number, characteristic_length_lu=self.char_length_lu, characteristic_length_pu=self.char_length_pu, characteristic_velocity_pu=self.char_velocity_pu ) def make_resolution(self, resolution: Union[int, List[int]], stencil: Optional['Stencil'] = None) -> List[int]: if isinstance(resolution, int): return [resolution] * (stencil.d or self.stencil.d) else: return resolution @property def obstacle_mask(self): return self._obstacle_mask @obstacle_mask.setter def obstacle_mask(self, m: np.ndarray | torch.Tensor): assert isinstance(m, np.ndarray) and m.shape == self.resolution self._obstacle_mask = m.astype(bool) def initial_pu(self): """initial flow field in physical units: - considers perturbation and dimensionality """ p = np.zeros_like(self.grid[0], dtype=float)[None, ...] u_max_pu = self.units.characteristic_velocity_pu * self._unit_vector() u_max_pu = append_axes(u_max_pu, self.stencil.d) """ initial velocity field: "u_init"-parameter # 0: uniform u=0 # 1: uniform u=1 or parabolic # depends on lateral_walls: # - bounceback => parabolic; # - slip, periodic => uniform """ u = (1 - self.solid_mask) * u_max_pu if self.u_init == 0: u = u * 0 # uniform u=0 else: if self.lateral_walls == 'bounceback': # parabolic along y, uniform along x and z (similar to poiseuille-flow) ny = self.resolution[1] # number of gridpoints in y direction ux_factor = np.zeros(ny) # vector for one column (u(x=0)) # multiply parabolic profile with every column of the velocity field: y_coordinates = np.linspace(0, ny, ny) ux_factor[1:-1] = (- y_coordinates[1:-1] * (y_coordinates[1:-1] - ny) * 1 / (ny / 2) ** 2) if self.stencil.d == 2: u = np.einsum('k,ijk->ijk', ux_factor, u) elif self.stencil.d == 3: u = np.einsum('k,ijkl->ijkl', ux_factor, u) else: # lateral_walls == periodic or slip # initial velocity u_PU=1 on every fluid node u = (1 - self.solid_mask) * u_max_pu ### perturb initial velocity field-symmetry (in y and z) # to trigger 'von Karman' vortex street if self.perturb_init: # perturb initial solution in y # overlays a sine-wave on the second column of nodes x_lu=1 (index 1) ny = self.grid[1].shape[1] if u.max() < 0.5 * self.units.characteristic_velocity_pu: # add perturbation for small velocities amplitude_y = (np.sin(np.linspace(0, ny, ny) / ny * 2 * np.pi) * self.units.characteristic_velocity_pu * 0.1) if self.stencil.d == 2: u[0][1] += amplitude_y elif self.stencil.d == 3: nz = self.grid[2].shape[2] # plane of ones plane_yz = np.ones_like(u[0, 1]) # plane of amplitude in y u[0][1] = np.einsum('y,yz->yz', amplitude_y, plane_yz) # amplitude in z amplitude_z = (np.sin(np.linspace(0, nz, nz) / nz * 2 * np.pi) * self.units.characteristic_velocity_pu * 0.1) u[0][1] += np.einsum('z,yz->yz', amplitude_z, plane_yz) else: # multiply scaled down perturbation if velocity field is already near u_char factor = 1 + np.sin(np.linspace(0, ny, ny) / ny * 2 * np.pi) * 0.1 if self.stencil.d == 2: u[0][1] *= factor elif self.stencil.d == 3: nz = self.grid[2].shape[1] plane_yz = np.ones_like(u[0, 1, :, :]) u[0][1] = np.einsum('y,yz->yz', factor, u[0][1]) # perturbation in z-direction factor = 1 + np.sin(np.linspace(0, nz, nz) / nz * 2 * np.pi) * 0.1 u[0][1] = np.einsum('z,yz->yz', factor, u[0][1]) return p, u def make_solid_boundary_data(self, x_center: float, y_center: float, radius: float): print("(!) CYLINDER OBSTACLE WARNING: make_solid_boundary_data() is not implemented yet!") # NOT YET IMPLEMENTED; OCC-version for index lists; # CURRENTLY only make_ibb_index_lists is used with analytic function (see below) # TODO (future): make reading of externally supplied SolidBoundaryData object a thing def make_ibb_index_lists(self, x_center: float, y_center: float, radius: float): """calculate interpolation constants and population indices for HWBB and IBB1 BBBC """ # INFO: this method relies on self.obstacle_mask too! f_index_lt = [] # indices of relevant populations with d<=0.5 f_index_gt = [] # indices of relevant populations with d>0.5 d_lt = [] # distances between node and boundary for d<0.5 d_gt = [] # distances between node and boundary for d>0.5 # searching boundary-fluid-interface and append indices to f_index, # amd distance to boundary to d if self.stencil.d == 2: # TODO (optional): the 2D and 3D options could be condensed/unified nx, ny = self.obstacle_mask.shape # domain size in x and y a, b = np.where(self.obstacle_mask) # x- and y-index of boundaryTRUE nodes for iteration over boundary area for p in range(0, len(a)): # for all TRUE-nodes in boundary.mask for i in range(0, self.stencil.q): # for all stencil-directions c_i (lattice.stencil.e in lettuce) # check for boundary-nodes neighboring the domain-border. # ...they have to take the periodicity into account... border = np.zeros(self.stencil.d, dtype=int) if a[p] == 0 and self.stencil.e[i][0] == -1: # searching border on left [x] border[0] = -1 elif a[p] == nx - 1 and self.stencil.e[i][0] == 1: # searching border on right [x] border[0] = 1 if b[p] == 0 and self.stencil.e[i][1] == -1: # searching border on left [y] border[1] = -1 elif b[p] == ny - 1 and self.stencil.e[i][1] == 1: # searching border on right [y] border[1] = 1 try: # try in case the neighboring cell does not exist # (= an f pointing out of the simulation domain) if not self.obstacle_mask[a[p] + self.stencil.e[i][0] - border[0] * nx, b[p] + self.stencil.e[i][1] - border[1] * ny]: # if the neighbor of p is False in the boundary.mask, # p is a solid node, neighboring a fluid node: # the direction pointing from the fluid neighbor # to solid p is marked on the neighbor # calculate intersection point of boundary surface and link -> # ...calculate distance between fluid node and boundary surface on the link px = a[p] + self.stencil.e[i][0] - border[0] * nx # fluid node x-coordinate py = b[p] + self.stencil.e[i][1] - border[1] * ny # fluid node y-coordinate cx = self.stencil.e[self.stencil.opposite[i]][ 0] # link-direction x to solid node cy = self.stencil.e[self.stencil.opposite[i]][ 1] # link-direction y to solid node # pq-formula h1 = ((px * cx + py * cy - cx * x_center - cy * y_center) / (cx * cx + cy * cy)) # p/2 h2 = (px * px + py * py + x_center * x_center + y_center * y_center - 2 * px * x_center - 2 * py * y_center - radius * radius) / ( cx * cx + cy * cy) # q d1 = - h1 + np.sqrt(h1 * h1 - h2) d2 = - h1 - np.sqrt(h1 * h1 - h2) # distance from fluid node to the "true" boundary location # choose correct d and assign d and f_index if d1 <= 1 and np.isreal(d1): # d should be between 0 and 1 if d1 <= 0.5: d_lt.append(d1) f_index_lt.append([self.stencil.opposite[i], a[p] + self.stencil.e[i][0] - border[0] * nx, b[p] + self.stencil.e[i][1] - border[1] * ny]) else: # d>0.5 d_gt.append(d1) f_index_gt.append([self.stencil.opposite[i], a[p] + self.stencil.e[i][0] - border[0] * nx, b[p] + self.stencil.e[i][1] - border[1] * ny]) elif d2 <= 1 and np.isreal(d2): # d should be between 0 and 1 if d2 <= 0.5: d_lt.append(d2) f_index_lt.append([self.stencil.opposite[i], a[p] + self.stencil.e[i][0] - border[0] * nx, b[p] + self.stencil.e[i][1] - border[1] * ny]) else: # d>0.5 d_gt.append(d2) f_index_gt.append([self.stencil.opposite[i], a[p] + self.stencil.e[i][0] - border[0] * nx, b[p] + self.stencil.e[i][1] - border[1] * ny]) else: # neither d1 or d2 is real and between 0 and 1 print("IBB WARNING: d1 is", d1, "; d2 is", d2, "for boundaryPoint x,y,ci", a[p], b[p], self.stencil.e[i][0], self.stencil.e[i][1]) except IndexError: pass # just ignore this iteration since there is no neighbor there if self.stencil.d == 3: # like 2D, but in 3D...guess what... nx, ny, nz = self.obstacle_mask.shape a, b, c = np.where(self.obstacle_mask) for p in range(0, len(a)): for i in range(0, self.stencil.q): border = np.zeros(self.stencil.d, dtype=int) # x - direction if a[p] == 0 and self.stencil.e[i][0] == -1: # searching border on left border[0] = -1 elif a[p] == nx - 1 and self.stencil.e[i][0] == 1: # searching border on right border[0] = 1 # y - direction if b[p] == 0 and self.stencil.e[i][1] == -1: # searching border on left border[1] = -1 elif b[p] == ny - 1 and self.stencil.e[i][1] == 1: # searching border on right border[1] = 1 # z - direction if c[p] == 0 and self.stencil.e[i][2] == -1: # searching border on left border[2] = -1 elif c[p] == nz - 1 and self.stencil.e[i][2] == 1: # searching border on right border[2] = 1 try: # try in case the neighboring cell does not exist # (an f pointing out of simulation domain) if not self.obstacle_mask[a[p] + self.stencil.e[i][0] - border[0] * nx, b[p] + self.stencil.e[i][1] - border[1] * ny, c[p] + self.stencil.e[i][2] - border[2] * nz]: # calculate intersection point of boundary surface and link -> # ...calculate distance between fluid node and boundary surface on the link px = a[p] + self.stencil.e[i][0] - border[0] * nx # fluid node x-coordinate py = b[p] + self.stencil.e[i][1] - border[1] * ny # fluid node y-coordinate # Z-coordinate not needed for cylinder ! cx = self.stencil.e[self.stencil.opposite[i]][0] # link-direction x to solid node cy = self.stencil.e[self.stencil.opposite[i]][1] # link-direction y to solid node # Z-coordinate not needed for cylinder ! # pq-formula h1 = ((px * cx + py * cy - cx * x_center - cy * y_center) / (cx * cx + cy * cy)) # p/2 h2 = (px * px + py * py + x_center * x_center + y_center * y_center - 2 * px * x_center - 2 * py * y_center - radius * radius) / ( cx * cx + cy * cy) # q d1 = - h1 + np.sqrt(h1 * h1 - h2) d2 = - h1 - np.sqrt(h1 * h1 - h2) # distance from fluid node to the "true" boundary location # choose correct d and assign d and f_index if d1 <= 1 and np.isreal(d1): # d should be between 0 and 1 if d1 <= 0.5: d_lt.append(d1) f_index_lt.append([self.stencil.opposite[i], a[p] + self.stencil.e[i][0] - border[0] * nx, b[p] + self.stencil.e[i][1] - border[1] * ny, c[p] + self.stencil.e[i][2] - border[2] * nz]) else: # d>0.5 d_gt.append(d1) f_index_gt.append([self.stencil.opposite[i], a[p] + self.stencil.e[i][0] - border[0] * nx, b[p] + self.stencil.e[i][1] - border[1] * ny, c[p] + self.stencil.e[i][2] - border[2] * nz]) elif d2 <= 1 and np.isreal(d2): # d should be between 0 and 1 if d2 <= 0.5: d_lt.append(d2) f_index_lt.append([self.stencil.opposite[i], a[p] + self.stencil.e[i][0] - border[0] * nx, b[p] + self.stencil.e[i][1] - border[1] * ny, c[p] + self.stencil.e[i][2] - border[2] * nz]) else: # d>0.5 d_gt.append(d2) f_index_gt.append([self.stencil.opposite[i], a[p] + self.stencil.e[i][0] - border[0] * nx, b[p] + self.stencil.e[i][1] - border[1] * ny, c[p] + self.stencil.e[i][2] - border[2] * nz]) else: # neither d1 nor d2 is real and between 0 and 1 print("IBB WARNING: d1 is", d1, "; d2 is", d2, "for boundaryPoint x,y,z,ci", a[p], b[p], c[p], self.stencil.e[i][0], self.stencil.e[i][1], self.stencil.e[i][2]) except IndexError: pass # just ignore this iteration since there is no neighbor there # output as np.array: f_index_lt, f_index_gt, d_lt, d_gt return [np.array(f_index_lt), np.array(f_index_gt), np.array(d_lt), np.array(d_gt)] @property def grid(self): xyz = tuple(self.units.convert_length_to_pu(np.linspace(0, n, n)) for n in self.resolution) # tuple of lists of x,y,(z)-values/indices return np.meshgrid(*xyz, indexing='ij') # meshgrid of x-, y- (und z-)values/indices @property def post_boundaries(self): # inlet ("left side", x[0],y[1:-1], z[:]) inlet_boundary = EquilibriumBoundaryPU(flow=self, context=self.context, mask=self.in_mask, velocity=self.u_inlet) # outlet ("right side" (positive x-direction), x[-1],y[:], (z[:])) if self.stencil.d == 2: outlet_boundary = EquilibriumOutletP(direction=[1, 0], flow=self) else: # self.stencil.d == 3: outlet_boundary = EquilibriumOutletP(direction=[1, 0, 0], flow=self) # create and return boundary-list return [ inlet_boundary, outlet_boundary, ] @property def post_streaming_boundaries(self): """walls that should use the efficient bounce back boundaries""" # LATERAL WALLS (optional) ("top and bottom walls", x[:], y[0,-1], z[:]) lateral_boundary = None # stays None if lateral walls are not specified... if self.lateral_walls == 'bounceback': if self.bc_type == 'hwbb' or self.bc_type == 'HWBB': # use halfway bounce back print("(!) OBST. CYLINDER: lateral walls can currenlty only be " "FWBB, HWBB requirest Solid Boundary Data which is not " "implemented yet for lateral walls!") lateral_boundary = FullwayBounceBackBoundary(self.context, self, self.wall_mask, periodicity=self.periodicity) # TODO (optional, low prio): implement ibb- and hwbb-option for lateral walls else: # else use fullway bounce back lateral_boundary = FullwayBounceBackBoundary(self.context, self, self.wall_mask, periodicity=self.periodicity) elif self.lateral_walls == 'slip' or self.bc_type == 'SLIP': # use slip-wall (symmetry boundary) print("(!) OBST. CYLINDER: lateral walls can currenlty only be " "FWBB, slip boundary is not implemented yet!") lateral_boundary = FullwayBounceBackBoundary(self.context, self, self.wall_mask, periodicity=self.periodicity) # TODO (optional, low prio): implement slip-option for lateral walls # obstacle # (for example: obstacle "cylinder" with radius centered at position x_pos, y_pos) # (!) the obstacle_boundary should always be the last boundary # in the list of boundaries to correctly calculate forces on the obstacle solid_boundary_data = SolidBoundaryData() solid_boundary_data.solid_mask = self.obstacle_mask # load index-lists for circular cylinder into SBD object # [f_index_lt, f_index_gt, d_lt, d_gt]: (solid_boundary_data.f_index_lt, solid_boundary_data.f_index_gt, solid_boundary_data.d_lt, solid_boundary_data.d_gt) = self.make_ibb_index_lists( x_center=self.x_pos_lu-1, y_center=self.y_pos_lu-1, radius=self.radius_lu) print("CYLINDER: the bc_type was given as:", self.bc_type) if self.bc_type.casefold() == 'fwbb' or self.bc_type == 'FWBB': obstacle_boundary = FullwayBounceBackBoundary(self.context, self, self.obstacle_mask, periodicity = self.periodicity, calc_force=self.calc_force_coefficients) elif self.bc_type.casefold() == 'hwbb' or self.bc_type == 'HWBB': obstacle_boundary = HalfwayBounceBackBoundary(self.context, self, solid_boundary_data, periodicity = self.periodicity, calc_force=self.calc_force_coefficients) elif self.bc_type.casefold() == 'ibb1' or self.bc_type == 'IBB1': obstacle_boundary = LinearInterpolatedBounceBackBoundary(self.context, self, solid_boundary_data, calc_force=self.calc_force_coefficients) else: # use basic mask Bounce Back print("OBSTACLE CYLINDER - WARNING: bc_type can't be interpreted... 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examples/advanced_projects/efficient_bounce_back_obstacle/reporter/observables_force_coefficients.py ================================================ """Observables: force coefficients calculated from the force on an obstacle - coefficient of drag - coefficient of lift """ import numpy as np import torch from lettuce import Flow, Observable from examples.advanced_projects.efficient_bounce_back_obstacle import (FullwayBounceBackBoundary, HalfwayBounceBackBoundary, LinearInterpolatedBounceBackBoundary) __all__ = ['DragCoefficient', 'LiftCoefficient'] # TODO (optional): write this as "force"-coefficient and make # X, Y, Z as 0,1,2 direction choosable (parameter) to unify drag and lift class DragCoefficient(Observable): """The coefficient of drag of an obstacle, calculated using momentum exchange method (MEM, MEA) - calculates the density, - gets the force in x direction on the obstacle boundary, - calculates the coefficient of drag """ def __init__(self, flow: 'Flow', obstacle_boundary: "FullwayBounceBackBoundary | HalfwayBounceBackBoundary | LinearInterpolatedBounceBackBoundary", solid_mask: np.ndarray | torch.Tensor, area_pu: float): super().__init__(flow) self.obstacle_boundary = obstacle_boundary self.solid_mask = solid_mask # cross-sectional area of obstacle in LU # (! mind length-dimension in 2D -> area-dimension = self.lattice.D-1) self.area_lu = (area_pu * (self.flow.units.characteristic_length_lu/ self.flow.units.characteristic_length_pu) ** (self.flow.stencil.d-1)) self.nan_tensor = self.context.convert_to_tensor(torch.nan) self.solid_mask = self.context.convert_to_tensor(self.solid_mask, dtype=bool) def __call__(self, f: torch.Tensor = None): rho_tmp = torch.where(self.solid_mask, self.nan_tensor, self.flow.rho(f)) rho_mean = torch.nanmean(rho_tmp) # get current force on obstacle in x direction force_x_lu = self.obstacle_boundary.force_sum[0] # calculate drag_coefficient in LU drag_coefficient = (force_x_lu / (0.5 * rho_mean * self.flow.units.characteristic_velocity_lu ** 2 * self.area_lu)) return drag_coefficient class LiftCoefficient(Observable): """The coefficient of lift of an obstacle, calculated using momentum exchange method (MEM, MEA) - calculates the density, - gets the force in y direction on the obstacle boundary, - calculates the coefficient of lift """ def __init__(self, flow: 'Flow', obstacle_boundary: "FullwayBounceBackBoundary | HalfwayBounceBackBoundary | LinearInterpolatedBounceBackBoundary", solid_mask: np.ndarray | torch.Tensor, area_pu: float): super().__init__(flow) self.obstacle_boundary = obstacle_boundary self.solid_mask = solid_mask # cross-sectional area of obstacle in LU # (! mind length-dimension in 2D -> area-dimension = self.lattice.D-1) self.area_lu = (area_pu * (self.flow.units.characteristic_length_lu/ self.flow.units.characteristic_length_pu) ** (self.flow.stencil.d-1)) self.nan_tensor = self.context.convert_to_tensor(torch.nan) self.solid_mask = self.context.convert_to_tensor(self.solid_mask, dtype=bool) def __call__(self, f: torch.Tensor = None): rho_tmp = torch.where(self.solid_mask, self.nan_tensor, self.flow.rho(f)) rho_mean = torch.nanmean(rho_tmp) # get current force on obstacle in y direction force_y_lu = self.obstacle_boundary.force_sum[1] # calculate lift_coefficient in LU lift_coefficient = force_y_lu / (0.5 * rho_mean * self.flow.units.characteristic_velocity_lu ** 2 * self.area_lu) return lift_coefficient ================================================ FILE: examples/advanced_projects/efficient_bounce_back_obstacle/reporter/reporter_ProfileReporter.py ================================================ import torch import numpy as np import os from typing import List from lettuce._simulation import Reporter, Simulation from lettuce import Flow class ProfileReporter(Reporter): """ reporter to save average velocity profiles -> use the ProfilePlotter in post-processing to further process and plot data! """ def __init__(self, interval: int, flow: 'Flow', position_lu: float, i_start: int): super().__init__(interval) self.flow = flow self.i_start = i_start self.x_position = int(round(position_lu, 0)) # linear interpolation of u if x_pos is off grid # ... positions x_pos# and weights w# self.interpol = False if position_lu % 1 != 0: self.interpol = True self.x_pos1 = int(np.floor(position_lu)) self.x_pos2 = int(np.ceil(position_lu)) self.w2 = position_lu - self.x_pos1 self.w1 = 1 - self.w2 print("ProfileReporter: requested position is off grid! " "Interpolating u(" + str(position_lu) + ") = " + str(self.w1) + " * u(" + str(self.x_pos1) + ") + " + str(self.w2) + " * u(" + str(self.x_pos2) + ")") self.i_out = [] self.out = [] def __call__(self, simulation: 'Simulation'): if simulation.flow.i % self.interval == 0 and simulation.flow.i >= self.i_start: # calculate or interpolate velocity profile in y-direction at position if self.interpol: u = self.flow.u(self.flow.f)[:, self.x_pos1] * self.w1 + \ self.flow.u(self.flow.f)[:, self.x_pos2] * self.w2 else: u = self.flow.u(self.flow.f)[:, self.x_position] u = self.flow.units.convert_velocity_to_pu(u).cpu().numpy() self.i_out.append(self.flow.i) if self.flow.stencil.d == 2: self.out.append(u) elif self.flow.stencil.d == 3: # average over z-axis (axial cross stream for cylinder obstacle) self.out.append(np.mean(u, axis=2)) ================================================ FILE: examples/advanced_projects/efficient_bounce_back_obstacle/reporter/reporter_advanced_vtk_reporter.py ================================================ import os from typing import Optional, List import numpy as np import pyevtk.hl as vtk import torch from lettuce._simulation import Reporter, Simulation from lettuce import Flow __all__ = [ "write_vtk", "VTKReporterAdvanced", "VTKsliceReporter" ] def write_vtk(point_dict, id=0, filename_base="./data/output", origin: tuple[float, float, float] = (0.0, 0.0, 0.0)): vtk.imageToVTK( path=f"{filename_base}_{id:08d}", origin=origin, spacing=(1.0, 1.0, 1.0), cellData=None, pointData=point_dict, fieldData=None, ) class VTKReporterAdvanced(Reporter): """ Advanced VTK reporter. Adds functionality: - take solid_mask and pin observable values inside the solid to 0 -> useful for suboptimal initialization or use of FWBB boundary """ def __init__(self, flow: 'Flow', interval: int = 50, filename_base: str ="./data/output", solid_mask: Optional[np.ndarray | torch.Tensor]=None, imin: int = 0, imax: int = None): super().__init__(interval) self.flow = flow if interval < 0: self.interval = 1 else: self.interval = interval self.filename_base = filename_base self.imin=imin if imax is None: self.imax = 1e15 elif imax <= 0: self.imax = 1 else: self.imax = imax if solid_mask is not None and flow.stencil.d == 2: self.solid_mask = solid_mask[..., None] else: self.solid_mask = solid_mask directory = os.path.dirname(filename_base) if not os.path.isdir(directory): os.makedirs(directory) self.point_dict = dict() def __call__(self, simulation: Simulation): if self.flow.i % self.interval == 0 and self.imin <= self.flow.i <= self.imax: u = self.flow.units.convert_velocity_to_pu(self.flow.u(self.flow.f)) p = self.flow.units.convert_density_lu_to_pressure_pu(self.flow.rho(self.flow.f)) #if you want density as well: # rho = self.flow.units.convert_density_to_pu(self.flow.rho(f)) if self.flow.stencil.d == 2: if self.solid_mask is None: self.point_dict["p"] = self.flow.context.convert_to_ndarray(p[0, ..., None]) else: self.point_dict["p"] = np.where(self.solid_mask, 0, self.flow.context.convert_to_ndarray(p[0, ..., None])) for d in range(self.flow.stencil.d): if self.solid_mask is None: self.point_dict[f"u{'xyz'[d]}"] = ( self.flow.context.convert_to_ndarray(u[d, ..., None])) else: self.point_dict[f"u{'xyz'[d]}"] = ( np.where(self.solid_mask, 0, self.flow.context.convert_to_ndarray(u[d, ..., None]))) #if you want density as well: # self.point_dict["rho"] = self.flow.context.convert_to_ndarray(rho[0, ..., None]) else: if self.solid_mask is None: self.point_dict["p"] = self.flow.context.convert_to_ndarray(p[0, ...]) else: self.point_dict["p"] = np.where(self.solid_mask, 0, self.flow.context.convert_to_ndarray(p[0, ...])) for d in range(self.flow.stencil.d): if self.solid_mask is None: self.point_dict[f"u{'xyz'[d]}"] = ( self.flow.context.convert_to_ndarray(u[d, ...])) else: self.point_dict[f"u{'xyz'[d]}"] = ( np.where(self.solid_mask, 0, self.flow.context.convert_to_ndarray(u[d, ...]))) #if you want density as well: # self.point_dict["rho"] = self.flow.context.convert_to_ndarray(rho[0, ...]) write_vtk(self.point_dict, self.flow.i, self.filename_base) def output_mask(self, mask: np.ndarray | torch.Tensor, outdir: Optional[str] = None, name: str ="mask", point: bool = False, no_offset: bool = False): """ output the mask as a vtk-file for visualizatione etc. (outputs mask as cell data) cell data represents the approx. location of solid boundaries, assuming Fullway or Halfway Bounce Back implementation, if translated by (-0.5,-0.5,-0.5) LU (see export with "_cell" suffix below). Attention: point data is misleading, looking at masks rendered as solid objects or point-clouds! USE of translated mask in Paraview -> use Filter: Threshold -> Above Upper Threshold (Upper Threshold 0.9) -> Solid Color -> Volume/Wireframe,... """ if outdir is None: filename_base = self.filename_base else: filename_base = outdir+"/"+str(name) mask_dict = dict() mask_dict["mask"] = mask.astype(int) if len(mask.shape) == 3 \ else mask[..., None].astype(int) # extension to pseudo-3D is needed for Paraview if point: vtk.imageToVTK( path=filename_base +"_point", pointData=mask_dict ) if no_offset: vtk.imageToVTK( path=filename_base +"_cell_noOffset", cellData=mask_dict ) vtk.imageToVTK( path=filename_base + "_cell", cellData=mask_dict, origin=(-0.5, -0.5, -0.5), spacing=(1.0, 1.0, 1.0) ) class VTKsliceReporter(Reporter): ''' reports a certain specified area portion of the domain as vtk-file ''' def __init__(self, flow: 'Flow', interval: int = 50, filename_base: str = "./data/output", solid_mask: Optional[np.ndarray | torch.Tensor]=None, sliceXY: Optional[List | tuple]=None, sliceZ: float | int=None, imin: int=0, imax=None): super().__init__(interval) self.flow = flow self.interval = interval self.filename_base = filename_base self.imin = imin if imax is None: self.imax = 1e15 elif imax <= 0: self.imax = 1 else: self.imax = imax if solid_mask is not None and self.flow.stencil.d == 2: self.solid_mask = solid_mask[..., None] else: self.solid_mask = solid_mask directory = os.path.dirname(filename_base) if not os.path.isdir(directory): os.makedirs(directory) self.point_dict = dict() if sliceXY is not None: if sliceZ is None: sliceZ = 0 self.xmin = sliceXY[0][0] self.ymin = sliceXY[1][0] self.xmax = sliceXY[0][1] self.ymax = sliceXY[1][1] self.z_index = sliceZ else: self.z_index = None # TODO (OPTIONAL): # check if xmin == xmax (and y... and z...) and if so, add a "None" # dimension and take only ONE value, so this becomes a slice. # -> Probably best to do this in call() from a tuple: # ([xmin, xmax],[ymin, ymax],[zmin, zmax]) def __call__(self, simulation: Simulation): if self.flow.i % self.interval == 0 and self.imin <= self.flow.i <= self.imax: if self.z_index is not None: f = self.flow.f[:,self.xmin:self.xmax+1, self.ymin:self.ymax+1, self.z_index, None] # (!) None is needed because single-slice omits the last dimension and will result # in bad dimension in conversion of u and rho/p below! else: f = self.flow.f u = self.flow.units.convert_velocity_to_pu(self.flow.u(f)) p = self.flow.units.convert_density_lu_to_pressure_pu(self.flow.rho(f)) # if you want the density: # rho = self.flow.units.convert_density_to_pu(self.flow.rho(f)) if self.flow.stencil.d == 2: if self.solid_mask is None: self.point_dict["p"] = self.flow.context.convert_to_ndarray(p[0, ..., None]) else: self.point_dict["p"] = np.where(self.solid_mask, 0, self.flow.context.convert_to_ndarray(p[0, ..., None])) for d in range(self.flow.stencil.d): if self.solid_mask is None: self.point_dict[f"u{'xyz'[d]}"] = ( self.flow.context.convert_to_ndarray(u[d, ..., None])) else: self.point_dict[f"u{'xyz'[d]}"] = np.where(self.solid_mask, 0, self.flow.context.convert_to_ndarray(u[d, ..., None])) # if you want the density: # self.point_dict["rho"] = self.flow.context.convert_to_ndarray(rho[0, ..., None]) else: if self.solid_mask is None: self.point_dict["p"] = self.flow.context.convert_to_ndarray(p[0, ...]) else: self.point_dict["p"] = np.where(self.solid_mask, 0, self.flow.context.convert_to_ndarray(p[0, ...])) for d in range(self.flow.stencil.d): if self.solid_mask is None: self.point_dict[f"u{'xyz'[d]}"] = ( self.flow.context.convert_to_ndarray(u[d, ...])) else: self.point_dict[f"u{'xyz'[d]}"] = np.where(self.solid_mask, 0, self.flow.context.convert_to_ndarray(u[d, ...])) # # if you want the density: # self.point_dict["rho"] = self.flow.context.convert_to_ndarray(rho[0, ...]) write_vtk(self.point_dict, self.flow.i, self.filename_base, origin=(self.xmin, self.ymin, self.z_index)) # origin added to show slice at correct position when superimposing on 3D vtk-data! def output_mask(self, mask: np.ndarray | torch.Tensor, outdir: Optional[str]=None, name: str="mask", point: bool=False, no_offset: bool=False): """ output the mask as a vtk-file for visualizatione etc. UPDATE 28.08.2024 (MBille: outputs mask as cell data. cell data represents the approx. location of solid boundaries, assuming Fullway or Halfway Bounce Back implementation, if translated by (-0.5,-0.5,-0.5) LU. Attention: point data is misleading, looking at masks rendered as solid objects or point-clouds! USE: in Paraview use Filter: Threshold -> Above Upper Threshold (Upper Threshold 0.9) -> Solid Color -> Volume/Wireframe,... """ if outdir is None: filename_base = self.filename_base else: filename_base = outdir + "/" + str(name) mask_dict = dict() mask_dict["mask"] = mask.astype(int) if len(mask.shape) == 3 else mask[..., None].astype( int) # extension to pseudo-3D is needed for vtk-export to work if point: vtk.imageToVTK( path=filename_base + "_point", pointData=mask_dict ) if no_offset: vtk.imageToVTK( path=filename_base + "_cell_noOffset", cellData=mask_dict ) vtk.imageToVTK( path=filename_base + "_cell", cellData=mask_dict, origin=(-0.5, -0.5, -0.5), spacing=(1.0, 1.0, 1.0) ) ================================================ FILE: examples/advanced_projects/efficient_bounce_back_obstacle/simulation/__init__.py ================================================ ================================================ FILE: examples/advanced_projects/efficient_bounce_back_obstacle/simulation/ebb_simulation.py ================================================ import torch from timeit import default_timer as timer from typing import List from lettuce._simulation import * from lettuce import Flow __all__ = ['EbbSimulation'] class EbbSimulation(Simulation): """ advanced simulation class, with - (additional) post_streaming_boundaries (substeps: C -> S -> B -> R) - post_collision population information for post_streaming_boundaries """ def __init__(self, flow: 'Flow', collision: 'Collision', reporter: List['Reporter']): flow.context.use_native = False super().__init__(flow, collision, reporter) if hasattr(flow.__class__, 'post_streaming_boundaries'): # list of boundaries that are applied AFTER the streaming step self.post_streaming_boundaries = flow.post_streaming_boundaries else: self.post_streaming_boundaries = [] # adjust No_collision_ and no_streaming_mask for use of post_streaming_boundaries (!) if len(self.post_streaming_boundaries) > 0: # create NCM and NSM, # ...if there were no pre_ or post_boundaries that triggered # ...their creation in super-class if self.no_collision_mask is None: # fill masks with value of self.collision_index (= number of pre-boundaries) self.no_collision_mask = self.context.full_tensor( flow.resolution, self.collision_index, dtype=torch.uint8) if self.no_streaming_mask is None: self.no_streaming_mask = self.context.full_tensor([flow.stencil.q, *flow.resolution], self.collision_index, dtype=torch.uint8) for i, boundary in enumerate(self.post_streaming_boundaries, start=self.collision_index + 1 + len(self.post_boundaries)): # FOR DEBUGGING: print("SIMULATION: creating no_collision_mask entries for: i, boundary = ", i, boundary) # FOR DEBUGGING: print("collision index is:", self.collision_index) ncm = boundary.make_no_collision_mask( [it for it in self.flow.f.shape[1:]], context=self.context) if ncm is not None: self.no_collision_mask[ncm] = i nsm = boundary.make_no_streaming_mask( [it for it in self.flow.f.shape], context=self.context) if nsm is not None: self.no_streaming_mask |= nsm # get indices of post_streaming_boundaries, that need f_collided to be stored self.store_f_collided_post_streaming_boundaries_index = [] for i, boundary in enumerate(self.post_streaming_boundaries): if hasattr(boundary.__class__, "store_f_collided"): # append boundary to lis of boundaries that need # ...f_collided state between collision and streaming substep self.store_f_collided_post_streaming_boundaries_index.append(i) if hasattr(boundary.__class__, "initialize_f_collided"): # initialize the f_collided storage in each of those boundaries boundary.initialize_f_collided() # redefine collide_and_stream() to include the storage of f_collided # ...for use in post_streaming_boundaries def collide_and_stream(*_, **__): self._collide() # pass f to post_streaming_boundaries between collision and streaming substep self._pass_f_collided() self._stream() self._collide_and_stream = collide_and_stream def _post_streaming_boundaries(self): # runs all the post_streaming_boundaries; required for efficient BBBC for boundary in self.post_streaming_boundaries: boundary(self.flow) def _pass_f_collided(self): # passes f to post_streaming_boundaries, for later use for idx in self.store_f_collided_post_streaming_boundaries_index: self.post_streaming_boundaries[idx].store_f_collided(self.flow.f) def __call__(self, num_steps: int) -> float: beg = timer() if self.flow.i == 0: self._report() for _ in range(num_steps): self._collide_and_stream(self) self._post_streaming_boundaries() self.flow.i += 1 self._report() end = timer() return num_steps * self.flow.rho().numel() / 1e6 / (end - beg) ================================================ FILE: examples/development/.gitignore ================================================ # don't trak manually_generate_cuda_native.py output directory cuda_native native_generated native_generated_orig native_hard_coded ================================================ FILE: examples/development/example_progress_reporter_based_on_simplest_TGV.py ================================================ """ This file showcases the simplicity of the lettuce code. The following code will run a two-dimensional Taylor-Green vortex on GPU. """ import torch import lettuce as lt from lettuce.ext._reporter.progress_reporter import ProgressReporter flow = lt.TaylorGreenVortex( lt.Context(dtype=torch.float64, use_native=False), # for running on cpu: device='cpu' resolution=128, reynolds_number=100, mach_number=0.05, stencil=lt.D2Q9 ) progress_reporter = ProgressReporter(interval=10000, t_max=40, i_target=100000, print_message=True, outdir="./data/") simulation = lt.Simulation( flow=flow, collision=lt.BGKCollision(tau=flow.units.relaxation_parameter_lu), reporter=[progress_reporter]) mlups = simulation(num_steps=100000) print("Performance in MLUPS:", mlups) ================================================ FILE: examples/development/manually_generate_cuda_native.py ================================================ import lettuce as lt from lettuce import cuda_native, StreamingStrategy from os.path import join, dirname, abspath, isdir, exists from os import mkdir, listdir, remove, rmdir def ensure_empty_dir(path): """ shutil.rmtree + os.mkdir is not always working. Therefore, this utility ensures the expected behaviour more safely. """ if exists(path): for filename in listdir(path): filepath = join(path, filename) if isdir(filepath): ensure_empty_dir(filepath) rmdir(filepath) else: remove(filepath) else: mkdir(path) def main(verbose=False, install=False): """ Generate a cuda_native module into a local directory to get insights. """ # Step 1: Create the collision, boundary, and equilibrium objects. collision = cuda_native.ext.NativeBGKCollision(0) boundaries = [ # cuda_native.ext.NativeBounceBackBoundary(1), # cuda_native.ext.NativeBounceBackBoundary(2) ] equilibrium = cuda_native.ext.NativeQuadraticEquilibrium() # Step 2: Initialize the generator with the D2Q9 lattice, collision, boundaries, and equilibrium. generator = cuda_native.Generator(lt.D2Q9(), collision, boundaries, equilibrium, streaming_strategy=StreamingStrategy.PRE_STREAMING) # Step 3: Prepare the directory where the generated cuda_native module will be saved. #generate_dir = abspath(join(dirname(__file__), 'native_hard_coded')) #generate_dir = abspath(join(dirname(__file__), 'native_generated')) generate_dir = abspath(join(dirname(__file__), 'native_generated_orig')) #ensure_empty_dir(generate_dir) # Step 4: Generate the cuda_native module and format it into the prepared directory. generator.format(generate_dir) # if verbose: # for key, buffer in structured_code.items(): # print(f"{key}:") # for line in buffer.splitlines(): # print(f"> {line}") # print() # Step 5: optionally install the generated directory #if install: generator.install(abspath(join(dirname(__file__), generate_dir))) if __name__ == '__main__': main() ================================================ FILE: examples/simple_flows/Couette.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Couette flow example\n", "This is an example of using the Couette flow\n", "A two dimensional Couette flow is initialized and simulated. Afterwards the energy and the velocity field is plotted." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T08:42:22.820354Z", "start_time": "2024-08-14T08:42:21.742139Z" } }, "source": [ "import lettuce as lt\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ], "outputs": [], "execution_count": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "* for running on GPU: device = \"cuda\". CUDA drivers are required!\n", "* dtype=torch.float32 for single precision - float64 for double precision\n", "* select collision model (here BGKCollision) - try also KBCCollision or RegularizedCollision\n", "\n", "### Code:\n", "* Reporters will grab the results in between simulation steps (see reporters.py and simulation.py)\n", "* Output: Column 1: simulation steps, Column 2: time in LU, Column 3: kinetic energy in PU\n", "* Output: separate VTK-file with ux,uy,(uz) and p for every 100. time step in ./output" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T08:43:11.864311Z", "start_time": "2024-08-14T08:42:24.022069Z" } }, "source": [ "nmax = 200000\n", "nconsole = 1000\n", "nreport = 100\n", "epsilon = 0.001 # convergence condition: .1 % relative change\n", "\n", "context = lt.Context()\n", "flow = lt.CouetteFlow2D(resolution=16, reynolds_number=100, mach_number=0.1,\n", " context=context)\n", "collision = lt.BGKCollision(tau=flow.units.relaxation_parameter_lu)\n", "simulation = lt.Simulation(flow=flow, collision=collision, reporter=[])\n", "\n", "energy = lt.IncompressibleKineticEnergy(flow)\n", "energy_reporter_internal = lt.ObservableReporter(energy, interval=nreport, out=None)\n", "simulation.reporter.append(energy_reporter_internal)\n", "simulation.reporter.append(lt.ObservableReporter(energy, interval=nconsole)) # print energy\n", "simulation.reporter.append(lt.VTKReporter(interval=nreport, filename_base=\"./data/couette\"))" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Could not resolve cuda_native extension.\n", "Installing Native module (/tmp/tmpi9u677l7) ...\n", "steps time IncompressibleKineticEnergy\n", "steps time IncompressibleKineticEnergy\n" ] } ], "execution_count": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run simulation" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T08:43:13.335152Z", "start_time": "2024-08-14T08:43:11.865374Z" } }, "source": [ "energy_new = 0\n", "mlups = 0\n", "iterations = int(nmax//nconsole)\n", "for _ in range(iterations):\n", " energy_old = energy_new\n", " energy_new = energy(flow.f).mean()\n", " mlups += simulation(nconsole)\n", " if abs((energy_new - energy_old)/energy_new) < epsilon:\n", " print(\"CONVERGENCE! Less than \", epsilon*100, \" % relative change\")\n", " break\n", "print(\"avg MLUPS: \", mlups/iterations)" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 0.0 0.0\n", "1000 3.6084391824351614 0.09987135976552963\n", "2000 7.216878364870323 0.12556108832359314\n", "3000 10.825317547305485 0.14315533638000488\n", "4000 14.433756729740645 0.15471573173999786\n", "5000 18.042195912175806 0.1619458794593811\n", "6000 21.65063509461097 0.1663307100534439\n", "7000 25.25907427704613 0.16895049810409546\n", "8000 28.86751345948129 0.17049835622310638\n", "9000 32.47595264191645 0.17140650749206543\n", "10000 36.08439182435161 0.171939417719841\n", "11000 39.692831006786776 0.1722515970468521\n", "12000 43.30127018922194 0.17243336141109467\n", "13000 46.9097093716571 0.17253637313842773\n", "14000 50.51814855409226 0.17259767651557922\n", "CONVERGENCE! Less than 0.1 % relative change\n", "avg MLUPS: 0.176756059042171\n" ] } ], "execution_count": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Post process\n", "### Energy Reporter\n", "* Grab output of kinetic energy reporter" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T08:43:13.431431Z", "start_time": "2024-08-14T08:43:13.335771Z" } }, "source": [ "energy = np.array(simulation.reporter[0].out)\n", "print(energy.shape)\n", "plt.plot(energy[:,1],energy[:,2])\n", "plt.title('Kinetic energy')\n", "plt.xlabel('Time')\n", "plt.ylabel('Energy in physical units')\n", "plt.show()" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(141, 3)\n" ] }, { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Velocity\n", "We calculate the speed in Lettuce units depending on the last 'f'. Then we convert this velocity into physical units. For further investigations the tensor must be converted into a Numpy-Array. The norm of the fractions in x and y direction is plotted afterwards." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T08:43:13.510481Z", "start_time": "2024-08-14T08:43:13.432197Z" } }, "source": [ "u_x = context.convert_to_ndarray(flow.u_pu)[0]\n", "plt.imshow(u_x.transpose())\n", "plt.colorbar()\n", "plt.show()" ], "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 5 }, { "metadata": {}, "cell_type": "code", "outputs": [], "execution_count": null, "source": "" } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.10" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: examples/simple_flows/DecayingTurbulence.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example: Decaying Turbulence" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T14:28:25.119966Z", "start_time": "2024-08-14T14:28:24.094478Z" } }, "source": [ "import lettuce as lt\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ], "outputs": [], "execution_count": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "The dimension of DecayingTurbulence vortex is defined by the stencil. Here the flow is two dimensional due to D2Q9. Special Inputs & standard values that can be set in the flow constructor are ...\n", "* the wavenumber_energy-peak k0 (= 20 as default) \n", "* the initial_energy (= 0.5 as default)" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T14:29:37.889444Z", "start_time": "2024-08-14T14:28:49.489958Z" } }, "source": [ "context = lt.Context()\n", "flow = lt.DecayingTurbulence(context=context, resolution=256,\n", " reynolds_number=10000, mach_number=0.05, k0=20,\n", " ic_energy=0.5, stencil=lt.D2Q9())\n", "energyreporter = lt.ObservableReporter(lt.EnergySpectrum(flow), interval=500, \n", " out=None)\n", "simulation = lt.Simulation(flow=flow,\n", " collision=lt.BGKCollision(flow.units.relaxation_parameter_lu),\n", " reporter=[energyreporter])" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "steps time EnergySpectrum\n", "Could not resolve cuda_native extension.\n", "Installing Native module (/tmp/tmpdsz8n6d8) ...\n" ] } ], "execution_count": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initialized flow\n", "The initialized velocity is randomly generated and depends on the wavenumber peak k0, the initial energy, which are given in the flow constructor, and a given energy spectrum." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T14:29:38.065690Z", "start_time": "2024-08-14T14:29:37.890659Z" } }, "source": [ "u = context.convert_to_ndarray(flow.u_pu)\n", "u_norm = np.linalg.norm(u, axis=0)\n", "plt.imshow(u_norm)\n", "plt.colorbar()\n", "plt.title('Initialized velocity')\n", "plt.show()" ], "outputs": [ { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Energy spectrum\n", "The energy spectrum, which is forced in the beginning is given with:" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T14:29:38.309385Z", "start_time": "2024-08-14T14:29:38.066376Z" } }, "source": [ "spectrum = flow.energy_spectrum\n", "plt.loglog(spectrum[1],spectrum[0])\n", "plt.title('Energy spectrum')\n", "plt.xlabel('Wavenumber')\n", "plt.ylabel('Energy')\n", "plt.show()" ], "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run simulation\n", "Before the simulation is executed, the pressure field is obtained by solving the pressure poisson equation. In addition, f_neq is initialized to obtain stress tensor at the start of the simulation." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T14:29:40.960830Z", "start_time": "2024-08-14T14:29:38.310242Z" } }, "source": [ "mlups = simulation(num_steps=15000)\n", "print(\"Performance in MLUPS:\", mlups)" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Performance in MLUPS: 371.1256836669622\n" ] } ], "execution_count": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Velocity\n", "* Velocity field after the simulation" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T14:29:41.085115Z", "start_time": "2024-08-14T14:29:40.961425Z" } }, "source": [ "u = context.convert_to_ndarray(flow.u_pu)\n", "u_norm = np.linalg.norm(u, axis=0)\n", "plt.imshow(u_norm)\n", "plt.colorbar()\n", "plt.title('Velocity after simulation')\n", "plt.show()" ], "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Vorticity\n", "* Vorticity field after the simulation" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T14:29:41.203837Z", "start_time": "2024-08-14T14:29:41.085730Z" } }, "source": [ "dx = flow.units.convert_length_to_pu(1.0)\n", "grad_u0 = np.gradient(u[0], dx)\n", "grad_u1 = np.gradient(u[1], dx)\n", "vorticity = (grad_u1[0] - grad_u0[1])\n", "plt.imshow(vorticity, cmap='Spectral')\n", "plt.colorbar()\n", "plt.title('Vorticity after simulation')\n", "plt.show()" ], "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Energy spectrum after simulation" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-08-14T14:29:41.445985Z", "start_time": "2024-08-14T14:29:41.204420Z" } }, "source": [ "spectrum_final = simulation.reporter[0].out[-1]\n", "plt.loglog(spectrum[1],spectrum[0],label='Initialized spectrum')\n", "plt.loglog(spectrum[1],spectrum_final[2:],label='After simulation')\n", "plt.title('Energy spectrum')\n", "plt.xlabel('Wavenumber')\n", "plt.ylabel('Energy')\n", "plt.ylim(top=1e-1, bottom=1e-8)\n", "plt.legend()\n", "plt.show()" ], "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 8 }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: examples/simple_flows/LambOseenVortex.py ================================================ """ This file showcases the simplicity of the lettuce code. The following code will run a two-dimensional Taylor-Green vortex on GPU. """ import torch import lettuce as lt import matplotlib.pyplot as plt import numpy as np context = lt.Context(dtype=torch.float64, use_native=False) flow = lt.LambOseenVortex2D( context, # for running on cpu: device='cpu' resolution=200, reynolds_number=1000, mach_number=0.05, stencil=lt.D2Q9 ) simulation = lt.Simulation( flow=flow, collision=lt.BGKCollision(tau=flow.units.relaxation_parameter_lu), reporter=[]) mlups = simulation(num_steps=100) print("Performance in MLUPS:", mlups) p = context.convert_to_ndarray(flow.p_pu)[0].transpose() plt.imshow(p, origin='lower') plt.show() u = np.linalg.norm(context.convert_to_ndarray(flow.u_pu),axis=0).transpose() plt.imshow(u, origin='lower') plt.show() ================================================ FILE: examples/simple_flows/Obstacle.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "8275480f", "metadata": {}, "source": [ "## Grid refinement study for the flow around an obstacle" ] }, { "cell_type": "code", "execution_count": 1, "id": "95c5e8f8", "metadata": {}, "outputs": [], "source": [ "import lettuce as lt\n", "from matplotlib import pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "id": "3a1158ed", "metadata": {}, "outputs": [], "source": [ "def run(ny=64, *axes):\n", " context = lt.Context()\n", " flow = lt.Obstacle(\n", " context=context, resolution=[2 * ny, ny], reynolds_number=50.0,\n", " mach_number=0.05, domain_length_x=10.1, stencil=lt.D2Q9()\n", " )\n", " x, y = flow.grid\n", " flow.mask = ((x >= 2) & (x < 3) & (y >= x) & (y <= 3))\n", " axes[0].imshow(context.convert_to_ndarray(flow.mask.T), origin=\"lower\")\n", "\n", " tau = flow.units.relaxation_parameter_lu\n", " sim = lt.Simulation(flow, lt.BGKCollision(tau), reporter=[])\n", " sim(ny * 100)\n", " u = flow.u_pu.cpu().numpy()\n", " print(\"Max Velocity:\", u.max())\n", " return axes[1].imshow(u[0, ...].T, origin=\"lower\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "5ab90e9e", "metadata": { "scrolled": true }, "outputs": [], "source": [ "def run_and_plot(n):\n", " fig, axes = plt.subplots(1, 2, figsize=(10, 3))\n", " fig.subplots_adjust(right=0.85)\n", " im2 = run(n, *axes)\n", " cbar_ax = fig.add_axes([0.88, 0.15, 0.04, 0.7])\n", " fig.colorbar(im2, cax=cbar_ax)" ] }, { "cell_type": "code", "execution_count": 4, "id": "c49cbef2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Max Velocity: 1.5470362\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": "run_and_plot(32)" }, { "cell_type": "code", "execution_count": 5, "id": "466ba599", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Max Velocity: 1.5507787\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": "run_and_plot(64)" }, { "metadata": {}, "cell_type": "code", "outputs": [], "execution_count": null, "source": "run_and_plot(128)", "id": "dc896c757ce5a2ed" }, { "cell_type": "code", "execution_count": null, "id": "f21453ce", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: examples/simple_flows/Poiseuille.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Poiseuille flow example\n", "This is an example of using the Poiseuille flow\n", "A two dimensional Poiseuille flow is initialized and simulated. Afterwards the energy and the velocity field is plotted." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-07-19T08:13:27.990498Z", "start_time": "2024-07-19T08:13:26.998855Z" } }, "source": [ "import lettuce as lt\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ], "outputs": [], "execution_count": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "* for running on GPU: device = \"cuda\". CUDA drivers are required!\n", "* dtype=torch.float32 for single precision - float64 for double precision\n", "* select collision model (here BGKCollision) - try also KBCCollision or RegularizedCollision\n", "\n", "### Code:\n", "* Reporters will grab the results in between simulation steps (see reporters.py and simulation.py)\n", "* Output: Column 1: simulation steps, Column 2: time in LU, Column 3: kinetic energy in PU\n", "* Output: separate VTK-file with ux,uy,(uz) and p for every 100. time step in ./output" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-07-19T08:13:28.267834Z", "start_time": "2024-07-19T08:13:27.991500Z" } }, "source": [ "nmax = 20000\n", "nconsole = 100\n", "nreport = 100\n", "epsilon = 0.001 # convergence condition: .1 % relative change\n", "\n", "context = lt.Context()\n", "flow = lt.PoiseuilleFlow2D(context=context, resolution=16, reynolds_number=1,\n", " mach_number=0.02)\n", "acceleration_lu = flow.units.convert_acceleration_to_lu(flow.acceleration)\n", "force = lt.ShanChen(context=context, tau=flow.units.relaxation_parameter_lu,\n", " acceleration=acceleration_lu)\n", "collision = lt.BGKCollision(tau=flow.units.relaxation_parameter_lu, force=force)\n", "simulation = lt.Simulation(flow=flow, collision=collision, reporter=[])\n", "\n", "Energy = lt.IncompressibleKineticEnergy(flow)\n", "energy_reporter_internal = lt.ObservableReporter(Energy, interval=nreport, out=None)\n", "simulation.reporter.append(energy_reporter_internal)\n", "simulation.reporter.append(lt.ObservableReporter(Energy, interval=nconsole)) # print energy\n", "simulation.reporter.append(lt.VTKReporter(interval=nreport, filename_base=\"./data/poiseuille/out\"))" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "steps time IncompressibleKineticEnergy\n", "steps time IncompressibleKineticEnergy\n" ] } ], "execution_count": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run simulation" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-07-19T08:13:29.162795Z", "start_time": "2024-07-19T08:13:28.268494Z" } }, "source": [ "energy_new = 0\n", "mlups = 0\n", "iterations = int(nmax//nconsole)\n", "for _ in range(iterations):\n", " energy_old = energy_new\n", " energy_new = Energy(simulation.f).mean()\n", " mlups += simulation.step(nconsole)\n", " if abs((energy_new - energy_old)/energy_new) < epsilon:\n", " print(\"CONVERGENCE! Less than \", epsilon*100, \" % relative change\")\n", " break\n", "print(\"avg MLUPS: \", mlups/iterations)" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 0.0 0.0\n", "100 0.07216878364870323 9.909340968468728e-10\n", "200 0.14433756729740646 2.0629821599380966e-09\n", "300 0.2165063509461097 2.665080671936491e-09\n", "400 0.2886751345948129 2.9576571846563504e-09\n", "500 0.3608439182435162 3.092687466056564e-09\n", "600 0.4330127018922194 3.1537139106196866e-09\n", "700 0.5051814855409227 3.1810481207676734e-09\n", "800 0.5773502691896258 3.193243341608845e-09\n", "900 0.6495190528383291 3.1986748430123624e-09\n", "1000 0.7216878364870324 3.2010920642647115e-09\n", "1100 0.7938566201357355 3.2021674502234276e-09\n", "CONVERGENCE! Less than 0.1 % relative change\n", "avg MLUPS: 0.02166252193834579\n" ] } ], "execution_count": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Post process\n", "### Energy Reporter\n", "* Grab output of kinetic energy reporter" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-07-19T08:13:29.264764Z", "start_time": "2024-07-19T08:13:29.163528Z" } }, "source": [ "energy = np.array(simulation.reporter[0].out)\n", "print(energy.shape)\n", "plt.plot(energy[:,1],energy[:,2])\n", "plt.title('Kinetic energy')\n", "plt.xlabel('Time')\n", "plt.ylabel('Energy in physical units')\n", "plt.show()" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(12, 3)\n" ] }, { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Velocity\n", "We calculate the speed in Lettuce units depending on the last 'f'. Then we convert this velocity into physical units. For further investigations the tensor must be converted into a Numpy-Array. The norm of the fractions in x and y direction is plotted afterwards." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2024-07-19T08:15:01.298521Z", "start_time": "2024-07-19T08:15:01.118639Z" } }, "source": [ "u_x = context.convert_to_ndarray(flow.u_pu)[0]\n", "plt.imshow(u_x.transpose())\n", "plt.colorbar()\n", "plt.show()" ], "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 6 }, { "metadata": {}, "cell_type": "code", "outputs": [], "execution_count": null, "source": "" } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.10" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: lettuce/__init__.py ================================================ """Top-level package for lettuce.""" __author__ = 'Andreas Kraemer' __email__ = 'kraemer.research@gmail.com' import lettuce._version __version__ = _version.get_versions()['version'] from ._context import * from ._stencil import * from ._unit import * from ._flow import * from ._simulation import * import lettuce.util import lettuce.ext from lettuce.util import * from lettuce.ext import * ================================================ FILE: lettuce/_context.py ================================================ from typing import List, Optional, Union import numpy as np import torch __all__ = ['Context'] class Context: def __init__(self, device: Optional[torch.device | str] = None, dtype: Optional[torch.dtype] = None, use_native: Optional[bool] = None): if device is str: assert 'cuda' in device or 'cpu' in device # check sanity of context configuration if device is None and use_native is None: device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') use_native = True if torch.cuda.is_available() else False elif device is None: assert not use_native or torch.cuda.is_available(), \ ('cuda_native extension explicitly requested but ' 'cuda is not available!') device = torch.device('cuda:0') elif use_native is None: if 'cuda' in str(device): assert torch.cuda.is_available(), \ ('cuda device explicitly requested but ' 'cuda is not available!') use_native = True else: assert 'cpu' in str(device), \ (f"lettuce is designed to work on cpu or cuda devices. " f"{device} is not supported!") use_native = False else: if 'cuda' in str(device): assert torch.cuda.is_available(), \ ('cuda device explicitly requested ' 'but cuda is not available!') else: assert 'cpu' in str(device), \ (f"lettuce is designed to work on cpu or cuda devices. " f"{device} is not supported!") assert not use_native, \ ('can not use explicitly requested cuda_native extension ' 'on explicitly requested cpu device!') dtype = dtype or torch.float32 # default dtype to single assert dtype in [torch.float16, torch.float32, torch.float64], \ (f"lettuce is designed to work with common float types " f"(16, 32 and 64 bit). {dtype.__name__} is not supported!") # store context configuration self.device = torch.device(device) self.dtype = dtype self.use_native = use_native def synchronize(self): """ Use explicit sync to catch errors right away. Notice cost of sync, so better call only when necessary or in setup code. """ if self.device.type == 'cuda': torch.cuda.synchronize(self.device) def empty_tensor(self, size: Union[List[int], torch.Size], *args, dtype=None, **kwargs) -> torch.Tensor: return torch.empty(size, *args, **kwargs, device=self.device, dtype=(dtype or self.dtype)) def zero_tensor(self, size: Union[List[int], torch.Size], *args, dtype=None, **kwargs) -> torch.Tensor: return torch.zeros(size, *args, **kwargs, device=self.device, dtype=(dtype or self.dtype)) def full_tensor(self, size: Union[List[int], torch.Size], value, *args, dtype=None, **kwargs) -> torch.Tensor: return torch.full(size, value, *args, **kwargs, device=self.device, dtype=(dtype or self.dtype)) def one_tensor(self, size: Union[List[int], torch.Size], *args, dtype=None, **kwargs) -> torch.Tensor: return torch.ones(size, *args, **kwargs, device=self.device, dtype=(dtype or self.dtype)) def convert_to_tensor(self, array, *args, dtype: Optional[torch.dtype] = None, **kwargs ) -> torch.Tensor: is_tensor = isinstance(array, torch.Tensor) new_dtype = dtype if dtype is None: bool_types = [bool, torch.bool, torch.uint8, np.uint8] if hasattr(array, 'dtype') and array.dtype in bool_types: new_dtype = torch.uint8 else: new_dtype = self.dtype # Convert nested lists to NumPy array first if is_tensor: return array.to(*args, **kwargs, device=self.device, dtype=new_dtype) array = np.array(array) array = np.ascontiguousarray(array) tensor = torch.from_numpy(array) tensor = tensor.to(*args, **kwargs, device=self.device, dtype=new_dtype) return tensor @staticmethod def convert_to_ndarray(tensor: Union[torch.Tensor, List]) -> np.ndarray: if isinstance(tensor, torch.Tensor): return tensor.detach().cpu().numpy() else: return np.array(tensor) ================================================ FILE: lettuce/_flow.py ================================================ import pickle import numpy as np import torch import warnings from typing import Optional, List, Union, Callable from abc import ABC, abstractmethod from . import TorchStencil from .util import torch_gradient, torch_jacobi from .cuda_native import NativeEquilibrium __all__ = ['Equilibrium', 'Flow', 'Boundary'] class Equilibrium(ABC): @abstractmethod def __call__(self, flow: 'Flow', rho=None, u=None) -> torch.Tensor: ... @abstractmethod def native_available(self) -> bool: ... @abstractmethod def native_generator(self) -> 'NativeEquilibrium': ... class Boundary(ABC): @abstractmethod def __call__(self, flow: 'Flow'): ... @abstractmethod def make_no_collision_mask(self, shape: List[int], context: 'Context' ) -> Optional[torch.Tensor]: ... @abstractmethod def make_no_streaming_mask(self, shape: List[int], context: 'Context' ) -> Optional[torch.Tensor]: ... @abstractmethod def native_available(self) -> bool: ... @abstractmethod def native_generator(self, index: int) -> 'NativeBoundary': ... class Flow(ABC): """ The Flow contains physical configurations and state of the simulation. """ # the context can not change # during the lifetime of the flow # therefore it is stored with it context: 'Context' # configuration of the flow resolution: List[int] units: 'UnitConversion' stencil: 'Stencil' torch_stencil: 'TorchStencil' equilibrium: 'Equilibrium' # pre_boundaries: List['Boundary'] # post_boundaries: List['Boundary'] initialize_pressure: bool = False initialize_fneq: bool = False # current physical state i: int f: torch.Tensor _f_next: Optional[torch.Tensor] def __init__(self, context: 'Context', resolution: List[int], units: 'UnitConversion', stencil: 'Stencil', equilibrium: 'Equilibrium'): self.context = context self.resolution = resolution self.units = units self.stencil = stencil self.torch_stencil = TorchStencil(stencil, context) self.equilibrium = equilibrium self.i = 0 self.f = context.empty_tensor([stencil.q, *resolution]) self._f_next = None self.initialize() @property # @abstractmethod def pre_boundaries(self) -> List['Boundary']: """boundaries""" return [] @property # @abstractmethod def post_boundaries(self) -> List['Boundary']: """boundaries""" warnings.warn( "This warning occurs because either post_boundaries is not defined within" "the flow class or the `boundaries` method is used" "which is deprecated and will be removed in a future version. " "Please use `pre_boundaries` or `post_boundaries` directly instead.", stacklevel=2 ) return self.boundaries @property # @abstractmethod def boundaries(self) -> List['Boundary']: """boundaries""" return [] @abstractmethod def initial_pu(self) -> (float, Union[np.array, torch.Tensor]): """initial solution in physical units""" ... def initialize(self): """initializing in equilibrium""" initial_p, initial_u = self.initial_pu() initial_rho = self.context.convert_to_tensor( self.units.convert_pressure_pu_to_density_lu(initial_p)) initial_u = self.context.convert_to_tensor( self.units.convert_velocity_to_lu(initial_u)) if self.initialize_pressure: initial_rho = pressure_poisson( self.units, initial_u, initial_rho ) self.f = self.equilibrium(self, rho=initial_rho, u=initial_u) self.f = self.equilibrium(self, rho=initial_rho, u=initial_u) if self.initialize_fneq: self.f = initialize_f_neq(self) @property def f_next(self) -> torch.Tensor: if self._f_next is None: # lazy creation of the f_next buffer self._f_next = self.context.empty_tensor( [self.stencil.q, *self.resolution]) return self._f_next @f_next.setter def f_next(self, f_next_: torch.Tensor): self._f_next = f_next_ def rho(self, f: Optional[torch.Tensor] = None) -> torch.Tensor: """density""" return torch.sum(self.f if f is None else f, dim=0)[None, ...] @property def rho_pu(self) -> torch.Tensor: return self.units.convert_density_to_pu(self.rho()) @property def p_pu(self) -> torch.Tensor: return self.units.convert_density_lu_to_pressure_pu(self.rho()) @property def u_pu(self): return self.units.convert_velocity_to_pu(self.u()) def j(self, f: Optional[torch.Tensor] = None) -> torch.Tensor: """momentum""" return torch.einsum("qd,q...->d...", [self.torch_stencil.e, self.f if f is None else f]) def u(self, f: Optional[torch.Tensor] = None, rho=None, acceleration=None ) -> torch.Tensor: """velocity; the `acceleration` is used to compute the correct velocity in the presence of a forcing scheme.""" rho = self.rho(f=f) if rho is None else rho v = self.j(f=f) / rho # apply correction due to forcing, which effectively averages the pre- # and post-collision velocity if acceleration is None: correction = 0.0 else: if len(acceleration.shape) == 1: index = [Ellipsis] + [None] * self.stencil.d acceleration = acceleration[index] correction = acceleration / (2 * rho) return v + correction @property def velocity(self, f: Optional[torch.Tensor] = None): f = self.f if f is None else f return self.j(f) / self.rho(f) def incompressible_energy(self, f: Optional[torch.Tensor] = None ) -> torch.Tensor: """incompressible kinetic energy""" f = self.f if f is None else f return 0.5 * torch.einsum("d...,d...->...", [self.u(f), self.u(f)]) def entropy(self, f: Optional[torch.Tensor] = None) -> torch.Tensor: """entropy according to the H-theorem""" f = self.f if f is None else f f_log = -torch.log(torch.einsum("q...,q...->...", [f, 1 / self.torch_stencil.w])) return torch.einsum("q...,q...->...", [f, f_log]) def pseudo_entropy_global(self, f: Optional[torch.Tensor] = None ) -> torch.Tensor: """pseudo_entropy derived by a Taylor expansion around the weights""" f = self.f if f is None else f f_w = torch.einsum("q...,q...->q...", [f, 1 / self.torch_stencil.w]) return self.rho() - torch.einsum("q...,q...->...", [f, f_w]) def pseudo_entropy_local(self, f: Optional[torch.Tensor] = None ) -> torch.Tensor: """pseudo_entropy derived by a Taylor expansion around the local equilibrium""" f = self.f if f is None else f f_feq = f / self.equilibrium(self) return self.rho(f) - torch.einsum("q...,q...->...", [f, f_feq]) def shear_tensor(self, f: Optional[torch.Tensor] = None) -> torch.Tensor: """computes the shear tensor of a given self.f in the sense Pi_{\alpha \beta} = f_i * e_{i \alpha} * e_{i \beta}""" shear = torch.einsum("qa,qb->qab", [self.torch_stencil.e, self.torch_stencil.e]) shear = torch.einsum("q...,qab->ab...", [self.f if f is None else f, shear]) return shear def einsum(self, equation, fields, *args) -> torch.Tensor: """Einstein summation on local fields.""" warnings.warn( "The `einsum` method is deprecated and will be removed in a future version. " "Please use `torch.einsum` directly instead.", DeprecationWarning, stacklevel=2 ) inputs, output = equation.split("->") inputs = inputs.split(",") for i, inp in enumerate(inputs): if len(inp) == len(fields[i].shape): pass elif len(inp) == len(fields[i].shape) - self.stencil.d: inputs[i] += "..." if not output.endswith("..."): output += "..." else: assert False, "Bad dimension." equation = ",".join(inputs) + "->" + output return torch.einsum(equation, fields, *args) def dump(self, filename): with open(filename, "wb") as file: pickle.dump(self.context.convert_to_ndarray(self.f), file) def load(self, filename): with open(filename, "rb") as file: self.f = self.context.convert_to_tensor(pickle.load(file), dtype=self.context.dtype) if self.context.use_native: self._f_next = self.context.empty_tensor(self.f.shape) def pressure_poisson(units: 'UnitConversion', u, rho0, tol_abs=1e-10, max_num_steps=100000): """ Solve the pressure poisson equation using a jacobi scheme. Parameters ---------- units : lettuce.UnitConversion The flow instance. u : torch.Tensor The velocity tensor. rho0 : torch.Tensor Initial guess for the density (i.e., pressure). tol_abs : float The tolerance for pressure convergence. Returns ------- rho : torch.Tensor The converged density (i.e., pressure). """ # convert to physical units dx = units.convert_length_to_pu(1.0) u = units.convert_velocity_to_pu(u) p = units.convert_density_lu_to_pressure_pu(rho0) # compute laplacian with torch.no_grad(): u_mod = torch.zeros_like(u[0]) dim = u.shape[0] for i in range(dim): for j in range(dim): derivative = torch_gradient( torch_gradient(u[i] * u[j], dx)[i], dx )[j] u_mod -= derivative # TODO(@MCBs): still not working in 3D p_mod = torch_jacobi( u_mod, p[0], dx, dim=2, tol_abs=tol_abs, max_num_steps=max_num_steps )[None, ...] return units.convert_pressure_pu_to_density_lu(p_mod) def initialize_pressure_poisson(flow: 'Flow', max_num_steps=100000, tol_pressure=1e-6): """Reinitialize equilibrium distributions with pressure obtained by a Jacobi solver. Note that this method has to be called before initialize_f_neq. """ u = flow.u() rho = pressure_poisson( flow.units, u, flow.rho(), tol_abs=tol_pressure, max_num_steps=max_num_steps ) return flow.equilibrium(flow=flow, rho=rho, u=u) def initialize_f_neq(flow: 'Flow'): """Initialize the distribution function values. The f^(1) contributions are approximated by finite differences. See Krüger et al. (2017). """ rho = flow.rho() u = flow.u() grad_u0 = torch_gradient(u[0], dx=1, order=6)[None, ...] grad_u1 = torch_gradient(u[1], dx=1, order=6)[None, ...] S = torch.cat([grad_u0, grad_u1]) if flow.stencil.d == 3: grad_u2 = torch_gradient(u[2], dx=1, order=6)[None, ...] S = torch.cat([S, grad_u2]) Pi_1 = (1.0 * flow.units.relaxation_parameter_lu * rho * S / flow.torch_stencil.cs ** 2) Q = (torch.einsum('ia,ib->iab', [flow.torch_stencil.e, flow.torch_stencil.e]) - torch.eye(flow.stencil.d, device=flow.torch_stencil.e.device) * flow.stencil.cs ** 2) Pi_1_Q = torch.einsum('ab...,iab->i...', [Pi_1, Q]) fneq = torch.einsum('i,i...->i...', [flow.torch_stencil.w, Pi_1_Q]) feq = flow.equilibrium(flow, rho, u) return feq - fneq ================================================ FILE: lettuce/_simulation.py ================================================ import warnings import torch import numpy as np from timeit import default_timer as timer from typing import List, Optional from abc import ABC, abstractmethod from . import * from .cuda_native import NativeCollision, Generator, StreamingStrategy # todo StreamingStrategy was aliased here but should, see StreamingStrategy for todo __all__ = ['Collision', 'Reporter', 'Simulation', 'BreakableSimulation', 'StreamingStrategy'] class Collision(ABC): @abstractmethod def __call__(self, flow: 'Flow'): ... @abstractmethod def native_available(self) -> bool: ... @abstractmethod def native_generator(self, index: int) -> 'NativeCollision': ... class Reporter(ABC): interval: int def __init__(self, interval: int): self.interval = interval @abstractmethod def __call__(self, simulation: 'Simulation'): ... class Simulation: flow: 'Flow' context: 'Context' collision: 'Collision' # pre_boundaries: List['Boundary'] # post_boundaries: List['Boundary'] no_collision_mask: Optional[torch.Tensor] no_streaming_mask: Optional[torch.Tensor] reporter: List['Reporter'] streaming_strategy: StreamingStrategy def __init__(self, flow: 'Flow', collision: 'Collision', reporter: List['Reporter'], streaming_strategy=StreamingStrategy.POST_STREAMING): self.flow = flow self.flow.collision = collision self.context = flow.context self.collision = collision self.collision_index = len(flow.pre_boundaries) ''' collision_index is an index of type int; It represents the index of the collision-substep, in a sequential list of boundary- and collision-substeps. Look at the following example: Example: [boundary0, boundary1, collision_index=2, boundary3], In this example, boundary0 and boundary1 are pre_boundaries and boundary3 is a post_boundary. So the collision_index ist 2, because collision is performed after two boundaries (index 0 and 1 of the list) -> see also: comments under the method _collide() below... ''' self.transformer = (flow.pre_boundaries or []) + [collision] + (flow.post_boundaries or []) self.reporter = reporter self.pre_boundaries = flow.pre_boundaries self.post_boundaries = flow.post_boundaries self.streaming_strategy = streaming_strategy # ==================================== # # initialise masks based on boundaries # # ==================================== # # if there are no boundaries # -> leave the masks uninitialised self.no_collision_mask = None ''' The no_collision_mask will mark all nodes on which collision is not applied. This is technically NOT a boolean mask, but an INT field with indices of boundaries and the collision-substep (see collision_index and pre_/post_boundaries above) All nodes that are marked with the value of self.collision_index will have the collision operator applied. ''' self.no_streaming_mask = None ''' The no_streaming_mask is a boolean mask that will mark all nodes where populations will not be altered during the streaming-substep ''' # else # -> initialise the masks based on the boundaries masks if len(self.pre_boundaries) + len(self.post_boundaries) > 0: # fill masks with value of self.collision_index # (= number of pre_boundaries) self.no_collision_mask = self.context.full_tensor( flow.resolution, self.collision_index, dtype=torch.uint8) self.no_streaming_mask = self.context.full_tensor( [flow.stencil.q, *flow.resolution], self.collision_index, dtype=torch.uint8) # iterate over pre_boundaries for i, boundary in enumerate(self.pre_boundaries): ''' if the i-th boundary returns a non-None no_collision_mask, write its index (i) to every entry in the global no_collision_mask that is True in the individual boundary's no_collision_mask ''' ncm = boundary.make_no_collision_mask( [it for it in self.flow.f.shape[1:]], context=self.context) if ncm is not None: self.no_collision_mask[ncm] = i ''' if the i-th boundary returns a non-None no_streaming_mask, ...add its True-entries to the global no_streaming_mask ''' nsm = boundary.make_no_streaming_mask( [it for it in self.flow.f.shape], context=self.context) if nsm is not None: self.no_streaming_mask |= nsm # iterate over post-boundaries '''(similar to pre-boundaries (above), but start with index collision_index+1); results in a numbering similar to the following example: [0: pre_boundary0, 1: pre_boundary1,2: collision_index, 3: post_boundary0, 4: post_boundary1] ''' for i, boundary in enumerate(self.post_boundaries,start=self.collision_index+1): ncm = boundary.make_no_collision_mask( [it for it in self.flow.f.shape[1:]], context=self.context) if ncm is not None: self.no_collision_mask[ncm] = i nsm = boundary.make_no_streaming_mask( [it for it in self.flow.f.shape], context=self.context) if nsm is not None: self.no_streaming_mask |= nsm # define streaming- and collision-strategy (order and components): if streaming_strategy.pre_streaming() and streaming_strategy.post_streaming(): def collide_and_stream(*_, **__): self._stream() self._collide() self._stream() elif streaming_strategy.post_streaming(): def collide_and_stream(*_, **__): self._collide() self._stream() elif streaming_strategy.pre_streaming(): def collide_and_stream(*_, **__): self._stream() self._collide() else: def collide_and_stream(*_, **__): self._collide() self._collide_and_stream = collide_and_stream # =================================== # # generate cuda_native implementation # # =================================== # if self.context.use_native: # check for availability of cuda_native for all components if (self.flow.equilibrium is not None and not self.flow.equilibrium.native_available()): name = self.flow.equilibrium.__class__.__name__ print(f"cuda_native was requested, but equilibrium '{name}' " f"does not support cuda_native.") if not self.collision.native_available(): name = self.collision.__class__.__name__ print(f"cuda_native was requested, but collision '{name}' " f"does not support cuda_native.") for boundary in self.pre_boundaries + self.post_boundaries: if not boundary.native_available(): name = boundary.__class__.__name__ print(f"cuda_native was requested, but boundary '{name}' " f"does not support cuda_native.") # create cuda_native equivalents native_equilibrium = None if self.flow.equilibrium is not None: native_equilibrium = self.flow.equilibrium.native_generator() native_collision = self.collision.native_generator(self.collision_index) native_pre_boundaries = [] for i, boundary in enumerate(self.pre_boundaries): native_pre_boundaries.append(boundary.native_generator(i)) native_post_boundaries = [] for i, boundary in enumerate(self.post_boundaries, start=self.collision_index+1): native_post_boundaries.append(boundary.native_generator(i)) # begin generating cuda_native module from cuda_native components generator = Generator(self.flow.stencil, collision = native_collision, pre_boundaries = native_pre_boundaries, post_boundaries = native_post_boundaries, equilibrium = native_equilibrium, streaming_strategy = streaming_strategy) native_kernel = generator.resolve() if native_kernel is None: buffer = generator.generate() directory = generator.format(buffer) generator.install(directory) native_kernel = generator.resolve() if native_kernel is None: print('Failed to install cuda_native Extension!') return # redirect collide and stream to cuda_native kernel self._collide_and_stream = native_kernel def step(self, num_steps: int): warnings.warn("lt.Simulation.step() is deprecated and will be " "removed in a future version. Instead, call simulation " "directly: simulation(num_steps)", DeprecationWarning) return self(num_steps) @property def units(self): return self.flow.units @staticmethod def __stream(f, i, e, d): return torch.roll(f[i], shifts=tuple(e[i]), dims=tuple(np.arange(d))) def _stream(self): for i in range(1, self.flow.stencil.q): if self.no_streaming_mask is None: self.flow.f[i] = self.__stream(self.flow.f, i, self.flow.stencil.e, self.flow.stencil.d) else: new_fi = self.__stream(self.flow.f, i, self.flow.stencil.e, self.flow.stencil.d) self.flow.f[i] = torch.where(torch.eq( self.no_streaming_mask[i], 1), self.flow.f[i], new_fi) return self.flow.f def _collide(self): # runs collision and all pre_- and post_boundaries # ...(which are pre- or post-COLLISION, but pre-STREAMING) if self.no_collision_mask is None: # COLLIDE EVERYWHERE # run pre-boundaries for boundary in self.pre_boundaries: self.flow.f = boundary(self.flow) # run collision self.flow.f = self.collision(self.flow) # run post-boundaries for boundary in self.post_boundaries: self.flow.f = boundary(self.flow) else: # SELECTIVE COLLISION ''' according to no_collision_mask, which is NOT a boolean mask! (see definition/initialization/comment above) INFO: boundary-indices in no_collision_mask are defined as: - pre_boundaries have indices: 0 to len(pre_boundaries)-1 - collision_index: len(pre_boundaries) - post_boundaries have indices: len(pre_boundaries)+1 to len(pre_boundaries)+len(post_boundaries) ''' # run pre-boundaries for i, boundary in enumerate(self.pre_boundaries): # where the collision-int equals the boundary-index, # ...apply the boundary, everywhere else: do nothing torch.where(torch.eq(self.no_collision_mask, i), boundary(self.flow), self.flow.f, out=self.flow.f) # run collision # where the collision-int equals the number of pre-boundaries, # ...apply collision, everywhere else: do nothing torch.where(torch.eq(self.no_collision_mask, self.collision_index), self.collision(self.flow), self.flow.f, out=self.flow.f) # run post-boundaries for i, boundary in enumerate(self.post_boundaries, start=self.collision_index+1): torch.where(torch.eq(self.no_collision_mask, i), boundary(self.flow), self.flow.f, out=self.flow.f) return self.flow.f def _report(self): for reporter in self.reporter: reporter(self) def __call__(self, num_steps): beg = timer() if self.flow.i == 0: self._report() for _ in range(num_steps): self._collide_and_stream(self) self.flow.i += 1 self._report() end = timer() return num_steps * self.flow.rho().numel() / 1e6 / (end - beg) class BreakableSimulation(Simulation): def __init__(self, flow: 'Flow', collision: 'Collision', reporter: List['Reporter']): super().__init__(flow, collision, reporter) def __call__(self, num_steps: int) -> float: beg = timer() if self.flow.i == 0: self._report() while self.flow.i < num_steps: # flow.i is used instead of _, # because it is mutable by the reporter! self._collide_and_stream(self) self.flow.i += 1 self._report() end = timer() return num_steps * self.flow.rho().numel() / 1e6 / (end - beg) ================================================ FILE: lettuce/_stencil.py ================================================ from abc import ABC from typing import cast from functools import cached_property from typing import List, Literal import torch import numpy as np from ._context import Context __all__ = ['Stencil', 'TorchStencil'] class Stencil(ABC): e: List[List[int]] w: List[float] opposite: List[int] cs: float = 1 / np.sqrt(3.0) @cached_property def d(self) -> int: assert len(self.e[0]) in [1, 2, 3] return len(self.e[0]) @cached_property def q(self) -> int: return len(self.e) class TorchStencil: e: torch.Tensor w: torch.Tensor opposite: torch.Tensor cs: float = 1 / np.sqrt(3.0) def __init__(self, stencil: 'Stencil', context: 'Context'): self.e = context.convert_to_tensor(stencil.e) self.w = context.convert_to_tensor(stencil.w) self.opposite = context.convert_to_tensor(stencil.opposite) self.e = context.convert_to_tensor(stencil.e) @cached_property def d(self) -> Literal[1, 2, 3]: assert self.e.shape[1] in [1, 2, 3] return cast(Literal[1, 2, 3], self.e.shape[1]) @cached_property def q(self) -> int: return self.e.shape[0] ================================================ FILE: lettuce/_unit.py ================================================ """ Unit conversion into lattice units and back. lu = lattice units pu = physical units """ import numpy as np __all__ = ["UnitConversion"] class UnitConversion: """ Provides unit conversions between physical units (pu) and lattice units (lu). Conversion methods should work for `floats`, `torch.tensors` and `np.arrays`. """ def __init__(self, reynolds_number, mach_number=0.05, characteristic_length_pu=1, characteristic_velocity_pu=1, characteristic_length_lu=1, characteristic_density_lu=1, characteristic_density_pu=1, cs=1 / np.sqrt(3.0)): self.cs = cs self.reynolds_number = reynolds_number self.mach_number = mach_number self.characteristic_length_pu = characteristic_length_pu self.characteristic_velocity_pu = characteristic_velocity_pu self.characteristic_length_lu = characteristic_length_lu self.characteristic_density_lu = characteristic_density_lu self.characteristic_density_pu = characteristic_density_pu @property def characteristic_velocity_lu(self): return self.cs * self.mach_number @property def characteristic_pressure_pu(self): return (self.characteristic_density_pu * self.characteristic_velocity_pu ** 2) @property def characteristic_pressure_lu(self): return (self.characteristic_density_lu * self.characteristic_velocity_lu ** 2) @property def viscosity_lu(self): return (self.characteristic_length_lu * self.characteristic_velocity_lu / self.reynolds_number) @property def viscosity_pu(self): return (self.characteristic_length_pu * self.characteristic_velocity_pu / self.reynolds_number) @property def relaxation_parameter_lu(self): return self.viscosity_lu / self.cs ** 2 + 0.5 def convert_velocity_to_pu(self, velocity_in_lu): return (velocity_in_lu / self.characteristic_velocity_lu * self.characteristic_velocity_pu) def convert_velocity_to_lu(self, velocity_in_pu): return (velocity_in_pu / self.characteristic_velocity_pu * self.characteristic_velocity_lu) def convert_acceleration_to_pu(self, acceleration_in_lu): x = (self.characteristic_velocity_lu ** 2 / self.characteristic_length_lu) y = (self.characteristic_velocity_pu ** 2 / self.characteristic_length_pu) return acceleration_in_lu / x * y def convert_acceleration_to_lu(self, acceleration_in_pu): x = (self.characteristic_velocity_lu ** 2 / self.characteristic_length_lu) y = (self.characteristic_velocity_pu ** 2 / self.characteristic_length_pu) return acceleration_in_pu / y * x def convert_time_to_pu(self, time_in_lu): x = self.characteristic_length_lu / self.characteristic_velocity_lu y = self.characteristic_length_pu / self.characteristic_velocity_pu return time_in_lu / x * y def convert_time_to_lu(self, time_in_pu): x = self.characteristic_length_lu / self.characteristic_velocity_lu y = self.characteristic_length_pu / self.characteristic_velocity_pu return time_in_pu / y * x def convert_density_lu_to_pressure_pu(self, density_lu): return self.convert_pressure_to_pu( (density_lu - self.characteristic_density_lu) * self.cs ** 2 ) def convert_pressure_pu_to_density_lu(self, pressure_pu): return (self.convert_pressure_to_lu(pressure_pu) / self.cs ** 2 + self.characteristic_density_lu) def convert_density_to_pu(self, density_lu): return (density_lu / self.characteristic_density_lu * self.characteristic_density_pu) def convert_density_to_lu(self, density_pu): return (density_pu / self.characteristic_density_pu * self.characteristic_density_lu) def convert_pressure_to_pu(self, pressure_lu): return (pressure_lu / self.characteristic_pressure_lu * self.characteristic_pressure_pu) def convert_pressure_to_lu(self, pressure_pu): return (pressure_pu / self.characteristic_pressure_pu * self.characteristic_pressure_lu) def convert_length_to_pu(self, length_lu): return (length_lu * self.characteristic_length_pu / self.characteristic_length_lu) def convert_length_to_lu(self, length_pu): return (length_pu * self.characteristic_length_lu / self.characteristic_length_pu) def convert_energy_to_pu(self, energy_lu): """Energy is defined here in units of [density * velocity**2]""" return (energy_lu * self.characteristic_pressure_pu / self.characteristic_pressure_lu) def convert_energy_to_lu(self, energy_pu): """Energy is defined here in units of [density * velocity**2]""" return (energy_pu * self.characteristic_pressure_lu / self.characteristic_pressure_pu) def convert_incompressible_energy_to_pu(self, energy_lu): """Energy in incompressible systems is defined in units of [velocity**2]""" return (energy_lu * (self.characteristic_velocity_pu ** 2) / (self.characteristic_velocity_lu ** 2)) def convert_incompressible_energy_to_lu(self, energy_pu): """Energy in incompressible systems is defined in units of [velocity**2]""" return (energy_pu * (self.characteristic_velocity_lu ** 2) / (self.characteristic_velocity_pu ** 2)) ================================================ FILE: lettuce/_version.py ================================================ # This file helps to compute a version number in source trees obtained from # git-archive tarball (such as those provided by githubs download-from-tag # feature). Distribution tarballs (built by setup.py sdist) and build # directories (produced by setup.py build) will contain a much shorter file # that just contains the computed version number. # This file is released into the public domain. Generated by # versioneer-0.21 (https://github.com/python-versioneer/python-versioneer) """Git implementation of _version.py.""" import errno import os import re import subprocess import sys from typing import Callable, Dict def get_keywords(): """Get the keywords needed to look up the version information.""" # these strings will be replaced by git during git-archive. # setup.py/versioneer.py will grep for the variable names, so they must # each be defined on a line of their own. _version.py will just call # get_keywords(). git_refnames = "$Format:%d$" git_full = "$Format:%H$" git_date = "$Format:%ci$" keywords = {"refnames": git_refnames, "full": git_full, "date": git_date} return keywords class VersioneerConfig: """Container for Versioneer configuration parameters.""" def get_config(): """Create, populate and return the VersioneerConfig() object.""" # these strings are filled in when 'setup.py versioneer' creates # _version.py cfg = VersioneerConfig() cfg.VCS = "git" cfg.style = "pep440" cfg.tag_prefix = "" cfg.parentdir_prefix = "lettuce-" cfg.versionfile_source = "lettuce/_version.py" cfg.verbose = False return cfg class NotThisMethod(Exception): """Exception raised if a method is not valid for the current scenario.""" LONG_VERSION_PY: Dict[str, str] = {} HANDLERS: Dict[str, Dict[str, Callable]] = {} def register_vcs_handler(vcs, method): # decorator """Create decorator to mark a method as the handler of a VCS.""" def decorate(f): """Store f in HANDLERS[vcs][method].""" if vcs not in HANDLERS: HANDLERS[vcs] = {} HANDLERS[vcs][method] = f return f return decorate def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None): """Call the given command(s).""" assert isinstance(commands, list) process = None for command in commands: try: dispcmd = str([command] + args) # remember shell=False, so use git.cmd on windows, not just git process = subprocess.Popen([command] + args, cwd=cwd, env=env, stdout=subprocess.PIPE, stderr=(subprocess.PIPE if hide_stderr else None)) break except OSError: e = sys.exc_info()[1] if e.errno == errno.ENOENT: continue if verbose: print("unable to run %s" % dispcmd) print(e) return None, None else: if verbose: print("unable to find command, tried %s" % (commands,)) return None, None stdout = process.communicate()[0].strip().decode() if process.returncode != 0: if verbose: print("unable to run %s (error)" % dispcmd) print("stdout was %s" % stdout) return None, process.returncode return stdout, process.returncode def versions_from_parentdir(parentdir_prefix, root, verbose): """Try to determine the version from the parent directory name. Source tarballs conventionally unpack into a directory that includes both the project name and a version string. We will also support searching up two directory levels for an appropriately named parent directory """ rootdirs = [] for _ in range(3): dirname = os.path.basename(root) if dirname.startswith(parentdir_prefix): return {"version": dirname[len(parentdir_prefix):], "full-revisionid": None, "dirty": False, "error": None, "date": None} rootdirs.append(root) root = os.path.dirname(root) # up a level if verbose: print("Tried directories %s but none started with prefix %s" % (str(rootdirs), parentdir_prefix)) raise NotThisMethod("rootdir doesn't start with parentdir_prefix") @register_vcs_handler("git", "get_keywords") def git_get_keywords(versionfile_abs): """Extract version information from the given file.""" # the code embedded in _version.py can just fetch the value of these # keywords. When used from setup.py, we don't want to import _version.py, # so we do it with a regexp instead. This function is not used from # _version.py. keywords = {} try: with open(versionfile_abs, "r") as fobj: for line in fobj: if line.strip().startswith("git_refnames ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["refnames"] = mo.group(1) if line.strip().startswith("git_full ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["full"] = mo.group(1) if line.strip().startswith("git_date ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["date"] = mo.group(1) except OSError: pass return keywords @register_vcs_handler("git", "keywords") def git_versions_from_keywords(keywords, tag_prefix, verbose): """Get version information from git keywords.""" if "refnames" not in keywords: raise NotThisMethod("Short version file found") date = keywords.get("date") if date is not None: # Use only the last line. Previous lines may contain GPG signature # information. date = date.splitlines()[-1] # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant # datestamp. However we prefer "%ci" (which expands to an "ISO-8601 # -like" string, which we must then edit to make compliant), because # it's been around since git-1.5.3, and it's too difficult to # discover which version we're using, or to work around using an # older one. date = date.strip().replace(" ", "T", 1).replace(" ", "", 1) refnames = keywords["refnames"].strip() if refnames.startswith("$Format"): if verbose: print("keywords are unexpanded, not using") raise NotThisMethod("unexpanded keywords, not a git-archive tarball") refs = {r.strip() for r in refnames.strip("()").split(",")} # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of # just "foo-1.0". If we see a "tag: " prefix, prefer those. TAG = "tag: " tags = {r[len(TAG):] for r in refs if r.startswith(TAG)} if not tags: # Either we're using git < 1.8.3, or there really are no tags. We use # a heuristic: assume all version tags have a digit. The old git %d # expansion behaves like git log --decorate=short and strips out the # refs/heads/ and refs/tags/ prefixes that would let us distinguish # between branches and tags. By ignoring refnames without digits, we # filter out many common branch names like "release" and # "stabilization", as well as "HEAD" and "master". tags = {r for r in refs if re.search(r'\d', r)} if verbose: print("discarding '%s', no digits" % ",".join(refs - tags)) if verbose: print("likely tags: %s" % ",".join(sorted(tags))) for ref in sorted(tags): # sorting will prefer e.g. "2.0" over "2.0rc1" if ref.startswith(tag_prefix): r = ref[len(tag_prefix):] # Filter out refs that exactly match prefix or that don't start # with a number once the prefix is stripped (mostly a concern # when prefix is '') if not re.match(r'\d', r): continue if verbose: print("picking %s" % r) return {"version": r, "full-revisionid": keywords["full"].strip(), "dirty": False, "error": None, "date": date} # no suitable tags, so version is "0+unknown", but full hex is still there if verbose: print("no suitable tags, using unknown + full revision id") return {"version": "0+unknown", "full-revisionid": keywords["full"].strip(), "dirty": False, "error": "no suitable tags", "date": None} @register_vcs_handler("git", "pieces_from_vcs") def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command): """Get version from 'git describe' in the root of the source tree. This only gets called if the git-archive 'subst' keywords were *not* expanded, and _version.py hasn't already been rewritten with a short version string, meaning we're inside a checked out source tree. """ GITS = ["git"] TAG_PREFIX_REGEX = "*" if sys.platform == "win32": GITS = ["git.cmd", "git.exe"] TAG_PREFIX_REGEX = r"\*" _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, hide_stderr=True) if rc != 0: if verbose: print("Directory %s not under git control" % root) raise NotThisMethod("'git rev-parse --git-dir' returned error") # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] # if there isn't one, this yields HEX[-dirty] (no NUM) describe_out, rc = runner(GITS, ["describe", "--tags", "--dirty", "--always", "--long", "--match", "%s%s" % (tag_prefix, TAG_PREFIX_REGEX)], cwd=root) # --long was added in git-1.5.5 if describe_out is None: raise NotThisMethod("'git describe' failed") describe_out = describe_out.strip() full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root) if full_out is None: raise NotThisMethod("'git rev-parse' failed") full_out = full_out.strip() pieces = {} pieces["long"] = full_out pieces["short"] = full_out[:7] # maybe improved later pieces["error"] = None branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], cwd=root) # --abbrev-ref was added in git-1.6.3 if rc != 0 or branch_name is None: raise NotThisMethod("'git rev-parse --abbrev-ref' returned error") branch_name = branch_name.strip() if branch_name == "HEAD": # If we aren't exactly on a branch, pick a branch which represents # the current commit. If all else fails, we are on a branchless # commit. branches, rc = runner(GITS, ["branch", "--contains"], cwd=root) # --contains was added in git-1.5.4 if rc != 0 or branches is None: raise NotThisMethod("'git branch --contains' returned error") branches = branches.split("\n") # Remove the first line if we're running detached if "(" in branches[0]: branches.pop(0) # Strip off the leading "* " from the list of branches. branches = [branch[2:] for branch in branches] if "master" in branches: branch_name = "master" elif not branches: branch_name = None else: # Pick the first branch that is returned. Good or bad. branch_name = branches[0] pieces["branch"] = branch_name # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] # TAG might have hyphens. git_describe = describe_out # look for -dirty suffix dirty = git_describe.endswith("-dirty") pieces["dirty"] = dirty if dirty: git_describe = git_describe[:git_describe.rindex("-dirty")] # now we have TAG-NUM-gHEX or HEX if "-" in git_describe: # TAG-NUM-gHEX mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) if not mo: # unparsable. Maybe git-describe is misbehaving? pieces["error"] = ("unable to parse git-describe output: '%s'" % describe_out) return pieces # tag full_tag = mo.group(1) if not full_tag.startswith(tag_prefix): if verbose: fmt = "tag '%s' doesn't start with prefix '%s'" print(fmt % (full_tag, tag_prefix)) pieces["error"] = ("tag '%s' doesn't start with prefix '%s'" % (full_tag, tag_prefix)) return pieces pieces["closest-tag"] = full_tag[len(tag_prefix):] # distance: number of commits since tag pieces["distance"] = int(mo.group(2)) # commit: short hex revision ID pieces["short"] = mo.group(3) else: # HEX: no tags pieces["closest-tag"] = None count_out, rc = runner(GITS, ["rev-list", "HEAD", "--count"], cwd=root) pieces["distance"] = int(count_out) # total number of commits # commit date: see ISO-8601 comment in git_versions_from_keywords() date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip() # Use only the last line. Previous lines may contain GPG signature # information. date = date.splitlines()[-1] pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) return pieces def plus_or_dot(pieces): """Return a + if we don't already have one, else return a .""" if "+" in pieces.get("closest-tag", ""): return "." return "+" def render_pep440(pieces): """Build up version string, with post-release "local version identifier". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty Exceptions: 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += plus_or_dot(pieces) rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = "0+untagged.%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" return rendered def render_pep440_branch(pieces): """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] . The ".dev0" means not master branch. Note that .dev0 sorts backwards (a feature branch will appear "older" than the master branch). Exceptions: 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: if pieces["branch"] != "master": rendered += ".dev0" rendered += plus_or_dot(pieces) rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = "0" if pieces["branch"] != "master": rendered += ".dev0" rendered += "+untagged.%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" return rendered def pep440_split_post(ver): """Split pep440 version string at the post-release segment. Returns the release segments before the post-release and the post-release version number (or -1 if no post-release segment is present). """ vc = str.split(ver, ".post") return vc[0], int(vc[1] or 0) if len(vc) == 2 else None def render_pep440_pre(pieces): """TAG[.postN.devDISTANCE] -- No -dirty. Exceptions: 1: no tags. 0.post0.devDISTANCE """ if pieces["closest-tag"]: if pieces["distance"]: # update the post release segment tag_version, post_version = pep440_split_post(pieces["closest-tag"]) rendered = tag_version if post_version is not None: rendered += ".post%d.dev%d" % (post_version + 1, pieces["distance"]) else: rendered += ".post0.dev%d" % (pieces["distance"]) else: # no commits, use the tag as the version rendered = pieces["closest-tag"] else: # exception #1 rendered = "0.post0.dev%d" % pieces["distance"] return rendered def render_pep440_post(pieces): """TAG[.postDISTANCE[.dev0]+gHEX] . The ".dev0" means dirty. Note that .dev0 sorts backwards (a dirty tree will appear "older" than the corresponding clean one), but you shouldn't be releasing software with -dirty anyways. Exceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += plus_or_dot(pieces) rendered += "g%s" % pieces["short"] else: # exception #1 rendered = "0.post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += "+g%s" % pieces["short"] return rendered def render_pep440_post_branch(pieces): """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] . The ".dev0" means not master branch. Exceptions: 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%d" % pieces["distance"] if pieces["branch"] != "master": rendered += ".dev0" rendered += plus_or_dot(pieces) rendered += "g%s" % pieces["short"] if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = "0.post%d" % pieces["distance"] if pieces["branch"] != "master": rendered += ".dev0" rendered += "+g%s" % pieces["short"] if pieces["dirty"]: rendered += ".dirty" return rendered def render_pep440_old(pieces): """TAG[.postDISTANCE[.dev0]] . The ".dev0" means dirty. Exceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" else: # exception #1 rendered = "0.post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" return rendered def render_git_describe(pieces): """TAG[-DISTANCE-gHEX][-dirty]. Like 'git describe --tags --dirty --always'. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render_git_describe_long(pieces): """TAG-DISTANCE-gHEX[-dirty]. Like 'git describe --tags --dirty --always -long'. The distance/hash is unconditional. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render(pieces, style): """Render the given version pieces into the requested style.""" if pieces["error"]: return {"version": "unknown", "full-revisionid": pieces.get("long"), "dirty": None, "error": pieces["error"], "date": None} if not style or style == "default": style = "pep440" # the default if style == "pep440": rendered = render_pep440(pieces) elif style == "pep440-branch": rendered = render_pep440_branch(pieces) elif style == "pep440-pre": rendered = render_pep440_pre(pieces) elif style == "pep440-post": rendered = render_pep440_post(pieces) elif style == "pep440-post-branch": rendered = render_pep440_post_branch(pieces) elif style == "pep440-old": rendered = render_pep440_old(pieces) elif style == "git-describe": rendered = render_git_describe(pieces) elif style == "git-describe-long": rendered = render_git_describe_long(pieces) else: raise ValueError("unknown style '%s'" % style) return {"version": rendered, "full-revisionid": pieces["long"], "dirty": pieces["dirty"], "error": None, "date": pieces.get("date")} def get_versions(): """Get version information or return default if unable to do so.""" # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have # __file__, we can work backwards from there to the root. Some # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which # case we can only use expanded keywords. cfg = get_config() verbose = cfg.verbose try: return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, verbose) except NotThisMethod: pass try: root = os.path.realpath(__file__) # versionfile_source is the relative path from the top of the source # tree (where the .git directory might live) to this file. Invert # this to find the root from __file__. for _ in cfg.versionfile_source.split('/'): root = os.path.dirname(root) except NameError: return {"version": "0+unknown", "full-revisionid": None, "dirty": None, "error": "unable to find root of source tree", "date": None} try: pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose) return render(pieces, cfg.style) except NotThisMethod: pass try: if cfg.parentdir_prefix: return versions_from_parentdir(cfg.parentdir_prefix, root, verbose) except NotThisMethod: pass return {"version": "0+unknown", "full-revisionid": None, "dirty": None, "error": "unable to compute version", "date": None} ================================================ FILE: lettuce/base.py ================================================ from typing import Optional class LatticeBase: """Class LatticeBase LatticeBase is the Base Class for all Lattice Components. LatticeBase is mainly used to make native implementations available to all Lattice Components in an uniform way. Sub Classes can overwrite `create_native` and create a valid native generator. If done so `native_available` should also be overwritten to return True. """ lattice: 'Lattice' def __init__(self, lattice): """Trivial Constructor for Class LatticeBase Parameters ---------- lattice: Lattice The associated lattice object. Used for configuration and to access other lattice components. """ self.lattice = lattice def native_available(self) -> bool: """native_available Returns ------- Whether a native generator is available. If so create native should return a valid native generator. """ return False def create_native(self) -> Optional['NativeLatticeBase']: """create_native Returns ------- A native generator that can be used to create a native implementation of this component. Native components are generally more performant than the default components but native components need to be compiled in the first place. Check `native_available` before using this method as not every component is guaranteed to provide a native generator. """ return None ================================================ FILE: lettuce/cli.py ================================================ # -*- coding: utf-8 -*- """Console script for lettuce. To get help for terminal commands, open a console and type: >>> lettuce --help """ import sys import cProfile import pstats import click import torch import numpy as np from lettuce import * from lettuce import __version__ as lettuce_version from lettuce.ext import (BGKCollision, ErrorReporter, VTKReporter, flow_by_name, Guo) @click.group() @click.version_option(version=lettuce_version) @click.option("--cuda/--no-cuda", default=True, help="Use cuda (default=True).") @click.option("-i", "--gpu-id", type=int, default=0, help="Device ID of the GPU (default=0).") @click.option("-p", "--precision", type=click.Choice(["half", "single", "double"]), default="double", help="Numerical Precision; 16, 32, or 64 bit per float " "(default=double).") @click.pass_context # pass parameters to sub-commands def main(ctx, cuda, gpu_id, precision): """Pytorch-accelerated Lattice Boltzmann Solver """ ctx.obj = {'device': None, 'dtype': None} if cuda: if not torch.cuda.is_available(): print("CUDA not found.") raise click.Abort device = torch.device("cuda:{}".format(gpu_id)) else: device = torch.device("cpu") dtype = {"half": torch.half, "single": torch.float, "double": torch.double}[precision] ctx.obj['device'] = device ctx.obj['dtype'] = dtype @main.command() @click.option("-s", "--steps", type=int, default=10, help="Number of time steps.") @click.option("-r", "--resolution", type=int, default=1024, help="Grid Resolution") @click.option("-o", "--profile-out", type=str, default="", help="File to write profiling information to (default=""; " "no profiling information gets written).") @click.option("-f", "--flow", type=click.Choice(list(flow_by_name.keys())), default="taylor2d") @click.option("-v", "--vtk-out", type=str, default="", help="VTK file basename to write the velocities and densities " "to (default=""; no info gets written).") @click.option("--use-cuda_native/--use-no-cuda_native", default=True, help="whether to use the cuda_native implementation or not.") @click.option("--streaming-strategy", type=click.Choice(["PRE_STREAMING", "POST_STREAMING"]), default="PRE_STREAMING", help="Streaming strategy for the simulation (default=PRE_STREAMING).") @click.pass_context # pass parameters to sub-commands def benchmark(ctx, steps, resolution, profile_out, flow, vtk_out, use_cuda_native, streaming_strategy): """Run a short simulation and print performance in MLUPS. """ # start profiling if profile_out: profile = cProfile.Profile() profile.enable() # setup and run simulation flow_class, stencil = flow_by_name[flow] context = Context(ctx.obj['device'], ctx.obj['dtype'], use_cuda_native) flow = flow_class(context, resolution=resolution, reynolds_number=1, mach_number=0.05, stencil=stencil) force = Guo( tau=flow.units.relaxation_parameter_lu, acceleration=flow.units.convert_acceleration_to_lu(flow.force) ) if hasattr(flow, "acceleration") else None collision = BGKCollision(tau=flow.units.relaxation_parameter_lu, force=force) reporter = [] if vtk_out: reporter.append(VTKReporter(interval=10)) selected_streaming_strategy = getattr(StreamingStrategy, streaming_strategy) simulation = Simulation(flow, collision, reporter, streaming_strategy=selected_streaming_strategy) mlups = simulation(num_steps=steps) # write profiling output if profile_out: profile.disable() stats = pstats.Stats(profile) stats.sort_stats('cumulative') stats.print_stats() profile.dump_stats(profile_out) click.echo(f"Saved profiling information to {profile_out}.") click.echo("Finished {} ({}, {}, {}) for {} steps in {} bit precision with {}. MLUPS: {:10.2f}".format( flow.__class__.__name__, flow.stencil.__class__.__name__, ctx.obj['device'], use_cuda_native, steps, str(ctx.obj['dtype']).replace("torch.float", ""), streaming_strategy, mlups)) return 0 @main.command() @click.option("--use-cuda_native/--use-no-cuda_native", default=True, help="whether to use the cuda_native implementation or not.") @click.pass_context def convergence(ctx, use_cuda_native): """Use Taylor Green 2D for convergence test in diffusive scaling.""" use_cuda_native &= ctx.obj['device'].type != 'cpu' context = Context(ctx.obj['device'], ctx.obj['dtype'], use_native=use_cuda_native) error_u_old = None error_p_old = None factor_u = None factor_p = None print(("{:>15} " * 6).format("resolution", "error (u)", "order (u)", "error (p)", "order (p)", "MLUPS")) for i in range(4, 9): resolution = 2 ** i mach_number = 8 / resolution # Simulation flow = TaylorGreenVortex(context, [resolution] * 2, reynolds_number=10000, mach_number=mach_number) collision = BGKCollision(tau=flow.units.relaxation_parameter_lu) error_reporter = ErrorReporter(flow.analytic_solution, interval=1, out=None) simulation = Simulation(flow, collision, [error_reporter]) mlups = simulation(10 * resolution) error_u, error_p = np.mean(np.abs(error_reporter.out), axis=0).tolist() factor_u = 0 if error_u_old is None else error_u_old / error_u factor_p = 0 if error_p_old is None else error_p_old / error_p error_u_old = error_u error_p_old = error_p print(f"{resolution:15} {error_u:15.2e} {factor_u / 2:15.2f} " f"{error_p:15.2e} {factor_p / 2:15.2f} {mlups:15.2f}") tol = 1e-1 if not (2 - tol) < factor_u / 2 < (2 + tol): print(f"FAILED: Velocity convergence order {factor_u / 2} is not in " f"[1.9, 2.1") sys.exit(1) if not (1 - tol) < factor_p / 2 < (1 + tol): print(f"FAILED: Pressure convergence order {factor_p / 2} is not in " f"[0.9, 1.1].") sys.exit(1) else: return 0 if __name__ == "__main__": # convergence([], use_native=False) #sys.exit(main(['--cuda', '-p', 'single', 'benchmark', '--steps', '100', # '--resolution', '2048', '--use-no-cuda_native'])) sys.exit(main(['--cuda', '-p', 'single', 'benchmark', '--steps', '5000', '--resolution', '2048', '--use-cuda_native'])) ================================================ FILE: lettuce/cuda_native/__init__.py ================================================ from ._util import * from ._transformer import * from ._template import * from ._registry import * from ._default_code_gen import * from ._generator import * import lettuce.cuda_native.ext ================================================ FILE: lettuce/cuda_native/_default_code_gen.py ================================================ import json from enum import Enum from functools import lru_cache as cached from typing import Optional, cast from . import * from ._registry import Registry from .. import Stencil __all__ = ['StreamingStrategy', 'DefaultCodeGeneration'] class StreamingStrategy(Enum): NO_STREAMING = 0b00 PRE_STREAMING = 0b10 POST_STREAMING = 0b01 DOUBLE_STREAMING = 0b11 @cached def pre_streaming(self) -> bool: return bool(self.value & StreamingStrategy.PRE_STREAMING.value) @cached def post_streaming(self) -> bool: return bool(self.value & StreamingStrategy.POST_STREAMING.value) class DefaultCodeGeneration(Registry): _xyz: str = "xyz" _cxyz: str = "cxyz" stencil: 'Stencil' transformer: List['NativeTransformer'] equilibrium: Optional['NativeEquilibrium'] streaming_strategy: StreamingStrategy _transformer_mask: bool def __init__(self, stencil: 'Stencil', transformer: Optional[List['NativeTransformer']], equilibrium: Optional['NativeEquilibrium'], streaming_strategy: StreamingStrategy): Registry.__init__(self, stencil.d) self.stencil = stencil self.transformer = transformer self.equilibrium = equilibrium self.streaming_strategy = streaming_strategy self._transformer_mask = len(transformer) > 1 # @property # def collision(self) -> NativeCollision: # return cast(NativeCollision, self.transformer[0]) # # @property # def boundaries(self) -> List[NativeBoundary]: # return cast(List[NativeBoundary], self.transformer[1:]) @cached def d(self) -> str: return f"static_cast({json.dumps(self.stencil.d)})" @cached def q(self) -> str: return f"static_cast({json.dumps(self.stencil.q)})" @cached def e(self, q: int, d: int) -> str: assert d in range(self.stencil.d) assert q in range(self.stencil.q) return f"static_cast({json.dumps(self.stencil.e[q][d])})" @cached def w(self, q: int) -> str: assert q in range(self.stencil.q) return f"static_cast({json.dumps(self.stencil.w[q])})" @cached def opposite(self, q: int) -> str: assert q in range(self.stencil.q) return f"static_cast({json.dumps(self.stencil.opposite[q])})" @cached def support_no_collision_mask(self) -> str: return str(self._transformer_mask).lower() @cached def support_no_streaming_mask(self) -> str: return str(self._transformer_mask).lower() @cached def thread_count(self, d: Optional[int] = None) -> str: if d is None: values = [self.thread_count(d) for d in range(self.stencil.d)] return self.cuda.variable('dim3', 'thread_count', f"dim3{{{','.join(values)}}}") assert d in range(self.stencil.d) value = 'static_cast(16)' if self.stencil.d < 3 else 'static_cast(8)' return value @cached def cuda_block_count(self, d: Optional[int] = None) -> str: if d is None: values = [f"({self.cuda_size(d + 1)} + {self.thread_count(d)} - 1)/{self.thread_count(d)}" for d in range(self.stencil.d)] return self.cuda.variable('dim3', 'block_count', f"dim3{{{','.join(values)}}}") assert d in range(self.stencil.d) return f"block_count.{'cxyz'[d]}" @cached def kernel_block_count(self, d: int) -> str: assert d in range(self.stencil.d) variable = self.cuda_block_count(d) return self.kernel_hook(variable, Parameter('index_t', variable.replace('.', '_'))) @cached def cuda_size(self, d: int) -> str: assert d in range(self.stencil.d + 1) if d == 0: return f"{self.stencil.q}" return f"static_cast({self.cuda_f()}.size({d}))" @cached def kernel_size(self, d: int) -> str: if d == 0: return f"{self.stencil.q}" return self.kernel_hook(self.cuda_size(d), Parameter('index_t', f"size_{'cxyz'[d]}")) @cached def kernel_index(self, d: int) -> str: assert d in range(self.stencil.d) variable = 'xyz'[d] expr = f"blockIdx.{'xyz'[d]}*blockDim.{'xyz'[d]}+threadIdx.{'xyz'[d]}" return self.pipes.variable('index_t', variable, expr) @cached def cuda_stride(self, d: int) -> str: assert d in range(self.stencil.d + 1) return f"static_cast({self.cuda_f()}.stride({d}))" @cached def kernel_stride(self, d: int) -> str: return self.kernel_hook(self.cuda_stride(d), Parameter('index_t', f"stride_{'cxyz'[d]}")) @cached def kernel_offset(self, d: int, v: int = 0) -> str: assert d in range(self.stencil.d) assert v in [-1, 0, 1] if v == 0: variable = f"offset_{'xyz'[d]}" code = f"{self.kernel_index(d)}*{self.kernel_stride(d + 1)}" elif v == 1: variable = f"offset_{'xyz'[d]}_h" # todo benchmark branch less optimization code = f"({self.kernel_index(d)}!={self.kernel_size(d + 1)}-1)*({self.kernel_index(d)}+1)*{self.kernel_stride(d + 1)}" # code = f"(({self.kernel_index(d)}=={self.kernel_size(d + 1)}-1)?0:{self.kernel_index(d)}+1)*{self.kernel_stride(d + 1)}" else: # v == -1: variable = f"offset_{'xyz'[d]}_l" # todo benchmark branch less optimization code = f"(({self.kernel_index(d)}-1)+(({self.kernel_size(d + 1)}-{self.kernel_index(d)})&(-({self.kernel_index(d)}==0))))*{self.kernel_stride(d + 1)}" # code = f"(({self.kernel_index(d)}==0)?{self.kernel_size(d + 1)}-1:{self.kernel_index(d)}-1)*{self.kernel_stride(d + 1)}" return self.pipes.variable('index_t', variable, code) @cached def kernel_base_index(self) -> str: expr = '+'.join(self.kernel_offset(d, 0) for d in range(self.stencil.d)) return self.pipes.variable('index_t', 'base_index', expr) def assert_ncm(self): assert self._transformer_mask code = "assert hasattr(simulation, 'no_collision_mask')" self.python_pre.append(code, guard=code) def assert_nsm(self): assert self._transformer_mask code = "assert hasattr(simulation, 'no_streaming_mask')" self.python_pre.append(code, guard=code) @cached def cuda_no_collision_mask(self) -> str: self.assert_ncm() return self.cuda_hook('simulation.no_collision_mask', Parameter('at::Tensor', 'ncm')) def cuda_ncm(self) -> str: return self.cuda_no_collision_mask() @cached def kernel_no_collision_mask(self) -> str: variable_ncm = self.cuda_no_collision_mask() return self.kernel_hook(f"{variable_ncm}.data_ptr()", Parameter('byte_t*', variable_ncm)) def kernel_ncm(self) -> str: return self.kernel_no_collision_mask() @cached def cuda_no_streaming_mask(self) -> str: self.assert_nsm() return self.cuda_hook('simulation.no_streaming_mask', Parameter('at::Tensor', 'nsm')) def cuda_nsm(self) -> str: return self.cuda_no_streaming_mask() @cached def kernel_no_streaming_mask(self) -> str: self.assert_nsm() variable_nsm = self.cuda_no_streaming_mask() return self.kernel_hook(f"{variable_nsm}.data_ptr()", Parameter('byte_t*', variable_nsm)) def kernel_nsm(self) -> str: return self.kernel_no_streaming_mask() @cached def cuda_f(self) -> str: return self.cuda_hook(f"simulation.flow.f", Parameter('at::Tensor', 'f')) @cached def kernel_f(self) -> str: variable = self.cuda_f() return self.kernel_hook(f"{variable}.data_ptr()", Parameter('scalar_t*', variable)) @cached def kernel_stream_offset(self, q: int) -> str: assert q in range(self.stencil.q) offsets = [self.kernel_offset(d, self.stencil.e[q][d]) for d in range(self.stencil.d)] return self.pipes.variable('index_t', f"stream_offset_{q}", '+'.join(offsets)) @cached def f_reg(self, q: int) -> str: assert q in range(self.stencil.q) variable = f"f_reg_{q}" f = self.kernel_f() stride_c = self.kernel_stride(0) if not self.streaming_strategy.pre_streaming(): # no pre streaming base = self.kernel_base_index() return self.pipes.mutable('scalar_t', variable, f"{f}[{q}*{stride_c}+{base}]") # pre streaming code = f"{f}[{q}*{stride_c}+{self.kernel_stream_offset(self.stencil.opposite[q])}]" if self._transformer_mask: base = self.kernel_base_index() # prepend optional no streaming code = f"{self.kernel_nsm()}[{q}*{stride_c}+{base}]?{f}[{q}*{stride_c}+{base}]:{code}" # pre streaming return self.pipes.mutable('scalar_t', variable, code) @cached def kernel_rho(self) -> str: return self.pipes.variable('scalar_t', 'rho', '+'.join(self.f_reg(q) for q in range(self.stencil.q))) @cached def kernel_rho_inv(self) -> str: return self.pipes.variable('scalar_t', 'rho_inv', f"1.0/{self.kernel_rho()}") @cached def kernel_u(self, d: int) -> str: assert d in range(self.stencil.d) exf = '+'.join(f"{self.e(q, d)}*{self.f_reg(q)}" for q in range(self.stencil.q)) if self.stencil.d > 1: expr = f"({exf})*{self.kernel_rho_inv()}" else: expr = f"({exf})/{self.kernel_rho()}" return self.pipes.variable('scalar_t', f"u_{'xyz'[d]}", expr) @cached def cuda_f_next(self) -> str: return self.cuda_hook('simulation.flow.f_next', Parameter('at::Tensor', 'f_next')) @cached def kernel_f_next(self) -> str: variable = self.cuda_f_next() return self.kernel_hook(f"{variable}.data_ptr()", Parameter('scalar_t*', variable)) def generate(self): for d in range(self.stencil.d): self.pipes.append(f"if({self.kernel_index(d)}>={self.kernel_size(d + 1)})return;") self.thread_count() self.cuda_block_count() for transformer in self.transformer: if self._transformer_mask: self.pipe.append(f"if({self.kernel_ncm()}[{self.kernel_base_index()}]=={transformer.index}){{") transformer.generate(self) self.pipe.append('}') else: transformer.generate(self) f_next = self.kernel_f_next() for q in range(self.stencil.q): stride_c = self.kernel_stride(0) if not self.streaming_strategy.post_streaming(): base = self.kernel_base_index() self.pipe.append(f"{f_next}[{q}*{stride_c}+{base}]={self.f_reg(q)};") continue if self._transformer_mask: base = self.kernel_base_index() self.pipe.append(f"if({self.kernel_no_streaming_mask()}[{q}*{stride_c}+{base}])") self.pipe.append(f" {f_next}[{q}*{stride_c}+{base}]={self.f_reg(q)};") self.pipe.append('else') # post streaming self.pipe.append(f"{f_next}[{q}*{stride_c}+{self.kernel_stream_offset(q)}]={self.f_reg(q)};") ================================================ FILE: lettuce/cuda_native/_generator.py ================================================ import importlib import os import os.path import shutil import site import subprocess import sys import tempfile from functools import cached_property from typing import Dict, Optional, List from . import * from .. import __version__, Stencil __all__ = ['Generator'] class Generator(DefaultCodeGeneration): """Formatter & build tool for lattice‐Boltzmann kernels.""" def __init__(self, stencil: 'Stencil', collision: 'NativeCollision', pre_boundaries: Optional[List['NativeBoundary']] = None, post_boundaries: Optional[List['NativeBoundary']] = None, equilibrium: Optional['NativeEquilibrium'] = None, streaming_strategy=StreamingStrategy.POST_STREAMING): transformer = (pre_boundaries or []) + [collision] + (post_boundaries or []) DefaultCodeGeneration.__init__(self, stencil, transformer, equilibrium, streaming_strategy) @property def version(self): return __version__ @cached_property def name(self): name = [self.stencil.__class__.__name__] # name += [self.collision.__class__.__name__ if self.collision else 'None'] name += [self.streaming_strategy.name] for t in self.transformer: name += [t.__class__.__name__] name += [self.version] return lettuce_hash(name) def generate(self) -> Dict[str, str]: if not hasattr(self, '_generate_result'): DefaultCodeGeneration.generate(self) buffers = {'name': self.name, 'version': self.version} buffers.update(self.joined_buffer()) setattr(self, '_generate_result', buffers) return getattr(self, '_generate_result') def format(self, generate_dir: Optional[str] = None) -> str: if generate_dir is None: generate_dir = tempfile.mkdtemp() sources = [] for input_path_template, input_text_template in template.items(): val = self.generate() input_path = input_path_template.format(**val) input_text = input_text_template.format(**val) input_dir, input_filename = os.path.split(input_path) output_dir: str = os.path.join(generate_dir, input_dir) os.makedirs(output_dir, exist_ok=True) output_path = os.path.join(output_dir, input_filename) if input_path.endswith('.cu') or input_path.endswith('.cpp') or input_path.endswith('.hpp'): sources.append(output_path) with open(output_path, 'w') as output_file: output_file.write(input_text) if shutil.which('clang-format') is not None: for src in sources: # subprocess.run(['clang-format', '-i', f"-style=file:{generate_dir}/.clang-format", src], check=True) subprocess.run(['clang-format', '-i', f"-style=file", src], check=True) return generate_dir def _resolve(self): try: importlib.reload(site) native = importlib.import_module(f"lettuce_{self.name}") # noinspection PyUnresolvedReferences return native.invoke except ModuleNotFoundError: print('Could not resolve cuda_native extension.') return None except AttributeError: print('Native module found but it is not working as expected.') return None def resolve(self, install: bool = True): step = self._resolve() if step is not None or not install: return step directory = self.format() self.install(directory) # do not try to install a second time return self.resolve(False) @staticmethod def install(directory: str): print(f"Installing Native module ({directory}) ...") cmd = [sys.executable, 'setup.py', 'install'] install_log_path = os.path.join(directory, 'install.log') with open(install_log_path, 'wb') as install_log: p = subprocess.run(cmd, shell=False, cwd=directory, stderr=install_log, stdout=install_log) if p.returncode != 0: print(f"Install failed! See log-File ({install_log_path}) for more Info.") raise subprocess.CalledProcessError(p.returncode, cmd) # after install the module is not registered, # so we need to reload the register importlib.reload(site) ================================================ FILE: lettuce/cuda_native/_registry.py ================================================ from dataclasses import dataclass from functools import cached_property from typing import Dict, Set, Hashable, Optional, Any, cast __all__ = ['Parameter', 'RegistryList', 'Registry', 'CodeRegistryList', 'ParameterRegistryList'] @dataclass class Parameter: variable_type: str variable: str def __str__(self): return f"{self.variable_type} {self.variable}" class RegistryList(list): dimensions: int guards: Set[int] def __init__(self, dimensions: int): list.__init__(self) self.dimensions = dimensions self.guards = set() def registered(self, guard: Optional[Hashable] = None, cond: bool = True) -> bool: if not cond: return True if guard is not None: guard = hash(guard) if guard in self.guards: return True return False def register(self, guard: Optional[Hashable] = None, cond: bool = True) -> bool: if not cond: return False if guard is not None: guard = hash(guard) if guard in self.guards: return False self.guards.add(guard) return True def append(self, item: Any, guard: Optional[Hashable] = None, cond: bool = True): if self.register(guard, cond): list.append(self, item) def extend(self, other, guard: Optional[Hashable] = None, cond: bool = True): if self.register(guard, cond): list.extend(self, other) def __str__(self): raise NotImplementedError() class CodeRegistryList(RegistryList): def mutable(self, variable_type: str, variable: str, value: str) -> str: self.append(f"auto {variable}=static_cast<{variable_type}>({value});", guard=variable) return variable def variable(self, variable_type: str, variable: str, value: str) -> str: self.append(f"const auto {variable}=static_cast<{variable_type}>({value});", guard=variable) return variable def __str__(self): return '\n '.join(self) class ParameterRegistryList(RegistryList): def __str__(self): return ','.join(self) class Registry: _buffers: Dict[str, RegistryList[str]] def __init__(self, d: int): self._buffers = { 'python_pre': CodeRegistryList(d), 'python_post': CodeRegistryList(d), 'cuda': CodeRegistryList(d), 'pipes': CodeRegistryList(d), 'pipe': CodeRegistryList(d), 'py_values': ParameterRegistryList(d), 'cuda_parameters': ParameterRegistryList(d), 'cuda_values': ParameterRegistryList(d), 'kernel_parameters': ParameterRegistryList(d), } def cuda_hook(self, py_value: str, cuda_param: Parameter, cond: bool = True, ) -> str: self._buffers['py_values'].append(str(py_value), cond=cond, guard=cuda_param.variable) self._buffers['cuda_parameters'].append(str(cuda_param), cond=cond, guard=cuda_param.variable) return cuda_param.variable def kernel_hook(self, cuda_value: str, kernel_param: Parameter, cond: bool = True) -> str: self._buffers['cuda_values'].append(str(cuda_value), cond=cond, guard=kernel_param.variable) self._buffers['kernel_parameters'].append(str(kernel_param), cond=cond, guard=kernel_param.variable) return kernel_param.variable @cached_property def python_pre(self) -> CodeRegistryList: return cast(CodeRegistryList, self._buffers['python_pre']) @cached_property def python_post(self) -> CodeRegistryList: return cast(CodeRegistryList, self._buffers['python_post']) @cached_property def cuda(self) -> CodeRegistryList: return cast(CodeRegistryList, self._buffers['cuda']) @cached_property def pipes(self) -> CodeRegistryList: return cast(CodeRegistryList, self._buffers['pipes']) @cached_property def pipe(self) -> CodeRegistryList: return cast(CodeRegistryList, self._buffers['pipe']) def joined_buffer(self) -> Dict[str, str]: return {k: str(v) for k, v in self._buffers.items()} ================================================ FILE: lettuce/cuda_native/_template.py ================================================ from typing import Dict __all__ = [ 'template', ] template: Dict[str, str] = { ".clang-format": """ BasedOnStyle: Mozilla TabWidth: 2 UseTab: Never ColumnLimit: 0 MaxEmptyLinesToKeep: 1 AllowShortFunctionsOnASingleLine: Empty AllowShortLambdasOnASingleLine: Empty AlignConsecutiveAssignments: true """, "lettuce_{name}/__init__.py": """ import torch import numpy as np import os import importlib __all__ = ['cuda_native', 'invoke'] if os.name == 'nt': # on windows add cuda path for cuda_native module to find all dll's os.add_dll_directory(os.path.join(os.environ['CUDA_PATH'], 'bin')) cuda_native = importlib.import_module("lettuce_{name}.cuda_native") def invoke(simulation): {python_pre} cuda_native.lettuce({py_values}) {python_post} simulation.flow.f, simulation.flow.f_next = simulation.flow.f_next, simulation.flow.f """, "lettuce.cpp": """ #include "lettuce.hpp" PYBIND11_MODULE(TORCH_EXTENSION_NAME,m){{m.def("lettuce",&lettuce,"lettuce");}} """, "lettuce.hpp": """ #ifndef LETTUCE_HPP #define LETTUCE_HPP #include void lettuce({cuda_parameters}); #endif """, "lettuce_cuda.cu": """ #include #include #include #include using index_t = int; using byte_t = unsigned char; template __global__ void lettuce_kernel({kernel_parameters}) {{ {pipes} {pipe} }} void lettuce({cuda_parameters}) {{ {cuda} AT_DISPATCH_FLOATING_TYPES(f.scalar_type(),"lettuce",[&]{{ lettuce_kernel<<>>( {cuda_values}); }}); torch::cuda::synchronize(-1); }} """, "setup.py": """ #!/usr/bin/env python from setuptools import setup, find_packages from torch.utils.cpp_extension import BuildExtension, CUDAExtension from os.path import join, dirname native_sources = [ join(dirname(__file__), 'lettuce.cpp'), join(dirname(__file__), 'lettuce_cuda.cu')] extra_compile_args = {{ 'cxx': [], 'nvcc': [ '--ptxas-options', '-v,--warn-on-spills,--allow-expensive-optimizations=true,-O3', '--use_fast_math', '--optimize', '3', '--maxrregcount', '128' ] }} setup( author='Robert Andreas Fritsch', author_email='info@robert-fritsch.de', maintainer='Robert Andreas Fritsch', maintainer_email='info@robert-fritsch.de', install_requires=['torch>=1.2'], license='MIT license', keywords='lettuce', name='lettuce_{name}', ext_modules=[ CUDAExtension( name='lettuce_{name}.cuda_native', sources=native_sources, extra_compile_args=extra_compile_args ) ], packages=find_packages(include=['lettuce_{name}']), setup_requires=[], url='https://github.com/lettucecfd/lettuce', version='{version}', cmdclass={{'build_ext': BuildExtension}}, zip_safe=False, ) """, } ================================================ FILE: lettuce/cuda_native/_transformer.py ================================================ from abc import abstractmethod, ABC from typing import Optional, List from . import * __all__ = [ 'NativeTransformer', 'NativeBoundary', 'NativeEquilibrium', 'NativeCollision', ] class NativeTransformer(ABC): index: int def __init__(self, index: int): self.index = index @staticmethod @abstractmethod def create(index: int): ... @abstractmethod def generate(self, reg: 'Registry'): ... class NativeBoundary(NativeTransformer, ABC): ... class NativeEquilibrium(ABC): @abstractmethod def f_eq(self, reg: 'Registry', q: int, rho: Optional[str] = None, u: Optional[List[str]] = None): ... class NativeCollision(NativeTransformer, ABC): ... ================================================ FILE: lettuce/cuda_native/_util.py ================================================ from typing import Union, List import mmh3 def lettuce_hash(value: Union[str, List[str]]) -> str: if isinstance(value, list): value = ' '.join(value) return mmh3.hash_bytes(value).hex()[:8] ================================================ FILE: lettuce/cuda_native/ext/__init__.py ================================================ from ._boundary import * from ._collision import * from ._equilibrium import * ================================================ FILE: lettuce/cuda_native/ext/_boundary/__init__.py ================================================ from .bounce_back_boundary import NativeBounceBackBoundary from .equilibrium_pu import NativeEquilibriumBoundaryPu from .no_boundary import NativeNoBoundary __all__ = [ 'NativeBounceBackBoundary', 'NativeEquilibriumBoundaryPu', 'NativeNoBoundary', ] ================================================ FILE: lettuce/cuda_native/ext/_boundary/bounce_back_boundary.py ================================================ from ... import * __all__ = ['NativeBounceBackBoundary'] class NativeBounceBackBoundary(NativeBoundary): def __init__(self, index): NativeBoundary.__init__(self, index) @staticmethod def create(index): return NativeBounceBackBoundary(index) def generate(self, reg: 'DefaultCodeGeneration'): reg.pipe.append('{') reg.pipe.append(f"scalar_t bounce[{reg.stencil.q}];") for q in range(reg.stencil.q): reg.pipe.append(f" bounce[{q}] = {reg.f_reg(reg.stencil.opposite[q])};") for q in range(reg.stencil.q): reg.pipe.append(f" {reg.f_reg(q)} = bounce[{q}];") reg.pipe.append('}') ================================================ FILE: lettuce/cuda_native/ext/_boundary/equilibrium_pu.py ================================================ from ... import NativeBoundary, DefaultCodeGeneration, Parameter, CodeRegistryList __all__ = ['NativeEquilibriumBoundaryPu'] class NativeEquilibriumBoundaryPu(NativeBoundary): def __init__(self, index): NativeBoundary.__init__(self, index) @staticmethod def create(index): return NativeEquilibriumBoundaryPu(index) def cuda_velocity(self, reg: 'DefaultCodeGeneration'): # py_value = f"simulation.flow.units.convert_velocity_to_lu(simulation.boundaries[{self.index}].velocity)" py_value = f"simulation.flow.units.convert_velocity_to_lu(simulation.transformer[{self.index}].velocity)" return reg.cuda_hook(py_value, Parameter('at::Tensor', f"velocity_{self.index}")) def cuda_velocity_size(self, reg: 'DefaultCodeGeneration', d: int): assert d in range(reg.stencil.d) variable = self.cuda_velocity(reg) return f"static_cast({variable}.sizes()[{d+1}])" #return f"static_cast({variable}.sizes()[{d}])" def kernel_velocity_size(self, reg: 'DefaultCodeGeneration', d: int): assert d in range(reg.stencil.d) variable = self.cuda_velocity_size(reg, d) return reg.kernel_hook(variable, Parameter('index_t', f"velocity_{self.index}_size_{'xyz'[d]}")) def kernel_velocity(self, reg: 'DefaultCodeGeneration', d: int): assert d in range(reg.stencil.d) variable = self.cuda_velocity(reg) p_variable = reg.kernel_hook(f"{variable}.data()", Parameter('scalar_t*', f"p_{variable}")) if not reg.pipe.registered(variable): code = CodeRegistryList(reg.stencil.d) code.append('[&]{') variable_i = code.mutable('index_t', f"{variable}_index", '0') variable_m = code.mutable('index_t', f"{variable}_multiplier", '1') for i in reversed(range(reg.stencil.d)): code.append(f"if(1<{self.kernel_velocity_size(reg, i)}) {{") code.append(f" {variable_i}+={reg.kernel_index(i)}*{variable_m};") code.append(f" {variable_m}*={reg.kernel_size(i+1)};", cond=bool(i)) code.append(f"}}") code.append(f"return &{p_variable}[{variable_i}*{reg.d()}];") code.append('}()') reg.pipes.variable('scalar_t*', variable, str(code)) return f"{variable}[{d}]" def cuda_density(self, reg: 'DefaultCodeGeneration'): # py_value = f"simulation.flow.units.convert_pressure_pu_to_density_lu(simulation.boundaries[{self.index}].pressure)" py_value = f"simulation.flow.units.convert_pressure_pu_to_density_lu(simulation.transformer[{self.index}].pressure)" return reg.cuda_hook(py_value, Parameter('at::Tensor', f"density_{self.index}")) def cuda_density_size(self, reg: 'DefaultCodeGeneration', d: int): assert d in range(reg.stencil.d) variable = self.cuda_density(reg) return f"static_cast({variable}.sizes()[{d+1}])" #return f"static_cast({variable}.sizes()[{d}])" def kernel_density_size(self, reg: 'DefaultCodeGeneration', d: int): assert d in range(reg.stencil.d) variable = self.cuda_density_size(reg, d) return reg.kernel_hook(variable, Parameter('index_t', f"density_{self.index}_size_{d}")) def kernel_density(self, reg: 'DefaultCodeGeneration'): variable = self.cuda_density(reg) p_variable = reg.kernel_hook(f"{variable}.data()", Parameter('scalar_t*', f"p_{variable}")) code = CodeRegistryList(reg.stencil.d) code.append('[&]{') variable_i = code.mutable('index_t', f"{variable}_index", '0') variable_m = code.mutable('index_t', f"{variable}_multiplier", '1') for i in reversed(range(reg.stencil.d)): code.append(f"if(1<{self.kernel_density_size(reg, i)}) {{") code.append(f" {variable_i}+={reg.kernel_index(i)}*{variable_m};") code.append(f" {variable_m}*={reg.kernel_size(i+1)};", cond=bool(i)) code.append(f"}}") code.append(f"return {p_variable}[{variable_i}];") code.append('}()') return reg.pipes.variable('scalar_t', variable, str(code)) def generate(self, reg: 'DefaultCodeGeneration'): velocity = [self.kernel_velocity(reg, d) for d in range(reg.stencil.d)] density = self.kernel_density(reg) for q in range(reg.stencil.q): f_reg_q = reg.f_reg(q) f_eq_q = reg.equilibrium.f_eq(reg, q, rho=density, u=velocity) reg.pipe.append(f" {f_reg_q} = {f_eq_q};") ================================================ FILE: lettuce/cuda_native/ext/_boundary/no_boundary.py ================================================ from ... import NativeBoundary __all__ = ['NativeNoBoundary'] class NativeNoBoundary(NativeBoundary): def __init__(self, index): NativeBoundary.__init__(self, index) @staticmethod def create(index): return NativeNoBoundary(index) def generate(self, generator: 'Generator'): return ================================================ FILE: lettuce/cuda_native/ext/_collision/__init__.py ================================================ from .bgk_collision import NativeBGKCollision from .no_collision import NativeNoCollision __all__ = [ 'NativeBGKCollision', 'NativeNoCollision', ] ================================================ FILE: lettuce/cuda_native/ext/_collision/bgk_collision.py ================================================ from typing import Optional from lettuce.cuda_native import DefaultCodeGeneration, Parameter from ... import NativeCollision __all__ = ['NativeBGKCollision'] class NativeBGKCollision(NativeCollision): def __init__(self, index: int, force: Optional['NativeForce'] = None): NativeCollision.__init__(self, index) self.force = force @staticmethod def create(index: int, force: Optional['NativeForce'] = None): if force is None: return None return NativeBGKCollision(index) def cuda_tau_inv(self, reg: 'DefaultCodeGeneration'): variable = f"tau_inv{hex(id(self))[2:]}" return reg.cuda_hook('1.0 / simulation.collision.tau', Parameter('double', variable)) def kernel_tau_inv(self, reg: 'DefaultCodeGeneration'): variable = self.cuda_tau_inv(reg) return reg.kernel_hook(f"static_cast({variable})", Parameter('scalar_t', variable)) def generate(self, reg: 'DefaultCodeGeneration'): tau_inv = self.kernel_tau_inv(reg) for q in range(reg.stencil.q): f_q = reg.f_reg(q) f_eq_q = reg.equilibrium.f_eq(reg, q) reg.pipe.append(f"{f_q}={f_q}-({tau_inv}*({f_q}-{f_eq_q}));") ================================================ FILE: lettuce/cuda_native/ext/_collision/no_collision.py ================================================ from typing import Optional from ... import NativeCollision __all__ = ['NativeNoCollision'] class NativeNoCollision(NativeCollision): @staticmethod def create(index: int, force: None = None): assert force is None return NativeNoCollision(index) def generate(self, _): return ================================================ FILE: lettuce/cuda_native/ext/_equilibrium/__init__.py ================================================ from .quadratic_equilibrium import NativeQuadraticEquilibrium __all__ = [ 'NativeQuadraticEquilibrium', ] ================================================ FILE: lettuce/cuda_native/ext/_equilibrium/quadratic_equilibrium.py ================================================ from lettuce.cuda_native import DefaultCodeGeneration from lettuce.cuda_native import NativeEquilibrium from lettuce.cuda_native import lettuce_hash __all__ = ['NativeQuadraticEquilibrium'] class NativeQuadraticEquilibrium(NativeEquilibrium): # noinspection PyMethodMayBeStatic def uxu(self, reg: 'DefaultCodeGeneration', u: [str] = None) -> str: u = u or [reg.kernel_u(d) for d in range(reg.stencil.d)] # create unique variable per u variable = f"uxu_{lettuce_hash(u)}" code = '+'.join([f"{u[d]}*{u[d]}" for d in range(reg.stencil.d)]) return reg.pipes.variable('scalar_t', variable, code) # noinspection PyMethodMayBeStatic def exu(self, reg: 'DefaultCodeGeneration', q: int, u: [str] = None) -> str: u = u or [reg.kernel_u(d) for d in range(reg.stencil.d)] # create unique variable per u variable = f"exu_{q}_{lettuce_hash(u)}" code = '+'.join([f"{reg.e(q, d)}*{u[d]}" for d in range(reg.stencil.d)]) return reg.pipes.variable('scalar_t', variable, code) # noinspection PyMethodMayBeStatic def cs_pow_two(self, _) -> str: return 'static_cast(1.0 / 3.0)' # noinspection PyMethodMayBeStatic def two_cs_pow_two(self, _) -> str: return 'static_cast(2.0 / 3.0)' def f_eq(self, reg: 'DefaultCodeGeneration', q: int, rho: str = None, u: [str] = None): rho = rho or reg.kernel_rho() u = u or [reg.kernel_u(d) for d in range(reg.stencil.d)] # dependencies uxu = self.uxu(reg, u) exu_q = self.exu(reg, q, u) cs_pow_two = self.cs_pow_two(reg) two_cs_pow_two = self.two_cs_pow_two(reg) h = lettuce_hash([rho] + u) variable_tmp = f"f_eq_{q}_{h}_tmp" variable = f"f_eq_{q}_{h}" reg.pipes.variable('scalar_t', variable_tmp, f"{exu_q}/{cs_pow_two}") code = f"{rho}*{reg.w(q)}*(({exu_q}+{exu_q}-{uxu})/{two_cs_pow_two}+0.5*{variable_tmp}*{variable_tmp}+1.0)" return reg.pipes.variable('scalar_t', variable, code) ================================================ FILE: lettuce/cuda_native/ext/_force/__init__.py ================================================ ================================================ FILE: lettuce/cuda_native/ext/_force/_force.py ================================================ class NativeForce: pass ================================================ FILE: lettuce/ext/__init__.py ================================================ from ._stencil import * from ._equilibrium import * from ._force import * from ._collision import * from ._boundary import * from ._flows import * from ._reporter import * ================================================ FILE: lettuce/ext/_boundary/__init__.py ================================================ from .anti_bounce_back_outlet import AntiBounceBackOutlet from .bounce_back_boundary import BounceBackBoundary from .equilibrium_boundary_pu import EquilibriumBoundaryPU from .equilibrium_outlet_p import EquilibriumOutletP from .partially_saturated_boundary import PartiallySaturatedBC __all__ = [ 'AntiBounceBackOutlet', 'BounceBackBoundary', 'EquilibriumBoundaryPU', 'EquilibriumOutletP', 'PartiallySaturatedBC' ] ================================================ FILE: lettuce/ext/_boundary/anti_bounce_back_outlet.py ================================================ from typing import List import numpy as np import torch from ... import Collision from .._collision import BGKCollision from ... import Boundary, Context __all__ = ['AntiBounceBackOutlet'] class AntiBounceBackOutlet(Boundary): """Allows distributions to leave domain unobstructed through this boundary. Based on equations from page 195 of "The lattice Boltzmann method" (2016 by Krüger et al.) Give the side of the domain with the boundary as list [x, y, z] with only one entry nonzero [1, 0, 0] for positive x-direction in 3D; [1, 0] for the same in 2D [0, -1, 0] is negative y-direction in 3D; [0, -1] for the same in 2D """ def __init__(self, direction: [List[int]], flow: 'Flow', collision: 'Collision' = None): self.collision = BGKCollision(tau=flow.units.relaxation_parameter_lu) \ if collision is None else collision context = flow.context assert len(direction) in [1, 2, 3], \ (f"Invalid direction parameter. Expected direction of of length " f"1, 2 or 3 but got {len(direction)}.") assert ((direction.count(0) == (len(direction) - 1)) and ((1 in direction) ^ (-1 in direction))), \ (f"Invalid direction parameter. Expected direction with all " f"entries 0 except one 1 or -1 but got {direction}.") self.stencil = flow.torch_stencil # select velocities to be bounced (the ones pointing in "direction") # needs to be np, because it is a list of indices instead of # torchean bool tensor self.velocities = np.concatenate( np.argwhere(np.matmul(context.convert_to_ndarray(flow.stencil.e), direction) > 1 - 1e-6), axis=0) # build indices of u and f that determine the side of the domain self.index = [] self.neighbor = [] for i in direction: if i == 0: self.index.append(slice(None)) self.neighbor.append(slice(None)) if i == 1: self.index.append(-1) self.neighbor.append(-2) if i == -1: self.index.append(0) self.neighbor.append(1) # construct indices for einsum and get w in proper shape for the # calculation in each dimension w = flow.torch_stencil.w[self.velocities] if len(direction) == 3: self.dims = 'dc, cxy -> dxy' self.w = w.view(1, -1).t().unsqueeze(1) if len(direction) == 2: self.dims = 'dc, cx -> dx' self.w = w.view(1, -1).t() if len(direction) == 1: self.dims = 'dc, c -> dc' self.w = w def __call__(self, flow: 'Flow'): # not 100% sure about this. But collision seem to stabilize the # boundary. # self.collision(flow) # actual algorithm u = flow.u() u_w = (u[tuple([slice(None)] + self.index)] + 0.5 * (u[tuple([slice(None)] + self.index)] - u[tuple([slice(None)] + self.neighbor)])) f = flow.f f[tuple([flow.context.convert_to_ndarray( flow.torch_stencil.opposite)[self.velocities]] + self.index)] = ( - flow.f[tuple([self.velocities] + self.index)] + self.w * flow.rho()[tuple([slice(None)] + self.index)] * (2 + torch.einsum(self.dims, flow.torch_stencil.e[self.velocities], u_w) ** 2 / flow.torch_stencil.cs ** 4 - (torch.norm(u_w, dim=0) / flow.torch_stencil.cs) ** 2) ) return f def make_no_streaming_mask(self, f_shape, context: 'Context'): no_stream_mask = torch.zeros(size=f_shape, dtype=torch.bool, device=context.device) no_stream_mask[tuple([context.convert_to_ndarray(self.stencil.opposite)[ self.velocities]] + self.index)] = 1 return no_stream_mask def make_no_collision_mask(self, shape: List[int], context: 'Context'): no_collision_mask = context.zero_tensor(shape, dtype=bool) no_collision_mask[tuple(self.index)] = 1 return no_collision_mask def native_available(self) -> bool: return False def native_generator(self, index: int) -> 'NativeBoundary': pass ================================================ FILE: lettuce/ext/_boundary/bounce_back_boundary.py ================================================ import torch from typing import List, Optional from ... import Boundary, Flow, Context from ...cuda_native.ext import NativeBounceBackBoundary __all__ = ['BounceBackBoundary'] class BounceBackBoundary(Boundary): _mask: torch.Tensor def __init__(self, mask: torch.Tensor): self._mask = mask return def __call__(self, flow: 'Flow'): return flow.f[flow.stencil.opposite] def make_no_streaming_mask(self, shape: List[int], context: 'Context' ) -> Optional[torch.Tensor]: return None def make_no_collision_mask(self, shape: List[int], context: 'Context' ) -> Optional[torch.Tensor]: return self._mask def native_available(self) -> bool: return True def native_generator(self, index: int) -> 'NativeBoundary': return NativeBounceBackBoundary(index) ================================================ FILE: lettuce/ext/_boundary/equilibrium_boundary_pu.py ================================================ import numbers from typing import List, Optional, Union import numpy as np import torch from ... import Boundary from ... import Context from ... import Flow from ...cuda_native.ext import NativeEquilibriumBoundaryPu __all__ = ['EquilibriumBoundaryPU'] class EquilibriumBoundaryPU(Boundary): """Sets distributions on this boundary to equilibrium with predefined velocity and pressure. Note that this behavior is generally not compatible with the Navier-Stokes equations. This boundary condition should only be used if no better options are available. """ @staticmethod def checked_tensor(t: Union[torch.Tensor, np.ndarray, list, tuple, numbers.Number], context: 'Context', flow: 'Flow') -> torch.Tensor: # 1) Base conversion to a tensor if not torch.is_tensor(t): if isinstance(t, numbers.Number): t = torch.tensor(t) # scalar → zero-dim tensor elif isinstance(t, (np.ndarray, list, tuple)): t = torch.as_tensor(t) # array-like → tensor else: raise TypeError(f"Cannot convert {type(t)} to tensor") # now t is a tensor of some dtype; we’ll set dtype/device at the end d = flow.stencil.d spatial = list(flow.f.shape[1:]) # e.g. [16,16] full_ndim = 1 + len(spatial) # 2) Expand scalars → [1,1,...,1] if t.ndim == 0: t = t.reshape([1] * full_ndim) # 3) Expand a length-d vector → [d,1,1,...,1] elif t.ndim == 1 and t.shape[0] == d: t = t.reshape([d] + [1] * len(spatial)) # 4) Expand a pure spatial field → [1, N₁, N₂, ...] elif t.ndim == len(spatial) and list(t.shape) == spatial: t = t.unsqueeze(0) # prepend a 1 in the component axis # 5) If it’s already [d, N₁,...,Nₖ], accept it; otherwise error elif t.ndim != full_ndim: raise ValueError(f"Tensor has wrong rank: expected {full_ndim}, got {t.ndim}") # 6) Check broadcast compatibility # dim 0 must be d or 1; dims 1... must be N_i or 1 for i in range(full_ndim): size = t.shape[i] if i == 0: if size not in (1, d): raise ValueError(f"Component dim must be 1 or {d}, got {size}") else: if size not in (1, spatial[i - 1]): raise ValueError(f"Spatial dim {i} must be 1 or {spatial[i - 1]}, got {size}") # 7) Finally send through context.convert_to_tensor to pick up dtype/device return context.convert_to_tensor(t) def __init__(self, context: 'Context', flow: 'Flow', mask, velocity, pressure=0): self.velocity = self.checked_tensor(velocity, context, flow) self.pressure = self.checked_tensor(pressure, context, flow) # velocity = [velocity] if not hasattr(velocity, 'len') else velocity # self.velocity = context.convert_to_tensor(velocity) # self.pressure = context.convert_to_tensor(pressure) self._mask = mask def __call__(self, flow: 'Flow'): rho = flow.units.convert_pressure_pu_to_density_lu(self.pressure) u = flow.units.convert_velocity_to_lu(self.velocity) feq = flow.equilibrium(flow, rho, u) feq = torch.einsum("q...,q...->q...", [feq, torch.ones_like(flow.f)]) return feq def make_no_collision_mask(self, shape: List[int], context: 'Context' ) -> Optional[torch.Tensor]: return self._mask def make_no_streaming_mask(self, shape: List[int], context: 'Context' ) -> Optional[torch.Tensor]: pass def native_available(self) -> bool: return True def native_generator(self, index: int) -> 'NativeBoundary': return NativeEquilibriumBoundaryPu(index) ================================================ FILE: lettuce/ext/_boundary/equilibrium_outlet_p.py ================================================ from typing import List, Optional import numpy as np import torch __all__ = ['EquilibriumOutletP'] from ... import Flow, Context, Boundary from . import AntiBounceBackOutlet class EquilibriumOutletP(AntiBounceBackOutlet): """Equilibrium outlet with constant pressure. """ def __init__(self, direction: List[int], flow: 'Flow', rho_outlet: float = 1.0): super(EquilibriumOutletP, self).__init__(direction, flow) self.context = flow.context self.rho_outlet = self.context.convert_to_tensor(rho_outlet) assert len(direction) in [1, 2, 3], \ (f"Invalid direction parameter. Expected direction of of length 1," f" 2 or 3 but got {len(direction)}.") assert ((direction.count(0) == (len(direction) - 1)) and ((1 in direction) ^ (-1 in direction))), \ (f"Invalid direction parameter. Expected direction with all " f"entries 0 except one 1 or -1 but got {direction}.") direction = np.array(direction) # select velocities to be bounced (the ones pointing in "direction") self.velocities = np.concatenate(np.argwhere(np.matmul( flow.stencil.e, direction) > 1 - 1e-6), axis=0) # build indices of u and f that determine the side of the domain self.index = [] self.neighbor = [] for i in direction: if i == 0: self.index.append(slice(None)) self.neighbor.append(slice(None)) if i == 1: self.index.append(-1) self.neighbor.append(-2) if i == -1: self.index.append(0) self.neighbor.append(1) # construct indices for einsum and get w in proper shape for the # calculation in each dimension if len(direction) == 3: self.dims = 'dc, cxy -> dxy' self.w = flow.torch_stencil.w[ self.velocities].view(1, -1).t().unsqueeze(1) if len(direction) == 2: self.dims = 'dc, cx -> dx' self.w = flow.torch_stencil.w[self.velocities].view(1, -1).t() if len(direction) == 1: self.dims = 'dc, c -> dc' self.w = flow.torch_stencil.w[self.velocities] def __call__(self, flow: 'Flow'): here = tuple([slice(None)] + self.index) other = tuple([slice(None)] + self.neighbor) rho = flow.rho() u = flow.u() rho_w = self.rho_outlet * torch.ones_like(rho[here]) u_w = u[other] feq = flow.f feq[here] = flow.equilibrium(flow, rho_w[..., None], u_w[..., None])[ ..., 0] return torch.einsum("q...,q...->q...", [feq, torch.ones_like(flow.f)]) def make_no_streaming_mask(self, shape: List[int], context: 'Context' ) -> Optional[torch.Tensor]: no_streaming_mask = context.zero_tensor(shape, dtype=bool) no_streaming_mask[tuple([np.setdiff1d(np.arange(shape[0]), self.velocities)] + self.index)] = 1 return no_streaming_mask def make_no_collision_mask(self, shape: List[int], context: 'Context'): no_collision_mask = context.zero_tensor(shape, dtype=torch.bool) no_collision_mask[tuple(self.index)] = 1 return no_collision_mask def native_available(self) -> bool: return False def native_generator(self, index: int): pass ================================================ FILE: lettuce/ext/_boundary/partially_saturated_boundary.py ================================================ from typing import List import torch from ... import Boundary class PartiallySaturatedBC(Boundary): """ Partially saturated boundary condition using a partial combination of standard full-way bounce back and BGK-Collision, first presented by Noble and Torczynski (1998), see Krüger et al., pp. 448. This may be just as efficient as a compact version, because the boundary is actually used on all nodes even within the object. """ def __init__(self, mask: torch.Tensor, tau: float, saturation: float): self._mask = mask self.tau = tau # B(epsilon, theta), Krüger p. 448ff self.B = saturation * (tau - 0.5) / ((1 - saturation) + (tau - 0.5)) return def __call__(self, flow: 'Flow'): feq = flow.equilibrium(flow) # TODO: benchmark and possibly use indices (like _compact) # and/or calculate feq twice within torch.where (like _less_memory) f = torch.where( self._mask, flow.f - (1.0 - self.B) / self.tau * (flow.f - feq) + self.B * ((flow.f[flow.stencil.opposite] - feq[flow.stencil.opposite]) - (flow.f - flow.equilibrium(flow, u=torch.zeros_like(flow.u())) ) ), flow.f) return f def make_no_collision_mask(self, shape: List[int], context: 'Context' ) -> torch.Tensor: return self._mask def make_no_streaming_mask(self, shape: List[int], context: 'Context' ): pass def native_available(self) -> bool: return False def native_generator(self, index: int) -> 'NativeBoundary': pass ================================================ FILE: lettuce/ext/_collision/__init__.py ================================================ from .bgk_collision import BGKCollision from .kbc_collision import KBCCollision3D, KBCCollision2D, KBCCollision from .mrt_collision import MRTCollision from .no_collision import NoCollision from .regularized_collision import RegularizedCollision from .smagorinsky_collision import SmagorinskyCollision from .trt_collision import TRTCollision __all__ = [ 'BGKCollision', 'KBCCollision2D', 'KBCCollision3D', 'KBCCollision', 'MRTCollision', 'NoCollision', 'RegularizedCollision', 'SmagorinskyCollision', 'TRTCollision' ] ================================================ FILE: lettuce/ext/_collision/bgk_collision.py ================================================ import torch from typing import Optional from ... import Flow, Collision from ...cuda_native.ext import NativeBGKCollision from .. import Force __all__ = ['BGKCollision'] class BGKCollision(Collision): def __init__(self, tau, force: Optional['Force'] = None): self.tau = tau self.force = force def __call__(self, flow: 'Flow') -> torch.Tensor: u_eq = 0 if self.force is None else self.force.u_eq(flow) u = flow.u() + u_eq feq = flow.equilibrium(flow, u=u) si = self.force.source_term(u) if self.force is not None else 0 return flow.f - 1.0 / self.tau * (flow.f - feq) + si def name(self) -> str: if self.force is not None: return f"{self.__class__.__name__}_{self.force.__class__.__name__}" return self.__class__.__name__ def native_available(self) -> bool: return self.force is None or self.force.native_available() def native_generator(self, index: int) -> 'NativeCollision': if self.force is not None: return NativeBGKCollision(self.force.native_generator()) return NativeBGKCollision(index) ================================================ FILE: lettuce/ext/_collision/kbc_collision.py ================================================ import warnings import torch from ... import Flow, Collision, TorchStencil from .. import D3Q27, D2Q9 __all__ = ['KBCCollision3D', 'KBCCollision2D', 'KBCCollision'] class KBCCollision(Collision): """ Entropic multi-relaxation time-relaxation time model according to Karlin et al. """ def __init__(self, tau: float = None): self.tau = tau self.beta = None self.M = None def kbc_moment_transform(self, f): pass def kbc_moment_transform_3d(self, f): """Transforms the f into the KBC moment representation""" m = torch.einsum('abcq,qmno', self.M, f) rho = m[0, 0, 0] m = m / rho m[0, 0, 0] = rho return m def kbc_moment_transform_2d(self, f): """Transforms the f into the KBC moment representation""" m = torch.einsum('abq,qmn', self.M, f) rho = m[0, 0] m = m / rho m[0, 0] = rho return m def compute_s_seq_from_m(self, f, m): pass def compute_s_seq_from_m_3d(self, f, m): s = torch.zeros_like(f) T = m[2, 0, 0] + m[0, 2, 0] + m[0, 0, 2] N_xz = m[2, 0, 0] - m[0, 0, 2] N_yz = m[0, 2, 0] - m[0, 0, 2] Pi_xy = m[1, 1, 0] Pi_xz = m[1, 0, 1] Pi_yz = m[0, 1, 1] s[0] = m[0, 0, 0] * -T s[1] = 1. / 6. * m[0, 0, 0] * (2 * N_xz - N_yz + T) s[2] = s[1] s[3] = 1. / 6. * m[0, 0, 0] * (2 * N_yz - N_xz + T) s[4] = s[3] s[5] = 1. / 6. * m[0, 0, 0] * (-N_xz - N_yz + T) s[6] = s[5] s[7] = 1. / 4 * m[0, 0, 0] * Pi_yz s[8] = s[7] s[9] = - 1. / 4 * m[0, 0, 0] * Pi_yz s[10] = s[9] s[11] = 1. / 4 * m[0, 0, 0] * Pi_xz s[12] = s[11] s[13] = -1. / 4 * m[0, 0, 0] * Pi_xz s[14] = s[13] s[15] = 1. / 4 * m[0, 0, 0] * Pi_xy s[16] = s[15] s[17] = -1. / 4 * m[0, 0, 0] * Pi_xy s[18] = s[17] return s def compute_s_seq_from_m_2d(self, f, m): s = torch.zeros_like(f) T = m[2, 0] + m[0, 2] N = m[2, 0] - m[0, 2] Pi_xy = m[1, 1] s[0] = m[0, 0] * -T s[1] = 1. / 2. * m[0, 0] * (0.5 * (T + N)) s[2] = 1. / 2. * m[0, 0] * (0.5 * (T - N)) s[3] = 1. / 2. * m[0, 0] * (0.5 * (T + N)) s[4] = 1. / 2. * m[0, 0] * (0.5 * (T - N)) s[5] = 1. / 4. * m[0, 0] * Pi_xy s[6] = -s[5] s[7] = 1. / 4 * m[0, 0] * Pi_xy s[8] = -s[7] return s def __call__(self, flow: 'Flow') -> torch.Tensor: if self.M is None: self.tau = flow.units.relaxation_parameter_lu self.beta = 1. / (2 * self.tau) if flow.stencil.d == 3: assert isinstance(flow.stencil, D3Q27), \ "KBC Collision is only implemented for D3Q27!" self.compute_s_seq_from_m = self.compute_s_seq_from_m_3d self.kbc_moment_transform = self.kbc_moment_transform_3d # Build a matrix that contains the indices self.M = flow.context.zero_tensor([3, 3, 3, 27]) torch_stencil = TorchStencil(D3Q27(), flow.context) for i in range(3): for j in range(3): for k in range(3): self.M[i, j, k] = (torch_stencil.e[:, 0] ** i * torch_stencil.e[:, 1] ** j * torch_stencil.e[:, 2] ** k) elif flow.stencil.d == 2: assert isinstance(flow.stencil, D2Q9), \ "KBC Collision is only implemented for D2Q9!" self.compute_s_seq_from_m = self.compute_s_seq_from_m_2d self.kbc_moment_transform = self.kbc_moment_transform_2d # Build a matrix that contains the indices self.M = flow.context.zero_tensor([3, 3, 9]) torch_stencil = TorchStencil(D2Q9(), flow.context) for i in range(3): for j in range(3): self.M[i, j] = (torch_stencil.e[:, 0] ** i * torch_stencil.e[:, 1] ** j) else: raise NotImplementedError("KBC Collision is only implemented " "for 2d and 3d!") # the deletes are not part of the algorithm, they just keep the memory # usage lower feq = flow.equilibrium(flow) # k = torch.zeros_like(f) m = self.kbc_moment_transform(flow.f) delta_s = self.compute_s_seq_from_m(flow.f, m) # k[1] = m[0, 0, 0] / 6. * (3. * m[1, 0, 0]) # k[0] = m[0, 0, 0] # k[2] = -k[1] # k[3] = m[0, 0, 0] / 6. * (3. * m[0, 1, 0]) # k[4] = -k[3] # k[5] = m[0, 0, 0] / 6. * (3. * m[0, 0, 1]) # k[6] = -k[5] m = self.kbc_moment_transform(feq) delta_s -= self.compute_s_seq_from_m(flow.f, m) del m delta_h = flow.f - feq - delta_s sum_s = flow.rho(delta_s * delta_h / feq) sum_h = flow.rho(delta_h * delta_h / feq) del feq gamma_stab = 1. / self.beta - (2 - 1. / self.beta) * sum_s / sum_h gamma_stab[gamma_stab < 1E-15] = 2.0 # Detect NaN gamma_stab[torch.isnan(gamma_stab)] = 2.0 f = flow.f - self.beta * (2 * delta_s + gamma_stab * delta_h) return f def native_available(self) -> bool: return False def native_generator(self, index: int) -> 'NativeCollision': pass class KBCCollision2D(KBCCollision): def __init__(self, tau: float = None): warnings.warn("KBCCollision2D is is deprecated! Use KBCCollision " "instead!") super().__init__() class KBCCollision3D(KBCCollision): def __init__(self, tau: float = None): warnings.warn("KBCCollision2D is is deprecated! Use KBCCollision " "instead!") super().__init__() ================================================ FILE: lettuce/ext/_collision/mrt_collision.py ================================================ import torch from ... import Flow, Collision __all__ = ['MRTCollision'] class MRTCollision(Collision): """ Multiple relaxation time collision operator This is an MRT operator in the most general sense of the word. The transform does not have to be linear and can, e.g., be any moment or cumulant transform. """ def __init__(self, transform: 'Transform', relaxation_parameters: list, context: 'Context'): self.transform = transform self.relaxation_parameters = context.convert_to_tensor( relaxation_parameters) def __call__(self, flow: 'Flow'): m = self.transform.transform(flow.f) meq = self.transform.equilibrium(m, flow) m = m - torch.einsum("q...,q...->q...", [1 / self.relaxation_parameters, m - meq]) f = self.transform.inverse_transform(m) return f def native_available(self) -> bool: return False def native_generator(self, index: int) -> 'NativeCollision': pass ================================================ FILE: lettuce/ext/_collision/no_collision.py ================================================ import torch from ... import Flow, Collision from ...cuda_native.ext import NativeNoCollision __all__ = ['NoCollision'] class NoCollision(Collision): def __call__(self, flow: 'Flow') -> torch.Tensor: return flow.f def native_available(self) -> bool: return True def native_generator(self, index: int) -> 'NativeCollision': return NativeNoCollision(index) ================================================ FILE: lettuce/ext/_collision/regularized_collision.py ================================================ import torch from ... import Flow, Collision __all__ = ['RegularizedCollision'] class RegularizedCollision(Collision): """ Regularized LBM according to Jonas Latt and Bastien Chopard (2006) """ def __init__(self, tau: float = None): self.tau = tau self.Q_matrix = None def __call__(self, flow: 'Flow'): if self.Q_matrix is None: self.tau = flow.units.relaxation_parameter_lu self.Q_matrix = torch.zeros([flow.stencil.q, flow.stencil.d, flow.stencil.d], device=flow.context.device, dtype=flow.context.dtype) for a in range(flow.stencil.q): for b in range(flow.stencil.d): for c in range(flow.stencil.d): self.Q_matrix[a, b, c] = ( flow.torch_stencil.e[a, b] * flow.torch_stencil.e[a, c]) if b == c: self.Q_matrix[a, b, c] -= (flow.torch_stencil.cs ** 2) feq = flow.equilibrium(flow) pi_neq = flow.shear_tensor(flow.f - feq) cs4 = flow.stencil.cs ** 4 pi_neq = torch.einsum("qab...,ab...->q...", [self.Q_matrix, pi_neq]) pi_neq = torch.einsum("q...,q...->q...", [flow.torch_stencil.w, pi_neq]) fi1 = pi_neq / (2 * cs4) f = feq + (1. - 1. / self.tau) * fi1 return f def native_available(self) -> bool: return False def native_generator(self, index: int) -> 'NativeCollision': pass ================================================ FILE: lettuce/ext/_collision/smagorinsky_collision.py ================================================ import torch from ... import Flow, Collision from .. import Force __all__ = ['SmagorinskyCollision'] class SmagorinskyCollision(Collision): """ Smagorinsky large eddy simulation (LES) collision model with BGK operator. """ def __init__(self, tau, smagorinsky_constant=0.17, force: 'Force' = None): self.force = force self.tau = tau self.iterations = 2 self.tau_eff = tau self.constant = smagorinsky_constant def __call__(self, flow: 'Flow'): rho = flow.rho() u_eq = 0 if self.force is None else self.force.u_eq(flow) u = flow.u() + u_eq feq = flow.equilibrium(flow, rho, u) S_shear = flow.shear_tensor(flow.f - feq) S_shear /= (2.0 * rho * flow.stencil.cs ** 2) self.tau_eff = self.tau nu = (self.tau - 0.5) / 3.0 for i in range(self.iterations): S = S_shear / self.tau_eff S = torch.einsum('ab...,ab...->...', [S, S]) nu_t = self.constant ** 2 * S nu_eff = nu + nu_t self.tau_eff = nu_eff * 3.0 + 0.5 Si = 0 if self.force is None else self.force.source_term(u) return flow.f - 1.0 / self.tau_eff * (flow.f - feq) + Si def native_available(self) -> bool: return False def native_generator(self, index: int) -> 'NativeCollision': pass ================================================ FILE: lettuce/ext/_collision/trt_collision.py ================================================ from ... import Flow, Collision __all__ = ['TRTCollision'] class TRTCollision(Collision): """ Two relaxation time collision model - standard implementation (cf. Krüger 2017) """ def __init__(self, tau, tau_minus=1.0): self.tau_plus = tau self.tau_minus = tau_minus def __call__(self, flow: 'Flow'): # rho = flow.rho() # u = flow.u() feq = flow.equilibrium(flow) f_diff_neq = (((flow.f + flow.f[flow.stencil.opposite]) - (feq + feq[flow.stencil.opposite])) / (2.0 * self.tau_plus)) f_diff_neq += (((flow.f - flow.f[flow.stencil.opposite]) - (feq - feq[flow.stencil.opposite])) / (2.0 * self.tau_minus)) f = flow.f - f_diff_neq return f def native_available(self) -> bool: return False def native_generator(self, index: int) -> 'NativeCollision': pass ================================================ FILE: lettuce/ext/_equilibrium/__init__.py ================================================ from .incompressible_quadratic_equilibrium import IncompressibleQuadraticEquilibrium from .quadratic_equilibrium import QuadraticEquilibrium from .quadratic_equilibrium_less_memory import QuadraticEquilibriumLessMemory __all__ = [ 'IncompressibleQuadraticEquilibrium', 'QuadraticEquilibrium', 'QuadraticEquilibriumLessMemory' ] ================================================ FILE: lettuce/ext/_equilibrium/incompressible_quadratic_equilibrium.py ================================================ import torch from ... import Flow, Equilibrium __all__ = ['IncompressibleQuadraticEquilibrium'] class IncompressibleQuadraticEquilibrium(Equilibrium): def __init__(self, rho0=1.0): self.rho0 = rho0 def __call__(self, flow: 'Flow', rho=None, u=None): rho = flow.rho() if rho is None else rho u = flow.u() if u is None else u exu = torch.einsum("qd,d...->q...", [flow.torch_stencil.e, u]) uxu = torch.einsum("d...,d...->...", [u, u]) feq = torch.einsum( "q...,q...->q...", [flow.torch_stencil.w, rho + self.rho0 * ( (2 * exu - uxu) / (2 * flow.torch_stencil.cs ** 2) + 0.5 * (exu / (flow.torch_stencil.cs ** 2)) ** 2 )] ) return feq ================================================ FILE: lettuce/ext/_equilibrium/quadratic_equilibrium.py ================================================ import torch from ... import Flow, Equilibrium __all__ = ['QuadraticEquilibrium'] from ...cuda_native.ext import NativeQuadraticEquilibrium class QuadraticEquilibrium(Equilibrium): def __call__(self, flow: 'Flow', rho=None, u=None): rho = flow.rho() if rho is None else rho u = flow.u() if u is None else u exu = torch.tensordot(flow.torch_stencil.e, u, dims=1) uxu = torch.einsum("d...,d...->...", [u, u]) feq = torch.einsum("q...,q...->q...", [flow.torch_stencil.w, rho * ( (2 * exu - uxu) / (2 * flow.torch_stencil.cs ** 2) + 0.5 * (exu / (flow.torch_stencil.cs ** 2)) ** 2 + 1)]) return feq def native_available(self) -> bool: return True def native_generator(self) -> 'NativeEquilibrium': return NativeQuadraticEquilibrium() ================================================ FILE: lettuce/ext/_equilibrium/quadratic_equilibrium_less_memory.py ================================================ import torch from ... import Flow, Equilibrium __all__ = ['QuadraticEquilibriumLessMemory'] class QuadraticEquilibriumLessMemory(Equilibrium): """ does the same as the normal equilibrium, however it uses somewhere around 20% less RAM, but runs about 2% slower on GPU and 11% on CPU """ def __call__(self, flow: 'Flow', rho=None, u=None): rho = flow.rho() if rho is None else rho u = flow.u() if u is None else u feq = torch.einsum( "q,q...->q...", [flow.torch_stencil.w, rho * ((2 * torch.tensordot(flow.torch_stencil.e, u, dims=1) - torch.einsum("d...,d...->...", [u, u])) / (2 * flow.torch_stencil.cs ** 2) + 0.5 * (torch.tensordot(flow.torch_stencil.e, u, dims=1) / (flow.torch_stencil.cs ** 2)) ** 2 + 1 ) ] ) return feq def native_available(self) -> bool: return False def native_generator(self) -> 'NativeEquilibrium': pass ================================================ FILE: lettuce/ext/_flows/__init__.py ================================================ from ._ext_flow import * from .taylorgreen import * from .couette import * from .poiseuille import * from .doublyshear import * from .decayingturbulence import * from .obstacle import * from .liddrivencavity import * from .lamboseenvortex import * from ._flow_by_name import * ================================================ FILE: lettuce/ext/_flows/_ext_flow.py ================================================ from abc import ABC, abstractmethod from typing import List, Optional, Union from .. import D1Q3, D2Q9, D3Q19, QuadraticEquilibrium from ... import Flow class ExtFlow(Flow, ABC): """ Most __init__ methods of Flow look the same while having a lot of parameters. This class implements a common parameter set and allows subclasses to only implement the creation of the unit conversion and resolution. """ def __init__(self, context: 'Context', resolution: Union[int, List[int]], reynolds_number, mach_number, stencil: Optional['Stencil'] = None, equilibrium: Optional['Equilibrium'] = None): # set stencil or default stencil based on dimension resolution = self.make_resolution(resolution, stencil) assert len(resolution) in [1, 2, 3], \ (f"flow supports dimensions 1, 2 and 3 but {len(resolution)} " f"dimensions where requested.") default_stencils = [D1Q3(), D2Q9(), D3Q19()] stencil = stencil or default_stencils[len(resolution) - 1] stencil = stencil() if callable(stencil) else stencil # set equilibrium or quadratic equilibrium equilibrium = equilibrium or QuadraticEquilibrium() Flow.__init__(self, context, resolution, self.make_units( reynolds_number, mach_number, resolution), stencil, equilibrium) @abstractmethod def make_resolution(self, resolution: Union[int, List[int]], stencil: Optional['Stencil'] = None) -> List[int]: ... @abstractmethod def make_units(self, reynolds_number, mach_number, resolution: List[int] ) -> 'UnitConversion': ... ================================================ FILE: lettuce/ext/_flows/_flow_by_name.py ================================================ from typing import Dict, Tuple, Type, AnyStr from ... import Stencil from .. import D2Q9, D3Q19, D3Q27 from . import (ExtFlow, TaylorGreenVortex, PoiseuilleFlow2D, DoublyPeriodicShear2D, CouetteFlow2D, DecayingTurbulence, LambOseenVortex2D) __all__ = ['flow_by_name'] flow_by_name: Dict[AnyStr, Tuple[Type['ExtFlow'], Type['Stencil']]] = { 'taylor2d': (TaylorGreenVortex, D2Q9), 'taylor3d_d3q19': (TaylorGreenVortex, D3Q19), 'taylor3d_d3q27': (TaylorGreenVortex, D3Q27), 'poiseuille2d': (PoiseuilleFlow2D, D2Q9), 'shear2d': (DoublyPeriodicShear2D, D2Q9), 'couette2d': (CouetteFlow2D, D2Q9), 'decay2d': (DecayingTurbulence, D2Q9), 'lamboseen': (LambOseenVortex2D, D2Q9),} ================================================ FILE: lettuce/ext/_flows/couette.py ================================================ """ Couette Flow """ from typing import Union, List, Optional import numpy as np import torch from ... import UnitConversion from .. import BounceBackBoundary, EquilibriumBoundaryPU from ._ext_flow import ExtFlow __all__ = ['CouetteFlow2D'] class CouetteFlow2D(ExtFlow): def __init__(self, context: 'Context', resolution: Union[int, List[int]], reynolds_number, mach_number, stencil: Optional['Stencil'] = None, equilibrium: Optional['Equilibrium'] = None): self.u0 = 0 # background velocity super().__init__(context, resolution, reynolds_number, mach_number, stencil, equilibrium) def make_resolution(self, resolution: Union[int, List[int]], stencil: Optional['Stencil'] = None) -> List[int]: if isinstance(resolution, int): return [resolution] * 2 else: return resolution def make_units(self, reynolds_number, mach_number, resolution: List[int] ) -> 'UnitConversion': return UnitConversion( reynolds_number=reynolds_number, mach_number=mach_number, characteristic_length_lu=resolution[0], characteristic_length_pu=1, characteristic_velocity_pu=self.u0 ) def analytic_solution(self): dvdy = 1/self.resolution[0] x, y = self.grid u = self.context.convert_to_tensor([dvdy*y + self.u0]) return u def initial_pu(self): zeros = self.context.zero_tensor(self.resolution) p = zeros[None, ...] u = torch.stack([zeros, zeros], dim=0) return p, u @property def grid(self): xyz = tuple(torch.linspace(0, 1, steps=n, device=self.context.device, dtype=self.context.dtype) for n in self.resolution) return torch.meshgrid(*xyz, indexing='ij') @property def post_boundaries(self): ktop = torch.zeros(self.resolution, dtype=torch.bool) ktop[:, 1] = True kbottom = torch.zeros(self.resolution, dtype=torch.bool) kbottom[:, -1] = True return [ # moving bounce back top EquilibriumBoundaryPU(flow=self, context=self.context, mask=ktop, velocity=np.array([1.0, 0.0])), # bounce back bottom BounceBackBoundary(kbottom) ] ================================================ FILE: lettuce/ext/_flows/decayingturbulence.py ================================================ """ DecayingTurbulence vortex in 2D and 3D. Dimension is set by the stencil. Special Inputs & standard value: wavenumber_energy-peak = 20, initial_energy = 0.5 Additional attributes / properties __________ energy_spectrum: returns a pair [spectrum, wavenumbers] """ from typing import Union, List, Optional import numpy as np import torch from ... import UnitConversion from .._stencil import D1Q3, D2Q9, D3Q19 from . import ExtFlow __all__ = ['DecayingTurbulence'] class DecayingTurbulence(ExtFlow): def __init__(self, context: 'Context', resolution: Union[int, List[int]], reynolds_number, mach_number, k0=20, ic_energy=0.5, stencil: Optional['Stencil'] = None, equilibrium: Optional['Equilibrium'] = None, initialize_pressure: bool = True, initialize_fneq: bool = True, randseed: Optional[int] = None): self.initialize_pressure = initialize_pressure self.initialize_fneq = initialize_fneq self.randseed = randseed self.k0 = k0 self.ic_energy = ic_energy self.wavenumbers = [] self.spectrum = [] default_stencils = [D1Q3(), D2Q9(), D3Q19()] stencil = stencil or default_stencils[len(resolution) - 1] stencil = stencil() if callable(stencil) else stencil if stencil.d != 2: self.initialize_pressure = False super().__init__(context, resolution, reynolds_number, mach_number, stencil, equilibrium) def make_resolution(self, resolution: Union[int, List[int]], stencil: Optional['Stencil'] = None) -> List[int]: if isinstance(resolution, int): return [resolution] * stencil.d else: return resolution def make_units(self, reynolds_number, mach_number, resolution ) -> 'UnitConversion': return UnitConversion( reynolds_number=reynolds_number, mach_number=mach_number, characteristic_length_lu=resolution[0], characteristic_length_pu=2 * np.pi, characteristic_velocity_pu=None ) def analytic_solution(self, x, t=0): return def _generate_wavenumbers(self): self.dimensions = self.grid[0].shape frequencies = [np.fft.fftfreq(dim, d=1 / dim) for dim in self.dimensions] wavenumber = np.meshgrid(*frequencies) wavenorms = np.linalg.norm(wavenumber, axis=0) self.wavenumbers = np.arange(int(np.max(wavenorms))) wavemask = ((wavenorms[..., None] > self.wavenumbers - 0.5) & (wavenorms[..., None] <= self.wavenumbers + 0.5)) return wavenorms, wavenumber, wavemask def _generate_spectrum(self): wavenorms, wavenumber, wavemask = self._generate_wavenumbers() ek = wavenorms ** 4 * np.exp(-2 * (wavenorms / self.k0) ** 2) ek /= np.sum(ek) ek *= self.ic_energy self.spectrum = ek[..., None] * wavemask self.spectrum = np.sum(self.spectrum, axis=tuple((np.arange(self.stencil.d)))) return ek, wavenumber def _generate_initial_velocity(self, ek, wavenumber): dx = self.units.convert_length_to_pu(1.0) np.random.seed(self.randseed) u = np.random.random(np.array(wavenumber).shape) * 2 * np.pi + 0j u = [np.fft.fftn(u[dim], axes=tuple((np.arange(self.stencil.d)))) for dim in range(self.stencil.d)] u_real = [u[dim].real for dim in range(self.stencil.d)] u_imag = [u[dim].imag for dim in range(self.stencil.d)] for dim in range(self.stencil.d): u_real[dim].ravel()[0] = 0 u_imag[dim].ravel()[0] = 0 u_real_h = [np.sqrt(2 / self.stencil.d * ek / (u_imag[dim] ** 2 + u_real[dim] ** 2 + 1.e-15)) * u_real[dim] for dim in range(self.stencil.d)] u_imag_h = [np.sqrt(2 / self.stencil.d * ek / (u_imag[dim] ** 2 + u_real[dim] ** 2 + 1.e-15)) * u_imag[dim] for dim in range(self.stencil.d)] for dim in range(self.stencil.d): u_real_h[dim].ravel()[0] = 0 u_imag_h[dim].ravel()[0] = 0 """ Remove divergence # modified wave number sin(k*dx) is used, as the gradient below uses # second order cental differences # Modify if other schemes are used or use kx, ky if you don't know # the modified wavenumber !!! """ wavenumber_modified = [np.sin(wavenumber[dim] * dx) / dx for dim in range(self.stencil.d)] wavenorm_modified = np.linalg.norm(wavenumber_modified, axis=0) + 1e-16 divergence_real = np.zeros(self.dimensions) divergence_imag = np.zeros(self.dimensions) for dim in range(self.stencil.d): divergence_real += wavenumber_modified[dim] * u_real_h[dim] divergence_imag += wavenumber_modified[dim] * u_imag_h[dim] u_real = [u_real_h[dim] - divergence_real * wavenumber_modified[dim] / wavenorm_modified ** 2 for dim in range(self.stencil.d)] u_imag = [u_imag_h[dim] - divergence_imag * wavenumber_modified[dim] / wavenorm_modified ** 2 for dim in range(self.stencil.d)] for dim in range(self.stencil.d): u_real[dim].ravel()[0] = 0 u_imag[dim].ravel()[0] = 0 # Scale velocity field to achieve the desired inicial energy e_kin = [np.sum(u_real[dim] ** 2 + u_imag[dim] ** 2) for dim in range(self.stencil.d)] e_kin = np.sum(e_kin) * .5 factor = np.sqrt(self.ic_energy / e_kin) u_real = [u_real[dim] * factor for dim in range(self.stencil.d)] u_imag = [u_imag[dim] * factor for dim in range(self.stencil.d)] # Backtransformation to physical space norm = ((self.resolution[0] * dx ** (1 - self.stencil.d) * np.sqrt( self.units.characteristic_length_pu)) if self.stencil.d == 3 else (self.resolution[0] / dx)) u = np.asarray([ (np.fft.ifftn(u_real[dim] + u_imag[dim] * 1.0j, axes=tuple((np.arange(self.stencil.d)))) * norm).real for dim in range(self.stencil.d)]) return u def _compute_initial_pressure(self): # TODO: use pressure_poisson (@12b02686) to approximate rho from # u-field return np.zeros(self.dimensions)[None, ...] def initial_pu(self): """Return initial solution. Note: this function sets the characteristic velocity in phyiscal units.""" ek, wavenumber = self._generate_spectrum() u = self._generate_initial_velocity(ek, wavenumber) self.units.characteristic_velocity_pu = np.array(u).max() p = self._compute_initial_pressure() self.units.characteristic_velocity_pu = np.linalg.norm(u, axis=0).max() return p, u @property def energy_spectrum(self): return self.spectrum, self.wavenumbers @property def grid(self) -> (torch.Tensor, ...): endpoints = [2 * torch.pi * (1 - 1 / n) for n in self.resolution] # like endpoint=False in np.linspace xyz = tuple(torch.linspace(0, endpoints[n], steps=self.resolution[n], device=self.context.device, dtype=self.context.dtype) for n in range(self.stencil.d)) return torch.meshgrid(*xyz, indexing='ij') @property def post_boundaries(self) -> List['Boundary']: return [] ================================================ FILE: lettuce/ext/_flows/doublyshear.py ================================================ """ Doubly shear layer in 2D. Special Inputs & standard value: shear_layer_width = 80, initial_perturbation_magnitude = 0.05 """ from typing import Union, List, Optional import numpy as np import torch from lettuce._unit import UnitConversion from . import ExtFlow __all__ = ['DoublyPeriodicShear2D'] from .._stencil import D2Q9 class DoublyPeriodicShear2D(ExtFlow): def __init__(self, context: 'Context', resolution: Union[int, List[int]], reynolds_number, mach_number, stencil: Optional['Stencil'] = None, equilibrium: Optional['Equilibrium'] = None, shear_layer_width=80, initial_perturbation_magnitude=0.05, initialize_fneq: bool = True): self.initialize_fneq = initialize_fneq self.initial_perturbation_magnitude = initial_perturbation_magnitude self.shear_layer_width = shear_layer_width self.stencil = D2Q9() if stencil is None else stencil super().__init__(context, resolution, reynolds_number, mach_number, self.stencil, equilibrium) def make_resolution(self, resolution: Union[int, List[int]], stencil: Optional['Stencil'] = None) -> List[int]: if isinstance(resolution, int): return [resolution] * self.stencil.d else: assert len(resolution) == 2, 'expected 2-dimensional resolution' return resolution def make_units(self, reynolds_number, mach_number, resolution: List[int]) -> 'UnitConversion': return UnitConversion( reynolds_number=reynolds_number, mach_number=mach_number, characteristic_length_lu=resolution[0], characteristic_length_pu=1, characteristic_velocity_pu=1 ) def analytic_solution(self, t=0): raise NotImplementedError def initial_pu(self) -> (float, Union[np.array, torch.Tensor]): pert = self.initial_perturbation_magnitude w = self.shear_layer_width u1 = self.context.convert_to_tensor(torch.where( self.grid[1] > 0.5, torch.tanh(w * (self.grid[1] - 0.25)), torch.tanh(w * (0.75 - self.grid[1])) )) u2 = pert * torch.sin(2 * torch.pi * (self.grid[0] + 0.25)) u = torch.stack([u1, u2]) p = torch.zeros_like(u1)[None, ...] return p, u @property def grid(self) -> (torch.Tensor, torch.Tensor): endpoints = [1 - 1 / n for n in self.resolution] # like endpoint=False in np.linspace xyz = tuple(torch.linspace(0, endpoints[n], steps=self.resolution[n], device=self.context.device, dtype=self.context.dtype) for n in range(self.stencil.d)) return torch.meshgrid(*xyz, indexing='ij') @property def post_boundaries(self): return [] ================================================ FILE: lettuce/ext/_flows/lamboseenvortex.py ================================================ """ Lamb-Oseen Vortex in 2D. This module defines the `LambOseenVortex2D` class, which simulates a 2D Lamb-Oseen vortex flow according to Wissocq et al. 2017 """ import warnings from typing import Union, List, Optional import torch from ... import UnitConversion from .._stencil import D2Q9 from . import ExtFlow __all__ = ['LambOseenVortex2D'] class LambOseenVortex2D(ExtFlow): def __init__(self, context: 'Context', resolution: Union[int, List[int]], reynolds_number, mach_number, stencil: Optional['Stencil'] = None, equilibrium: Optional['Equilibrium'] = None, initialize_fneq: bool = True, velocity_init = 1, K = None, xc: int = None): # Store configuration parameters self.initialize_fneq = initialize_fneq self.velocity_init = velocity_init # Set default stencil if not provided if stencil is None and not isinstance(resolution, list): self.stencil = D2Q9() else: self.stencil = stencil() if callable(stencil) else stencil # Set the vortex center x-coordinate. if isinstance(resolution, int): self.xc = resolution // 2 if xc is None else xc else: self.xc = resolution[0] // 2 if xc is None else xc # Call the base class constructor. This also sets self.context, self.resolution, # self.units, and self.equilibrium. ExtFlow.__init__(self, context, resolution, reynolds_number, mach_number, self.stencil, equilibrium) def make_resolution(self, resolution: Union[int, List[int]], stencil: Optional['Stencil'] = None) -> List[int]: if isinstance(resolution, int): return [resolution] * self.stencil.d else: assert len(resolution) == 2, 'expected 2-dimensional resolution' return resolution def make_units(self, reynolds_number, mach_number, resolution: List[int]) -> 'UnitConversion': return UnitConversion( reynolds_number=reynolds_number, mach_number=mach_number, characteristic_length_lu=resolution[0], characteristic_length_pu=1, characteristic_velocity_pu=1 ) @property def grid(self): endpoints = self.resolution xyz = tuple([ self.units.convert_length_to_pu(torch.arange(0, endpoints[0], device=self.context.device, dtype=self.context.dtype)), self.units.convert_length_to_pu(torch.arange(0, endpoints[1], device=self.context.device, dtype=self.context.dtype)) ]) return torch.meshgrid(*xyz, indexing='ij') def initial_pu(self) -> (torch.Tensor, torch.Tensor): p, U = self.initial_lamboseenvortex() return p, U def initial_lamboseenvortex(self) -> (torch.Tensor, torch.Tensor): # Vortex center coordinates in lattice units # xc is already in lattice units from __init__ yc = self.resolution[1] * 0.5 # y-center in lattice units # Get grid coordinates in physical units and then convert to lattice units for calculation x_pu, y_pu = self.grid # x_pu, y_pu are (nx, ny) in physical units # Convert grid coordinates to lattice units for calculation with Rc (which is a characteristic length in LU) x_lu = self.units.convert_length_to_lu(x_pu) y_lu = self.units.convert_length_to_lu(y_pu) # Initial characteristic velocity in lattice units ux0_lu = self.units.convert_velocity_to_lu(self.velocity_init) # Lamb-Oseen vortex specific constants (these might be configurable in a more advanced version) beta = 0.5 # Parameter related to circulation Rc = 20.0 # Characteristic radius of the vortex core in lattice units gamma = 0.5 # Adiabatic index or similar thermodynamic constant Cv = 1.0 / 3.0 # Specific heat capacity at constant volume or similar constant # Calculate the squared distance from the vortex center in lattice units r2_lu = (x_lu - self.xc) ** 2 + (y_lu - yc) ** 2 # Calculate density (d) based on the thermodynamic relation for the vortex # This formula describes the density perturbation due to the vortex. d_lu = torch.pow( 1.0 - (beta * ux0_lu) ** 2 / (2.0 * Cv) * torch.exp(1.0 - r2_lu / (2.0 * Rc)), 1.0 / (gamma - 1.0) ) # Calculate the exponential decay term for velocity exp_term = torch.exp(-r2_lu / (2.0 * Rc)) # Calculate velocity components (u_x, u_y) in lattice units # These are derived from the stream function of the Lamb-Oseen vortex u_x_lu = (ux0_lu - beta * ux0_lu * (y_lu - yc) / Rc * exp_term) u_y_lu = beta * ux0_lu * (x_lu - self.xc) / Rc * exp_term # Convert density (d) to pressure (p) in physical units # Assuming 'd' here acts like a density in LU that can be directly converted to pressure in PU. # This might depend on the specific LBM pressure definition. p_pu = self.units.convert_density_lu_to_pressure_pu(d_lu) # Convert velocity components from lattice units to physical units u_x_pu = self.units.convert_velocity_to_pu(u_x_lu) u_y_pu = self.units.convert_velocity_to_pu(u_y_lu) # Stack velocity components into a single tensor (2, nx, ny) U_pu = torch.stack([u_x_pu, u_y_pu], dim=0) return p_pu, U_pu @property def post_boundaries(self) -> List['Boundary']: return [] ================================================ FILE: lettuce/ext/_flows/liddrivencavity.py ================================================ """ Cavity flow """ from typing import List, Union, Optional import numpy as np import torch from ... import UnitConversion from .. import BounceBackBoundary, EquilibriumBoundaryPU from ._ext_flow import ExtFlow class Cavity2D(ExtFlow): def __init__(self, context: 'Context', resolution, reynolds_number, mach_number): super().__init__(context, resolution, reynolds_number, mach_number) def make_resolution(self, resolution: Union[int, List[int]], stencil: Optional['Stencil'] = None) -> List[int]: if isinstance(resolution, int): return [resolution] * 2 else: assert len(resolution) == 2, 'expected 2-dimensional resolution' return resolution def make_units(self, reynolds_number, mach_number, resolution: List[int]) -> 'UnitConversion': return UnitConversion( reynolds_number=reynolds_number, mach_number=mach_number, characteristic_length_lu=resolution[0], characteristic_length_pu=1, characteristic_velocity_pu=1 ) def initial_pu(self): return (torch.stack([torch.zeros_like(self.grid[0])]), torch.stack([torch.zeros_like(self.grid[0])] * 2)) # p, u @property def grid(self) -> (torch.Tensor, torch.Tensor): endpoints = [1 - 1 / n for n in self.resolution] # like endpoint=False in np.linspace xyz = tuple(torch.linspace(0, endpoints[n], steps=self.resolution[n], device=self.context.device, dtype=self.context.dtype) for n in range(self.stencil.d)) return torch.meshgrid(*xyz, indexing='ij') @property def post_boundaries(self): x, *y = self.grid boundary = self.context.zero_tensor(x.shape, dtype=bool) top = self.context.zero_tensor(x.shape, dtype=bool) boundary[[0, -1], 1:] = True # left and right boundary[:, 0] = True # bottom top[:, -1] = True # top return [ # bounce back walls BounceBackBoundary(boundary), # moving fluid on top# moving bounce back top EquilibriumBoundaryPU( self.context, top, [float(self.units.characteristic_velocity_pu), 0.0] ), ] ================================================ FILE: lettuce/ext/_flows/obstacle.py ================================================ import warnings from typing import Union, List, Optional import numpy as np import torch from . import ExtFlow from ... import UnitConversion, Context, Stencil, Equilibrium from ...util import append_axes from .. import (EquilibriumBoundaryPU, BounceBackBoundary, EquilibriumOutletP, AntiBounceBackOutlet) __all__ = ['Obstacle'] class Obstacle(ExtFlow): """ Flow class to simulate the flow around an object (mask). It consists of one inflow (equilibrium boundary) and one outflow (anti-bounce-back-boundary), leading to a flow in positive x direction. Parameters ---------- resolution : Tuple[int]: Grid resolution. domain_length_x : float Length of the domain in physical units. Attributes ---------- _mask : torch.Tensor with dtype = bool Boolean mask to define the obstacle. The shape of this object is the shape of the grid. Initially set to zero (no obstacle). Examples -------- Initialization of flow around a cylinder: >>> from lettuce import D2Q9 >>> flow = Obstacle( >>> shape=(101, 51), >>> reynolds_number=100, >>> mach_number=0.1, >>> stencil=D2Q9, >>> domain_length_x=10.1 >>> ) >>> x, y = flow.grid >>> condition = np.sqrt((x-2.5)**2+(y-2.5)**2) < 1. >>> flow.mask[np.where(condition)] = 1 """ def __init__(self, context: Context, resolution: Union[int, List[int]], reynolds_number, mach_number, domain_length_x, char_length=1, char_velocity=1, stencil: Optional[Stencil] = None, equilibrium: Optional[Equilibrium] = None): self.char_length_lu = resolution[0] / domain_length_x * char_length self.char_length = char_length self.char_velocity = char_velocity self.resolution = self.make_resolution(resolution, stencil) self._mask = torch.zeros(self.resolution, dtype=torch.bool) ExtFlow.__init__(self, context, resolution, reynolds_number, mach_number, stencil, equilibrium) def make_units(self, reynolds_number, mach_number, resolution: List[int] ) -> 'UnitConversion': return UnitConversion( reynolds_number=reynolds_number, mach_number=mach_number, characteristic_length_lu=self.char_length_lu, characteristic_length_pu=self.char_length, characteristic_velocity_pu=self.char_velocity ) def make_resolution(self, resolution: Union[int, List[int]], stencil: Optional['Stencil'] = None) -> List[int]: if isinstance(resolution, int): return [resolution] * (stencil.d or self.stencil.d) else: return resolution @property def mask(self): return self._mask @mask.setter def mask(self, m): assert ((isinstance(m, np.ndarray) or isinstance(m, torch.Tensor)) and all(m.shape[dim] == self.resolution[dim] for dim in range( self.stencil.d))) self._mask = self.context.convert_to_tensor(m, dtype=torch.bool) def initial_pu(self) -> (float, Union[np.array, torch.Tensor]): p = np.zeros_like(self.grid[0], dtype=float)[None, ...] u_char = self.units.characteristic_velocity_pu * self._unit_vector() u_char = append_axes(u_char, self.stencil.d) u = ~self.mask * u_char return p, u @property def grid(self): xyz = tuple(self.units.convert_length_to_pu(torch.arange(n)) for n in self.resolution) return torch.meshgrid(*xyz, indexing='ij') @property def post_boundaries(self): x = self.grid[0] return [ EquilibriumBoundaryPU(flow=self, context=self.context, mask=torch.abs(x) < 1e-6, velocity=self.units. characteristic_velocity_pu * self._unit_vector() ), AntiBounceBackOutlet(self._unit_vector().tolist(), self), # EquilibriumOutletP(direction=self._unit_vector().tolist(), # self, rho_outlet=0), BounceBackBoundary(self.mask) ] def _unit_vector(self, i=0): return torch.eye(self.stencil.d)[i] def Obstacle2D(context: 'Context', resolution: Union[int, List[int]], reynolds_number, mach_number, stencil: 'Stencil', char_length_lu): warnings.warn("Obstacle2D is deprecated. Use Obstacle instead", DeprecationWarning) resolution_x = resolution[0] if isinstance(resolution, list) \ else resolution domain_length_x = resolution_x / char_length_lu return Obstacle(context=context, resolution=resolution, reynolds_number=reynolds_number, mach_number=mach_number, domain_length_x=domain_length_x, stencil=stencil) def Obstacle3D(context: 'Context', resolution: Union[int, List[int]], reynolds_number, mach_number, stencil: 'Stencil', char_length_lu): warnings.warn("Obstacle3D is deprecated. Use Obstacle instead", DeprecationWarning) resolution_x = resolution[0] if isinstance(resolution, list) \ else resolution domain_length_x = resolution_x / char_length_lu return Obstacle(context=context, resolution=resolution, reynolds_number=reynolds_number, mach_number=mach_number, domain_length_x=domain_length_x, stencil=stencil) ================================================ FILE: lettuce/ext/_flows/poiseuille.py ================================================ """ Poiseuille Flow """ from typing import Union, List, Optional import numpy as np import torch from . import ExtFlow from lettuce._unit import UnitConversion from lettuce.ext._boundary import BounceBackBoundary __all__ = ['PoiseuilleFlow2D'] from .._stencil import D2Q9 class PoiseuilleFlow2D(ExtFlow): def __init__(self, context: 'Context', resolution: Union[int, List[int]], reynolds_number, mach_number, stencil: Optional['Stencil'] = None, equilibrium: Optional['Equilibrium'] = None, initialize_with_zeros=True): self.stencil = D2Q9() if stencil is None else stencil self.initialize_with_zeros = initialize_with_zeros super().__init__(context, resolution, reynolds_number, mach_number, self.stencil, equilibrium) def analytic_solution(self, t=0) -> (torch.Tensor, torch.Tensor): half_lattice_spacing = 0.5 / self.resolution[0] x, y = self.grid nu = self.units.viscosity_pu rho = 1 ux = (self.acceleration[0] / (2 * rho * nu) * ((y - half_lattice_spacing) * (1 - half_lattice_spacing - y))) uy = self.context.zero_tensor(self.resolution) u = torch.stack([ux, uy], dim=0) p = y * 0 + self.units.convert_density_lu_to_pressure_pu(rho) return p, u def initial_pu(self): if self.initialize_with_zeros: zeros = self.context.zero_tensor(self.resolution) p = zeros[None, ...] u = torch.stack(2*[zeros], dim=0) return p, u else: return self.analytic_solution() def make_units(self, reynolds_number, mach_number, resolution: List[int]) -> 'UnitConversion': return UnitConversion( reynolds_number=reynolds_number, mach_number=mach_number, characteristic_length_lu=resolution[0]-1, characteristic_length_pu=1, characteristic_velocity_pu=1 ) def make_resolution(self, resolution: Union[int, List[int]], stencil: Optional['Stencil'] = None) -> List[int]: if isinstance(resolution, list): assert len(resolution) == self.stencil.d if isinstance(resolution, int): resolution = [resolution] * self.stencil.d return resolution @property def grid(self): xyz = tuple(torch.linspace(0, 1, steps=n, device=self.context.device, dtype=self.context.dtype) for n in self.resolution) return torch.meshgrid(*xyz, indexing='ij') @property def post_boundaries(self): mask = self.context.zero_tensor(self.resolution, dtype=bool) mask[:, [0, -1]] = True boundary = BounceBackBoundary(mask=mask) return [boundary] @property def acceleration(self): return self.context.convert_to_tensor([0.001, 0]) ================================================ FILE: lettuce/ext/_flows/taylorgreen.py ================================================ """ Taylor-Green vortex in 2D and 3D. """ import warnings from typing import Union, List, Optional import torch from ... import UnitConversion from .._stencil import D2Q9 from . import ExtFlow __all__ = ['TaylorGreenVortex', 'TaylorGreenVortex2D', 'TaylorGreenVortex3D'] class TaylorGreenVortex(ExtFlow): def __init__(self, context: 'Context', resolution: Union[int, List[int]], reynolds_number, mach_number, stencil: Optional['Stencil'] = None, equilibrium: Optional['Equilibrium'] = None, initialize_fneq: bool = True): self.initialize_fneq = initialize_fneq if stencil is None and not isinstance(resolution, list): warnings.warn("Requiring information about dimensionality!" " Either via stencil or resolution. Setting " "dimension to 2.", UserWarning) self.stencil = D2Q9() else: self.stencil = stencil() if callable(stencil) else stencil ExtFlow.__init__(self, context, resolution, reynolds_number, mach_number, stencil, equilibrium) def make_resolution(self, resolution: Union[int, List[int]], stencil: Optional['Stencil'] = None) -> List[int]: if isinstance(resolution, int): return [resolution] * self.stencil.d else: assert len(resolution) in [2, 3], ('the resolution of a ' 'taylor-green-vortex ' 'must be 2- or 3-dimensional!') return resolution def make_units(self, reynolds_number, mach_number, resolution) -> 'UnitConversion': return UnitConversion( reynolds_number=reynolds_number, mach_number=mach_number, characteristic_length_lu=resolution[0] / (2 * torch.pi), characteristic_length_pu=1, characteristic_velocity_pu=1) @property def grid(self): endpoints = [2 * torch.pi * (1 - 1 / n) for n in self.resolution] # like endpoint=False in np.linspace xyz = tuple(torch.linspace(0, endpoints[n], steps=self.resolution[n], device=self.context.device, dtype=self.context.dtype) for n in range(self.stencil.d)) return torch.meshgrid(*xyz, indexing='ij') def initial_pu(self) -> (torch.Tensor, torch.Tensor): return self.analytic_solution(t=0) def analytic_solution(self, t: float) -> (torch.Tensor, torch.Tensor): if t > 0 and self.stencil.d > 2: warnings.warn("The analytic solution is only true for the 2D TGV!") grid = self.grid nu = self.context.convert_to_tensor(self.units.viscosity_pu) if len(self.resolution) == 2: u = torch.stack( [torch.cos(grid[0]) * torch.sin(grid[1]) * torch.exp(-2 * nu * t), -torch.sin(grid[0]) * torch.cos(grid[1]) * torch.exp(-2 * nu * t)]) p = -torch.stack( [0.25 * (torch.cos(2 * grid[0]) + torch.cos(2 * grid[1])) * torch.exp(-4 * nu * t)]) else: u = torch.stack( [torch.sin(grid[0]) * torch.cos(grid[1]) * torch.cos(grid[2]), -torch.cos(grid[0]) * torch.sin(grid[1]) * torch.cos(grid[2]), torch.zeros_like(grid[0])]) p = torch.stack( [1 / 16. * (torch.cos(2 * grid[0]) + torch.cos(2 * grid[1])) * (torch.cos(2 * grid[2]) + 2)]) return p, u @property def post_boundaries(self) -> List['Boundary']: return [] def TaylorGreenVortex3D(context: 'Context', resolution: Union[int, List[int]], reynolds_number, mach_number, stencil: Optional['Stencil'] = None, equilibrium: Optional['Equilibrium'] = None): warnings.warn("TaylorGreenVortex3D is deprecated. Use TaylorGreenVortex" " instead", DeprecationWarning) return TaylorGreenVortex(context=context, resolution=resolution, reynolds_number=reynolds_number, mach_number=mach_number, stencil=stencil, equilibrium=equilibrium) def TaylorGreenVortex2D(context: 'Context', resolution: Union[int, List[int]], reynolds_number, mach_number, stencil: Optional['Stencil'] = None, equilibrium: Optional['Equilibrium'] = None): warnings.warn("TaylorGreenVortex2D is deprecated. Use TaylorGreenVortex" " instead", DeprecationWarning) return TaylorGreenVortex(context=context, resolution=resolution, reynolds_number=reynolds_number, mach_number=mach_number, stencil=stencil, equilibrium=equilibrium) ================================================ FILE: lettuce/ext/_force/__init__.py ================================================ from ._force import * from .guo import * from .shan_chen import * __all__ = [ 'Force', 'Guo', 'ShanChen' ] ================================================ FILE: lettuce/ext/_force/_force.py ================================================ from abc import ABC, abstractmethod __all__ = ['Force'] class Force(ABC): @abstractmethod def __init__(self, flow: 'Flow', tau, acceleration): ... @abstractmethod def source_term(self, u): ... @abstractmethod def u_eq(self, flow: 'Flow'): ... @property @abstractmethod def ueq_scaling_factor(self): ... @abstractmethod def native_available(self) -> bool: ... @abstractmethod def native_generator(self) -> 'NativeForce': ... ================================================ FILE: lettuce/ext/_force/guo.py ================================================ import torch from . import Force from lettuce.util import append_axes __all__ = ['Guo'] class Guo(Force): def __init__(self, flow, tau, acceleration): self.flow = flow self.tau = tau self.acceleration = flow.context.convert_to_tensor(acceleration) def source_term(self, u): emu = append_axes(self.flow.torch_stencil.e, self.flow.torch_stencil.d) - u eu = torch.einsum("ib,b...->i...", [self.flow.torch_stencil.e, u]) eeu = torch.einsum("ia,i...->ia...", [self.flow.torch_stencil.e, eu]) emu_eeu = (emu / (self.flow.torch_stencil.cs ** 2) + eeu / (self.flow.torch_stencil.cs ** 4)) emu_eeuF = torch.einsum("ia...,a->i...", [emu_eeu, self.acceleration]) weemu_eeuF = (append_axes(self.flow.torch_stencil.w, self.flow.torch_stencil.d) * emu_eeuF) return (1 - 1 / (2 * self.tau)) * weemu_eeuF def u_eq(self, flow: 'Flow' = None): flow = self.flow if flow is None else flow return (self.ueq_scaling_factor * append_axes(self.acceleration, flow.torch_stencil.d) / flow.rho()) @property def ueq_scaling_factor(self): return 0.5 def native_available(self) -> bool: return False def native_generator(self) -> 'NativeForce': pass ================================================ FILE: lettuce/ext/_force/shan_chen.py ================================================ from . import Force from lettuce.util import append_axes __all__ = ['ShanChen'] class ShanChen(Force): def __init__(self, flow, tau, acceleration): self.tau = tau self.acceleration = flow.context.convert_to_tensor(acceleration) def source_term(self, u): return 0 def u_eq(self, flow: 'Flow'): return (self.ueq_scaling_factor * append_axes(self.acceleration, flow.stencil.d) / flow.rho()) @property def ueq_scaling_factor(self): return self.tau * 1 def native_available(self) -> bool: return False def native_generator(self) -> 'NativeForce': pass ================================================ FILE: lettuce/ext/_reporter/__init__.py ================================================ from .error_reporter import * from .observable_reporter import * from .vtk_reporter import * from .write_image import * from .failure_reporter import * from .progress_reporter import * ================================================ FILE: lettuce/ext/_reporter/error_reporter.py ================================================ import sys import torch from ... import Reporter __all__ = ['ErrorReporter'] class ErrorReporter(Reporter): """Reports numerical errors with respect to analytic solution.""" def __init__(self, analytical_solution, interval=1, out=sys.stdout): Reporter.__init__(self, interval) self.analytical_solution = analytical_solution self.out = [] if out is None else out if not isinstance(self.out, list): print("#error_u error_p", file=self.out) def __call__(self, simulation: 'Simulation'): i, t, f = simulation.flow.i, simulation.units.convert_time_to_pu( simulation.flow.i), simulation.flow.f if i % self.interval == 0: pref, uref = self.analytical_solution(t=t) pref = simulation.flow.context.convert_to_tensor(pref) uref = simulation.flow.context.convert_to_tensor(uref) p = simulation.flow.p_pu u = simulation.flow.u_pu resolution = torch.pow(torch.prod( simulation.flow.context.convert_to_tensor(p.size())), 1 / simulation.flow.stencil.d) err_u = (torch.norm(u - uref) / resolution ** (simulation.flow.stencil.d / 2)) err_p = (torch.norm(p - pref) / resolution ** (simulation.flow.stencil.d / 2)) if isinstance(self.out, list): self.out.append([err_u.item(), err_p.item()]) else: print(err_u.item(), err_p.item(), file=self.out) ================================================ FILE: lettuce/ext/_reporter/failure_reporter.py ================================================ import torch import os from typing import List, Tuple, Optional from abc import ABC, abstractmethod from ... import Reporter, BreakableSimulation from .vtk_reporter import write_vtk __all__ = ["FailureReporterBase", "NaNReporter", "HighMaReporter"] class FailureReporterBase(Reporter, ABC): """ abstract base class for reporters that detect a failing simulation, due to conditions like NaN, high Mach number etc. - relies on BreakableSimulation class (!) """ #TODO (optional): make STOPPING the simulation optional # -> for example the HighMa-Reporter only reports high Mach numbers and # their location, but the simulation just continues normally. # This could be useful and "ok" in some cases: EXAMPLE, in the event # of a settling period (transient high velocities) at the beginning # of the run. def __init__(self, interval: int, k: int = 100, outdir: Optional[str]=None, vtk_out: bool=False): super().__init__(interval) self.k = k self.outdir = outdir self.name = "FailureReporter" self.vtk_out = vtk_out self.failed_iteration = None def __call__(self, simulation: 'BreakableSimulation'): if simulation.flow.i % self.interval == 0: if self.is_failed(simulation): results = self.get_results(simulation) self.failed_iteration = simulation.flow.i if self.outdir is not None: self._write_log(simulation, results) self.save_vtk(simulation) print( f'(!) ABORT MESSAGE: {self.name}Reporter detected ' f'{self.name} (reporter-interval = {self.interval}) ' f'at iteration {simulation.flow.i}. ' f'See log in {self.outdir} for details!') # telling simulation to abort simulation by setting i too high simulation.flow.i = int(simulation.flow.i + 1e10) # TODO: make this more robust with a failed-flag in simulation # and not rely on flow.i to be high "enough" to be higher than # the target steps number given to simulation(steps)? # the 1e10 is "a lot", but in a very unlikely case of a very long simulation, # where num_steps >=1e10, this would not work... def _get_top_failures(self, mask: torch.Tensor, values: torch.Tensor) -> List[Tuple]: """extract coordinates and values at nodes (mask); returns list of (pos, val) tuples""" failed_values = values[mask] all_coords = torch.nonzero(mask) num_to_extract = min(self.k, failed_values.numel()) if torch.isnan(failed_values).any(): top_indices = torch.arange(num_to_extract, device=values.device) else: _, top_indices = torch.topk(failed_values, k=num_to_extract, largest=True, sorted=True) top_coords = all_coords[top_indices].cpu().numpy() top_values = failed_values[top_indices].cpu().numpy() return [ (list(c.astype(int)), float(v)) for c, v in zip(top_coords, top_values) ] def _write_log(self, simulation: 'BreakableSimulation', results: List[Tuple]): """writes results to file and logs flow.i""" if not os.path.exists(self.outdir): os.mkdir(self.outdir) filepath = os.path.join(self.outdir, f"{self.name}_reporter.log") with open(filepath, "w") as file: file.write(f"(!) {self.name} detected in step {simulation.flow.i} " f"at following locations (top {self.k} listed):\n") file.write(" ") for location in self.locations_string(simulation): file.write(f"{location:6} ") file.write("\n") for pos, val in results: line="" for pos_i in pos: line = line + f"{int(pos_i):6} " file.write(f"{line:<20} | {val:<15.6f}\n") file.write("\n") def save_vtk(self, simulation: 'BreakableSimulation'): """saves vtk file to outdir""" point_dict = dict() u = simulation.flow.u_pu p = simulation.flow.p_pu if simulation.flow.stencil.d == 2: point_dict["p"] = simulation.flow.context.convert_to_ndarray(p[0, ..., None]) for d in range(simulation.flow.stencil.d): point_dict[f"u{'xyz'[d]}"] = simulation.flow.context.convert_to_ndarray( u[d, ..., None]) else: point_dict["p"] = simulation.flow.context.convert_to_ndarray(p[0, ...]) for d in range(simulation.flow.stencil.d): point_dict[f"u{'xyz'[d]}"] = simulation.flow.context.convert_to_ndarray(u[d, ...]) write_vtk(point_dict, simulation.flow.i, self.outdir + f"/{self.name}_frame") @abstractmethod def locations_string(self, simulation: 'BreakableSimulation') -> List[str]: ... @abstractmethod def is_failed(self, simulation: 'BreakableSimulation') -> torch.Tensor: """checks if simulation meets criterion""" ... @abstractmethod def get_results(self, simulation: 'BreakableSimulation') -> List[Tuple]: """calls specific method to create list of locations at which simulation failed""" ... class NaNReporter(FailureReporterBase): def __init__(self, interval: int, k: int =100, outdir: Optional[str]=None, vtk_out: bool =False): super().__init__(interval, k, outdir, vtk_out) self.name = "NaN" def is_failed(self, simulation: 'BreakableSimulation') -> torch.Tensor: return torch.isnan(simulation.flow.f).any() def get_results(self, simulation: 'BreakableSimulation') -> List[Tuple]: nan_mask = torch.isnan(simulation.flow.f) return self._get_top_failures(nan_mask, simulation.flow.f) def locations_string(self, simulation: 'BreakableSimulation') -> List[str]: """create locations string as a header for the table of locations and values in the output""" locations = ["q", "x"] if simulation.flow.stencil.d > 1: locations.append("y") if simulation.flow.stencil.d > 2: locations.append("z") return locations class HighMaReporter(FailureReporterBase): def __init__(self, interval: int, threshold: float =0.3, k: int =100, outdir: Optional[str]=None, vtk_out: bool =False): super().__init__(interval, k, outdir, vtk_out) self.threshold = threshold self.name = "HighMa" def is_failed(self, simulation: 'BreakableSimulation') -> torch.Tensor: u = simulation.flow.u() ma = torch.norm(u, dim=0) / simulation.flow.stencil.cs return (ma > self.threshold).any() def get_results(self, simulation: 'BreakableSimulation') -> List[Tuple]: u = simulation.flow.u() ma = torch.norm(u, dim=0) / simulation.flow.stencil.cs mask = ma > self.threshold return self._get_top_failures(mask, ma) def locations_string(self, simulation: 'BreakableSimulation') -> List[str]: """create locations string as a header for the table of locations and values in the output""" locations = ["x"] if simulation.flow.stencil.d > 1: locations.append("y") if simulation.flow.stencil.d > 2: locations.append("z") locations.append("Ma") return locations ================================================ FILE: lettuce/ext/_reporter/observable_reporter.py ================================================ import sys from abc import ABC, abstractmethod from typing import Optional import torch import numpy as np from ... import Reporter, Flow from ...util import torch_gradient from packaging import version __all__ = ['Observable', 'ObservableReporter', 'MaximumVelocity', 'IncompressibleKineticEnergy', 'Enstrophy', 'EnergySpectrum', 'Mass'] class Observable(ABC): def __init__(self, flow: 'Flow'): self.context = flow.context self.flow = flow @abstractmethod def __call__(self, f: Optional[torch.Tensor] = None): ... class MaximumVelocity(Observable): """Maximum velocitiy""" def __call__(self, f: Optional[torch.Tensor] = None): return torch.norm(self.flow.u_pu, dim=0).max() class IncompressibleKineticEnergy(Observable): """Total kinetic energy of an incompressible flow.""" def __call__(self, f: Optional[torch.Tensor] = None): dx = self.flow.units.convert_length_to_pu(1.0) kinE = self.flow.units.convert_incompressible_energy_to_pu( torch.sum(self.flow.incompressible_energy())) kinE *= dx ** self.flow.stencil.d return kinE class Enstrophy(Observable): """The integral of the vorticity Notes ----- The function only works for periodic domains """ def __call__(self, f: Optional[torch.Tensor] = None): u0 = self.flow.units.convert_velocity_to_pu(self.flow.u()[0]) u1 = self.flow.units.convert_velocity_to_pu(self.flow.u()[1]) dx = self.flow.units.convert_length_to_pu(1.0) grad_u0 = torch_gradient(u0, dx=dx, order=6) grad_u1 = torch_gradient(u1, dx=dx, order=6) vorticity = torch.sum((grad_u0[1] - grad_u1[0]) * (grad_u0[1] - grad_u1[0])) if self.flow.stencil.d == 3: u2 = self.flow.units.convert_velocity_to_pu(self.flow.u()[2]) grad_u2 = torch_gradient(u2, dx=dx, order=6) vorticity += torch.sum( (grad_u2[1] - grad_u1[2]) * (grad_u2[1] - grad_u1[2]) + ((grad_u0[2] - grad_u2[0]) * (grad_u0[2] - grad_u2[0])) ) return vorticity * dx ** self.flow.stencil.d class EnergySpectrum(Observable): """The kinetic energy spectrum""" def __init__(self, flow: Flow): super(EnergySpectrum, self).__init__(flow) self.dx = self.flow.units.convert_length_to_pu(1.0) self.dimensions = self.flow.resolution frequencies = [self.context.convert_to_tensor( np.fft.fftfreq(dim, d=1 / dim) ) for dim in self.dimensions] wavenumbers = torch.stack(torch.meshgrid(*frequencies, indexing='ij')) wavenorms = torch.norm(wavenumbers, dim=0) if self.flow.stencil.d == 3: self.norm = self.dimensions[0] * np.sqrt(2 * np.pi) / self.dx ** 2 else: self.norm = self.dimensions[0] / self.dx self.wavenumbers = torch.arange(int(torch.max(wavenorms))) self.wavemask = ( (wavenorms[..., None] > self.wavenumbers.to( dtype=self.context.dtype, device=self.context.device) - 0.5) & (wavenorms[..., None] <= self.wavenumbers.to( dtype=self.context.dtype, device=self.context.device) + 0.5) ) def __call__(self, f: Optional[torch.Tensor] = None): u = self.flow.u() return self.spectrum_from_u(u) def spectrum_from_u(self, u): u = self.flow.units.convert_velocity_to_pu(u) ekin = self._ekin_spectrum(u) ek = ekin[..., None] * self.wavemask.to(dtype=self.context.dtype) ek = ek.sum(torch.arange(self.flow.stencil.d).tolist()) return ek def _ekin_spectrum(self, u): """distinguish between different torch versions""" torch_ge_18 = (version.parse(torch.__version__) >= version.parse( "1.8.0")) if torch_ge_18: return self._ekin_spectrum_torch_ge_18(u) else: return self._ekin_spectrum_torch_lt_18(u) def _ekin_spectrum_torch_lt_18(self, u): zeros = torch.zeros(self.dimensions, dtype=self.context.dtype, device=self.context.device)[..., None] uh = (torch.stack( [torch.fft(torch.cat((u[i][..., None], zeros), self.flow.stencil.d), signal_ndim=self.flow.stencil.d) for i in range(self.flow.stencil.d) ]) / self.norm) ekin = torch.sum(0.5 * (uh[..., 0] ** 2 + uh[..., 1] ** 2), dim=0) return ekin def _ekin_spectrum_torch_ge_18(self, u): uh = (torch.stack([ torch.fft.fftn(u[i], dim=tuple(torch.arange(self.flow.stencil.d))) for i in range(self.flow.stencil.d) ]) / self.norm) ekin = torch.sum(0.5 * (uh.imag ** 2 + uh.real ** 2), dim=0) return ekin class Mass(Observable): """Total mass in lattice units. Parameters ---------- no_mass_mask : torch.Tensor Boolean mask that defines grid points which do not count into the total mass (e.g. bounce-back boundary). """ def __init__(self, flow: Flow, no_mass_mask=None): super(Mass, self).__init__(flow) self.mask = no_mass_mask def __call__(self, f: Optional[torch.Tensor] = None): mass = f[..., 1:-1, 1:-1].sum() if self.mask is not None: mass -= (f * self.mask.to(dtype=torch.float)).sum() return mass class ObservableReporter(Reporter): """A reporter that prints an observable every few iterations. Examples -------- Create an Enstrophy reporter. >>> from lettuce import TaylorGreenVortex3D, Enstrophy, D3Q27, Context >>> context = Context(device=torch.device("cpu")) >>> flow = TaylorGreenVortex(context, 50, 300, 0.1, D3Q27()) >>> simulation = ... >>> enstrophy = Enstrophy(flow) >>> reporter = ObservableReporter(enstrophy, interval=10) >>> simulation.reporter.append(reporter) """ def __init__(self, observable, interval=1, out=sys.stdout): super().__init__(interval) self.observable = observable self.out = [] if out is None else out self._parameter_name = observable.__class__.__name__ if out is not None: print('steps ', 'time ', self._parameter_name) def __call__(self, simulation: 'Simulation'): if simulation.flow.i % self.interval == 0: observed = self.observable.context.convert_to_ndarray( self.observable(simulation.flow.f)) assert len(observed.shape) < 2 if len(observed.shape) == 0: observed = [observed.item()] else: observed = observed.tolist() entry = ([simulation.flow.i, simulation.units.convert_time_to_pu(simulation.flow.i)] + observed) if isinstance(self.out, list): self.out.append(entry) else: print(*entry, file=self.out) ================================================ FILE: lettuce/ext/_reporter/progress_reporter.py ================================================ import os import datetime from timeit import default_timer as timer from lettuce import Reporter, Simulation def append_txt_file(filename, line: str): ''' append a line to a file with an added linebreak''' file = open(filename, "a") file.write(line + "\n") file.close() class ProgressReporter(Reporter): ''' Progress reporter that logs: current wall time, elapsed wall time, elapsed steps and estimates wall time remaining. future feature: Option to write a checkpoint file, when t_max is reached. (Sim. can be restarted from checkpoint) (!) This reporter does not export other reporters observable values etc., so make sure you save them in other ways, if sim. is stopped by host system (e.g. HPC cluster)! ''' # TODO (future improvements): # - implement checkpointing functionality in lettuce # - make progress reporter able to end simulation (similar to failureReporter) def __init__(self, interval=1000, t_max=0, i_target=0, i_start=0, outdir=None, print_message=False, checkpoint=False): ## initialize local attributes... self.t_max = t_max self.i_start = i_start self.i_target = i_target # should be equivalent to sim.num_steps self.outdir= str(outdir) self.print_message = print_message self.checkpoint = checkpoint ## check output directory for file (if path is not NONE) # ...OR write to sys.out (print) if self.outdir is not None: if not os.path.exists(self.outdir): os.mkdir(self.outdir) self.running = False self.t_start = 0 # mutability of this up for discussion for sims that are # ...started from a checkpoint self.t_elapsed = 0 # mutability of this up for discussion for sims that are # ...started from a checkpoint super().__init__(interval) def __call__(self, simulation: 'Simulation'): """start internal timer, if timer is not running; get timestamp, calculate elapsed time, estimate time remaining etc. write data to file. """ if not self.running: self.start_timer() elif simulation.flow.i % self.interval == 0: timestamp = datetime.datetime.now() timestamp_str = timestamp.strftime("%y%m%d_%H%M%S") t_now = timer() self.t_elapsed = t_now - self.t_start if simulation.flow.i == self.i_start: t_per_step = self.t_elapsed / self.interval else: t_per_step = self.t_elapsed / (simulation.flow.i - self.i_start) i_remaining = self.i_target - simulation.flow.i t_remaining_estimate = t_per_step * i_remaining datetime_finish_estimate = timestamp + datetime.timedelta(seconds=t_remaining_estimate) t_total_estimate = self.t_elapsed + t_remaining_estimate # write DATA and warn if t_total_estimate > t_max base_message = (timestamp_str.ljust(13) + " " + str(simulation.flow.i).rjust(10) + " " + "{:.2f}".format(t_now).rjust(10) + " " + "{:.2f}".format(self.t_elapsed).rjust(10) + " " + "{:.6f}".format(t_per_step).rjust(10) + " " + "{:.2f}".format(t_remaining_estimate).rjust(15) + " " + "{:.2f}".format(t_total_estimate).rjust(15) + " " + str(datetime_finish_estimate.strftime( '%Y-%m-%d %H:%M:%S')).ljust(20)) # (for reference) table column widths: # 13, 10, 7+2.(rjust10), 7+2.(rjust10), 1+6.(rjust10), 7+2.(rjust10), 7+2.(rjust15), 27 if t_total_estimate > self.t_max: message = base_message + " WARNING t_total>t_max=" + str(self.t_max) else: message = base_message append_txt_file(self.outdir + "/progress_reporter_log.txt", message) if self.print_message: print(message) # (NOT IMPLEMENTET YET) write checkpoint if t_elapsed > t_max if self.checkpoint and self.t_elapsed > self.t_max: # checkpointing seems to be missing in current master... print("PROGRESS REPORTER: checkpoint was requested, " "but current version of lettuce does not support " "checkpointing... sorry :(") # TO BE IMPLEMENTED IN THE FUTURE: # simulation.save_checkpoint(self.outdir # + "/" + timestamp_str + "_f_" # + str(simulation.flow.i) + ".cpt") def start_timer(self): """ start the internal timer and write table header to output file""" self.running = True self.t_start = timer() self.t_elapsed = 0 #print("starting timer") print("(!) PROGRESS REPORTER ACTIVE:\nt_start: " + str(self.t_start) + ", interval: " + str(self.interval) + ", i_target: " + str(self.i_target)) table_header = ("timestamp ".center(13) +"|"+"step".center(10) +"|"+"t_now".center(10) +"|"+"t_elapsed".center(10) +"|"+"t_per_step".center(10) +"|"+"t_remain(est)".center(15) +"|"+"t_total(est)".center(15) +"|"+"DATE_FINISH(est)".center(20) +"|"+" T WARNING") if self.print_message: print(table_header) else: print(f"(ProgressReporter) print_message == False: see " f"'{self.outdir}/progress_reporter_log.txt' for output") append_txt_file(self.outdir+"/progress_reporter_log.txt", "t_start: "+str(self.t_start)+", interval: "+str(self.interval) +", i_target: "+str(self.i_target)) append_txt_file(self.outdir+"/progress_reporter_log.txt", table_header) # sizes: 13, 10, 7+2.(rjust10), 7+2.(rjust10), 1+6.(rjust10), 7+2.(rjust10), 7+2.(rjust15), 27 ================================================ FILE: lettuce/ext/_reporter/vtk_reporter.py ================================================ import numpy as np import pyevtk.hl as vtk import os from ... import Reporter __all__ = ['VTKReporter', 'write_vtk'] def write_vtk(point_dict, id=0, filename_base="./data/output"): vtk.gridToVTK(f"{filename_base}_{id:08d}", np.arange(0, point_dict["p"].shape[0]), np.arange(0, point_dict["p"].shape[1]), np.arange(0, point_dict["p"].shape[2]), pointData=point_dict) class VTKReporter(Reporter): """General VTK Reporter for velocity and pressure""" def __init__(self, interval=50, filename_base="./data/output"): super().__init__(interval) self.filename_base = filename_base directory = os.path.dirname(filename_base) if not os.path.isdir(directory): os.mkdir(directory) self.point_dict = dict() def __call__(self, simulation: 'Simulation'): if simulation.flow.i % self.interval == 0: u = simulation.flow.u_pu p = simulation.flow.p_pu if simulation.flow.stencil.d == 2: self.point_dict["p"] = ( simulation.flow.context.convert_to_ndarray( p[0, ..., None])) for d in range(simulation.flow.stencil.d): self.point_dict[f"u{'xyz'[d]}"] = ( simulation.flow.context.convert_to_ndarray( u[d, ..., None])) else: self.point_dict["p"] = ( simulation.flow.context.convert_to_ndarray(p[0, ...])) for d in range(simulation.flow.stencil.d): self.point_dict[f"u{'xyz'[d]}"] = ( simulation.flow.context.convert_to_ndarray(u[d, ...])) write_vtk(self.point_dict, simulation.flow.i, self.filename_base) def output_mask(self, simulation: 'Simulation'): """Outputs the no_collision_mask of the simulation object as VTK-file with range [0,1] Usage: vtk_reporter.output_mask(simulation.no_collision_mask)""" point_dict = dict() if simulation.flow.stencil.d == 2: point_dict["mask"] = simulation.flow.context.convert_to_ndarray( simulation.no_collision_mask)[..., None].astype(int) else: point_dict["mask"] = simulation.flow.context.convert_to_ndarray( simulation.no_collision_mask).astype(int) vtk.gridToVTK(self.filename_base + "_mask", np.arange(0, point_dict["mask"].shape[0]), np.arange(0, point_dict["mask"].shape[1]), np.arange(0, point_dict["mask"].shape[2]), pointData=point_dict) ================================================ FILE: lettuce/ext/_reporter/write_image.py ================================================ __all__ = ['write_image'] def write_image(filename, array2d): from matplotlib import pyplot as plt fig, ax = plt.subplots() plt.tight_layout() ax.imshow(array2d) ax.set_xlabel('') ax.set_ylabel('') ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) plt.savefig(filename) ================================================ FILE: lettuce/ext/_stencil/__init__.py ================================================ from .d1q3 import D1Q3 from .d2q9 import D2Q9 from .d3q15 import D3Q15 from .d3q19 import D3Q19 from .d3q27 import D3Q27 __all__ = [ 'D1Q3', 'D2Q9', 'D3Q15', 'D3Q19', 'D3Q27' ] ================================================ FILE: lettuce/ext/_stencil/d1q3.py ================================================ from ... import Stencil __all__ = ['D1Q3'] class D1Q3(Stencil): def __init__(self): self.e = [[0], [1], [-1]] self.w = [2.0 / 3.0, 1.0 / 6.0, 1.0 / 6.0] self.opposite = [0, 2, 1] ================================================ FILE: lettuce/ext/_stencil/d2q9.py ================================================ from ... import Stencil __all__ = ['D2Q9'] class D2Q9(Stencil): def __init__(self): self.e = [[0, 0], [1, 0], [0, 1], [-1, 0], [0, -1], [1, 1], [-1, 1], [-1, -1], [1, -1]] self.w = [4.0 / 9.0] + [1.0 / 9.0] * 4 + [1.0 / 36.0] * 4 self.opposite = [0, 3, 4, 1, 2, 7, 8, 5, 6] ================================================ FILE: lettuce/ext/_stencil/d3q15.py ================================================ from ... import Stencil __all__ = ['D3Q15'] class D3Q15(Stencil): def __init__(self): self.e = [[0, 0, 0], [1, 0, 0], [-1, 0, 0], [0, 1, 0], [0, -1, 0], [0, 0, 1], [0, 0, -1], [1, 1, 1], [-1, -1, -1], [1, 1, -1], [-1, -1, 1], [1, -1, 1], [-1, 1, -1], [1, -1, -1], [-1, 1, 1]] self.w = [2.0 / 9.0] + [1.0 / 9.0] * 6 + [1.0 / 72.0] * 8 self.opposite = [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13] ================================================ FILE: lettuce/ext/_stencil/d3q19.py ================================================ from ... import Stencil __all__ = ['D3Q19'] class D3Q19(Stencil): def __init__(self): self.e = [[0, 0, 0], [1, 0, 0], [-1, 0, 0], [0, 1, 0], [0, -1, 0], [0, 0, 1], [0, 0, -1], [0, 1, 1], [0, -1, -1], [0, 1, -1], [0, -1, 1], [1, 0, 1], [-1, 0, -1], [1, 0, -1], [-1, 0, 1], [1, 1, 0], [-1, -1, 0], [1, -1, 0], [-1, 1, 0]] self.w = [1.0 / 3.0] + [1.0 / 18.0] * 6 + [1.0 / 36.0] * 12 self.opposite = [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15, 18, 17] ================================================ FILE: lettuce/ext/_stencil/d3q27.py ================================================ from ... import Stencil __all__ = ['D3Q27'] class D3Q27(Stencil): def __init__(self): self.e = [[0, 0, 0], [1, 0, 0], [-1, 0, 0], [0, 1, 0], [0, -1, 0], [0, 0, 1], [0, 0, -1], [0, 1, 1], [0, -1, -1], [0, 1, -1], [0, -1, 1], [1, 0, 1], [-1, 0, -1], [1, 0, -1], [-1, 0, 1], [1, 1, 0], [-1, -1, 0], [1, -1, 0], [-1, 1, 0], [1, 1, 1], [-1, -1, -1], [1, 1, -1], [-1, -1, 1], [1, -1, 1], [-1, 1, -1], [1, -1, -1], [-1, 1, 1]] self.w = [8.0 / 27.0] + [2.0 / 27.0] * 6 + [1.0 / 54.0] * 12 + [1.0 / 216.0] * 8 self.opposite = [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15, 18, 17, 20, 19, 22, 21, 24, 23, 26, 25] ================================================ FILE: lettuce/util/__init__.py ================================================ ''' Utility functions. ''' from .utility import * # not importing moments due to cyclic import issue # from .moments import * from .datautils import * from .utility import get_subclasses __all__ = [ 'get_subclasses', 'LettuceException', 'LettuceWarning', 'InefficientCodeWarning', 'ExperimentalWarning', 'torch_gradient', 'grid_fine_to_coarse', 'torch_jacobi', 'append_axes', 'HDF5Reporter', 'LettuceDataset', # 'moment_tensor', # 'get_default_moment_transform', # 'Moments', # 'Transform', # 'D1Q3Transform', # 'D2Q9Lallemand', # 'D2Q9Dellar', # 'D3Q27Hermite' ] ================================================ FILE: lettuce/util/datautils.py ================================================ """ datautils for writing/reading hdf5 files. """ import h5py from torch.utils import data from lettuce._version import get_versions import pickle import io import numpy as np from lettuce._simulation import Reporter __all__ = ["HDF5Reporter", "LettuceDataset"] class HDF5Reporter(Reporter): """ HDF5 _reporter for distribution function f in lettuce containing metadata of the simulation. Parameters ---------- filebase : string Path to the hdf5 file with annotations. metadata : dictionary Optional metadata can be saved. The passed values must be of type string. >>> metadata = {"attr_1": "str_value_1", "attr_2": "str_value_2"} interval : integer Define the step interval after the _reporter is applied. The _reporter will save f every "interval" step. Examples -------- Create a HDF5 _reporter. >>> import lettuce as lt >>> context = Context() >>> flow = lt.TaylorGreenVortex(context, [50, 50], 300, 0.1) >>> _collision = ... >>> simulation = ... >>> hdf5_reporter = lt.HDF5Reporter( >>> context=context, >>> flow=flow, >>> _collision=_collision, >>> interval= 100, >>> filebase="./h5_output") >>> simulation.reporters.append(hdf5_reporter) """ def __init__(self, flow, collision, interval, filebase='./output', metadata=None): self.context = flow.context self.interval = interval self.filebase = filebase fs = h5py.File(self.filebase + '.h5', 'w') fs.attrs['lettuce_version'] = get_versions()['version'] fs.attrs["flow"] = self._pickle_to_h5(flow) fs.attrs['_collision'] = self._pickle_to_h5(collision) if metadata: for attr in metadata: fs.attrs[attr] = metadata[attr] self.shape = (flow.stencil.q, *flow.grid[0].shape) fs.create_dataset(name="f", shape=(0, *self.shape), maxshape=(None, *self.shape)) fs.close() def __call__(self, simulation: 'Simulation'): # i, t, f): if simulation.flow.i % self.interval == 0: with h5py.File(self.filebase + '.h5', 'r+') as fs: fs["f"].resize(fs["f"].shape[0] + 1, axis=0) fs["f"][-1, ...] = self.context.convert_to_ndarray( simulation.flow.f) fs.attrs['data'] = str(fs["f"].shape[0]) fs.attrs['steps'] = str(simulation.flow.i) @staticmethod def _pickle_to_h5(instance): bytes_io = io.BytesIO() pickle.dump(instance, bytes_io) bytes_io.seek(0) return np.void(bytes_io.getvalue()) class LettuceDataset(data.Dataset): """ Custom dataset for HDF5 files in lettuce that can be used by torch's dataloader. Parameters ---------- filebase : string Path to the hdf5 file with annotations. transform : class object Optional transform to be applied on a f loaded from HDF5 file. target : logical operation (True, False) Returns also the next dataset[idx + skip_idx_to_target] - default=False skip_idx_to_target : integer Define which next target dataset is returned if target is True - default=1 Examples -------- Create a data loader. >>> import lettuce as lt >>> import torch >>> lattice = lt.Lattice(lt.D3Q27, device="cpu") >>> dataset_train = lt.LettuceDataset(lattice=lattice, >>> filebase= "./hdf5_output.h5", >>> target=True) >>> train_loader = torch.utils.data.DataLoader(dataset_train, shuffle=True) >>> for (f, target, idx) in train_loader: >>> ... """ def __init__(self, filebase, transform=None, target=False, skip_idx_to_target=1): super().__init__() self.filebase = filebase self.transform = transform self.target = target self.skip_idx_to_target = skip_idx_to_target self.fs = h5py.File(self.filebase, "r") self.shape = self.fs["f"].shape self.keys = list(self.fs.keys()) self.context = self._unpickle_from_h5(self.fs.attrs["flow"]).context def __str__(self): for attr, value in self.fs.attrs.items(): if attr in ('flow', '_collision'): print(attr + ": " + str(self._unpickle_from_h5(self.fs.attrs[attr]))) else: print(attr + ": " + str(value)) return "" def __len__(self): return self.shape[0] - self.skip_idx_to_target if self.target else self.shape[0] def __getitem__(self, idx): f = self.get_data(idx) target = [] if self.target: target = self.get_data(idx + self.skip_idx_to_target) if self.transform: f = self.transform(f) if self.target: target = self.transform(target) return (f, target, idx) if self.target else (f, idx) def __del__(self): self.fs.close() def get_data(self, idx): return self.context.convert_to_tensor(self.fs["f"][idx]) def get_attr(self, attr): return self.fs.attrs[attr] @staticmethod def _unpickle_from_h5(byte_str): return pickle.load(io.BytesIO(byte_str)) ================================================ FILE: lettuce/util/moments.py ================================================ """ Moments and cumulants of the distribution function. """ import warnings from typing import List import torch from lettuce._flow import Flow import lettuce from lettuce.util import (LettuceException, InefficientCodeWarning, get_subclasses, ExperimentalWarning) from lettuce._stencil import Stencil from lettuce.ext._stencil import * import numpy as np __all__ = [ "moment_tensor", "get_default_moment_transform", "Moments", "Transform", "D1Q3Transform", "D2Q9Lallemand", "D2Q9Dellar", "D3Q27Hermite" ] _ALL_STENCILS = get_subclasses(Stencil, module=lettuce) def moment_tensor(e: List[List[int]], multiindex): if isinstance(e, torch.Tensor): return torch.prod(torch.pow(e, multiindex[..., None, :]), dim=-1) else: return np.prod(np.power(e, multiindex[..., None, :]), axis=-1) def get_default_moment_transform(stencil: 'Stencil', context: 'Context'): if stencil == D1Q3 or isinstance(stencil, D1Q3): return D1Q3Transform(stencil, context) if stencil == D2Q9 or isinstance(stencil, D2Q9): return D2Q9Lallemand(stencil, context) else: raise LettuceException(f"No default moment transform for lattice " f"{stencil}.") class Moments: def __init__(self, lattice): self.rho = moment_tensor(lattice.e, lattice.convert_to_tensor( np.zeros(lattice.D))) self.j = moment_tensor(lattice.e, lattice.convert_to_tensor( np.eye(lattice.D))) # ... TODO ... class Transform: """Base class that defines the signature for all moment (and cumulant) transforms. """ def __init__(self, stencil: 'Stencil', context: 'Context', names=None): self.context = context self.names = [f"m{i}" for i in range(stencil.q)]\ if names is None else names self.stencil = stencil def __getitem__(self, moment_names): if not isinstance(moment_names, tuple): moment_names = [moment_names] return [self.names.index(name) for name in moment_names] def transform(self, f): return f def inverse_transform(self, m): return m def equilibrium(self, m: torch.Tensor, flow: 'Flow'): """A very inefficient and basic implementation of the equilibrium moments. """ warnings.warn( "Transform.equilibrium is a poor man's implementation of " "the moment equilibrium. Please consider implementing the " "equilibrium moments for your transform by hand.", InefficientCodeWarning ) f = self.inverse_transform(m) feq = flow.equilibrium(flow, flow.rho(None, f), flow.u(None, f)) return self.transform(feq) def einsum(self, equation, fields, *args) -> torch.Tensor: """Einstein summation on local fields.""" inputs, output = equation.split("->") inputs = inputs.split(",") for i, inp in enumerate(inputs): if len(inp) == len(fields[i].shape): pass elif len(inp) == len(fields[i].shape) - self.stencil.d: inputs[i] += "..." if not output.endswith("..."): output += "..." else: assert False, "Bad dimension." equation = ",".join(inputs) + "->" + output return torch.einsum(equation, fields, *args) def mv(self, m, v) -> torch.Tensor: """matrix-vector multiplication""" return self.einsum("ij,j->i", [m, v]) class D1Q3Transform(Transform): matrix = np.array([ [1, 1, 1], [0, 1, -1], [0, 1, 1] ]) inverse = np.array([ [1, 0, -1], [0, 1 / 2, 1 / 2], [0, -1 / 2, 1 / 2] ]) names = ["rho", "j", "e"] supported_stencils = [D1Q3] def __init__(self, stencil: 'Stencil', context: 'Context'): super(D1Q3Transform, self).__init__(stencil, context, self.names) self.matrix = self.context.convert_to_tensor(self.matrix) self.inverse = self.context.convert_to_tensor(self.inverse) def transform(self, f): return self.mv(self.matrix, f) def inverse_transform(self, m): return self.mv(self.inverse, m) # def _equilibrium(self, m): # # TODO # raise NotImplementedError class D2Q9Dellar(Transform): matrix = np.array( [[1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 0, -1, 0, 1, -1, -1, 1], [0, 0, 1, 0, -1, 1, 1, -1, -1], [-3 / 2, 3, -3 / 2, 3, -3 / 2, 3, 3, 3, 3], [0, 0, 0, 0, 0, 9, -9, 9, -9], [-3 / 2, -3 / 2, 3, -3 / 2, 3, 3, 3, 3, 3], [1, -2, -2, -2, -2, 4, 4, 4, 4], [0, -2, 0, 2, 0, 4, -4, -4, 4], [0, 0, -2, 0, 2, 4, 4, -4, -4]] ) inverse = np.array( [[4 / 9, 0, 0, -4 / 27, 0, -4 / 27, 1 / 9, 0, 0], [1 / 9, 1 / 3, 0, 2 / 27, 0, -1 / 27, -1 / 18, -1 / 12, 0], [1 / 9, 0, 1 / 3, -1 / 27, 0, 2 / 27, -1 / 18, 0, -1 / 12], [1 / 9, -1 / 3, 0, 2 / 27, 0, -1 / 27, -1 / 18, 1 / 12, 0], [1 / 9, 0, -1 / 3, -1 / 27, 0, 2 / 27, -1 / 18, 0, 1 / 12], [1 / 36, 1 / 12, 1 / 12, 1 / 54, 1 / 36, 1 / 54, 1 / 36, 1 / 24, 1 / 24], [1 / 36, -1 / 12, 1 / 12, 1 / 54, -1 / 36, 1 / 54, 1 / 36, -1 / 24, 1 / 24], [1 / 36, -1 / 12, -1 / 12, 1 / 54, 1 / 36, 1 / 54, 1 / 36, -1 / 24, -1 / 24], [1 / 36, 1 / 12, -1 / 12, 1 / 54, -1 / 36, 1 / 54, 1 / 36, 1 / 24, -1 / 24]] ) names = ['rho', 'jx', 'jy', 'Pi_xx', 'Pi_xy', 'PI_yy', 'N', 'Jx', 'Jy'] supported_stencils = [D2Q9] def __init__(self, stencil: 'Stencil', context: 'Context'): super(D2Q9Dellar, self).__init__(stencil, context, self.names) self.matrix = self.context.convert_to_tensor(self.matrix) self.inverse = self.context.convert_to_tensor(self.inverse) def transform(self, f): return self.mv(self.matrix, f) def inverse_transform(self, m): return self.mv(self.inverse, m) def equilibrium(self, m, flow: 'Flow'): warnings.warn("I am not 100% sure if this equilibrium is correct.", ExperimentalWarning) meq = torch.zeros_like(m) rho = m[0] jx = m[1] jy = m[2] Pi_xx = jx * jx / rho * 9 / 2 Pi_xy = jx * jy / rho * 9 Pi_yy = jy * jy / rho * 9 / 2 meq[0] = rho meq[1] = jx meq[2] = jy meq[3] = Pi_xx meq[4] = Pi_xy meq[5] = Pi_yy return meq class D2Q9Lallemand(Transform): matrix = np.array( [[1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 0, -1, 0, 1, -1, -1, 1], [0, 0, 1, 0, -1, 1, 1, -1, -1], [0, 1, -1, 1, -1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, -1, 1, -1], [-4, -1, -1, -1, -1, 2, 2, 2, 2], [0, -2, 0, 2, 0, 1, -1, -1, 1], [0, 0, -2, 0, 2, 1, 1, -1, -1], [4, -2, -2, -2, -2, 1, 1, 1, 1]] ) inverse = np.array( [[1 / 9, 0, 0, 0, 0, -1 / 9, 0, 0, 1 / 9], [1 / 9, 1 / 6, 0, 1 / 4, 0, -1 / 36, -1 / 6, 0, -1 / 18], [1 / 9, 0, 1 / 6, -1 / 4, 0, -1 / 36, 0, -1 / 6, -1 / 18], [1 / 9, -1 / 6, 0, 1 / 4, 0, -1 / 36, 1 / 6, 0, -1 / 18], [1 / 9, 0, -1 / 6, -1 / 4, 0, -1 / 36, 0, 1 / 6, -1 / 18], [1 / 9, 1 / 6, 1 / 6, 0, 1 / 4, 1 / 18, 1 / 12, 1 / 12, 1 / 36], [1 / 9, -1 / 6, 1 / 6, 0, -1 / 4, 1 / 18, -1 / 12, 1 / 12, 1 / 36], [1 / 9, -1 / 6, -1 / 6, 0, 1 / 4, 1 / 18, -1 / 12, -1 / 12, 1 / 36], [1 / 9, 1 / 6, -1 / 6, 0, -1 / 4, 1 / 18, 1 / 12, -1 / 12, 1 / 36]] ) names = ['rho', 'jx', 'jy', 'pxx', 'pxy', 'e', 'qx', 'qy', 'eps'] supported_stencils = [D2Q9] def __init__(self, stencil: 'Stencil', context: 'Context'): super(D2Q9Lallemand, self).__init__(stencil, context, self.names) self.matrix = self.context.convert_to_tensor(self.matrix) self.inverse = self.context.convert_to_tensor(self.inverse) def transform(self, f): return self.mv(self.matrix, f) def inverse_transform(self, m): return self.mv(self.inverse, m) def equilibrium(self, m, flow: 'Flow'): """From Lallemand and Luo""" warnings.warn("I am not 100% sure if this equilibrium is correct.", ExperimentalWarning) meq = torch.zeros_like(m) rho = m[0] jx = m[1] jy = m[2] c1 = -2 alpha2 = -8 alpha3 = 4 gamma1 = 2 / 3 gamma2 = 18 gamma3 = 2 / 3 gamma4 = -18 e = 1 / 4 * alpha2 * rho + 1 / 6 * gamma2 * (jx ** 2 + jy ** 2) eps = 1 / 4 * alpha3 * rho + 1 / 6 * gamma4 * (jx ** 2 + jy ** 2) qx = 1 / 2 * c1 * jx qy = 1 / 2 * c1 * jy pxx = 1 / 2 * gamma1 * (jx ** 2 - jy ** 2) pxy = 1 / 2 * gamma3 * (jx * jy) meq[0] = rho meq[1] = jx meq[2] = jy meq[3] = pxx meq[4] = pxy meq[5] = e meq[6] = qx meq[7] = qy meq[8] = eps return meq """ D3Q19 is not implemented, yet. Also, the moments should be ordered so that 1...D+1 correspond to momentum, which is no the case for this matrix. """ """ class D3Q19DHumieres(NaturalMomentTransform): matrix = np.array( [[1 / 1, 1, 1, 1, 1, 1, 1, 1, 1 / 1, 1, 1, 1, 1, 1, 1, 1, 1 / 1, 1, 1], [-30, -11, -11, -11 / 1, -11, -11, -11, 8, 8, 8, 8 / 1, 8, 8, 8, 8, 8, 8, 8, 8 / 1], [12, -4, -4, -4, -4, -4 / 1, -4, 1, 1, 1, 1, 1, 1, 1 / 1, 1, 1, 1, 1, 1], [0, 1 / 1, 0, -1, 0, 0, 0, 1, -1, -1, 1, 1, 1, -1, -1, 0, 0, 0, 0], [0, -4, 0, 4, 0, 0, 0, 1 / 1, -1, -1, 1, 1, 1, -1, -1, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, 1, 0, 0, 0, 0, -1, 1, 1, -1, -1, 1 / 1, 1, -1], [0, 0, 0, 0, 0, 4, -4, 0, 0, 0, 0, -1, 1, 1, -1, -1, 1, 1, -1 / 1], [0, 0, 1, 0, -1, 0, 0, 1, 1, -1, -1, 0, 0, 0, 0, 1, 1, -1, -1], [0, 0, -4, 0, 4, 0, 0, 1, 1, -1, -1, 0, 0, 0, 0, 1, 1 / 1, -1, -1], [0, 2, -1, 2 / 1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, -2, -2, -2, -2 / 1], [0, -4, 2, -4, 2, 2 / 1, 2, 1, 1, 1, 1, 1, 1, 1 / 1, 1, -2, -2, -2, -2], [0, 0, -1, 0, -1, 1, 1, -1, -1 / 1, -1, -1, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 2 / 1, 0, 2, -2, -2, -1, -1, -1 / 1, -1, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, -1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, -1, 1], [0, 0, 0, 0, 0, 0, 0, 1, -1, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, -1 / 1, 1, 1, -1, 1, 1, -1, -1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, -1, 1, -1, 1, 1, -1], [0, 0, 0, 0, 0, 0, 0, 1, 1, -1, -1, 0, 0, 0, 0, -1. / 1, -1, 1, 1]] ) inverse = np.array( [[1 / 19, -5 / 399, 1 / 21, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1 / 19, -11 / 2394, -1 / 63, 1 / 10, -1 / 10, 0, 0, 0, 0, 1 / 18, -1 / 18, 0, 0, 0, 0, 0, 0, 0, 0], [1 / 19, -11 / 2394, -1 / 63, 0, 0, 0, 0, 1 / 10, -1 / 10, -1 / 36, 1 / 36, -1 / 12, 1 / 12, 0, 0, 0, 0, 0, 0], [1 / 19, -11 / 2394, -1 / 63, -1 / 10, 1 / 10, 0, 0, 0, 0, 1 / 18, -1 / 18, 0, 0, 0, 0, 0, 0, 0, 0], [1 / 19, -11 / 2394, -1 / 63, 0, 0, 0, 0, -1 / 10, 1 / 10, -1 / 36, 1 / 36, -1 / 12, 1 / 12, 0, 0, 0, 0, 0, 0], [1 / 19, -11 / 2394, -1 / 63, 0, 0, -1. / 10, 1 / 10, 0, 0, -1 / 36, 1 / 36, 1 / 12, -1 / 12, 0, 0, 0, 0, 0, 0], [1 / 19, -11. / 2394, -1 / 63, 0, 0, 1 / 10, -1. / 10, 0, 0, -1 / 36, 1 / 36, 1 / 12, -1 / 12, 0, 0, 0, 0, 0, 0], [1 / 19, 4 / 1197, 1. / 252, 1 / 10, 1 / 40, 0, 0, 1 / 10, 1 / 40, 1 / 36, 1 / 72, -1 / 12, -1 / 24, 0, 0, 1 / 4, -1 / 8, 0, 1. / 8], [1 / 19, 4 / 1197, 1 / 252, -1 / 10, -1 / 40, 0, 0, 1 / 10, 1 / 40, 1 / 36, 1 / 72, -1 / 12, -1 / 24, 0, 0, -1 / 4, 1. / 8, 0, 1 / 8], [1 / 19, 4. / 1197, 1 / 252, -1 / 10, -1 / 40, 0, 0, -1 / 10, -1 / 40, 1 / 36, 1 / 72, -1. / 12, -1 / 24, 0, 0, 1 / 4, 1 / 8, 0, -1 / 8], [1 / 19, 4 / 1197, 1. / 252, 1 / 10, 1 / 40, 0, 0, -1. / 10, -1 / 40, 1 / 36, 1 / 72, -1 / 12, -1. / 24, 0, 0, -1 / 4, -1 / 8, 0, -1 / 8], [1 / 19, 4 / 1197, 1 / 252, 1. / 10, 1 / 40, -1 / 10, -1 / 40, 0, 0, 1 / 36, 1 / 72, 1 / 12, 1 / 24, -1 / 4, 0, 0, 1 / 8, 1 / 8, 0], [1 / 19, 4 / 1197, 1 / 252, 1 / 10, 1 / 40, 1. / 10, 1 / 40, 0, 0, 1 / 36, 1. / 72, 1 / 12, 1 / 24, 1 / 4, 0, 0, 1 / 8, - 1 / 8, 0], [1. / 19, 4 / 1197, 1 / 252, - 1 / 10, - 1 / 40, 1. / 10, 1 / 40, - 0, - 0, 1 / 36, 1 / 72, 1 / 12, 1 / 24, - 1 / 4, 0, 0, -1. / 8, -1 / 8, 0], [1 / 19, 4. / 1197, 1 / 252, -1 / 10, -1 / 40, -1 / 10, -1. / 40, 0, 0, 1 / 36, 1 / 72, 1 / 12, 1 / 24, 1 / 4, 0, 0, -1 / 8, 1 / 8, 0], [1 / 19, 4 / 1197, 1 / 252, 0, 0, -1 / 10, -1 / 40, 1 / 10, 1 / 40, -1 / 18, -1 / 36, 0, 0, 0, -1. / 4, 0, 0, -1 / 8, -1 / 8], [1 / 19, 4 / 1197, 1 / 252, 0, 0, 1. / 10, 1 / 40, 1 / 10, 1 / 40, -1 / 18, -1. / 36, 0, 0, 0, 1 / 4, 0, 0, 1 / 8, -1 / 8], [1 / 19, 4. / 1197, 1 / 252, 0, 0, 1 / 10, 1. / 40, -1 / 10, -1 / 40, -1 / 18, -1 / 36, 0, 0, 0, -1 / 4, 0, 0, 1 / 8, 1 / 8], [1 / 19, 4 / 1197, 1. / 252, 0, 0, -1 / 10, -1 / 40, -1. / 10, -1 / 40, -1 / 18, -1 / 36, 0, 0, 0, 1 / 4, 0, 0, -1 / 8, 1. / 8]] ) names = ['rho', 'e', 'eps', 'jx', 'qx', 'jy', 'qy', 'jz', 'qz', '3pxx', '3pixx', 'pww', 'piww', 'pxy', 'pxz', 'pxx', 'mx', 'my', 'mz'] def __init__(self, lattice): assert lattice._stencil == D3Q19 super(D3Q19DHumieres, self).__init__( lattice, lattice.convert_to_tensor(self.matrix), lattice.convert_to_tensor(self.inverse) ) class D3Q27CumulantTransform(Transform): def __init__(self, lattice): raise NotImplementedError """ class D3Q27Hermite(Transform): matrix = np.array([ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1], [0, 0, 0, 1, -1, 0, 0, 1, -1, 1, -1, 0, 0, 0, 0, 1, -1, -1, 1, 1, -1, 1, -1, -1, 1, -1, 1], [0, 0, 0, 0, 0, 1, -1, 1, -1, -1, 1, 1, -1, -1, 1, 0, 0, 0, 0, 1, -1, -1, 1, 1, -1, -1, 1], [-1 / 3, 2 / 3, 2 / 3, -1 / 3, -1 / 3, -1 / 3, -1 / 3, -1 / 3, -1 / 3, -1 / 3, -1 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, -1, -1, 0, 0, 0, 0, 1, 1, -1, -1, 1, 1, -1, -1], [-1 / 3, -1 / 3, -1 / 3, 2 / 3, 2 / 3, -1 / 3, -1 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, -1 / 3, -1 / 3, -1 / 3, -1 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3], [0, 0, 0, 0, 0, 0, 0, 1, 1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, -1, -1, -1, -1, 1, 1], [-1 / 3, -1 / 3, -1 / 3, -1 / 3, -1 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, -1 / 3, -1 / 3, -1 / 3, -1 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3], [0, 0, 0, -1 / 3, 1 / 3, 0, 0, -1 / 3, 1 / 3, -1 / 3, 1 / 3, 0, 0, 0, 0, 2 / 3, -2 / 3, -2 / 3, 2 / 3, 2 / 3, -2 / 3, 2 / 3, -2 / 3, -2 / 3, 2 / 3, -2 / 3, 2 / 3], [0, 0, 0, 0, 0, -1 / 3, 1 / 3, -1 / 3, 1 / 3, 1 / 3, -1 / 3, 2 / 3, -2 / 3, -2 / 3, 2 / 3, 0, 0, 0, 0, 2 / 3, -2 / 3, -2 / 3, 2 / 3, 2 / 3, -2 / 3, -2 / 3, 2 / 3], [0, -1 / 3, 1 / 3, 0, 0, 0, 0, 0, 0, 0, 0, -1 / 3, 1 / 3, -1 / 3, 1 / 3, 2 / 3, -2 / 3, 2 / 3, -2 / 3, 2 / 3, -2 / 3, 2 / 3, -2 / 3, 2 / 3, -2 / 3, 2 / 3, -2 / 3], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, -1, 1, -1, 1, 1, -1], [0, -1 / 3, 1 / 3, 0, 0, 0, 0, 0, 0, 0, 0, 2 / 3, -2 / 3, 2 / 3, -2 / 3, -1 / 3, 1 / 3, -1 / 3, 1 / 3, 2 / 3, -2 / 3, 2 / 3, -2 / 3, 2 / 3, -2 / 3, 2 / 3, -2 / 3], [0, 0, 0, 0, 0, -1 / 3, 1 / 3, 2 / 3, -2 / 3, -2 / 3, 2 / 3, -1 / 3, 1 / 3, 1 / 3, -1 / 3, 0, 0, 0, 0, 2 / 3, -2 / 3, -2 / 3, 2 / 3, 2 / 3, -2 / 3, -2 / 3, 2 / 3], [0, 0, 0, -1 / 3, 1 / 3, 0, 0, 2 / 3, -2 / 3, 2 / 3, -2 / 3, 0, 0, 0, 0, -1 / 3, 1 / 3, 1 / 3, -1 / 3, 2 / 3, -2 / 3, 2 / 3, -2 / 3, -2 / 3, 2 / 3, -2 / 3, 2 / 3], [1 / 9, -2 / 9, -2 / 9, -2 / 9, -2 / 9, 1 / 9, 1 / 9, -2 / 9, -2 / 9, -2 / 9, -2 / 9, -2 / 9, -2 / 9, -2 / 9, -2 / 9, 4 / 9, 4 / 9, 4 / 9, 4 / 9, 4 / 9, 4 / 9, 4 / 9, 4 / 9, 4 / 9, 4 / 9, 4 / 9, 4 / 9], [0, 0, 0, 0, 0, 0, 0, -1 / 3, -1 / 3, 1 / 3, 1 / 3, 0, 0, 0, 0, 0, 0, 0, 0, 2 / 3, 2 / 3, -2 / 3, -2 / 3, -2 / 3, -2 / 3, 2 / 3, 2 / 3], [1 / 9, -2 / 9, -2 / 9, 1 / 9, 1 / 9, -2 / 9, -2 / 9, -2 / 9, -2 / 9, -2 / 9, -2 / 9, 4 / 9, 4 / 9, 4 / 9, 4 / 9, -2 / 9, -2 / 9, -2 / 9, -2 / 9, 4 / 9, 4 / 9, 4 / 9, 4 / 9, 4 / 9, 4 / 9, 4 / 9, 4 / 9], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1 / 3, -1 / 3, 1 / 3, 1 / 3, 0, 0, 0, 0, 2 / 3, 2 / 3, -2 / 3, -2 / 3, 2 / 3, 2 / 3, -2 / 3, -2 / 3], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1 / 3, -1 / 3, 1 / 3, 1 / 3, 2 / 3, 2 / 3, 2 / 3, 2 / 3, -2 / 3, -2 / 3, -2 / 3, -2 / 3], [1 / 9, 1 / 9, 1 / 9, -2 / 9, -2 / 9, -2 / 9, -2 / 9, 4 / 9, 4 / 9, 4 / 9, 4 / 9, -2 / 9, -2 / 9, -2 / 9, -2 / 9, -2 / 9, -2 / 9, -2 / 9, -2 / 9, 4 / 9, 4 / 9, 4 / 9, 4 / 9, 4 / 9, 4 / 9, 4 / 9, 4 / 9], [0, 0, 0, 0, 0, 1 / 9, -1 / 9, -2 / 9, 2 / 9, 2 / 9, -2 / 9, -2 / 9, 2 / 9, 2 / 9, -2 / 9, 0, 0, 0, 0, 4 / 9, -4 / 9, -4 / 9, 4 / 9, 4 / 9, -4 / 9, -4 / 9, 4 / 9], [0, 0, 0, 1 / 9, -1 / 9, 0, 0, -2 / 9, 2 / 9, -2 / 9, 2 / 9, 0, 0, 0, 0, -2 / 9, 2 / 9, 2 / 9, -2 / 9, 4 / 9, -4 / 9, 4 / 9, -4 / 9, -4 / 9, 4 / 9, -4 / 9, 4 / 9], [0, 1 / 9, -1 / 9, 0, 0, 0, 0, 0, 0, 0, 0, -2 / 9, 2 / 9, -2 / 9, 2 / 9, -2 / 9, 2 / 9, -2 / 9, 2 / 9, 4 / 9, -4 / 9, 4 / 9, -4 / 9, 4 / 9, -4 / 9, 4 / 9, -4 / 9], [-1 / 27, 2 / 27, 2 / 27, 2 / 27, 2 / 27, 2 / 27, 2 / 27, -4 / 27, -4 / 27, -4 / 27, -4 / 27, -4 / 27, -4 / 27, -4 / 27, -4 / 27, -4 / 27, -4 / 27, -4 / 27, -4 / 27, 8 / 27, 8 / 27, 8 / 27, 8 / 27, 8 / 27, 8 / 27, 8 / 27, 8 / 27], ]) inverse = np.array([ [8 / 27, 0, 0, 0, -4 / 9, 0, 0, -4 / 9, 0, -4 / 9, 0, 0, 0, 0, 0, 0, 0, 2 / 3, 0, 2 / 3, 0, 0, 2 / 3, 0, 0, 0, -1], [2 / 27, 2 / 9, 0, 0, 2 / 9, 0, 0, -1 / 9, 0, -1 / 9, 0, 0, -1 / 3, 0, -1 / 3, 0, 0, -1 / 3, 0, -1 / 3, 0, 0, 1 / 6, 0, 0, 1 / 2, 1 / 2], [2 / 27, -2 / 9, 0, 0, 2 / 9, 0, 0, -1 / 9, 0, -1 / 9, 0, 0, 1 / 3, 0, 1 / 3, 0, 0, -1 / 3, 0, -1 / 3, 0, 0, 1 / 6, 0, 0, -1 / 2, 1 / 2], [2 / 27, 0, 2 / 9, 0, -1 / 9, 0, 0, 2 / 9, 0, -1 / 9, -1 / 3, 0, 0, 0, 0, 0, -1 / 3, -1 / 3, 0, 1 / 6, 0, 0, -1 / 3, 0, 1 / 2, 0, 1 / 2], [2 / 27, 0, -2 / 9, 0, -1 / 9, 0, 0, 2 / 9, 0, -1 / 9, 1 / 3, 0, 0, 0, 0, 0, 1 / 3, -1 / 3, 0, 1 / 6, 0, 0, -1 / 3, 0, -1 / 2, 0, 1 / 2], [2 / 27, 0, 0, 2 / 9, -1 / 9, 0, 0, -1 / 9, 0, 2 / 9, 0, -1 / 3, 0, 0, 0, -1 / 3, 0, 1 / 6, 0, -1 / 3, 0, 0, -1 / 3, 1 / 2, 0, 0, 1 / 2], [2 / 27, 0, 0, -2 / 9, -1 / 9, 0, 0, -1 / 9, 0, 2 / 9, 0, 1 / 3, 0, 0, 0, 1 / 3, 0, 1 / 6, 0, -1 / 3, 0, 0, -1 / 3, -1 / 2, 0, 0, 1 / 2], [1 / 54, 0, 1 / 18, 1 / 18, -1 / 36, 0, 0, 1 / 18, 1 / 6, 1 / 18, -1 / 12, -1 / 12, 0, 0, 0, 1 / 6, 1 / 6, -1 / 12, -1 / 4, -1 / 12, 0, 0, 1 / 6, -1 / 4, -1 / 4, 0, -1 / 4], [1 / 54, 0, -1 / 18, -1 / 18, -1 / 36, 0, 0, 1 / 18, 1 / 6, 1 / 18, 1 / 12, 1 / 12, 0, 0, 0, -1 / 6, -1 / 6, -1 / 12, -1 / 4, -1 / 12, 0, 0, 1 / 6, 1 / 4, 1 / 4, 0, -1 / 4], [1 / 54, 0, 1 / 18, -1 / 18, -1 / 36, 0, 0, 1 / 18, -1 / 6, 1 / 18, -1 / 12, 1 / 12, 0, 0, 0, -1 / 6, 1 / 6, -1 / 12, 1 / 4, -1 / 12, 0, 0, 1 / 6, 1 / 4, -1 / 4, 0, -1 / 4], [1 / 54, 0, -1 / 18, 1 / 18, -1 / 36, 0, 0, 1 / 18, -1 / 6, 1 / 18, 1 / 12, -1 / 12, 0, 0, 0, 1 / 6, -1 / 6, -1 / 12, 1 / 4, -1 / 12, 0, 0, 1 / 6, -1 / 4, 1 / 4, 0, -1 / 4], [1 / 54, 1 / 18, 0, 1 / 18, 1 / 18, 0, 1 / 6, -1 / 36, 0, 1 / 18, 0, 1 / 6, -1 / 12, 0, 1 / 6, -1 / 12, 0, -1 / 12, 0, 1 / 6, -1 / 4, 0, -1 / 12, -1 / 4, 0, -1 / 4, -1 / 4], [1 / 54, -1 / 18, 0, -1 / 18, 1 / 18, 0, 1 / 6, -1 / 36, 0, 1 / 18, 0, -1 / 6, 1 / 12, 0, -1 / 6, 1 / 12, 0, -1 / 12, 0, 1 / 6, -1 / 4, 0, -1 / 12, 1 / 4, 0, 1 / 4, -1 / 4], [1 / 54, 1 / 18, 0, -1 / 18, 1 / 18, 0, -1 / 6, -1 / 36, 0, 1 / 18, 0, -1 / 6, -1 / 12, 0, 1 / 6, 1 / 12, 0, -1 / 12, 0, 1 / 6, 1 / 4, 0, -1 / 12, 1 / 4, 0, -1 / 4, -1 / 4], [1 / 54, -1 / 18, 0, 1 / 18, 1 / 18, 0, -1 / 6, -1 / 36, 0, 1 / 18, 0, 1 / 6, 1 / 12, 0, -1 / 6, -1 / 12, 0, -1 / 12, 0, 1 / 6, 1 / 4, 0, -1 / 12, -1 / 4, 0, 1 / 4, -1 / 4], [1 / 54, 1 / 18, 1 / 18, 0, 1 / 18, 1 / 6, 0, 1 / 18, 0, -1 / 36, 1 / 6, 0, 1 / 6, 0, -1 / 12, 0, -1 / 12, 1 / 6, 0, -1 / 12, 0, -1 / 4, -1 / 12, 0, -1 / 4, -1 / 4, -1 / 4], [1 / 54, -1 / 18, -1 / 18, 0, 1 / 18, 1 / 6, 0, 1 / 18, 0, -1 / 36, -1 / 6, 0, -1 / 6, 0, 1 / 12, 0, 1 / 12, 1 / 6, 0, -1 / 12, 0, -1 / 4, -1 / 12, 0, 1 / 4, 1 / 4, -1 / 4], [1 / 54, 1 / 18, -1 / 18, 0, 1 / 18, -1 / 6, 0, 1 / 18, 0, -1 / 36, -1 / 6, 0, 1 / 6, 0, -1 / 12, 0, 1 / 12, 1 / 6, 0, -1 / 12, 0, 1 / 4, -1 / 12, 0, 1 / 4, -1 / 4, -1 / 4], [1 / 54, -1 / 18, 1 / 18, 0, 1 / 18, -1 / 6, 0, 1 / 18, 0, -1 / 36, 1 / 6, 0, -1 / 6, 0, 1 / 12, 0, -1 / 12, 1 / 6, 0, -1 / 12, 0, 1 / 4, -1 / 12, 0, -1 / 4, 1 / 4, -1 / 4], [1 / 216, 1 / 72, 1 / 72, 1 / 72, 1 / 72, 1 / 24, 1 / 24, 1 / 72, 1 / 24, 1 / 72, 1 / 24, 1 / 24, 1 / 24, 1 / 8, 1 / 24, 1 / 24, 1 / 24, 1 / 24, 1 / 8, 1 / 24, 1 / 8, 1 / 8, 1 / 24, 1 / 8, 1 / 8, 1 / 8, 1 / 8], [1 / 216, -1 / 72, -1 / 72, -1 / 72, 1 / 72, 1 / 24, 1 / 24, 1 / 72, 1 / 24, 1 / 72, -1 / 24, -1 / 24, -1 / 24, -1 / 8, -1 / 24, -1 / 24, -1 / 24, 1 / 24, 1 / 8, 1 / 24, 1 / 8, 1 / 8, 1 / 24, -1 / 8, -1 / 8, -1 / 8, 1 / 8], [1 / 216, 1 / 72, 1 / 72, -1 / 72, 1 / 72, 1 / 24, -1 / 24, 1 / 72, -1 / 24, 1 / 72, 1 / 24, -1 / 24, 1 / 24, -1 / 8, 1 / 24, -1 / 24, 1 / 24, 1 / 24, -1 / 8, 1 / 24, -1 / 8, 1 / 8, 1 / 24, -1 / 8, 1 / 8, 1 / 8, 1 / 8], [1 / 216, -1 / 72, -1 / 72, 1 / 72, 1 / 72, 1 / 24, -1 / 24, 1 / 72, -1 / 24, 1 / 72, -1 / 24, 1 / 24, -1 / 24, 1 / 8, -1 / 24, 1 / 24, -1 / 24, 1 / 24, -1 / 8, 1 / 24, -1 / 8, 1 / 8, 1 / 24, 1 / 8, -1 / 8, -1 / 8, 1 / 8], [1 / 216, 1 / 72, -1 / 72, 1 / 72, 1 / 72, -1 / 24, 1 / 24, 1 / 72, -1 / 24, 1 / 72, -1 / 24, 1 / 24, 1 / 24, -1 / 8, 1 / 24, 1 / 24, -1 / 24, 1 / 24, -1 / 8, 1 / 24, 1 / 8, -1 / 8, 1 / 24, 1 / 8, -1 / 8, 1 / 8, 1 / 8], [1 / 216, -1 / 72, 1 / 72, -1 / 72, 1 / 72, -1 / 24, 1 / 24, 1 / 72, -1 / 24, 1 / 72, 1 / 24, -1 / 24, -1 / 24, 1 / 8, -1 / 24, -1 / 24, 1 / 24, 1 / 24, -1 / 8, 1 / 24, 1 / 8, -1 / 8, 1 / 24, -1 / 8, 1 / 8, -1 / 8, 1 / 8], [1 / 216, 1 / 72, -1 / 72, -1 / 72, 1 / 72, -1 / 24, -1 / 24, 1 / 72, 1 / 24, 1 / 72, -1 / 24, -1 / 24, 1 / 24, 1 / 8, 1 / 24, -1 / 24, -1 / 24, 1 / 24, 1 / 8, 1 / 24, -1 / 8, -1 / 8, 1 / 24, -1 / 8, -1 / 8, 1 / 8, 1 / 8], [1 / 216, -1 / 72, 1 / 72, 1 / 72, 1 / 72, -1 / 24, -1 / 24, 1 / 72, 1 / 24, 1 / 72, 1 / 24, 1 / 24, -1 / 24, -1 / 8, -1 / 24, 1 / 24, 1 / 24, 1 / 24, 1 / 8, 1 / 24, -1 / 8, -1 / 8, 1 / 24, 1 / 8, 1 / 8, -1 / 8, 1 / 8], ]) names = ['rho', 'jx', 'jy', 'jz', 'Pi_xx', 'Pi_xy', 'PI_xz', 'PI_yy', 'PI_yz', 'PI_zz', 'J_xxy', 'J_xxz', 'J_xyy', 'J_xyz', 'J_xzz', 'J_yyz', 'J_yzz', 'J_xxyy', 'J_xxyz', 'J_xxzz', 'J_xyyz', 'J_xyzz', 'J_yyzz', 'J_xxyyz', 'J_xxyzz', 'J_xyyzz', 'J_xyxzyz'] supported_stencils = [D3Q27] def __init__(self, stencil: 'Stencil', context: 'Context'): super(D3Q27Hermite, self).__init__(stencil, context, self.names) self.matrix = self.context.convert_to_tensor(self.matrix) self.inverse = self.context.convert_to_tensor(self.inverse) def transform(self, f): return self.mv(self.matrix, f) def inverse_transform(self, m): return self.mv(self.inverse, m) def equilibrium(self, m, flow: 'Flow'): meq = torch.zeros_like(m) rho = m[0] jx = m[1] jy = m[2] jz = m[3] meq[0] = rho meq[1] = jx meq[2] = jy meq[3] = jz meq[4] = jx * jx / rho meq[5] = jx * jy / rho meq[6] = jx * jz / rho meq[7] = jy * jy / rho meq[8] = jy * jz / rho meq[9] = jz * jz / rho meq[10] = jx * jx * jy / rho ** 2 meq[11] = jx * jx * jz / rho ** 2 meq[12] = jx * jy * jy / rho ** 2 meq[13] = jx * jy * jz / rho ** 2 meq[14] = jx * jz * jz / rho ** 2 meq[15] = jy * jy * jz / rho ** 2 meq[16] = jy * jz * jz / rho ** 2 meq[17] = jx * jx * jy * jy / rho ** 3 meq[18] = jx * jx * jy * jz / rho ** 3 meq[19] = jx * jx * jz * jz / rho ** 3 meq[20] = jx * jy * jy * jz / rho ** 3 meq[21] = jx * jy * jz * jz / rho ** 3 meq[22] = jy * jy * jz * jz / rho ** 3 meq[23] = jx * jx * jy * jy * jz / rho ** 4 meq[24] = jx * jx * jy * jz * jz / rho ** 4 meq[25] = jx * jy * jy * jz * jz / rho ** 4 meq[26] = jx * jy * jx * jz * jy * jz / rho ** 5 return meq ================================================ FILE: lettuce/util/utility.py ================================================ import inspect as _inspect import torch as _torch __all__ = ['get_subclasses', 'LettuceException', 'LettuceWarning', 'InefficientCodeWarning', 'ExperimentalWarning' , 'torch_gradient', 'grid_fine_to_coarse', 'torch_jacobi', 'append_axes'] def get_subclasses(cls, module): for name, obj in _inspect.getmembers(module): if hasattr(obj, "__bases__") and cls in obj.__bases__: yield obj class LettuceException(Exception): pass class LettuceWarning(UserWarning): pass class InefficientCodeWarning(LettuceWarning): pass class ExperimentalWarning(LettuceWarning): pass def torch_gradient(f, dx=1, order=2): """ Function to calculate the first derivative of tensors. Orders O(h²); O(h⁴); O(h⁶) are implemented. Notes ----- See [1]_. The function only works for periodic domains References ---------- .. [1] Fornberg B. (1988) Generation of Finite Difference Formulas on Arbitrarily Spaced Grids, Mathematics of Computation 51, no. 184 : 699-706. `PDF `_. """ dim = f.ndim weights = { 2: [-1 / 2, 1 / 2, 0, 0, 0, 0], 4: [1 / 12, -2 / 3, 2 / 3, -1 / 12, 0, 0], 6: [-1 / 60, 3 / 20, -3 / 4, 3 / 4, -3 / 20, 1 / 60], } weight = weights.get(order) if dim == 2: dims = (0, 1) stencil = { 2: [[[1, 0], [-1, 0], [0, 0], [0, 0], [0, 0], [0, 0]], [[0, 1], [0, -1], [0, 0], [0, 0], [0, 0], [0, 0]]], 4: [[[2, 0], [1, 0], [-1, 0], [-2, 0], [0, 0], [0, 0]], [[0, 2], [0, 1], [0, -1], [0, -2], [0, 0], [0, 0]]], 6: [[[3, 0], [2, 0], [1, 0], [-1, 0], [-2, 0], [-3, 0]], [[0, 3], [0, 2], [0, 1], [0, -1], [0, -2], [0, -3]]], } shift = stencil.get(order) elif dim == 3: dims = (0, 1, 2) stencil = { 2: [[[1, 0, 0], [-1, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 1, 0], [0, -1, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 1], [0, 0, -1], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]], 4: [[[2, 0, 0], [1, 0, 0], [-1, 0, 0], [-2, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 2, 0], [0, 1, 0], [0, -1, 0], [0, -2, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 2], [0, 0, 1], [0, 0, -1], [0, 0, -2], [0, 0, 0], [0, 0, 0]]], 6: [[[3, 0, 0], [2, 0, 0], [1, 0, 0], [-1, 0, 0], [-2, 0, 0], [-3, 0, 0]], [[0, 3, 0], [0, 2, 0], [0, 1, 0], [0, -1, 0], [0, -2, 0], [0, -3, 0]], [[0, 0, 3], [0, 0, 2], [0, 0, 1], [0, 0, -1], [0, 0, -2], [0, 0, -3]]] } shift = stencil.get(order) else: raise LettuceException("Invalid dimension!") with _torch.no_grad(): out = _torch.cat(dim * [f[None, ...]]) for i in range(dim): out[i, ...] = ( weight[0] * f.roll(shifts=shift[i][0], dims=dims) + weight[1] * f.roll(shifts=shift[i][1], dims=dims) + weight[2] * f.roll(shifts=shift[i][2], dims=dims) + weight[3] * f.roll(shifts=shift[i][3], dims=dims) + weight[4] * f.roll(shifts=shift[i][4], dims=dims) + weight[5] * f.roll(shifts=shift[i][5], dims=dims) ) * _torch.tensor(1.0 / dx, dtype=f.dtype, device=f.device) return out def grid_fine_to_coarse(flow: 'Flow', f_fine, tau_fine, tau_coarse): if f_fine.shape.__len__() == 3: f_eq = flow.equilibrium(flow, rho=flow.rho(f_fine[:, ::2, ::2]), u=flow.u(f_fine[:, ::2, ::2])) f_neq = f_fine[:, ::2, ::2] - f_eq elif f_fine.shape.__len__() == 4: f_eq = flow.equilibrium(flow, rho=flow.rho(f_fine[:, ::2, ::2, ::2]), u=flow.u(f_fine[:, ::2, ::2, ::2])) f_neq = f_fine[:, ::2, ::2, ::2] - f_eq else: raise LettuceException("Invalid dimension!") f_coarse = f_eq + 2 * tau_coarse / tau_fine * f_neq return f_coarse def torch_jacobi(f, p, dx, dim, tol_abs=1e-10, max_num_steps=100000): """Jacobi solver for the Poisson pressure equation""" ## transform to torch.tensor # p = torch.tensor(p, device=device, dtype=torch.double) # dx = torch.tensor(dx, device=device, dtype=torch.double) error, it = 1, 0 while error > tol_abs and it < max_num_steps: it += 1 if dim == 2: # Difference quotient for second derivative O(h²) for index i=0,1 p = (f * (dx ** 2) - (p.roll(shifts=1, dims=0) + p.roll(shifts=1, dims=1) + p.roll(shifts=-1, dims=0) + p.roll(shifts=-1, dims=1))) * -1 / 4 residuum = f - (p.roll(shifts=1, dims=0) + p.roll(shifts=1, dims=1) + p.roll(shifts=-1, dims=0) + p.roll(shifts=-1, dims=1) - 4 * p) / (dx ** 2) if dim == 3: # Difference quotient for second derivative O(h²) for index i=0,1,2 p = (f * (dx ** 2) - (p.roll(shifts=1, dims=0) + p.roll(shifts=1, dims=1) + p.roll(shifts=1, dims=2) + p.roll(shifts=-1, dims=0) + p.roll(shifts=-1, dims=1) + p.roll(shifts=-1, dims=2))) * -1 / 6 residuum = f - (p.roll(shifts=1, dims=0) + p.roll(shifts=1, dims=1) + p.roll(shifts=1, dims=2) + p.roll(shifts=-1, dims=0) + p.roll(shifts=-1, dims=1) + p.roll(shifts=-1, dims=2) - 6 * p) / (dx ** 2) # Error is defined as the mean value of the residuum error = _torch.mean(residuum ** 2) return p def append_axes(array, n): index = (Ellipsis,) + (None,) * n return array[index] ================================================ FILE: native_cuda_synopsis.md ================================================ The native-cuda update introduced some major updates to the lettuce structure. Here, you find the main changes. Below, you find the steps to update your runfiles: ## Changes 1. `lt.Lattice` is deprecated. The `device` and `dtype` fields moved to `lt.Context`. The `Stencil` object or class (either works) is passed to `Flow.__init__`. 2. `lt.Streaming` is deprecated. Streaming is now always handled as defined in `lt.StandardStreaming`. 3. `lt.Stencil` is stored in a field of `lt.Flow` and `D` is now lower case. So, the Dimensions can be accessed via `flow.stencil.d` instead of `flow.units.lattice.D`. 4. Further changes: - `lt.Collision` does not neet `lattice` definition. - `reporters` was renamed to `reporter` - `no_collision_mask` now includes the boundary's identifier (important for custom flows). - `flow.u_pu` directly returns velocity field in physical units as a property. Same counts for `flow.p_pu` (pressure) and `flow.rho_pu` (density). - `units` (a `lt.UnitConverter`) can be called from both `lt.Simulation` and `lt.Flow` ## Updating runfiles 1. Move device and dtype declarations into lt.Context and remove all lattice declarations. 2. Move stencil declaration to lt.Flow and add context declaration. 3. Remove lt.Streaming initialization and occurences. 4. Rename reporters to reporter ================================================ FILE: pyproject.toml ================================================ [build-system] requires = ["setuptools>=61", "wheel", "versioneer>=0.28"] build-backend = "setuptools.build_meta" [project] name = "lettuce" version = "0.2.3" description = "Lattice Boltzmann Python GPU" readme = "README.md" requires-python = "==3.12.*" license = { text = "MIT" } dependencies = [ "click>=8.1.7", "h5py==3.11.0", "matplotlib>=3.9.2", "mmh3>=4.1.0", "numpy>=2.1.0", "packaging>=24.1", "pyevtk>=1.6.0", "pytest>=8.3.2", "setuptools>=78.1.1", "torch>=2.6.0", "vtk>=9.5.0", ] [project.scripts] lettuce = "lettuce.cli:main" [project.urls] Homepage = "https://github.com/lettucecfd/lettuce" [tool.setuptools.packages.find] include = ["lettuce*"] [tool.versioneer] VCS = "git" style = "pep440" versionfile_source = "lettuce/_version.py" versionfile_build = "lettuce/_version.py" tag_prefix = "" parentdir_prefix_version = "lettuce-" [project.optional-dependencies] cpu = [ "torch>=2.7.0", ] cu124 = [ "torch>=2.6.0", ] cu126 = [ "torch>=2.7.0", ] cu128 = [ "torch>=2.7.0", ] cu130 = [ "torch>=2.9.0", ] [tool.uv] conflicts = [ [ { extra = "cpu" }, { extra = "cu124" }, { extra = "cu126" }, { extra = "cu128" }, { extra = "cu130" }, ], ] [tool.uv.sources] torch = [ # Korrekte Marker, die an die Extra-Marker gebunden sind. # macOS wird standardmäßig auf CPU gesetzt { index = "pytorch-cpu", extra = "cpu", marker = "sys_platform == 'linux' or sys_platform == 'win32' or sys_platform == 'darwin'"}, { index = "pytorch-cu124", extra = "cu124", marker = "sys_platform == 'linux' or sys_platform == 'win32'"}, { index = "pytorch-cu126", extra = "cu126", marker = "sys_platform == 'linux' or sys_platform == 'win32'"}, { index = "pytorch-cu128", extra = "cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'"}, { index = "pytorch-cu130", extra = "cu130", marker = "sys_platform == 'linux' or sys_platform == 'win32'"}, ] [[tool.uv.index]] name = "pytorch" url = "https://download.pytorch.org/whl/cu124" explicit = true [[tool.uv.index]] name = "pytorch-cpu" url = "https://download.pytorch.org/whl/cpu" explicit = true [[tool.uv.index]] name = "pytorch-cu124" url = "https://download.pytorch.org/whl/cu124" explicit = true [[tool.uv.index]] name = "pytorch-cu126" url = "https://download.pytorch.org/whl/cu126" explicit = true [[tool.uv.index]] name = "pytorch-cu128" url = "https://download.pytorch.org/whl/cu128" explicit = true [[tool.uv.index]] name = "pytorch-cu130" url = "https://download.pytorch.org/whl/cu130" explicit = true ================================================ FILE: requirements.txt ================================================ # This file was autogenerated by uv via the following command: # uv pip compile pyproject.toml -o requirements.txt click==8.1.7 # via lettuce (pyproject.toml) contourpy==1.3.2 # via matplotlib cycler==0.12.1 # via matplotlib filelock==3.20.3 # via torch fonttools==4.61.0 # via matplotlib fsspec==2025.7.0 # via torch h5py==3.11.0 # via lettuce (pyproject.toml) iniconfig==2.1.0 # via pytest jinja2==3.1.6 # via torch kiwisolver==1.4.8 # via matplotlib markupsafe==3.0.2 # via jinja2 matplotlib==3.9.2 # via # lettuce (pyproject.toml) # vtk mmh3==4.1.0 # via lettuce (pyproject.toml) mpmath==1.3.0 # via sympy networkx==3.5 # via torch numpy==2.1.0 # via # lettuce (pyproject.toml) # contourpy # h5py # matplotlib # pyevtk nvidia-cublas-cu12==12.8.4.1 # via # nvidia-cudnn-cu12 # nvidia-cusolver-cu12 # torch nvidia-cuda-cupti-cu12==12.8.90 # via torch nvidia-cuda-nvrtc-cu12==12.8.93 # via torch nvidia-cuda-runtime-cu12==12.8.90 # via torch nvidia-cudnn-cu12==9.10.2.21 # via torch nvidia-cufft-cu12==11.3.3.83 # via torch nvidia-cufile-cu12==1.13.1.3 # via torch nvidia-curand-cu12==10.3.9.90 # via torch nvidia-cusolver-cu12==11.7.3.90 # via torch nvidia-cusparse-cu12==12.5.8.93 # via # nvidia-cusolver-cu12 # torch nvidia-cusparselt-cu12==0.7.1 # via torch nvidia-nccl-cu12==2.27.3 # via torch nvidia-nvjitlink-cu12==12.8.93 # via # nvidia-cufft-cu12 # nvidia-cusolver-cu12 # nvidia-cusparse-cu12 # torch nvidia-nvtx-cu12==12.8.90 # via torch packaging==24.1 # via # lettuce (pyproject.toml) # matplotlib # pytest pillow==11.3.0 # via matplotlib pluggy==1.6.0 # via pytest pyevtk==1.6.0 # via lettuce (pyproject.toml) pyparsing==3.2.3 # via matplotlib pytest==8.3.2 # via lettuce (pyproject.toml) python-dateutil==2.9.0.post0 # via matplotlib setuptools==80.9.0 # via # lettuce (pyproject.toml) # torch # triton six==1.17.0 # via python-dateutil sympy==1.14.0 # via torch torch==2.8.0 # via lettuce (pyproject.toml) triton==3.4.0 # via torch typing-extensions==4.14.1 # via torch vtk==9.5.0 # via lettuce (pyproject.toml) ================================================ FILE: tests/__init__.py ================================================ """Top-level package for tests.""" __all__ = ["conftest"] ================================================ FILE: tests/boundary/test_antibounceback_outlet_bc.py ================================================ from copy import copy from tests.conftest import * def test_anti_bounce_back_outlet(fix_configuration, fix_stencil): """Compares the result of the application of the boundary to f to the result using the formula taken from page 195 of "The lattice Boltzmann method" (2016 by Krüger et al.) if both are similar it is assumed to be working fine.""" device, dtype, use_native = fix_configuration if use_native: pytest.skip("This test does not depend on the native implementation.") context = Context(device=device, dtype=dtype, use_native=False) flow = TestFlow(context, resolution=fix_stencil.d * [16], reynolds_number=1, mach_number=0.1, stencil=fix_stencil) f = flow.f # generates reference value of f using non-dynamic formula f_ref = copy(f) u = flow.u() D = flow.stencil.d Q = flow.stencil.q if D == 3: direction = [1, 0, 0] if Q == 27: u_w = u[:, -1, :, :] + 0.5 * (u[:, -1, :, :] - u[:, -2, :, :]) u_w_norm = torch.norm(u_w, dim=0) for i in [1, 11, 13, 15, 17, 19, 21, 23, 25]: stencil_e_tensor = torch.tensor(flow.stencil.e[i], device=f.device, dtype=f.dtype) f_ref[flow.stencil.opposite[i], -1, :, :] = ( - f_ref[i, -1, :, :] + (flow.stencil.w[i] * flow.rho()[0, -1, :, :] * (2 + torch.einsum( 'c, cyz -> yz', stencil_e_tensor, u_w ) ** 2 / flow.stencil.cs ** 4 - (u_w_norm / flow.stencil.cs) ** 2)) ) if Q == 19: u_w = u[:, -1, :, :] + 0.5 * (u[:, -1, :, :] - u[:, -2, :, :]) u_w_norm = torch.norm(u_w, dim=0) for i in [1, 11, 13, 15, 17]: stencil_e_tensor = torch.tensor(flow.stencil.e[i], device=f.device, dtype=f.dtype) f_ref[flow.stencil.opposite[i], -1, :, :] = ( - f_ref[i, -1, :, :] + (flow.stencil.w[i] * flow.rho()[0, -1, :, :] * (2 + torch.einsum('c, cyz -> yz', stencil_e_tensor, u_w) ** 2 / flow.stencil.cs ** 4 - (u_w_norm / flow.stencil.cs) ** 2))) if D == 2 and Q == 9: direction = [1, 0] u_w = u[:, -1, :] + 0.5 * (u[:, -1, :] - u[:, -2, :]) u_w_norm = torch.norm(u_w, dim=0) for i in [1, 5, 8]: stencil_e_tensor = torch.tensor(flow.stencil.e[i], device=f.device, dtype=f.dtype) f_ref[flow.stencil.opposite[i], -1, :] = - f_ref[i, -1, :] + ( flow.stencil.w[i] * flow.rho()[0, -1, :] * (2 + torch.einsum('c, cy -> y', stencil_e_tensor, u_w) ** 2 / flow.stencil.cs ** 4 - ( u_w_norm / flow.stencil.cs) ** 2)) if D == 1 and Q == 3: direction = [1] u_w = u[:, -1] + 0.5 * (u[:, -1] - u[:, -2]) u_w_norm = torch.norm(u_w, dim=0) for i in [1]: stencil_e_tensor = torch.tensor(flow.stencil.e[i], device=f.device, dtype=f.dtype) f_ref[flow.stencil.opposite[i], -1] = ( - f_ref[i, -1] + (flow.stencil.w[i] * flow.rho()[0, -1] * (2 + torch.einsum('c, x -> x', stencil_e_tensor, u_w) ** 2 / flow.stencil.cs ** 4 - (u_w_norm / flow.stencil.cs) ** 2) ) ) # generates value from actual boundary implementation abb_outlet = AntiBounceBackOutlet(direction=direction, flow=flow) f = abb_outlet(flow) assert f.cpu().numpy() == pytest.approx(f_ref.cpu().numpy()) ================================================ FILE: tests/boundary/test_bc_masks.py ================================================ from tests.conftest import * def test_masks(fix_configuration): """test if masks are applied from boundary conditions""" device, dtype, native = fix_configuration context = Context(device=device, dtype=dtype, use_native=native) flow = Obstacle(context=context, resolution=[16, 16], reynolds_number=100, mach_number=0.1, domain_length_x=2) all_native_boundaries_in_Obstacle = sum([ _.native_available() for _ in flow.boundaries]) == flow.boundaries if native and not all_native_boundaries_in_Obstacle: pytest.skip("Some boundaries in Obstacle are still not available for " "cuda_native (probably AntiBounceBackOutlet)") flow.mask[1, 1] = 1 collision = BGKCollision(1.0) simulation = Simulation(flow, collision, []) assert simulation.no_streaming_mask.any() assert simulation.no_collision_mask.any() ================================================ FILE: tests/boundary/test_bounceback_bc.py ================================================ from tests.conftest import * from copy import copy def test_bounce_back_boundary(fix_stencil, fix_configuration): device, dtype, use_native = fix_configuration if use_native: pytest.skip("This test does not depend on the native implementation.") context = Context(device=device, dtype=dtype, use_native=False) flow = TestFlow(context, resolution=fix_stencil.d * [16], reynolds_number=1, mach_number=0.1, stencil=fix_stencil) f_old = copy(flow.f) mask = context.one_tensor(flow.resolution, dtype=bool) # will contain all # points bounce_back = BounceBackBoundary(mask) f_bounced = bounce_back(flow) assert (f_old[flow.stencil.opposite].cpu().numpy() == pytest.approx(f_bounced.cpu().numpy())) def test_bounce_back_boundary_not_applied_if_mask_empty(fix_stencil, fix_configuration): device, dtype, use_native = fix_configuration if use_native: pytest.skip("This test does not depend on the native implementation.") context = Context(device=device, dtype=dtype, use_native=False) flow = TestFlow(context, resolution=fix_stencil.d * [16], reynolds_number=1, mach_number=0.1, stencil=fix_stencil) f_old = copy(flow.f) mask = context.zero_tensor(flow.resolution, dtype=bool) # will not contain # any points bounce_back = BounceBackBoundary(mask) bounce_back(flow) assert (flow.f.cpu().numpy() == pytest.approx(f_old.cpu().numpy())) ================================================ FILE: tests/boundary/test_equilibrium_bc_outlet_p.py ================================================ from tests.conftest import * # TODO: Implement cuda_native generator and test suite def test_equilibrium_outlet_p_algorithm(fix_stencil, fix_configuration): """ Test for the equilibrium outlet p boundary algorithm. This test verifies that the algorithm correctly computes the equilibrium outlet pressure by comparing its output to manually calculated equilibrium values. """ device, dtype, use_native = fix_configuration if use_native: pytest.skip("This test does not depend on the native implementation.") context = Context(device=device, dtype=dtype, use_native=False) stencil = fix_stencil flow = TestFlow(context, stencil=stencil, resolution=16, reynolds_number=1, mach_number=0.1) direction = [0] * (stencil.d - 1) + [1] boundary_cpu = EquilibriumOutletP(flow=flow, direction=direction, rho_outlet=1.2) f_post_boundary = boundary_cpu(flow)[..., -1] u_slice = [stencil.d, *flow.resolution[:stencil.d - 1], 1] rho_slice = [1, *flow.resolution[:stencil.d - 1], 1] u = flow.units.convert_velocity_to_lu(context.one_tensor(u_slice)) rho = context.one_tensor(rho_slice) * 1.2 reference = flow.equilibrium(flow, rho=rho, u=u)[..., 0] assert reference.cpu().numpy() == pytest.approx( f_post_boundary.cpu().numpy(), rel=1e-2) # TODO rel is too big! ================================================ FILE: tests/boundary/test_equilibrium_bc_pu.py ================================================ from tests.conftest import * def moment_dims_params(): from itertools import product for stencil in stencil_params(): for p in product([1, 16], repeat=stencil.d): yield stencil, list(p) def moment_dims_ids(): buffer = [] for stencil, dims in moment_dims_params(): buffer.append(f"{stencil.__class__.__name__}-MomentDims" f"{'x'.join([str(d) for d in dims])}") return buffer @pytest.fixture(params=moment_dims_params(), ids=moment_dims_ids()) def fix_stencil_x_moment_dims(request): return request.param class DummyEquilibriumBoundary(EquilibriumBoundaryPU): # def make_no_collision_mask(self, shape: List[int], context: 'Context' # ) -> Optional[torch.Tensor]: # m = context.zero_tensor(shape, dtype=bool) # m[..., :1] = True # return m def make_no_streaming_mask(self, shape: List[int], context: 'Context') -> Optional[torch.Tensor]: return context.one_tensor(shape, dtype=bool) def test_equilibrium_boundary_pu_algorithm(fix_stencil, fix_configuration): """ Test for the equilibrium boundary algorithm. This test verifies that the algorithm correctly computes the equilibrium outlet pressure by comparing its output to manually calculated equilibrium values. """ device, dtype, native = fix_configuration context = Context(device=device, dtype=dtype, use_native=native) pressure = 0.01 velocity = context.convert_to_tensor([0.2] * fix_stencil.d) class DummyEQBC(TestFlow): @property def post_boundaries(self) -> List['Boundary']: m = self.context.zero_tensor(self.resolution, dtype=bool) m[..., :1] = True return [DummyEquilibriumBoundary(context=self.context, flow=self, mask=m, velocity=velocity, pressure=pressure)] flow_1 = DummyEQBC(context, resolution=fix_stencil.d * [16], reynolds_number=1, mach_number=0.1, stencil=fix_stencil) flow_2 = TestFlow(context, resolution=16, reynolds_number=1, mach_number=0.1, stencil=fix_stencil) simulation = Simulation(flow=flow_1, collision=NoCollision(), reporter=[]) simulation(num_steps=1) # manually calculate the forced feq rho = flow_2.units.convert_pressure_pu_to_density_lu(pressure) u = flow_2.units.convert_velocity_to_lu(velocity) feq = flow_2.equilibrium(flow_2, rho, u) # apply manually calculated feq to f flow_2.f[..., :1] = torch.einsum("q...,q...->q...", [feq, torch.ones_like( flow_2.f)])[..., :1] assert context.convert_to_ndarray(flow_1.f) == pytest.approx( context.convert_to_ndarray(flow_2.f)) def test_equilibrium_boundary_pu_tgv(fix_stencil, fix_configuration): if fix_stencil.d not in [2, 3]: pytest.skip("TGV Test can only be done in 2D or 3D.") device, dtype, native = fix_configuration context = Context(device=device, dtype=dtype, use_native=native) class DummyEQBC(TaylorGreenVortex): @property def post_boundaries(self): # u = self.context.one_tensor(self.stencil.d) * 0.1 u = self.context.one_tensor(self.stencil.d) * 0.1 p = 0 # self.context.zero_tensor([1, 1, 1]) m = self.context.zero_tensor(self.resolution, dtype=bool) m[..., :1] = True return [DummyEquilibriumBoundary( self.context, self, m, u, p)] flow_1 = DummyEQBC(context, resolution=16, reynolds_number=1, mach_number=0.1, stencil=fix_stencil) flow_2 = DummyTGV(context, resolution=16, reynolds_number=1, mach_number=0.1, stencil=fix_stencil) simulation = Simulation(flow=flow_1, collision=NoCollision(), reporter=[]) simulation(num_steps=1) pressure = 0 velocity = 0.1 * np.ones(flow_2.stencil.d) feq = flow_2.equilibrium( flow_2, context.convert_to_tensor( flow_2.units.convert_pressure_pu_to_density_lu(pressure)), context.convert_to_tensor( flow_2.units.convert_velocity_to_lu(velocity)) ) flow_2.f[..., :1] = torch.einsum("q,q...->q...", feq, torch.ones_like(flow_2.f))[..., :1] assert flow_1.f.cpu().numpy() == pytest.approx(flow_2.f.cpu().numpy()) def test_equilibrium_boundary_pu_native(fix_stencil_x_moment_dims, fix_dtype): if not torch.cuda.is_available(): pytest.skip(reason="CUDA is not available on this machine.") stencil, moment_dims = fix_stencil_x_moment_dims context_native = Context(device=torch.device('cuda'), dtype=fix_dtype, use_native=True) context_cpu = Context(device=torch.device('cpu'), dtype=fix_dtype, use_native=False) velocity = 0.2 * np.ones([stencil.d] + moment_dims) pressure = 0.02 * np.ones([1] + moment_dims) class DummyEQBC(TestFlow): @property def post_boundaries(self) -> List[Boundary]: m = self.context.zero_tensor(self.resolution, dtype=bool) m[..., :1] = True return [DummyEquilibriumBoundary(flow=self, context=self.context, mask=m, velocity=self.context. convert_to_tensor(velocity), pressure=self.context. convert_to_tensor(pressure))] flow_native = DummyEQBC(context_native, resolution=16, reynolds_number=1, mach_number=0.1, stencil=stencil) flow_cpu = DummyEQBC(context_cpu, resolution=16, reynolds_number=1, mach_number=0.1, stencil=stencil) simulation_native = Simulation(flow=flow_native, collision=NoCollision(), reporter=[]) simulation_cpu = Simulation(flow=flow_cpu, collision=NoCollision(), reporter=[]) simulation_native(num_steps=1) simulation_cpu(num_steps=1) assert flow_cpu.f.cpu().numpy() == pytest.approx( flow_native.f.cpu().numpy()) ================================================ FILE: tests/boundary/test_equilibrium_pressure_outlet.py ================================================ from tests.conftest import * def test_equilibrium_pressure_outlet(fix_configuration, fix_stencil): pytest.skip("TODO rtol is too big or simulation time too short!") if fix_stencil.d not in [2, 3]: pytest.skip("Obstacle to test EQ P outlet only implemented for 2D " "and 3D") outlet_directions = [[0, -1], [0, 1], [1, 0]] if fix_stencil.d == 3: outlet_directions = [direction + [0] for direction in outlet_directions] device, dtype, native = fix_configuration context = Context(device=device, dtype=dtype, use_native=native) class MyObstacle(Obstacle): @property def boundaries(self, *args): x, y, *z = self.grid z = z if z else None outlets = [EquilibriumOutletP(_, self) for _ in outlet_directions] inflow = [self.units.characteristic_velocity_pu, 0] \ if self.stencil.d == 2 \ else [self.units.characteristic_velocity_pu, 0, 0] return [ EquilibriumBoundaryPU( self.context, np.abs(x) < 1e-6, inflow ), *outlets, BounceBackBoundary(self.mask) ] flow = MyObstacle(context=context, resolution=[32] * fix_stencil.d, reynolds_number=10, mach_number=0.1, domain_length_x=3, stencil=fix_stencil) all_native_boundaries_in_MyObstacle = sum([ _.native_available() for _ in flow.boundaries]) == flow.boundaries if native and not all_native_boundaries_in_MyObstacle: pytest.skip("Some boundaries in Obstacle are still not available for " "cuda_native (probably EquilibriumOutletP)") # mask = np.zeros_like(flow.grid[0], dtype=bool) # mask[10:20, 10:20] = 1 flow.mask[10:20, 10:20] = True simulation = Simulation(flow, RegularizedCollision( flow.units.relaxation_parameter_lu), [VTKReporter(1, filename_base=f"./data/output_" f"D{fix_stencil.d}Q" f"{fix_stencil.q}")]) simulation(30) rho = flow.rho() u = flow.u() feq = flow.equilibrium(flow, torch.ones_like(rho), u) p = flow.units.convert_density_lu_to_pressure_pu(rho) zeros = torch.zeros_like(p[0, -1, :]) # TODO rtol is too big or simulation time too short! assert torch.allclose(zeros, p[:, -1, :], rtol=0, atol=1e-4) assert torch.allclose(zeros, p[:, :, 0], rtol=0, atol=1e-4) assert torch.allclose(zeros, p[:, :, -1], rtol=0, atol=1e-4) assert torch.allclose(feq[:, -1, 1:-1], feq[:, -2, 1:-1]) ================================================ FILE: tests/boundary/test_partiallysaturated_bc.py ================================================ from tests.conftest import * from copy import copy def test_partiallysaturated_boundary(fix_stencil, fix_configuration): device, dtype, use_native = fix_configuration if use_native: pytest.skip("This test does not depend on the native implementation.") context = Context(device=device, dtype=dtype, use_native=False) flow = TestFlow(context, resolution=fix_stencil.d * [16], reynolds_number=1, mach_number=0.1, stencil=fix_stencil) tau = flow.units.relaxation_parameter_lu collision = BGKCollision(tau) mask = context.one_tensor(flow.resolution, dtype=bool) # will contain all # points flow.boundaries = [PartiallySaturatedBC(mask, flow.units.relaxation_parameter_lu, saturation=0.5)] simulation = Simulation(flow, collision, []) simulation(2) def test_fullysaturated_like_neq_bounceback(fix_stencil, fix_configuration): device, dtype, use_native = fix_configuration if use_native: pytest.skip("This test does not depend on the native implementation.") if dtype == torch.float32: pytest.skip("This test does not work for single precision.") context = Context(device=device, dtype=dtype, use_native=False) flow = TestFlow(context, resolution=fix_stencil.d * [16], reynolds_number=1, mach_number=0.1, stencil=fix_stencil) flow.f = torch.rand_like(flow.f, dtype=dtype)*flow.f mask = context.one_tensor(flow.resolution, dtype=bool) # will contain all # points # create neq part tau = flow.units.relaxation_parameter_lu collision = BGKCollision(tau) flow.f = collision(flow) fneq_bounceback = (flow.equilibrium(flow) - flow.f)[flow.stencil.opposite] PS_BC = PartiallySaturatedBC(mask, tau, saturation=1.0) f_fullysaturated = PS_BC(flow) flow2 = copy(flow) flow2.f = f_fullysaturated fneq_fullysaturated = flow2.equilibrium(flow2) - flow2.f assert (fneq_bounceback.cpu().numpy() == pytest.approx(fneq_fullysaturated.cpu().numpy())) def test_partiallysaturated_boundary_not_applied_if_mask_empty(fix_stencil, fix_configuration): device, dtype, use_native = fix_configuration if use_native: pytest.skip("This test does not depend on the native implementation.") context = Context(device=device, dtype=dtype, use_native=False) flow = TestFlow(context, resolution=fix_stencil.d * [16], reynolds_number=1, mach_number=0.1, stencil=fix_stencil) tau = flow.units.relaxation_parameter_lu mask = context.zero_tensor(flow.resolution, dtype=bool) # will not contain # any points f_old = copy(flow.f) PS_BC = PartiallySaturatedBC(mask, tau, saturation=0.5) f_bounced = PS_BC(flow) assert (f_bounced.cpu().numpy() == pytest.approx(f_old.cpu().numpy())) ================================================ FILE: tests/collision/test_collision_conserves_mass.py ================================================ from tests.conftest import * def test_collision_conserves_mass(fix_conserving_collision, fix_configuration, fix_stencil): if (fix_conserving_collision == KBCCollision and type(fix_stencil) not in [D2Q9, D3Q27]): pytest.skip("KBCCollision only implemented for D2Q9 and D3Q27") device, dtype, use_native = fix_configuration context = Context(device=device, dtype=dtype, use_native=use_native) flow = TestFlow(context=context, resolution=[16] * fix_stencil.d, reynolds_number=100, mach_number=0.1, stencil=fix_stencil) collision = fix_conserving_collision(tau=0.51) f_collided = collision(flow) assert (flow.rho().cpu().numpy() == pytest.approx(flow.rho(f_collided).cpu().numpy())) ================================================ FILE: tests/collision/test_collision_conserves_momentum.py ================================================ from tests.conftest import * def test_collision_conserves_momentum(fix_conserving_collision, fix_configuration, fix_stencil): if (fix_conserving_collision == KBCCollision and type(fix_stencil) not in [D2Q9, D3Q27]): pytest.skip("KBCCollision only implemented for D2Q9 and D3Q27") device, dtype, use_native = fix_configuration if use_native: pytest.skip("This test does not depend on the native implementation.") context = Context(device=device, dtype=dtype, use_native=False) flow = TestFlow(context=context, resolution=[16] * fix_stencil.d, reynolds_number=100, mach_number=0.1, stencil=fix_stencil) collision = fix_conserving_collision(tau=0.51) f_collided = collision(flow) assert (flow.j().cpu().numpy() == pytest.approx(flow.j(f_collided).cpu().numpy(), abs=1e-5)) ================================================ FILE: tests/collision/test_collision_fixpoint_2x.py ================================================ from tests.conftest import * @pytest.mark.parametrize("Collision", [BGKCollision]) def test_collision_fixpoint_2x(Collision, fix_configuration, fix_stencil): device, dtype, use_native = fix_configuration if use_native: pytest.skip("This test does not depend on the native implementation.") context = Context(device=device, dtype=dtype, use_native=False) flow = TestFlow(context=context, resolution=[16] * fix_stencil.d, reynolds_number=100, mach_number=0.1, stencil=fix_stencil) collision = Collision(0.5) f_old = copy(flow.f) flow.f = collision(flow) flow.f = collision(flow) assert f_old.cpu().numpy() == pytest.approx(flow.f.cpu().numpy(), abs=1e-5) ================================================ FILE: tests/collision/test_collision_fixpoint_2x_MRT.py ================================================ from lettuce.util.moments import D2Q9Lallemand, D2Q9Dellar from tests.conftest import * @pytest.mark.parametrize("Transform", [D2Q9Lallemand, D2Q9Dellar]) def test_collision_fixpoint_2x_MRT(Transform, fix_configuration): # TODO: Migrate lattice methods to be independend of Flow or pass Flow # to Transform pytest.skip("Transform() currently still uses Lattice methods.") device, dtype, use_native = fix_configuration if use_native: pytest.skip("This test does not depend on the native implementation.") context = Context(device=device, dtype=dtype, use_native=False) np.random.seed(1) # arbitrary, but deterministic stencil = D2Q9() f = context.convert_to_tensor(np.random.random([stencil.q] + [3] * stencil.d)) f_old = copy(f) flow = DummyFlow(context, 1) collision = MRTCollision(Transform(stencil), [0.5] * 9, context=context) f = collision(collision(flow)) assert f.cpu().numpy() == pytest.approx(f_old.cpu().numpy(), abs=1e-5) ================================================ FILE: tests/collision/test_collision_optimizes_pseudo_entropy.py ================================================ from tests.conftest import * def test_collision_optimizes_pseudo_entropy(fix_configuration, fix_stencil): """checks if the pseudo-entropy of the KBC collision model is at least higher than the BGK pseudo-entropy""" if type(fix_stencil) not in [D2Q9, D3Q27]: pytest.skip("KBCCollision only implemented for D2Q9 and D3Q27.") device, dtype, use_native = fix_configuration if use_native: pytest.skip("This test does not depend on the native implementation.") context = Context(device=device, dtype=dtype, use_native=False) flow = TestFlow(context=context, resolution=[16] * fix_stencil.d, reynolds_number=100, mach_number=0.1, stencil=fix_stencil) np.random.seed(1) # arbitrary, but deterministic flow.f = flow.context.convert_to_tensor(np.random.random( [flow.stencil.q] + [3] * flow.stencil.d)) tau = 0.5003 coll_kbc = KBCCollision(tau) coll_bgk = BGKCollision(tau) f_kbc = coll_kbc(flow) f_bgk = coll_bgk(flow) entropy_kbc = flow.pseudo_entropy_local(f_kbc) entropy_bgk = flow.pseudo_entropy_local(f_bgk) assert (entropy_bgk < entropy_kbc).all() ================================================ FILE: tests/collision/test_collision_relaxes_shear_moments.py ================================================ from tests.conftest import * @pytest.mark.parametrize("Collision", [BGKCollision, TRTCollision, KBCCollision, RegularizedCollision]) def test_collision_relaxes_shear_moments(Collision, fix_configuration, fix_stencil): """checks whether the collision models relax the shear moments according to the prescribed relaxation time""" if Collision == KBCCollision and type(fix_stencil) not in [D2Q9, D3Q27]: pytest.skip("KBCCollision only implemented for D2Q9 and D3Q27") device, dtype, use_native = fix_configuration if use_native: pytest.skip("This test does not depend on the native implementation.") context = Context(device=device, dtype=dtype, use_native=False) flow = TestFlow(context=context, resolution=[16] * fix_stencil.d, reynolds_number=100, mach_number=0.1, stencil=fix_stencil) feq = flow.equilibrium(flow) shear_pre = flow.shear_tensor() shear_eq_pre = flow.shear_tensor(feq) tau = 0.6 coll = Collision(tau) f_post = coll(flow) shear_post = flow.shear_tensor(f_post) assert (shear_post.cpu().numpy() == pytest.approx((shear_pre - 1 / tau * (shear_pre - shear_eq_pre) ).cpu().numpy(), abs=1e-5)) ================================================ FILE: tests/collision/test_force.py ================================================ from tests.conftest import * import matplotlib.pyplot as plt @pytest.mark.parametrize("ForceType", [Guo, ShanChen]) def test_force(ForceType, fix_device): if fix_device.type == 'cuda' and not torch.cuda.is_available(): pytest.skip('CUDA not available') context = Context(device=fix_device, dtype=torch.float64, use_native=False) # TODO cuda_native Fails here, probably because this test requires uneven # grid points flow = PoiseuilleFlow2D(context=context, resolution=17, reynolds_number=1, mach_number=0.02, initialize_with_zeros=True) acceleration_lu = flow.units.convert_acceleration_to_lu(flow.acceleration) force = ForceType(flow=flow, tau=flow.units.relaxation_parameter_lu, acceleration=acceleration_lu) collision = BGKCollision(tau=flow.units.relaxation_parameter_lu, force=force) report_interval = 100 errorreporter = ErrorReporter(flow.analytic_solution, interval=report_interval, out=None) simulation = Simulation(flow=flow, collision=collision, reporter=[errorreporter]) simulation(1000) # compare with reference solution u_sim = flow.u(acceleration=acceleration_lu) u_sim = flow.units.convert_velocity_to_pu(u_sim) _, u_ref = flow.analytic_solution() # print(flow.boundaries) # print(flow.pre_boundaries) # print(flow.post_boundaries) fluidnodes = torch.eq(simulation.no_collision_mask, 0) # np.where(np.logical_not(flow.boundaries[0].mask.cpu())) print() print(("{:>15} " * 3).format("iterations", "error (u)", "error (p)")) for i in range(len(errorreporter.out)): error_u, error_p = np.abs(errorreporter.out[i]).tolist() print(f"{i * report_interval:15} {error_u:15.2e} {error_p:15.2e}") for dim in range(2): u_sim_i = context.convert_to_ndarray(u_sim[dim][fluidnodes]) u_ref_i = context.convert_to_ndarray(u_ref[dim][fluidnodes]) assert u_sim_i.max() == pytest.approx(u_ref_i.max(), rel=0.01) assert u_sim_i == pytest.approx(u_ref_i, rel=None, abs=0.01 * u_ref[0].max()) ================================================ FILE: tests/conftest.py ================================================ import pytest import numpy as np import torch from copy import copy from lettuce import * from lettuce.util.moments import * def dtype_params(): return [torch.float64, torch.float32] def dtype_ids(): return ['Float64', 'Float32'] def stencil1d_params(): return [D1Q3()] def stencil2d_params(): return [D2Q9()] def stencil3d_params(): return [D3Q15(), D3Q19(), D3Q27()] def stencil_params(): return stencil1d_params() + stencil2d_params() + stencil3d_params() def stencil1d_ids(): return [p.__class__.__name__ for p in stencil1d_params()] def stencil2d_ids(): return [p.__class__.__name__ for p in stencil2d_params()] def stencil3d_ids(): return [p.__class__.__name__ for p in stencil3d_params()] def stencil_ids(): return stencil1d_ids() + stencil2d_ids() + stencil3d_ids() def device_params(): return [torch.device('cpu'), torch.device('cuda')] def device_ids(): return ['CPU', 'CUDA'] def native_params(): return [True, False] def native_ids(): return ['Native', 'NonNative'] def configuration_params(): for device in device_params(): for dtype in dtype_params(): for native in native_params(): if not (device == torch.device('cpu') and native): yield device, dtype, native def configuration_ids(): buffer = [] for device in device_ids(): for dtype in dtype_ids(): for native in native_ids(): if not (device == 'CPU' and native == 'Native'): if native == 'Native': buffer.append(f"{device}-{dtype}-{native}") else: buffer.append(f"{device}-{dtype}") return buffer def transform_params(): Transforms = [ D1Q3Transform, D2Q9Dellar, D2Q9Lallemand, D3Q27Hermite ] Stencils = [ D1Q3, D2Q9, D2Q9, D3Q27 ] return zip(Transforms, Stencils) def transform_ids(): return ["D1Q3", "D2Q9Dellar", "D2Q9Lallemand", "D3Q27"] @pytest.fixture(params=transform_params(), ids=transform_ids()) def fix_transform(request): return request.param COLLISIONS = list(get_subclasses(Collision, lettuce.ext._collision)) @pytest.fixture(params=COLLISIONS) def fix_collision(request): return request.param def conserving_collision_params(): return [ BGKCollision, KBCCollision, TRTCollision, RegularizedCollision, SmagorinskyCollision ] def conserving_collision_ids(): return [ 'BGKCollision', 'KBCCollision', 'TRTCollision', 'RegularizedCollision', 'SmagorinskyCollision' ] @pytest.fixture(params=dtype_params(), ids=dtype_ids()) def fix_dtype(request): return request.param @pytest.fixture(params=stencil1d_params(), ids=stencil1d_ids()) def fix_stencil1d(request): return request.param @pytest.fixture(params=stencil2d_params(), ids=stencil2d_ids()) def fix_stencil2d(request): return request.param @pytest.fixture(params=stencil3d_params(), ids=stencil3d_ids()) def fix_stencil3d(request): return request.param @pytest.fixture(params=stencil_params(), ids=stencil_ids()) def fix_stencil(request): return request.param @pytest.fixture(params=device_params(), ids=device_ids()) def fix_device(request): if 'cuda' in request.param.type and not torch.cuda.is_available(): pytest.skip(reason="CUDA is not available on this machine.", allow_module_level=True) return request.param @pytest.fixture(params=native_params(), ids=native_ids()) def fix_native(request): if request.param[0] and not torch.cuda.is_available(): pytest.skip(reason="CUDA is not available on this machine.", allow_module_level=True) return request.param @pytest.fixture(params=configuration_params(), ids=configuration_ids()) def fix_configuration(request): if 'cuda' in request.param[0].type and not torch.cuda.is_available(): pytest.skip(reason="CUDA is not available on this machine.", allow_module_level=True) return request.param @pytest.fixture(params=conserving_collision_params(), ids=conserving_collision_ids()) def fix_conserving_collision(request): return request.param class TestFlow(ExtFlow): __test__ = False def __init__(self, context: 'Context', resolution: Union[int, List[int]], reynolds_number, mach_number, stencil: Optional['Stencil'] = None, equilibrium: Optional['Equilibrium'] = None): self._boundaries = [] super().__init__(context, resolution, reynolds_number, mach_number, stencil, equilibrium) def make_resolution(self, resolution: List[int], stencil: Optional['Stencil'] = None) -> List[int]: if isinstance(resolution, int): if stencil is None: return [resolution] else: return [resolution] * stencil.d else: return resolution def make_units(self, reynolds_number, mach_number, resolution: List[int]) -> 'UnitConversion': return UnitConversion(reynolds_number, mach_number, characteristic_length_lu=resolution[0]) def initial_pu(self) -> (float, Union[np.array, torch.Tensor]): u = 1.01 * np.ones([self.stencil.d] + self.resolution) p = 0.01 * np.ones([1] + self.resolution) return p, u @property def boundaries(self) -> List['Boundary']: return self._boundaries @boundaries.setter def boundaries(self, boundaries: List['Boundary']): self._boundaries = boundaries def DummyTGV(context: 'Context', resolution: Union[int, List[int]], reynolds_number, mach_number, stencil: Optional['Stencil'] = None, equilibrium: Optional['Equilibrium'] = None): return TaylorGreenVortex(context, resolution, reynolds_number, mach_number, stencil, equilibrium) class DummyFlow(ExtFlow): def __init__(self, context: Context, resolution: int = 16): ExtFlow.__init__(self, context, resolution, 1.0, 1.0) def make_resolution(self, resolution: Union[int, List[int]], stencil: Optional['Stencil'] = None) -> List[int]: return [resolution, resolution] if isinstance(resolution, int)\ else resolution def make_units(self, reynolds_number, mach_number, _: List[int] ) -> 'UnitConversion': return UnitConversion(reynolds_number=reynolds_number, mach_number=mach_number) def initial_pu(self) -> (float, List[float]): ... def initialize(self): ... @property def boundaries(self) -> List['Boundary']: return [] ================================================ FILE: tests/flow/test_divergence.py ================================================ from tests.conftest import * @pytest.mark.parametrize("stencil2d3d", [D2Q9(), D3Q27()]) def test_divergence(stencil2d3d, fix_configuration): device, dtype, use_native = fix_configuration context = Context(device=device, dtype=dtype, use_native=use_native) flow = DecayingTurbulence(context=context, resolution=[50] * stencil2d3d.d, reynolds_number=1, mach_number=0.05, ic_energy=0.5) collision = BGKCollision(tau=flow.units.relaxation_parameter_lu) simulation = Simulation(flow=flow, collision=collision, reporter=[]) ekin = (flow.units.convert_incompressible_energy_to_pu( torch.sum(flow.incompressible_energy())) * flow.units.convert_length_to_pu(1.0) ** stencil2d3d.d) u0 = flow.u_pu[0] u1 = flow.u_pu[1] dx = flow.units.convert_length_to_pu(1.0) grad_u0 = torch_gradient(u0, dx=dx, order=6).cpu().numpy() grad_u1 = torch_gradient(u1, dx=dx, order=6).cpu().numpy() divergence = np.sum(grad_u0[0] + grad_u1[1]) if stencil2d3d.d == 3: u2 = flow.u_pu[2] grad_u2 = torch_gradient(u2, dx=dx, order=6).cpu().numpy() divergence += np.sum(grad_u2[2]) assert (flow.ic_energy == pytest.approx(context.convert_to_ndarray(ekin), rel=1)) assert (0 == pytest.approx(divergence, abs=2e-3)) ================================================ FILE: tests/flow/test_einsum.py ================================================ from lettuce._flow import initialize_f_neq from tests.conftest import * class EinsumFlow(TestFlow): def __init__(self, context, res): super().__init__(context, resolution=res, reynolds_number=1, mach_number=0.01) def j(self, f: Optional[torch.Tensor] = None) -> torch.Tensor: """momentum""" return self.einsum("qd,q->d", [self.torch_stencil.e, self.f if f is None else f]) def incompressible_energy(self, f: Optional[torch.Tensor] = None ) -> torch.Tensor: """incompressible kinetic energy""" f = self.f if f is None else f return 0.5 * self.einsum("d,d->", [self.u(f), self.u(f)]) def entropy(self, f: Optional[torch.Tensor] = None) -> torch.Tensor: """entropy according to the H-theorem""" f = self.f if f is None else f f_log = -torch.log(self.einsum("q,q->q", [f, 1 / self.torch_stencil.w])) return self.einsum("q,q->", [f, f_log]) def pseudo_entropy_global(self, f: Optional[torch.Tensor] = None ) -> torch.Tensor: """pseudo_entropy derived by a Taylor expansion around the weights""" f = self.f if f is None else f f_w = self.einsum("q,q->q", [f, 1 / self.torch_stencil.w]) return self.rho() - self.einsum("q,q->", [f, f_w]) def pseudo_entropy_local(self, f: Optional[torch.Tensor] = None ) -> torch.Tensor: """pseudo_entropy derived by a Taylor expansion around the local equilibrium""" f = self.f if f is None else f f_feq = f / self.equilibrium(self) return self.rho(f) - self.einsum("q,q->", [f, f_feq]) def shear_tensor(self, f: Optional[torch.Tensor] = None) -> torch.Tensor: """computes the shear tensor of a given self.f in the sense Pi_{\alpha \beta} = f_i * e_{i \alpha} * e_{i \beta}""" shear = self.einsum("qa,qb->qab", [self.torch_stencil.e, self.torch_stencil.e]) shear = self.einsum("q,qab->ab", [self.f if f is None else f, shear]) return shear def einsum(self, equation, fields, *args) -> torch.Tensor: """Einstein summation on local fields.""" inputs, output = equation.split("->") inputs = inputs.split(",") for i, inp in enumerate(inputs): if len(inp) == len(fields[i].shape): pass elif len(inp) == len(fields[i].shape) - self.stencil.d: inputs[i] += "..." if not output.endswith("..."): output += "..." else: assert False, "Bad dimension." equation = ",".join(inputs) + "->" + output return torch.einsum(equation, fields, *args) def initialize_f_neq_floweinsum(flow: 'EinsumFlow'): """Initialize the distribution function values. The f^(1) contributions are approximated by finite differences. See Krüger et al. (2017). """ rho = flow.rho() u = flow.u() grad_u0 = torch_gradient(u[0], dx=1, order=6)[None, ...] grad_u1 = torch_gradient(u[1], dx=1, order=6)[None, ...] S = torch.cat([grad_u0, grad_u1]) if flow.stencil.d == 3: grad_u2 = torch_gradient(u[2], dx=1, order=6)[None, ...] S = torch.cat([S, grad_u2]) Pi_1 = (1.0 * flow.units.relaxation_parameter_lu * rho * S / flow.torch_stencil.cs ** 2) Q = (torch.einsum('ia,ib->iab', [flow.torch_stencil.e, flow.torch_stencil.e]) - torch.eye(flow.stencil.d, device=flow.torch_stencil.e.device) * flow.stencil.cs ** 2) Pi_1_Q = flow.einsum('ab,iab->i', [Pi_1, Q]) fneq = flow.einsum('i,i->i', [flow.torch_stencil.w, Pi_1_Q]) feq = flow.equilibrium(flow, rho, u) return feq - fneq def initialize_f_neq_torcheinsum(flow: 'Flow'): """Initialize the distribution function values. The f^(1) contributions are approximated by finite differences. See Krüger et al. (2017). """ rho = flow.rho() u = flow.u() grad_u0 = torch_gradient(u[0], dx=1, order=6)[None, ...] grad_u1 = torch_gradient(u[1], dx=1, order=6)[None, ...] S = torch.cat([grad_u0, grad_u1]) if flow.stencil.d == 3: grad_u2 = torch_gradient(u[2], dx=1, order=6)[None, ...] S = torch.cat([S, grad_u2]) Pi_1 = (1.0 * flow.units.relaxation_parameter_lu * rho * S / flow.torch_stencil.cs ** 2) Q = (torch.einsum('ia,ib->iab', [flow.torch_stencil.e, flow.torch_stencil.e]) - torch.eye(flow.stencil.d, device=flow.torch_stencil.e.device) * flow.stencil.cs ** 2) Pi_1_Q = torch.einsum('ab...,iab->i...', [Pi_1, Q]) fneq = torch.einsum('i,i...->i...', [flow.torch_stencil.w, Pi_1_Q]) feq = flow.equilibrium(flow, rho, u) return feq - fneq class RegularizedEinsum(RegularizedCollision): def __call__(self, flow: 'Flow'): if self.Q_matrix is None: self.tau = flow.units.relaxation_parameter_lu self.Q_matrix = torch.zeros([flow.stencil.q, flow.stencil.d, flow.stencil.d], device=flow.context.device, dtype=flow.context.dtype) for a in range(flow.stencil.q): for b in range(flow.stencil.d): for c in range(flow.stencil.d): self.Q_matrix[a, b, c] = ( flow.torch_stencil.e[a, b] * flow.torch_stencil.e[a, c]) if b == c: self.Q_matrix[a, b, c] -= (flow.torch_stencil.cs ** 2) feq = flow.equilibrium(flow) pi_neq = flow.shear_tensor(flow.f - feq) cs4 = flow.stencil.cs ** 4 pi_neq = flow.einsum("qab,ab->q", [self.Q_matrix, pi_neq]) pi_neq = flow.einsum("q,q->q", [flow.torch_stencil.w, pi_neq]) fi1 = pi_neq / (2 * cs4) f = feq + (1. - 1. / self.tau) * fi1 return f class GuoEinsum(Guo): def source_term(self, u): emu = append_axes(self.flow.torch_stencil.e, self.flow.torch_stencil.d) - u eu = self.flow.einsum("ib,b->i", [self.flow.torch_stencil.e, u]) eeu = self.flow.einsum("ia,i->ia", [self.flow.torch_stencil.e, eu]) emu_eeu = (emu / (self.flow.torch_stencil.cs ** 2) + eeu / (self.flow.torch_stencil.cs ** 4)) emu_eeuF = self.flow.einsum("ia,a->i", [emu_eeu, self.acceleration]) weemu_eeuF = (append_axes(self.flow.torch_stencil.w, self.flow.torch_stencil.d) * emu_eeuF) return (1 - 1 / (2 * self.tau)) * weemu_eeuF @pytest.mark.parametrize("fix_dim", [1, 2, 3]) def test_einsum(fix_dim): context = Context(dtype=torch.float64) flow = EinsumFlow(context, [16] * fix_dim) flow.f = torch.rand_like(flow.f) f0 = flow.j(flow.f) f1 = flow.einsum("qd,q->d", [flow.torch_stencil.e, flow.f]) f2 = torch.einsum("qd,q...->d...", [flow.torch_stencil.e, flow.f]) assert torch.allclose(f0, f1) assert torch.allclose(f1, f2) f0 = flow.incompressible_energy(flow.f) # also in QuadraticEquilibrium, IncompressibleQuadraticEquilibrium, # QuadraticEquilibriumLessMemory f1 = 0.5 * flow.einsum("d,d->", [flow.u(flow.f), flow.u(flow.f)]) f2 = 0.5 * torch.einsum("d...,d...->...", [flow.u(flow.f), flow.u(flow.f)]) assert torch.allclose(f0, f1) assert torch.allclose(f1, f2) f0 = flow.entropy() # also __call__ in EquilibriumBoundaryPU, EquilibriumOutletP, MRTCollision, # RegularizedCollision, QuadraticEquilibrium, # IncompressibleQuadraticEquilibrium, QuadraticEquilibriumLessMemory, # test_equilibrium_boundary_pu_algorithm f_log = -torch.log(flow.einsum("q,q->q", [flow.f, 1 / flow.torch_stencil.w])) f1 = flow.einsum("q,q->", [flow.f, f_log]) f_log = -torch.log(torch.einsum("q...,q...->q...", [flow.f, 1 / flow.torch_stencil.w])) f2 = torch.einsum("q...,q...->...", [flow.f, f_log]) assert torch.allclose(f0, f1) assert torch.allclose(f1, f2) f0 = flow.pseudo_entropy_global() f_w = flow.einsum("q,q->q", [flow.f, 1 / flow.torch_stencil.w]) f1 = flow.rho() - flow.einsum("q,q->", [flow.f, f_w]) f_w = torch.einsum("q...,q...->q...", [flow.f, 1 / flow.torch_stencil.w]) f2 = flow.rho() - torch.einsum("q...,q...->...", [flow.f, f_w]) assert torch.allclose(f0, f1) assert torch.allclose(f1, f2) f0 = flow.pseudo_entropy_local(flow.f) f_feq = flow.f / flow.equilibrium(flow) f1 = flow.rho(flow.f) - flow.einsum("q,q->", [flow.f, f_feq]) f2 = flow.rho(flow.f) - torch.einsum("q...,q...->...", [flow.f, f_feq]) assert torch.allclose(f0, f1) assert torch.allclose(f1, f2) f0 = flow.shear_tensor(flow.f) shear0 = flow.einsum("qa,qb->qab", [flow.torch_stencil.e, flow.torch_stencil.e]) shear0 = flow.einsum("q,qab->ab", [flow.f, shear0]) f1 = shear0 shear1 = torch.einsum("qa,qb->qab", [flow.torch_stencil.e, flow.torch_stencil.e]) shear1 = torch.einsum("q...,qab->ab...", [flow.f, shear1]) f2 = shear1 assert torch.allclose(f0, f1) assert torch.allclose(f1, f2) # in SmagorinskyCollision f0 = flow.einsum('ab,ab->', [shear0, shear0]) f1 = torch.einsum('ab...,ab...->...', [shear1, shear1]) assert torch.allclose(f0, f1) if fix_dim in [2, 3]: f0 = initialize_f_neq_floweinsum(flow) f1 = initialize_f_neq_torcheinsum(flow) f2 = initialize_f_neq(flow) assert torch.allclose(f0, f1) assert torch.allclose(f1, f2) # flow.einsum("qab,ab->q", [self.Q_matrix, pi_neq]) # torch.einsum("qab...,ab...->q...", [self.Q_matrix, pi_neq]) coll0 = RegularizedCollision(0.6) f0 = coll0(flow) coll1 = RegularizedEinsum(0.6) f1 = coll1(flow) assert torch.allclose(f0, f1) # from IncompressibleQuadraticEquilibrium exu0 = flow.einsum("qd,d->q", [flow.torch_stencil.e, flow.u()]) exu1 = torch.einsum("qd,d...->q...", [flow.torch_stencil.e, flow.u()]) assert torch.allclose(exu0, exu1) # Guo force source term a = [1] + [0] * (fix_dim - 1) guo0 = GuoEinsum(flow, 0.6, a) source_term0 = guo0.source_term(flow.u()) guo1 = Guo(flow, 0.6, a) source_term1 = guo1.source_term(flow.u()) assert torch.allclose(source_term0, source_term1) ================================================ FILE: tests/flow/test_flow.py ================================================ from tests.conftest import * @pytest.mark.parametrize("flowname", flow_by_name.keys()) def test_flow(flowname, fix_configuration): device, dtype, use_native = fix_configuration if use_native: pytest.skip("This test does not depend on the native implementation.") context = Context(device=device, dtype=dtype, use_native=False) IncompressibleFlow, stencil = flow_by_name[flowname] stencil = stencil() if callable(stencil) else stencil flow = IncompressibleFlow(context=context, resolution=[16] * stencil.d, reynolds_number=1, mach_number=0.05, stencil=stencil) collision = BGKCollision(tau=flow.units.relaxation_parameter_lu) simulation = Simulation(flow=flow, collision=collision, reporter=[]) simulation(1) ================================================ FILE: tests/flow/test_initialize_fneq.py ================================================ """Testing that initializing fneq improves TGV solution""" import pytest from tests.conftest import * @pytest.mark.parametrize("Case", [DecayingTurbulence, TaylorGreenVortex, DoublyPeriodicShear2D]) def test_initialize_fneq(fix_configuration, fix_stencil, Case): # fixture setup if fix_stencil.d == 1: pytest.skip("Testflows not working for 1D") if Case is DoublyPeriodicShear2D and fix_stencil.d != 2: pytest.skip("DoublyPeriodicShear2D only working for 2D") device, dtype, use_native = fix_configuration # if dtype == torch.float32: # pytest.skip("TGV is not accurate enough for single precision.") context = Context(device, dtype, use_native) # setting up flows with and without fneq if Case is DecayingTurbulence: randseed = np.random.randint(1) flow_neq = Case(context=context, resolution=32, reynolds_number=1000, mach_number=0.1, stencil=fix_stencil, initialize_pressure=False, randseed=randseed) flow_eq = Case(context=context, resolution=32, reynolds_number=1000, mach_number=0.1, stencil=fix_stencil, initialize_pressure=False, initialize_fneq=False, randseed=randseed) else: flow_neq = Case(context=context, resolution=32, reynolds_number=1000, mach_number=0.1, stencil=fix_stencil) flow_eq = Case(context=context, resolution=32, reynolds_number=1000, mach_number=0.1, stencil=fix_stencil, initialize_fneq=False) # initializing with and without fneq flow_eq.initialize() flow_neq.initialize() # comparing densitiy, velocity, and kinetic energy with and without fneq # (should be equal) rho_eq = flow_eq.rho() u_eq = flow_eq.u() ke_eq = flow_eq.incompressible_energy() rho_neq = flow_neq.rho() u_neq = flow_neq.u() ke_neq = flow_neq.incompressible_energy() tol = 1e-6 assert (context.convert_to_ndarray(rho_neq) == pytest.approx(context.convert_to_ndarray(rho_eq), rel=0.0, abs=tol)) assert (context.convert_to_ndarray(u_neq) == pytest.approx(context.convert_to_ndarray(u_eq), rel=0.0, abs=tol)) assert (context.convert_to_ndarray(ke_neq) == pytest.approx(context.convert_to_ndarray(ke_eq), rel=0.0, abs=tol)) # comparing to analytic solution of TGV if Case is TaylorGreenVortex and fix_stencil.d == 2: collision = BGKCollision(tau=flow_neq.units.relaxation_parameter_lu) error_reporter_neq = ErrorReporter(flow_neq.analytic_solution, interval=1, out=None) simulation_neq = Simulation(flow_neq, collision, [error_reporter_neq]) error_reporter_eq = ErrorReporter(flow_eq.analytic_solution, interval=1, out=None) simulation_eq = Simulation(flow_eq, collision, [error_reporter_eq]) simulation_neq(10) simulation_eq(10) error_u_neq, error_p_neq = np.mean(np.abs(error_reporter_neq.out), axis=0).tolist() error_u_eq, error_p_eq = np.mean(np.abs(error_reporter_eq.out), axis=0).tolist() assert error_u_neq < error_u_eq print(f"Relative improvement in velocity error: " f"{(error_u_neq - error_u_eq) / error_u_eq}") ================================================ FILE: tests/flow/test_initialize_pressure.py ================================================ from lettuce._flow import pressure_poisson from tests.conftest import * def test_initialize_pressure(fix_dtype): context = Context(device='cpu', dtype=fix_dtype, use_native=False) flow = TaylorGreenVortex(context=context, resolution=[32] * 2, reynolds_number=1000, mach_number=0.05, initialize_fneq=False) """First part: Testing initialize_pressure against analytic solution of TGV.""" # getting analytical p and u p0, u0 = flow.analytic_solution(t=0) # get TGV velocity field u_lu = flow.units.convert_velocity_to_lu(u0) # manually setting rho=1 (p=0) rho_lu = torch.ones_like(p0) # initializing as if not knowing analytic pressure solution f_before = flow.equilibrium(flow, rho=rho_lu, u=u_lu) p_before = flow.units.convert_density_lu_to_pressure_pu(flow.rho(f_before)) # getting poisson calculation for better f's rho_poisson = pressure_poisson(flow.units, u_lu, rho_lu, tol_abs=1e-6, max_num_steps=1000 ) f_after = flow.equilibrium(flow, rho=rho_poisson, u=u_lu) p_after = flow.units.convert_density_lu_to_pressure_pu(flow.rho(f_after)) # assert that pressure is much closer to analytic solution assert (p_after - p0).abs().sum() < 5e-2 * (p_before - p0).abs().sum() # feedback on deviation from analytic solution print() print("Average absolute deviation of zero-density from analytic " "solution: ", (p_before - p0).abs().mean().item()) print("Average absolute deviation of naive poisson pressure from analytic " "solution: ", (p_after - p0).abs().mean().item()) print("Relative improvement in this case (not representative): ", (p_after - p0).abs().mean().item() / (p_before - p0).abs().mean().item()) print("Maximum deviation of poisson pressure from analytic solution: ", (p_after - p0).abs().max().item()) # assert that pressure is converged up to 0.02 (max p) p0, p_before, p_after = [context.convert_to_ndarray(_) for _ in [p0, p_before, p_after]] assert p_after == pytest.approx(p0, rel=0.0, abs=2e-2) """Second part: Testing initialize_pressure to not break if initialized with proper pressure.""" # getting analytical p and u in LU p0, u0 = flow.analytic_solution(t=0) u_lu = flow.units.convert_velocity_to_lu(u0) rho_lu = flow.units.convert_pressure_pu_to_density_lu(p0) # initializing with analytic pressure solution f_before = flow.equilibrium(flow, rho=rho_lu, u=u_lu) p_before = flow.units.convert_density_lu_to_pressure_pu(flow.rho(f_before)) # getting poisson calculation for 'better' f's rho_poisson = pressure_poisson(flow.units, u_lu, rho_lu, tol_abs=1e-6, max_num_steps=1000 ) f_after = flow.equilibrium(flow, rho=rho_poisson, u=u_lu) p_after = flow.units.convert_density_lu_to_pressure_pu(flow.rho(f_after)) # feedback on deviation from analytic solution print() print("Average absolute deviation of smarter poisson pressure from " "analytic solution: ", (p_after - p0).abs().mean().item()) print("Maximum deviation of smarter poisson pressure from analytic " "solution: ", (p_after - p0).abs().max().item()) print("Average absolute deviation 'improvement' (negative -> worse " "solution): ", (p_before - p0).abs().mean().item() - (p_after - p0).abs().mean().item()) # assert that equilibrium pressure is similar to analytic solution p0, p_before, p_after = [context.convert_to_ndarray(_) for _ in [p0, p_before, p_after]] atol = 1e-10 if fix_dtype is torch.float64 else 1e-4 assert p_before == pytest.approx(p0, rel=0.0, abs=atol) # assert that initialized pressure is similar to analytic solution to 0.02 assert p_after == pytest.approx(p0, rel=0.0, abs=2e-2) ================================================ FILE: tests/flow/test_obstacle.py ================================================ from tests.conftest import * @pytest.mark.parametrize("stencil2d3d", [D2Q9(), D3Q27()]) def test_obstacle(stencil2d3d, fix_configuration): device, dtype, use_native = fix_configuration context = Context(device=device, dtype=dtype, use_native=use_native) nx = 32 ny = 16 nz = 16 resolution = [nx, ny, nz] if stencil2d3d.d == 3 else [nx, ny] mask = np.zeros(resolution) if stencil2d3d.d == 3: mask[3:6, 3:6, :] = 1 else: mask[3:6, 3:6] = 1 flow = Obstacle(context=context, resolution=resolution, reynolds_number=100, mach_number=0.1, domain_length_x=3) all_native_boundaries_in_Obstacle = sum([ _.native_available() for _ in flow.boundaries]) == flow.boundaries if use_native and not all_native_boundaries_in_Obstacle: pytest.skip("Some boundaries in Obstacle are still not available for " "cuda_native (probably AntiBounceBackOutlet)") collision = BGKCollision(tau=flow.units.relaxation_parameter_lu) flow.mask = mask != 0 simulation = Simulation(flow, collision, []) simulation(2) ================================================ FILE: tests/flow/test_pressure_poisson.py ================================================ from lettuce._flow import pressure_poisson from tests.conftest import * @pytest.mark.parametrize("Stencil", [D2Q9, D3Q27]) def test_pressure_poisson(fix_configuration, Stencil): if Stencil == D3Q27: pytest.skip("D3Q27 pressure_poisson does not work.") device, dtype, use_native = fix_configuration context = Context(device, dtype, use_native) flow = TaylorGreenVortex(context=context, resolution=32, reynolds_number=100, mach_number=0.05, stencil=Stencil) p0, u = flow.initial_pu() u = flow.units.convert_velocity_to_lu(u) rho0 = flow.units.convert_pressure_pu_to_density_lu(p0) rho = pressure_poisson(flow.units, u, torch.ones_like(rho0)) pfinal = context.convert_to_ndarray( flow.units.convert_density_lu_to_pressure_pu(rho)) p0 = context.convert_to_ndarray(p0) assert pfinal == pytest.approx(p0, rel=0.0, abs=0.05) ================================================ FILE: tests/moments/test_conserved_moments_d2q9.py ================================================ from lettuce.util.moments import D2Q9Dellar, D2Q9Lallemand, moment_tensor from tests.conftest import * @pytest.mark.parametrize("MomentSet", (D2Q9Dellar, D2Q9Lallemand)) def test_conserved_moments_d2q9(MomentSet): multiindices = np.array([ [0, 0], [1, 0], [0, 1] ]) m = moment_tensor(D2Q9().e, multiindices) assert m == pytest.approx(MomentSet.matrix[:3, :]) ================================================ FILE: tests/moments/test_getitem.py ================================================ from tests.conftest import * def test_getitem(fix_device, fix_dtype): moments = D2Q9Lallemand(D2Q9(), Context(fix_device, fix_dtype)) assert moments["jx", "jy"] == [1, 2] assert moments["rho"] == [0] ================================================ FILE: tests/moments/test_inverse_transform.py ================================================ from tests.conftest import * def test_inverse_transform(fix_transform): Transform, Stencil = fix_transform stencil = Stencil() context = Context() torch_stencil = TorchStencil(stencil, context) transform = Transform(torch_stencil, context) f = context.convert_to_tensor( np.random.random([stencil.q] + [3] * stencil.d)) original = context.convert_to_ndarray(f) retransformed = context.convert_to_ndarray( transform.inverse_transform(transform.transform(f))) assert retransformed == pytest.approx(original, abs=1e-5) ================================================ FILE: tests/moments/test_moment_equilibrium_D3Q27Hermite.py ================================================ from tests.conftest import * def test_moment_equilibrium_D3Q27Hermite(fix_device, fix_dtype): context = Context(fix_device, fix_dtype) stencil = D3Q27() moments = D3Q27Hermite(stencil, context) flow = TestFlow(context, 10, 1, 0.1, stencil=stencil) meq1 = context.convert_to_ndarray(moments.transform(flow.equilibrium( flow))) meq2 = context.convert_to_ndarray(moments.equilibrium(moments.transform( flow.f), flow)) same_moments = moments['rho', 'jx', 'jy', 'jz', 'Pi_xx', 'Pi_xy', 'PI_xz', 'PI_yy', 'PI_yz', 'PI_zz'] assert meq1[same_moments] == pytest.approx(meq2[same_moments], abs=1e-5) ================================================ FILE: tests/moments/test_moment_equilibrium_dellar.py ================================================ from tests.conftest import * def test_moment_equilibrium_dellar(fix_device, fix_dtype): context = Context(fix_device, fix_dtype) stencil = D2Q9() moments = D2Q9Dellar(stencil, context) flow = TestFlow(context, 10, 1, 0.1, stencil=stencil) meq1 = context.convert_to_ndarray(moments.transform(flow.equilibrium( flow))) meq2 = context.convert_to_ndarray(moments.equilibrium(moments.transform( flow.f), flow)) assert meq1 == pytest.approx(meq2, abs=1e-5) ================================================ FILE: tests/moments/test_moment_equilibrium_lallemand.py ================================================ from tests.conftest import * def test_moment_equilibrium_lallemand(fix_device, fix_dtype): context = Context(fix_device, fix_dtype) stencil = D2Q9() moments = D2Q9Lallemand(stencil, context) flow = TestFlow(context, 10, 1, 0.1, stencil=stencil) meq1 = context.convert_to_ndarray(moments.transform(flow.equilibrium( flow))) meq2 = context.convert_to_ndarray(moments.equilibrium(moments.transform( flow.f), flow)) same_moments = moments["rho", "jx", "jy", "qx", "qy"] assert meq1[same_moments] == pytest.approx(meq2[same_moments], abs=1e-5) ================================================ FILE: tests/moments/test_moments_density.py ================================================ from lettuce.util.moments import moment_tensor from tests.conftest import * def test_moments_density_array(fix_stencil): rho_tensor = moment_tensor(fix_stencil.e, np.array([0] * fix_stencil.d)) assert rho_tensor == pytest.approx(np.ones(fix_stencil.q)) def test_more_moments_density_array(fix_stencil): rho_tensor = moment_tensor(fix_stencil.e, np.array([[0] * fix_stencil.d])) assert rho_tensor == pytest.approx(np.ones((1, fix_stencil.q))) def test_moments_density_tensor(fix_stencil, fix_device): context = Context(fix_device) rho_tensor = moment_tensor(context.convert_to_tensor(fix_stencil.e), context.convert_to_tensor(([0] * fix_stencil.d))) assert rho_tensor.shape == (fix_stencil.q,) assert (context.convert_to_ndarray(rho_tensor) == pytest.approx(np.ones((fix_stencil.q)))) def test_more_moments_density_tensor(fix_stencil, fix_device): context = Context(fix_device) rho_tensor = moment_tensor(context.convert_to_tensor(fix_stencil.e), context.convert_to_tensor(([[0] * fix_stencil.d]))) assert rho_tensor.shape == (1, fix_stencil.q) assert (context.convert_to_ndarray(rho_tensor) == pytest.approx(np.ones((1, fix_stencil.q)))) ================================================ FILE: tests/moments/test_orthogonality.py ================================================ from tests.conftest import * def test_orthogonality(fix_device, fix_dtype, fix_transform): Transform, Stencil = fix_transform stencil = Stencil() if stencil.d == 1: pytest.skip("No othogonality for 1D") context = Context(fix_device, fix_dtype) tranform = Transform(stencil, context) M = context.convert_to_ndarray(tranform.matrix) if Transform is D2Q9Lallemand: Md = np.round(M @ M.T, 4) else: Md = np.round(M @ np.diag(stencil.w) @ M.T, 4) assert np.where(np.diag(np.ones(stencil.q)), Md != 0.0, Md == 0.0).all() ================================================ FILE: tests/native/__init__.py ================================================ from tests.conftest import * if not torch.cuda.is_available(): pytest.skip(reason="CUDA is not available on this machine, " "so cuda_native will not be tested..", allow_module_level=True) ================================================ FILE: tests/native/test_native_bgk_collision.py ================================================ import pytest import torch.cuda from lettuce import Context, UnitConversion, Simulation from lettuce.ext import ExtFlow, BGKCollision from typing import List, Union, Optional class DummyBGK(ExtFlow): def __init__(self, context: Context): ExtFlow.__init__(self, context, 16, 1.0, 1.0) def make_resolution(self, resolution: Union[int, List[int]], stencil: Optional['Stencil'] = None) -> List[int]: return [resolution, resolution] if isinstance(resolution, int) \ else resolution def make_units(self, reynolds_number, mach_number, _: List[int] ) -> 'UnitConversion': return UnitConversion(reynolds_number=reynolds_number, mach_number=mach_number) def initial_pu(self) -> (float, List[float]): ... def initialize(self): self.f[:, :, :] = 1.0 self.f[:, 2, 2] = 2.0 @property def boundaries(self) -> List['Boundary']: return [] def test_native_bgk_collision(): cpu_context = Context('cpu') cpu_flow = DummyBGK(cpu_context) assert cpu_flow.f.shape[0] == 9 assert cpu_flow.f.shape[1] == 16 assert cpu_flow.f.shape[2] == 16 native_context = Context(use_native=True) native_flow = DummyBGK(native_context) assert native_flow.f.shape[0] == 9 assert native_flow.f.shape[1] == 16 assert native_flow.f.shape[2] == 16 collision = BGKCollision(2.0) cpu_simulation = Simulation(cpu_flow, collision, []) native_simulation = Simulation(native_flow, collision, []) assert cpu_flow.f.cpu().numpy() == pytest.approx( native_flow.f.cpu().numpy()) cpu_simulation(1) native_simulation(1) # for i in range(9): # for j in range(16): # print() # print(f"[{i}, {j}, :] cpu ", cpu_flow.f.cpu().numpy()[i, j, :]) # print(f"[{i}, {j}, :] nat ", native_flow.f.cpu().numpy()[i, j, :] # ) for i in range(9): for j in range(16): assert cpu_flow.f.cpu().numpy()[i, j, :] == pytest.approx( native_flow.f.cpu().numpy()[i, j, :]), f"[{i}, {j}, :]" # print() # cpu_index = int((cpu_flow.f.cpu()[i, :, :] == float(i + 1)). # nonzero(as_tuple=True)[0]) # native_index = int((native_flow.f.cpu()[i, :, :] == float(i + 1)). # nonzero(as_tuple=True)[0]) # print(f"cpu distribution {i} row {cpu_index}: ", cpu_flow.f.cpu() # [i, :, :][cpu_index]) # print(f"cuda_native distribution {i} row {native_index}: ", # native_flow.f.cpu()[i, :, :][native_index]) ================================================ FILE: tests/native/test_native_bounce_back.py ================================================ from typing import List, Optional import pytest import torch.cuda from lettuce import Context, Simulation from lettuce.ext import NoCollision, BounceBackBoundary from tests.conftest import DummyFlow class MyBounceBackBoundary(BounceBackBoundary): def make_no_collision_mask(self, shape: List[int], context: 'Context' ) -> Optional[torch.Tensor]: mask = context.zero_tensor(shape, dtype=bool) mask[0, :] = True mask[:, 0] = True mask[2:, :] = True mask[:, 2:] = True return mask class DummyBBBC(DummyFlow): def initialize(self): self.f.zero_() self.f[:, 1, 1] = 1.0 @property def boundaries(self) -> List['Boundary']: return [MyBounceBackBoundary(torch.ones(self.resolution))] def test_native_bounce_back(): cpu_context = Context(torch.device('cpu'), use_native=False) cpu_flow = DummyBBBC(cpu_context) assert cpu_flow.f.shape[0] == 9 assert cpu_flow.f.shape[1] == 16 assert cpu_flow.f.shape[2] == 16 native_context = Context(torch.device('cuda'), use_native=True) native_flow = DummyBBBC(native_context) assert native_flow.f.shape[0] == 9 assert native_flow.f.shape[1] == 16 assert native_flow.f.shape[2] == 16 collision = NoCollision() cpu_simulation = Simulation(cpu_flow, collision, []) native_simulation = Simulation(native_flow, collision, []) assert cpu_flow.f.cpu().numpy() == pytest.approx( native_flow.f.cpu().numpy()) # print() # for i in range(9): # print(native_flow.f.cpu()[i, 0:4, 0:4]) cpu_simulation(1) native_simulation(1) assert cpu_flow.f.cpu().numpy() == pytest.approx( native_flow.f.cpu().numpy()) # print() # for i in range(9): # print(native_flow.f.cpu()[i, 0:4, 0:4]) cpu_simulation(1) native_simulation(1) assert cpu_flow.f.cpu().numpy() == pytest.approx( native_flow.f.cpu().numpy()) # print() # for i in range(9): # print(native_flow.f.cpu()[i, 0:4, 0:4]) ================================================ FILE: tests/native/test_native_equilibrium_pu.py ================================================ """ Test boundary conditions. """ import pytest from lettuce import * from lettuce.ext import * from tests.conftest import DummyTGV class my_equilibrium_boundary_mask(EquilibriumBoundaryPU): def make_no_collision_mask(self, shape: List[int], context: 'Context' ) -> Optional[torch.Tensor]: a = context.zero_tensor(shape, dtype=bool) a[:, 1] = True return a # return None def make_no_streaming_mask(self, shape: List[int], context: 'Context' ) -> Optional[torch.Tensor]: return context.one_tensor(shape, dtype=bool) # return None def test_equilibrium_boundary_pu_native(): context_native = Context(device=torch.device('cuda'), dtype=torch.float64, use_native=True) context_cpu = Context(device=torch.device('cpu'), dtype=torch.float64, use_native=False) flow_native = DummyTGV(context_native, resolution=[16, 16], reynolds_number=1, mach_number=0.1) flow_cpu = DummyTGV(context_cpu, resolution=[16, 16], reynolds_number=1, mach_number=0.1) '''Works as expected''' u = np.ones([2, 16, 16]) rho = np.ones([16, 16]) '''Does not work''' # u = np.ones([2,16,1]) # rho = np.ones([16,1]) '''Does not work''' # u = np.ones([2,1,1]) # rho = np.ones([1,1]) boundary_native = my_equilibrium_boundary_mask(context_native, flow_native, u, rho) boundary_cpu = my_equilibrium_boundary_mask(context_cpu, flow_cpu, u, rho) flow_native.boundaries.append(boundary_native) flow_cpu.boundaries.append(boundary_cpu) simulation_native = Simulation(flow=flow_native, collision=NoCollision(), reporter=[]) simulation_cpu = Simulation(flow=flow_cpu, collision=NoCollision(), reporter=[]) simulation_native(num_steps=1) simulation_cpu(num_steps=1) print() print("Print the first 4 rows/columns of the velocities ux and uy for " "better visualization and comparison:") print("Native:") print(flow_native.velocity[:, :4, :4]) print("CPU:") print(flow_cpu.velocity[:, :4, :4]) assert flow_cpu.f.cpu().numpy() == pytest.approx( flow_native.f.cpu().numpy(), rel=1e-6) ================================================ FILE: tests/native/test_native_no_streaming_mask.py ================================================ from tests.conftest import * def test_native_no_streaming_mask(): """test if """ if not torch.cuda.is_available(): pytest.skip("Native test skipped") context = Context(dtype=torch.float32, device=torch.device("cuda"), use_native=True) flow = TestFlow(context, 16, 1, 0.01) collision = NoCollision() simulation = Simulation(flow, collision, []) simulation.no_streaming_mask = context.zero_tensor(flow.resolution, dtype=bool) f0 = copy(flow.f) simulation(64) f1 = flow.f assert torch.isclose(f0, f1).all() ================================================ FILE: tests/native/test_native_streaming.py ================================================ import pytest import torch.cuda from lettuce import Context, Simulation from lettuce.ext import NoCollision from tests.conftest import DummyFlow class DummyStreaming(DummyFlow): def initialize(self): self.f.zero_() self.f[0, 1, 1] = 1.0 self.f[1, 2, 2] = 2.0 self.f[2, 3, 3] = 3.0 self.f[3, 4, 4] = 4.0 self.f[4, 5, 5] = 5.0 self.f[5, 6, 6] = 6.0 self.f[6, 7, 7] = 7.0 self.f[7, 8, 8] = 8.0 self.f[8, 9, 9] = 9.0 def test_native_streaming(): cpu_context = Context(torch.device('cpu'), use_native=False) cpu_flow = DummyStreaming(cpu_context) assert cpu_flow.f.shape[0] == 9 assert cpu_flow.f.shape[1] == 16 assert cpu_flow.f.shape[2] == 16 native_context = Context(torch.device('cuda'), use_native=True) native_flow = DummyStreaming(native_context) assert native_flow.f.shape[0] == 9 assert native_flow.f.shape[1] == 16 assert native_flow.f.shape[2] == 16 collision = NoCollision() cpu_simulation = Simulation(cpu_flow, collision, []) native_simulation = Simulation(native_flow, collision, []) assert cpu_flow.f.cpu().numpy() == pytest.approx( native_flow.f.cpu().numpy()) cpu_simulation(1) native_simulation(1) assert cpu_flow.f.cpu().numpy() == pytest.approx( native_flow.f.cpu().numpy()) # for i in range(9): # print() # cpu_index = int((cpu_flow.f.cpu()[i, :, :] == float(i + 1)).nonzero( # as_tuple=True)[0]) # native_index = int((native_flow.f.cpu()[i, :, :] == # float(i + 1)).nonzero(as_tuple=True)[0]) # print(f"cpu distribution {i} row {cpu_index}: ", cpu_flow.f.cpu( # )[i, :, :][cpu_index]) # print(f"cuda_native distribution {i} row {native_index}: ", # native_flow.f.cpu()[i, :, :][native_index]) ================================================ FILE: tests/native/test_native_streaming_strategy.py ================================================ import pytest import torch.cuda from lettuce import Context, Simulation, StreamingStrategy, NoCollision from lettuce.ext import BGKCollision from .test_native_bgk_collision import DummyBGK @pytest.mark.parametrize("streaming_strategy", [StreamingStrategy.NO_STREAMING, StreamingStrategy.PRE_STREAMING, StreamingStrategy.POST_STREAMING, StreamingStrategy.DOUBLE_STREAMING]) def test_native_streaming_strategy(streaming_strategy): cpu_context = Context('cpu') cpu_flow = DummyBGK(cpu_context) assert cpu_flow.f.shape[0] == 9 assert cpu_flow.f.shape[1] == 16 assert cpu_flow.f.shape[2] == 16 native_context = Context(use_native=True) native_flow = DummyBGK(native_context) assert native_flow.f.shape[0] == 9 assert native_flow.f.shape[1] == 16 assert native_flow.f.shape[2] == 16 collision = BGKCollision(2.0) cpu_simulation = Simulation(cpu_flow, collision, [], streaming_strategy) native_simulation = Simulation(native_flow, collision, [], streaming_strategy) assert cpu_flow.f.cpu().numpy() == pytest.approx(native_flow.f.cpu().numpy()) cpu_simulation(1) native_simulation(1) print() print(f"native_flow:") for i in range(9): for x in range(16): for y in range(16): print(native_flow.f.cpu().numpy()[i, x, y], sep=' ', end=' ') print() print() print() print() print(f"cpu_flow:") for i in range(9): for x in range(16): for y in range(16): print(cpu_flow.f.cpu().numpy()[i, x, y], sep=' ', end=' ') print() print() print() for i in range(9): for j in range(16): assert cpu_flow.f.cpu().numpy()[i, j, :] == pytest.approx( native_flow.f.cpu().numpy()[i, j, :]), f"[{i}, {j}, :]" ================================================ FILE: tests/reporter/test_HDF5Reporter.py ================================================ import os from tests.conftest import * def test_HDF5Reporter(tmpdir): step = 3 context = Context(device='cpu') flow = TaylorGreenVortex(context=context, resolution=[16, 16], reynolds_number=10, mach_number=0.05) collision = BGKCollision(tau=flow.units.relaxation_parameter_lu) simulation = Simulation(flow=flow, collision=collision, reporter=[]) hdf5_reporter = HDF5Reporter(flow=flow, collision=collision, interval=step, filebase=tmpdir / "output") simulation.reporter.append(hdf5_reporter) simulation(step) assert os.path.isfile(tmpdir / "output.h5") dataset_train = LettuceDataset(filebase=tmpdir / "output.h5", target=True) train_loader = torch.utils.data.DataLoader(dataset_train, shuffle=False) print(dataset_train) for (f, target, idx) in train_loader: assert idx in (0, 1, 2) assert f.shape == (1, 9, 16, 16) assert target.shape == (1, 9, 16, 16) ================================================ FILE: tests/reporter/test_energy_spectrum.py ================================================ from tests.conftest import * @pytest.mark.parametrize("flowname", flow_by_name.keys()) def test_energy_spectrum(tmpdir, flowname): context = Context(device=torch.device('cpu')) IncompressibleFlow, stencil = flow_by_name[flowname] if IncompressibleFlow is CouetteFlow2D: pytest.skip("CouetteFlow2D has nan energy spectrum") stencil = stencil() if callable(stencil) else stencil flow = IncompressibleFlow(context=context, resolution=[20] * stencil.d, reynolds_number=1600, mach_number=0.01, stencil=stencil) collision = BGKCollision(tau=flow.units.relaxation_parameter_lu) spectrum = context.convert_to_ndarray(EnergySpectrum(flow)()) energy = IncompressibleKineticEnergy(flow)().item() if Flow == DecayingTurbulence: # check that the reported spectrum agrees with the spectrum used for # initialization ek_ref, _ = flow.energy_spectrum assert (spectrum == pytest.approx(ek_ref, rel=0.0, abs=0.1)) if Flow == TaylorGreenVortex: # check that flow has only one mode ek_max = sorted(spectrum, reverse=True) assert ek_max[0] * 1e-5 > ek_max[1] assert (energy == pytest.approx(np.sum(spectrum), rel=1e-3, abs=1e-1)) ================================================ FILE: tests/reporter/test_generic_reporters.py ================================================ from tests.conftest import * @pytest.mark.parametrize("Observable", [Enstrophy, EnergySpectrum, MaximumVelocity, IncompressibleKineticEnergy, Mass]) @pytest.mark.parametrize("Case", [[32]*2, [32]*3]) def test_generic_reporters(Observable, Case, fix_configuration): device, dtype, use_native = fix_configuration context = Context(device=device, dtype=dtype, use_native=use_native) flow = TaylorGreenVortex(context, Case, 10000, 0.05) collision = BGKCollision(tau=flow.units.relaxation_parameter_lu) reporter = ObservableReporter(Observable(flow), interval=1, out=None) simulation = Simulation(flow, collision, [reporter]) simulation(2) values = np.asarray(reporter.out) if Observable is EnergySpectrum: assert values[1, 2:] == pytest.approx(values[0, 2:], rel=0.0, abs=values[0, 2:].sum() / 10) else: assert values[1, 2] == pytest.approx(values[0, 2], rel=0.05) ================================================ FILE: tests/reporter/test_high_ma_reporter.py ================================================ import os from tests.conftest import * def test_high_ma_reporter(tmpdir): flow = Obstacle(context=Context(), resolution=[16, 16], reynolds_number=10, mach_number=0.2, domain_length_x=16) flow.mask = ((2 < flow.grid[0]) & (flow.grid[0] < 10) & (2 < flow.grid[1]) & (flow.grid[1] < 10)) collision = BGKCollision(tau=flow.units.relaxation_parameter_lu) reporter = HighMaReporter(1, outdir=tmpdir, vtk_out=True) simulation = BreakableSimulation(flow, collision, [reporter]) simulation(100) assert os.path.isfile(tmpdir / "HighMa_reporter.log") assert os.path.isfile(tmpdir / "HighMa_frame_00000013.vtr") assert flow.i > 100 assert reporter.failed_iteration == 13 ================================================ FILE: tests/reporter/test_nan_reporter.py ================================================ import os from tests.conftest import * def test_nan_reporter(tmpdir): flow = TaylorGreenVortex(context=Context(), resolution=[16, 16], reynolds_number=1e12, mach_number=1) collision = BGKCollision(tau=flow.units.relaxation_parameter_lu) reporter = NaNReporter(10, outdir=tmpdir, vtk_out=True) simulation = BreakableSimulation(flow, collision, [reporter]) simulation(100) assert os.path.isfile(tmpdir / "NaN_reporter.log") print(os.listdir(tmpdir)) assert os.path.isfile(tmpdir / "NaN_frame_00000070.vtr") or os.path.isfile(tmpdir / "NaN_frame_00000080.vtr") assert flow.i > 100 assert reporter.failed_iteration in [70, 80] ================================================ FILE: tests/reporter/test_vtk_reporter_mask.py ================================================ import os from tests.conftest import * def test_vtk_reporter_mask(tmpdir): flow = PoiseuilleFlow2D(context=Context(), resolution=16, reynolds_number=10, mach_number=0.05) collision = BGKCollision(tau=flow.units.relaxation_parameter_lu) simulation = Simulation(flow, collision, []) vtk_reporter = VTKReporter(interval=1, filename_base=tmpdir / "output") simulation.reporter.append(vtk_reporter) vtk_reporter.output_mask(simulation) simulation(2) assert os.path.isfile(tmpdir / "output_mask.vtr") assert os.path.isfile(tmpdir / "output_00000000.vtr") assert os.path.isfile(tmpdir / "output_00000001.vtr") ================================================ FILE: tests/reporter/test_vtk_reporter_no_mask.py ================================================ import os from tests.conftest import * def test_vtk_reporter_no_mask(tmpdir): context = Context(device="cpu") flow = TaylorGreenVortex(context=context, resolution=[16, 16], reynolds_number=10, mach_number=0.05) collision = BGKCollision(tau=flow.units.relaxation_parameter_lu) simulation = Simulation(flow, collision, []) vtk_reporter = VTKReporter(interval=1, filename_base=tmpdir / "output") simulation.reporter.append(vtk_reporter) simulation(2) assert os.path.isfile(tmpdir / "output_00000000.vtr") assert os.path.isfile(tmpdir / "output_00000001.vtr") ================================================ FILE: tests/reporter/test_write_image.py ================================================ from tests.conftest import * import os def test_write_image(tmpdir): # pytest.skip("matplotlib not working") context = Context() flow = TaylorGreenVortex(context=context, resolution=[16, 16], reynolds_number=10, mach_number=0.05) p, _ = flow.initial_pu() write_image(tmpdir / "p.png", context.convert_to_ndarray(p[0])) print(tmpdir / "p.png") assert os.path.isfile(tmpdir / "p.png") ================================================ FILE: tests/reporter/test_write_vtk.py ================================================ from tests.conftest import * from lettuce.ext._reporter.vtk_reporter import write_vtk import os def test_write_vtk(tmpdir): context = Context(device='cpu') flow = TaylorGreenVortex(context, resolution=[16, 16], reynolds_number=10, mach_number=0.05) p, u = flow.initial_pu() point_dict = {"p": context.convert_to_ndarray(p[0, ..., None])} write_vtk(point_dict, id=1, filename_base=tmpdir / "output") assert os.path.isfile(tmpdir / "output_00000001.vtr") ================================================ FILE: tests/stencil/test_first_zero.py ================================================ from tests.conftest import * def test_first_zero(fix_stencil): """Test that the zeroth velocity is 0.""" assert (fix_stencil.e[0] == pytest.approx(np.zeros_like(fix_stencil.e[0]))) ================================================ FILE: tests/stencil/test_opposite.py ================================================ from tests.conftest import * def test_opposite(fix_stencil): """Test if the opposite field holds the index of the opposite direction.""" context = Context() torch_stencil = TorchStencil(fix_stencil, context) assert torch.isclose(torch_stencil.e[fix_stencil.opposite], -torch_stencil.e).all() ================================================ FILE: tests/stencil/test_symmetry.py ================================================ from tests.conftest import * def test_symmetry(fix_stencil): """Test if the stencil is symmetric""" assert np.array(fix_stencil.e).sum() == pytest.approx(0.0) ================================================ FILE: tests/stencil/test_weights.py ================================================ from tests.conftest import * def test_weights(fix_stencil): """Test if the sum of all weights equals one.""" assert np.array(fix_stencil.w).sum() == pytest.approx(1.0) ================================================ FILE: tests/test_checkpoint.py ================================================ from copy import deepcopy from tests.conftest import * def test_checkpoint(tmpdir): context = Context() flow = PoiseuilleFlow2D(context=context, resolution=[512, 512], reynolds_number=1, mach_number=0.02, initialize_with_zeros=False) f0 = deepcopy(flow.f) filename = tmpdir / 'PoiseuilleFlow2D' flow.dump(filename) simulation = Simulation(flow, BGKCollision(flow.units.relaxation_parameter_lu), []) simulation(10) flow.load(filename) assert torch.eq(f0, flow.f).all() """ This could be a way to test a dump-load workflow which stores all flow attributes. I did not get it to work, yet. context = Context('cuda') flow = PoiseuilleFlow2D(context=context, resolution=16, reynolds_number=1, mach_number=0.02, initialize_with_zeros=False) f0 = deepcopy(flow.f) filename = './PoiseuilleFlow2D' flow.dump(filename) simulation = Simulation(flow, BGKCollision(flow.units.relaxation_parameter_lu), []) simulation(10) context2 = Context('cpu') flow2 = PoiseuilleFlow2D(context=context2, resolution=32, reynolds_number=10, mach_number=0.01, initialize_with_zeros=True) flow2.load(filename) assert flow2.resolution == flow.resolution assert flow2.units.reynolds_number == flow.units.reynolds_number assert flow2.units.mach_number == flow.units.mach_number assert flow2.initialize_with_zeros == flow.initialize_with_zeros assert torch.eq(f0, flow2.f).all() """ ================================================ FILE: tests/test_cli.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- """Tests for `lettuce` package.""" import pytest from click.testing import CliRunner from lettuce import cli @pytest.fixture def response(): """Sample pytest fixture. See more at: http://doc.pytest.org/en/latest/fixture.html """ # import requests # return requests.get('https://github.com/audreyr/cookiecutter-pypackage') def test_content(response): """Sample pytest test function with the pytest fixture as an argument.""" # from bs4 import BeautifulSoup # assert 'GitHub' in BeautifulSoup(response.content).title.string def test_command_line_interface(): """Test the CLI.""" runner = CliRunner() result = runner.invoke(cli.main) assert result.exit_code == 0 assert 'Boltzmann' in result.output help_result = runner.invoke(cli.main, ['--help']) assert help_result.exit_code == 0 assert 'Show this message and exit.' in help_result.output ================================================ FILE: tests/test_equilibrium.py ================================================ from tests.conftest import * @pytest.mark.parametrize("fix_equilibrium", [QuadraticEquilibrium]) def test_equilibrium_conserves_mass(fix_equilibrium, fix_configuration, fix_stencil): device, dtype, use_native = fix_configuration if use_native: pytest.skip("This test does not depend on the native implementation.") context = Context(device=device, dtype=dtype, use_native=False) flow = TestFlow(context=context, resolution=[16] * fix_stencil.d, reynolds_number=100, mach_number=0.1, stencil=fix_stencil) equilibrium = fix_equilibrium() feq = equilibrium(flow) assert (flow.rho(feq).cpu().numpy() == pytest.approx(flow.rho().cpu().numpy())) @pytest.mark.parametrize("fix_equilibrium", [QuadraticEquilibrium]) def test_equilibrium_conserves_momentum(fix_equilibrium, fix_configuration, fix_stencil): device, dtype, use_native = fix_configuration if use_native: pytest.skip("This test does not depend on the native implementation.") context = Context(device=device, dtype=dtype, use_native=False) flow = TestFlow(context=context, resolution=[16] * fix_stencil.d, reynolds_number=100, mach_number=0.1, stencil=fix_stencil) equilibrium = fix_equilibrium() feq = equilibrium(flow) assert (flow.j(feq).cpu().numpy() == pytest.approx(flow.j().cpu().numpy(), abs=1e-6)) ================================================ FILE: tests/unit/__init__.py ================================================ from tests.conftest import * def create_default_unit_conversion(): return UnitConversion( reynolds_number=1000, mach_number=0.05, characteristic_length_pu=2 * np.pi, characteristic_velocity_pu=2, characteristic_length_lu=100, characteristic_density_pu=0.7) ================================================ FILE: tests/unit/test_consistency.py ================================================ from tests.conftest import * from tests.unit import create_default_unit_conversion def test_consistency(): rho = 0.9 u = 0.1 units = create_default_unit_conversion() assert (units.convert_density_to_pu(rho) * units.convert_velocity_to_pu(u) ** 2 == pytest.approx(units.convert_energy_to_pu(rho * u * u)) ) assert (units.convert_velocity_to_pu(u) ** 2 == pytest.approx(units.convert_incompressible_energy_to_pu(u * u))) ================================================ FILE: tests/unit/test_conversion_reversible.py ================================================ from tests.conftest import * from tests.unit import create_default_unit_conversion def test_conversion_reversible(): approx_two = pytest.approx(2.0) units = create_default_unit_conversion() assert approx_two == units.convert_velocity_to_lu(units.convert_velocity_to_pu(2.0)) assert approx_two == units.convert_time_to_lu(units.convert_time_to_pu(2.0)) assert approx_two == units.convert_length_to_lu(units.convert_length_to_pu(2.0)) assert approx_two == units.convert_density_to_lu(units.convert_density_to_pu(2.0)) assert approx_two == units.convert_pressure_to_lu(units.convert_pressure_to_pu(2.0)) assert approx_two == units.convert_density_lu_to_pressure_pu(units.convert_pressure_pu_to_density_lu(2.0)) assert approx_two == units.convert_energy_to_lu(units.convert_energy_to_pu(2.0)) assert approx_two == units.convert_incompressible_energy_to_lu(units.convert_incompressible_energy_to_pu(2.0)) ================================================ FILE: tests/unit/test_reynolds_number_consistent.py ================================================ from tests.conftest import * from tests.unit import create_default_unit_conversion def test_reynolds_number_consistent(): units = create_default_unit_conversion() re_lu = units.characteristic_velocity_lu * units.characteristic_length_lu / units.viscosity_lu re_pu = units.characteristic_velocity_pu * units.characteristic_length_pu / units.viscosity_pu assert re_lu == pytest.approx(re_pu) ================================================ FILE: tests/util/test_grid_fine_to_coarse.py ================================================ from tests.conftest import * @pytest.mark.parametrize("dims", [2, 3]) def test_grid_fine_to_coarse(dims): context = Context(device='cpu', dtype=torch.float64) flow_f = TaylorGreenVortex(context, [40] * dims, 1600, 0.15) flow_c = TaylorGreenVortex(context, [20] * dims, 1600, 0.15) f_c = grid_fine_to_coarse(flow_f, flow_f.f, flow_f.units.relaxation_parameter_lu, flow_c.units.relaxation_parameter_lu) p_init, u_init = flow_c.initial_pu() rho_init = flow_c.units.convert_pressure_pu_to_density_lu(p_init) u_init = flow_c.units.convert_velocity_to_lu(u_init) shear_c_init = flow_c.shear_tensor(flow_c.f) shear_c = flow_c.shear_tensor(f_c) assert torch.isclose(flow_c.u(f_c), u_init).all() assert torch.isclose(flow_c.rho(f_c), rho_init).all() assert torch.isclose(f_c, flow_c.f).all() assert torch.isclose(shear_c_init, shear_c).all() ================================================ FILE: tests/util/test_torch_gradient.py ================================================ from tests.conftest import * @pytest.mark.parametrize("dims", [2, 3]) @pytest.mark.parametrize("order", [2, 4, 6]) def test_torch_gradient(dims, order): context = Context() flow = TaylorGreenVortex(context=context, resolution=[100] * dims, reynolds_number=1, mach_number=0.05) _, u = flow.initial_pu() dx = 2 * torch.pi / 100 u0_grad = torch_gradient(u[0], dx=dx, order=order) u = context.convert_to_ndarray(u) u0_grad = context.convert_to_ndarray(u0_grad) u0_grad_np = np.array(np.gradient(u[0], dx)) if dims == 2: x, y = [context.convert_to_ndarray(x) for x in flow.grid] u0_grad_analytic = np.array([ -np.sin(x) * np.sin(y), np.cos(x) * np.cos(y), ]) elif dims == 3: x, y, z = [context.convert_to_ndarray(x) for x in flow.grid] u0_grad_analytic = np.array([ np.cos(x) * np.cos(y) * np.cos(z), np.sin(x) * np.sin(y) * (-np.cos(z)), np.sin(x) * (-np.cos(y)) * np.sin(z) ]) else: return assert np.allclose(u0_grad_analytic, u0_grad, rtol=1e-3, atol=1e-3) if dims == 2: assert (u0_grad_np[:, 2:-2, 2:-2] == pytest.approx(u0_grad[:, 2:-2, 2:-2], rel=1e-3, abs=1e-3)) if dims == 3: assert (u0_grad_np[:, 2:-2, 2:-2, 2:-2] == pytest.approx(u0_grad[:, 2:-2, 2:-2, 2:-2], rel=1e-3, abs=1e-3)) ================================================ FILE: versioneer.py ================================================ # Version: 0.21 """The Versioneer - like a rocketeer, but for versions. The Versioneer ============== * like a rocketeer, but for versions! * https://github.com/python-versioneer/python-versioneer * Brian Warner * License: Public Domain * Compatible with: Python 3.6, 3.7, 3.8, 3.9 and pypy3 * [![Latest Version][pypi-image]][pypi-url] * [![Build Status][travis-image]][travis-url] This is a tool for managing a recorded version number in distutils-based python projects. The goal is to remove the tedious and error-prone "update the embedded version string" step from your release process. Making a new release should be as easy as recording a new tag in your version-control system, and maybe making new tarballs. ## Quick Install * `pip install versioneer` to somewhere in your $PATH * add a `[versioneer]` section to your setup.cfg (see [Install](INSTALL.md)) * run `versioneer install` in your source tree, commit the results * Verify version information with `python setup.py version` ## Version Identifiers Source trees come from a variety of places: * a version-control system checkout (mostly used by developers) * a nightly tarball, produced by build automation * a snapshot tarball, produced by a web-based VCS browser, like github's "tarball from tag" feature * a release tarball, produced by "setup.py sdist", distributed through PyPI Within each source tree, the version identifier (either a string or a number, this tool is format-agnostic) can come from a variety of places: * ask the VCS tool itself, e.g. "git describe" (for checkouts), which knows about recent "tags" and an absolute revision-id * the name of the directory into which the tarball was unpacked * an expanded VCS keyword ($Id$, etc) * a `_version.py` created by some earlier build step For released software, the version identifier is closely related to a VCS tag. Some projects use tag names that include more than just the version string (e.g. "myproject-1.2" instead of just "1.2"), in which case the tool needs to strip the tag prefix to extract the version identifier. For unreleased software (between tags), the version identifier should provide enough information to help developers recreate the same tree, while also giving them an idea of roughly how old the tree is (after version 1.2, before version 1.3). Many VCS systems can report a description that captures this, for example `git describe --tags --dirty --always` reports things like "0.7-1-g574ab98-dirty" to indicate that the checkout is one revision past the 0.7 tag, has a unique revision id of "574ab98", and is "dirty" (it has uncommitted changes). The version identifier is used for multiple purposes: * to allow the module to self-identify its version: `myproject.__version__` * to choose a name and prefix for a 'setup.py sdist' tarball ## Theory of Operation Versioneer works by adding a special `_version.py` file into your source tree, where your `__init__.py` can import it. This `_version.py` knows how to dynamically ask the VCS tool for version information at import time. `_version.py` also contains `$Revision$` markers, and the installation process marks `_version.py` to have this marker rewritten with a tag name during the `git archive` command. As a result, generated tarballs will contain enough information to get the proper version. To allow `setup.py` to compute a version too, a `versioneer.py` is added to the top level of your source tree, next to `setup.py` and the `setup.cfg` that configures it. This overrides several distutils/setuptools commands to compute the version when invoked, and changes `setup.py build` and `setup.py sdist` to replace `_version.py` with a small static file that contains just the generated version data. ## Installation See [INSTALL.md](./INSTALL.md) for detailed installation instructions. ## Version-String Flavors Code which uses Versioneer can learn about its version string at runtime by importing `_version` from your main `__init__.py` file and running the `get_versions()` function. From the "outside" (e.g. in `setup.py`), you can import the top-level `versioneer.py` and run `get_versions()`. Both functions return a dictionary with different flavors of version information: * `['version']`: A condensed version string, rendered using the selected style. This is the most commonly used value for the project's version string. The default "pep440" style yields strings like `0.11`, `0.11+2.g1076c97`, or `0.11+2.g1076c97.dirty`. See the "Styles" section below for alternative styles. * `['full-revisionid']`: detailed revision identifier. For Git, this is the full SHA1 commit id, e.g. "1076c978a8d3cfc70f408fe5974aa6c092c949ac". * `['date']`: Date and time of the latest `HEAD` commit. For Git, it is the commit date in ISO 8601 format. This will be None if the date is not available. * `['dirty']`: a boolean, True if the tree has uncommitted changes. Note that this is only accurate if run in a VCS checkout, otherwise it is likely to be False or None * `['error']`: if the version string could not be computed, this will be set to a string describing the problem, otherwise it will be None. It may be useful to throw an exception in setup.py if this is set, to avoid e.g. creating tarballs with a version string of "unknown". Some variants are more useful than others. Including `full-revisionid` in a bug report should allow developers to reconstruct the exact code being tested (or indicate the presence of local changes that should be shared with the developers). `version` is suitable for display in an "about" box or a CLI `--version` output: it can be easily compared against release notes and lists of bugs fixed in various releases. The installer adds the following text to your `__init__.py` to place a basic version in `YOURPROJECT.__version__`: from ._version import get_versions __version__ = get_versions()['version'] del get_versions ## Styles The setup.cfg `style=` configuration controls how the VCS information is rendered into a version string. The default style, "pep440", produces a PEP440-compliant string, equal to the un-prefixed tag name for actual releases, and containing an additional "local version" section with more detail for in-between builds. For Git, this is TAG[+DISTANCE.gHEX[.dirty]] , using information from `git describe --tags --dirty --always`. For example "0.11+2.g1076c97.dirty" indicates that the tree is like the "1076c97" commit but has uncommitted changes (".dirty"), and that this commit is two revisions ("+2") beyond the "0.11" tag. For released software (exactly equal to a known tag), the identifier will only contain the stripped tag, e.g. "0.11". Other styles are available. See [details.md](details.md) in the Versioneer source tree for descriptions. ## Debugging Versioneer tries to avoid fatal errors: if something goes wrong, it will tend to return a version of "0+unknown". To investigate the problem, run `setup.py version`, which will run the version-lookup code in a verbose mode, and will display the full contents of `get_versions()` (including the `error` string, which may help identify what went wrong). ## Known Limitations Some situations are known to cause problems for Versioneer. This details the most significant ones. More can be found on Github [issues page](https://github.com/python-versioneer/python-versioneer/issues). ### Subprojects Versioneer has limited support for source trees in which `setup.py` is not in the root directory (e.g. `setup.py` and `.git/` are *not* siblings). The are two common reasons why `setup.py` might not be in the root: * Source trees which contain multiple subprojects, such as [Buildbot](https://github.com/buildbot/buildbot), which contains both "master" and "slave" subprojects, each with their own `setup.py`, `setup.cfg`, and `tox.ini`. Projects like these produce multiple PyPI distributions (and upload multiple independently-installable tarballs). * Source trees whose main purpose is to contain a C library, but which also provide bindings to Python (and perhaps other languages) in subdirectories. Versioneer will look for `.git` in parent directories, and most operations should get the right version string. However `pip` and `setuptools` have bugs and implementation details which frequently cause `pip install .` from a subproject directory to fail to find a correct version string (so it usually defaults to `0+unknown`). `pip install --editable .` should work correctly. `setup.py install` might work too. Pip-8.1.1 is known to have this problem, but hopefully it will get fixed in some later version. [Bug #38](https://github.com/python-versioneer/python-versioneer/issues/38) is tracking this issue. The discussion in [PR #61](https://github.com/python-versioneer/python-versioneer/pull/61) describes the issue from the Versioneer side in more detail. [pip PR#3176](https://github.com/pypa/pip/pull/3176) and [pip PR#3615](https://github.com/pypa/pip/pull/3615) contain work to improve pip to let Versioneer work correctly. Versioneer-0.16 and earlier only looked for a `.git` directory next to the `setup.cfg`, so subprojects were completely unsupported with those releases. ### Editable installs with setuptools <= 18.5 `setup.py develop` and `pip install --editable .` allow you to install a project into a virtualenv once, then continue editing the source code (and test) without re-installing after every change. "Entry-point scripts" (`setup(entry_points={"console_scripts": ..})`) are a convenient way to specify executable scripts that should be installed along with the python package. These both work as expected when using modern setuptools. When using setuptools-18.5 or earlier, however, certain operations will cause `pkg_resources.DistributionNotFound` errors when running the entrypoint script, which must be resolved by re-installing the package. This happens when the install happens with one version, then the egg_info data is regenerated while a different version is checked out. Many setup.py commands cause egg_info to be rebuilt (including `sdist`, `wheel`, and installing into a different virtualenv), so this can be surprising. [Bug #83](https://github.com/python-versioneer/python-versioneer/issues/83) describes this one, but upgrading to a newer version of setuptools should probably resolve it. ## Updating Versioneer To upgrade your project to a new release of Versioneer, do the following: * install the new Versioneer (`pip install -U versioneer` or equivalent) * edit `setup.cfg`, if necessary, to include any new configuration settings indicated by the release notes. See [UPGRADING](./UPGRADING.md) for details. * re-run `versioneer install` in your source tree, to replace `SRC/_version.py` * commit any changed files ## Future Directions This tool is designed to make it easily extended to other version-control systems: all VCS-specific components are in separate directories like src/git/ . The top-level `versioneer.py` script is assembled from these components by running make-versioneer.py . In the future, make-versioneer.py will take a VCS name as an argument, and will construct a version of `versioneer.py` that is specific to the given VCS. It might also take the configuration arguments that are currently provided manually during installation by editing setup.py . Alternatively, it might go the other direction and include code from all supported VCS systems, reducing the number of intermediate scripts. ## Similar projects * [setuptools_scm](https://github.com/pypa/setuptools_scm/) - a non-vendored build-time dependency * [minver](https://github.com/jbweston/miniver) - a lightweight reimplementation of versioneer * [versioningit](https://github.com/jwodder/versioningit) - a PEP 518-based setuptools plugin ## License To make Versioneer easier to embed, all its code is dedicated to the public domain. The `_version.py` that it creates is also in the public domain. Specifically, both are released under the Creative Commons "Public Domain Dedication" license (CC0-1.0), as described in https://creativecommons.org/publicdomain/zero/1.0/ . [pypi-image]: https://img.shields.io/pypi/v/versioneer.svg [pypi-url]: https://pypi.python.org/pypi/versioneer/ [travis-image]: https://img.shields.io/travis/com/python-versioneer/python-versioneer.svg [travis-url]: https://travis-ci.com/github/python-versioneer/python-versioneer """ # pylint:disable=invalid-name,import-outside-toplevel,missing-function-docstring # pylint:disable=missing-class-docstring,too-many-branches,too-many-statements # pylint:disable=raise-missing-from,too-many-lines,too-many-locals,import-error # pylint:disable=too-few-public-methods,redefined-outer-name,consider-using-with # pylint:disable=attribute-defined-outside-init,too-many-arguments import configparser import errno import json import os import re import subprocess import sys from typing import Callable, Dict class VersioneerConfig: """Container for Versioneer configuration parameters.""" def get_root(): """Get the project root directory. We require that all commands are run from the project root, i.e. the directory that contains setup.py, setup.cfg, and versioneer.py . """ root = os.path.realpath(os.path.abspath(os.getcwd())) setup_py = os.path.join(root, "setup.py") versioneer_py = os.path.join(root, "versioneer.py") if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)): # allow 'python path/to/setup.py COMMAND' root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0]))) setup_py = os.path.join(root, "setup.py") versioneer_py = os.path.join(root, "versioneer.py") if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)): err = ("Versioneer was unable to run the project root directory. " "Versioneer requires setup.py to be executed from " "its immediate directory (like 'python setup.py COMMAND'), " "or in a way that lets it use sys.argv[0] to find the root " "(like 'python path/to/setup.py COMMAND').") raise VersioneerBadRootError(err) try: # Certain runtime workflows (setup.py install/develop in a setuptools # tree) execute all dependencies in a single python process, so # "versioneer" may be imported multiple times, and python's shared # module-import table will cache the first one. So we can't use # os.path.dirname(__file__), as that will find whichever # versioneer.py was first imported, even in later projects. my_path = os.path.realpath(os.path.abspath(__file__)) me_dir = os.path.normcase(os.path.splitext(my_path)[0]) vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0]) if me_dir != vsr_dir: print("Warning: build in %s is using versioneer.py from %s" % (os.path.dirname(my_path), versioneer_py)) except NameError: pass return root def get_config_from_root(root): """Read the project setup.cfg file to determine Versioneer config.""" # This might raise OSError (if setup.cfg is missing), or # configparser.NoSectionError (if it lacks a [versioneer] section), or # configparser.NoOptionError (if it lacks "VCS="). See the docstring at # the top of versioneer.py for instructions on writing your setup.cfg . setup_cfg = os.path.join(root, "setup.cfg") parser = configparser.ConfigParser() with open(setup_cfg, "r") as cfg_file: parser.read_file(cfg_file) VCS = parser.get("versioneer", "VCS") # mandatory # Dict-like interface for non-mandatory entries section = parser["versioneer"] cfg = VersioneerConfig() cfg.VCS = VCS cfg.style = section.get("style", "") cfg.versionfile_source = section.get("versionfile_source") cfg.versionfile_build = section.get("versionfile_build") cfg.tag_prefix = section.get("tag_prefix") if cfg.tag_prefix in ("''", '""'): cfg.tag_prefix = "" cfg.parentdir_prefix = section.get("parentdir_prefix") cfg.verbose = section.get("verbose") return cfg class NotThisMethod(Exception): """Exception raised if a method is not valid for the current scenario.""" # these dictionaries contain VCS-specific tools LONG_VERSION_PY: Dict[str, str] = {} HANDLERS: Dict[str, Dict[str, Callable]] = {} def register_vcs_handler(vcs, method): # decorator """Create decorator to mark a method as the handler of a VCS.""" def decorate(f): """Store f in HANDLERS[vcs][method].""" HANDLERS.setdefault(vcs, {})[method] = f return f return decorate def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None): """Call the given command(s).""" assert isinstance(commands, list) process = None for command in commands: try: dispcmd = str([command] + args) # remember shell=False, so use git.cmd on windows, not just git process = subprocess.Popen([command] + args, cwd=cwd, env=env, stdout=subprocess.PIPE, stderr=(subprocess.PIPE if hide_stderr else None)) break except OSError: e = sys.exc_info()[1] if e.errno == errno.ENOENT: continue if verbose: print("unable to run %s" % dispcmd) print(e) return None, None else: if verbose: print("unable to find command, tried %s" % (commands,)) return None, None stdout = process.communicate()[0].strip().decode() if process.returncode != 0: if verbose: print("unable to run %s (error)" % dispcmd) print("stdout was %s" % stdout) return None, process.returncode return stdout, process.returncode LONG_VERSION_PY['git'] = r''' # This file helps to compute a version number in source trees obtained from # git-archive tarball (such as those provided by githubs download-from-tag # feature). Distribution tarballs (built by setup.py sdist) and build # directories (produced by setup.py build) will contain a much shorter file # that just contains the computed version number. # This file is released into the public domain. Generated by # versioneer-0.21 (https://github.com/python-versioneer/python-versioneer) """Git implementation of _version.py.""" import errno import os import re import subprocess import sys from typing import Callable, Dict def get_keywords(): """Get the keywords needed to look up the version information.""" # these strings will be replaced by git during git-archive. # setup.py/versioneer.py will grep for the variable names, so they must # each be defined on a line of their own. _version.py will just call # get_keywords(). git_refnames = "%(DOLLAR)sFormat:%%d%(DOLLAR)s" git_full = "%(DOLLAR)sFormat:%%H%(DOLLAR)s" git_date = "%(DOLLAR)sFormat:%%ci%(DOLLAR)s" keywords = {"refnames": git_refnames, "full": git_full, "date": git_date} return keywords class VersioneerConfig: """Container for Versioneer configuration parameters.""" def get_config(): """Create, populate and return the VersioneerConfig() object.""" # these strings are filled in when 'setup.py versioneer' creates # _version.py cfg = VersioneerConfig() cfg.VCS = "git" cfg.style = "%(STYLE)s" cfg.tag_prefix = "%(TAG_PREFIX)s" cfg.parentdir_prefix = "%(PARENTDIR_PREFIX)s" cfg.versionfile_source = "%(VERSIONFILE_SOURCE)s" cfg.verbose = False return cfg class NotThisMethod(Exception): """Exception raised if a method is not valid for the current scenario.""" LONG_VERSION_PY: Dict[str, str] = {} HANDLERS: Dict[str, Dict[str, Callable]] = {} def register_vcs_handler(vcs, method): # decorator """Create decorator to mark a method as the handler of a VCS.""" def decorate(f): """Store f in HANDLERS[vcs][method].""" if vcs not in HANDLERS: HANDLERS[vcs] = {} HANDLERS[vcs][method] = f return f return decorate def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None): """Call the given command(s).""" assert isinstance(commands, list) process = None for command in commands: try: dispcmd = str([command] + args) # remember shell=False, so use git.cmd on windows, not just git process = subprocess.Popen([command] + args, cwd=cwd, env=env, stdout=subprocess.PIPE, stderr=(subprocess.PIPE if hide_stderr else None)) break except OSError: e = sys.exc_info()[1] if e.errno == errno.ENOENT: continue if verbose: print("unable to run %%s" %% dispcmd) print(e) return None, None else: if verbose: print("unable to find command, tried %%s" %% (commands,)) return None, None stdout = process.communicate()[0].strip().decode() if process.returncode != 0: if verbose: print("unable to run %%s (error)" %% dispcmd) print("stdout was %%s" %% stdout) return None, process.returncode return stdout, process.returncode def versions_from_parentdir(parentdir_prefix, root, verbose): """Try to determine the version from the parent directory name. Source tarballs conventionally unpack into a directory that includes both the project name and a version string. We will also support searching up two directory levels for an appropriately named parent directory """ rootdirs = [] for _ in range(3): dirname = os.path.basename(root) if dirname.startswith(parentdir_prefix): return {"version": dirname[len(parentdir_prefix):], "full-revisionid": None, "dirty": False, "error": None, "date": None} rootdirs.append(root) root = os.path.dirname(root) # up a level if verbose: print("Tried directories %%s but none started with prefix %%s" %% (str(rootdirs), parentdir_prefix)) raise NotThisMethod("rootdir doesn't start with parentdir_prefix") @register_vcs_handler("git", "get_keywords") def git_get_keywords(versionfile_abs): """Extract version information from the given file.""" # the code embedded in _version.py can just fetch the value of these # keywords. When used from setup.py, we don't want to import _version.py, # so we do it with a regexp instead. This function is not used from # _version.py. keywords = {} try: with open(versionfile_abs, "r") as fobj: for line in fobj: if line.strip().startswith("git_refnames ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["refnames"] = mo.group(1) if line.strip().startswith("git_full ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["full"] = mo.group(1) if line.strip().startswith("git_date ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["date"] = mo.group(1) except OSError: pass return keywords @register_vcs_handler("git", "keywords") def git_versions_from_keywords(keywords, tag_prefix, verbose): """Get version information from git keywords.""" if "refnames" not in keywords: raise NotThisMethod("Short version file found") date = keywords.get("date") if date is not None: # Use only the last line. Previous lines may contain GPG signature # information. date = date.splitlines()[-1] # git-2.2.0 added "%%cI", which expands to an ISO-8601 -compliant # datestamp. However we prefer "%%ci" (which expands to an "ISO-8601 # -like" string, which we must then edit to make compliant), because # it's been around since git-1.5.3, and it's too difficult to # discover which version we're using, or to work around using an # older one. date = date.strip().replace(" ", "T", 1).replace(" ", "", 1) refnames = keywords["refnames"].strip() if refnames.startswith("$Format"): if verbose: print("keywords are unexpanded, not using") raise NotThisMethod("unexpanded keywords, not a git-archive tarball") refs = {r.strip() for r in refnames.strip("()").split(",")} # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of # just "foo-1.0". If we see a "tag: " prefix, prefer those. TAG = "tag: " tags = {r[len(TAG):] for r in refs if r.startswith(TAG)} if not tags: # Either we're using git < 1.8.3, or there really are no tags. We use # a heuristic: assume all version tags have a digit. The old git %%d # expansion behaves like git log --decorate=short and strips out the # refs/heads/ and refs/tags/ prefixes that would let us distinguish # between branches and tags. By ignoring refnames without digits, we # filter out many common branch names like "release" and # "stabilization", as well as "HEAD" and "master". tags = {r for r in refs if re.search(r'\d', r)} if verbose: print("discarding '%%s', no digits" %% ",".join(refs - tags)) if verbose: print("likely tags: %%s" %% ",".join(sorted(tags))) for ref in sorted(tags): # sorting will prefer e.g. "2.0" over "2.0rc1" if ref.startswith(tag_prefix): r = ref[len(tag_prefix):] # Filter out refs that exactly match prefix or that don't start # with a number once the prefix is stripped (mostly a concern # when prefix is '') if not re.match(r'\d', r): continue if verbose: print("picking %%s" %% r) return {"version": r, "full-revisionid": keywords["full"].strip(), "dirty": False, "error": None, "date": date} # no suitable tags, so version is "0+unknown", but full hex is still there if verbose: print("no suitable tags, using unknown + full revision id") return {"version": "0+unknown", "full-revisionid": keywords["full"].strip(), "dirty": False, "error": "no suitable tags", "date": None} @register_vcs_handler("git", "pieces_from_vcs") def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command): """Get version from 'git describe' in the root of the source tree. This only gets called if the git-archive 'subst' keywords were *not* expanded, and _version.py hasn't already been rewritten with a short version string, meaning we're inside a checked out source tree. """ GITS = ["git"] TAG_PREFIX_REGEX = "*" if sys.platform == "win32": GITS = ["git.cmd", "git.exe"] TAG_PREFIX_REGEX = r"\*" _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, hide_stderr=True) if rc != 0: if verbose: print("Directory %%s not under git control" %% root) raise NotThisMethod("'git rev-parse --git-dir' returned error") # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] # if there isn't one, this yields HEX[-dirty] (no NUM) describe_out, rc = runner(GITS, ["describe", "--tags", "--dirty", "--always", "--long", "--match", "%%s%%s" %% (tag_prefix, TAG_PREFIX_REGEX)], cwd=root) # --long was added in git-1.5.5 if describe_out is None: raise NotThisMethod("'git describe' failed") describe_out = describe_out.strip() full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root) if full_out is None: raise NotThisMethod("'git rev-parse' failed") full_out = full_out.strip() pieces = {} pieces["long"] = full_out pieces["short"] = full_out[:7] # maybe improved later pieces["error"] = None branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], cwd=root) # --abbrev-ref was added in git-1.6.3 if rc != 0 or branch_name is None: raise NotThisMethod("'git rev-parse --abbrev-ref' returned error") branch_name = branch_name.strip() if branch_name == "HEAD": # If we aren't exactly on a branch, pick a branch which represents # the current commit. If all else fails, we are on a branchless # commit. branches, rc = runner(GITS, ["branch", "--contains"], cwd=root) # --contains was added in git-1.5.4 if rc != 0 or branches is None: raise NotThisMethod("'git branch --contains' returned error") branches = branches.split("\n") # Remove the first line if we're running detached if "(" in branches[0]: branches.pop(0) # Strip off the leading "* " from the list of branches. branches = [branch[2:] for branch in branches] if "master" in branches: branch_name = "master" elif not branches: branch_name = None else: # Pick the first branch that is returned. Good or bad. branch_name = branches[0] pieces["branch"] = branch_name # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] # TAG might have hyphens. git_describe = describe_out # look for -dirty suffix dirty = git_describe.endswith("-dirty") pieces["dirty"] = dirty if dirty: git_describe = git_describe[:git_describe.rindex("-dirty")] # now we have TAG-NUM-gHEX or HEX if "-" in git_describe: # TAG-NUM-gHEX mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) if not mo: # unparsable. Maybe git-describe is misbehaving? pieces["error"] = ("unable to parse git-describe output: '%%s'" %% describe_out) return pieces # tag full_tag = mo.group(1) if not full_tag.startswith(tag_prefix): if verbose: fmt = "tag '%%s' doesn't start with prefix '%%s'" print(fmt %% (full_tag, tag_prefix)) pieces["error"] = ("tag '%%s' doesn't start with prefix '%%s'" %% (full_tag, tag_prefix)) return pieces pieces["closest-tag"] = full_tag[len(tag_prefix):] # distance: number of commits since tag pieces["distance"] = int(mo.group(2)) # commit: short hex revision ID pieces["short"] = mo.group(3) else: # HEX: no tags pieces["closest-tag"] = None count_out, rc = runner(GITS, ["rev-list", "HEAD", "--count"], cwd=root) pieces["distance"] = int(count_out) # total number of commits # commit date: see ISO-8601 comment in git_versions_from_keywords() date = runner(GITS, ["show", "-s", "--format=%%ci", "HEAD"], cwd=root)[0].strip() # Use only the last line. Previous lines may contain GPG signature # information. date = date.splitlines()[-1] pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) return pieces def plus_or_dot(pieces): """Return a + if we don't already have one, else return a .""" if "+" in pieces.get("closest-tag", ""): return "." return "+" def render_pep440(pieces): """Build up version string, with post-release "local version identifier". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty Exceptions: 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += plus_or_dot(pieces) rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = "0+untagged.%%d.g%%s" %% (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" return rendered def render_pep440_branch(pieces): """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] . The ".dev0" means not master branch. Note that .dev0 sorts backwards (a feature branch will appear "older" than the master branch). Exceptions: 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: if pieces["branch"] != "master": rendered += ".dev0" rendered += plus_or_dot(pieces) rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = "0" if pieces["branch"] != "master": rendered += ".dev0" rendered += "+untagged.%%d.g%%s" %% (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" return rendered def pep440_split_post(ver): """Split pep440 version string at the post-release segment. Returns the release segments before the post-release and the post-release version number (or -1 if no post-release segment is present). """ vc = str.split(ver, ".post") return vc[0], int(vc[1] or 0) if len(vc) == 2 else None def render_pep440_pre(pieces): """TAG[.postN.devDISTANCE] -- No -dirty. Exceptions: 1: no tags. 0.post0.devDISTANCE """ if pieces["closest-tag"]: if pieces["distance"]: # update the post release segment tag_version, post_version = pep440_split_post(pieces["closest-tag"]) rendered = tag_version if post_version is not None: rendered += ".post%%d.dev%%d" %% (post_version+1, pieces["distance"]) else: rendered += ".post0.dev%%d" %% (pieces["distance"]) else: # no commits, use the tag as the version rendered = pieces["closest-tag"] else: # exception #1 rendered = "0.post0.dev%%d" %% pieces["distance"] return rendered def render_pep440_post(pieces): """TAG[.postDISTANCE[.dev0]+gHEX] . The ".dev0" means dirty. Note that .dev0 sorts backwards (a dirty tree will appear "older" than the corresponding clean one), but you shouldn't be releasing software with -dirty anyways. Exceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%%d" %% pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += plus_or_dot(pieces) rendered += "g%%s" %% pieces["short"] else: # exception #1 rendered = "0.post%%d" %% pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += "+g%%s" %% pieces["short"] return rendered def render_pep440_post_branch(pieces): """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] . The ".dev0" means not master branch. Exceptions: 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%%d" %% pieces["distance"] if pieces["branch"] != "master": rendered += ".dev0" rendered += plus_or_dot(pieces) rendered += "g%%s" %% pieces["short"] if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = "0.post%%d" %% pieces["distance"] if pieces["branch"] != "master": rendered += ".dev0" rendered += "+g%%s" %% pieces["short"] if pieces["dirty"]: rendered += ".dirty" return rendered def render_pep440_old(pieces): """TAG[.postDISTANCE[.dev0]] . The ".dev0" means dirty. Exceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%%d" %% pieces["distance"] if pieces["dirty"]: rendered += ".dev0" else: # exception #1 rendered = "0.post%%d" %% pieces["distance"] if pieces["dirty"]: rendered += ".dev0" return rendered def render_git_describe(pieces): """TAG[-DISTANCE-gHEX][-dirty]. Like 'git describe --tags --dirty --always'. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render_git_describe_long(pieces): """TAG-DISTANCE-gHEX[-dirty]. Like 'git describe --tags --dirty --always -long'. The distance/hash is unconditional. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render(pieces, style): """Render the given version pieces into the requested style.""" if pieces["error"]: return {"version": "unknown", "full-revisionid": pieces.get("long"), "dirty": None, "error": pieces["error"], "date": None} if not style or style == "default": style = "pep440" # the default if style == "pep440": rendered = render_pep440(pieces) elif style == "pep440-branch": rendered = render_pep440_branch(pieces) elif style == "pep440-pre": rendered = render_pep440_pre(pieces) elif style == "pep440-post": rendered = render_pep440_post(pieces) elif style == "pep440-post-branch": rendered = render_pep440_post_branch(pieces) elif style == "pep440-old": rendered = render_pep440_old(pieces) elif style == "git-describe": rendered = render_git_describe(pieces) elif style == "git-describe-long": rendered = render_git_describe_long(pieces) else: raise ValueError("unknown style '%%s'" %% style) return {"version": rendered, "full-revisionid": pieces["long"], "dirty": pieces["dirty"], "error": None, "date": pieces.get("date")} def get_versions(): """Get version information or return default if unable to do so.""" # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have # __file__, we can work backwards from there to the root. Some # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which # case we can only use expanded keywords. cfg = get_config() verbose = cfg.verbose try: return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, verbose) except NotThisMethod: pass try: root = os.path.realpath(__file__) # versionfile_source is the relative path from the top of the source # tree (where the .git directory might live) to this file. Invert # this to find the root from __file__. for _ in cfg.versionfile_source.split('/'): root = os.path.dirname(root) except NameError: return {"version": "0+unknown", "full-revisionid": None, "dirty": None, "error": "unable to find root of source tree", "date": None} try: pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose) return render(pieces, cfg.style) except NotThisMethod: pass try: if cfg.parentdir_prefix: return versions_from_parentdir(cfg.parentdir_prefix, root, verbose) except NotThisMethod: pass return {"version": "0+unknown", "full-revisionid": None, "dirty": None, "error": "unable to compute version", "date": None} ''' @register_vcs_handler("git", "get_keywords") def git_get_keywords(versionfile_abs): """Extract version information from the given file.""" # the code embedded in _version.py can just fetch the value of these # keywords. When used from setup.py, we don't want to import _version.py, # so we do it with a regexp instead. This function is not used from # _version.py. keywords = {} try: with open(versionfile_abs, "r") as fobj: for line in fobj: if line.strip().startswith("git_refnames ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["refnames"] = mo.group(1) if line.strip().startswith("git_full ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["full"] = mo.group(1) if line.strip().startswith("git_date ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["date"] = mo.group(1) except OSError: pass return keywords @register_vcs_handler("git", "keywords") def git_versions_from_keywords(keywords, tag_prefix, verbose): """Get version information from git keywords.""" if "refnames" not in keywords: raise NotThisMethod("Short version file found") date = keywords.get("date") if date is not None: # Use only the last line. Previous lines may contain GPG signature # information. date = date.splitlines()[-1] # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant # datestamp. However we prefer "%ci" (which expands to an "ISO-8601 # -like" string, which we must then edit to make compliant), because # it's been around since git-1.5.3, and it's too difficult to # discover which version we're using, or to work around using an # older one. date = date.strip().replace(" ", "T", 1).replace(" ", "", 1) refnames = keywords["refnames"].strip() if refnames.startswith("$Format"): if verbose: print("keywords are unexpanded, not using") raise NotThisMethod("unexpanded keywords, not a git-archive tarball") refs = {r.strip() for r in refnames.strip("()").split(",")} # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of # just "foo-1.0". If we see a "tag: " prefix, prefer those. TAG = "tag: " tags = {r[len(TAG):] for r in refs if r.startswith(TAG)} if not tags: # Either we're using git < 1.8.3, or there really are no tags. We use # a heuristic: assume all version tags have a digit. The old git %d # expansion behaves like git log --decorate=short and strips out the # refs/heads/ and refs/tags/ prefixes that would let us distinguish # between branches and tags. By ignoring refnames without digits, we # filter out many common branch names like "release" and # "stabilization", as well as "HEAD" and "master". tags = {r for r in refs if re.search(r'\d', r)} if verbose: print("discarding '%s', no digits" % ",".join(refs - tags)) if verbose: print("likely tags: %s" % ",".join(sorted(tags))) for ref in sorted(tags): # sorting will prefer e.g. "2.0" over "2.0rc1" if ref.startswith(tag_prefix): r = ref[len(tag_prefix):] # Filter out refs that exactly match prefix or that don't start # with a number once the prefix is stripped (mostly a concern # when prefix is '') if not re.match(r'\d', r): continue if verbose: print("picking %s" % r) return {"version": r, "full-revisionid": keywords["full"].strip(), "dirty": False, "error": None, "date": date} # no suitable tags, so version is "0+unknown", but full hex is still there if verbose: print("no suitable tags, using unknown + full revision id") return {"version": "0+unknown", "full-revisionid": keywords["full"].strip(), "dirty": False, "error": "no suitable tags", "date": None} @register_vcs_handler("git", "pieces_from_vcs") def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command): """Get version from 'git describe' in the root of the source tree. This only gets called if the git-archive 'subst' keywords were *not* expanded, and _version.py hasn't already been rewritten with a short version string, meaning we're inside a checked out source tree. """ GITS = ["git"] TAG_PREFIX_REGEX = "*" if sys.platform == "win32": GITS = ["git.cmd", "git.exe"] TAG_PREFIX_REGEX = r"\*" _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, hide_stderr=True) if rc != 0: if verbose: print("Directory %s not under git control" % root) raise NotThisMethod("'git rev-parse --git-dir' returned error") # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] # if there isn't one, this yields HEX[-dirty] (no NUM) describe_out, rc = runner(GITS, ["describe", "--tags", "--dirty", "--always", "--long", "--match", "%s%s" % (tag_prefix, TAG_PREFIX_REGEX)], cwd=root) # --long was added in git-1.5.5 if describe_out is None: raise NotThisMethod("'git describe' failed") describe_out = describe_out.strip() full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root) if full_out is None: raise NotThisMethod("'git rev-parse' failed") full_out = full_out.strip() pieces = {} pieces["long"] = full_out pieces["short"] = full_out[:7] # maybe improved later pieces["error"] = None branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], cwd=root) # --abbrev-ref was added in git-1.6.3 if rc != 0 or branch_name is None: raise NotThisMethod("'git rev-parse --abbrev-ref' returned error") branch_name = branch_name.strip() if branch_name == "HEAD": # If we aren't exactly on a branch, pick a branch which represents # the current commit. If all else fails, we are on a branchless # commit. branches, rc = runner(GITS, ["branch", "--contains"], cwd=root) # --contains was added in git-1.5.4 if rc != 0 or branches is None: raise NotThisMethod("'git branch --contains' returned error") branches = branches.split("\n") # Remove the first line if we're running detached if "(" in branches[0]: branches.pop(0) # Strip off the leading "* " from the list of branches. branches = [branch[2:] for branch in branches] if "master" in branches: branch_name = "master" elif not branches: branch_name = None else: # Pick the first branch that is returned. Good or bad. branch_name = branches[0] pieces["branch"] = branch_name # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] # TAG might have hyphens. git_describe = describe_out # look for -dirty suffix dirty = git_describe.endswith("-dirty") pieces["dirty"] = dirty if dirty: git_describe = git_describe[:git_describe.rindex("-dirty")] # now we have TAG-NUM-gHEX or HEX if "-" in git_describe: # TAG-NUM-gHEX mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) if not mo: # unparsable. Maybe git-describe is misbehaving? pieces["error"] = ("unable to parse git-describe output: '%s'" % describe_out) return pieces # tag full_tag = mo.group(1) if not full_tag.startswith(tag_prefix): if verbose: fmt = "tag '%s' doesn't start with prefix '%s'" print(fmt % (full_tag, tag_prefix)) pieces["error"] = ("tag '%s' doesn't start with prefix '%s'" % (full_tag, tag_prefix)) return pieces pieces["closest-tag"] = full_tag[len(tag_prefix):] # distance: number of commits since tag pieces["distance"] = int(mo.group(2)) # commit: short hex revision ID pieces["short"] = mo.group(3) else: # HEX: no tags pieces["closest-tag"] = None count_out, rc = runner(GITS, ["rev-list", "HEAD", "--count"], cwd=root) pieces["distance"] = int(count_out) # total number of commits # commit date: see ISO-8601 comment in git_versions_from_keywords() date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip() # Use only the last line. Previous lines may contain GPG signature # information. date = date.splitlines()[-1] pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) return pieces def do_vcs_install(manifest_in, versionfile_source, ipy): """Git-specific installation logic for Versioneer. For Git, this means creating/changing .gitattributes to mark _version.py for export-subst keyword substitution. """ GITS = ["git"] if sys.platform == "win32": GITS = ["git.cmd", "git.exe"] files = [manifest_in, versionfile_source] if ipy: files.append(ipy) try: my_path = __file__ if my_path.endswith(".pyc") or my_path.endswith(".pyo"): my_path = os.path.splitext(my_path)[0] + ".py" versioneer_file = os.path.relpath(my_path) except NameError: versioneer_file = "versioneer.py" files.append(versioneer_file) present = False try: with open(".gitattributes", "r") as fobj: for line in fobj: if line.strip().startswith(versionfile_source): if "export-subst" in line.strip().split()[1:]: present = True break except OSError: pass if not present: with open(".gitattributes", "a+") as fobj: fobj.write(f"{versionfile_source} export-subst\n") files.append(".gitattributes") run_command(GITS, ["add", "--"] + files) def versions_from_parentdir(parentdir_prefix, root, verbose): """Try to determine the version from the parent directory name. Source tarballs conventionally unpack into a directory that includes both the project name and a version string. We will also support searching up two directory levels for an appropriately named parent directory """ rootdirs = [] for _ in range(3): dirname = os.path.basename(root) if dirname.startswith(parentdir_prefix): return {"version": dirname[len(parentdir_prefix):], "full-revisionid": None, "dirty": False, "error": None, "date": None} rootdirs.append(root) root = os.path.dirname(root) # up a level if verbose: print("Tried directories %s but none started with prefix %s" % (str(rootdirs), parentdir_prefix)) raise NotThisMethod("rootdir doesn't start with parentdir_prefix") SHORT_VERSION_PY = """ # This file was generated by 'versioneer.py' (0.21) from # revision-control system data, or from the parent directory name of an # unpacked source archive. Distribution tarballs contain a pre-generated copy # of this file. import json version_json = ''' %s ''' # END VERSION_JSON def get_versions(): return json.loads(version_json) """ def versions_from_file(filename): """Try to determine the version from _version.py if present.""" try: with open(filename) as f: contents = f.read() except OSError: raise NotThisMethod("unable to read _version.py") mo = re.search(r"version_json = '''\n(.*)''' # END VERSION_JSON", contents, re.M | re.S) if not mo: mo = re.search(r"version_json = '''\r\n(.*)''' # END VERSION_JSON", contents, re.M | re.S) if not mo: raise NotThisMethod("no version_json in _version.py") return json.loads(mo.group(1)) def write_to_version_file(filename, versions): """Write the given version number to the given _version.py file.""" os.unlink(filename) contents = json.dumps(versions, sort_keys=True, indent=1, separators=(",", ": ")) with open(filename, "w") as f: f.write(SHORT_VERSION_PY % contents) print("set %s to '%s'" % (filename, versions["version"])) def plus_or_dot(pieces): """Return a + if we don't already have one, else return a .""" if "+" in pieces.get("closest-tag", ""): return "." return "+" def render_pep440(pieces): """Build up version string, with post-release "local version identifier". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty Exceptions: 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += plus_or_dot(pieces) rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = "0+untagged.%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" return rendered def render_pep440_branch(pieces): """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] . The ".dev0" means not master branch. Note that .dev0 sorts backwards (a feature branch will appear "older" than the master branch). Exceptions: 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: if pieces["branch"] != "master": rendered += ".dev0" rendered += plus_or_dot(pieces) rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = "0" if pieces["branch"] != "master": rendered += ".dev0" rendered += "+untagged.%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" return rendered def pep440_split_post(ver): """Split pep440 version string at the post-release segment. Returns the release segments before the post-release and the post-release version number (or -1 if no post-release segment is present). """ vc = str.split(ver, ".post") return vc[0], int(vc[1] or 0) if len(vc) == 2 else None def render_pep440_pre(pieces): """TAG[.postN.devDISTANCE] -- No -dirty. Exceptions: 1: no tags. 0.post0.devDISTANCE """ if pieces["closest-tag"]: if pieces["distance"]: # update the post release segment tag_version, post_version = pep440_split_post(pieces["closest-tag"]) rendered = tag_version if post_version is not None: rendered += ".post%d.dev%d" % (post_version+1, pieces["distance"]) else: rendered += ".post0.dev%d" % (pieces["distance"]) else: # no commits, use the tag as the version rendered = pieces["closest-tag"] else: # exception #1 rendered = "0.post0.dev%d" % pieces["distance"] return rendered def render_pep440_post(pieces): """TAG[.postDISTANCE[.dev0]+gHEX] . The ".dev0" means dirty. Note that .dev0 sorts backwards (a dirty tree will appear "older" than the corresponding clean one), but you shouldn't be releasing software with -dirty anyways. Exceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += plus_or_dot(pieces) rendered += "g%s" % pieces["short"] else: # exception #1 rendered = "0.post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += "+g%s" % pieces["short"] return rendered def render_pep440_post_branch(pieces): """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] . The ".dev0" means not master branch. Exceptions: 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%d" % pieces["distance"] if pieces["branch"] != "master": rendered += ".dev0" rendered += plus_or_dot(pieces) rendered += "g%s" % pieces["short"] if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = "0.post%d" % pieces["distance"] if pieces["branch"] != "master": rendered += ".dev0" rendered += "+g%s" % pieces["short"] if pieces["dirty"]: rendered += ".dirty" return rendered def render_pep440_old(pieces): """TAG[.postDISTANCE[.dev0]] . The ".dev0" means dirty. Exceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" else: # exception #1 rendered = "0.post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" return rendered def render_git_describe(pieces): """TAG[-DISTANCE-gHEX][-dirty]. Like 'git describe --tags --dirty --always'. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render_git_describe_long(pieces): """TAG-DISTANCE-gHEX[-dirty]. Like 'git describe --tags --dirty --always -long'. The distance/hash is unconditional. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render(pieces, style): """Render the given version pieces into the requested style.""" if pieces["error"]: return {"version": "unknown", "full-revisionid": pieces.get("long"), "dirty": None, "error": pieces["error"], "date": None} if not style or style == "default": style = "pep440" # the default if style == "pep440": rendered = render_pep440(pieces) elif style == "pep440-branch": rendered = render_pep440_branch(pieces) elif style == "pep440-pre": rendered = render_pep440_pre(pieces) elif style == "pep440-post": rendered = render_pep440_post(pieces) elif style == "pep440-post-branch": rendered = render_pep440_post_branch(pieces) elif style == "pep440-old": rendered = render_pep440_old(pieces) elif style == "git-describe": rendered = render_git_describe(pieces) elif style == "git-describe-long": rendered = render_git_describe_long(pieces) else: raise ValueError("unknown style '%s'" % style) return {"version": rendered, "full-revisionid": pieces["long"], "dirty": pieces["dirty"], "error": None, "date": pieces.get("date")} class VersioneerBadRootError(Exception): """The project root directory is unknown or missing key files.""" def get_versions(verbose=False): """Get the project version from whatever source is available. Returns dict with two keys: 'version' and 'full'. """ if "versioneer" in sys.modules: # see the discussion in cmdclass.py:get_cmdclass() del sys.modules["versioneer"] root = get_root() cfg = get_config_from_root(root) assert cfg.VCS is not None, "please set [versioneer]VCS= in setup.cfg" handlers = HANDLERS.get(cfg.VCS) assert handlers, "unrecognized VCS '%s'" % cfg.VCS verbose = verbose or cfg.verbose assert cfg.versionfile_source is not None, \ "please set versioneer.versionfile_source" assert cfg.tag_prefix is not None, "please set versioneer.tag_prefix" versionfile_abs = os.path.join(root, cfg.versionfile_source) # extract version from first of: _version.py, VCS command (e.g. 'git # describe'), parentdir. This is meant to work for developers using a # source checkout, for users of a tarball created by 'setup.py sdist', # and for users of a tarball/zipball created by 'git archive' or github's # download-from-tag feature or the equivalent in other VCSes. get_keywords_f = handlers.get("get_keywords") from_keywords_f = handlers.get("keywords") if get_keywords_f and from_keywords_f: try: keywords = get_keywords_f(versionfile_abs) ver = from_keywords_f(keywords, cfg.tag_prefix, verbose) if verbose: print("got version from expanded keyword %s" % ver) return ver except NotThisMethod: pass try: ver = versions_from_file(versionfile_abs) if verbose: print("got version from file %s %s" % (versionfile_abs, ver)) return ver except NotThisMethod: pass from_vcs_f = handlers.get("pieces_from_vcs") if from_vcs_f: try: pieces = from_vcs_f(cfg.tag_prefix, root, verbose) ver = render(pieces, cfg.style) if verbose: print("got version from VCS %s" % ver) return ver except NotThisMethod: pass try: if cfg.parentdir_prefix: ver = versions_from_parentdir(cfg.parentdir_prefix, root, verbose) if verbose: print("got version from parentdir %s" % ver) return ver except NotThisMethod: pass if verbose: print("unable to compute version") return {"version": "0+unknown", "full-revisionid": None, "dirty": None, "error": "unable to compute version", "date": None} def get_version(): """Get the short version string for this project.""" return get_versions()["version"] def get_cmdclass(cmdclass=None): """Get the custom setuptools/distutils subclasses used by Versioneer. If the package uses a different cmdclass (e.g. one from numpy), it should be provide as an argument. """ if "versioneer" in sys.modules: del sys.modules["versioneer"] # this fixes the "python setup.py develop" case (also 'install' and # 'easy_install .'), in which subdependencies of the main project are # built (using setup.py bdist_egg) in the same python process. Assume # a main project A and a dependency B, which use different versions # of Versioneer. A's setup.py imports A's Versioneer, leaving it in # sys.modules by the time B's setup.py is executed, causing B to run # with the wrong versioneer. Setuptools wraps the sub-dep builds in a # sandbox that restores sys.modules to it's pre-build state, so the # parent is protected against the child's "import versioneer". By # removing ourselves from sys.modules here, before the child build # happens, we protect the child from the parent's versioneer too. # Also see https://github.com/python-versioneer/python-versioneer/issues/52 cmds = {} if cmdclass is None else cmdclass.copy() # we add "version" to both distutils and setuptools from distutils.core import Command class cmd_version(Command): description = "report generated version string" user_options = [] boolean_options = [] def initialize_options(self): pass def finalize_options(self): pass def run(self): vers = get_versions(verbose=True) print("Version: %s" % vers["version"]) print(" full-revisionid: %s" % vers.get("full-revisionid")) print(" dirty: %s" % vers.get("dirty")) print(" date: %s" % vers.get("date")) if vers["error"]: print(" error: %s" % vers["error"]) cmds["version"] = cmd_version # we override "build_py" in both distutils and setuptools # # most invocation pathways end up running build_py: # distutils/build -> build_py # distutils/install -> distutils/build ->.. # setuptools/bdist_wheel -> distutils/install ->.. # setuptools/bdist_egg -> distutils/install_lib -> build_py # setuptools/install -> bdist_egg ->.. # setuptools/develop -> ? # pip install: # copies source tree to a tempdir before running egg_info/etc # if .git isn't copied too, 'git describe' will fail # then does setup.py bdist_wheel, or sometimes setup.py install # setup.py egg_info -> ? # we override different "build_py" commands for both environments if 'build_py' in cmds: _build_py = cmds['build_py'] elif "setuptools" in sys.modules: from setuptools.command.build_py import build_py as _build_py else: from distutils.command.build_py import build_py as _build_py class cmd_build_py(_build_py): def run(self): root = get_root() cfg = get_config_from_root(root) versions = get_versions() _build_py.run(self) # now locate _version.py in the new build/ directory and replace # it with an updated value if cfg.versionfile_build: target_versionfile = os.path.join(self.build_lib, cfg.versionfile_build) print("UPDATING %s" % target_versionfile) write_to_version_file(target_versionfile, versions) cmds["build_py"] = cmd_build_py if 'build_ext' in cmds: _build_ext = cmds['build_ext'] elif "setuptools" in sys.modules: from setuptools.command.build_ext import build_ext as _build_ext else: from distutils.command.build_ext import build_ext as _build_ext class cmd_build_ext(_build_ext): def run(self): root = get_root() cfg = get_config_from_root(root) versions = get_versions() _build_ext.run(self) if self.inplace: # build_ext --inplace will only build extensions in # build/lib<..> dir with no _version.py to write to. # As in place builds will already have a _version.py # in the module dir, we do not need to write one. return # now locate _version.py in the new build/ directory and replace # it with an updated value target_versionfile = os.path.join(self.build_lib, cfg.versionfile_build) print("UPDATING %s" % target_versionfile) write_to_version_file(target_versionfile, versions) cmds["build_ext"] = cmd_build_ext if "cx_Freeze" in sys.modules: # cx_freeze enabled? from cx_Freeze.dist import build_exe as _build_exe # nczeczulin reports that py2exe won't like the pep440-style string # as FILEVERSION, but it can be used for PRODUCTVERSION, e.g. # setup(console=[{ # "version": versioneer.get_version().split("+", 1)[0], # FILEVERSION # "product_version": versioneer.get_version(), # ... class cmd_build_exe(_build_exe): def run(self): root = get_root() cfg = get_config_from_root(root) versions = get_versions() target_versionfile = cfg.versionfile_source print("UPDATING %s" % target_versionfile) write_to_version_file(target_versionfile, versions) _build_exe.run(self) os.unlink(target_versionfile) with open(cfg.versionfile_source, "w") as f: LONG = LONG_VERSION_PY[cfg.VCS] f.write(LONG % {"DOLLAR": "$", "STYLE": cfg.style, "TAG_PREFIX": cfg.tag_prefix, "PARENTDIR_PREFIX": cfg.parentdir_prefix, "VERSIONFILE_SOURCE": cfg.versionfile_source, }) cmds["build_exe"] = cmd_build_exe del cmds["build_py"] if 'py2exe' in sys.modules: # py2exe enabled? from py2exe.distutils_buildexe import py2exe as _py2exe class cmd_py2exe(_py2exe): def run(self): root = get_root() cfg = get_config_from_root(root) versions = get_versions() target_versionfile = cfg.versionfile_source print("UPDATING %s" % target_versionfile) write_to_version_file(target_versionfile, versions) _py2exe.run(self) os.unlink(target_versionfile) with open(cfg.versionfile_source, "w") as f: LONG = LONG_VERSION_PY[cfg.VCS] f.write(LONG % {"DOLLAR": "$", "STYLE": cfg.style, "TAG_PREFIX": cfg.tag_prefix, "PARENTDIR_PREFIX": cfg.parentdir_prefix, "VERSIONFILE_SOURCE": cfg.versionfile_source, }) cmds["py2exe"] = cmd_py2exe # we override different "sdist" commands for both environments if 'sdist' in cmds: _sdist = cmds['sdist'] elif "setuptools" in sys.modules: from setuptools.command.sdist import sdist as _sdist else: from distutils.command.sdist import sdist as _sdist class cmd_sdist(_sdist): def run(self): versions = get_versions() self._versioneer_generated_versions = versions # unless we update this, the command will keep using the old # version self.distribution.metadata.version = versions["version"] return _sdist.run(self) def make_release_tree(self, base_dir, files): root = get_root() cfg = get_config_from_root(root) _sdist.make_release_tree(self, base_dir, files) # now locate _version.py in the new base_dir directory # (remembering that it may be a hardlink) and replace it with an # updated value target_versionfile = os.path.join(base_dir, cfg.versionfile_source) print("UPDATING %s" % target_versionfile) write_to_version_file(target_versionfile, self._versioneer_generated_versions) cmds["sdist"] = cmd_sdist return cmds CONFIG_ERROR = """ setup.cfg is missing the necessary Versioneer configuration. You need a section like: [versioneer] VCS = git style = pep440 versionfile_source = src/myproject/_version.py versionfile_build = myproject/_version.py tag_prefix = parentdir_prefix = myproject- You will also need to edit your setup.py to use the results: import versioneer setup(version=versioneer.get_version(), cmdclass=versioneer.get_cmdclass(), ...) Please read the docstring in ./versioneer.py for configuration instructions, edit setup.cfg, and re-run the installer or 'python versioneer.py setup'. """ SAMPLE_CONFIG = """ # See the docstring in versioneer.py for instructions. Note that you must # re-run 'versioneer.py setup' after changing this section, and commit the # resulting files. [versioneer] #VCS = git #style = pep440 #versionfile_source = #versionfile_build = #tag_prefix = #parentdir_prefix = """ OLD_SNIPPET = """ from ._version import get_versions __version__ = get_versions()['version'] del get_versions """ INIT_PY_SNIPPET = """ from . import {0} __version__ = {0}.get_versions()['version'] """ def do_setup(): """Do main VCS-independent setup function for installing Versioneer.""" root = get_root() try: cfg = get_config_from_root(root) except (OSError, configparser.NoSectionError, configparser.NoOptionError) as e: if isinstance(e, (OSError, configparser.NoSectionError)): print("Adding sample versioneer config to setup.cfg", file=sys.stderr) with open(os.path.join(root, "setup.cfg"), "a") as f: f.write(SAMPLE_CONFIG) print(CONFIG_ERROR, file=sys.stderr) return 1 print(" creating %s" % cfg.versionfile_source) with open(cfg.versionfile_source, "w") as f: LONG = LONG_VERSION_PY[cfg.VCS] f.write(LONG % {"DOLLAR": "$", "STYLE": cfg.style, "TAG_PREFIX": cfg.tag_prefix, "PARENTDIR_PREFIX": cfg.parentdir_prefix, "VERSIONFILE_SOURCE": cfg.versionfile_source, }) ipy = os.path.join(os.path.dirname(cfg.versionfile_source), "__init__.py") if os.path.exists(ipy): try: with open(ipy, "r") as f: old = f.read() except OSError: old = "" module = os.path.splitext(os.path.basename(cfg.versionfile_source))[0] snippet = INIT_PY_SNIPPET.format(module) if OLD_SNIPPET in old: print(" replacing boilerplate in %s" % ipy) with open(ipy, "w") as f: f.write(old.replace(OLD_SNIPPET, snippet)) elif snippet not in old: print(" appending to %s" % ipy) with open(ipy, "a") as f: f.write(snippet) else: print(" %s unmodified" % ipy) else: print(" %s doesn't exist, ok" % ipy) ipy = None # Make sure both the top-level "versioneer.py" and versionfile_source # (PKG/_version.py, used by runtime code) are in MANIFEST.in, so # they'll be copied into source distributions. Pip won't be able to # install the package without this. manifest_in = os.path.join(root, "MANIFEST.in") simple_includes = set() try: with open(manifest_in, "r") as f: for line in f: if line.startswith("include "): for include in line.split()[1:]: simple_includes.add(include) except OSError: pass # That doesn't cover everything MANIFEST.in can do # (http://docs.python.org/2/distutils/sourcedist.html#commands), so # it might give some false negatives. Appending redundant 'include' # lines is safe, though. if "versioneer.py" not in simple_includes: print(" appending 'versioneer.py' to MANIFEST.in") with open(manifest_in, "a") as f: f.write("include versioneer.py\n") else: print(" 'versioneer.py' already in MANIFEST.in") if cfg.versionfile_source not in simple_includes: print(" appending versionfile_source ('%s') to MANIFEST.in" % cfg.versionfile_source) with open(manifest_in, "a") as f: f.write("include %s\n" % cfg.versionfile_source) else: print(" versionfile_source already in MANIFEST.in") # Make VCS-specific changes. For git, this means creating/changing # .gitattributes to mark _version.py for export-subst keyword # substitution. do_vcs_install(manifest_in, cfg.versionfile_source, ipy) return 0 def scan_setup_py(): """Validate the contents of setup.py against Versioneer's expectations.""" found = set() setters = False errors = 0 with open("setup.py", "r") as f: for line in f.readlines(): if "import versioneer" in line: found.add("import") if "versioneer.get_cmdclass()" in line: found.add("cmdclass") if "versioneer.get_version()" in line: found.add("get_version") if "versioneer.VCS" in line: setters = True if "versioneer.versionfile_source" in line: setters = True if len(found) != 3: print("") print("Your setup.py appears to be missing some important items") print("(but I might be wrong). Please make sure it has something") print("roughly like the following:") print("") print(" import versioneer") print(" setup( version=versioneer.get_version(),") print(" cmdclass=versioneer.get_cmdclass(), ...)") print("") errors += 1 if setters: print("You should remove lines like 'versioneer.VCS = ' and") print("'versioneer.versionfile_source = ' . This configuration") print("now lives in setup.cfg, and should be removed from setup.py") print("") errors += 1 return errors if __name__ == "__main__": cmd = sys.argv[1] if cmd == "setup": errors = do_setup() errors += scan_setup_py() if errors: sys.exit(1)